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 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 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: 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 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: 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: 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: 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 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 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: 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: 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: 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 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: 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 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 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.

Description: Abstract Zhang (2019) criticizes several of the assumptions and parameter choices of the model of Kuil et al. (2018), and claims that, due to an inconsistency in the irrigation equation, the key findings should be interpreted with much caution. We address each of the comments and show that the conclusions of Kuil et al. (2018) remain fully valid.

Description: Abstract A paper has recently been published in Water Resources Research, which explored the influence of smallholder's perceptions regarding water availability on crop choice and water allocation through social‐hydrological modeling. The present paper attempts to discuss and comment on that paper. The authors have developed a social‐hydrological model, behind which some assumptions of the hydrological aspects (including the omission of deep percolation and the complete separation of transpiration and evaporation for the growing season and the off‐season) are not well justified. Further, a few parameter values seem to be mistakenly presented by the authors. Most importantly, the equation used for calculating the irrigation depth for crop 1 was incorrectly derived, which could lead to apparent biases in the simulated soil moisture, drought memories and crop fractions. Hence, the key findings of Kuil et al. (2018) must be associated with large uncertainties, and should be interpreted with much caution.

Description: Abstract The aim of this paper is to improve semi‐seasonal forecast of groundwater availability in response to climate variables, surface water availability, groundwater level variations, and human water management using a two‐step data‐driven modeling approach. First, we implement ensemble artificial neural networks (ANN) for the three hundred wells across the High Plains aquifer (US). The modelling framework includes a method to choose the most relevant input variables and time lags; an assessment of the effect of exogenous variables on the predictive capabilities of models; and the estimation of the forecast skill based on the Nash‐Sutcliffe efficiency index (NSE), the normalized root mean squared error and the coefficient of determination (R2). Then, for the ANNs with low accuracy, a Multi Model Combination (MuMoC) based on a hybrid of ANN and instance‐based learning method is applied. MuMoC uses forecasts from neighboring wells to improve the accuracy of ANNs. An exhaustive‐search optimization algorithm is employed to select the best neighboring wells based on the cross‐correlation and predictive accuracy criteria. The results show high average ANN forecasting skills across the aquifer (average NSE 〉 0.9). Spatially distributed metrics of performance showed also higher error in areas of strong interaction between hydro‐meteorological forcings, irrigation intensity, and the aquifer. In those areas, the integration of the spatial information into MuMoC lead to an improvement of the model accuracy (NSE increased by 0.12), with peaks higher than 0.3 when the optimization objectives for selecting the neighbors were maximized.

Description: Abstract The recent intensification of international trade has led to a growing disconnection between consumer demand for goods and services and the water resources that support them. The important role of household demands on the exploitation of distant freshwater resources is widely recognized, yet the different socioeconomic drivers underlying the trends in domestic and foreign water use remain poorly quantified. In this work, the main mechanisms governing the exploitation of domestic and foreign freshwater resources are quantified by undertaking a structural decomposition analysis over the period 1994‐2010 in 186 countries. Our results show that growth in affluence has been the main determinant of rising water consumption trends worldwide. There are indications that consumers in developed countries tend to increase their affluence by intensifying the use of foreign water resources. Conversely, affluence growth in developing regions seems to rely on the exploitation of local water resources revealing a significant imbalance among economies. The growth of affluence and population have been only partially offset by improvements in the intensity of blue water use by producers and, to a lesser extent, by changes in the interdependences among sectors.

