ALBERT

Description: ABSTRACT Atmospheric models such as the Weather Research and Forecasting (WRF) model provide a tool to evaluate the behavior of regional hydrological cycle components, including precipitation, evapotranspiration, soil water storage and runoff. Recent model developments have focused on coupled atmospheric‐hydrological modeling systems, such as WRF‐Hydro, in order to account for subsurface, overland, and river flow and potentially improve the representation of land‐atmosphere interactions. The aim of this study is to investigate the contribution of lateral terrestrial water flow to the regional hydrological cycle, with the help of a joint soil‐vegetation‐atmospheric water tagging (SVA‐TAG) procedure newly developed in the so‐called WRF‐tag and WRF‐Hydro‐tag models. An application of both models for the high precipitation event on 15 August 2008 in the German and Austrian parts of the upper Danube river basin (94,100 km2) is presented. The precipitation that fell in the basin during this event is considered as a water source, is tagged and subsequently tracked for a 40 month‐period until December 2011. At the end of the study period, in both simulations, approximately 57% of the tagged water has run off, while 41% has evaporated back to the atmosphere, including 2% that has recycled in the upper Danube river basin as precipitation. In WRF‐Hydro‐tag, the surface evaporation of tagged water is slightly enhanced by surface flow infiltration, and slightly reduced by subsurface lateral water flow in areas with low topography gradients. This affects the source precipitation recycling only in a negligible amount.

Description: Abstract Snow acts as a vital source of water especially in areas where streamflow relies on snowmelt. The spatio‐temporal pattern of snow cover has tremendous value for snowmelt modeling. Instantaneous snow extent can be observed by remote sensing. Cloud cover often interferes. Many complex methods exist to resolve this, but often have requirements which delay the availability of the data and prohibit its use for real‐time modeling. In this research, we propose a new method for spatially modeling snow cover throughout the melting season. The method ingests multiple years of MODIS snow cover data and combines it using principal component analysis (PCA) to produce a spatial melt‐pattern model. Development and application of this model relies on the inter‐annual recurrence of the seasonal melting pattern. This recurrence has long been accepted as fact, but to our knowledge has not been utilized in remote sensing of snow. We develop and test the model in a large watershed in Wyoming using 17 years of remotely sensed snow cover images. When applied to images from two years that were not used in its development, the model represents snow covered area with accuracy of 84.9‐97.5% at varied snow covered areas. The model also effectively removes cloud cover if any portion of the interface between land and snow is visible in a cloudy image. This new PCA method for modeling the inter‐annually recurring spatial melt pattern exclusively from remotely sensed images possesses its own intrinsic merit, in addition to those associated with its applications.

Description: Abstract The development of the unconventional gas and CO2 sequestration is moving to deep formations. Because of the small flow pathways in the matrix, the Knudsen number might be high even though the gas is dense. In fact, due to the relatively high pressure at in situ conditions, gas flow in microfractures usually manifests a strong slip and nonideal gas effects. Therefore, understanding the coupling mechanism of these two on gas flow in rough‐walled microfractures is required to accurately model subsurface flow behavior. In this study, pressure‐driven gas flow in rough‐walled microfracture is analyzed in depth. Starting from the local governing equations for gas flow, a local flow model that includes gas slip and nonideal gas effects is derived by solving the Stokes equation with a first‐order slip boundary condition. Focusing at the representative elementary volume scale, the upscaled solutions to gas flow in a fracture with sinusoidal surface are derived to obtain the apparent permeability. The impact of nonideal gas effects, fracture roughness and aperture, and the tangential momentum accommodation coefficient on CH4 and CO2 flow is analyzed. The results show that fracture roughness introduces a high degree of heterogeneity in gas flow. At in situ conditions effects of gas slip, fracture roughness and tangential momentum accommodation coefficient on gas flow are reduced. The ideal gas law is capable of estimating CH4 flow to some extent. However, it fails to estimate CO2 flow in microfractures.

Description: Abstract In recent years, climatology, variability, hydrological impact, and climatic drivers of atmospheric rivers (ARs) are widely explored based on various AR identification algorithms. Different algorithms, varying in their tracing variables, thresholds, and geometric metrics criteria, will introduce uncertainty in further study of AR. Herein, a novel AR identification algorithm is proposed to address some current limitations. A coupled quantile and Gaussian kernel smoothing technique is proposed to make a balance in capturing the spatiotemporal variation of integrated water vapor transport climatology and avoiding largely biased estimation. In spite of variety of AR shape, orientation, and curvature, more reliable AR metrics (e.g., length and width) can be calculated based on the generated smooth AR trajectory, which is realized by modifying and integrating the concepts of local regression and K‐nearest neighbors. An unprecedented and novel metric (i.e., turning angle series) is delivered to quantify AR curvature, serves as the key to distinguish tropical cyclone‐like features, which often indicate occurrences of tropical cyclones. It also bridges ARs to their associated atmospheric circulation patterns. A pilot application of the algorithm is presented to identify persistent AR events related to flood triggering extreme precipitation sequences in the Yangtze River Basin (YRB). A dominating AR route, which connects Arabian Sea, Bay of Bengal, South China Sea, to Southeast China and YRB, terminates in the North Pacific, is found principal to the flood triggering extreme precipitation sequences in the YRB. In addition, this algorithm is extensible to other regions, even global domain.

Description: Abstract A storage‐discharge relation tells us how discharge will change when new water enters a hydrologic system, but not which water is released. Does an incremental increase in discharge come from faster turnover of older water already in storage? Or are the recent inputs rapidly delivered to the outlet, ‘short‐circuiting’ the bulk of the system? Here I demonstrate that the concepts of storage‐discharge relationships and transit time distributions can be unified into a single relationship that can usefully address these questions: the age‐ranked storage‐discharge relation. This relationship captures how changes in total discharge arise from changes in the turn‐over rate of younger and older water in storage, and provides a window into both the celerity and velocity of water in a catchment. This leads naturally to a distinction between cases where an increase in total discharge is accompanied by an increase (old water acceleration), no change (old water steadiness), or a decrease in the rate of discharge of older water in storage (old water suppression). The simple theoretical case of a power‐law age‐ranked storage‐discharge relations is explored to illustrate these cases. Example applications to data suggest that the apparent presence of old water acceleration or suppression is sensitive to the functional form chosen to fit to the data, making it difficult to draw decisive conclusions. This suggests new methods are needed that do not require a functional form to be chosen, and provide age‐dependent uncertainty bounds.

Description: Abstract Hydrogeological field studies rely often on a single conceptual representation of the subsurface. This is problematic since the impact of a poorly chosen conceptual model on predictions might be significantly larger than the one caused by parameter uncertainty. Furthermore, conceptual models often need to incorporate geological concepts and patterns in order to provide meaningful uncertainty quantification and predictions. Consequently, several geologically‐realistic conceptual models should ideally be considered and evaluated in terms of their relative merits. Here, we propose a full Bayesian methodology based on Markov chain Monte Carlo (MCMC) to enable model selection among 2D conceptual models that are sampled using training images and concepts from multiple‐point statistics (MPS). More precisely, power posteriors for the different conceptual subsurface models are sampled using sequential geostatistical resampling and Graph Cuts. To demonstrate the methodology, we compare and rank five alternative conceptual geological models that have been proposed in the literature to describe aquifer heterogeneity at the MAcroDispersion Experiment (MADE) site in Mississippi, USA. We consider a small‐scale tracer test (MADE‐5) for which the spatial distribution of hydraulic conductivity impacts multilevel solute concentration data observed along a 2D transect. The thermodynamic integration and the stepping‐stone sampling methods were used to compute the evidence and associated Bayes factors using the computed power posteriors. We find that both methods are compatible with MPS‐based inversions and provide a consistent ranking of the competing conceptual models considered.

Description: Abstract Laboratory experiments examined the impact of model vegetation on wave‐driven resuspension. Model canopies were constructed from cylinders with three diameters (d = 0.32, 0.64, and 1.26 cm) and 12 densities (cylinders/m2) up to a solid volume fraction (ϕ) of 10%. The sediment bed consisted of spherical grains with d50 = 85 μm. For each experiment, the wave velocity was gradually adjusted by increasing the amplitude of 2‐s waves in a stepwise fashion. A Nortek Vectrino sampled the velocity at z = 1.3 cm above the bed. The critical wave orbital velocity for resuspension was inferred from records of suspended sediment concentration (measured with optical backscatter) as a function of wave velocity. The critical wave velocity decreased with increasing solid volume fraction. The reduction in critical wave velocity was linked to stem‐generated turbulence, which, for the same wave velocity, increased with increasing solid volume fraction. The measured turbulence was consistent with a wave‐modified version of a stem‐turbulence model. The measurements suggested that a critical value of turbulent kinetic energy was needed to initiate resuspension, and this was used to define the critical wave velocity as a function of solid volume fraction. The model predicted the measured critical wave velocity for stem diameters d = 0.64 to 2 cm. Combining the critical wave velocity with an existing model for wave damping defined the meadow size for which wave damping would be sufficient to suppress wave‐induced sediment suspension within the interior of the meadow.

Description: Abstract The impacts of aquatic vegetation on bed load transport rate and bedform characteristics were quantified using flume measurements with model emergent vegetation. First, a model for predicting the turbulent kinetic energy, kt, in vegetated channels from channel average velocity U and vegetation volume fraction ϕ was validated for mobile sediment beds. Second, using data from several studies, the predicted kt was shown to be a good predictor of bed load transport rate, Qs, allowing Qs to be predicted from U and ϕ for vegetated channels. The control of Qs by kt was explained by statistics of individual grain motion recorded by a camera, which showed that the number of sediment grains in motion per bed area was correlated with kt. Third, ripples were observed and characterized in channels with and without model vegetation. For low vegetation solid volume fraction (ϕ ≤ 0.012), the ripple wavelength was constrained by stem spacing. However, at higher vegetation solid volume fraction (ϕ=0.025), distinct ripples were not observed, suggesting a transition to sheet flow, which is sediment transport over a plane bed without the formation of bedforms. The fraction of the bed load flux carried by migrating ripples decreased with increasing ϕ, again suggesting that vegetation facilitated the formation of sheet flow.

Description: Abstract Reliable estimation of the volume and timing of snowmelt runoff is vital for water supply and flood forecasting in snow‐dominated regions. Snowmelt is often simulated using temperature‐index (TI) models due to their applicability in data‐sparse environments. Previous research has shown that a modified‐TI model, which uses a radiation‐derived proxy temperature instead of air temperature as its surrogate for available energy, can produce more accurate snow covered area (SCA) maps than a traditional TI model. However, it is unclear whether the improved SCA maps are associated with improved snow water equivalent (SWE) estimation across the watershed or improved snowmelt‐derived streamflow simulation. This paper evaluates whether a modified‐TI model produces better streamflow estimates than a TI model when they are used within a fully‐distributed hydrologic model. It further evaluates the performance of the two models when they are calibrated using either point SWE measurements or SCA maps. The Senator Beck Basin in Colorado is used as the study site because its surface is largely bedrock, which reduces the role of infiltration and emphasizes the role of the SWE pattern on streamflow generation. Streamflow is simulated using both models for six years. The modified‐TI model produces more accurate streamflow estimates (including flow volume and peak flow rate) than the TI model, likely because the modified‐TI model better reproduces the SWE pattern across the watershed. Both models also produce better performance when calibrated with SCA maps instead of point SWE data, likely because the SCA maps better constrain the space‐time pattern of SWE.

Description: Abstract This study aims at proposing novel approaches for integrating qualitative flow observations in a lumped hydrologic routing model and assessing their usefulness for improving flood estimation. Routing is based on a three‐parameter Muskingum model used to propagate streamflow in five different rivers in the United States. Qualitative flow observations, synthetically generated from observed flow, are converted into fuzzy observations using flow characteristic for defining fuzzy classes. A model states updating method and a model output correction technique are implemented. An innovative application of Interacting Multiple Models, which use was previously demonstrated on tracking in ballistic missile applications, is proposed as state updating method, together with the traditional Kalman filter. The output corrector approach is based on the fuzzy error corrector, which was previously used for robots navigation. This study demonstrates the usefulness of integrating qualitative flow observations for improving flood estimation. In particular, state updating methods outperform the output correction approach in terms of average improvement of model performances, while the latter is found to be less sensitive to biased observations and to the definition of fuzzy sets used to represent qualitative observations.

Description: Abstract The Sustainable Development Goals (SDGs) of the United Nations Agenda 2030 represent an ambitious blueprint to reduce inequalities globally and achieve a sustainable future for all mankind. Meeting the SDGs for water requires an integrated approach to managing and allocating water resources, by involving all actors and stakeholders, and considering how water resources link different sectors of society. To date, water management practice is dominated by technocratic, scenario‐based approaches that may work well in the short‐term, but can result in unintended consequences in the long‐term due to limited accounting of dynamic feedbacks between the natural, technical and social dimensions of human‐water systems. The discipline of socio‐hydrology has an important role to play in informing policy by developing a generalizable understanding of phenomena that arise from interactions between water and human systems. To explain these phenomena, socio‐hydrology must address several scientific challenges to strengthen the field and broaden its scope. These include engagement with social scientists to accommodate social heterogeneity, power relations, trust, cultural beliefs, and cognitive biases, which strongly influence the way in which people alter, and adapt to, changing hydrological regimes. It also requires development of new methods to formulate and test alternative hypotheses for the explanation of emergent phenomena generated by feedbacks between water and society. Advancing socio‐hydrology in these ways therefore represents a major contribution towards meeting the targets set by the SDGs, the societal grand challenge of our time.

Description: Abstract Field data of topography, water levels, and peat hydraulic conductivity collected over a 28‐year period have revealed the impacts of marginal drainage on uncut raised bog ecohydrology and its peat properties. Drainage of the regional groundwater body has induced changes in the hydraulic properties of deep peat, with peat compression decreasing hydraulic conductivity and storativity while simultaneously introducing localized secondary porosity and effective storage. Where peat has increased in hydraulic conductivity, there is a corresponding decline in vertical hydraulic gradients and significant localized increases in recharge to the underlying substrate. Repeated topographic surveys show intense localized areas of peat consolidation (〉5%) where it is underlain by highly permeable (〉10 m/day) glacial till deposits. More widely, continued subsidence (4–6 mm/year) of the bog surface has been measured over 900 m from the bog margin, resulting in the progressive loss of approximately 40% of actively growing raised bog since 1991. This loss has thus been shown to be attributable to changes in the underlying groundwater head due to deep‐cut drainage, rather than near‐surface peatland drainage. However, although reinstating regional hydrostatic pressures in order to restore this ombrotrophic peatland may control the rapid drainage through preferential flow pathways, this may not eliminate the ecological impacts resulting from changed surface morphology arising from subsidence. Hence, this longitudinal study provides new insights into the role that aquifer systems and groundwater bodies play in maintaining hydrogeological processes in ombrotrophic peatland systems, while highlighting the difficulty in ecological restoration where regional groundwater dependencies are significant.

