ALBERT

Description: Biocides used in film protection products leaching from facades are known to be a potential threat to the environment. This study identifies individual sources and entry pathways in a small-scale urban area. We investigate emissions of commonly used biocides (terbutryn, diuron, and octylisothiazolinone – OIT) and some of their transformation products (TPs; diuron-desmethyl, terbumeton, terbuthylazine-2-hydroxy, and terbutryn-desethyl) from a 2 ha residential area 13 years after construction has ended. Sampling utilizes existing urban water infrastructure representative for decentralized storm water management in central and northern Europe and applies a two-step approach to (a) determine the occurrence of biocides above water quality limits (i.e., predicted no-effect concentration, PNEC) and (b) identify source areas and characterize entry pathways into surface and groundwater. Monitoring focuses on the analysis of selected biocides and TPs by liquid chromatography–mass spectrometry/mass spectrometry (LC-MS/MS) in water samples taken from facades, rainwater pipes, drainage, and storm water infiltration systems. In standing water in a swale, we found high concentrations of diuron (174 ng L−1) and terbutryn (40 ng L−1) above PNEC for surface water. We confirmed expected sources, i.e., facades. Sampling of rain downpipes from flat roofs identified additional sources of all biocides and two TPs of terbutryn and one TP of diuron. Diuron and terbutryn were found in three drainage pipes representing different entry pathways of biocides. In one drainage pipe collecting road runoff, only diuron-desmethyl and terbutryn-desethyl were detected. In two other drainage pipes collecting infiltrated water through soil, terbuthylazine-2-hydroxy was additionally detected. One of the pipes collecting infiltrated water through soil concentration showed the highest concentrations of terbutryn and two of its TPs (terbutryn-desethyl and terbuthylazine-2-hydroxy). This suggests a high leaching potential of terbutryn. The applied two-step approach determined sources and pathways of biocide and their TPs. This study contributes to expanding knowledge on their entry and distribution and, thus, eventually towards reducing emissions.

Description: Recent advances in soil moisture remote sensing have produced satellite data sets with improved soil moisture mapping under vegetation and with higher spatial and temporal resolutions. In this study, we evaluate the potential of a new, experimental version of the Advanced Scatterometer (ASCAT) soil water index data set for multiple objective calibrations of a conceptual hydrologic model. The analysis is performed in 213 catchments in Austria for the period 2000–2014. An HBV (Hydrologiska Byråns Vattenbalansavdelning)-type hydrologic model is calibrated based on runoff data, ASCAT soil moisture data, and Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover data for various calibration variants. Results show that the inclusion of soil moisture data in the calibration mainly improves the soil moisture simulations, the inclusion of snow data mainly improves the snow simulations, and the inclusion of both of them improves both soil moisture and snow simulations to almost the same extent. The snow data are more efficient at improving snow simulations than the soil moisture data are at improving soil moisture simulations. The improvements of both runoff and soil moisture model efficiencies are larger in low elevation and agricultural catchments than in others. The calibrated snow-related parameters are strongly affected by including snow data and, to a lesser extent, by soil moisture data. In contrast, the soil-related parameters are only affected by the inclusion of soil moisture data. The results indicate that the use of multiple remote sensing products in hydrological modeling can improve the representation of hydrological fluxes and prediction of runoff hydrographs at the catchment scale.

Description: Crop yield is reduced by heat and water stress and even more when these conditions co-occur. Yet, compound effects of air temperature and water availability on crop heat stress are poorly quantified. Existing crop models, by relying at least partially on empirical functions, cannot account for the feedbacks of plant traits and response to heat and water stress on canopy temperature. We developed a fully mechanistic model, coupling crop energy and water balances, to determine canopy temperature as a function of plant traits, stochastic environmental conditions, and irrigation applications. While general, the model was parameterized for wheat. Canopy temperature largely followed air temperature under well-watered conditions. But, when soil water potential was more negative than −0.14 MPa, further reductions in soil water availability led to a rapid rise in canopy temperature – up to 10 ∘C warmer than air at soil water potential of −0.62 MPa. More intermittent precipitation led to higher canopy temperatures and longer periods of potentially damaging crop canopy temperatures. Irrigation applications aimed at keeping crops under well-watered conditions could reduce canopy temperature but in most cases were unable to maintain it below the threshold temperature for potential heat damage; the benefits of irrigation in terms of reduction of canopy temperature decreased as average air temperature increased. Hence, irrigation is only a partial solution to adapt to warmer and drier climates.

Description: The MARINE (Model of Anticipation of Runoff and INundations for Extreme events) hydrological model is a distributed model dedicated to flash flood simulation. Recent developments of the MARINE model are explored in this work. On one hand, transfers of water through the subsurface, formerly relying on water height, now take place in a homogeneous soil column based on the soil saturation degree (SSF model). On the other hand, the soil column is divided into two layers, which represent, respectively, the upper soil layer and the deep weathered rocks (SSF–DWF model). The aim of the present work is to assess the accuracy of these new representations for the simulation of soil moisture during flash flood events. An exploration of the various products available in the literature for soil moisture estimation is performed. The efficiency of the models for soil saturation degree simulation is estimated with respect to several products either at the local scale or spatially distributed: (i) the gridded soil moisture product provided by the operational modeling chain SAFRAN-ISBA-MODCOU; (ii) the gridded soil moisture product provided by the LDAS-Monde assimilation chain, which is based on the ISBA-A-gs land surface model and assimilating satellite derived data; (iii) the upper soil water content hourly measurements taken from the SMOSMANIA observation network; and (iv) the Soil Water Index provided by the Copernicus Global Land Service (CGLS), which is derived from Sentinel-1 C-SAR and ASCAT satellite data. The case study is performed over two French Mediterranean catchments impacted by flash flood events over the 2017–2019 period. The local comparison of the MARINE outputs with the SMOSMANIA measurements, as well as the comparison at the basin scale of the MARINE outputs with the gridded LDAS-Monde and CGLS data, lead to the following conclusion: both the dynamics and the amplitudes of the soil saturation degree simulated with the SSF and SSF–DWF models are better correlated with both the SMOSMANIA measurements and the LDAS-Monde data than the outputs of the base model. Finally, the soil saturation degree simulated by the two-layers model for the deep layer is compared to the soil saturation degree provided by the LDAS-Monde product at corresponding depths. In conclusion, the developments presented for the representation of subsurface flow in the MARINE model enhance the soil saturation degree simulation during flash floods with respect to both gridded data and local soil moisture measurements.

Description: Compound dry and hot conditions frequently cause large impacts on ecosystems and societies worldwide. A suite of indices is available for the assessment of droughts and heatwaves, yet there is no index available for incorporating the joint variability of dry and hot conditions at the sub-monthly scale. Here we introduce a daily-scale index, called the standardized compound drought and heat index (SCDHI), to assess compound dry-hot conditions. The SCDHI is based on a daily drought index (the standardized antecedent precipitation evapotranspiration index – SAPEI), the daily-scale standardized temperature index (STI), and a joint probability distribution method. The new index is verified against real-world compound dry and hot events and associated observed vegetation impacts in China. The SCDHI can not only capture compound dry and hot events at both monthly and sub-monthly scales, but is also a good indicator for associated vegetation impacts. Using the SCDHI, we quantify the frequency, severity, duration, and intensity of compound dry-hot events during the historical period and assess the ability of climate models to reproduce these characteristics in China. We find that compound events whose severity is at least light and which last longer than 2 weeks generally persisted for 20–35 d in China. Southern China suffered from compound events most frequently, and the most severe compound events were mainly detected in this region. Climate models generally overestimate the frequency, duration, severity, and intensity of compound events in China, especially for western regions, which can be attributed to a too strong dependence between the SAPEI and STI in those models. The SCDHI provides a new tool to quantify sub-monthly characteristics of compound dry and hot events and to monitor their initiation, development, and decay. This is important information for decision-makers and stakeholders to release early and timely warnings.

Description: Unconventional sources of data that enhance our understanding of internal interactions between socio-economic and hydrological processes are central to modeling human–water systems. Participatory modeling (PM) departs from conventional modeling tools by informing and conceptualizing human–water systems through stakeholder engagement. However, the implementation of many PM processes remains biased, particularly in regions where marginalized communities are present. Many PM processes are not cognizant of differentiation and diversity within a society and tend to treat communities as homogeneous units with similar capabilities, needs, and interests. This undifferentiation leads to the exclusion of key actors, many of whom are associated with marginalized communities. In this study, a participatory model-building framework (PMBF), aiming to ensure the inclusiveness of marginalized stakeholders – who (1) have low literacy, (2) are comparatively powerless, and/or (3) are associated with a marginalized language – in participatory modeling, is proposed. The adopted approach employs interdisciplinary storylines to inform and conceptualize human–water systems. The suggested method is underpinned by the multi-level perspective (MLP) framework, which was developed by Geels et al. (2002) to conceptualize socio-technical transitions and modified in this study to accommodate the development of interdisciplinary storylines. A case study was conducted in Atitlán Basin, Guatemala, to understand the relationships that govern the lake's cultural eutrophication problem. This research integrated key stakeholders from the Indigenous Mayan community, associated with diverse literacy ranges, and emerging from three different marginalized linguistic backgrounds (Kaqchikel, Tz'utujil, and K'iche'), in the PM activity. The proposed approach facilitated the participation of marginalized stakeholders. Moreover, it (1) helped develop an understanding of mechanisms governing the eutrophication of the lake, (2) initiated a dialogue between Indigenous Peoples and non-Indigenous stakeholders, and (3) extracted potential solutions targeting the system's leverage points. The participatory model-building activity generated three submodules: (1) agriculture, (2) tourism, and (3) environmental awareness. Each submodule contained socioculturally specific mechanisms associated with nutrient discharge to Lake Atitlán. The delineation of such nuanced relationships helps develop well-targeted policies and best management practices (BMPs). Additionally, the suggested process helped decrease the impact of power imbalances in water resources management and empowered community-based decision-making.

Description: With the increasing frequency and severity of fire, there is an increasing desire to better manage fuels and minimize, as much as possible, the impacts of fire on soils and other natural resources. Piling and/or burning slash is one method of managing fuels and reducing the risk and consequences of wildfire, but the repercussions to the soil, although very localized, can be significant and often irreversible. In an effort to provide a tool to better understand the impact of fire on soils, this study outlines the improvements to and the in situ validation of a nonequilibrium model for simulating the coupled interactions and transport of heat, moisture and water vapor during fires. Improvements to the model eliminate the following two important (but heretofore universally overlooked) inconsistencies: one that describes the relationship between evaporation and condensation in the parameterization of the nonequilibrium vapor source term, and the other that is the incorrect use of the apparent thermal conductivity in the soil heat flow equation. The first of these made a small enhancement in the stability and performance of the model. The second is an important improvement in the physics underpinning the model but had less of an impact on the model's performance and stability than the first. This study also (a) develops a general heating function that describes the energy input to the soil surface by the fire and (b) discusses the complexities and difficulties of formulating the upper boundary condition from a surface energy balance approach. The model validation uses (in situ temperature, soil moisture and heat flux) data obtained in a 2004 experimental slash pile burn. Important temperature-dependent corrections to the instruments used for measuring soil heat flux and moisture are also discussed and assessed. Despite any possible ambiguities in the calibration of the sensors or the simplicity of the parameterization of the surface heating function, the difficulties and complexities of formulating the upper boundary condition and the obvious complexities of the dynamic response of the soil's temperature and heat flux, the model produced at least a very credible, if not surprisingly good, simulation of the observed data. This study then continues with a discussion and sensitivity analysis of some important feedbacks (some of which are well known and others that are more hypothetical) that are not included in the present (or any extant) model, but that undoubtedly are dynamically influencing the physical properties of the soil in situ during the fire and, thereby, modulating the behavior of the soil temperature and moisture. This paper concludes with a list of possible future observational and modeling studies and how they would advance the research and findings discussed here.

Description: Acquiring in situ data of tidal flooding is key for the successful restoration planning of intertidal wetlands such as salt marshes and mangroves. However, monitoring spatially explicit inundation time series and tidal currents can be costly and technically challenging. With the increasing availability of low-cost sensors and data loggers, customized solutions can now be designed to monitor intertidal hydrodynamics with direct applications for restoration and management. In this study, we present the design, calibration, and application of the “Mini Buoy”, a low-cost underwater float containing an acceleration data logger for monitoring tidal inundation characteristics and current velocities derived from single-axis equilibrium acceleration (i.e. logger tilt). The acceleration output of the Mini Buoys was calibrated against water-level and current-velocity data in the hypertidal Bay of Fundy, Canada, and in a tidally reconnected former aquaculture pond complex in North Sumatra, Indonesia. Key parameters, such as submersion time and current velocities during submergence, can be determined over several months using the Mini Buoy. An open-source application was developed to generate ecologically meaningful hydrological information from the Mini Buoy data for mangrove restoration planning. We present this specific SE Asian mangrove restoration application alongside a flexible concept design for the Mini Buoy to be customized for research and management of intertidal wetlands worldwide.

Description: Spatiotemporally continuous estimates of the hydrologic cycle are often generated through hydrologic modeling, reanalysis, or remote sensing (RS) methods and are commonly applied as a supplement to, or a substitute for, in situ measurements when observational data are sparse or unavailable. This study compares estimates of precipitation (P), actual evapotranspiration (ET), runoff (R), snow water equivalent (SWE), and soil moisture (SM) from 87 unique data sets generated by 47 hydrologic models, reanalysis data sets, and remote sensing products across the conterminous United States (CONUS). Uncertainty between hydrologic component estimates was shown to be high in the western CONUS, with median uncertainty (measured as the coefficient of variation) ranging from 11 % to 21 % for P, 14 % to 26 % for ET, 28 % to 82 % for R, 76 % to 84 % for SWE, and 36 % to 96 % for SM. Uncertainty between estimates was lower in the eastern CONUS, with medians ranging from 5 % to 14 % for P, 13 % to 22 % for ET, 28 % to 82 % for R, 53 % to 63 % for SWE, and 42 % to 83 % for SM. Interannual trends in estimates from 1982 to 2010 show common disagreement in R, SWE, and SM. Correlating fluxes and stores against remote-sensing-derived products show poor overall correlation in the western CONUS for ET and SM estimates. Study results show that disagreement between estimates can be substantial, sometimes exceeding the magnitude of the measurements themselves. The authors conclude that multimodel ensembles are not only useful but are in fact a necessity for accurately representing uncertainty in research results. Spatial biases of model disagreement values in the western United States show that targeted research efforts in arid and semiarid water-limited regions are warranted, with the greatest emphasis on storage and runoff components, to better describe complexities of the terrestrial hydrologic system and reconcile model disagreement.

Description: Irrigated agriculture is threatened by soil salinity in numerous arid and semi-arid areas of the world, chiefly caused by the use of highly salinity irrigation water, compounded by excessive evapotranspiration. Given this threat, efficient field assessment methods are needed to monitor the dynamics of soil salinity in salt-affected irrigated lands and evaluate the performance of management strategies. In this study, we report on the results of an irrigation experiment with the main objective of evaluating time-lapse inversion of electromagnetic induction (EMI) data and hydrological modelling in field assessment of soil salinity dynamics. Four experimental plots were established and irrigated 12 times during a 2-month period, with water at four different salinity levels (1, 4, 8 and 12 dS m−1) using a drip irrigation system. Time-lapse apparent electrical conductivity (σa) data were collected four times during the experiment period using the CMD Mini-Explorer. Prior to inversion of time-lapse σa data, a numerical experiment was performed by 2D simulations of the water and solute infiltration and redistribution process in synthetic transects, generated by using the statistical distribution of the hydraulic properties in the study area. These simulations gave known spatio-temporal distribution of water contents and solute concentrations and thus of bulk electrical conductivity (σb), which in turn were used to obtain known structures of apparent electrical conductivity, σa. These synthetic distributions were used for a preliminary understanding of how the physical context may influence the EMI-based σa readings carried out in the monitored transects as well as being used to optimize the smoothing parameter to be used in the inversion of σa readings. With this prior information at hand, we inverted the time-lapse field σa data and interpreted the results in terms of concentration distributions over time. The proposed approach, using preliminary hydrological simulations to understand the potential role of the variability of the physical system to be monitored by EMI, may actually allow for a better choice of the inversion parameters and interpretation of EMI readings, thus increasing the potentiality of using the electromagnetic induction technique for rapid and non-invasive investigation of spatio-temporal variability in soil salinity over large areas.

Description: We present a method to simulate fluid flow with reactive solute transport in structured, partially saturated soils using a Lagrangian perspective. In this context, we extend the scope of the Lagrangian Soil Water and Solute Transport Model (LAST) (Sternagel et al., 2019) by implementing vertically variable, non-linear sorption and first-order degradation processes during transport of reactive substances through a partially saturated soil matrix and macropores. For sorption, we develop an explicit mass transfer approach based on Freundlich isotherms because the common method of using a retardation factor is not applicable in the particle-based approach of LAST. The reactive transport method is tested against data of plot- and field-scale irrigation experiments with the herbicides isoproturon and flufenacet at different flow conditions over various periods. Simulations with HYDRUS 1-D serve as an additional benchmark. At the plot scale, both models show equal performance at a matrix-flow-dominated site, but LAST better matches indicators of preferential flow at a macropore-flow-dominated site. Furthermore, LAST successfully simulates the effects of adsorption and degradation on the breakthrough behaviour of flufenacet with preferential leaching and remobilization. The results demonstrate the feasibility of the method to simulate reactive solute transport in a Lagrangian framework and highlight the advantage of the particle-based approach and the structural macropore domain to simulate solute transport as well as to cope with preferential bypassing of topsoil and subsequent re-infiltration into the subsoil matrix.