Description: Abstract Uncertainty analysis is an integral part of any scientific modelling, particularly within the domain of hydrological sciences given the various types and sources of uncertainty. At the centre of uncertainty rests the concept of equifinality, i.e. reaching a given endpoint (finality) through different pathways. The operational definition of equifinality in hydrological modelling is that various model structures and/or parameter sets (i.e. equal pathways) are equally capable of reproducing a similar (not necessarily identical) hydrological outcome (i.e. finality). Here we argue that there is more to model‐equifinality than model structures/parameters, i.e. other model components can give rise to model‐equifinality and/or could be used to explore equifinality within model space. We identified six facets of model‐equifinality namely model structure, parameters, performance metrics, initial and boundary conditions, inputs, and internal fluxes. Focusing on model internal fluxes, we developed a methodology called Flux Mapping that has fundamental implications in understanding and evaluating model process‐representation within the paradigm of multiple working hypotheses. To illustrate this, we examine the equifinality of runoff fluxes of a conceptual rainfall‐runoff model for a number of different Australian catchments. We demonstrate how flux maps can give new insights into the model behaviour that cannot be captured by conventional model evaluation methods. We discuss the advantages of flux space, as a sub‐space of the model space not usually examined, over parameter space. We further discuss the utility of flux mapping in hypothesis generation and testing, extendable to any field of scientific modelling of open complex systems under uncertainty.

Description: Abstract Fire suppression in Western US mountains has caused dense forests with high water demands to grow. Restoring natural wildfire regimes to these forests could affect hydrology by changing vegetation composition and structure, but the specific effects on water balance are unknown. Mountain watersheds supply water to much of the Western USA, so understanding the relationship between fire regime and water yield is essential to inform management. We used a distributed hydrological model to quantify hydrologic response to a restored fire regime in the Illilouette Creek Basin (ICB) within Yosemite National Park, California. Over the past 45 years, as successive fires reduced the ICB's forest cover approximately 25%, model results show that annual streamflow, subsurface water storage, and peak snowpack increased relative to a fire‐suppressed control, while evapotranspiration and climatic water deficit decreased. A second model experiment compared the water balance in the ICB under two vegetation cover scenarios: 2012 vegetation, representing a frequent‐fire landscape, and 1969 vegetation, representing fire suppression. These two model landscapes were run with observed weather data from 1972 to 2017 in order to capture natural variations in precipitation and temperature. This experiment showed that wet years experienced greater fire‐related reductions in evapotranspiration and increases in streamflow, while reductions in climatic water deficit were greater in dry years. Spring snowmelt runoff was higher under burned conditions, while summer baseflow was relatively unaffected. Restoring wildfire to the fire‐suppressed ICB likely increased downstream water availability, shifted streamflows slightly earlier, and reduced water stress to forests.

Description: Abstract The Truckee/Carson Basin, like other semi‐arid basins in the western United States, faces challenges to water management and planning under a changing climate. We analyzed tree‐ring data, along with instrumental climatic and hydrologic records, to provide a perspective on extreme drought in the 21st century. Drought indices highlighted a recent increase in the average duration of hydroclimatic episodes: in the new millennium average duration was 74% longer for SPI‐24 and 62% longer for PDSI than in the previous century. Average SWE declined 7% per decade from 1965 to 2018. The 2012‐2015 drought, in particular, stood out for its intensity and expression in snowpack, streamflow, and drought indices. Likely because of recent warming, this four‐year drought event had a very low likelihood based on observed Carson River flows from the first half of the 20th century. A 501‐year tree‐ring reconstruction (1500‐2000 CE) of average water‐year streamflow for the Carson River indicated that positive (wet) spells had slightly longer duration (mean of 2.7 years and range from 1 to 10 years) than negative (dry) intervals (mean of 2.4 years and range from 1 to 9 years). The early 1900s pluvial, i.e. 1905‐1911 in this record, was the third strongest episode in the entire reconstruction. The driest years were 1580 and 1934, both well‐known widespread and severe droughts in the western US. Noise‐added reconstructions suggest that 2012‐2015, while not unique in the 401 years prior to the start of the Carson River gaged flows in 1901, was a less than one‐in‐a‐century event.