Description: Abstract The mismatch between water demand and water availability in many megacities poses vexing water management challenges. Managers are forced to take remedial efforts to address these challenges, often with a heavy focus on infrastructure solutions such as building reservoirs or interbasin transfers to meet demand, which may in fact exacerbate the problem through unintended consequences that arise from neglect of social, economic, and environmental factors. Such a situation awaits Beijing, China, which faces major water management challenges in spite of the addition of a large interbasin transfer to meet increasing demand. In this study, a sociohydrologic model is developed for investigating Beijing's future water sustainability from a holistic and dynamic perspective. Using the model, we first explore the sociohydrologic mechanisms that contributed to Beijing's worsening water situation during 1988–2014. We then use the model to assess possible future impacts of the South to North Water Diversion Project on Beijing's water supply prospects for the 2015–2035 period. Alternative futures are explored by combining three different sustainable management strategies. The model results show that the source of Beijing's dominant water pressure experienced a transformation from productive to domestic water use over the last 30 years. They also indicate that the transfer water via South to North Water Diversion Project cannot fundamentally reverse Beijing's water shortage in the long term and that demand‐oriented management measures will be required for alleviating the city's water stress. These findings provide guidance not only for Beijing's water management but also for other less developed cities around the world.

Description: Abstract The dynamic system response curve (DSRC) method has been shown to effectively use error feedback correction to obtain updated areal estimates of mean rainfall and thereby improve the accuracy of real‐time flood forecasts. In this study, we address two main shortcomings of the existing method. First, ridge estimation is used to deal with ill‐conditioning of the normal equation coefficient matrix when the method is applied to small basins, or when the length of updating rainfall series is short. Second, the effects of spatial heterogeneity of rainfall on rainfall error estimates are accounted for using a simple index. The improved performance of the method is demonstrated using both synthetic and real data studies. For smaller basins with relatively homogeneous spatial distributions of rainfall, the use of ridge regression provides more accurate and robust results. For larger‐scale basins with significant spatial heterogeneity of rainfall, spatial rainfall error updating provides significant improvements. Overall, combining the two strategies results in the best performance for all cases, with the effects of ridge estimation and spatially distributed updating complementing each other.

Description: Abstract Understanding how spatial variability in stream discharge and water chemistry decrease with increasing catchment area is required to improve our ability to predict hydrological and biogeochemical processes in ungauged basins. We investigated differences in this decrease of variability with increasing catchment area among catchments, and among specific discharge (Qs) and water chemistry parameters. We defined the slope of the decrease in the variability with increasing catchment area as the rate of decrease in the standard deviation and coefficient of variation (δSD and δCV, respectively), both of which are −0.5 for the simple mixing of random variables (random mixing). All δSD and δCV values of Qs were less than −0.5, while those of most water chemistry values were greater than −0.5, indicating that with increased catchment area the spatial variability of Qs decreased more steeply than for random mixing, while for water chemistry they decreased less steeply. δSD and δCV had linear relationships with both the spatial dissimilarity index and relative changes in parameters’ mean values with increasing catchment area. It suggested that differences in δSD or δCV for Qs and water chemistry can be explained by the different spatial structures, where dissimilar values of Qs and similar values of water chemistry, respectively, are located close together in space. Differences in δSD and δCV according to Qs and water chemistry should significantly affect the determination of representative elementary area (REA), and therefore need to be considered when predicting REA from spatial variability of low‐order streams.

Description: Abstract The Ensemble Kalman Filter (EnKF) has been proved as a useful algorithm to merge coarse resolution Gravity Recovery and Climate Experiment (GRACE) data with hydrologic model results. However, in order for the EnKF to perform optimally a correct forecast error covariance is needed. The EnKF estimates this error covariance through an ensemble of model simulations with perturbed forcing data. Consequently a correct specification of perturbation magnitude is essential for the EnKF to work optimally. To this end, an Adaptive EnKF (AEnKF), a variant of the EnKF with an additional component that dynamically detects and corrects error misspecifications during the filtering process, has been applied. Due to the low spatial and temporal resolution of GRACE data, the efficiency of this method could be different than for other hydrologic applications. Therefore, instead of spatially or temporally averaging the internal diagnostic (normalized innovations) to detect the misspecifications, spatiotemporal averaging was used. First, sensitivity of the estimation accuracy to the degree of error in forcing perturbations was investigated. Second, efficiency of the AEnKF for GRACE assimilation was explored using two synthetic and one real data experiment. Results show that there is considerable benefit in using this method to estimate the forcing error magnitude, and that the AEnKF can efficiently estimate this magnitude.

Description: Abstract The scarcity of groundwater storage change data at the global scale hinders our ability to monitor groundwater resources effectively. In this study, we assimilate a state‐of‐the‐art terrestrial water storage (TWS) product derived from Gravity Recovery and Climate Experiment (GRACE) satellite observations into NASA's Catchment land surface model (CLSM) at the global scale, with the goal of generating groundwater storage time series that are useful for drought monitoring and other applications. Evaluation using in situ data from nearly 4,000 wells shows that GRACE data assimilation improves the simulation of groundwater, with estimation errors reduced by 36% and 10% and correlation improved by 16% and 22% at the regional and point scales, respectively. The biggest improvements are observed in regions with large interannual variability in precipitation, where simulated groundwater responds too strongly to changes in atmospheric forcing. The positive impacts of GRACE data assimilation are further demonstrated using observed low flow data. CLSM and GRACE data assimilation performance is also examined across different permeability categories. The evaluation reveals that GRACE data assimilation fails to compensate for the lack of a groundwater withdrawal scheme in CLSM when it comes to simulating realistic groundwater variations in regions with intensive groundwater abstraction. CLSM simulated groundwater correlates strongly with 12‐month precipitation anomalies in low and mid‐latitude areas. A groundwater drought indicator based on GRACE data assimilation generally agrees with other regional‐scale drought indicators, with discrepancies mainly in their estimated drought severity.

Description: Abstract Groundwater transit time is an essential hydrologic metric for groundwater resources management. However, especially in tropical environments studies on the transit time distribution (TTD) of groundwater infiltration and its corresponding mean transit time (mTT) have been extremely limited due to data sparsity. In this study, we primarily use stable isotopes to examine the TTDs and their mTTs of both vertical and horizontal infiltration at a riverbank infiltration area in the Vietnamese Mekong Delta (VMD), representative of the tropical climate in Asian Monsoon regions. Precipitation, river water, groundwater, and local ponding surface water were sampled for three to nine years and analyzed for stable isotopes (δ18O and δ2H), providing a unique data set of stable isotope records for a tropical region. We quantified the contribution that the two sources contributed to the local shallow groundwater by a novel concept of two‐component lumped parameter models (LPMs) that are solved using δ18O records. The study illustrates that two‐component LPMs, in conjunction with hydrological and isotopic measurements, are able to identify subsurface flow conditions and water mixing at riverbank infiltration systems. However, the predictive skill and the reliability of the models decrease for locations farther from the river, where recharge by precipitation dominates, and a low‐permeable aquitard layer above the highly permeable aquifer is present. This specific setting impairs the identifiability of model parameters. For river infiltration short mTTs (〈40 weeks) were determined for sites closer to the river (〈200 m), whereas for the precipitation infiltration the mTTs were longer (〉80 weeks) and independent of the distance to the river. The results not only enhance the understanding of the groundwater recharge dynamics in the VMD but also suggest that the highly complex mechanisms of surface‐groundwater interaction can be conceptualized by exploiting two‐component LPMs in general. The model concept could thus be a powerful tool for better understanding both the hydrological functioning of mixing processes and the movement of different water components in riverbank infiltration systems.

Description: Abstract Physically‐based models are useful frameworks for testing intervention strategies designed to reduce elevated sediment loads in agricultural catchments. Evaluating the success of these strategies depends on model accuracy, generally established by a calibration and evaluation process. In this contribution, the physically‐based SHETRAN model was assessed in two similar UK agricultural catchments. The model was calibrated on the Blackwater catchment (18 km2) and evaluated in the adjacent Kit Brook catchment (22 km2) using 4‐years of 15‐minute discharge and suspended sediment flux data. Model sensitivity to changes in single and multiple combinations of parameters as well as sensitivity to changes in Digital Elevation Model (DEM) resolution were assessed. Model flow performance was reasonably accurate; with a Nash‐Sutcliffe efficiency coefficient (NSE) of 0.78 in Blackwater and 0.60 in Kit Brook. In terms of event prediction, the mean of the absolute percentage of difference (μAbsdiff) between measured and simulated flow volume (Qv), peak discharge (Qp), sediment yield (Sy) and peak sediment flux (Sp) showed larger values in Kit Brook (48% [Qv], 66% [Qp], 298% [Sy], 438% [Sp]) compared to the Blackwater catchment (30% [Qv], 41% [Qp], 106% [Sy], 86% [Sp]). Results indicate that SHETRAN can produce reasonable flow prediction but performs less well in estimation of sediment flux, despite reasonably similar hydro‐sedimentary behaviour between catchments. The sensitivity index showed flow volume sensitive to saturated hydraulic conductivity and peak discharge to the Strickler coefficient; sediment yield was sensitive to the overland flow erodibility coefficient and peak sediment flux to raindrop/leaf soil erodibility coefficient. The multi‐parameter sensitivity analysis showed that different combinations of parameters produced similar model responses. Model sensitivity to grid resolution presented similar flow volumes for different DEM resolutions, whereas event peak and duration (for both flow and sediment flux) were highly sensitive to changes in grid size.

Description: Abstract High‐frequency stable isotope data are useful for validating atmospheric moisture circulation models and provide improved understanding of the mechanisms controlling isotopic compositions in tropical rainfall. Here we present a near‐continuous 6‐month record of O‐ and H‐isotope compositions in both water vapour and daily rainfall from Northeast Australia measured by laser spectroscopy. The data set spans both Wet and Dry Seasons to help address a significant data and knowledge gap in the southern hemisphere tropics. We interpret the isotopic records for water vapour and rainfall in the context of contemporaneous meteorological observations. Surface air moisture provided near‐continuous tracking of the links between isotopic variations and meteorological events on local to regional spatial scales. Power spectrum analysis of the isotopic variation showed a range of significant periodicities, from hourly to monthly scales and cross‐wavelet analysis identified significant regions of common power for hourly‐averaged water vapour isotopic composition and relative humidity, wind direction and solar radiation. Relative humidity had the greatest sub‐diurnal influence on isotopic composition. On longer timescales (weeks to months) isotope variability was strongly correlated with both wind direction and relative humidity. The high‐frequency records showed diurnal isotopic variations in O‐ and H‐isotope compositions due to local dew formation and, for deuterium excess, as a result of evapotranspiration. Several significant negative isotope anomalies on a daily scale were associated with the activity of regional mesoscale convective systems and the occurrence of two tropical cyclones. Calculated air parcel back‐trajectories identified the predominant moisture transport paths from the Southwest Pacific Ocean while moisture transport from northerly directions occurred mainly during the Wet Season monsoonal air flow. Water vapour isotope compositions reflected the same meteorological events as recorded in rainfall isotopes but provided much more detailed and continuous information on atmospheric moisture cycling than the intermittent isotopic record provided by rainfall. Improved global coverage of stable isotope data for atmospheric water vapour is likely to improve simulations of future changes to climate drivers of the hydrological cycle.

Description: Abstract The USDA National Agricultural Statistics Survey (NASS) collects and publishes crop growth status and soil moisture conditions in major US agricultural regions. The operationally produced weekly reports are based on survey information. The surveys are based on visual assessments and – in the case of soil moisture – report soil moisture levels in one of four categories (Very Short, Short, Adequate and Surplus). In this study, we show that these reports have remarkable correspondence with the NASA Soil Moisture Active Passive (SMAP) Level‐4 Soil Moisture (L4SM) product. This consistency allows for combining the two different types of data to produce a value‐added assessment, which enables cropland soil moisture mapping and state‐level statistics. Moreover, it enables daily assessment rather than weekly. In this study classification thresholds are derived for L4SM by mapping cumulative distribution functions of L4SM surface and root‐zone SM to the categorical NASS SM conditions. The results show that, year‐over‐year, the SMAP cumulative SM distributions are consistent with the NASS SM conditions and, furthermore, that the temporal evolution of the SMAP‐derived thresholds is consistent with the seasonal crop growth cycles from year to year. The results signify that the SMAP SM retrievals are relatable to SM estimation conducted in agricultural crop land by land managers and farmers, which underlines the general applicability of the SMAP data.

Description: Abstract Repeated measures experiments can be conducted to empirically estimate the uncertainty of a streamgauging method, such as the widespread moving‐boat acoustic Doppler current profilers (ADCP) approach. Previous ADCP repeated measures experiments, a.k.a. inter‐laboratory comparisons, provided a credible range of uncertainty estimates reflecting the quality of the site conditions. However, the method, which is a one‐way analysis of variance (ANOVA), only addresses the uncertainty of one lumped factor that combines several distinct factors: instrument, operator, procedure and cross‐section effects. To decompose the uncertainty of ADCP streamflow measurements due to cross‐section selection and team effects, a large repeated measures experiment has been conducted in the Taurion River (France). The experiment design was crossed and balanced, with two sets of 24 teams circulated over two sets of 12 cross‐sections. A constant flow rate was released from a dam, located immediately upstream of the experimental site. Prior to the statistical analysis, a data quality review was performed using the U.S. Geological Survey (USGS) QRev software to clean the dataset from avoidable errors and to homogenize the discharge computations. A two‐way ANOVA was applied to quantify the cross‐section effect, the team effect and their interaction, which was found to dominate the pure cross‐section effect. It was then possible to predict the average uncertainty of multiple‐transect ADCP discharge measurements, depending on the number of teams, cross‐sections and repeated transects included in the discharge average. The method opens interesting avenues for documenting difficult‐to‐estimate uncertainty sources of streamgauging techniques in other measuring conditions, especially the most adverse ones.

Description: Abstract Snow albedo is a dominant control on snowmelt in many parts of the world. An empirical albedo decay equation, developed over 60 years ago, is still used in snowmelt models. Several empirical snow albedo models developed since show wide spread in results. Remotely sensed snow albedos have been used in a few studies, but validations are scarce because of the difficulty in making accurate in situ measurements. Reconstruction of snow water equivalent (SWE), where the snowpack is built in reverse, is especially sensitive to albedo. We present two new contributions: (1) an updated albedo model where grain size and light absorbing particle (LAP) content are solved for simultaneously; (2) multiyear comparisons of remotely sensed and in situ albedo measurements from three high‐altitude sites in the western U.S. Our remotely sensed albedos show 4 to 6% RMSE and negligible bias. In comparison, empirical albedo decay models, which require extensive in situ measurements, show RMSE values of 7 to 17% with biases of ‐6 to ‐14%. We examine the sensitivity of SWE reconstructions to albedo error at two sites. With no simulated error in albedo, reconstructed SWE had MAE values of 7 to 13% and 5‐6% bias. The accuracy actually improved with some simulated added error, likely because of a fundamental bias in the reconstruction approach. Conversely, the best age‐based decay model showed an 18‐20% MAE and bias in reconstructed SWE. We conclude that remotely sensed albedos where available are superior to age‐based approaches in all aspects except simplicity.

Description: Abstract Describing the space‐time variability of hydrologic extremes in relation to climate is important for scientific and operational purposes. Many studies demonstrated the role of large‐scale modes of climate variability such as the El Nino Southern Oscillation (ENSO) or the North Atlantic Oscillation (NAO), amongst many others. Climate indices have hence frequently been used as predictors in probabilistic models describing hydrologic extremes. However, standard climate indices such as ENSO/NAO are poor predictors in some regions. Consequently, this paper describes an innovative method to avoid relying on standard climate indices, based on the following idea: the relevant climate indices are effectively unknown (they are hidden), and they should therefore be estimated directly from hydrologic data. In statistical terms, this corresponds to a Bayesian hierarchical model describing extreme occurrences, with hidden climate indices treated as latent variables. This approach is illustrated using three case studies. A synthetic case study first shows that identifying hidden climate indices from occurrence data alone is feasible. A second case study using flood occurrences at 42 East‐Australian sites confirms that the model correctly identifies their ENSO‐related climate driver. The third case study is based on 207 sites in France, where standard climate indices poorly predict flood occurrence. The hidden climate indices model yields a reliable description of flood occurrences, in particular their clustering in space and their large interannual variability. Moreover, some hidden climate indices are linked with specific patterns in atmospheric variables, making them interpretable in terms of climate variability and opening the way for predictive applications.