Description: The spatiotemporal variability of rainfall in the dry (October–March) and wet (April–September) seasons over eastern China is examined from 1901–2016 based on the gridded rainfall dataset from the University of East Anglia Climatic Research Unit. Principal component analysis is employed to identify the dominant variability modes, wavelet coherence is utilized to investigate the spectral features of the leading modes of precipitation and their coherences with the large-scale modes of climate variability, and the Bayesian dynamical linear model is adopted to quantify the time-varying correlations between climate variability modes and rainfall in the dry and wet seasons. Results show that first and second principal components (PCs) account for 34.2 % (16.1 %) and 13.4 % (13.9 %) of the variance in the dry (wet) season, and their variations are roughly coincident with phase shifts of the El Niño–Southern Oscillation (ENSO) in both seasons. The anomalous moisture fluxes responsible for the occurrence of precipitation events in eastern China exhibit an asymmetry between high and light rainfall years in the dry (wet) season. The ENSO has a 4- to 8-year signal of the statistically positive (negative) association with rainfall during the dry (wet) season over eastern China. The statistically significant positive (negative) associations between the Pacific Decadal Oscillation (PDO) and precipitation are found with a 9- to 15-year (4- to 7-year) signal. The impacts of the PDO on rainfall in eastern China exhibit multiple timescales as compared to the ENSO episodes, while the PDO triggers a stronger effect on precipitation in the wet season than the dry half year. The interannual and interdecadal variations in rainfall over eastern China are substantially modulated by drivers originated from the Pacific Ocean. During the wet season, the ENSO exerted a gradually weakening effect on eastern China rainfall from 1901 to 2016, while the effects of the PDO decreased before the 1980s, and then shifted into increases after the 2000s. The finding provides a metric for assessing the capability of climate models and guidance of seasonal prediction.

Description: Ecohydrological sensitivity, defined as the response intensity of streamflow to per unit vegetation change is an integrated indicator for assessing hydrological sensitivity to vegetation change. Understanding ecohydrological sensitivity and its influencing factors is crucial for managing water supply, reducing water-related hazards and ensuring aquatic functions by vegetation management. Yet, there is still a systematic assessment on ecohydrological sensitivity and associated driving factors especially at a seasonal scale lacking. In this study, 14 large watersheds across various environmental gradients in China were selected to quantify their ecohydrological sensitivities at a seasonal scale and to examine the role of associated influencing factors such as climate, vegetation, topography, soil and landscape. Based on the variables identified by correlation analysis and factor analysis, prediction models of seasonal ecohydrological sensitivity were constructed to test their utilities for the design of watershed management and protection strategies. Our key findings were the following: (1) ecohydrological sensitivities were more sensitive under dry conditions than wet conditions – for example, 1 % LAI (leaf area index) change, on average, induced 5.05 % and 1.96 % change in the dry and wet season streamflow, respectively; (2) seasonal ecohydrological sensitivities were highly variable across the study watersheds with different climate conditions, dominant soil types and hydrological regimes; and (3) the dry season ecohydrological sensitivity was mostly determined by topography (slope, slope length, valley depth and downslope distance gradient), soil (topsoil organic carbon and topsoil bulk density) and vegetation (LAI), while the wet season ecohydrological sensitivity was mainly controlled by soil (topsoil-available water-holding capacity), landscape (edge density) and vegetation (leaf area index). Our study provided a useful and practical framework to assess and predict ecohydrological sensitivities at the seasonal scale. The established ecohydrological sensitivity prediction models can be applied to ungauged watersheds or watersheds with limited hydrological data to help decision makers and watershed managers effectively manage hydrological impacts through vegetation restoration programs. We conclude that ecohydrological sensitivities at the seasonal scale are varied by climate, vegetation and watershed property, and their understanding can greatly support the management of hydrological risks and protection of aquatic functions.

Description: Probabilistic seasonal forecasts are important for many water-intensive activities requiring long-term planning. Among the different techniques used for seasonal forecasting, the ensemble streamflow prediction (ESP) approach has long been employed due to the singular dependence on past meteorological records. The Swedish Meteorological and Hydrological Institute is currently extending the use of long-range forecasts within its operational warning service, which requires a thorough analysis of the suitability and applicability of different methods with the national S-HYPE hydrological model. To this end, we aim to evaluate the skill of ESP forecasts over 39 493 catchments in Sweden, understand their spatio-temporal patterns, and explore the main hydrological processes driving forecast skill. We found that ESP forecasts are generally skilful for most of the country up to 3 months into the future but that large spatio-temporal variations exist. Forecasts are most skilful during the winter months in northern Sweden, except for the highly regulated hydropower-producing rivers. The relationships between forecast skill and 15 different hydrological signatures show that forecasts are most skilful for slow-reacting, baseflow-dominated catchments and least skilful for flashy catchments. Finally, we show that forecast skill patterns can be spatially clustered in seven unique regions with similar hydrological behaviour. Overall, these results contribute to identifying in which areas and seasons and how long into the future ESP hydrological forecasts provide an added value, not only for the national forecasting and warning service, but also, most importantly, for guiding decision-making in critical services such as hydropower management and risk reduction.

Description: Accurate, timely, and reliable precipitation observations are mandatory for hydrological forecast and early warning systems. In the case of convective precipitation, traditional rain gauge networks often miss precipitation maxima, due to density limitations and the high spatial variability of the rainfall field. Despite several limitations like attenuation or partial beam blocking, the use of C-band weather radar has become operational in most European weather services. Traditionally, weather-radar-based quantitative precipitation estimation (QPE) is derived from horizontal reflectivity data. Nevertheless, dual-polarization weather radar can overcome several shortcomings of the conventional horizontal-reflectivity-based estimation. As weather radar archives are growing, they are becoming increasingly important for climatological purposes in addition to operational use. For the first time, the present study analyses one of the longest datasets from fully operational polarimetric C-band weather radars; these are located in Estonia and Italy, in very different climate conditions and environments. The length of the datasets used in the study is 5 years for both Estonia and Italy. The study focuses on long-term observations of summertime precipitation and their quantitative estimations by polarimetric observations. From such derived QPEs, accumulations for 1 h, 24 h, and 1-month durations are calculated and compared with reference rain gauges to quantify uncertainties and evaluate performances. Overall, the radar products showed similar results in Estonia and Italy when compared to each other. The product where radar reflectivity and specific differential phase were combined based on a threshold exhibited the best agreement with gauge values in all accumulation periods. In both countries reflectivity-based rainfall QPE underestimated and specific differential-phase-based product overestimated gauge measurements.

Description: Karst development influences the hydrological response of catchments. However, such an impact is poorly documented and even less quantified, especially over short scales of space and time. The aim of this article is thus to define karst influence on the different hydrological processes driving runoff generation, including interbasin groundwater flow (IGF) for elementary catchments at the storm-event timescale. IGFs are estimated at the scale of the river reach, by comparing inlet and outlet flows as well as the effective rainfall from the topographic elementary catchment. Three types of storm-event descriptors (characterizing water balance, hydrograph shape and lateral exchanges) were calculated for the 20 most important storm events of 108 stations in three French regions (Cévennes Mountains, Jura Mountains and Normandy), representative of different karst settings. These descriptors were compared and analysed according to catchment geology (karst, non-karst or mixed) and seasonality in order to explore the specific impact of karst areas on water balance, hydrograph shape, lateral exchanges and hydrogeological basin area. A statistical approach showed that, despite the variations with study areas, karst promotes (i) higher water infiltration from rivers during storm events, (ii) increased characteristic flood times and peak-flow attenuation, and (iii) lateral outflow. These influences are interpreted as mainly due to IGF loss that can be significant at the storm-event scale, representing around 50 % of discharge and 20 % of rainfall in the intermediate catchment. The spatial variability of such effects is also linked to contrasting lithology and karst occurrence. Our work thus provides a generic framework for assessing karst impact on the hydrological response of catchments to storm events; moreover, it can analyse flood-event characteristics in various hydro-climatic settings and can help with testing the influence of other physiographic parameters on runoff generation.

Description: This study investigates the combined hydrogen deuterium and triple oxygen isotope hydrology of the Salar del Huasco, an endorheic salt flat with shallow lakes at its centre that is located on the Altiplano Plateau, N Chile. This lacustrine system is hydrologically dynamic and complex because it receives inflow from multiple surface and groundwater sources. It undergoes seasonal flooding, followed by rapid shrinking of the water body at the prevailing arid climate with very high evaporation rates. At any given point in time, ponds, lakes, and recharge sources capture a large range of evaporation degrees. Samples taken between 2017 and 2019 show a range of δ18O between −13.3 ‰ and 14.5 ‰, d-excess between 7 ‰ and −100 ‰, and 17O-excess between 19 and −108 per meg. A pan evaporation experiment conducted on-site was used to derive the turbulence coefficient of the Craig–Gordon isotope evaporation model for the local wind regime. This, along with sampling of atmospheric vapour at the salar (-21.0±3.3 ‰ for δ18O, 34±6 ‰ for d-excess and 23±9 per meg for 17O-excess), enabled the accurate reproduction of measured ponds and lake isotope data by the Craig–Gordon model. In contrast to classic δ2H–δ18O studies, the 17O-excess data not only allow one to distinguish two different types of evaporation – evaporation with and without recharge – but also to identify mixing processes between evaporated lake water and fresh flood water. Multiple generations of infiltration events can also be inferred from the triple oxygen isotope composition of inflow water, indicating mixing of sources with different evaporation histories. These processes cannot be resolved using classic δ2H–δ18O data alone. Adding triple oxygen isotope measurements to isotope hydrology studies may therefore significantly improve the accuracy of a lake's hydrological balance – i.e. the evaporation-to-inflow ratio (E / I) – estimated by water isotope data and application of the Craig–Gordon isotope evaporation model.

Description: The Soil Moisture Active Passive (SMAP) Level-4 (L4) product provides global estimates of surface soil moisture (SSM) and root-zone soil moisture (RZSM) via the assimilation of SMAP brightness temperature (Tb) observations into the NASA Catchment Land Surface Model (CLSM). Here, using in situ measurements from 2474 sites in China, we evaluate the performance of soil moisture estimates from the L4 data assimilation (DA) system and from a baseline “open-loop” (OL) simulation of CLSM without Tb assimilation. Using random forest regression, the efficiency of the L4 DA system (i.e., the performance improvement in DA relative to OL) is attributed to eight control factors related to the CLSM as well as τ–ω radiative transfer model (RTM) components of the L4 system. Results show that the Spearman rank correlation (R) for L4 SSM with in situ measurements increases for 77 % of the in situ measurement locations (relative to that of OL), with an average R increase of approximately 14 % (ΔR=0.056). RZSM skill is improved for about 74 % of the in situ measurement locations, but the average R increase for RZSM is only 7 % (ΔR=0.034). Results further show that the SSM DA skill improvement is most strongly related to the difference between the RTM-simulated Tb and the SMAP Tb observation, followed by the error in precipitation forcing data and estimated microwave soil roughness parameter h. For the RZSM DA skill improvement, these three dominant control factors remain the same, although the importance of soil roughness exceeds that of the Tb simulation error, as the soil roughness strongly affects the ingestion of DA increments and further propagation to the subsurface. For the skill of the L4 and OL estimates themselves, the top two control factors are the precipitation error and the SSM–RZSM coupling strength error, both of which are related to the CLSM component of the L4 system. Finally, we find that the L4 system can effectively filter out errors in precipitation. Therefore, future development of the L4 system should focus on improving the characterization of the SSM–RZSM coupling strength.

Description: Recent studies have shown evidence of increasing and decreasing trends for average floods and flood quantiles across Europe. Studies attributing observed changes in flood peaks to their drivers have mostly focused on the average flood behaviour, without distinguishing small and large floods. This paper proposes a new framework for attributing flood changes to potential drivers, as a function of return period (T), in a regional context. We assume flood peaks to follow a non-stationary regional Gumbel distribution, where the median flood and the 100-year growth factor are used as parameters. They are allowed to vary in time and between catchments as a function of the drivers quantified by covariates. The elasticities of floods with respect to the drivers and the contributions of the drivers to flood changes are estimated by Bayesian inference. The prior distributions of the elasticities of flood quantiles to the drivers are estimated by hydrological reasoning and from the literature. The attribution model is applied to European flood and covariate data and aims at attributing the observed flood trend patterns to specific drivers for different return periods at the regional scale. We analyse flood discharge records from 2370 hydrometric stations in Europe over the period 1960–2010. Extreme precipitation, antecedent soil moisture and snowmelt are the potential drivers of flood change considered in this study. Results show that, in northwestern Europe, extreme precipitation mainly contributes to changes in both the median (q2) and 100-year flood (q100), while the contributions of antecedent soil moisture are of secondary importance. In southern Europe, both antecedent soil moisture and extreme precipitation contribute to flood changes, and their relative importance depends on the return period. Antecedent soil moisture is the main contributor to changes in q2, while the contributions of the two drivers to changes in larger floods (T〉10 years) are comparable. In eastern Europe, snowmelt drives changes in both q2 and q100.

Description: Hydrological models are useful tools for exploring the impact of climate change. To prioritize parameters for calibration and to evaluate hydrological model functioning, sensitivity analysis can be conducted. Parameter sensitivity, however, varies over climate, and therefore climate change could influence parameter sensitivity. In this study we explore the change in parameter sensitivity for the mean discharge and the timing of the discharge, within a plausible climate change rate. We investigate whether changes in sensitivity propagate into the calibration strategy and diagnostically compare three hydrological models based on the sensitivity results. We employed three frequently used hydrological models (SAC, VIC, and HBV) and explored parameter sensitivity changes across 605 catchments in the United States by comparing GCM(RCP8.5)-forced historical and future periods. Consistent among all hydrological models and both for the mean discharge and the timing of the discharge is that the sensitivity of snow parameters decreases in the future. Which other parameters increase in sensitivity is less consistent among the hydrological models. In 45 % to 55 % of the catchments, dependent on the hydrological model, at least one parameter changes in the future in the top-5 most sensitive parameters for mean discharge. For the timing, this varies between 40 % and 88 %. This requires an adapted calibration strategy for long-term projections, for which we provide several suggestions. The disagreement among the models on the processes that become more relevant in future projections also calls for a strict evaluation of the adequacy of the model structure for long-term simulations.

Description: Agricultural watersheds are significant contributors to downstream nutrient excess issues. The timing and magnitude of nutrient mobilization in these watersheds are driven by a combination of anthropogenic, hydrologic, and biogeochemical factors that operate across a range of spatial and temporal scales. However, how, when, and where these complex factors drive nutrient mobilization has previously been difficult to capture with low-frequency or spatially limited data sets. To address this knowledge gap, we analyzed daily nitrate concentration (c) and discharge (Q) data for a 4-year period (2016–2019) from five nested, agricultural watersheds in the midwestern United States that contribute nutrient loads to the Gulf of Mexico. These records allow us to investigate nutrient mobilization patterns at a temporal and spatial resolution not previously possible. The watersheds span two distinct landforms shaped by differences in glacial history, resulting in natural soil properties that necessitated different drainage infrastructure across the study area. To investigate nutrient export patterns under different hydrologic conditions, we partitioned the hydrograph into stormflow and baseflow periods and examined those periods separately through the analysis of their concentration–discharge (c–Q) relationships on annual, seasonal, and event timescales. Stormflow showed consistent chemostatic patterns across all seasons, while baseflow showed seasonally dynamic c–Q patterns. Baseflow exhibited chemodynamic conditions in the summer and fall and more chemostatic conditions in the winter and spring, suggesting that water source contributions during baseflow were nonstationary. Baseflow chemodynamic behavior was driven by low-flow, low-NO3- conditions during which in-stream and near-stream biological processing likely moderated in-stream NO3- concentrations. Additionally, inputs from deeper groundwater with longer residence times and lower-NO3- concentration likely contributed to low-NO3- conditions in stream, particularly in the larger watersheds. Stormflow c–Q behavior was consistent across watersheds, but baseflow c–Q behavior was linked to the intensity of agriculture and the density of built drainage infrastructure, with more drainage infrastructure associated with higher loads and more chemostatic export patterns across the watersheds. This suggests that the way humans replumb the subsurface in response to geologic conditions has implications for hydrologic connectivity, homogenization of source areas, and, subsequently, nutrient export during both baseflow and stormflow. Our analysis also showed that anomalous flow periods greatly influenced overall c–Q patterns, suggesting that the analysis of high-resolution records at multiple scales is critical when interpreting seasonal or annual patterns.

Description: Major multi-reservoir cascades represent a primary mechanism for dealing with hydrologic variability and extremes within institutionally complex river basins worldwide. These coordinated management processes fundamentally reshape water balance dynamics. Yet, multi-reservoir coordination processes have been largely ignored in the increasingly sophisticated representations of reservoir operations within large-scale hydrological models. The aim of this paper is twofold, namely (i) to provide evidence that the common modeling practice of parameterizing each reservoir in a cascade independently from the others is a significant approximation and (ii) to demonstrate potential unintended consequences of this independence approximation when simulating the dynamics of hydrological extremes in complex reservoir cascades. We explore these questions using the Water Balance Model, which features detailed representations of the human infrastructure coupled to the natural processes that shape water balance dynamics. It is applied to the Upper Snake River basin in the western US and its heavily regulated multi-reservoir cascade. We employ a time-varying sensitivity analysis that utilizes the method of Morris factor screening to explicitly track how the dominant release rule parameters evolve both along the cascade and in time according to seasonal high- and low-flow events. This enables us to address aim (i) by demonstrating how the progressive and cumulative dominance of upstream releases significantly dampens the ability of downstream reservoir rules' parameters to influence flow conditions. We address aim (ii) by comparing simulation results with observed reservoir operations during critical low-flow and high-flow events in the basin. Our time-varying parameter sensitivity analysis with the method of Morris clarifies how independent single-reservoir parameterizations and their tacit assumption of independence leads to reservoir release behaviors that generate artificial water shortages and flooding, whereas the observed coordinated cascade operations avoided these outcomes for the same events. To further explore the role of (non-)coordination in the large deviations from the observed operations, we use an offline multi-reservoir water balance model in which adding basic coordination mechanisms drawn from the observed emergency operations is sufficient to correct the deficiencies of the independently parameterized reservoir rules from the hydrological model. These results demonstrate the importance of understanding the state–space context in which reservoir releases occur and where operational coordination plays a crucial role in avoiding or mitigating water-related extremes. Understanding how major infrastructure is coordinated and controlled in major river basins is essential for properly assessing future flood and drought hazards in a changing world.