Description: Abstract The community Noah land surface model with multi‐parameterization options (Noah‐MP) provides a plethora of model configurations with varying complexity for land surface modeling. The practical application of this model requires a basic understanding of the relative abilities of its various parameterization configurations in representing spatiotemporal variability and hydrologic connectivity. We designed an ensemble of 288 experiments from multi‐parameterization schemes of six physical processes to assess and reduce the structural uncertainty for land surface modeling over ten hydrologic regions in China for the period 2001–2010. The observed latent heat (LH) was well reproduced by the ensemble. Meanwhile, most experiments underestimated sensible heat (SH) throughout the year and overestimated the cold season but underestimated the warm season terrestrial water storage anomaly (TWSA). The sensitive processes and best‐performing schemes varied not only with regions but also among variables. The LH and SH were most sensitive to runoff‐groundwater (RUN), surface heat exchange coefficient (SFC), and radiation transfer (RAD). The TWSA was dominated by RUN and RAD, while largely influenced by soil moisture factor for stomatal resistance (BTR) and frozen soil permeability (INF) over some limited regions. By contrast, supercooled liquid water (FRZ) had little influence on all variables. Our optimization for individual variables produced high mean Taylor skill scores that ranged from 0.95–0.99 for LH, 0.82–0.99 for SH, and 0.63–0.95 for TWSA depending on regions. The simultaneous optimization made tradeoff among the three variables, which improved TWSA performance at the cost of reducing the skill for LH and SH over a few regions.

Description: Abstract River wetted width (RWW) is an important variable in the study of river hydrological and biogeochemical processes. Presently, RWW is often measured from remotely sensed imagery and the accuracy of RWW estimation is typically low when coarse spatial resolution imagery is used because river boundaries often run through pixels that represent a region that is a mixture of water and land. Thus, when conventional hard classification methods are used in the estimation of RWW, the mixed pixel problem can become a large source of error. To address this problem, this paper proposes a novel approach to measure RWW at the sub‐pixel scale. Spectral unmixing is first applied to the imagery to obtain a water fraction image that indicates the proportional coverage of water in image pixels. A fine spatial resolution river map from which RWW may be estimated is then produced from the water fraction image by super‐resolution mapping (SRM). In the SRM analysis, a deep convolutional neural network (CNN) is used to eliminate the negative effects of water fraction errors and reconstruct the geographical distribution of water. The proposed approach is assessed in two experiments, with the results demonstrating that the CNN based SRM model can effectively estimate sub‐pixel scale details of rivers, and that the accuracy of RWW estimation is substantially higher than that obtained from the use of a conventional hard image classification. The improvement shows that the proposed method has great potential to derive more accurate RWW values from remotely sensed imagery.

Description: Abstract Irrigated agriculture will have to increase production to meet the demand for food of the population of the world. A simple physically‐based method is presented that allows to determine appropriate irrigation rate and duration to avoid runoff, thus contributing to the design of efficient irrigation. The method relies on the infiltration capacity curve of the soil under interest. This curve allows determination of two important relationships: (a) maximal irrigation rate vs. irrigation dose, and (b) irrigation duration vs. rate. Two case studies illustrate the application of the method to adapt irrigation to the reduction of soil infiltration capacity resulting from the quality of the irrigation water (treated wastewater) or soil surface sealing. The range of irrigation rates for which duration can be estimated as the ratio between the required dose and the application rate is defined.

Description: Abstract Multi‐reservoir systems are designed to serve multiple conflicting demands over varying time scales that may be out of phase with the system's hydroclimatic inputs. Adaptive, nonlinear reservoir control policies are often best suited to serve these needs. However, nonlinear operating policies are often difficult to interpret, and so water managers tend to favor simple, static rules that may not effectively manage conflicts between the system's multisectoral demands. In this study, we introduce an analytical framework for opening the black‐box of optimized nonlinear operating policies, decomposing their time‐varying information sensitivities to show how their adaptive and coordinated release prescriptions better manage hydrologic variability. Interestingly, these information sensitivities vary significantly across policies depending on how they negotiate tradeoffs between conflicting objectives. We illustrate this analysis in the Red River basin of Vietnam, where four major reservoirs serve to protect the capital of Hanoi from flooding while also providing the surrounding region with electric power and meeting multi‐sectoral water demands for the agricultural and urban economies. Utilizing Evolutionary Multi‐Objective Direct Policy Search (EMODPS), we are able to design policies that, using the same information as sequential if/then/else‐based operating guidelines developed by the government, outperform these traditional rules with respect to every objective. Policy diagnostics using time‐varying sensitivity analysis illustrate how the EMODPS operations better adapt and coordinate information use to reduce food‐energy‐water conflicts in the basin. These findings accentuate the benefits of transitioning to dynamic operating policies in order to manage evolving hydroclimatic variability and socioeconomic demands in multi‐purpose reservoir networks.