Description: Hydrological Processes, Volume 33, Issue 19, Page 2499-2501, 15 September 2019.

Description: Abstract Monitoring and estimation of snow depth in alpine catchments is needed for a proper assessment of management alternatives for water supply in these water resources systems. The distribution of snowpack thickness is usually approached by using field data that come from snow samples collected at a given number of locations that constitute the monitoring network. Optimal design of this network is required to obtain the best possible estimates. Assuming that there is an existing monitoring network, its optimization may imply the selection of an optimal network as a subset of the existing one (if there are not funds to maintain them) or enlarging the existing network by one or more stations (optimal augmentation problem). We propose an optimization procedure that minimizes the total variance in the estimate of snowpack thickness. The novelty of this work is to treat, for the first time, the problem of snow observation network optimization for an entire mountain range rather than for small catchments as done in previous studies. Taking into account the reduced data available, which is a common problem in many mountain ranges, the importance of a proper design of these observation networks is even larger. Snowpack thickness is estimated by combining regression models to approach the effect of the explanatory variables and kriging techniques to consider the influence of the stakes location. We solve the optimization problems under different hypotheses, studying the impacts of augmentation and reduction, both, one by one and in pairs. We also analyse the sensitivity of results to non‐snow measurements deduced from satellite information. Finally, we design a new optimal network by combining the reduction and augmentation methods. The methodology has been applied to the Sierra Nevada mountain range (southern Spain), where very limited resources are employed to monitor snowfall and where an optimal snow network design could prove critical. An optimal snow observation network is defined by relocating some observation points. It would reduce the estimation variance by around 600 cm2 (15%).

Description: Abstract The effects of root systems on soil detachment by overland flow are closely related to vegetation types. The objective of this study was to quantify the effects of two gramineous roots (Paspalum mandiocanum with shallow roots and Pennisetum giganteum with deep roots) on soil detachment capacity, rill erodibility and critical shear stress on alluvial fans of benggang in southeast China. A 4 m long and 0.12 m wide flume was used. Slope steepness ranged from 9% to 27%, and unit flow discharge ranged from 1.39×10‐3 to 4.19×10‐3 m2 s‐1. The mean detachment capacities of Paspalum mandiocanum and Pennisetum giganteum lands were 18% and 38% lower than that of bare land, respectively, and the effects of root on reducing soil detachment were mainly reflected in the 0‐5 cm soil layer. The most important factors in characterizing soil detachment capacity were root length density and soil cohesion, and soil detachment capacity of the two grass lands could be estimated using flow shear stress, soil cohesion, and root length density (NSE=0.90). With the increase in soil depth, rill erodibility increased, while shear stress decreased. The mean rill erodibilities of Paspalum mandiocanum and Pennisetum giganteum lands were 81% and 61% as much as that of bare land, respectively. Additionally, rill erodibilities of the two grass lands could be estimated as an exponential function by root length density and soil cohesion (NSE=0.88). The mean critical shear stress of Paspalum mandiocanum and Pennisetum giganteum lands were 1.29 and 1.39 times that of bare land, respectively, and it could be estimated with a linear function by root length density (NSE=0.76). This study demonstrated that planting of the two grasses Paspalum mandiocanum and Pennisetum giganteum could effectively reduce soil detachment and enhance soil resistance to erosion on alluvial fans, with the deep roots of Pennisetum giganteum being more effective than the shallow roots of Paspalum mandiocanum. The results are helpful for understanding the influencing mechanism of root systems on soil detachment process.

Description: Abstract The particle filter‐based data assimilation method is an effective tool to adjust model states based on observations. In this study, we proposed a modified particle filter‐based data assimilation method with a local weighting procedure (MPFDA‐LW) for a high‐precision two‐dimensional hydrodynamic model (HydroM2D) in dam‐break flood simulation. Moreover, a particle filter‐based data assimilation method with a global weighting procedure (PFDA‐GW) for the HydroM2D model was also investigated. The MPFDA‐LW and the PFDA‐GW for the HydroM2D model, respectively, adopted spatially nonuniform and uniform Manning's roughness coefficients. The MPFDA‐LW considering spatial‐temporal variability of Manning's roughness coefficient could significantly improve the performances of the HydroM2D model in simulating water stages at all gauges simultaneously, whereas the PFDA‐GW considering temporal variability of Manning's roughness coefficient could only slightly improve the performances of the HydroM2D model in simulating water stages at a few gauges. The MPFDA‐LW is more suitable for improving the performance of 2‐D hydrodynamic models in flood inundation simulation than the PFDA‐GW.

Description: Abstract The physics of disconnection between interrelated surface and groundwater has evolved considerably in recent years, especially since conjunctive use of water resources is increasingly dependent on groundwater resilience, but methods to measure disconnection on a river basin scale are lacking especially for managed‐ephemeral and irrigated‐agricultural systems. Multiyear drought limited surface water along Rincon Valley within the Elephant Butte Irrigation District (EBID) in the arid, Lower Rio Grande Basin of south‐central New Mexico, USA, and effects were compounded by continued extraction of groundwater to meet crop requirements. Average year‐end water table elevations in recent years have been below the average elevation of the riverbed, indicating potential disconnection between the river and the aquifer even when the river flows during the irrigation season. This study analyzed data from EBID groundwater monitoring wells adjacent to the river, infiltration determined from river flows, and riverbed measurements along the Rincon Valley reach to determine net annual seepage discharge to the aquifer and annual average pressure head below the river. Annual assessment from 2010 to 2017 confirmed that the drought shifted the system from connection to transition and then to disconnection. Nonlinear regression was used to quantify this shift to disconnection and back, enabled determination of several disconnection process metrics, and was also used to confirm that nonlinear disconnection behavior was reversible without significant hysteresis. The method developed herein confirms that the total head difference transition threshold can be determined from river/riparian monitoring sites over reach to basin scales.

Description: Abstract Dissolution trapping is one of the primary mechanisms of carbon dioxide (CO2) storage in a geological formation. In this study, a numerical model was used to examine the impacts of single and multiple fractures on the transport of dissolved CO2 plumes in various geological settings. The effects of the fracture angle, fracture‐matrix permeability ratio, fracture intersection, and matrix heterogeneity on density‐driven CO2 convection were systematically investigated. The fractures were found to play time‐varying roles in both homogeneous and heterogeneous media by serving as preferential pathways for both CO2‐rich plumes (fingers) and CO2‐free water. The competition between the enhancement of convective mixing and the inhibition of finger growth by the upward flow of freshwater generated a complex flow system. The interaction between the strong upward flow of freshwater through the fractures and the falling CO2‐rich fingers through the porous matrix induced a positive feedback, resulting in accelerated domain‐scale circulation and CO2 dissolution. While the CO2‐rich fingers grew relatively evenly at the top boundary in the homogeneous media, they selectively developed through the high permeable zones in the heterogeneous media. Compared with homogeneous media, the heterogeneous media preserving fractures particularly generated a more dynamic fracture‐matrix mass transfer, resulting in more rapid CO2 dissolution. The findings of this study were extended to examine the effects of fracture connectivity on the enhancement of CO2 transport and dissolution on a field scale.

Description: Abstract Discharge from multiple wastewater treatment plants (WWTPs) distributed in urbanized river basins contributes to impairments of river water‐quality and aquatic ecosystem integrity, with size and location of WWTPs determined by population distribution within a river basin. Here we used geo‐referenced data for WWTPs in Germany to investigate the spatial organization of three attributes of interest in this study: population, population equivalents (the aggregated population served by each WWTP), and the number/sizes of WWTPs. To this end, we selected as case studies three large urbanized river basins (Weser, Elbe, and Rhine), home to about 70% of the population in Germany. We employed fractal river networks as structural platforms to examine the spatial patterns from two perspectives: spatial hierarchy (stream order) and patterns along longitudinal flow paths (width function). Moreover, we proposed three dimensionless scaling indices to quantify (1) human settlement preferences by stream order, (2) non‐sanitary flow contribution to total wastewater treated at WWTPs, and (3) degree of centralization in WWTPs locations. Across the three river basins, we found scale‐invariant distributions for each of the three attributes with stream order, quantified using extended Horton scaling ratios. We found a weak downstream clustering of population in the three basins. Variations in population equivalent clustering among different class‐sizes of WWTPs reflected the size, number, and locations of urban agglomerations in these river basins. We discussed the applicability of this approach to other large urbanized basins to analyze spatial organization of population and WWTPs.

Description: Abstract Natural flood management (NFM) is increasingly promoted as a sustainable flood risk management (FRM) option, but significant barriers remain to its implementation. We assess the barriers to uptake and implementation of NFM using an approach in which we conceptualise a catchment as a social‐ecological system. We investigate the barriers relating to multiple stakeholders, biophysical and social components and the interactions between these different system elements. Semi‐structured interviews were undertaken with land managers and practitioners of FRM in the UK. Data were analysed using qualitative methods, including thematic coding and categorisation. Key barriers of 25 identified were: economic constraints for land managers, the current lack of scientific evidence to support NFM and current lack of governance over long‐term responsibility for NFM, which hinders future monitoring and maintenance. Practitioners within some sectors were less likely to recognise barriers noted by land managers, including cultural challenges, catchment planning concerns and lack of perceived control. For successful wider implementation of NFM, it is crucial that practitioners recognise the barriers that land managers experience, and that projects should build monitoring programmes into their funding bids, to assess impacts on flood risk and maintenance needs and to build the evidence base to guide future NFM implementation. This article is protected by copyright. All rights reserved.

Description: Abstract The aim of this study was to investigate how the spatial distribution of grass influenced runoff and erosion from a hillslope with loess and cinnamon soils in the rocky area of Northern China. We set up a trial to test the two soils with different treatments, including bare soil (BS), grass strips on the upper (UGS) and lower (DGS) parts of the slope, grass cover over the entire slope (GS), and a grass carpet on the lower part of the slope (GC), under simulated rainfall conditions. The results showed that the runoff coefficients for the loess and cinnamon soils decreased by between 4% and 20% and by between 2% and 37%, respectively, when covered with grass. Grass spatial distribution had little effect on the runoff, but more effect on erosion than vegetation coverage degree. The most effective location of grass cover for decreasing hillslope erosion was at the foot, and the high efficiency was mainly due to controlling of rill formation and sediment deposition. The soil loss from GS, DGS, and GC on the loess and cinnamon soils was between 77% and 93% less and 55% and 80% less, respectively, compared to the loss from BS. However, the soil characteristics had little effect on soil erosion for well‐vegetated slopes. The results highlight the importance of vegetation re‐establishment at the foot of hillslope in controlling soil erosion.

Description: Abstract Rain‐on‐snow (RoS) events have caused severe floods in mountainous catchments in the recent past. Challenges in forecasting such events are uncertainties in meteorological input variables, the accurate estimation of snow cover and deficits in process understanding during runoff formation. Here, we evaluate the potential of the European Centre for Medium‐Range Weather Forecasts Integrated Forecasting System (ECMWF IFS) to forecast RoS disposition (i.e. minimum rainfall amounts, initial snow cover and meltwater contribution) several days ahead. We thereby evaluate forecasts of rain and snowfall with disdrometer observations and show that ensemble‐based forecasts have larger potential than the high‐resolution forecast of ECMWF IFS. Then, we use ECMWF IFS weather forecasts as input to a conceptual hydrological model, which is calibrated using estimates of snow‐covered area (SCA), snow water equivalent (SWE) as well as discharge observations. We show that the forecast skill of this model chain is reasonably high with respect to SCA and SWE, even several days ahead. However, a number of RoS events are missed in the forecast, mainly due to an underestimation of rainfall amounts. These misses can be reduced by lowering the rainfall amount threshold for the forecast as compared to the analysis, being accompanied by only a moderate increase in false alarm rates. In contrast, the forecast of RoS disposition is found to be less sensitive to thresholds of initial snow cover and meltwater contribution. We conclude that valuable disposition warnings for RoS events can be issued several days ahead, and we illustrate this idea with a case study.

Description: Abstract The water quality and ecosystem health of river corridors depend on the biogeochemical processes occurring in the hyporheic zones (HZs) of the beds and banks of rivers. HZs in riverbeds often form because of bedforms. Despite widespread and persistent variation in river flow, how the discharge‐ and grainsize‐dependent geometry of bedforms and how bedform migration collectively and systematically affects hyporheic exchange flux, solute transport and biogeochemical reaction rates are unknown. We investigated these linked processes through morphodynamically‐consistent multiphysics numerical simulation experiments. Several realistic ripple geometries based on bedform stability criteria using mean river flow velocity and median sediment grainsize were designed. Ripple migration rates were estimated based primarily on the river velocity. The ripple geometries and migration rates were used to drive hyporheic flow and reactive transport models which quantified HZ nitrogen transformation. Results from fixed bedform simulations were compared with matching migrating bedform scenarios. We found that the turnover exchange due to ripple migration has a large impact on reactant supply and reaction rates. The nitrate removal efficiency increased asymptotically with Damköhler number for both mobile and immobile ripples, but the immobile ripple always had a higher nitrate removal efficiency. Since moving ripples remove less nitrogen, and may even be net nitrifying at times, consideration for bedform morphodynamics may therefore lead to reduction of model‐based estimates of denitrification. The connection between nitrate removal efficiency and Damköhler number can be integrated into frameworks for quantifying transient, network‐scale, HZ nitrate dynamics.

Description: Abstract This study addresses the evaluation of flow resistance in natural gravel‐bed rivers. Through a new dataset collected on 136 reaches of 78 gravel‐bed rivers (Calabrian fiumare) in southern Italy, different conventional flow resistance equations to predict mean flow velocity in gravel‐bed rivers were tested in their original form. These equations have shown considerable disagreement with observed data, especially in river reaches characterized by high bed load conditions and for the domains of intermediate‐ and large‐scale roughness. This disagreement produced in almost all the cases an underestimation of the flow resistance, which can be corrected by introducing the Froude number and a particular form of the Shields sediment mobility parameter into the Manning, Chezy, and Darcy‐Weisbach equations. Through analyses carried out both on the whole dataset and on its sub‐sets, we propose a semiempirical approach with which, on the one hand the tractive forces exerted by the flow on the bed are taken into account by considering the ratio between the sediment mobility parameter and its critical value, and on the other hand water surface distortions are evaluated using the Froude number. This approach has been further validated using a literature‐based dataset showing, even in this case, excellent performances. Finally, the literature‐based dataset allowed to improve the performances of the proposed approach in the field of large‐scale roughness. Efficiency tests indicate that the new equations can better reproduce the flow velocity in river reach where conventional flow resistance equations are not able to explain the entire dissipative process.