Description: Although the parameters of hydrological models are usually regarded as constant, temporal variations can occur in a changing environment. Thus, effectively estimating time-varying parameters becomes a significant challenge. Two methods, including split-sample calibration (SSC) and data assimilation, have been used to estimate time-varying parameters. However, SSC is unable to consider the parameter temporal continuity, while data assimilation assumes parameters vary at every time step. This study proposed a new method that combines (1) the basic concept of split-sample calibration, whereby parameters are assumed to be stable for one sub-period, and (2) the parameter continuity assumption; i.e. the differences between parameters in consecutive time steps are small. Dynamic programming is then used to determine the optimal parameter trajectory by considering two objective functions: maximization of simulation accuracy and maximization of parameter continuity. The efficiency of the proposed method is evaluated by two synthetic experiments, one with a simple 2-parameter monthly model and the second using a more complex 15-parameter daily model. The results show that the proposed method is superior to SSC alone and outperforms the ensemble Kalman filter if the proper sub-period length is used. An application to the Wuding River basin indicates that the soil water capacity parameter varies before and after 1972, which can be interpreted according to land use and land cover changes. A further application to the Xun River basin shows that parameters are generally stationary on an annual scale but exhibit significant changes over seasonal scales. These results demonstrate that the proposed method is an effective tool for identifying time-varying parameters in a changing environment.

Description: The term “flash drought” is frequently invoked to describe droughts that develop rapidly over a relatively short timescale. Despite extensive and growing research on flash drought processes, predictability, and trends, there is still no standard quantitative definition that encompasses all flash drought characteristics and pathways. Instead, diverse definitions have been proposed, supporting wide-ranging studies of flash drought but creating the potential for confusion as to what the term means and how to characterize it. Use of different definitions might also lead to different conclusions regarding flash drought frequency, predictability, and trends under climate change. In this study, we compared five previously published definitions, a newly proposed definition, and an operational satellite-based drought monitoring product to clarify conceptual differences and to investigate the sensitivity of flash drought inventories and trends to the choice of definition. Our analyses indicate that the newly introduced Soil Moisture Volatility Index definition effectively captures flash drought onset in both humid and semi-arid regions. Analyses also showed that estimates of flash drought frequency, spatial distribution, and seasonality vary across the contiguous United States depending upon which definition is used. Definitions differ in their representation of some of the largest and most widely studied flash droughts of recent years. Trend analysis indicates that definitions that include air temperature show significant increases in flash droughts over the past 40 years, but few trends are evident for definitions based on other surface conditions or fluxes. These results indicate that “flash drought” is a composite term that includes several types of events and that clarity in definition is critical when monitoring, forecasting, or projecting the drought phenomenon.

Description: Drought is a natural extreme climate phenomenon that presents great challenges in forecasting and monitoring for water management purposes. Previous studies have examined the use of Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage anomalies to measure the amount of water missing from a drought-affected region, and other studies have attempted statistical approaches to drought recovery forecasting based on joint probabilities of precipitation and soil moisture. The goal of this study is to combine GRACE data and historical precipitation observations to quantify the amount of precipitation required to achieve normal storage conditions in order to estimate a likely drought recovery time. First, linear relationships between terrestrial water storage anomaly (TWSA) and cumulative precipitation anomaly are established across a range of conditions. Then, historical precipitation data are statistically modeled to develop simplistic precipitation forecast skill based on climatology and long-term trend. Two additional precipitation scenarios are simulated to predict the recovery period by using a standard deviation in climatology and long-term trend. Precipitation scenarios are convolved with water deficit estimates (from GRACE) to calculate the best estimate of a drought recovery period. The results show that, in the regions of strong seasonal amplitude (like a monsoon belt), drought continues even with above-normal precipitation until its wet season. The historical GRACE-observed drought recovery period is used to validate the approach. Estimated drought for an example month demonstrated an 80 % recovery period, as observed by the GRACE.

Description: The effect of macropore flow on solute transport has spurred much research over the last forty years. In this study, non-reactive solute transport in water-saturated columns filled with porous media crossed by a macropore was experimentally and numerically investigated. The emphasis was put on the study of exit effects, whose very existence is inherent to the finite size of any experimental column. We specifically investigated the impact of a filter at the column outlet on water flow and solute transport in macroporous systems. Experiments involving breakthrough measurements and magnetic resonance imaging (MRI) showed that solute transport displayed some significant non-unidirectional features, with a strong mass exchange at the interface between the macropore and the matrix. Fluid dynamics and transport simulations indicated that this was due to the non-unidirectional nature of the flow field close to the outlet filter. The flow near the exit of the column was shown to be strongly impacted by the presence of the outlet filter, which acts as a barrier and redistributes water from the macropore to the matrix. This impact was apparent on the breakthrough curves and the MRI images. It was also confirmed by computer simulations and could, if not properly taken into account, impede the accurate inference of the transport properties of macroporous media from breakthrough experiments.

Description: Future climate scenarios for the Mediterranean region indicate a possible decrease in annual precipitation associated with an intensification of extreme rainfall events in the coming years. A major challenge in this region is to evaluate the impacts of changing precipitation patterns on extreme hydrological events such as droughts and floods. For this, it is important to understand the impact of climate change on soil moisture since it is a proxy for agricultural droughts, and the antecedent soil moisture condition plays a key role on runoff generation. This study focuses on 10 sites, located in southern France, with available soil moisture, temperature, and precipitation observations for a 10-year time period. Soil moisture is simulated at each site at the hourly time step using a model of soil water content. The sensitivity of the simulated soil moisture to different changes in precipitation and temperature is evaluated by simulating the soil moisture response to temperature and precipitation scenarios generated using a delta change method for temperature and a stochastic model (the Neyman–Scott rectangular pulse model) for precipitation. Results show that soil moisture is more impacted by changes in precipitation intermittence than precipitation intensity and temperature. Overall, increased temperature and precipitation intensity associated with more intermittent precipitation leads to decreased soil moisture and an increase in the annual number of days with dry soil moisture conditions. In particular, a temperature increase of +4 ∘C combined with a decrease of annual rainfall between 10 % and 20 %, corresponding to the current available climate scenarios for the Mediterranean, lead to a lengthening of the drought period from June to October with an average of +28 d of soil moisture drought per year.

Description: In the context of major changes (climate, demography, economy, etc.), the southern Mediterranean area faces serious challenges with intrinsically low, irregular, and continuously decreasing water resources. In some regions, the proper growth both in terms of cropping density and surface area of irrigated areas is so significant that it needs to be included in future scenarios. A method for estimating the future evolution of irrigation water requirements is proposed and tested in the Tensift watershed, Morocco. Monthly synthetic crop coefficients (Kc) of the different irrigated areas were obtained from a time series of remote sensing observations. An empirical model using the synthetic Kc and rainfall was developed and fitted to the actual data for each of the different irrigated areas within the study area. The model consists of a system of equations that takes into account the monthly trend of Kc, the impact of yearly rainfall, and the saturation of Kc due to the presence of tree crops. The impact of precipitation change is included in the Kc estimate and the water budget. The anthropogenic impact is included in the equations for Kc. The impact of temperature change is only included in the reference evapotranspiration, with no impact on the Kc cycle. The model appears to be reliable with an average r2 of 0.69 for the observation period (2000–2016). However, different subsampling tests of the number of calibration years showed that the performance is degraded when the size of the training dataset is reduced. When subsampling the training dataset to one-third of the 16 available years, r2 was reduced to 0.45. This score has been interpreted as the level of reliability that could be expected for two time periods after the full training years (thus near to 2050). The model has been used to reinterpret a local water management plan and to incorporate two downscaled climate change scenarios (RCP4.5 and RCP8.5). The examination of irrigation water requirements until 2050 revealed that the difference between the two climate scenarios was very small (

Description: Forest evaporation exports a vast amount of water vapor from land ecosystems into the atmosphere. Meanwhile, evaporation during rain events is neglected or considered of minor importance in dense ecosystems. Air convection moves the water vapor upwards leading to the formation of large invisible vapor plumes, while the identification of visible vapor plumes has not yet been studied. This work describes the formation process of vapor plumes in a tropical wet forest as evidence of evaporation processes happening during rain events. In the dry season of 2018 at La Selva Biological Station (LSBS) in Costa Rica it was possible to spot visible vapor plumes within the forest canopy. The combination of time-lapse videos at the canopy top with conventional meteorological measurements along the canopy profile allowed us to identify the driver conditions required for this process to happen. This phenomenon happened only during rain events. Visible vapor plumes during the daytime occurred when the following three conditions are accomplished: presence of precipitation (P), air convection, and a lifting condensation level value smaller than 100 m at 43 m height (zlcl.43).

Description: Although surface velocities are key in the calibration of physically based urban drainage models, the shallow water depths developed during non-extreme precipitation and the potential risks during flood events limit the availability of this type of data in urban catchments. In this context, imaging velocimetry techniques are being investigated as suitable non-intrusive methods to estimate runoff velocities, when the possible influence of rain has yet to be analyzed. This study carried out a comparative assessment of different seeded and unseeded imaging velocimetry techniques based on large-scale particle image velocimetry (LSPIV) and bubble image velocimetry (BIV) through six realistic but laboratory-controlled experiments, in which the runoff generated by three different rain intensities was recorded. First, the use of naturally generated bubbles and water shadows and glares as tracers allows unseeded techniques to measure extremely shallow flows. However, these techniques are more affected by raindrop impacts, which even lead to erroneous velocities in the case of high rain intensities. At the same time, better results were obtained for high intensities and in complex flows with techniques that use artificial particles. Finally, the study highlights the potential of these imaging techniques for measuring surface velocities in real field applications as well as the importance of considering rain properties to interpret and assess the results obtained. The robustness of the techniques for real-life applications yet remains to be proven by means of further studies in non-controlled environments.

Description: The previous comparative studies on watersheds were mostly based on the comparison of dispersive characteristics, which lacked systemicity and causality. We proposed a causal structure-based framework for basin comparison based on the Bayesian network (BN) and focus on the basin-scale water–energy–food–ecology (WEFE) nexus. We applied it to the Syr Darya River basin (SDB) and the Amu Darya River basin (ADB), of which poor water management caused the Aral Sea disaster. The causality of the nexus was effectively compared and universality of this framework was discussed. In terms of changes in the nexus, the sensitive factor for the water supplied to the Aral Sea changed from the agricultural development during the Soviet Union period to the disputes in the WEFE nexus after the disintegration. The water–energy contradiction of the SDB is more severe than that of the ADB, partly due to the higher upstream reservoir interception capacity. It further made management of the winter surplus water downstream of the SDB more controversial. Due to this, the water–food–ecology conflict between downstream countries may escalate and turn into a long-term chronic problem. Reducing water inflow to depressions and improving the planting structure prove beneficial to the Aral Sea ecology, and this effect of the SDB is more significant. The construction of reservoirs on the Panj River of the upstream ADB should be cautious to avoid an intense water–energy conflict such as the SDB's. It is also necessary to promote the water-saving drip irrigation and to strengthen the cooperation.

Description: Timely and accurate estimation of reference evapotranspiration (ET0) is indispensable for agricultural water management for efficient water use. This study aims to estimate the amount of ET0 with machine learning approaches by using minimum meteorological parameters in the Corum region, which has an arid and semi-arid climate and is regarded as an important agricultural centre of Turkey. In this context, monthly averages of meteorological variables, i.e. maximum and minimum temperature; sunshine duration; wind speed; and average, maximum, and minimum relative humidity, are used as inputs. Two different kernel-based methods, i.e. Gaussian process regression (GPR) and support vector regression (SVR), together with a Broyden–Fletcher–Goldfarb–Shanno artificial neural network (BFGS-ANN) and long short-term memory (LSTM) models were used to estimate ET0 amounts in 10 different combinations. The results showed that all four methods predicted ET0 amounts with acceptable accuracy and error levels. The BFGS-ANN model showed higher success (R2=0.9781) than the others. In kernel-based GPR and SVR methods, the Pearson VII function-based universal kernel was the most successful (R2=0.9771). Scenario 5, with temperatures including average temperature, maximum and minimum temperature, and sunshine duration as inputs, gave the best results. The second best scenario had only the sunshine duration as the input to the BFGS-ANN, which estimated ET0 having a correlation coefficient of 0.971 (Scenario 8). Conclusively, this study shows the better efficacy of the BFGS in ANNs for enhanced performance of the ANN model in ET0 estimation for drought-prone arid and semi-arid regions.

Description: A reservoir operator does not favor storage above a certain level in situations such as the pre-release operation prior to a flood; scheduled engineering construction; or mechanical excavation of sediment in the impoundments, drawdown, and empty flushing, etc. This paper selects empty flushing as the case study, and a method is presented to promote the feasibility of emptying the reservoir storage. The impact of emptying reservoir on water supply is minimized through appropriate joint operation in a multi-reservoir system, where drawdown and empty flushing is carried out in a primary reservoir, and the other reservoir provides backup water for supply. This method prioritizes allocating the storage in the primary reservoir for water supply during specific periods prior to its emptying. If the storage of every reservoir achieves its predefined conditions, drawdown of the primary reservoir is activated and followed by empty flushing. Previously preserved storage in the other reservoir ensures adequate water supply during the periods of emptying the primary reservoir. Flushing of the primary reservoir is continued until either the accumulative released water exceeds the specified volume, storage in the backup reservoir drops below the predefined threshold, or the inflow to the primary reservoir recedes from the flood peak to be below the releasing capacity of outlets. This behavior is simulated and linked with a nonlinear optimization algorithm to calibrate the optimal parameters defining the activation and termination of empty flushing. The optimized strategy limits the incremental water shortage within the acceptable threshold and maximizes the expected benefits of emptying reservoir.

Description: Long Short-Term Memory (LSTM) networks have been applied to daily discharge prediction with remarkable success. Many practical applications, however, require predictions at more granular timescales. For instance, accurate prediction of short but extreme flood peaks can make a lifesaving difference, yet such peaks may escape the coarse temporal resolution of daily predictions. Naively training an LSTM on hourly data, however, entails very long input sequences that make learning difficult and computationally expensive. In this study, we propose two multi-timescale LSTM (MTS-LSTM) architectures that jointly predict multiple timescales within one model, as they process long-past inputs at a different temporal resolution than more recent inputs. In a benchmark on 516 basins across the continental United States, these models achieved significantly higher Nash–Sutcliffe efficiency (NSE) values than the US National Water Model. Compared to naive prediction with distinct LSTMs per timescale, the multi-timescale architectures are computationally more efficient with no loss in accuracy. Beyond prediction quality, the multi-timescale LSTM can process different input variables at different timescales, which is especially relevant to operational applications where the lead time of meteorological forcings depends on their temporal resolution.

Description: Frozen soil processes are of great importance in controlling surface water and energy balances during the cold season and in cold regions. Over recent decades, considerable frozen soil degradation and surface soil warming have been reported over the Tibetan Plateau and North China, but most land surface models have difficulty in capturing the freeze–thaw cycle, and few validations focus on the effects of frozen soil processes on soil thermal characteristics in these regions. This paper addresses these issues by introducing a physically more realistic and computationally more stable and efficient frozen soil module (FSM) into a land surface model – the third-generation Simplified Simple Biosphere Model (SSiB3-FSM). To overcome the difficulties in achieving stable numerical solutions for frozen soil, a new semi-implicit scheme and a physics-based freezing–thawing scheme were applied to solve the governing equations. The performance of this model as well as the effects of frozen soil process on the soil temperature profile and soil thermal characteristics were investigated over the Tibetan Plateau and North China using observation sites from the China Meteorological Administration and models from 1981 to 2005. Results show that the SSiB3 model with the FSM produces a more realistic soil temperature profile and its seasonal variation than that without FSM during the freezing and thawing periods. The freezing process in soil delays the winter cooling, while the thawing process delays the summer warming. The time lag and amplitude damping of temperature become more pronounced with increasing depth. These processes are well simulated in SSiB3-FSM. The freeze–thaw processes could increase the simulated phase lag days and land memory at different soil depths as well as the soil memory change with the soil thickness. Furthermore, compared with observations, SSiB3-FSM produces a realistic change in maximum frozen soil depth at decadal scales. This study shows that the soil thermal characteristics at seasonal to decadal scales over frozen ground can be greatly improved in SSiB3-FSM, and SSiB3-FSM can be used as an effective model for TP and NC simulation during cold season. Overall, this study could help understand the vertical soil thermal characteristics over the frozen ground and provide an important scientific basis for land–atmosphere interactions.