Description: Abstract We propose a time series modeling approach based on nonlinear dynamical systems to recover the underlying dynamics and predictability of streamflow and to produce projections with identifiable skill. First, a wavelet spectral analysis is performed on the time series to identify the dominant quasi‐periodic bands. The time series is then reconstructed across these bands and summed to obtain a signal time series. This signal is embedded in a D‐dimensional space with an appropriate lag τ to reconstruct the phase space in which the dynamics unfolds. Time varying predictability is assessed by quantifying the divergence of trajectories in the phase space with time, using Local Lyapunov Exponents (LLE). Ensembles of projections from a current time are generated by block resampling trajectories of desired projection length, from the K‐nearest neighbors of the current vector in the phase space. This modeling approach was applied to the naturalized historical and paleo reconstructed streamflow at Lees Ferry gauge on the Colorado River which offered three interesting insights. (i) The flows exhibited significant epochal variations in predictability. (ii) The predictability of the flow quantified by LLE is related to the variance of the flow signal and selected climate indices. (iii) Blind projections of flow during epochs identified as highly predictable showed good skill in capturing the distributional and threshold exceedance statistics and poor performance during low predictability epochs. The ability to assess the potential skill of these long lead projections opens opportunities to perceive hydrologic predictability and consequently water management in a new paradigm.

Description: Abstract Groundwater levels are typically measured at only a limited number of points in a catchment. Thus, upscaling these point measurements to the catchment scale is necessary to determine subsurface flow paths and runoff source areas. Here, we present a data‐driven approach comprised of time series clustering and topography‐based upscaling of shallow, perched groundwater dynamics using groundwater data from 51 monitoring sites in a 20 ha pre‐alpine headwater catchment in Switzerland. The agreement between the upscaled (modeled) and measured groundwater dynamics was strong for most of the 19‐month study period for the upslope and footslope locations, but weaker at the beginning of events and for the midslope locations. However, these differences between measured and modeled groundwater levels did not significantly affect modeled groundwater activation, i.e., the time when groundwater levels were within the more transmissive soil layers near the soil surface. The resulting groundwater activation maps represent the groundwater response across the catchment and highlight the dynamic expansion and contraction of the subsurface runoff source areas, particularly along the channel network. This is in agreement with the variable source area concept. However, there were also isolated active zones that did not get connected to the stream during rainfall events, highlight the need to distinguish between variable active and variable stream‐connected runoff source areas. Our data‐driven approach to upscale point measurements of shallow groundwater levels appears useful for studying catchment scale variations in groundwater storage and connectivity and thus may help to better understand runoff generation in mountain catchments.

Description: Abstract We propose in this article a regional study of Intensity‐Duration‐Area‐Frequency (IDAF) relationships of annual rainfall maxima in southern France. For this we develop a regional extreme value IDAF model based on space‐time scale invariance hypotheses. The model allows us to link the statistical distributions of rainfall maxima over any duration and area. It provides in particular an analytical expression of the Areal Reduction Factor (ARF), which expresses how the statistical distribution of rainfall maxima changes as the area increases, for any fixed duration. It also provides an analytical expression of areal return level for the continuum of area and duration. The model is applied to radar reanalysis data covering a 13,000 km2 region of southern France featuring contrasted rainfall regimes (2008‐2015). We estimate the IDAF relationships centered on any radar pixel of the region in the range 3‐48 h and 1‐2025 km2. We obtain in particular a spatial distribution of the ARF, which allows us to distinguish different rainfall regimes in the region. The overall IDAF model provides also a regional quantification of areal rainfall risk by allowing the computation of rainfall return level maps for any area and duration in the applicable range. Despite inevitable sampling issues due to the shortness of the data sample, we highlight important differences in the spatial distribution of areal rainfall risk depending on the area and duration, illustrating that a comprehensive storm risk evaluation should consider the continuum of area and duration rather than arbitrarily predefined ones.