Description: Abstract The spatiotemporal distribution of pore water in the vadose zone can have a critical control on many processes in the near‐surface Earth, such as the onset of landslides, crop yield, groundwater recharge, and runoff generation. Electrical geophysics has been widely used to monitor the moisture content (θ) distribution in the vadose zone at field sites, and often resistivity (ρ) or conductivity (σ) is converted to moisture contents through petrophysical relationships (e.g., Archie's law). Though both the petrophysical relationships (i.e., choices of appropriate model and parameterization) and the derived moisture content are known to be subject to uncertainty, they are commonly treated as exact and error‐free. This study examines the impact of uncertain petrophysical relationships on the moisture content estimates derived from electrical geophysics. We show from a collection of data from multiple core samples that significant variability in the θ(ρ) relationship can exist. Using rules of error propagation, we demonstrate the combined effect of inversion and uncertain petrophysical parameterization on moisture content estimates and derive their uncertainty bounds. Through investigation of a water injection experiment, we observe that the petrophysical uncertainty yields a large range of estimated total moisture volume within the water plume. The estimates of changes in water volume, however, generally agree within (large) uncertainty bounds. Our results caution against solely relying on electrical geophysics to estimate moisture content in the field. The uncertainty propagation approach is transferrable to other field studies of moisture content estimation.

Description: Abstract Water vapor adsorption/desorption isotherms are measured on five shales from Illinois basin by dynamic vapor sorption method. The experimental adsorption data are modeled by the Guggenheim, Anderson, and De Boer model and the Freundlich model over the entire range of measured relative humidity (Rh) values (0–0.95). Modeling results show that shale hydration is controlled by surface chemistry at low Rh through a strong intermolecular bonding, while is mainly influenced by the pore structure at high Rh (〉0.9) through capillary condensation. This is consistent with the progressive decrease of isosteric heat of adsorption with water content, obtained by the Clausius‐Clapeyron equation. Exceptionally, for the one shale containing 8.6% montmorillonite, mesopore condensation only accounts for 33% of the measured water adsorption even at Rh ~0.95 due to the limited external pores and the important role of clay swelling. The specific surface area defined by Guggenheim, Anderson, and De Boer analysis as available for water adsorption is larger than that available for low‐pressure N2 adsorption due to the complex surface chemistry. The one shale rich in expansive montmorillonite and with a large interlayer capacity for water but inaccessible to N2 molecules conditions this result. Among the other four shales, one with high kerogen content behaves the highest water adsorption, possibly due to the high content of oxygen‐containing functional groups and the potentially high pore volume of kerogen. These findings contribute to a better understanding of water storage and transport behavior in shales and impact behavior relevant to structures and reservoirs founded in such media.

Description: Abstract One of the main problems of hydrologic/hydrodynamic routing models is defining the right set of parameters, especially on inaccessible and/or large basins. Remote Sensing techniques provide measurements of the basin topography, drainage system and channel width, however current methods for estimating riverbed elevation are not as accurate. This paper presents methods of altimetry data assimilation for estimating effective bathymetry of a hydrodynamic model. We tested past altimetry observations from satellites ENVISAT, ICESAT and JASON 2 and synthetic altimetry data from satellites ICESAT 2, JASON 3, SARAL and SWOT to assess future/present mission's potential. The data assimilation (DA) methods used were Direct Insertion, Linear Interpolation, the SCE‐UA optimization algorithm and an adapted Kalman Filter developed with hydraulically based variance and covariance introduced in this paper. The past satellite altimetry data assimilation was evaluated comparing simulated and observed water surface elevation (WSE) while the synthetic altimetry DA were assessed through a direct comparison with a “true” bathymetry. The SCE‐UA and hydraulically based Kalman Filter methods presented the best performances, reducing WSE error in 65% in past altimetry data experiment and reducing biased bathymetry error in 75% in the synthetic experiment, however the latter method is much less computationally expensive. Regarding satellites, it was observed that the performance is related to the satellite inter‐track distance, as higher number of observation sites allows more accurate bed elevation estimation.

Description: Abstract Resilience of soil moisture regimes (SMRs) describes the stability of a particular SMR and its ability to withstand disturbances. This study analyzes the resilience of SMRs with quantifiable ecological (ECO‐) and engineering (ENG‐) metrics for a stochastic dynamic soil moisture system. The SMR is defined by the stationary state, described by a stationary probability distribution function (pdf), of the soil moisture dynamical system, and further classified into arid, semi‐arid, semi‐wet and wet classes. Applying the stationary pdf of soil moisture dynamics derived by Rodriguez‐Iturbe et al. [1999] and Laio et al. [2001a], the ENG‐ and ECO‐ resilience metrics of the various SMRs are quantified. We show that the recovery rate of soil moisture is a convex function of the expected soil moisture at the stationary state — the recovery rate reaches a minimum value at some intermediate soil moisture status. We also show that the maximum acceptable changes in the infiltration condition indicate the capacity of a system to avoid possible regime shifts. SMR shifts are characterized by phenomena of stagnation and hysteresis, which suggest two distinct thresholds for SMR shifts and their reversion. In particular, the semi‐wet SMR that is favorable to agriculture requires stricter infiltration conditions than other SMRs. This resilience analysis provides better understanding of how natural hydrological conditions control soil moisture, which helps provide guidance on maintaining SMRs suitable for agricultural activities and desertification prevention.

Description: Abstract A fundamental understanding of the fluid movement and dynamic partitioning process at fracture intersections is important to accurately predict water infiltration and contaminant transport in networks of fractures. We present an experimental study on the flow‐splitting behavior at a T‐shaped intersection. Different combinations of apertures of the vertical (bv) and horizontal (bh) fractures are considered. Experimental results confirm that the gravity‐driven flow in the vertical fracture transitions from droplet to rivulet mode as the flow rate increases. We quantify the flow dynamics through the intersection and especially focus on the partitioning efficiency (η) defined as the percentage of flow partitioned into the horizontal fracture. We identify three regimes of flow partitioning at the intersection for the case of bv 〈 bh: total partitioning (η → 1), splitting or partial bypass (0 〈 η 〈 1), and total bypass (η → 0). The total bypass regime is associated with the rivulet mode with a flow rate higher than ~1.5 ml/min. We find a simple relationship between η and the flow rate Q for droplet flow, η = min(1, ChQ−1), where Ch is a threshold flow rate below which droplets almost completely imbibe into the horizontal fracture, leading to η → 1. A force balance analysis links Ch to a critical droplet length for the transition from complete partitioning to path splitting. The obtained relationship is further supported by numerical simulations of droplet flow through intersections. The results and analysis from this study may provide insights and physical constraints on construction of reduced order unsaturated flow models based on simplified discrete fracture networks.

Description: The manuscript analyses links between flood spatial arrangement and soil water balance in a plain watershed, and, with that purpose, landscape metrics are calculated in maps obtain by remote sensed data in different hydrological scenarios. In contrast with previous works that investigate connectivity, we applied landscape metrics focusing on flooding pattern, their spatial and temporal variability, and their relationship with soil water balance. In addition, the analysis of patterns allows highlighting the internal heterogeneity that plain landscapes usually exhibit. Abstract In areas with very mild relief, water drains in a disordered way due to the lack of a developed drainage network, as it occurs in extremely flat sedimentary regions like the Argentine Pampas. The study analysed the flood spatial arrangements in 2014 by calculating landscape metrics and relating them to soil water balance. The study area is located at Del Azul creek lower basin (Pampa Ecoregion, Argentina). Daily soil water balances were obtained, and seven landscape metrics were calculated in 15 windows in five LandSat images, all along 2014, to explore the relationship between hydrological scenarios and spatial pattern summarized with principal component analysis. Water excess concentrated in winter (June and August); deficits were in late spring and summer (January and November), whereas the beginning of autumn (March) was an intermediate situation. Principal component 1 (44.7%) reflected area and shape metrics and correlated positively with water table level; principal component 2 (32.3%) summarized aggregation ones and was negatively associated with accumulated water excesses or deficits in previous 30 days and useful reserve. Both exhibited possible threshold‐driven behaviour. Internal heterogeneity between NW and SE zones within the study area coincided with the existence of ancient alluvial fans. The results highlight the peculiarities of the flood spatial patterns in regions with very mild relief, where landforms usually determine water flows.

Description: Abstract Streamflow simulation of the headwater catchment of the Yellow River basin (HCYRB) in China is important for water resources management of the Yellow River basin. A statistical‐dynamical model, combining regular vine copulas with an optimization method for structure estimation, is presented with an application for simulating the monthly streamflow with local climate drivers at HCYRB. Local climate drivers for streamflow in every month are analyzed using rank‐based correlation. Precipitation, evaporation, and temperature generally show strong associations with streamflow. Winter streamflows relate to total precipitation of the wet season, and total evaporation of Oct and Nov, while unfrozen‐month streamflows are correlated with evaporation and precipitation of current and previous one months in the wet season. Both canonical vine and D‐vine copulas are applied to develop different conditional quantile functions for streamflows in different months with their dynamical covariates. The covariates are selected from historical streamflows and climate drivers with appropriate lags using partial correlations. The optimal vine trees are selected using the sequential maximum spanning tree algorithm with the weight based on both dependence and goodness of fit. The model demonstrates higher skill than existing vine‐based models and the seasonal autoregressive integrated moving average model. The enhanced skill of the hybrid statistical‐dynamical model comes from an improved capability of capturing nonlinear correlation and tail dependence of streamflow and climate drivers with the optimization of vine structure selection. The model provides an effective advance to enhance water resources planning and management for HCYRB and the whole basin.

Description: Journal of Flood Risk Management, Volume 12, Issue 3, September 2019.

Description: Journal of Flood Risk Management, Volume 12, Issue 3, September 2019.

Description: Abstract Before the solid waste is dumped in landfills, the collection process for large Spanish cities starts from a regular collection of household waste municipal service which is carried out through street containers. When an urban flood occurs those containers may lose their stability, thereby allowing debris (i.e., solid waste contained) and leachate to escape from the container and contaminate the flood water. Moreover, once a container loses its stability it can further constrict a narrow street and increase flooding, thereby creating a closed basin with no outlet for runoff and exacerbating the effects of flooding. Therefore, the waste containers stability when exposed to flooding is definitely an environmental, safety and health concern to be addressed. In this research stability functions for waste containers exposed to urban floods have been derived. These thresholds have been employed to analyse the containers' potential behaviour during floods in Barcelona. In order to validate the model a historical rainfall has been modelled and low‐return‐period design storms (i.e., 1, 5, and 10 years) have been used to assess the containers vulnerability against floods for frequent rainfall events. Once the number of potentially unstable containers has been estimated, an adaptation measure has been proposed in order to increase the resilience of waste sector against urban floods in Barcelona.

Description: Abstract This work introduces water–air two‐phase flow into integrated surface–subsurface flow by simulating rainfall infiltration and run‐off production on a soil slope with the finite element method. The numerical model is formulated by partial differential equations for hydrostatic shallow flow and water–air two‐phase flow in the shallow subsurface. Finite element computing formats and solution strategies are presented to obtain a numerical solution for the coupled model. An unsaturated seepage flow process is first simulated by water–air two‐phase flow under the atmospheric pressure boundary condition to obtain the rainfall infiltration rate. Then, the rainfall infiltration rate is used as an input parameter to solve the surface run‐off equations and determine the value of the surface run‐off depth. In the next iteration, the pressure boundary condition of unsaturated seepage flow is adjusted by the surface run‐off depth. The coupling process is achieved by updating the rainfall infiltration rate and surface run‐off depth sequentially until the convergence criteria are reached in a time step. A well‐conducted surface run‐off experiment and traditional surface–subsurface model are used to validate the new model. Comparisons with the traditional surface–subsurface model show that the initiation time of surface run‐off calculated by the proposed model is earlier and that the water depth is larger, thus providing values that are closer to the experimental results.

Description: Abstract We model mudstone permeability during consolidation and grain rotation, and during fluid injection by simulating porous media flow using the lattice Boltzmann method. We define the mudstone structure using clay platelet thickness, aspect ratio, orientation, and pore widths. Over the representative range of clay platelet lengths (0.1–3 μm), aspect ratios (length/thickness = 20–50), and porosities (ϕ = 0.07–0.80) our permeability results match mudstone datasets well. Homogenous kaolinite and smectite models document a log linear decline in vertical permeability from 8.31 × 10−15–6.84 × 10−17 m2 at ϕ = 0.76–0.80 to 6.33 × 10−19–1.30 × 10−23 m2 at ϕ = 0.14–0.16, showing good correlation with experimental data (R2 = 0.42 and 0.56).We employ our methodology to predict the permeability of two natural mudstone samples composed of smectite, illite, and chlorite grains. Over ϕ = 0.32–0.58, the permeability trends of two models replicating the mineralogical composition of the natural mudstone samples match experimental datasets well (R2 = 0.78 and 0.74). We extend our methodology to evaluate how vertical permeability might evolve during microfracture network growth or macrofracture propagation upon fluid injection in compacted mudstone. Fluid injection results in a permeability increase from 1.02 × 10−20 m2 at ϕ = 0.07 to 2.07 × 10−16 m2 at ϕ = 0.29 for growth of a microfracture network, and from 1.02 × 10−20 m2 at ϕ = 0.07 to 1.23 × 10−16 m2 at ϕ = 0.32 for macrofracture propagation. Our results suggest that a distributed microfracture network results in greater permeability during fluid injection in compacted mudstones (ϕ = 0.07–0.32) in comparison to a wide macrofracture. Our modeling approach provides a simple means to estimate permeability during burial and compaction or fluid injection based on knowledge of porosity and mineralogy.

Description: Abstract Phase five of the Coupled Model Intercomparison Project (CMIP5) enabled a range of decadal modelling experiments where climate models were initialised with observations and allowed to evolve freely for 10‐30 years. However, climate models struggle to realistically simulate rainfall and the skill of rainfall prediction in decadal experiments is poor. Here, we examine how predictions of sea surface temperature anomaly (SSTA) indices from CMIP5 decadal experiments can provide skilful rainfall forecasts at interannual timescales for Australia. Forecasts of commonly used SSTA indices relevant to Australian seasonal rainfall are derived from decadal hindcasts of six different climate models and corrected for model drift. The corrected indices are then combined to form a multi‐model ensemble. The resultant forecasts are used as predictors in a statistical rainfall model developed in this study. As SSTA forecasts lose skill with increasing lead time, a new methodology for predicting interannual rainfall is proposed. We allow our statistical prediction model to evolve with lead time while accounting for the loss of skill in SSTA forecasts instead of using one statistical model for all lead times. Results in this pilot study across two of the largest climate zones in Australia show that SSTA outputs from the decadal experiments provide enhanced skill in rainfall prediction over using the conventional model (based purely on lagged observed indices) up to a maximum of three years ahead. This methodology could be used more broadly for other regions around the world where rainfall variability is known to have strong links to ocean temperatures.

Description: Abstract Probabilistic modelling of streamflow in ephemeral catchments, where streamflow is frequently zero or negligible, is a major scientific and operational challenge. This paper evaluates the benefits of an explicit treatment of zero flows in the residual error models used for hydrological model calibration and prediction. In this approach, the lower bound of zero for streamflow is implemented using a censoring approach. The explicit approach is compared to a simpler pragmatic approach, which imposes the zero streamflow bound in prediction but not in calibration. Following a theoretical exposition, empirical comparisons are reported using a daily rainfall‐runoff model (GR4J), four residual error schemes (based on log, log‐sinh and Box‐Cox (BC) transformations with λ = 0.2 and 0.5), 74 Australian catchments with diverse hydroclimatology, and five performance metrics (reliability, precision, bias, proportion of zero flow days and CRPS skill score). The key findings are: (1) in “mid‐ephemeral” catchments (5‐50% zero flows) the explicit approach improves predictive performance, especially reliability, through better characterization of residual errors; (2) BC0.2 and BC0.5 schemes are Pareto optimal in mid‐ephemeral catchments (when the explicit approach is used): BC0.2 achieves better reliability and is recommended for probabilistic prediction, whereas BC0.5 attains lower volumetric bias; (3) in “low‐ephemeral” catchments (〈5% zero flows) the pragmatic approach is sufficient; (4) in “high‐ephemeral” catchments (〉50% zero flows) theoretical limitations result in poor performance of these particular explicit and pragmatic approaches, and further development is needed. The findings provide guidance on improving probabilistic streamflow predictions in ephemeral catchments.