Description: The drought of 2018 in central and northern Europe showed once more the large impact that this natural hazard can have on the environment and society. Such droughts are often seen as slowly developing phenomena. However, root zone soil moisture deficits can rapidly develop during periods lacking precipitation and meteorological conditions that favor high evapotranspiration rates. These periods of soil moisture stress can persist for as long as the meteorological drought conditions last, thereby negatively affecting vegetation and crop health. In this study, we aim to characterize past soil moisture stress events over the croplands of southwestern Germany and, furthermore, to relate the characteristics of these past events to different soil and climate properties. We first simulated daily soil moisture over the period 1989–2018 on a 1 km resolution grid, using the physically based hydrological model TRAIN. We then derived various soil moisture stress characteristics, including probability, development time, and persistence, from the simulated time series of all agricultural grid cells (n≈15 000). Logistic regression and correlation were then applied to relate the derived characteristics to the plant-available storage capacity of the root zone and to the climatological setting. Finally, sensitivity analyses were carried out to investigate how results changed when using a different parameterization of the root zone, i.e., soil based or fixed, or when assessing soil moisture drought (anomaly) instead of stress. Results reveal that the majority of agricultural grid cells across the study region reached soil moisture stress during prominent drought years. The development time of these soil moisture stress events varied substantially, from as little as 10 d to over 4 months. The persistence of soil moisture stress varied as well and was especially high for the drought of 2018. A strong control on the probability and development time of soil moisture stress was found to be the storage capacity of the root zone, whereas the persistence was not strongly linearly related to any of the considered controls. On the other hand, the sensitivity analyses revealed the increased control of climate on soil moisture stress characteristics when using a fixed instead of a soil-based root zone storage. Thus, the strength of different controls depends on the assumptions made during modeling. Nonetheless, the storage capacity of the root zone, whether it is a characteristic of the soil or a difference between a shallow or deep rooting crop, remains an important control on soil moisture stress characteristics. This is different for SM drought characteristics, which have little or contrasting relation with the storage capacity of the root zone. Overall, the results give insight to the large spatial and temporal variability in soil moisture stress characteristics and suggest the importance of considering differences in root zone soil storage for agricultural drought assessments.

Description: Floods and flash floods are complex events, depending on weather dynamics, basin physiographical characteristics, land use cover and water management. For this reason, the prediction of such events usually deals with very accurate model tuning and validation, which is usually site-specific and based on climatological data, such as discharge time series or flood databases. In this work, we developed and tested two hydrological-stress indices for flood detection in the Italian Central Apennine District: a heterogeneous geographical area, characterized by complex topography and medium-to-small catchment extension. The proposed indices are threshold-based and developed considering operational requirements of National Civil Protection Department end-users. They are calibrated and tested through the application of signal theory, in order to overcome data scarcity over ungauged areas, as well as incomplete discharge time series. The validation has been carried out on a case study basis, using flood reports from various sources of information, as well as hydrometric-level time series, which represent the actual hydrological quantity monitored by Civil Protection operators. Obtained results show that the overall accuracy of flood prediction is greater than 0.8, with false alarm rates below 0.5 and the probability of detection ranging from 0.51 to 0.80. Moreover, the different nature of the proposed indices suggests their application in a complementary way, as the index based on drained precipitation appears to be more sensitive to rapid flood propagation in small tributaries, while the discharge-based index is particularly responsive to main-channel dynamics.

Description: Water recycled through transpiring forests influences the spatial distribution of precipitation in the Amazon and has been shown to play a role in the initiation of the wet season. However, due to the challenges and costs associated with measuring evapotranspiration (ET) directly and high uncertainty in remote-sensing ET retrievals, the spatial and temporal patterns in Amazon ET remain poorly understood. In this study, we estimated ET over the Amazon and 10 sub-basins using a catchment-balance approach, whereby ET is calculated directly as the balance between precipitation, runoff, and change in groundwater storage. We compared our results with ET from remote-sensing datasets, reanalysis, models from Phase 5 and Phase 6 of the Coupled Model Intercomparison Projects (CMIP5 and CMIP6 respectively), and in situ flux tower measurements to provide a comprehensive overview of current understanding. Catchment-balance analysis revealed a gradient in ET from east to west/southwest across the Amazon Basin, a strong seasonal cycle in basin-mean ET primarily controlled by net incoming radiation, and no trend in ET over the past 2 decades. This approach has a degree of uncertainty, due to errors in each of the terms of the water budget; therefore, we conducted an error analysis to identify the range of likely values. Satellite datasets, reanalysis, and climate models all tended to overestimate the magnitude of ET relative to catchment-balance estimates, underestimate seasonal and interannual variability, and show conflicting positive and negative trends. Only two out of six satellite and model datasets analysed reproduced spatial and seasonal variation in Amazon ET, and captured the same controls on ET as indicated by catchment-balance analysis. CMIP5 and CMIP6 ET was inconsistent with catchment-balance estimates over all scales analysed. Overall, the discrepancies between data products and models revealed by our analysis demonstrate a need for more ground-based ET measurements in the Amazon as well as a need to substantially improve model representation of this fundamental component of the Amazon hydrological cycle.

Description: Quantifying how vegetation mediates water partitioning at different spatial and temporal scales in complex, managed catchments is fundamental for long-term sustainable land and water management. Estimations from ecohydrological models conceptualising how vegetation regulates the interrelationships between evapotranspiration losses, catchment water storage dynamics, and recharge and runoff fluxes are needed to assess water availability for a range of ecosystem services and evaluate how these might change under increasing extreme events, such as droughts. Currently, the feedback mechanisms between water and mosaics of different vegetation and land cover are not well understood across spatial scales, and the effects of different scales on the skill of ecohydrological models needs to be clarified. We used the tracer-aided ecohydrological model EcH2O-iso in an intensively monitored 66 km2 mixed land use catchment in northeastern Germany to quantify water flux–storage–age interactions at four model grid resolutions (250, 500, 750, and 1000 m). This used a fusion of field (including precipitation, soil water, groundwater, and stream isotopes) and remote sensing data in the calibration. Multicriteria calibration across the catchment at each resolution revealed some differences in the estimation of fluxes, storages, and water ages. In general, model sensitivity decreased and uncertainty increased with coarser model resolutions. Larger grids were unable to replicate observed streamflow and distributed isotope dynamics in the way smaller pixels could. However, using isotope data in the calibration still helped constrain the estimation of fluxes, storage, and water ages at coarser resolutions. Despite using the same data and parameterisation for calibration at different grid resolutions, the modelled proportion of fluxes differed slightly at each resolution, with coarse models simulating higher evapotranspiration, lower relative transpiration, increased overland flow, and slower groundwater movement. Although the coarser resolutions also revealed higher uncertainty and lower overall model performance, the overall results were broadly similar. The study shows that tracers provide effective calibration constraints on larger resolution ecohydrological modelling and help us understand the influence of grid resolution on the simulation of vegetation–soil interactions. This is essential in interpreting associated uncertainty in estimating land use influence on large-scale “blue” (ground and surface water) and “green” (vegetation and evaporated water) fluxes, particularly for future environmental change.

Description: The biophysical processes occurring in the unsaturated zone have a direct impact on the water table dynamics. Representing these processes through the application of unsaturated zone models of different complexity has an impact on the estimates of the volumes of water flowing between the unsaturated zone and the aquifer. These fluxes, known as net recharge, are often used as the shared variable that couples unsaturated to groundwater models. However, as recharge estimates are always affected by a degree of uncertainty, model–data fusion methods, such as data assimilation, can be used to inform these coupled models and reduce uncertainty. This study assesses the effect of unsaturated zone models complexity (conceptual versus physically based) to update groundwater model outputs, through the assimilation of actual evapotranspiration rates, for a water-limited site in South Australia. Actual evapotranspiration rates are assimilated because they have been shown to be related to the water table dynamics and thus form the link between remote sensing data and the deeper parts of the soil profile. Results have been quantified using standard metrics, such as the root mean square error and Pearson correlation coefficient, and reinforced by calculating the continuous ranked probability score, which is specifically designed to determine a more representative error in stochastic models. It has been found that, once properly calibrated to reproduce the actual evapotranspiration–water table dynamics, a simple conceptual model may be sufficient for this purpose; thus using one configuration over the other should be motivated by the specific purpose of the simulation and the information available.

Description: With respect to ongoing discussions about the causes of energy imbalance and approaches to force energy balance closure, a method has been proposed that allows partial latent heat flux closure (Widmoser and Wohlfahrt, 2018). In the present paper, this method is applied to four measurement stations over grassland under humid and semiarid climates, where lysimeter (LY) and eddy covariance (EC) measurements were taken simultaneously. The results differ significantly from the ones reported in the literature. We distinguish between the resulting EC values being weakly and strongly correlated to LY observations as well as systematic and random deviations between the LY and EC values. Overall, an excellent match could be achieved between the LY and EC measurements after applying evaporation-linked weights. But there remain large differences between the standard deviations of the LY and adjusted EC values. For further studies we recommend data collected at time intervals even below 0.5 h. No correlation could be found between evaporation weights and weather indices. Only for some datasets, a positive correlation between evaporation and the evaporation weight could be found. This effect appears pronounced for cases with high radiation and plant water stress. Without further knowledge of the causes of energy imbalance one might perform full closure using equally distributed weights. Full closure, however, is not dealt with in this paper.

Description: The streamflow of the Yellow River (YR) is strongly affected by human activities like irrigation and dam operation. Many attribution studies have focused on the long-term trends of streamflows, yet the contributions of these anthropogenic factors to streamflow fluctuations have not been well quantified with fully mechanistic models. This study aims to (1) demonstrate whether the mechanistic global land surface model ORCHIDEE (ORganizing Carbon and Hydrology in Dynamic EcosystEms) is able to simulate the streamflows of this complex rivers with human activities using a generic parameterization for human activities and (2) preliminarily quantify the roles of irrigation and dam operation in monthly streamflow fluctuations of the YR from 1982 to 2014 with a newly developed irrigation module and an offline dam operation model. Validations with observed streamflows near the outlet of the YR demonstrated that model performances improved notably with incrementally considering irrigation (mean square error (MSE) decreased by 56.9 %) and dam operation (MSE decreased by another 30.5 %). Irrigation withdrawals were found to substantially reduce the river streamflows by approximately 242.8±27.8×108 m3 yr−1 in line with independent census data (231.4±31.6×108 m3 yr−1). Dam operation does not change the mean streamflows in our model, but it impacts streamflow seasonality, more than the seasonal change of precipitation. By only considering generic operation schemes, our dam model is able to reproduce the water storage changes of the two large reservoirs, LongYangXia and LiuJiaXia (correlation coefficient of ∼ 0.9). Moreover, other commonly neglected factors, such as the large operation contribution from multiple medium/small reservoirs, the dominance of large irrigation districts for streamflows (e.g., the Hetao Plateau), and special management policies during extreme years, are highlighted in this study. Related processes should be integrated into models to better project future YR water resources under climate change and optimize adaption strategies.

Description: Streamflow is often the only variable used to evaluate hydrological models. In a previous international comparison study, eight research groups followed an identical protocol to calibrate 12 hydrological models using observed streamflow of catchments within the Meuse basin. In the current study, we quantify the differences in five states and fluxes of these 12 process-based models with similar streamflow performance, in a systematic and comprehensive way. Next, we assess model behavior plausibility by ranking the models for a set of criteria using streamflow and remote-sensing data of evaporation, snow cover, soil moisture and total storage anomalies. We found substantial dissimilarities between models for annual interception and seasonal evaporation rates, the annual number of days with water stored as snow, the mean annual maximum snow storage and the size of the root-zone storage capacity. These differences in internal process representation imply that these models cannot all simultaneously be close to reality. Modeled annual evaporation rates are consistent with Global Land Evaporation Amsterdam Model (GLEAM) estimates. However, there is a large uncertainty in modeled and remote-sensing annual interception. Substantial differences are also found between Moderate Resolution Imaging Spectroradiometer (MODIS) and modeled number of days with snow storage. Models with relatively small root-zone storage capacities and without root water uptake reduction under dry conditions tend to have an empty root-zone storage for several days each summer, while this is not suggested by remote-sensing data of evaporation, soil moisture and vegetation indices. On the other hand, models with relatively large root-zone storage capacities tend to overestimate very dry total storage anomalies of the Gravity Recovery and Climate Experiment (GRACE). None of the models is systematically consistent with the information available from all different (remote-sensing) data sources. Yet we did not reject models given the uncertainties in these data sources and their changing relevance for the system under investigation.

Description: Briggs (1897) deduced capillary flow from deviation of the equilibrium between capillarity and gravity. Richards (1931) raised capillary flow to an unproven soil hydrological dogma. Attempts to correct the dogma led to concepts of non-equilibrium flow, macropore flow, and preferential flow during infiltration. Viscous film flow is proposed as an alternative approach to capillarity-driven flow during unsaturated infiltration.

Description: Reliable historical manual measurements of snow depths are available for many years, sometimes decades, across the globe, and increasingly snow depth data are also available from automatic stations and remote sensing platforms. In contrast, records of snow water equivalent (SWE) are sparse, which is significant as SWE is commonly the most important snowpack feature for hydrology, climatology, agriculture, natural hazards, and other fields. Existing methods of modeling SWE either rely on detailed meteorological forcing being available or are not intended to simulate individual SWE values, such as seasonal “peak SWE”. Here we present a new semiempirical multilayer model, Δsnow, for simulating SWE and bulk snow density solely from a regular time series of snow depths. The model, which is freely available as an R package, treats snow compaction following the rules of Newtonian viscosity, considers errors in measured snow depth, and treats overburden loads due to new snow as additional unsteady compaction; if snow is melted, the water mass is stepwise distributed from top to bottom in the snowpack. Seven model parameters are subject to calibration. Snow observations of 67 winters from 14 stations, well-distributed over different altitudes and climatic regions of the Alps, are used to find an optimal parameter setting. Data from another 71 independent winters from 15 stations are used for validation. Results are very promising: median bias and root mean square error for SWE are only −3.0 and 30.8 kg m−2, and +0.3 and 36.3 kg m−2 for peak SWE, respectively. This is a major advance compared to snow models relying on empirical regressions, and even sophisticated thermodynamic snow models do not necessarily perform better. As such, the new model offers a means to derive robust SWE estimates from historical snow depth data and, with some modification, to generate distributed SWE from remotely sensed estimates of spatial snow depth distribution.

Description: The theory that forms the basis of TOPMODEL (a topography-based hydrological model) was first outlined by Mike Kirkby some 45 years ago. This paper recalls some of the early developments, the rejection of the first journal paper, the early days of digital terrain analysis, model calibration and validation, the various criticisms of the simplifying assumptions, and the relaxation of those assumptions in the dynamic forms of TOPMODEL. A final section addresses the question of what might be done now in seeking a simple, parametrically parsimonious model of hillslope and small catchment processes if we were starting again.

Description: The number of personal weather stations (PWSs) with data available through the internet is increasing gradually in many parts of the world. The purpose of this study is to investigate the applicability of these data for the spatial interpolation of precipitation using a novel approach based on indicator correlations and rank statistics. Due to unknown errors and biases of the observations, rainfall amounts from the PWS network are not considered directly. Instead, it is assumed that the temporal order of the ranking of these data is correct. The crucial step is to find the stations which fulfil this condition. This is done in two steps – first, by selecting the locations using the time series of indicators of high precipitation amounts. Then, the remaining stations are then checked for whether they fit into the spatial pattern of the other stations. Thus, it is assumed that the quantiles of the empirical distribution functions are accurate. These quantiles are then transformed to precipitation amounts by a quantile mapping using the distribution functions which were interpolated from the information from the German National Weather Service (Deutscher Wetterdienst – DWD) data only. The suggested procedure was tested for the state of Baden-Württemberg in Germany. A detailed cross validation of the interpolation was carried out for aggregated precipitation amount of 1, 3, 6, 12 and 24 h. For each of these temporal aggregations, nearly 200 intense events were evaluated, and the improvement of the interpolation was quantified. The results show that the filtering of observations from PWSs is necessary as the interpolation error after the filtering and data transformation decreases significantly. The biggest improvement is achieved for the shortest temporal aggregations.

Description: Declining soil-saturated hydraulic conductivity (Ks) as a result of saline and sodic irrigation water is a major cause of soil degradation. While it is understood that the mechanisms that lead to degradation can cause irreversible changes in Ks, existing models do not account for hysteresis between the degradation and rehabilitation processes. We develop the first model for the effect of saline and sodic water on Ks that explicitly includes hysteresis. As such, the idea that a soil's history of degradation and rehabilitation determines its future Ks lies at the center of this model. By means of a “weight” function, the model accounts for soil-specific differences, such as clay content. The weight function also determines the form of the hysteresis curves, which are not restricted to a single shape, as in some existing models for irreversible soil processes. The concept of the weight function is used to develop a reversibility index, which allows for the quantitative comparison of different soils and their susceptibility to irreversible degradation. We discuss the experimental setup required to find a soil's weight function and show how the weight function determines the degree to which Ks is reversible for a given soil. We demonstrate the feasibility of this procedure by presenting experimental results showcasing the presence of hysteresis in soil Ks and using these results to calculate a weight function. Past experiments and models on the decline of Ks due to salinity and sodicity focus on degradation alone, ignoring any characterization of the degree to which declines in Ks are reversible. Our model and experimental results emphasize the need to measure “reversal curves”, which are obtained from rehabilitation measurements following mild declines in Ks. The developed model has the potential to significantly improve our ability to assess the risk of soil degradation by allowing for the consideration of how the accumulation of small degradation events can cause significant land degradation.