Description: Abstract Rivers typically present heterogeneus bed material, but the effects of sediment non‐uniformity on river bar characteristics are still unclear. This work investigates the impact of sediment size heterogeneity on alternate bars with a morphodynamic numerical model. The model is firstly used to reproduce a laboratory experiment showing alternate bar formation with non‐uniform bed material. Subsequently, the influence of sediment size heterogeneity on alternate bars is investigated distinguishing hybrid from free bars, definition based on the presence/absence of morphodynamic forcing, considering the results of nine scenarios. In four of them, a transverse obstacle is used to generate forcing. The computations are carried out with the Telemac‐Mascaret system solving the two‐dimensional shallow‐water equations with a finite‐element approach, accounting for horizontal and vertical sediment sorting processes. The results show that sediment heterogeneity affects free migrating and hybrid bars in a different way. The difference lies in the presence/absence of a migration front, so that distinct relations between bed topography, bed shear stress and sediment sorting are obtained. Sediment sorting and associated planform redistribution of bed roughness only slightly modify free migrating bar morphodynamics, whereas hybrid bars are greatly impacted, with decreased amplitude and increased wavelength. Increased sediment size heterogeneity increases the degree of sediment sorting, while the sorting pattern remains the same for both free and hybrid bars. Moreover, it produces averagely higher, longer and faster free bars, while in the case of hybrid bars their wavelength is increased but no general trend can be determined for their amplitude.

Description: Abstract The retrieval of detailed, co‐located snow depth and canopy cover information from airborne lidar has advanced our understanding of links between forest snow distribution and canopy structure. In this study, we present two recent high‐resolution (1 m) lidar data sets acquired in (i) a 2017 mission in the Eastern Swiss Alps and (ii) NASA's 2017 SnowEx field campaign at Grand Mesa, Colorado. Validation of derived snow depth maps against extensive manual measurements revealed a RMSE of 6 and 3 cm for plot‐level mean and standard deviation of snow depth, respectively, demonstrating that within‐stand snow distribution patterns were captured reliably. Lidar data were further processed to obtain canopy structure metrics. To this end, we developed a novel approach involving a continuous measure of local distance to canopy edge (DCE), which enabled creating spatially aggregated nondirectional and directional descriptors of the canopy structure. DCE‐based canopy metrics were correlated to mean and standard deviation of snow depth over areas representing grid‐cell sizes typical of watershed and regional model applications (20–200 m). Snow depth increased along the DCE gradient from dense canopy to the center of canopy gaps for all sites and acquisition times, while directional effects particularly evolved during the ablation season. These findings highlight the control of canopy gap distribution on snow distribution in discontinuous forests, with higher snow depths where the open fraction is concentrated in few large gaps rather than many fragmented small gaps. In these environments, dedicated canopy structure metrics such as DCE should advance spatially distributed snow modeling.

Description: Water Resources Research, Volume 0, Issue ja, -Not available-.

Description: Abstract Predicting specific yield for a given aquifer remains a great challenge due to its dynamic characteristics, especially under periodic (e.g., seasonal and diurnal) groundwater level fluctuations. In this study, a dimensionless period of groundwater level fluctuations, which depends on the saturated hydraulic conductivity, the length of the groundwater level fluctuation period, and the soil water retention properties, is first introduced. Then, the relationship between the predicted specific yield and the dimensionless period of groundwater level fluctuations is defined based on the theory of variably saturated flow. The proposed approximate formula is tested by series of simulations of variably saturated flow within homogeneous and isotropic porous media with the Hydrus‐1D program. The results of numerical experiments demonstrate an invariant relationship between the predicted specific yield and the dimensionless period for a wide range of changes in the dimensionless period of groundwater level oscillations. Furthermore, the sensitivity analysis of the dependence of the predicted specific yield on the van Genuchten parameters indicates the applicability of estimating the specific yield via van Genuchten parameters, which can be predicted by the soil texture classes for a given length of the oscillation period and a known saturated hydraulic conductivity in the zone of groundwater level fluctuations. The proposed model can be used for predicting the dynamics of the specific yield under seasonal or diurnal groundwater level fluctuations.