Description: Scaling issues in snow hydrology persist due to limitations in instrumentation and inability to measure physical properties and processes at spatiotemporal scales required for analysis. Snow depth and water equivalent (SWE) across scale estimated using time‐lapse photos, transects, and model grids (Canadian Meteorological Centre depth, GlobSnow SWE) were found to represent different physical processes and have substantially different statistical moments. Findings have implications for understanding limitations of distributing snowpack measurements, data assimilation, and validation of remotely sensed estimates. Abstract This study investigates scaling issues by evaluating snow processes and quantifying bias in snowpack properties across scale in a northern Great Lakes–St. Lawrence forest. Snow depth and density were measured along transects stratified by land cover over the 2015/2016 and 2016/2017 winters. Daily snow depth was measured using a time‐lapse (TL) camera at each transect. Semivariogram analysis of the transect data was conducted, and no autocorrelation was found, indicating little spatial structure along the transects. Pairwise differences in snow depth and snow water equivalent (SWE) between land covers were calculated and compared across scales. Differences in snowpack between forested sites at the TL points corresponded to differences in canopy cover, but this relationship was not evident at the transect scale, indicating a difference in observed process across scale. TL and transect estimates had substantial bias, but consistency in error was observed, which indicates that scaling coefficients may be derived to improve point scale estimates. TL and transect measurements were upscaled to estimate grid scale means. Upscaled estimates were compared and found to be consistent, indicating that appropriately stratified point scale measurements can be used to approximate a grid scale mean when transect data are not available. These findings are important in remote regions such as the study area, where frequent transect data may be difficult to obtain. TL, transect, and upscaled means were compared with modelled depth and SWE. Model comparisons with TL and transect data indicated that bias was dependent on land cover, measurement scale, and seasonality. Modelled means compared well with upscaled estimates, but model SWE was underestimated during spring melt. These findings highlight the importance of understanding the spatial representativeness of in situ measurements and the processes those measurements represent when validating gridded snow products or assimilating data into models.

Description: Abstract Microbially induced carbonate precipitation (MICP) is a promising technique that could be used for soil stabilization, for permeability control in porous and fractured media, for sealing leaky hydrocarbon wells, and for immobilizing contaminants. Many further field trials are required before optimum treatment strategies can be established. These field trials will be costly and time consuming to \carry out and are currently a barrier to transitioning MICP from a lab‐scale process to a practical field‐scale deployable technology. To narrow down the range of potential treatment options into a manageable number, we present a field‐scale reactive transport model of MICP that captures the key processes of bacteria transport and attachment, urea hydrolysis, tractable CaCO3 precipitation, and modification to the porous media in terms of porosity and permeability. The model, named biogroutFoam, is implemented in OpenFOAM, and results are presented for MICP treatment in a planar fracture, three‐dimensional sand media at pore scale, and at continuum scale for an array of nine injection/abstraction wells. Results indicate that it is necessary to model bacterial attachment, that bacterial attachment should be a function of fluid velocity, and that phased injection strategies may lead to the most uniform precipitation in a porous media.

Description: No abstract is available for this article.

Description: Abstract A novel procedure for estimation of the vulnerability to seepage inducing piping processes in earthen levees affected by animal burrows is presented. The proposed methodology combines an available procedure of seepage vulnerability assessment for undamaged levees with the result of a finite element analysis software, which is used for identifying the seepage path and hydraulics head profile of both damaged and undamaged levees. The main steps of the procedure for estimating the impact of burrows in increasing the vulnerability of levees are presented. Twenty‐one levees along the Tanaro River (north‐western Italy) are used as a case study, and the results show that the critical conditions for the onset of inner erosion are achieved for shorter flood durations in damaged levees. If burrows occur, the probability of inner erosion (seepage probability) increases resulting in a potential increase of forming longer tunnels. This approach is a first attempt to quantify the seepage probability of extended levee systems affected by burrows and is applied for simplified geometrical and two‐dimensional representation of the cavities. This procedure can be applied by the hydraulic authorities to set the priorities in levees maintenance. Future research would focus on the analysis of more realistic burrows conditions.

Description: Abstract A Bayesian model that uses the spatial dependence induced by the river network topology, and the leading principal components of regional tree‐ring chronologies for paleo‐streamflow reconstruction is presented. In any river basin, a convergent, dendritic network of tributaries comes together to form the main stem of a river. Consequently, it is natural to think of a spatial Markov process that recognizes this topological structure to develop a spatially consistent basin‐scale streamflow reconstruction model that uses the information in streamflow and tree‐ring chronology data to inform the reconstructed flows, while maintaining the space‐time correlation structure of flows that is critical for water resource assessments and management. Given historical data from multiple streamflow gauges along a river, their tributaries in a watershed, and regional tree‐ring chronologies, the model is fit and used to simultaneously reconstruct the full network of paleo‐streamflow at all gauges in the basin progressing upstream to downstream along the river. Our application to eighteen streamflow gauges in the Upper Missouri River Basin shows that the mean adjusted‐R2 for the basin is approximately 0.5 with good overall cross‐validated skill as measured by five different skill metrics. The spatial network structure produced a substantial reduction in the uncertainty associated with paleo‐streamflow as one proceeds downstream in the network aggregating information from upstream gauges and tree‐ring chronologies. Uncertainty was reduced by more than 50% at six gauges, between 6 and 50% at one gauge, and by less than 5% at the remaining eleven gauges when compared with the traditional PCR reconstruction model.

Description: Abstract Forest canopies present irregular surfaces that alter both the quantity and spatiotemporal variability of precipitation inputs. The drop size distribution (DSD) of rainfall varies with rainfall event characteristics and is altered substantially by the forest stand properties. Yet, the influence of two major European tree species, European beech (Fagus sylvatica L.) and Norway spruce (Picea abies (L.) H. Karst), on throughfall DSD is largely unknown. In order to assess the impact of these two species with differing canopy structures on throughfall DSD, two optical disdrometers, one above and one below the canopy of each European beech and Norway spruce, measured DSD of both incident rainfall and throughfall over two months at a 10 second resolution. Fractions of different throughfall categories were analysed for single precipitation events of different intensities. While penetrating the canopies, clear shifts in drop size and temporal distributions of incoming rainfall were observed. Beech and spruce, however, had different DSD, behaved differently in their effect on diameter volume percentiles as well as width of drop spectrum. The maximum drop sizes under beech were higher than under spruce. The mean ± standard deviation of the median volume drops size (D50) over all rain events was 2.7 ± 0.28 mm for beech and 0.80 ± 0.04 mm for spruce, respectively. In general, there was a high DSD variability within events indicating varying amounts of the different throughfall fractions. These findings help to better understand the effects of different tree species on rainfall partitioning processes and small scale variations in subcanopy rainfall inputs, thereby demonstrating the need for further research in high resolution spatial and temporal properties of rainfall and throughfall.

Description: Abstract The interleaving of impermeable and permeable surfaces along a runoff flow path controls the hillslope hydrograph, the spatial pattern of infiltration, and the distribution of flow velocities in landscapes dominated by overland flow. Predictions of the relationship between the pattern of (im)permeable surfaces and hydrological outcomes tend to fall into two categories: (i) generalized metrics of landscape pattern, often referred to as connectivity metrics, and (ii) direct simulation of specific hillslopes. Unfortunately, the success of using connectivity metrics for prediction is mixed, while direct simulation approaches are computationally expensive and hard to generalize. Here we present a new approach for prediction based on emulation of a coupled Saint Venant equation‐Richards equation model with random forest regression. The emulation model predicts infiltration and peak flow velocities for every location on a hillslope with an arbitrary spatial pattern of impermeable and permeable surfaces but fixed soil, slope, and storm properties. It provides excellent fidelity to the physically based model predictions and is generalizable to novel spatial patterns. The spatial pattern features that explain most of the hydrological variability are not stable across different soils, slopes, and storms, potentially explaining some of the difficulties associated with direct use of spatial metrics for predicting landscape function. Although the current emulator relies on strong assumptions, including smooth topography, binary permeability fields, and only a small collection of soils, slope, and storm scenarios, it offers a promising way forward for applications in dryland and urban settings and in supporting the development of potential connectivity indices.

Description: A recently theoretically deduced rill flow resistance equation, based on a power‐velocity profile, was tested experimentally on plots of varying slopes (ranging from 9% to 26%) in which mobile and fixed bed rills were incised. The measurements carried out in both conditions confirmed that the Darcy–Weisbach friction factor can be accurately estimated using the proposed theoretical approach. For the investigated conditions, the effect of sediment transport on the flow resistance law is negligible respect to the grain roughness effect. Abstract Rills caused by run‐off concentration on erodible hillslopes have very irregular profiles and cross‐section shapes. Rill erosion directly depends on the hydraulics of flow in the rills, which may differ greatly from hydraulics of flow in larger and regular channels. In this paper, a recently theoretically deduced rill flow resistance equation, based on a power–velocity profile, was tested experimentally on plots of varying slopes (ranging from 9% to 26%) in which mobile and fixed bed rills were incised. Initially, measurements of flow velocity, water depth, cross‐section area, wetted perimeter, and bed slope, carried out in 320 reaches of mobile bed rills and in 165 reaches of fixed rills, were used for calibrating the theoretical flow resistance equation. Then the relationship between the velocity profile parameter Γ, the channel slope, and the flow Froude number was separately calibrated for the mobile bed rills and for the fixed ones. The measurements carried out in both conditions (fixed and mobile bed rills) confirmed that the Darcy–Weisbach friction factor can be accurately estimated using the proposed theoretical approach. For mobile bed rills, the data were supportive of the slope independence hypothesis of velocity, due to the feedback mechanism, stated by Govers. The feedback mechanism was able to produce quasicritical flow conditions. For fixed bed rills, obtained by fixing the rill channel, by a glue, at the end of the experimental run with a mobile bed rill, the slope independence of the flow velocity measurements was also detected. Therefore, an experimental run carried out by a rill bed fixed after modelling flow action is useful to detect the feedback mechanism. Finally, the analysis showed that, for the investigated conditions, the effect of sediment transport on the flow resistance law can be considered negligible respect to the grain roughness effect.

Description: Analyses of stem water stable isotope composition were used to identify water taken up by plants. The Populus trees and Shepherdia and Symphoricarpos shrubs had contrasting functional rooting depths as illustrated by the different seasonal patterns of change in deuterium excess values, which indicated greater shallow soil water use by the shrub species. Abstract Riparian cottonwood forests in dry regions of western North America do not typically receive sufficient growing season precipitation to completely support their relatively high transpiration requirements. Water used in transpiration by riparian ecosystems must include alluvial groundwater or water stored in the potentially large reservoir of the unsaturated soil zone. We used the stable oxygen and hydrogen isotope composition of stem xylem water to evaluate water sources used by the dominant riparian cottonwood (Populus spp.) trees and shrubs (Shepherdia argentea and Symphoricarpos occidentalis) in Lethbridge, Alberta, during 3 years of contrasting environmental conditions. Cottonwoods did not exclusively take up alluvial groundwater but made extensive use of water sourced from the unsaturated soil zone. The oxygen and hydrogen isotope compositions of cottonwood stem water did not strongly overlap with those of alluvial groundwater, which were closely associated with the local meteoric water line. Instead, cottonwood stem water δ18O and δ2H values were located below the local meteoric water line, forming a line with a low slope that was indicative of water exposed to evaporative enrichment of heavy isotopes. In addition, cottonwood xylem water isotope compositions had negative values of deuterium excess (d‐excess) and line‐conditioned (deuterium) excess (lc‐excess), both of which provided evidence that water taken up by the cottonwoods had been exposed to fractionation during evaporation. The shrub species had lower values of d‐excess and lc‐excess than had the cottonwood trees due to shallower rooting depths, and the d‐excess values declined during the growing season, as shallow soil water that was taken up by the plants was exposed to increasing, cumulative evaporative enrichment. The apparent differences in functional rooting pattern between cottonwoods and the shrub species, strongly influenced the ratio of net photosynthesis to stomatal conductance (intrinsic water‐use efficiency), as shown by variation among species in the δ13C values of leaf tissue.

Description: Abstract The physical parameterization of key processes in land surface models (LSMs) remains uncertain, and new techniques are required to evaluate LSMs accuracy over large spatial scales. Given the role of soil moisture in the partitioning of surface water fluxes (between infiltration, runoff, and evapotranspiration), surface soil moisture (SSM) estimates represent an important observational benchmark for such evaluations. Here, we apply SSM estimates from the NASA Soil Moisture Active Passive Level‐4 product (SMAP_L4) to diagnose bias in the correlation between SSM and surface runoff for multiple Noah‐Multiple Physics (Noah‐MP) LSM parameterization cases. Results demonstrate that Noah‐MP surface runoff parameterizations often underestimate the correlation between prestorm SSM and the event‐scale runoff coefficient (RC; defined as the ratio between event‐scale streamflow and precipitation volumes). This bias can be quantified against an observational benchmark calculated using streamflow observations and SMAP_L4 SSM and applied to explain a substantial fraction of the observed basin‐to‐basin (and case‐to‐case) variability in the skill of event‐scale RC estimates from Noah‐MP. Most notably, a low bias in LSM‐predicted SSM/RC correlation squanders RC information contained in prestorm SSM and reduces LSM RC estimation skill. Based on this concept, a novel case selection strategy for ungauged basins is introduced and demonstrated to successfully identify poorly performing Noah‐MP parameterization cases.

Description: Abstract We demonstrate a simple, cheap method for pore size characterization of porous media that generates a distribution of pore radii for improved flow and transport modeling. The new method for pore structure characterization utilizes recent theoretical developments in non‐Newtonian fluids. Numerical evaluations and validations with synthetic porous media showed potential for obtaining a distribution of effective pore radii and their contribution to total flow only by complementing water with non‐Newtonian fluids in saturated infiltration experiments. To demonstrate this ability on real sands, a series of one‐dimensional column experiments was conducted with varying porous medium packings, including Accusands and a polydisperse sand/glass bead mixture. For each packing, distilled water and varying concentrations of guar and xanthan gum were injected over a range of flow rates and pressure gradients. The model‐generated pore radii were compared with pore radius distributions measured by X‐ray microcomputed tomography (μCT), with results demonstrating good agreement between the model and μCT data. Simulations of saturated water flow and drainage curves using model‐generated pore radii compared favorably to experimental data, with errors typically between 2% and 10% for single‐phase flow and approaching the error of the μCT measured radius distributions for the drainage curves.

Description: Hydrological Processes, Volume 33, Issue 17, Page 2263-2265, 15 August 2019.

Description: Abstract Because of the possibility of getting the right answers for the wrong reasons, the predictive performance of a complex systems model is not by itself a reliable indicator of hypothesis quality for the purposes of scientific learning about processes. The predictive performance of a structurally adequate model should be an emergent property of its functional performance. In this context, any Pareto trade‐off between measures of predictive performance versus functional performance indicates process‐level error in the model; this trade‐off, if it exists, indicates that the model's predictions are right for the wrong functional reasons. This paper demonstrates a novel concept based on information theory that is capable of attributing observed errors to specific processes. To demonstrate that the concept and method hold true for models and observations of real systems, we employ a minimal single‐parameter‐variation sensitivity analysis using a sophisticated ecohydrology model, MLCan, for a well‐monitored field site (Bondville IL Ameriflux Soybean). We identify both functional and predictive error in MLCan, and also evidence of the hypothesized tradeoffs between the two. This trade‐off indicates structural error within MLCan. For example, the sensible heat flux process can be calibrated to achieve good predictive performance at the cost of poor functional performance. In contrast, we find little structural error for processes driven by solar radiation, which appear “right for the right reasons.” This method could be applied broadly to pinpoint process error and structural error in a wide range of system models, beyond the ecohydrological scope demonstrated here.