Description: Being an extensively produced natural fiber on earth, cotton is of importance for economies. Although the plant is broadly adapted to varying environments, the growth of and irrigation water demand on cotton may be challenged by future climate change. To study the impacts of climate change on cotton productivity in different regions across the world and the irrigation water requirements related to it, we use the process-based, spatially detailed biosphere and hydrology model LPJmL (Lund–Potsdam–Jena managed land). We find our modeled cotton yield levels in good agreement with reported values and simulated water consumption of cotton production similar to published estimates. Following the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) protocol, we employ an ensemble of five general circulation models under four representative concentration pathways (RCPs) for the 2011–2099 period to simulate future cotton yields. We find that irrigated cotton production does not suffer from climate change if CO2 effects are considered, whereas rainfed production is more sensitive to varying climate conditions. Considering the overall effect of a changing climate and CO2 fertilization, cotton production on current cropland steadily increases for most of the RCPs. Starting from ∼65 million tonnes in 2010, cotton production for RCP4.5 and RCP6.0 equates to 83 and 92 million tonnes at the end of the century, respectively. Under RCP8.5, simulated global cotton production rises by more than 50 % by 2099. Taking only climate change into account, projected cotton production considerably shrinks in most scenarios, by up to one-third or 43 million tonnes under RCP8.5. The simulation of future virtual water content (VWC) of cotton grown under elevated CO2 results for all scenarios in less VWC compared to ambient CO2 conditions. Under RCP6.0 and RCP8.5, VWC is notably decreased by more than 2000 m3 t−1 in areas where cotton is produced under purely rainfed conditions. By 2040, the average global VWC for cotton declines in all scenarios from currently 3300 to 3000 m3 t−1, and reduction continues by up to 30 % in 2100 under RCP8.5. While the VWC decreases by the CO2 effect, elevated temperature acts in the opposite direction. Ignoring beneficial CO2 effects, global VWC of cotton would increase for all RCPs except RCP2.6, reaching more than 5000 m3 t−1 by the end of the simulation period under RCP8.5. Given the economic relevance of cotton production, climate change poses an additional stress and deserves special attention. Changes in VWC and water demands for cotton production are of special importance, as cotton production is known for its intense water consumption. The implications of climate impacts on cotton production on the one hand and the impact of cotton production on water resources on the other hand illustrate the need to assess how future climate change may affect cotton production and its resource requirements. Our results should be regarded as optimistic, because of high uncertainty with respect to CO2 fertilization and the lack of implementing processes of boll abscission under heat stress. Still, the inclusion of cotton in LPJmL allows for various large-scale studies to assess impacts of climate change on hydrological factors and the implications for agricultural production and carbon sequestration.

Description: The transboundary Helmand River basin (HRB) is the main drainage system for large parts of Afghanistan and the Sistan region of Iran. Due to the reliance of this arid region on water from the Helmand River, a better understanding of hydrological-drought pattern and the underlying drivers in the region is critically required for effective management of the available water. The objective of this paper is therefore to analyze and quantify spatiotemporal pattern of drought and the underlying processes in the study region. More specifically we test for the Helmand River basin the following hypotheses for the 1970–2006 period: (1) drought characteristics, including frequency and severity, systematically changed over the study period; (2) the spatial pattern and processes of drought propagation through the Helmand River basin also changed; and (3) the relative roles of climate variability and human influence on changes in hydrological droughts can be quantified. It was found that drought characteristics varied throughout the study period but largely showed no systematic trends. The same was observed for the time series of drought indices SPI (standard precipitation index) and SPEI (standardized precipitation evapotranspiration index), which exhibited considerable spatial coherence and synchronicity throughout the basin, indicating that, overall, droughts similarly affect the entire HRB with few regional or local differences. In contrast, analysis of the SDI (streamflow drought index) exhibited significant negative trends in the lower parts of the basin, indicating an intensification of hydrological droughts. It could be shown that with a mean annual precipitation of ∼ 250 mm yr−1, streamflow deficits and thus hydrological drought throughout the HRB are largely controlled by precipitation deficits, whose annual anomalies on average account for ±50 mm yr−1, or ∼ 20 % of the water balance of the HRB, while anomalies of total evaporative fluxes on average only account for ±20 mm yr−1. Assuming no changes in the reservoir management practices over the study period, the results suggest that the two reservoirs in the HRB only played a minor role for the downstream propagation of streamflow deficits, as indicated by the mean difference between inflow and outflow during drought periods, which did not exceed ∼ 0.5 % of the water balance of the HRB. Irrigation water abstraction had a similarly limited effect on the magnitude of streamflow deficits, accounting for ∼ 10 % of the water balance of the HRB. However, the downstream parts of the HRB moderated the further propagation of streamflow deficits and associated droughts because of the minor effects of reservoir operation and very limited agricultural water in the early decades of the study period. This drought moderation function of the lower basin was gradually and systematically inverted by the end of the study period, when the lower basin eventually amplified the downstream propagation of flow deficits and droughts. Our results provide plausible evidence that this shift from drought moderation to drought amplification in the lower basin is likely a consequence of increased agricultural activity and the associated increases in irrigation water demand, from ∼ 13 mm yr−1 at the beginning of the study period to ∼ 23 mm yr−1 at the end, and thus in spite of being only a minor fraction of the water balance. Overall the results of this study illustrate that flow deficits and the associated droughts in the HRB clearly reflect the dynamic interplay between temporally varying regional differences in hydro-meteorological variables together with subtle and temporally varying effects linked to direct human intervention.

Description: In cities around the world, urban green spaces provide a range of benefits and ecosystem services. However, recent years have shown how prolonged warm and dry periods can affect urban water resources and lead to water stress in vegetation in urban green spaces, even in temperate regions. Consequently, quantitative knowledge about ecohydrological partitioning in different types of urban green space is crucial for balancing sustainable water needs in cities during future challenges of increasing urbanization and climate warming. Using isotopic tracers in precipitation and soil water, along with conventional hydrometric measurements in a plot-scale study in Berlin, Germany, we investigated water partitioning under different generic types of urban vegetation (grassland, shrub and trees). This allowed for the assessment of urban vegetation effects on evapotranspiration, subsurface flow paths and storage during a prolonged drought period with episodic rainfall. Despite higher soil evaporation losses under urban grassland, higher interception and transpiration likely contributed to slower turnover of soil water and older groundwater recharge under urban trees. Shrub vegetation seemed to be most resilient to prolonged drought periods, with lower evapotranspiration losses. Our results contribute to a better understanding of ecohydrological partitioning under mixed urban vegetation communities and an evidence base for better adaptive management of urban water and irrigation strategies to sustainably meet the water demands of urban green spaces in the future.

Description: Satellite observations can provide valuable information for a better understanding of hydrological processes and thus serve as valuable tools for model structure development and improvement. While model calibration and evaluation have in recent years started to make increasing use of spatial, mostly remotely sensed information, model structural development largely remains to rely on discharge observations at basin outlets only. Due to the ill-posed inverse nature and the related equifinality issues in the modelling process, this frequently results in poor representations of the spatio-temporal heterogeneity of system-internal processes, in particular for large river basins. The objective of this study is thus to explore the value of remotely sensed, gridded data to improve our understanding of the processes underlying this heterogeneity and, as a consequence, their quantitative representation in models through a stepwise adaptation of model structures and parameters. For this purpose, a distributed, process-based hydrological model was developed for the study region, the poorly gauged Luangwa River basin. As a first step, this benchmark model was calibrated to discharge data only and, in a post-calibration evaluation procedure, tested for its ability to simultaneously reproduce (1) the basin-average temporal dynamics of remotely sensed evaporation and total water storage anomalies and (2) their temporally averaged spatial patterns. This allowed for the diagnosis of model structural deficiencies in reproducing these temporal dynamics and spatial patterns. Subsequently, the model structure was adapted in a stepwise procedure, testing five additional alternative process hypotheses that could potentially better describe the observed dynamics and pattern. These included, on the one hand, the addition and testing of alternative formulations of groundwater upwelling into wetlands as a function of the water storage and, on the other hand, alternative spatial discretizations of the groundwater reservoir. Similar to the benchmark, each alternative model hypothesis was, in a next step, calibrated to discharge only and tested against its ability to reproduce the observed spatio-temporal pattern in evaporation and water storage anomalies. In a final step, all models were re-calibrated to discharge, evaporation and water storage anomalies simultaneously. The results indicated that (1) the benchmark model (Model A) could reproduce the time series of observed discharge, basin-average evaporation and total water storage reasonably well. In contrast, it poorly represented time series of evaporation in wetland-dominated areas as well as the spatial pattern of evaporation and total water storage. (2) Stepwise adjustment of the model structure (Models B–F) suggested that Model F, allowing for upwelling groundwater from a distributed representation of the groundwater reservoir and (3) simultaneously calibrating the model with respect to multiple variables, i.e. discharge, evaporation and total water storage anomalies, provided the best representation of all these variables with respect to their temporal dynamics and spatial patterns, except for the basin-average temporal dynamics in the total water storage anomalies. It was shown that satellite-based evaporation and total water storage anomaly data are not only valuable for multi-criteria calibration, but can also play an important role in improving our understanding of hydrological processes through the diagnosis of model deficiencies and stepwise model structural improvement.

Description: Prediction of mean annual runoff is of great interest but still poses a challenge in ungauged basins. The present work diagnoses the prediction in mean annual runoff affected by the uncertainty in estimated distribution of soil water storage capacity. Based on a distribution function, a water balance model for estimating mean annual runoff is developed, in which the effects of climate variability and the distribution of soil water storage capacity are explicitly represented. As such, the two parameters in the model have explicit physical meanings, and relationships between the parameters and controlling factors on mean annual runoff are established. The estimated parameters from the existing data of watershed characteristics are applied to 35 watersheds. The results showed that the model could capture 88.2 % of the actual mean annual runoff on average across the study watersheds, indicating that the proposed new water balance model is promising for estimating mean annual runoff in ungauged watersheds. The underestimation of mean annual runoff is mainly caused by the underestimation of the area percentage of low soil water storage capacity due to neglecting the effect of land surface and bedrock topography. Higher spatial variability of soil water storage capacity estimated through the height above the nearest drainage (HAND) and topographic wetness index (TWI) indicated that topography plays a crucial role in determining the actual soil water storage capacity. The performance of mean annual runoff prediction in ungauged basins can be improved by employing better estimation of soil water storage capacity including the effects of soil, topography, and bedrock. It leads to better diagnosis of the data requirement for predicting mean annual runoff in ungauged basins based on a newly developed process-based model finally.

Description: A popular parameterized soil water retention curve (SWRC) has a hydraulic conductivity curve associated with it that can have a physically unacceptable infinite slope at saturation. The problem was eliminated before by giving the SWRC a non-zero air entry value. This improved version still has an asymptote at the dry end, which limits its usefulness for dry conditions and causes its integral to diverge for commonly occurring parameter values. We therefore joined the parameterizations' sigmoid midsection to a logarithmic dry section ending at zero water content for a finite matric potential, as was done previously for a power-law-type SWRC. We selected five SWRC parameterizations that had been proven to produce unproblematic near-saturation conductivities and fitted these and our new curve to data from 21 soils. The logarithmic dry branch gave more realistic extrapolations into the dry end of both the retention and the conductivity curves than an asymptotic dry branch. We tested the original curve, its first improvement, and our second improvement by feeding them into a numerical model that calculated evapotranspiration and deep drainage for nine combinations of soils and climates. The new curve was more robust than the other two. The new curve was better able to produce a conductivity curve with a substantial drop during the early stages of drying than the earlier improvement. It therefore generated smaller amounts of more evenly distributed deep drainage compared to the spiked response to rainfall produced by the earlier improvement.

Description: Groundwater is the main source of freshwater and maintains streamflow during drought. Potential future groundwater and baseflow drought hazards depend on the systems' sensitivity to altered recharge conditions. We performed groundwater model experiments using three different generic stress tests to estimate the groundwater and baseflow drought sensitivity to changes in recharge. The stress tests stem from a stakeholder co-design process that specifically followed the idea of altering known drought events from the past, i.e. asking whether altered recharge could have made a particular event worse. Across Germany, groundwater responses to the stress tests are highly heterogeneous, with groundwater heads in the north more sensitive to long-term recharge and in the Central German Uplands to short-term recharge variations. Baseflow droughts are generally more sensitive to intra-annual dynamics, and baseflow responses to the stress tests are smaller compared to the groundwater heads. The groundwater drought recovery time is mainly driven by the hydrogeological conditions, with slow (fast) recovery in the porous (fractured rock) aquifers. In general, a seasonal shift of recharge (i.e. less summer recharge and more winter recharge) will have lesser effects on groundwater and baseflow drought severity. A lengthening of dry spells might cause much stronger responses, especially in regions with slow groundwater response to precipitation. Water management may need to consider the spatially different sensitivities of the groundwater system and the potential for more severe groundwater droughts in the large porous aquifers following prolonged meteorological droughts, particularly in the context of climate change projections indicating stronger seasonality and more severe drought events.

Description: This paper concerns the problem of optimal monitoring network layout using information-theoretical methods. Numerous different objectives based on information measures have been proposed in recent literature, often focusing simultaneously on maximum information and minimum dependence between the chosen locations for data collection stations. We discuss these objective functions and conclude that a single-objective optimization of joint entropy suffices to maximize the collection of information for a given number of stations. We argue that the widespread notion of minimizing redundancy, or dependence between monitored signals, as a secondary objective is not desirable and has no intrinsic justification. The negative effect of redundancy on total collected information is already accounted for in joint entropy, which measures total information net of any redundancies. In fact, for two networks of equal joint entropy, the one with a higher amount of redundant information should be preferred for reasons of robustness against failure. In attaining the maximum joint entropy objective, we investigate exhaustive optimization, a more computationally tractable greedy approach that adds one station at a time, and we introduce the “greedy drop” approach, where the full set of stations is reduced one at a time. We show that no greedy approach can exist that is guaranteed to reach the global optimum.

Description: Alpine streams are particularly valuable for downstream water resources and of high ecological relevance; however, a detailed understanding of water storage and release in such heterogeneous environments is often still lacking. Observations of naturally occurring tracers, such as stable isotopes of water or electrical conductivity, are frequently used to track and explain hydrologic patterns and processes. Importantly, some of these hydrologic processes also create microhabitat variations in Alpine aquatic systems, each inhabited by characteristic organismal communities. The inclusion of such ecological diversity in a hydrologic assessment of an Alpine system may improve our understanding of hydrologic flows while also delivering biological information. Recently, the application of environmental DNA (eDNA) to assess biological diversity in water and connected habitats has gained popularity in the field of aquatic ecology. A few of these studies have started to link aquatic diversity with hydrologic processes but hitherto never in an Alpine system. Here, we collected water from an Alpine catchment in Switzerland and compared the genetic information of eukaryotic organisms conveyed by eDNA with the hydrologic information conveyed by naturally occurring hydrologic tracers. Between March and September 2017, we sampled water at multiple time points at 10 sites distributed over the 13.4 km2 Vallon de Nant catchment (Switzerland). The sites corresponded to three different water types and habitats, namely low-flow or ephemeral tributaries, groundwater-fed springs, and the main channel receiving water from both previous mentioned water types. Accompanying observations of typical physicochemical hydrologic characteristics with eDNA revealed that in the main channel and in the tributaries, the biological richness increases according to the change in streamflow, dq/dt, whereas, in contrast, the richness in springs increased in correlation with electrical conductivity. At the catchment scale, our results suggest that transport of additional, and probably terrestrial, DNA into water storage or flow compartments occurs with increasing streamflow. Such processes include overbank flow, stream network expansion, and hyporheic exchange. In general, our results highlight the importance of considering the at-site sampling habitat in combination with upstream connected habitats to understand how streams integrate eDNA over a catchment and to interpret spatially distributed eDNA samples, both for hydrologic and biodiversity assessments. At the intersection of two disciplines, our study provides complementary knowledge gains and identifies the next steps to be addressed for using eDNA to achieve complementary insights into Alpine water sources. Finally, we provide recommendations for future observation of eDNA in Alpine stream ecosystems.

Description: Billions of people rely on groundwater as being an accessible source of drinking water and for irrigation, especially in times of drought. Its importance will likely increase with a changing climate. It is still unclear, however, how climate change will impact groundwater systems globally and, thus, the availability of this vital resource. Groundwater recharge is an important indicator for groundwater availability, but it is a water flux that is difficult to estimate as uncertainties in the water balance accumulate, leading to possibly large errors in particular in dry regions. This study investigates uncertainties in groundwater recharge projections using a multi-model ensemble of eight global hydrological models (GHMs) that are driven by the bias-adjusted output of four global circulation models (GCMs). Pre-industrial and current groundwater recharge values are compared with recharge for different global warming (GW) levels as a result of three representative concentration pathways (RCPs). Results suggest that projected changes strongly vary among the different GHM–GCM combinations, and statistically significant changes are only computed for a few regions of the world. Statistically significant GWR increases are projected for northern Europe and some parts of the Arctic, East Africa, and India. Statistically significant decreases are simulated in southern Chile, parts of Brazil, central USA, the Mediterranean, and southeastern China. In some regions, reversals of groundwater recharge trends can be observed with global warming. Because most GHMs do not simulate the impact of changing atmospheric CO2 and climate on vegetation and, thus, evapotranspiration, we investigate how estimated changes in GWR are affected by the inclusion of these processes. In some regions, inclusion leads to differences in groundwater recharge changes of up to 100 mm per year. Most GHMs with active vegetation simulate less severe decreases in groundwater recharge than GHMs without active vegetation and, in some regions, even increases instead of decreases are simulated. However, in regions where GCMs predict decreases in precipitation and where groundwater availability is the most important, model agreement among GHMs with active vegetation is the lowest. Overall, large uncertainties in the model outcomes suggest that additional research on simulating groundwater processes in GHMs is necessary.

Description: Inspired by a quotation from Howard Cook in 1946, this paper traces the evolution of the infiltration theory of runoff from the work of Robert Horton and LeRoy Sherman in the 1930s to the early digital computer models of the 1970s and 1980s. The reasons for the popularity of the infiltration theory are considered and its impact on the way in which hydrological responses were perceived by several generations of hydrologists. Reconsideration of the perceptual model for many catchments, partly as a result of the greater appreciation of the contribution of subsurface flows to the hydrograph indicated by tracer studies, suggests a more precise utilisation of hydrological terms and, in particular, that the use of runoff and surface runoff should be avoided.