Description: Abstract The water table fluctuation method of estimating recharge is widely used because it is conceptually simple and easy to implement. The major source of uncertainty in the recharge estimates come from the specific yield. The apparent specific yield has a dependence on the depth to water table that makes its measurement difficult (if not impossible) for appropriate use in the water table fluctuation method. This study has treated the specific yield as a conceptual parameter that cannot be measured and has constrained it using a rejection sampling approach using probabilistic estimates of net recharge from the chloride mass balance method and excess water derived from the difference between precipitation and remotely sensed actual evapotranspiration. The method developed here provided probabilistic estimates of the ultimate specific yield and a probabilistic time series of gross recharge, both important in shallow water table environments. An additional benefit of the method is that by jointly constraining the three different recharge types (i.e., excess water, gross and net recharge) they are assured of being internally consistent. The method was implemented for 58 bores across four catchments in Northern Australia that may see increased development in coming years.

Description: Abstract Characterizing snowmelt both spatially and temporally from in‐situ observation remains a challenge. Available sensors (i.e. sonic ranger, lidar, airborne photogrammetry) provide either timeseries of local point measurements, or sporadic surveys covering larger areas. We propose a methodology to recover from a minimum of three synchronized time‐lapse cameras changes in snow depth and snow cover extent over area smaller or equivalent to 0.12 km2. Our method uses photogrammetry to compute point clouds from a set of three or more images, and automatically repeat this task for the entire timeseries. The challenges were 1) finding an optimal experimental setup deployable in the field, 2) estimating the error associated with this technique, and 3) being able to minimize the input of manual work in the data processing pipeline. Developed and tested in the field in Finse, Norway, over one month during the 2018 melt season, we estimated a median melt of 2.12±0.48 m derived from three cameras 1.2 km away from the region of interest. The closest weather station recorded 1.94 m of melt. Other parameters like snow cover extent and duration could be estimated over a 300x400 m region. The software is open‐source and applicable to a broader range of geomorphologic processes like glacier dynamic, snow accumulation, or any other processes of surface deformation, with the conditions of 1) having fixed visible points within the area of interest, and 2) resolving sufficient surface textures in the photographs.

Description: Abstract This paper is focused on the contribution that the two‐dimensional Shallow Water Equations (2‐D SWEs) could provide in respect to the fields of research devoted to the river networks analysis. The novelty introduced in this work is represented, in particular, by the hydraulic characterization of the river drainage networks, starting from flow patterns simulated by the 2‐D SWEs on high‐resolution DEM, through the analysis of water depths flowing down the hillslopes, channelized in both the main river and in all the tributaries, or stored in small depressions. The first finding of this research is the determination of scaling laws that describe the relations between the water depth threshold, used to identify the network cells, and a dimensionless area, related to the total area of the network cells themselves. The observed bimodal scaling behavior has been considered as representative of the flow patterns belonging to the channel networks (CN) and hillslope plus channel networks (HCN), the physical and geomorphological interpretation of which has been provided from a multifractal point of view. In particular, the analysis of the multifractal spectra highlights significant variations in the multifractal signatures, between the CN and the HCN structures, leading to the proposal of a novel criterion for channels heads detection that has provided encouraging predictions of field observations.

Description: Abstract Groundwater aquifers provide an important “insurance” against climate variability. Due to prolonged droughts and/or irrigation demands, groundwater exploitation results in significant groundwater storage depletion. Managed aquifer recharge (MAR) is a promising management practice that intentionally places or retains more water in groundwater aquifers than would otherwise naturally occur. In this study, we examine the possibility of using large irrigated agricultural areas as potential MAR locations (Ag‐MAR). Using the California Central Valley Groundwater‐Surface Water Simulation Model (C2VSim) we tested four different agricultural recharge land distributions, two streamflow diversion locations, eight recharge target amounts and five recharge timings. These scenarios allowed a systematic evaluation of Ag‐MAR on changes in regional, long‐term groundwater storage, streamflow, and groundwater levels. The results show that overall availability of stream water for recharge is critical for Ag‐MAR systems. If stream water availability is limited, longer recharge periods at lower diversion rates allow diverting larger volumes and more efficient recharge compared to shorter diversion periods with higher rates. The recharged streamwater increases both, groundwater storage and net groundwater contributions to streamflow. During the first decades of Ag‐MAR operation, the diverted water contributed mainly to groundwater storage. After 80 years of Ag‐MAR operation about 34% of the overall diverted water remained in groundwater storage while 66% discharged back to streams, enhancing baseflow during months with no recharge diversions. Groundwater level rise is shown to vary with the spatial and temporal distribution of Ag‐MAR. Overall, Ag‐MAR is shown to provide long‐term benefits for water availability, in groundwater and in streams.