Description: Abstract Monthly evapotranspiration (ET) rates for 1979–2015 were estimated by the latest, calibration‐free version of the complementary relationship (CR) of evaporation over the conterminous United States. The results were compared to similar estimates of three land surface models (Noah, VIC, Mosaic), two reanalysis products (National Centers of Environmental Protection Reanalysis II, ERA‐Interim), two remote‐sensing‐based (Global Land Evaporation Amsterdam Model, Penman‐Monteith‐Leuning) algorithms, and the spatially upscaled eddy‐covariance ET measurements of FLUXNET‐MTE. Model validations were performed via simplified water‐balance derived ET rates employing Parameter‐Elevation Regressions on Independent Slopes Model precipitation, United States Geological Survey two‐ and six‐digit Hydrologic Unit Code (HUC2 and HUC6) discharge, and terrestrial water storage anomalies from Gravity Recovery and Climate Experiment, the latter for 2003–2015. The CR outperforms all other multiyear mean annual HUC2‐averaged ET estimates with root‐mean‐square error = 51 mm/year, R = 0.98, relative bias of −1%, and Nash‐Sutcliffe efficiency = 0.94, respectively. Inclusion of the Gravity Recovery and Climate Experiment data into the annual water balances for the shorter 2003–2015 period does not have much effect on model performance. Similarly, the CR outperforms all other models for the linear trend of the annual ET rates over the HUC2 basins. Over the significantly smaller HUC6 basins where the water‐balance validation is more uncertain, the CR still outperforms all other models except FLUXNET‐MTE, which has the advantage of possible local ET measurements, a benefit that clearly diminishes at the HUC2 scale. As the employed CR is calibration‐free and requires only very few meteorological inputs, yet it yields superior ET performance at the regional scale, it may serve as a diagnostic and benchmarking tool for more complex and data intensive models of terrestrial evapotranspiration rates.

Description: Abstract Recent advances in machine learning open new opportunities to gain deeper insight into hydrological systems, where some relevant system quantities remain difficult to measure. We use deep learning methods trained on numerical simulations of the physical processes to explore the possibilities to close the information gap of missing system quantities. As an illustrative example we study the estimation of velocity fields in numerical and laboratory experiments of density‐driven solute transport. Using high resolution observations of the solute concentration distribution, we demonstrate the capability of the method to structurally incorporate the representation of the physical processes. Velocity field estimation for synthetic data for both, variable and uniform concentration boundary conditions, showed equal results. This capability is remarkable because only the latter was employed for training the network. Applying the method to measured concentration distributions of density‐driven solute transport in a Hele‐Shaw cell makes the velocity field assessable in the experiment. This assessability of the velocity field even holds for regions with negligible solute concentration between the density fingers, where the velocity field is otherwise inaccessible.

Description: Abstract In view of the rapid proliferation of water infrastructures worldwide, balancing human and ecosystem needs for water resources is a critical environmental challenge of global significance. While there is abundant literature on the environmental impacts of individual water infrastructures, little attention has been paid to their cumulative effects in river networks, which may have basin‐to‐global impacts on freshwater ecology. Here we developed a methodological framework based on Pareto frontier analysis for optimizing trade‐offs between water withdrawal and ecological indicators. We applied this framework to a mountainous Ecuadorian headwater river network that is part of a continental water transfer for supply and demand management to optimize ecological conditions and the operation of 11 water intake structures used to provide potable water to the city of Quito. We found that the current water intake configuration has an important effect on the total length of fifth‐order stream sections (65% reduction compared to premanaged condition) and isolates 70.9% of the headwater stream length. The Pareto frontier analysis identified water intake portfolios (i.e., different combinations of intake sites) that decreased ecological impacts by 7.8% points (pp) and 13.0 pp for connectivity and stream order change, respectively, while meeting Quito's water demands. Additional portfolios accounting for monthly variability in water demand and resources further decrease the ecological impact up to 9.6 pp in connectivity and 13.4 pp in stream order. These eco‐friendly portfolios suggest that adaptive management at basin level may help optimize water withdrawal to fulfill urban demands while preserving ecological integrity.

Description: Abstract Hydroelectric dams often create highly dynamic downstream flows that promote surface water‐groundwater (SW‐GW) interactions including bank storage, the temporary storage of river water in the riverbank. Previous research on SW‐GW exchanges in dammed rivers have been local studies conducted within the bed or the bank, limiting the understanding of these exchanges which occur over potentially hundreds of kilometers. This study evaluates how dam releases affect SW‐GW exchange continuously over a 100 km distance. This is accomplished by longitudinally routing water releases through a synthetic river and modeling bed and bank fluid and solute exchange across transverse transects spaced along the reach. Peak and square dam release hydrograph shapes with three magnitudes (0.5, 1.0, and 1.5 m) were considered. The effect of four ambient groundwater flow conditions (very slightly losing, neutral, and two gaining from the perspective of the river) were evaluated for each dam release scenario. Both types of dam release shapes cause SW‐GW interaction over the entire 100 km distance, and our results show square type releases cause bank storage exchange well beyond this distance. Strongly gaining conditions reduce the amount of exchange and allow flushing of river‐sourced solute out of the bank after the dam pulse has passed. Both neutral and losing conditions have larger fluid and solute flux into the bank and limit the amount of solute that returns to the river. Our results support that river corridors downstream of dams have increased river‐aquifer connectivity, and that this enhanced connectivity can extend at least 100 km downstream.

Description: Abstract Watershed studies often rely on the assumption that interannual storage changes are negligible in the hydrologic balance of a watershed. The assumption can be useful and is sometimes necessary, but it is widely acknowledged as unrealistic. Identifying and understanding systematic deviations from hydrologic steady state has important implications for both hydrologic research and water management. To that end, we evaluated the magnitude of interannual changes in storage for nearly 1000 watersheds in the conterminous US (CONUS) for the ten‐year period 2002 to 2011 using ground‐based and remotely sensed data. We evaluated relationships between changes in storage (i.e., deviations from hydrologic steady state), vegetation cover, and hydroclimatic variables. Analysis of results using a Budyko framework revealed that, in general, greater evaporative partitioning led to smaller deviations from hydrologic steady state. Additional analysis using gradient boosted regression tree modeling identified an inverse relationship between forest cover and the magnitude of deviations from hydrologic steady state. In fact, modeling showed forest cover to be a stronger driver of variability in deviations from steady state than any hydroclimatic variable. We discuss ecohydrological feedbacks capable of contributing to steady state conditions in forested watersheds, and we discuss implications of these results for the co‐evolution of watersheds, vegetation, and climate.

Description: Abstract Flood damage processes are complex and vary between events and regions. State‐of‐the‐art flood loss models are often developed on basis of empirical damage data from specific case studies and do not perform well when spatially and temporally transferred. This is due to the fact, that such localized models often cover only a small set of possible damage processes from one event and a region. On the other hand, a single generalized model covering multiple events and different regions ignores the variability in damage processes across regions and events due to variables that are not explicitly accounted for individual households. We implement a Hierarchical Bayesian approach to parameterize widely used depth‐damage functions resulting in a Hierarchical (multi‐level) Bayesian Model (HBM) for flood loss estimation that accounts for spatio‐temporal heterogeneity in damage processes. We test and prove the hypothesis that, in transfer scenarios, HBMs are superior compared to generalized and localized regression models. In order to improve loss predictions for regions and events for which no empirical damage data is available, we use variables pertaining to specific region‐ and event‐characteristics representing commonly available expert knowledge as group‐level predictors within the HBM.

Description: Abstract Most existing numerical research on tide‐induced groundwater dynamics assume a constant surface water salinity on the seaward boundary (constant salinity case). Few studies have investigated the influence of tidally‐varying salinity on shallow groundwater dynamics in coastal aquifers (tidal salinity case). We compiled field observations of tidally‐varying salinity in multiple estuaries across the eastern coast of China and a tidal creek in North inlet‐Winyah Bay, the USA. Numerical simulations were then conducted to explore the effect of tidally‐varying salinity on groundwater flow and salt transport in an idealized creek‐marsh aquifer. Results showed that the upper saline plume and classical saltwater wedge appeared in all cases, but the salinity in the saltwater wedge was diluted in the tidal salinity cases. Notably, groundwater transit times were shorter in the tidal salinity case than in the constant salinity case, especially under the creek bottom. Quantitative analyses indicated that tidally‐varying salinity significantly enhanced surface water‐groundwater exchange, increasing submarine groundwater discharge by 10% and the total inflow of surface water across the water‐sediment interface by 7%. As the density of groundwater differs from that of the overlying surface water, fingered saltwater flow formed in sediments under the creek bottom, leading to some small local water circulation cells. These small cells reduced groundwater transit times, and almost doubled the water exchange rate. Coupling the density‐dependent flow to a simplified nitrogen reaction network revealed that the tidally‐varying salinity may have the potential to influence nitrogen biogeochemical transformations that modify nitrogen loads prior to discharge.

Description: Abstract Nowadays, national and international requirements and laws emphasize the “natural” development of river‐floodplain systems. One goal is to increase the connectivity between the river and its floodplains and thus reactivate floodplains as flooding areas, which potentially increases the mobility of fine sediments. The objective of this study is to analyze the long‐term effects of reactivated floodplains on the mobility of floodplain deposits of small rivers based on two river restoration scenarios: elevating the riverbed or lowering the floodplains. Past channel fixation and degradation as well as the subsequent increase in the floodplain elevation led to the decoupling of the channel and floodplain morphodynamics associated with the reduction of the habitat connectivity. Here, the floodplain sedimentation rates were determined using a numerical model based on the Delft3D software. The novelty of this numerical investigations is the morphological long‐term analysis over time scales of decades, which is not comparable to other short‐term hydro‐ and morphodynamic studies for small meandering lowland rivers. The results of 11 river restoration scenarios show that lowering the floodplain and raising the riverbed elevation both lead to an increase in the fine sediment deposition on the floodplain. However, lowering the floodplain elevation is generally more effective. Based on the numerical model results and the assumption of a fixed river channel, only anthropogenic activity might have increased the amount of fine sediments deposited on floodplains and has accelerated the decoupling of the floodplains from the riverbed in the past centuries.

Description: Abstract Crowd‐sourcing incorporates common citizens as rich sources of data, and is promising for environmental monitoring. In this paper, we propose and test the idea of incorporating incentives to crowd‐sourcing management for rainfall monitoring. Specifically, we model the allocation of incentives (quantitatively measurable and limited rewards) among crowd‐sourcing participants for a theoretical rainfall monitoring case. For this purpose, we develop an integrated model comprising a reward allocation component to represent the decision‐making process of a central manager, an agent‐based model to simulate the interactions between the manager and participants, and a rainfall simulation model to evaluate the effectiveness of various reward allocation policies. We simulate six reward allocation policies of varying levels of administrative cost, and consideration of participant and rainfall spatial heterogeneities. The results suggest the performance of each policy to improve with the reward budget and their spatial uniformity. Among the six policies tested, we find that the participant density weighted maximum participation policy (MDPP) yields the most accurate estimation of rainfall intensity due to its more explicit consideration of the spatial distribution of participants; however, this policy associates with a high administrative cost. This highlights the tradeoff between performance and cost in designing effective reward allocation policies. This paper provides a physical and behavior simulation modeling tool to study the feasibility and complexity of reward‐based participant management for crowd‐sourcing rainfall monitoring. The proposed crowd‐sourcing method is beneficial for a wide range of applications that require rainfall data with fine resolution, such as stormwater management and water availability and biomass assessment for food and energy crops.

Description: Hydrological Processes, Volume 0, Issue ja, -Not available-.

Description: Abstract Flood risk planning and emergency response at community levels rely on fast access to accurate inundation models that identify geographic areas, assets, and populations that may be flooded. However, limited flood modeling resources are available to support these events and activities. We present a computationally‐efficient flood model for facilitating rapid risk analysis across a wide range of scenarios and decision support to operational, crisis action, local flood‐fight, and community planning efforts. Our flood depth regression method converts publicly‐available river stage heights to flood depths, then downscales the depths from gage locations onto high resolution National Hydrography Dataset flowlines and estimates areas and depths of flooding by subtraction of the National Elevation Dataset from modeled water surface elevations. We demonstrate proof‐of‐principle analyses for historic 2009 Red River of the North flooding in the United States, achieving comprehensive mainstem flood estimation for the length of the river and depth accuracy of 1.4 ft (0.4 m) compared to gage observations, remote sensing, and higher‐resolution hydrologic models. We also demonstrate the utility of the method to inform planning and response decisions in preparation for flooding in a companion scenario for Yerington, Nevada, and call for further research and operationalization of riverine inundation mapping techniques. This article is protected by copyright. All rights reserved.

Description: Abstract Increasingly variable hydrologic regimes combined with more frequent and intense extreme events are challenging water systems management worldwide. These trends emphasize the need of accurate medium‐ to long‐term predictions to timely prompt anticipatory operations. Despite in some locations global climate oscillations and particularly the El Niño Southern Oscillation (ENSO) may contribute to extending forecast lead times, in other regions there is no consensus on how ENSO can be detected and used as local conditions are also influenced by other concurrent climate signals. In this work, we introduce the Climate State Intelligence framework to capture the state of multiple global climate signals via artificial intelligence and improve seasonal forecasts. These forecasts are used as additional inputs for informing water system operations and their value is quantified as the corresponding gain in system performance. We apply the framework to the Lake Como basin, a regulated lake in northern Italy mainly operated for flood control and irrigation supply. Numerical results show the existence of notable teleconnection patterns dependent on both ENSO and the North Atlantic Oscillation over the Alpine region, which contribute in generating skilful seasonal precipitation and hydrologic forecasts. The use of this information for conditioning the lake operations produces an average 44% improvement in system performance with respect to a baseline solution not informed by any forecast, with this gain that further increases during extreme drought episodes. Our results also suggest that observed preseason SST anomalies appear more valuable than hydrologic‐based seasonal forecasts, producing an average 59% improvement in system performance.

Description: Abstract Meltwater from glaciers is not only a stable source of water but also affects downstream streamflow dynamics. One of these dynamics is the interannual variability of streamflow. Glaciers can moderate streamflow variability, because the runoff in the glacierised part, driven by temperature, correlates negatively with the runoff in the non‐glacierised part of a catchment, driven by precipitation, thereby counterbalancing each other. This is also called the glacier compensation effect (GCE) and the effect is assumed to depend on relative glacier cover. Previous studies found a convex relationship between streamflow variability and glacier cover of different glacierised catchments, with lowest streamflow variability at a certain optimum glacier cover. In this study we aim to revisit these previously found curves to find out if a universal relationship between interannual streamflow variability and glacier cover exists, which could potentially be used in a space‐for‐time substitution analysis. Moreover, we test the hypothesis that the dominant climate drivers (here precipitation and temperature) switch around the suggested optimum of the curve. First a set of virtual nested catchments, with the same absolute glacier area but varying non‐glacierised area, were modelled to isolate the effect of glacier cover on streamflow variability. The modelled relationship was then compared to a multi‐catchment dataset of gauged glacierised catchments in the European Alps. In a third step, changes of the GCE curve over time were analysed. Model results showed a convex relationship and the optimum in the simulated curve aligned with a switch in the dominant climate driver. However, the multi‐catchment data and the time change analyses did not suggest the existence of a universal convex relationship. Overall, we conclude that GCE is complex due to entangled controls and changes over time in glacierised catchments. Therefore, care should be taken to use a GCE curve for estimating and/or predicting interannual streamflow variability in glacierised catchments.