Description: Drought is a devastating natural hazard that is difficult to define, detect and quantify. The increased availability of both meteorological and remotely sensed data provides an opportunity to develop new methods to identify drought conditions and characterize how drought changes over space and time. In this paper, we applied the surface energy balance model, SEBS (Surface Energy Balance System), for the period 2001–2018, to estimate evapotranspiration and other energy fluxes over the dehesa area of the Iberian Peninsula, with a monthly temporal resolution and 0.05∘ pixel size. A satisfactory agreement was found between the fluxes modeled and the measurements obtained for 3 years by two flux towers located over representative sites (RMSD = 21 W m−2 and R2=0.76, on average, for all energy fluxes and both sites). The estimations of the convective fluxes (LE and H) showed higher deviations, with RMSD = 26 W m−2 on average, than Rn and G, with RMSD = 15 W m−2. At both sites, annual evapotranspiration (ET) was very close to total precipitation, with the exception of a few wet years in which intense precipitation events that produced high runoff were observed. The analysis of the anomalies of the ratio of ET to reference ET (ETo) was used as an indicator of agricultural drought on monthly and annual scales. The hydrological years 2004/2005 and 2011/2012 stood out for their negative values. The first one was the most severe of the series, with the highest impact observed on vegetation coverage and grain production. On a monthly scale, this event was also the longest and most intense, with peak negative values in January–February and April–May 2005, explaining its great impact on cereal production (up to 45 % reduction). During the drier events, the changes in the grasslands' and oak trees' ground cover allowed for a separate analysis of the strategies adopted by the two strata to cope with water stress. These results indicate that the drought events characterized for the period did not cause any permanent damage to the vegetation of dehesa systems. The approach tested has proven useful for providing insight into the characteristics of drought events over this ecosystem and will be helpful to identify areas of interest for future studies at finer resolutions.

Description: The vulnerability of coastal wetlands to future sea-level rise (SLR) has been extensively studied in recent years, and models of coastal wetland evolution have been developed to assess and quantify the expected impacts. Coastal wetlands respond to SLR by vertical accretion and landward migration. Wetlands accrete due to their capacity to trap sediments and to incorporate dead leaves, branches, stems and roots into the soil, and they migrate driven by the preferred inundation conditions in terms of salinity and oxygen availability. Accretion and migration strongly interact, and they both depend on water flow and sediment distribution within the wetland, so wetlands under the same external flow and sediment forcing but with different configurations will respond differently to SLR. Analyses of wetland response to SLR that do not incorporate realistic consideration of flow and sediment distribution, like the bathtub approach, are likely to result in poor estimates of wetland resilience. Here, we investigate how accretion and migration processes affect wetland response to SLR using a computational framework that includes all relevant hydrodynamic and sediment transport mechanisms that affect vegetation and landscape dynamics, and it is efficient enough computationally to allow the simulation of long time periods. Our framework incorporates two vegetation species, mangrove and saltmarsh, and accounts for the effects of natural and manmade features like inner channels, embankments and flow constrictions due to culverts. We apply our model to simplified domains that represent four different settings found in coastal wetlands, including a case of a tidal flat free from obstructions or drainage features and three other cases incorporating an inner channel, an embankment with a culvert, and a combination of inner channel, embankment and culvert. We use conditions typical of south-eastern Australia in terms of vegetation, tidal range and sediment load, but we also analyse situations with 3 times the sediment load to assess the potential of biophysical feedbacks to produce increased accretion rates. We find that all wetland settings are unable to cope with SLR and disappear by the end of the century, even for the case of increased sediment load. Wetlands with good drainage that improves tidal flushing are more resilient than wetlands with obstacles that result in tidal attenuation and can delay wetland submergence by 20 years. Results from a bathtub model reveal systematic overprediction of wetland resilience to SLR: by the end of the century, half of the wetland survives with a typical sediment load, while the entire wetland survives with increased sediment load.

Description: In this study, we explored the use of statistical machine learning and long-term groundwater nitrate monitoring data to estimate vadose zone and saturated zone lag times in an irrigated alluvial agricultural setting. Unlike most previous statistical machine learning studies that sought to predict groundwater nitrate concentrations within aquifers, the focus of this study was to leverage available groundwater nitrate concentrations and other environmental variables to determine mean regional vertical velocities (transport rates) of water and solutes in the vadose zone and saturated zone (3.50 and 3.75 m yr−1, respectively). The statistical machine learning results are consistent with two primary recharge processes in this western Nebraska aquifer, namely (1) diffuse recharge from irrigation and precipitation across the landscape and (2) focused recharge from leaking irrigation conveyance canals. The vadose zone mean velocity yielded a mean recharge rate (0.46 m yr−1) consistent with previous estimates from groundwater age dating in shallow wells (0.38 m yr−1). The saturated zone mean velocity yielded a recharge rate (1.31 m yr−1) that was more consistent with focused recharge from leaky irrigation canals, as indicated by previous results of groundwater age dating in intermediate-depth wells (1.22 m yr−1). Collectively, the statistical machine learning model results are consistent with previous observations of relatively high water fluxes and short transit times for water and nitrate in the primarily oxic aquifer. Partial dependence plots from the model indicate a sharp threshold in which high groundwater nitrate concentrations are mostly associated with total travel times of 7 years or less, possibly reflecting some combination of recent management practices and a tendency for nitrate concentrations to be higher in diffuse infiltration recharge than in canal leakage water. Limitations to the machine learning approach include the non-uniqueness of different transport rate combinations when comparing model performance and highlight the need to corroborate statistical model results with a robust conceptual model and complementary information such as groundwater age.

Description: The improvement of a forecasting system and the continuous evaluation of its quality are recurrent steps in operational practice. However, the systematic evaluation of forecast value or usefulness for better decision-making is less frequent, even if it is also essential to guide strategic planning and investments. In the hydropower sector, several operational systems use medium-range hydrometeorological forecasts (up to 7–10 d ahead) and energy price predictions as input to models that optimize hydropower production. The operation of hydropower systems, including the management of water stored in reservoirs, is thus partially impacted by weather and hydrological conditions. Forecast value can be quantified by the economic gains obtained with the optimization of operations informed by the forecasts. In order to assess how much improving the quality of hydrometeorological forecasts will improve their economic value, it is essential to understand how the system and its optimization model are sensitive to sequences of input forecasts of different quality. This paper investigates the impact of 7 d streamflow forecasts of different quality on the management of hydroelectric reservoirs and the economic gains generated from a linear programming optimization model. The study is based on a conceptual approach. Flows from 10 catchments in France are synthetically generated over a 4-year period to obtain forecasts of different quality in terms of accuracy and reliability. These forecasts define the inflows to 10 hydroelectric reservoirs, which are conceptually parameterized. Relationships between forecast quality and economic value (hydropower revenue) show that forecasts with a recurrent positive bias (overestimation) and low accuracy generate the highest economic losses when compared to the reference management system where forecasts are equal to observed inflows. The smallest losses are observed for forecast systems with underdispersion reliability bias, while forecast systems with negative bias (underestimation) show intermediate losses. Overall, the losses (which amount to millions of Euros) represent approximately 1 % to 3 % of the revenue over the study period. Besides revenue, the quality of the forecasts also impacts spillage, stock evolution, production hours and production rates, with systematic over- and underestimations being able to generate some extreme reservoir management situations.

Description: Understanding travel times and hydrological pathways of rain and snowmelt water transported through the landscape to recipient surface waters is critical in many hydrological and biogeochemical investigations. In this study, a particle-tracking model approach in Mike SHE was used to investigate the pathway and its associated travel time of water in 14 partly nested, long-term monitored boreal sub-catchments of the Krycklan catchment (0.12–68 km2). This region is characterized by long and snow-rich winters with little groundwater recharge and highly dynamic runoff during spring snowmelt. The geometric mean of the annual travel time distribution (MTTgeo) for the studied sub-catchments varied from 0.8 to 2.7 years. The variations were related to the different landscape types and their varying hydrological responses during different seasons. Winter MTTgeo ranged from 1.2 to 7.7 years, while spring MTTgeo varied from 0.5 to 1.9 years. The modelled variation in annual and seasonal MTTgeo and the fraction of young water (

Description: In this paper, we present a novel approach, enabling the measurement of nitrate concentrations in natural soil porewater containing natural soil dissolved organic carbon (DOC). The method is based on UV absorbance spectroscopy, combined with fluorescence spectroscopy, for simultaneous analysis of DOC and nitrate concentrations. The analytical procedure involves deduction of the absorption caused by the DOC from the total absorbance in the UV range that is attributed to both DOC and nitrate in the water solution. The analytical concept has been successfully tested in soil water samples obtained from five agricultural sites, as well as in water samples obtained from a commercial humus soil mixture. We believe that the new analytical concept can provide a scientific foundation for developing a sensor for real-time nitrate concentration measurements in agricultural soils. As such, it can play a significant role in reducing nitrate pollution in water resources, optimizing input application in agriculture, and decreasing food production costs.

Description: A better understanding of the reasons why hydrological model performance is unsatisfying represents a crucial part of meaningful model evaluation. However, current evaluation efforts are mostly based on aggregated efficiency measures such as Kling–Gupta efficiency (KGE) or Nash–Sutcliffe efficiency (NSE). These aggregated measures provide a relative gradation of model performance. Especially in the case of a weak model performance it is important to identify the different errors which may have caused such unsatisfactory predictions. These errors may originate from the model parameters, the model structure, and/or the input data. In order to provide more insight, we define three types of errors which may be related to their source: constant error (e.g. caused by consistent input data error such as precipitation), dynamic error (e.g. structural model errors such as a deficient storage routine) and timing error (e.g. caused by input data errors or deficient model routines/parameters). Based on these types of errors, we propose the novel diagnostic efficiency (DE) measure, which accounts for these three error types. The disaggregation of DE into its three metric terms can be visualized in a plain radial space using diagnostic polar plots. A major advantage of this visualization technique is that error contributions can be clearly differentiated. In order to provide a proof of concept, we first generated time series artificially with the three different error types (i.e. simulations are surrogated by manipulating observations). By computing DE and the related diagnostic polar plots for the reproduced errors, we could then supply evidence for the concept. Finally, we tested the applicability of our approach for a modelling example. For a particular catchment, we compared streamflow simulations realized with different parameter sets to the observed streamflow. For this modelling example, the diagnostic polar plot suggests that dynamic errors explain the overall error to a large extent. The proposed evaluation approach provides a diagnostic tool for model developers and model users and the diagnostic polar plot facilitates interpretation of the proposed performance measure as well as a relative gradation of model performance similar to the well-established efficiency measures in hydrology.

Description: Spatial rainfall patterns exert a key control on the catchment-scale hydrologic response. Despite recent advances in radar-based rainfall sensing, rainfall observation remains a challenge, particularly in mountain environments. This paper analyzes the importance of high-density rainfall observations for a 13.4 km2 catchment located in the Swiss Alps, where rainfall events were monitored during 3 summer months using a network of 12 low-cost, drop-counting rain gauges. We developed a data-based analysis framework to assess the importance of high-density rainfall observations to help predict the hydrological response. The framework involves the definition of spatial rainfall distribution metrics based on hydrological and geomorphological considerations and a regression analysis of how these metrics explain the hydrologic response in terms of runoff coefficient and lag time. The gained insights on dominant predictors are then used to investigate the optimal rain gauge network density for predicting the streamflow response metrics, including an extensive test of the effect of down-sampled rain gauge networks and an event-based rainfall–runoff model to evaluate the resulting optimal rain gauge network configuration. The analysis unravels that, besides rainfall amount and intensity, the rainfall distance from the outlet along the stream network is a key spatial rainfall metric. This result calls for more detailed observations of stream network expansions and the parameterization of along-stream processes in rainfall–runoff models. In addition, despite the small spatial scale of this case study, the results show that an accurate representation of the rainfall field (with at least three rain gauges) is of prime importance for capturing the key characteristics of the hydrologic response in terms of generated runoff volumes and delay for the studied catchment (0.22 rain gauges per square kilometer). The potential of the developed rainfall monitoring and analysis framework for rainfall–runoff analysis in small catchments remains to be fully unraveled in future studies, potentially also including urban catchments.

Description: Connectivity of the hillslope and the stream is a non-stationary and non-linear phenomenon dependent on many controls. The objective of this study is to identify these controls by examining the spatial and temporal patterns of the similarity between shallow groundwater and soil moisture dynamics and streamflow dynamics in the Hydrological Open Air Laboratory (HOAL), a small (66 ha) agricultural headwater catchment in Lower Austria. We investigate the responses to 53 precipitation events and the seasonal dynamics of streamflow, groundwater and soil moisture over 2 years. The similarity, in terms of Spearman correlation coefficient, hysteresis index and peak-to-peak time, of groundwater to streamflow shows a clear spatial organization, which is best correlated with topographic position index, topographic wetness index and depth to the groundwater table. The similarity is greatest in the riparian zone and diminishes further away from the stream where the groundwater table is deeper. Soil moisture dynamics show high similarity to streamflow but no clear spatial pattern. This is reflected in a low correlation of the similarity with site characteristics. However, the similarity increases with increasing catchment wetness and rainfall duration. Groundwater connectivity to the stream on the seasonal scale is higher than that on the event scale, indicating that groundwater contributes more to the baseflow than to event runoff.

Description: We employ an approach based on the ensemble Kalman filter coupled with stochastic moment equations (MEs-EnKF) of groundwater flow to explore the dependence of conductivity estimates on the type of available information about hydraulic heads in a three-dimensional randomly heterogeneous field where convergent flow driven by a pumping well takes place. To this end, we consider three types of observation devices corresponding to (i) multi-node monitoring wells equipped with packers (Type A) and (ii) partially (Type B) and (iii) fully (Type C) screened wells. We ground our analysis on a variety of synthetic test cases associated with various configurations of these observation wells. Moment equations are approximated at second order (in terms of the standard deviation of the natural logarithm, Y, of conductivity) and are solved by an efficient transient numerical scheme proposed in this study. The use of an inflation factor imposed to the observation error covariance matrix is also analyzed to assess the extent at which this can strengthen the ability of the MEs-EnKF to yield appropriate conductivity estimates in the presence of a simplified modeling strategy where flux exchanges between monitoring wells and aquifer are neglected. Our results show that (i) the configuration associated with Type A monitoring wells leads to conductivity estimates with the (overall) best quality, (ii) conductivity estimates anchored on information from Type B and C wells are of similar quality, (iii) inflation of the measurement-error covariance matrix can improve conductivity estimates when a simplified flow model is adopted, and (iv) when compared with the standard Monte Carlo-based EnKF method, the MEs-EnKF can efficiently and accurately estimate conductivity and head fields.

Description: Many scenarios of future climate evolution and its anthropogenic drivers include considerable amounts of bioenergy as a fuel source, as a negative emission technology, and for providing electricity. The associated freshwater abstractions for irrigation of dedicated biomass plantations might be substantial and therefore potentially increase water limitation and stress in affected regions; however, assumptions and quantities of water use provided in the literature vary strongly. This paper reviews existing global assessments of freshwater abstractions for bioenergy production and puts these estimates into the context of scenarios of other water-use sectors. We scanned the available literature and (out of 430 initial hits) found 16 publications (some of which include several bioenergy-water-use scenarios) with reported values on global irrigation water abstractions for biomass plantations, suggesting water withdrawals in the range of 128.4 to 9000 km3 yr−1, which would come on top of (or compete with) agricultural, industrial, and domestic water withdrawals. To provide an understanding of the origins of this large range, we present the diverse underlying assumptions, discuss major study differences, and calculate an inverse water-use efficiency (iwue), which facilitates comparison of the required freshwater amounts per produced biomass harvest. We conclude that due to the potentially high water demands and the tradeoffs that might go along with them, bioenergy should be an integral part of global assessments of freshwater demand and use. For interpreting and comparing reported estimates of possible future bioenergy water abstractions, full disclosure of parameters and assumptions is crucial. A minimum set should include the complete water balances of bioenergy production systems (including partitioning of blue and green water), bioenergy crop species and associated water-use efficiencies, rainfed and irrigated bioenergy plantation locations (including total area and meteorological conditions), and total biomass harvest amounts. In the future, a model intercomparison project with standardized parameters and scenarios would be helpful.

Description: Riparian countries have their respective values and priorities for water management, and their values of shared water have fundamental impacts for their propensity to be involved in cooperative management and adhere to treaties/agreements. However, there is limited understanding of the changing values and interests of each riparian country. Taking the Lancang–Mekong River basin as a case study, this paper aims to analyse the evolution of water conflict and cooperation dynamics from the perspectives of multiple countries. Newspaper articles were used as the key data source as they provide insights into events reported on by the media that are representative of each country/sector they are published within. The results depict a trend of cooperative sentiments towards water events occurring within the region. The six riparian countries (China, Myanmar, Laos, Thailand, Cambodia, and Vietnam) had a greater average sentiment score (0.5) for cooperation than international countries (0.16) for the majority of the study period. The trend also shows that countries further downstream showed lower cooperative sentiments, except for Vietnam (China 0.86, Myanmar 0.58, Laos 0.46, Thailand 0.34, Cambodia 0.13, Vietnam 0.91). Dam infrastructure was often negatively reported (60 % of negatively reported articles), which is therefore a major contributor to conflict for the Lancang–Mekong River basin. Events that are positively reported are those that aid in connecting leaders and project developers between riparian countries including meetings, bilateral and multilateral cooperation, and development projects. These findings provide the basis for further revealing the mechanism of cooperation and conflicts as well as more proactively managing cooperation and conflict in the Lancang–Mekong River basin and beyond.