Description: Abstract Base flows are important for tropical regions with pronounced dry seasons which are facing increasing water demands. Base flow generation, however, is one of the most challenging hydrological processes to characterize in the tropics. In many years during the May‐December wet season in the Panama Canal Watershed (PCW), base flows in rivers abruptly increase. This increase persists until the start of the December‐April dry season. Understanding this unusual base flow jump (BFJ) behavior is critical to improve water provisioning in seasonal tropics, especially during droughts and extended dry seasons. This study developed an integrated approach combining piecewise regression on cumulative average base flow and sensitivity analysis to calculate the timing and magnitude of BFJ. Rainfall, forest cover, mean land surface slope, catchment area and estimated subsurface storage were tested as predictors for the occurrence and magnitude of the BFJs in seven sub‐catchments of the PCW. Sensitivity analysis on correlated predictors allowed ranking of predictor contributions due to isolated and cross correlation effects. Correlations between observed BFJs and BFJs predicted by watershed and rainfall‐related predictors were 0.92 and 0.65 for BFJ timing and magnitude, respectively. Forest cover was the second most significant predictor after cumulative rainfall for jump magnitude, owing to larger subsurface storage and groundwater recharge in forests than pastures. Catchments in the mountainous eastern PCW always generated larger jumps due to their higher rainfall and greater forest cover than the western PCW catchments. The cross‐correlations between predictors contributed to more than 50% of the jump variances. The results demonstrate the importance of rainfall gradient and catchment characteristics in affecting the sudden and sustained BFJs, which can help inform land management decisions intended to enhance water supplies in tropics. This study underscores the need for more research to further understand the hydrological processes involved in the BFJ phenomenon, including better BFJ models and field characterizations, to help improve tropical ecosystem services under a changing environment.

Description: Abstract Starting from their first applications in the late 1960s, the mass balance equations for separating storm hydrographs have been analyzed with respect to their solvability and sensitivity to errors. By changing them into a time perspective, it is shown how an iterative extension of the standard two‐component separation can lead to a system of balance equations that allows for separating n time components using one single stable isotope tracer. This new approach is tested for the case n=6 by using the experimental data of the Zastler catchment (18.4 km2, southern Black Forest, Germany). The behavior of several time components is demonstrated. By separating the event water of the last and next‐to‐last event, it is shown how the event water of a certain rainfall‐runoff event contributes to stream discharge over the next two subsequent events. The new separation model offers the possibility of tracing event water over a much longer period after the initial event.

Description: Abstract Satellite estimates of inland water quality have the potential to vastly expand our ability to observe and monitor the dynamics of large water bodies. For almost 50 years, we have been able to remotely sense key water quality constituents like Total Suspended Sediment (TSS), Dissolved Organic Carbon (DOC), Chlorophyll a, and Secchi Disk Depth (SDD). Nonetheless, remote sensing of water quality is poorly integrated into inland water sciences, in part due to a lack of publicly available training data and a perception that remote estimates are unreliable. Remote sensing models of water quality can be improved by training and validation on larger datasets of coincident field and satellite observations, here called matchups. To facilitate model development and deeper integration of remote sensing into inland water science, we have built AquaSat, the largest such matchup dataset ever assembled. AquaSat contains more than 600,000 matchups, covering 1984‐2019, of ground‐based TSS, DOC, Chlorophyll a, and SDD measurements paired with spectral reflectance from Landsat 5, 7, and 8 collected within +/‐1 day of each other. To build AquaSat, we developed open source tools in R and Python and applied them to existing public datasets covering the contiguous United States, including the Water Quality Portal, LAGOS‐NE, and the Landsat archive. In addition to publishing the dataset, we are also publishing our full code architecture to facilitate expanding and improving AquaSat. We anticipate that this work will help make remote sensing of inland water accessible to more hydrologists, ecologists, and limnologists while facilitating novel data‐driven approaches to monitoring and understanding critical water resources at large spatiotemporal scales.

Description: Abstract Rock deformation induced by pore‐fluid pressure carries useful information about fluid flow owing to hydromechanical coupling. Thus, obtaining spatiotemporal changes in rock deformation could provide improved understanding of the fluid and pressure migration in aquifers or the role of fluid in the evolution of rainfall‐induced landslide. Here we deployed high‐resolution Rayleigh‐scattering‐type distributed fiber optic strain sensing (DFOSS) to measure rock deformation while injecting water into low‐permeability dry sandstone. X‐ray computed tomography imaging was simultaneously used to visualize the water migration. DFOSS measurements showed the rock developed a dilation deformation that grew during water saturating process. The movement of water wetting front can be revealed by the changes in the measured distributed strain. Strain changes were shaped by poroelastic changes due to the fluid pressure buildup and swelling by the water–clay reaction (i.e., adsorption). The latter mechanism caused increase in the strain when water first entered the dry pore spaces and the change in pore pressure was slight. The mechanism continued contributed to the overall deformation to peak magnitude of ~600 μϵ together with poroelastic mechanism. However, after the rock was fully saturated, further deformation during the flow test can be explained by the poroelastic mechanism alone. Our study suggests that the two factors can be employed as signatures for effectively monitoring fluid behavior in natural sediments using DFOSS. Moreover, we obtained the spatial hydromechanical properties, permeability and stiffness, from the distributed strain measurement and pressure responses. Using DFOSS in the field may substantially improve our ability to monitor and model fluid activity related to rock deformations in reservoirs and rainfall‐induced landslides that would help in warning people of risks and preventing disasters.

Description: Abstract Flood hazard maps are useful tools for land‐planning and flood‐risk management in order to increase the safety of flood‐prone areas that can be inundated in the event of levee failure. However, flood hazard assessment is affected by various uncertainties, both aleatory and epistemic. The flood hazard analysis should hence take into account the main sources of uncertainty and quantify the confidence of the results for a given design flood event. To this end, this paper presents a probabilistic method for flood hazard mapping which considers uncertainty due to breach location and failure time. A reliability analysis of the discretized levee system, performed using the concept of fragility function, enables the pre‐selection of a set of levee sections more susceptible to failure. The probabilities of the breach scenarios (characterized by different breach locations and times) are then calculated using the probability multiplication rule, neglecting multiple breaches. The method is applied to a 96 km levee‐protected reach in the central portion of the Po River (northern Italy) and to an adjacent 1900 km2 flood‐prone area on the right‐hand side of the river, with a focus on the piping breach mechanism. The numerical simulations are performed through a combined 1D‐2D hydrodynamic model using widespread free software. The results show that the method is effective for probabilistic inundation and flood hazard mapping. In addition, it has the advantage of requiring a smaller computational effort in comparison with the methods based on a classic Monte Carlo procedure.

Description: Abstract Changes in river flow may appear from shifts in land cover, constructions in the river channel, and climatic change, but currently there is a lack of understanding of the relative importance of these drivers. Therefore, we collected gauged river‐flow time‐series from 1961 to 2018 from across Sweden for 34 disturbed catchments to quantify how the various types of disturbances have affected river flow. We used trend analysis and the differences in observations versus hydrological modelling to explore the effects on river flow from: (1) land cover changes from wildfires, storms and urbanization; (2) dam constructions with regulations for hydropower production; and (3) climate‐change impact in otherwise undisturbed catchments. A mini model‐ensemble, consisting of three versions of the S‐HYPE model, was used and the three models gave similar results. We searched for changes in annual and daily stream flow, seasonal flow regime and flow duration curves. The results show that regulation of river flow has the largest impact, reducing spring floods with up to 100% and increasing winter flow by several orders of magnitude, with substantial effects transmitted far downstream. Climate changed the total river flow up to 20%. Tree removal by wildfires and storms has minor impacts at medium and large‐scales. Urbanization, on the contrary, showed a 20% increase in high flows also at medium scales. This study emphasizes the benefits of combining observed time‐series with numerical modelling to exclude the effect of varying weather conditions, when quantifying the effects of various drivers on long‐term streamflow shifts.

Description: Abstract Elevated soil moisture and heavy precipitation contribute to landslides worldwide. These environmental variables are now being resolved with satellites at spatiotemporal scales that could offer new perspectives on the development of landslide warning systems. However, the application of these data to hydro‐meteorological thresholds (which account for antecedent soil moisture and rainfall) first need to be evaluated with respect to proven, direct measurement‐based thresholds that use rain gages and in situ soil moisture sensors. Here, we compare ground‐based hydrologic data to overlapping satellite‐based data before, during, and after a recent season of widespread shallow landsliding in the San Francisco Bay Area (California, USA). We then explore how the remotely sensed information could be used to empirically define hypothetical thresholds for shallow landsliding. We find that the ground‐based thresholds developed with a single monitoring station show superior performance because the in situ soil saturation data better reflect the gravity‐dominated subsurface flow conditions that are characteristic of hillslopes during the rainy season. Although the satellite‐based thresholds can identify most of the landslide days, they include a greater number of false alarms due to overestimates of soil moisture between major storm events. To avoid the type of false alarms that are characteristic of our satellite‐based thresholds, further post‐processing of the near‐surface hydrologic response data should be integrated into satellite‐based model outputs to better reflect gravity‐dominated drainage. Our results encourage further deployment of ground stations in landslide‐prone terrain and cautious exploration of satellite‐based hydro‐meteorological thresholds where in situ networks are nonexistent.

Description: Abstract River discharge gauging is scarce in high mountainous regions, especially on the Tibetan Plateau, where rivers are widely distributed. Although remote sensing is an important mean of monitoring river discharge, previous methods are most suitable for large rivers, and the ability to monitor small rivers (with widths less than 100 m) is limited. To resolve this issue, a multiple pixel ratio (MPR) method is presented for monitoring the discharge of small rivers based on the relationship between river discharge and the near‐infrared reflectivity. Utilizing 281 Landsat images (1990‐2015), we monitored river discharges in two sub‐basins in the upstream region of the Heihe River located on the northeastern Tibetan Plateau. Our results indicate that the performance of the MPR method is more stable than the previous calibration/measurement (C/M) method. The monitoring accuracy was correlated with the length and location of the selected inundated river channel (SIRC). Using SIRC lengths between 300 and 600 m can provide better monitoring accuracy. The Nash‐Sutcliffe efficiency (NSE) of monitoring results (2013‐2014) of Qilian station was 0.82; the Zhamashike station (2015) was 0.45; the two stations multi‐years (1990‐2013) monitoring results was 0.32, 0.41, respectively; the ungauged basin was 0.45. Our results suggest that the MPR method can expand the ability of remote sensing to monitor discharge in small rivers with widths greater than 30 m on the Tibetan Plateau. In addition, the new method also has the potential to monitor the discharge of ungauged small rivers (with widths greater than 30 m).

Description: Abstract Flow‐duration curve (FDC) based streamflow estimation methods involve estimating an FDC at an ungaged or partially gaged location and using the time series of nonexceedance probabilities estimated from donor streamgage sites to generate estimates of streamflow. We develop a mathematical framework to illustrate the connection between copulas and prior FDC‐based approaches. The performance of copula methods is compared to several other streamflow estimation methods using a decade of daily streamflow data from 74 sites located within two river basins in the southeast United States with different climate characteristics and physiographic properties. We show that copula approaches: (1) outperform other methods in the limiting case of perfect information with regard to the rank‐based correlation structure and FDCs across the gaging network; (2) provide a hedge against poor performance when donor information becomes sparser and less informative; (3) outperform other methods when used for partially gaged sites with several years of available data; and (4) remain a competitive albeit nondominating method for ungaged sites and partially gaged sites with limited data when realistic error is introduced in the estimation of FDCs and correlations across the gaging network.

Description: Abstract Overbank sedimentation is predominantly due to fine sediments transported under suspension that become trapped and settle in floodplains when high‐flow conditions occur in rivers. In a compound channel, the processes of exchanging water and fine sediments between the main channel and floodplains regulate the geomorphological evolution and are crucial for the maintenance of the ecosystem functions of the floodplains. These hydrodynamic and morphodynamic processes depend on variables such as the flow‐depth ratio between the water depth in the main channel and the water depth in the floodplain, the width ratio between the width of the main channel and the width of the floodplain, and the floodplain land cover characterized by the type of roughness. This paper examines, by means of laboratory experiments, how these variables are interlinked and how the deposition of sediments in the compound channel is jointly determined by them. The combination of these compound channel characteristics modulates the production of vertically axised large turbulent vortical structures in the mixing interface. Such vortical structures determine the water mass exchange between the main channel and the floodplain, conditioning in turn the transport of sediment particles conveyed in the water, and, therefore, the resulting overbank sedimentation. The existence and pattern of sedimentation are conditioned by both the hydrodynamic variables (the flow‐depth ratio and the width ratio) and the floodplain land cover simulated in terms of smooth walls, meadow‐type roughness, sparse‐wood‐type roughness, and dense‐wood‐type roughness.

Description: No abstract is available for this article.

Description: Abstract Peatlands cover many low‐lying areas in the tropics. Tropical peatlands are intriguing systems because of their tight coupling between hydrology and carbon storage: They accumulate carbon over thousands of years because of waterlogging, and they remain waterlogged after growing into domed shapes because peat restricts drainage. This feedback between waterlogging and landscape morphology generates landforms with special hydrologic properties that enable simplifications of standard watershed models. In natural tropical peatlands, the water table is always near the surface and infiltration is almost immediate. In addition, water table fluctuations relative to the peat surface are remarkably uniform across tropical peatlands because these peatlands acquire shapes with a uniform topographic wetness index. In this paper, we show that because of these distinctive properties, simple hydrologic models that represent the hydraulic state of a catchment by a scalar quantity that describes total water storage are useful and physically meaningful in tropical peatlands. We demonstrate how to efficiently derive hillslope‐scale parameterizations of transmissivity and specific yield as functions of water table height for a tropical peatland from water table, rainfall, and topographic data. Our findings suggest that natural tropical peatland subcatchments could be usefully modeled as single hydrologic response units for river flow and flood forecasting.

Description: Melting seasonal ground ice reduces potential evapotranspiration and may be a mechanism for peatland persistence in the Western Boreal Plain Abstract Peatlands in the Western Boreal Plains act as important water sources in the landscape. Their persistence, despite potential evapotranspiration (PET) often exceeding annual precipitation, is attributed to various water storage mechanisms. One storage element that has been understudied is seasonal ground ice (SGI). This study characterized spring SGI conditions and explored its impacts on available energy, actual evapotranspiration, water table, and near surface soil moisture in a western boreal plains peatland. The majority of SGI melt took place over May 2017. Microtopography had limited impact on melt rates due to wet conditions. SGI melt released 139mm in ice water equivalent (IWE) within the top 30cm of the peat, and weak significant relationships with water table and surface moisture suggest that SGI could be important for maintaining vegetation transpiration during dry springs. Melting SGI decreased available energy causing small reductions in PET (〈10mm over the melt period) and appeared to reduce actual evapotranspiration variability but not mean rates, likely due to slow melt rates. This suggests that melting SGI supplies water, allowing evapotranspiration to occur at near potential rates, but reduces the overall rate at which evapotranspiration could occur (PET). The role of SGI may help peatlands in headwater catchments act as a conveyor of water to downstream landscapes during the spring while acting as a supply of water for the peatland. Future work should investigate SGI influences on evapotranspiration under differing peatland types, wet and dry spring conditions, and if the spatial variability of SGI melt leads to spatial variability in evapotranspiration.