Description: Surface runoff represents a major pathway for pesticide transport from agricultural areas to surface waters. The influence of artificial structures (e.g. roads, hedges, and ditches) on surface runoff connectivity has been shown in various studies. In Switzerland, so-called hydraulic shortcuts (e.g. inlet and maintenance shafts of road or field storm drainage systems) have been shown to influence surface runoff connectivity and related pesticide transport. Their occurrence and their influence on surface runoff and pesticide connectivity have, however, not been studied systematically. To address that deficit, we randomly selected 20 study areas (average size of 3.5 km2) throughout the Swiss plateau, representing arable cropping systems. We assessed shortcut occurrence in these study areas using three mapping methods, namely field mapping, drainage plans, and high-resolution aerial images. Surface runoff connectivity in the study areas was analysed using a 2×2 m digital elevation model and a multiple-flow algorithm. Parameter uncertainty affecting this analysis was addressed by a Monte Carlo simulation. With our approach, agricultural areas were divided into areas that are either directly, indirectly (i.e. via hydraulic shortcuts), or not at all connected to surface waters. Finally, the results of this connectivity analysis were scaled up to the national level, using a regression model based on topographic descriptors, and were then compared to an existing national connectivity model. Inlet shafts of the road storm drainage system were identified as the main shortcuts. On average, we found 0.84 inlet shafts and a total of 2.0 shafts per hectare of agricultural land. In the study catchments, between 43 % and 74 % of the agricultural area is connected to surface waters via hydraulic shortcuts. On the national level, this fraction is similar and lies between 47 % and 60 %. Considering our empirical observations led to shifts in estimated fractions of connected areas compared to the previous connectivity model. The differences were most pronounced in flat areas of river valleys. These numbers suggest that transport through hydraulic shortcuts is an important pesticide flow path in a landscape where many engineered structures exist to drain excess water from fields and roads. However, this transport process is currently not considered in Swiss pesticide legislation and authorization. Therefore, current regulations may fall short in addressing the full extent of the pesticide problem. However, independent measurements of water flow and pesticide transport to quantify the contribution of shortcuts and validating the model results are lacking. Overall, the findings highlight the relevance of better understanding the connectivity between fields and receiving waters and the underlying factors and physical structures in the landscape.

Description: The two-parameter recursive digital filter method (Eckhardt) and the conductivity mass balance (CMB) method are two widely used baseflow separation methods favored by hydrologists. Some divergences in the application of these two methods have emerged in recent years. Some scholars believe that deviation of baseflow separation results of the two methods is due to uncertainty of the parameters of the Eckhardt method and that the Eckhardt method should be corrected by reference to the CMB method. However, other scholars attribute the deviation to the fact that they contain different transient water components. This study aimed to resolve this disagreement by analyzing the effectiveness of the CMB method for correcting the Eckhardt method through application of the methods to 26 basins in the United States by comparison of the biases between the generated daily baseflow series. The results showed that the approach of calibrating the Eckhardt method against the CMB method provides a “false” calibration of total baseflow by offsetting the inherent biases in the baseflow sequences generated by the two methods. The baseflow sequence generated by the Eckhardt method usually includes slow interflow and bank storage return flow, whereas that of the CMB method usually includes high-conductivity water flushed from swamps and depressions by rainfall, but not low-conductivity interflow and bank storage return flow. This difference results in obvious peak misalignment and periodic deviation between the baseflow sequences obtained by the two methods, thereby preventing calibration. However, multi-component separation of streamflow can be achieved through comparison. Future research should recognize the deviations between the separation results obtained by the different methods, identify the reasons for these differences, and explore the hydrological information contained therein.

Description: Pedotransfer functions are used to relate gridded databases of soil texture information to the soil hydraulic and thermal parameters of land surface models. The parameters within these pedotransfer functions are uncertain and calibrated through analyses of point soil samples. How these calibrations relate to the soil parameters at the spatial scale of modern land surface models is unclear because gridded databases of soil texture represent an area average. We present a novel approach for calibrating such pedotransfer functions to improve land surface model soil moisture prediction by using observations from the Soil Moisture Active Passive (SMAP) satellite mission within a data assimilation framework. Unlike traditional calibration procedures, data assimilation always takes into account the relative uncertainties given to both model and observed estimates to find a maximum likelihood estimate. After performing the calibration procedure, we find improved estimates of soil moisture and heat flux for the Joint UK Land Environment Simulator (JULES) land surface model (run at a 1 km resolution) when compared to estimates from a cosmic-ray soil moisture monitoring network (COSMOS-UK) and three flux tower sites. The spatial resolution of the COSMOS probes is much more representative of the 1 km model grid than traditional point-based soil moisture sensors. For 11 cosmic-ray neutron soil moisture probes located across the modelled domain, we find an average 22 % reduction in root mean squared error, a 16 % reduction in unbiased root mean squared error and a 16 % increase in correlation after using data assimilation techniques to retrieve new pedotransfer function parameters.

Description: Groundwater table dynamics extensively modify the volume of the hyporheic zone and the rate of hyporheic exchange processes. Understanding the effects of daily groundwater table fluctuations on the tightly coupled flow and heat transport within hyporheic zones is crucial for water resources management. With this aim in mind, a physically based model is used to explore hyporheic responses to varying groundwater table fluctuation scenarios. The effects of different timing and amplitude of groundwater table daily drawdowns under gaining and losing conditions are explored in hyporheic zones influenced by natural flood events and diel river temperature fluctuations. We find that both diel river temperature fluctuations and daily groundwater table drawdowns play important roles in determining the spatiotemporal variability of hyporheic exchange rates, temperature of exfiltrating hyporheic fluxes, mean residence times, and hyporheic denitrification potentials. Groundwater table dynamics present substantially distinct impacts on hyporheic exchange under gaining or losing conditions. The timing of groundwater table drawdown has a direct influence on hyporheic exchange rates and hyporheic buffering capacity on thermal disturbances. Consequently, the selection of aquifer pumping regimes has significant impacts on the dispersal of pollutants in the aquifer and thermal heterogeneity in the sediment.

Description: Lowland karst aquifers can generate unique wetland ecosystems which are caused by groundwater fluctuations that result in extensive groundwater–surface water interactions (i.e. flooding). However, the complex hydrogeological attributes of these systems, linked to extremely fast aquifer recharge processes and flow through well-connected conduit networks, often present difficulty in predicting how they will respond to changing climatological conditions. This study investigates the predicted impacts of climate change on a lowland karst catchment by using a semi-distributed pipe network model of the karst aquifer populated with output from the high spatial resolution (4 km) Consortium for Small-scale Modelling Climate Lokalmodell (COSMO-CLM) regional climate model simulations for Ireland. An ensemble of projections for the future Irish climate were generated by downscaling from five different global climate models (GCMs), each based on four Representative Concentration Pathways (RCPs; RCP2.6, RCP4.5, RCP6.0 and RCP8.5) to account for the uncertainty in the estimation of future global emissions of greenhouse gases. The one-dimensional hydraulic/hydrologic karst model incorporates urban drainage software to simulate open channel and pressurised flow within the conduits, with flooding on the land surface represented by storage nodes with the same stage volume properties of the physical turlough basins. The lowland karst limestone catchment is located on the west coast of Ireland and is characterised by a well-developed conduit-dominated karst aquifer which discharges to the sea via intertidal and submarine springs. Annual above ground flooding associated with this complex karst system has led to the development of unique wetland ecosystems in the form of ephemeral lakes known as turloughs; however, extreme flooding of these features causes widespread damage and disruption in the catchment. This analysis has shown that mean, 95th and 99th percentile flood levels are expected to increase by significant proportions for all future emission scenarios. The frequency of events currently considered to be extreme is predicted to increase, indicating that more significant groundwater flooding events seem likely to become far more common. The depth and duration of flooding is of extreme importance, both from an ecological perspective in terms of wetland species distribution and for extreme flooding in terms of the disruption to homes, transport links and agricultural land inundated by flood waters. The seasonality of annual flooding is also predicted to shift later in the flooding season, which could have consequences in terms of ecology and land use in the catchment. The investigation of increasing mean sea levels, however, showed that anticipated rises would have very little impact on groundwater flooding due to the marginal impact on ebb tide outflow volumes. Overall, this study highlights the relative vulnerability of lowland karst systems to future changing climate conditions, mainly due to the extremely fast recharge which can occur in such systems. The study presents a novel and highly effective methodology for studying the impact of climate change in lowland karst systems by coupling karst hydrogeological models with the output from high-resolution climate simulations.

Description: Within the critical zone, regolith plays a key role in the fundamental hydrological functions of water collection, storage, mixing and release. Electrical resistivity tomography (ERT) is recognized as a remarkable tool for characterizing the geometry and properties of the regolith, overcoming limitations inherent to conventional borehole-based investigations. For exploring shallow layers, a small electrode spacing (ES) will provide a denser set of apparent resistivity measurements of the subsurface. As this option is cumbersome and time-consuming, larger ES – albeit offering poorer shallow apparent resistivity data – is often preferred for large horizontal ERT surveys. To investigate the negative trade-off between larger ES and reduced accuracy of the inverted ERT images for shallow layers, we use a set of synthetic “conductive–resistive–conductive” three-layered soil–saprock/saprolite–bedrock models in combination with a reference field dataset. Our results suggest that an increase in ES causes a deterioration of the accuracy of the inverted ERT images in terms of both resistivity distribution and interface delineation and, most importantly, that this degradation increases sharply when the ES exceeds the thickness of the top subsurface layer. This finding, which is obvious for the characterization of shallow layers, is also relevant even when solely aiming for the characterization of deeper layers. We show that an oversized ES leads to overestimations of depth to bedrock and that this overestimation is even more important for subsurface structures with high resistivity contrast. To overcome this limitation, we propose adding interpolated levels of surficial apparent resistivity relying on a limited number of ERT profiles with a smaller ES. We demonstrate that our protocol significantly improves the accuracy of ERT profiles when using large ES, provided that the top layer has a rather constant thickness and resistivity. For the specific case of large-scale ERT surveys the proposed upgrading procedure is cost-effective in comparison to protocols based on small ES.

Description: In late winter, solar radiation is the main driver of water motion in ice-covered lakes. The resulting circulation and mixing determine the spatial distribution of heat within the lake and affect the heat budget of the ice cover. Although under-ice lake warming is often modeled as a one-dimensional (1D) vertical process, lake bathymetry induces a relative excess heating of shallow waters, creating horizontal density gradients. This study shows that the dynamic response to these gradients depends sensitively on lake size and latitude – Earth's rotation – and is controlled by the Rossby number. In the ageostrophic limit, horizontal density gradients drive cross-shore circulation that transports excess heat to the lake interior, accelerating the under-ice warming there. In the geostrophic regime, the circulation of the near- and off-shore waters decouples, and excess heat is retained in the shallows. The flow regime controls the fate of this excess heat and its contribution to water-induced ice melt.

Description: Soil water is a major requirement for biomass production and, therefore, one of the most important factors for agriculture productivity. As agricultural droughts are related to declining soil moisture, this paper examines soil moisture drought in the transboundary Koshi River basin (KRB) in the central Himalayan region. By applying the J2000 hydrological model, daily spatially distributed soil moisture is derived for the entire basin over a 28-year period (1980–2007). A multi-site and multi-variable approach – streamflow data at one station and evapotranspiration data at three stations – was used for the calibration and validation of the J2000 model. In order to identify drought conditions based on the simulated soil moisture, the soil moisture deficit index (SMDI) was then calculated, considering the derivation of actual soil moisture from long-term soil moisture on a weekly timescale. To spatially subdivide the variations in soil moisture, the river basin is partitioned into three distinct geographical regions, namely trans-Himalaya, the mountains, and the plains. Further, the SMDI is aggregated temporally to four seasons – winter, pre-monsoon, monsoon, and post-monsoon – based on wetness and dryness patterns observed in the study area. This has enabled us to look at the magnitude, extent, and duration of soil moisture drought. The results indicated that the J2000 model can simulate the hydrological processes of the basin with good accuracy. Considerable variation in soil moisture was observed in the three physiographic regions and across the four seasons due to high variation in precipitation and temperature conditions. The year 1992 was the driest year and 1998 was the wettest at the basin scale in both magnitude and duration. Similarly, the year 1992 also has the highest number of weeks under drought. Comparing the SMDI with the standardised precipitation index (SPI) suggested that SMDI can reflect a higher variation in drought conditions than SPI. Our results suggested that both the occurrence and severity of droughts have increased in the Koshi River basin over the last 3 decades, especially in the winter and pre-monsoon seasons. The insights provided into the frequency, spatial coverage, and severity of drought conditions can provide valuable contributions towards an improved management of water resources and greater agricultural productivity in the region.

Description: Evaluating land surface models (LSMs) using available observations is important for understanding the potential and limitations of current Earth system models in simulating water- and carbon-related variables. To reveal the error sources of a LSM, five essential climate variables have been evaluated in this paper (i.e., surface soil moisture, evapotranspiration, leaf area index, surface albedo, and precipitation) via simulations with the IPSL (Institute Pierre Simon Laplace) LSM ORCHIDEE (Organizing Carbon and Hydrology in Dynamic Ecosystems) model, particularly focusing on the difference between (i) forced simulations with atmospheric forcing data (WATCH Forcing Data ERA-Interim – WFDEI) and (ii) coupled simulations with the IPSL atmospheric general circulation model. Results from statistical evaluation, using satellite- and ground-based reference data, show that ORCHIDEE is well equipped to represent spatiotemporal patterns of all variables in general. However, further analysis against various landscape and meteorological factors (e.g., plant functional type, slope, precipitation, and irrigation) suggests potential uncertainty relating to freezing and/or snowmelt, temperate plant phenology, irrigation, and contrasted responses between forced and coupled mode simulations. The biases in the simulated variables are amplified in the coupled mode via surface–atmosphere interactions, indicating a strong link between irrigation–precipitation and a relatively complex link between precipitation–evapotranspiration that reflects the hydrometeorological regime of the region (energy limited or water limited) and snow albedo feedback in mountainous and boreal regions. The different results between forced and coupled modes imply the importance of model evaluation under both modes to isolate potential sources of uncertainty in the model.

Description: Soil microwave permittivity is a crucial parameter in passive microwave retrieval algorithms but remains a challenging variable to measure. To validate and improve satellite microwave data products, precise and reliable estimations of the relative permittivity (εr=ε/ε0=ε′-jε′′; unitless) of soils are required, particularly for frozen soils. In this study, permittivity measurements were acquired using two different instruments: the newly designed open-ended coaxial probe (OECP) and the conventional Stevens HydraProbe. Both instruments were used to characterize the permittivity of soil samples undergoing several freeze–thaw cycles in a laboratory environment. The measurements were compared to soil permittivity models. The OECP measured frozen (εfrozen′=[3.5; 6.0], εfrozen′′=[0.46; 1.2]) and thawed (εthawed′=[6.5; 22.8], εthawed′′=[1.43; 5.7]) soil microwave permittivity. We also demonstrate that cheaper and widespread soil permittivity probes operating at lower frequencies (i.e., Stevens HydraProbe) can be used to estimate microwave permittivity given proper calibration relative to an L-band (1–2 GHz) probe. This study also highlighted the need to improve dielectric soil models, particularly during freeze–thaw transitions. There are still important discrepancies between in situ and modeled estimates and no current model accounts for the hysteresis effect shown between freezing and thawing processes, which could have a significant impact on freeze–thaw detection from satellites.

Description: Plant–soil water isotopic dynamics in northern forests have been understudied relative to other forest types; nevertheless, such information can provide insight into how such forests may respond to hydroclimatic change. This study examines the co-evolution of xylem water and soil water stable isotopic compositions in a northern mixed forest in Ontario, Canada. Gross precipitation, bulk soil water and xylem water were sampled from pre-leaf out to post-senescence in 2016 for eastern white cedar, eastern hemlock, red oak and eastern white pine. Near-bole soil water contents and mobile soil water isotopic compositions were measured for the last three species. Mobile soil water did not deviate significantly from the local meteoric water line (LMWL). In contrast, near-surface bulk soil water showed significant evaporative enrichment relative to the LMWL from pre-leaf out to peak leaf out under all tree canopies, while xylem water was significantly depleted in 18O and particularly 2H relative to bulk soil water throughout the growing season. Inter-species differences in deviation of xylem water from the LMWL and their temporal changes emerged during the growing season, with coniferous species xylem water becoming isotopically enriched, while that of red oak became more depleted in 2H and 18O. These divergences occurred despite thin soil cover (generally

Description: Precipitation orographic enhancement is the result of both synoptic circulation and topography. Since high-elevation headwaters are often sparsely instrumented, the magnitude and distribution of this enhancement, as well as how they affect precipitation lapse rates, remain poorly understood. Filling this knowledge gap would allow a significant step ahead for hydrologic forecasting procedures and water management in general. Here, we hypothesized that spatially distributed, manual measurements of snow depth (courses) could provide new insights into this process. We leveraged over 11 000 snow course data upstream of two reservoirs in the western European Alps (Aosta Valley, Italy) to estimate precipitation orographic enhancement in the form of lapse rates and, consequently, improve predictions of a snow hydrologic modeling chain (Flood-PROOFS). We found that snow water equivalent (SWE) above 3000 m a.s.l. (above sea level) was between 2 and 8.5 times higher than recorded cumulative seasonal precipitation below 1000 m a.s.l., with gradients up to 1000 mm w.e. km−1. Enhancement factors, estimated by blending precipitation gauge and snow course data, were consistent between the two hydropower headwaters (median values above 3000 m a.s.l. between 4.1 and 4.8). Including blended gauge course lapse rates in an iterative precipitation spatialization procedure allowed Flood-PROOFS to remedy underestimations both of SWE above 3000 m a.s.l. (up to 50 %) and – importantly – of precipitation vs. observed streamflow. Annual runoff coefficients based on blended lapse rates were also more consistent from year to year than those based on precipitation gauges alone (standard deviation of 0.06 and 0.19, respectively). Thus, snow courses bear a characteristic signature of orographic precipitation, which opens a window of opportunity for leveraging these data sets to improve our understanding of the mountain water budget. This is all the more important due to the essential role of high-elevation headwaters in supporting water security and ecosystem services worldwide.