Description: Abstract In urban areas, the presence of impervious surfaces limits natural drainage and routes water to stormwater infrastructure with finite capacity, making these areas especially prone to flooding. Though large floods are responsible for endangering lives and causing extensive damage, there is growing evidence that more frequent floods with shallow water depths, termed nuisance flooding, can have a high cumulative cost and many direct and indirect damages. To determine whether locations of nuisance flooding may be linked to topography, we took a parsimonious, spatially distributed approach to explore whether high topographic index values co‐occur with citizen‐reported nuisance flooding. We obtained nuisance flood reports from the municipal data service 311 for several watersheds in New York City and Baltimore, USA. Our analysis tested two topographic indices (TI)—topographic wetness index (TWI) and sink depth—both calculated from high‐resolution (~1 m) digital elevation models. Generally, our findings suggest that not all but many locations of reported flooding tend to coincide with deep sinks or large TWI. However, nuisance flooding reports most commonly coincided with deep sinks and high TWI when using a maximum, instead of coincident, TI value extracted around each reported location of flooding, an approach we used due to the uncertainty in location accuracy of flooding reports. Overall, our results show promise for application of topographic indices, typically applied in more natural settings, as indicators of nuisance flooding areas in urbanized environments. Although limitations to this approach exist, the application of TIs and crowd‐sourced reporting in tandem could provide a useful starting point for mapping flood‐prone areas in many cities with technologically adept community members and ample geospatial data.

Description: 1 There was a poor correlation between the flow intensity indices of velocity category and nondimensional sediment transport capacity.2 Ignoring the partition phenomenon of the relation curves, stream power can be used to predict sediment transport capacity, with a coefficient of determination of 0.85.3 An empirical formula for predicting sediment transport with a coefficient of determination of 0.90 was established by multiple regression analysis based on the general flow intensity index. Abstract Soil erosion is a major contributor to land degradation in the Loess Plateau in China. To clarify the sediment transport capacity of overland flow influenced by hydraulic parameters, such as shear stress, sand shear stress (hydraulic gradient partition method and hydraulic radius partition method), mean flow velocity, Froude number, stream power, and unit stream power, indoor experiments with eight‐unit‐width flow discharges from 0.0667 × 10−3 to 0.3333 × 10−3 m2·s−1, six slope gradients from 3.49 to 20.79%, and two kinds of sand soils (d50 = 0.17 and 0.53 mm) were systematically investigated. A nondimensional method was adopted in data processing. Results showed that there was a partition phenomenon of relation curves because of the different median grain diameters. The correlation between the nondimensional stream power and nondimensional sediment transport capacity was the highest, followed by the correlation between the nondimensional unit stream power and nondimensional sediment transport capacity. However, there was a poor correlation between the flow intensity indices of velocity category and nondimensional sediment transport capacity. Nondimensional stream power, nondimensional unit stream power, and nondimensional shear stress could predict sediment transport capacity well. Ignoring the partition phenomenon of the relation curves, stream power could be used to predict sediment transport capacity, with a coefficient of determination of .85. Furthermore, a general flow intensity index was obtained to predict sediment transport capacity of overland flow. Finally, an empirical formula for predicting sediment transport capacity with a coefficient of determination of .90 was established by multiple regression analyses based on the general flow intensity index. During the analysis between measured sediment transport capacities in present study and predicted values based on Zhang model, Mahmoodabadi model, and Wu model, it was found that these three models could not accurately predict sediment transport capacities of this study because different models are estimated on the basis of different experimental conditions.

Description: Abstract Although steady, isotropic Darcy flows are inherently laminar and non‐mixing in the absence of diffusion, it is well understood that transient forcing via engineered pumping schemes can induce rapid, chaotic mixing flows in groundwater. In this study we explore the propensity for such mixing to arise in natural groundwater systems subject to cyclical forcings, e.g. tidal or seasonal influences. Using a conventional linear groundwater flow model subject to tidal forcing, we show that under certain conditions these flows generate Lagrangian transport and mixing phenomena (chaotic advection) near the tidal boundary. We show that aquifer heterogeneity, storativity, and forcing magnitude cause reversals in flow direction over the forcing cycle which, in turn, generate coherent Lagrangian structures and chaos. These features significantly augment fluid mixing and transport, leading to anomalous residence time distributions, flow segregation, and the potential for profoundly altered reaction kinetics. We define the dimensionless parameter groups which govern this phenomenon and explore these groups in connection with a set of well‐characterised tidal systems. The potential for Lagrangian chaos to be present near discharge boundaries must be recognized and assessed in field studies.

Description: Abstract The temperature of river water plays a crucial role in many physical, chemical and aquatic ecological processes. Despite the importance of having detailed information on this environmental variable at locally relevant scales (≤ 50km), high‐resolution simulations of water temperature on a large scale are currently lacking. We have developed the dynamical 1‐D water‐energy routing model (DynWat), that solves both the energy and water balance, to simulate river temperatures for the period 1960‐2014 at a nominal 10km and 50km resolution. The DynWat model accounts for surface water abstraction, reservoirs, riverine flooding and formation of ice, enabling a realistic representation of the water temperature. We present a novel 10km water temperature dataset at the global scale for all major rivers, lakes and reservoirs. Validated results against 358 stations worldwide indicate a decrease in the simulated Root Mean Squared Error (0.2°C) and bias (0.7°C), going from 50km to 10km simulations. We find an average global increase in water temperature of 0.16°C per decade between 1960‐2014, with more rapid warming towards 2014. Results show increasing trends for the annual daily maxima in the Northern Hemisphere (0.62°C per decade) and the annual daily minima in the Southern Hemisphere (0.45°C per decade) for 1960‐2014. The high‐resolution modelling framework not only improves the model performance, it also positively impacts the relevance of the simulations for regional scale studies and impact assessments in a region without observations. The resulting global water temperature dataset could help to improve the accuracy of decision‐support systems that depend on water temperature estimates.

Description: Abstract Sensitivity analysis (SA) is a critical part in the construction of all models, including environmental and water resources simulation models. For example, SA functions to characterize which model inputs the model outputs are overly‐sensitive or insensitive to. However, the quality of SA results is rarely assessed. If assessed, bootstrapping of the sensitivity results is used to determine the reliability of the SA output. Bootstrapping, however, is known to be inappropriate with small sample sizes. In contrast, increasing model computational burdens continue to drive researchers to apply existing SA techniques, and develop new ones, with smaller and smaller sample sizes. The new Model Variable Augmentation (MVA) approach is therefore introduced here to assess the quality of SA results without performing any additional model runs or requiring bootstrapping. MVA augments the original model input variables with additional variables of known properties. The sensitivities of the augmented model variables are used to draw conclusions on the reliability of the other "original" model parameters' sensitivities. The MVA method is applied to two global SA methods: the variance‐based Sobol' method and the moment‐independent PAWN method. MVA is scrutinized using analytical benchmark functions and then used to quality‐check sensitivity results of two hydrologic models. Results show: 1) MVA is a framework to quality check the implementation of a SA method, 2) for Sobol' and PAWN analyses, MVA‐assisted ranking of input sensitivity measures outperforms the standard ranking procedure without MVA, and 3) MVA provides reasonable estimation of the uncertainty of sensitivity estimates.

Description: Abstract A few relatively large reservoirs, hundreds of small reservoirs and numerous farm dams were built in the upper Gan River Basin, China. The operation of such a reservoir network can serve as a significant source of variability in the local hydrological regime and should be included in research to better understand the interaction between multiple hydrological processes and watershed management. In this study, a reservoir network module that included reservoirs of multiple sizes was developed and fully integrated into a coupled land surface and distributed hydrologic model, CLHMS, for a detailed description of the hydrological impact of a reservoir network. A generalized release scheme was employed to determine the outflow of both large and small reservoirs. The integrated model was then evaluated against observations and reanalysis data, which indicate that the model can reasonably reconstruct the reservoir operation, streamflow and other hydrological variables. Results quantitatively demonstrate that a reservoir network can result in an increased streamflow in dry seasons, a decreased streamflow in wet seasons, a generally larger groundwater discharge, higher groundwater level, a slightly damper soil condition and a larger amount of evapotranspiration at the basin level. With the integrated model, it is feasible to achieve more sustainable watershed planning and management.

Description: Abstract In efforts to reduce uncertainties about effects of minor changes in the pore structure of porous media on dynamic non‐equilibrium effects (DNE), we monitored water saturation‐capillary pressure relationships in both dynamic flow and static states in stepwise drainage processes of a sandy medium. The acquired data were used to detect DNE and their changes with time in each drainage step, and construct new parameters to characterize DNE. Effects of factors affecting water flow—including capillary number, water saturation, rate of change of water saturation with time (△Sw/△t) and the maximum value of this rate (RSMAX)—on the magnitude of the DNE were then determined. Results show that entry pressures are considerably larger under dynamic flow conditions than under static flow conditions, and DNE only occurs when the water saturation exceeds a threshold value in a drainage process. During either a smooth drainage or drainage step, the maximum capillary pressure (PMAX) is reached before the minimum water saturation (SMIN). Moreover, in drainage steps RSMAX occurs before the maximum difference between the capillary pressures associated with the static and dynamic states (DPMAX), except when they start from the saturated state. Accordingly, DNE in a drainage step are mainly reflected in the time between PMAX and SMIN (△t), DPMAX, the average sum of the squares of the difference between capillary pressures of the static and dynamic states (DPAVE), and the dynamic coefficient τ (which is often used to characterize DNE). Furthermore, our results indicate that the water saturation and its rate of change with time are more important parameters than the capillary number for modeling DNE in a drainage process, and we detected no clear correlation between either maximum or minimum values of τ and the timing of the most significant DNE during drainages. τ appears to characterize DNE effectively in smooth drainage processes, while △t, DPAVE, and DPMAX constitute meaningful supplementary parameters for the characterization of DNE in a drainage step of a stepwise drainage.

Description: Abstract Seasonal snowpacks in marginal snow environments are typically warm and nearly isothermal, exhibiting high inter‐ and intra‐annual variability. Measurements of snow depth and snow water equivalent were made across a small subalpine catchment in the Australian Alps over two snow seasons in order to investigate the extent and implications of snowpack spatial variability in this marginal setting. The distribution and dynamics of the snowpack were found to be influenced by upwind terrain, vegetation, solar radiation and slope. The role of upwind vegetation was quantified using a novel parameter based on gridded vegetation height. The elevation range of the catchment was relatively modest (185 m), and elevation impacted distribution but not dynamics. Two characteristic features of marginal snowpack behaviour are presented. Firstly, the evolution of the snowpack is described in terms of a relatively unstable accumulation state and a highly stable ablation state, as revealed by temporal variations in the mean and standard deviation of snow water equivalent. Secondly, the validity of partitioning the snow season into distinct accumulation and ablation phases is shown to be compromised in such a setting. Snow at the most marginal locations may undergo complete melt several times during a season and, even where snow cover is more persistent, ablation processes begin to have an effect on the distribution of the snowpack early in the season. Our results are consistent with previous research showing that individual point measurements are unable to fully represent the variability in the snowpack across a catchment, and we show that recognising and addressing this variability is particularly important for studies in marginal snow environments.

Description: Abstract Spatiotemporal heterogeneity in soil water content is recognized as a common phenomenon, but heterogeneity in the hydrogen and oxygen isotope composition of soil water, which can reveal processes of water cycling within soils, has not been well studied. New advances are being driven by measurement approaches allowing sampling with high density in both space and time. Using in situ soil water vapor probe techniques, combined with conventional soil and plant water vacuum distillation extraction, we monitored the hydrogen and oxygen stable isotopic composition of soil and plant waters at paired sites dominated by grasses and Gambel's oak (Quercus gambelli) within a semi‐arid montane ecosystem over the course of a growing season. We found that sites spaced only 20 m apart had profoundly different soil water isotopic and volumetric conditions. We document patterns of depth‐ and time‐explicit variation in soil water isotopic conditions at these sites, and consider mechanisms for the observed heterogeneity. We found that soil water content and isotopic variability was damped under Quercus gambelli, perhaps due in part to hydraulic redistribution of deep soil water or groundwater by Quercus gambelli in these soils relative to the grass‐dominated site. We also found some support for H isotope discrimination effects during water uptake by Quercus gambelli. In this ecosystem, the soil water content was higher than that at the neighboring grass site, and thus 25% more water was available for transpiration by Quercus gambelli compared to the grass site. This work highlights the role of plants in governing soil water variation and demonstrates that they can also strongly influence the isotope ratios of soil water. The resulting fine‐scale heterogeneity has implications for the use of isotope tracers to study soil hydrology and evaporation and transpiration fluxes to improve understanding of water cycling through the soil‐plant‐atmosphere continuum.

Description: Abstract While we know that rainfall interception (the rain caught, stored, and evaporated from aboveground vegetative surfaces and ground litter) is affected by rain and throughfall drop size, what was unknown until now is the relative proportion of each throughfall type (free throughfall, splash throughfall, canopy drip) beneath coniferous and broadleaved trees. Based on a multi‐national dataset of 〉 120 million throughfall drops, we found that the type, number, and volume of throughfall drops are different between coniferous and broadleaved tree species, leaf states, and timing within rain events. Compared to leafed broadleaved trees, conifers had a lower percentage of canopy drip (51% vs. 69% with respect to total throughfall volume) and slightly smaller diameter splash throughfall and canopy drip. Canopy drip from leafless broadleaved trees consisted of fewer and smaller diameter drops (D50_DR, fiftieth cumulative drop volume percentile for canopy drip, of 2.24 mm) than leafed broadleaved trees (D50_DR of 4.32 mm). Canopy drip was much larger in diameter under woody drip points (D50_DR of 5.92 mm) than leafed broadleaved trees. Based on throughfall volume, the percentage of canopy drip was significantly different between conifers, leafed broadleaved trees, leafless broadleaved trees, and woody surface drip points (p ranged from 〈 0.001 to 0.005). These findings are partly attributable to differences in canopy structure and plant surface characteristics between plant functional types and canopy state (leaf, leafless), among other factors. Hence, our results demonstrating the importance of drop‐size dependent partitioning between coniferous and broadleaved tree species could be useful to those requiring more detailed information on throughfall fluxes to the forest floor.

Description: Abstract California is expected to experience great spatial/temporal variations evaporation. These variations arise from strong north‐south, east‐west gradients in rainfall and vegetation, strong interannual variability in rainfall (+/‐30%) and strong seasonal variability in the supply and demand for moisture. We used the Breathing Earth System Simulator, BESS, to evaluate the rates and sums of evaporation across California, over the 2001‐2017 period. BESS is a bottom‐up, biophysical model that couples subroutines that calculate the surface energy balance, photosynthesis and stomatal conductance. The model is forced with high resolution remote sensing data (1 km). The questions we address are: how much water is evaporated across the natural and managed ecosystems of California?; how much does evaporation vary during the booms and busts in annual rainfall?; and is evaporation increasing with time due to a warming climate? Mean annual evaporation, averaged over the 2001‐2017 period was relatively steady (393 +/‐ 21 mm y‐1) given the high interannual variation in precipitation (519 +/‐ 140 mm y‐1). No significant trend in evaporation at the state‐wide level was detected over this time period, despite a background of a warming climate. Irrigated agricultural crops and orchards, at 1 km, scale, use less water than inferred estimates for individual fields. This leaves the potential for sharing water, a scarce resource, more equitably among competing stakeholders, e.g. farms, fish, people and ecosystems.