Description: The interior of western Canada, like many similar cold mid- to high-latitude regions worldwide, is undergoing extensive and rapid climate and environmental change, which may accelerate in the coming decades. Understanding and predicting changes in coupled climate–land–hydrological systems are crucial to society yet limited by lack of understanding of changes in cold-region process responses and interactions, along with their representation in most current-generation land-surface and hydrological models. It is essential to consider the underlying processes and base predictive models on the proper physics, especially under conditions of non-stationarity where the past is no longer a reliable guide to the future and system trajectories can be unexpected. These challenges were forefront in the recently completed Changing Cold Regions Network (CCRN), which assembled and focused a wide range of multi-disciplinary expertise to improve the understanding, diagnosis, and prediction of change over the cold interior of western Canada. CCRN advanced knowledge of fundamental cold-region ecological and hydrological processes through observation and experimentation across a network of highly instrumented research basins and other sites. Significant efforts were made to improve the functionality and process representation, based on this improved understanding, within the fine-scale Cold Regions Hydrological Modelling (CRHM) platform and the large-scale Modélisation Environmentale Communautaire (MEC) – Surface and Hydrology (MESH) model. These models were, and continue to be, applied under past and projected future climates and under current and expected future land and vegetation cover configurations to diagnose historical change and predict possible future hydrological responses. This second of two articles synthesizes the nature and understanding of cold-region processes and Earth system responses to future climate, as advanced by CCRN. These include changing precipitation and moisture feedbacks to the atmosphere; altered snow regimes, changing balance of snowfall and rainfall, and glacier loss; vegetation responses to climate and the loss of ecosystem resilience to wildfire and disturbance; thawing permafrost and its influence on landscapes and hydrology; groundwater storage and cycling and its connections to surface water; and stream and river discharge as influenced by the various drivers of hydrological change. Collective insights, expert elicitation, and model application are used to provide a synthesis of this change over the CCRN region for the late 21st century.

Description: The transboundary Lancang–Mekong River basin has experienced dynamics of cooperation over the past several decades, which is a common emergent response in transboundary coupled human–water systems. Downstream countries rely on the Mekong River for fisheries, agriculture, navigation and ecological services, while upstream countries have been constructing dams to generate hydropower. The dam construction and operation in upstream countries have changed the seasonality of streamflow in downstream countries, affecting their economic benefits. More recently, cooperation between upstream and downstream countries has been enhanced throughout the river basin. In this study, we introduce a quantitative socio-hydrological model to simulate hydrological processes, reservoir operations, economic benefits, policy feedbacks and therefore dynamics of cooperation within the Lancang–Mekong River basin. The model reproduces the observed dynamics of cooperation in the basin revealed by sentiment analysis of news articles. Hydrological variability such as droughts and human activities associated with reservoir operations affect dynamics of cooperation between the riparian countries, with importance attached to indirect political benefits of upstream playing an important role in the enhancement of cooperation. In this way, our study generated understanding of emergent cooperation dynamics in this transboundary river basin, and the socio-hydrological model used here provides a useful new framework to investigate and improve transboundary water management elsewhere.

Description: Soil moisture is highly variable in space and time, and deficits (i.e., droughts) play an important role in modulating crop yields. Limited hydroclimate and yield data, however, hamper drought impact monitoring and assessment at the farm field scale. This study demonstrates the potential of using field-scale soil moisture simulations to support high-resolution agricultural yield prediction and drought monitoring at the smallholder farm field scale. We present a multiscale modeling approach that combines HydroBlocks – a physically based hyper-resolution land surface model (LSM) – with machine learning. We used HydroBlocks to simulate root zone soil moisture and soil temperature in Zambia at 3 h 30 m resolution. These simulations, along with remotely sensed vegetation indices, meteorological data, and descriptors of the physical landscape (related to topography, land cover, and soils) were combined with district-level maize data to train a random forest (RF) model to predict maize yields at district and field scales (250 m). Our model predicted yields with an average testing coefficient of determination (R2) of 0.57 and mean absolute error (MAE) of 310 kg ha−1 using year-based cross-validation. Our predicted maize losses due to the 2015–2016 El Niño drought agreed well with losses reported by the Food and Agriculture Organization (FAO). Our results reveal that soil moisture is the strongest and most reliable predictor of maize yield, driving its spatial and temporal variability. Soil moisture was also a more effective indicator of drought impacts on crops than precipitation, soil and air temperatures, and remotely sensed normalized difference vegetation index (NDVI)-based drought indices. This study demonstrates how field-scale modeling can help bridge the spatial-scale gap between drought monitoring and agricultural impacts.

Description: Agricultural production and food prices are affected by hydroclimatic extremes. There has been a growing amount of literature measuring the impacts of individual extreme events (heat stress or water stress) on agricultural and human systems. Yet, we lack a comprehensive understanding of the significance and the magnitude of the impacts of compound extremes. This study combines a fine-scale weather product with outputs of a hydrological model to construct functional metrics of individual and compound hydroclimatic extremes for agriculture. Then, a yield response function is estimated with individual and compound metrics, focusing on corn in the United States during the 1981–2015 period. Supported by statistical evidence, the findings suggest that metrics of compound hydroclimatic extremes are better predictors of corn yield variations than metrics of individual extremes. The results also confirm that wet heat is more damaging than dry heat for corn. This study shows the average yield damage from heat stress has been up to four times more severe when combined with water stress.

Description: Pesticides may impact aquatic ecosystems when entering water bodies. Measures for mitigation against pesticide inputs include vegetated treatment systems (VTSs). Some of these systems have very short hydraulic retention time (

Description: Gully erosion can be a major disruptor to global fluvial sediment budgets. Gully erosion in the catchments of the Great Barrier Reef is attributed to ∼40 % of fine suspended sediment pollution to the freshwater and marine ecosystems downstream. Mitigating this source of erosion will have a lasting positive impact on the water quality of connected rivers and the receiving marine environment. Here we conduct a preliminary evaluation of the ability of intensive landscape-scale gully remediation to reduce suspended sediment and associated nutrient export from a catchment draining to the Great Barrier Reef. The gully remediation method was a first attempt, in the region, at investing a high level of financial (total cost of remediation AUD ∼90 000) and logistical effort (e.g. intensive earthworks, including the establishment of an on-site quarry) to develop long-lasting erosion mitigation measures (i.e. regraded, compacted, and battered gully walls, rock armouring of banks and channel, and installation of rock check dams). A novel suspended sediment monitoring network, comprised of a suite of new and established automated monitoring methods capable of operating in remote environments, was used to evaluate the water quality of a remediated gully, a control gully, and their respective catchments. The recently developed pumped active suspended sediment (PASS) sampler optimised to sample ephemeral water flows was deployed in gully outlets and catchment runoff flow paths. This study demonstrates how the combination of low- and high-cost water quality monitoring techniques can be deployed in a configuration that ensures sample collection redundancy and complementary data collection between methods. Monitoring was conducted during two consecutive wet seasons and, thus, can only provide preliminary information. Monitoring over longer timescales (i.e. 5–10 years) will need to be carried out in order to validate the findings discussed herein. Samples collected from the remediated gully had significantly lower suspended sediment concentrations compared to the control gully, providing preliminary evidence that the remediation works were successful in stabilising erosion within the gully. Dissolved and particulate nutrient concentrations were also significantly lower in the remediated gully samples, consistent with the decreased suspended sediment concentrations. The novel combination of suspended sediment measurements from both the gully channels and overland flows in the surrounding gully catchments suggests that sediment and nutrients at the remediated site are likely sourced from erosion processes occurring within the catchment of the gully (at relatively low concentrations). In contrast, the primary source of suspended sediment and associated nutrients at the control gully was erosion from within the gully itself. This study demonstrates the potential of landscape-scale remediation as an effective mitigation action for reducing suspended sediment and nutrient export from alluvial gullies. It also provides a useful case study for the monitoring effort required to appropriately assess the effectiveness of this type of erosion control.

Description: A total of 13 satellite missions have been launched since 1985, with different types of radar altimeters on board. This study intends to make a comprehensive evaluation of historic and currently operational satellite radar altimetry missions for lake water level retrieval over the same set of lakes and to develop a strategy for constructing consistent long-term water level records for inland lakes at global scale. The lake water level estimates produced by different retracking algorithms (retrackers) of the satellite missions were compared with the gauge measurements over 12 lakes in four countries. The performance of each retracker was assessed in terms of the data missing rate, the correlation coefficient r, the bias, and the root mean square error (RMSE) between the altimetry-derived lake water level estimates and the concurrent gauge measurements. The results show that the model-free retrackers (e.g., OCOG/Ice-1/Ice) outperform the model-based retrackers for most of the missions, particularly over small lakes. Among the satellite altimetry missions, Sentinel-3 gave the best results, followed by SARAL. ENVISAT has slightly better lake water level estimates than Jason-1 and Jason-2, but its data missing rate is higher. For small lakes, ERS-1 and ERS-2 missions provided more accurate lake water level estimates than the TOPEX/Poseidon mission. In contrast, for large lakes, TOPEX/Poseidon is a better option due to its lower data missing rate and shorter repeat cycle. GeoSat and GeoSat Follow-On (GFO) both have an extremely high data missing rate of lake water level estimates. Although several contemporary radar altimetry missions provide more accurate lake level estimates than GFO, GeoSat was the sole radar altimetry mission, between 1985 and 1990, that provided the lake water level estimates. With a full consideration of the performance and the operational duration, the best strategy for constructing long-term lake water level records should be a two-step bias correction and normalization procedure. In the first step, use Jason-2 as the initial reference to estimate the systematic biases with TOPEX/Poseidon, Jason-1, and Jason-3 and then normalize them to form a consistent TOPEX/Poseidon–Jason series. Then, use the TOPEX/Poseidon–Jason series as the reference to estimate and remove systematic biases with other radar altimetry missions to construct consistent long-term lake water level series for ungauged lakes.

Description: It is now well established to use shallow artificial neural networks (ANNs) to obtain accurate and reliable groundwater level forecasts, which are an important tool for sustainable groundwater management. However, we observe an increasing shift from conventional shallow ANNs to state-of-the-art deep-learning (DL) techniques, but a direct comparison of the performance is often lacking. Although they have already clearly proven their suitability, shallow recurrent networks frequently seem to be excluded from the study design due to the euphoria about new DL techniques and its successes in various disciplines. Therefore, we aim to provide an overview on the predictive ability in terms of groundwater levels of shallow conventional recurrent ANNs, namely non-linear autoregressive networks with exogenous input (NARX) and popular state-of-the-art DL techniques such as long short-term memory (LSTM) and convolutional neural networks (CNNs). We compare the performance on both sequence-to-value (seq2val) and sequence-to-sequence (seq2seq) forecasting on a 4-year period while using only few, widely available and easy to measure meteorological input parameters, which makes our approach widely applicable. Further, we also investigate the data dependency in terms of time series length of the different ANN architectures. For seq2val forecasts, NARX models on average perform best; however, CNNs are much faster and only slightly worse in terms of accuracy. For seq2seq forecasts, mostly NARX outperform both DL models and even almost reach the speed of CNNs. However, NARX are the least robust against initialization effects, which nevertheless can be handled easily using ensemble forecasting. We showed that shallow neural networks, such as NARX, should not be neglected in comparison to DL techniques especially when only small amounts of training data are available, where they can clearly outperform LSTMs and CNNs; however, LSTMs and CNNs might perform substantially better with a larger dataset, where DL really can demonstrate its strengths, which is rarely available in the groundwater domain though.

Description: Despite showing great success of applications in many commercial fields, machine learning and data science models generally show limited success in many scientific fields, including hydrology (Karpatne et al., 2017). The approach is often criticized for its lack of interpretability and physical consistency. This has led to the emergence of new modelling paradigms, such as theory-guided data science (TGDS) and physics-informed machine learning. The motivation behind such approaches is to improve the physical meaningfulness of machine learning models by blending existing scientific knowledge with learning algorithms. Following the same principles in our prior work (Chadalawada et al., 2020), a new model induction framework was founded on genetic programming (GP), namely the Machine Learning Rainfall–Runoff Model Induction (ML-RR-MI) toolkit. ML-RR-MI is capable of developing fully fledged lumped conceptual rainfall–runoff models for a watershed of interest using the building blocks of two flexible rainfall–runoff modelling frameworks. In this study, we extend ML-RR-MI towards inducing semi-distributed rainfall–runoff models. The meaningfulness and reliability of hydrological inferences gained from lumped models may tend to deteriorate within large catchments where the spatial heterogeneity of forcing variables and watershed properties is significant. This was the motivation behind developing our machine learning approach for distributed rainfall–runoff modelling titled Machine Induction Knowledge Augmented – System Hydrologique Asiatique (MIKA-SHA). MIKA-SHA captures spatial variabilities and automatically induces rainfall–runoff models for the catchment of interest without any explicit user selections. Currently, MIKA-SHA learns models utilizing the model building components of two flexible modelling frameworks. However, the proposed framework can be coupled with any internally coherent collection of building blocks. MIKA-SHA's model induction capabilities have been tested on the Rappahannock River basin near Fredericksburg, Virginia, USA. MIKA-SHA builds and tests many model configurations using the model building components of the two flexible modelling frameworks and quantitatively identifies the optimal model for the watershed of concern. In this study, MIKA-SHA is utilized to identify two optimal models (one from each flexible modelling framework) to capture the runoff dynamics of the Rappahannock River basin. Both optimal models achieve high-efficiency values in hydrograph predictions (both at catchment and subcatchment outlets) and good visual matches with the observed runoff response of the catchment. Furthermore, the resulting model architectures are compatible with previously reported research findings and fieldwork insights of the watershed and are readily interpretable by hydrologists. MIKA-SHA-induced semi-distributed model performances were compared against existing lumped model performances for the same basin. MIKA-SHA-induced optimal models outperform the lumped models used in this study in terms of efficiency values while benefitting hydrologists with more meaningful hydrological inferences about the runoff dynamics of the Rappahannock River basin.

Description: Surface waters are under pressure from diffuse pollution from agricultural activities, and groundwater is known to be a connection between the agricultural fields and streams. This paper is one of the first to calculate long-term in-stream concentrations of tritium, chloride, and nitrate using dynamic groundwater travel time distributions (TTDs) derived from a distributed, transient, 3D groundwater flow model using forward particle tracking. We tested our approach in the Springendalse Beek catchment, a lowland stream in the east of the Netherlands, for which we collected a long time series of chloride and nitrate concentrations (1969–2018). The Netherlands experienced a sharp decrease in concentrations of solutes leaching to groundwater in the 1980s due to legislations on the application of nitrogen to agricultural fields. Stream measurements of chloride and nitrate showed that the corresponding trend reversal in the groundwater-fed stream occurred after a time lag of 5–10 years. By combining calculated TTDs with the known history of nitrogen and chloride inputs, we found that the variable contribution of different groundwater flow paths to stream water quality reasonably explained the majority of long-term and seasonal variation in the measured stream nitrate concentrations. However, combining only TTDs and inputs underestimated the time lag between the peak in nitrogen input and the following trend reversal of nitrate in the stream. This feature was further investigated through an exploration of the model behaviour under different scenarios. A time lag of several years, and up to decades, can occur due to (1) a thick unsaturated zone adding a certain travel time, (2) persistent organic matter with a slow release of N in the unsaturated zone, (3) a long mean travel time (MTT) compared to the rate of the reduction in nitrogen application, (4) areas with a high application of nitrogen (agricultural fields) being located further away from the stream or drainage network, or (5) a higher presence of nitrate attenuating processes close to the stream or drainage network compared to the rest of the catchment. By making the connection between dynamic groundwater travel time distributions and in-stream concentration measurements, we provide a method for validating the travel time approach and make the step towards application in water quality modelling and management.

Description: An accurate estimate of precipitation is essential to improve the reliability of hydrological models and helps in decision making in agriculture and economy. Merged radar–rain-gauge products provide precipitation estimates at high spatial and temporal resolution. In this study, we assess the ability of the INCA (Integrated Nowcasting through Comprehensive Analysis) precipitation analysis product provided by ZAMG (the Austrian Central Institute for Meteorology and Geodynamics) in detecting and estimating precipitation for 12 years in southeastern Austria. The blended radar–rain-gauge INCA precipitation analyses are evaluated using WegenerNet – a very dense rain-gauge network with about one station per 2 km2 – as “true precipitation”. We analyze annual, seasonal, and extreme precipitation of the 1 km  × 1 km INCA product and its development from 2007 to 2018. From 2007 to 2011, the annual area-mean precipitation in INCA was slightly higher than WegenerNet, except in 2009. However, INCA underestimates precipitation in grid cells farther away from the two ZAMG meteorological stations in the study area (which are used as input for INCA), especially from May to September (“wet season”). From 2012 to 2014, INCA's overestimation of the annual-mean precipitation amount is even higher, with an average of 25 %, but INCA performs better close to the two ZAMG stations. Since new radars were installed during this period, we conclude that this increase in the overestimation is due to new radars' systematic errors. From 2015 onwards, the overestimation is still dominant in most cells but less pronounced than during the second period, with an average of 12.5 %. Regarding precipitation detection, INCA performs better during the wet seasons. Generally, false events in INCA happen less frequently in the cells closer to the ZAMG stations than in other cells. The number of true events, however, is comparably low closer to the ZAMG stations. The difference between INCA and WegenerNet estimates is more noticeable for extremes. We separate individual events using a 1 h minimum inter-event time (MIT) and demonstrate that INCA underestimates the events' peak intensity until 2012 and overestimates this value after mid-2012 in most cases. In general, the precipitation rate and the number of grid cells with precipitation are higher in INCA. Considering four extreme convective short-duration events, there is a time shift in peak intensity detection. The relative differences in the peak intensity in these events can change from approximately −40 % to 40 %. The results show that the INCA analysis product has been improving; nevertheless, the errors and uncertainties of INCA to estimate short-duration convective rainfall events and the peak of extreme events should be considered for future studies. The results of this study can be used for further improvements of INCA products as well as for future hydrological studies in regions with moderately hilly topography and convective dominance in summer.