Description: Abstract In the past, the analytical model developed for a radially divergent heat flow in an aquifer thermal energy storage (ATES) system considers only the process of either thermal conduction or thermal dispersion. In addition, the existing models commonly regarded the inner boundary at the injection well as the constant‐temperature condition, which does not meet the continuity condition of heat flux at the wellbore. We herein propose an analytical model for a realistic representation of heat flow in an ATES system by considering the effects of both thermal conduction and thermal dispersion in the heat transfer equation and a Robin‐type boundary condition at the injection well. The model consists of three heat flow equations depicting the temperature distributions in the confined aquifer and its underlying and overlying rocks. The Laplace transform method is applied to solve the proposed model. The solutions for the cases of dispersion‐ and conduction‐dominant flow fields are also developed and discussed. Comparisons between the present solutions with five existing solutions developed for similar heated water injection problems are made. A global sensitivity method is also performed to analyze the thermal response to the change in each of the aquifer parameters. Finally, our solution is validated through the comparison with the finite difference solution and observed data from an ATES experiment site in Mobile, Alabama.

Description: Abstract The darkening of the snow surface by light absorbing particles (LAPs) impacts snow albedo directly by increasing absorption of shortwave radiation in the visible wavelengths. This indirectly enhances the rate of snow grain coarsening, which determines absorption in the near‐infrared wavelengths. In combination, these processes reduce snow albedo over the full range of snow reflectance, accelerating melt, and impacting regional climate and hydrology. Accurate parameterizations of snow albedo should represent both the direct and indirect radiative impacts. Here, dust influenced snow cover evolution was simulated at Senator Beck Basin Study Area, San Juan Mountains, CO with a multi‐layer physically based snow process model. The model was modified to track dust stratigraphy, and coupled to a snow/aerosol radiative transfer model to inform reflected shortwave radiation based on snow properties, dust concentrations, and region specific dust optical properties. This varies from previous efforts to constrain the magnitude of accelerated melt due to dust by directly and physically representing the processes that determine the radiative impacts. Model outputs, including effective grain size, dust stratigraphy, timing of dust emergence, and albedo were validated with a near daily snow and LAP physical and optical property dataset, and were well simulated. Daily mean radiative forcing (RF) ranged from 2 to 109 W m‐2, and was 30 W m‐2 on average over the full simulation, advancing snowmelt by 30 days. A partitioning of direct and indirect radiative impacts shows that direct absorption by dust contributes ~80% of total RF, with grain coarsening accounting for ~20%.

Description: Abstract Organic matter (OM) and suspended sediment are abundant, and interact with each other, in rivers and lakes. OM is usually adsorbed by suspended sediment and causes either particle stabilization or flocculation. In this study, the OM composition and suspended sediment flocculation potential of river water were regularly measured throughout the year 2016. The OM composition of the river water samples was measured with a liquid chromatography‐organic carbon detection system and fluorescence excitation‐emission matrix spectroscopy, and the flocculation potential was measured in a standard jar test experiment. Results from the OM analyses and flocculation potential tests, in association with a multivariate data analysis, demonstrated that the OM composition and flocculation potential of the river water were dynamic under different meteorological, hydrological, ecological, and anthropogenic conditions and closely correlated with each other. Dry seasons with low rainfall and water discharge induced a lacustrine condition and led to the OM composition being more aquagenic and flocculation‐favorable. The most favorable condition for the enhancement of flocculation was during algae bloom and associated with the production of biopolymers from algae. In contrast, rainy seasons were advantageous for stabilization of suspended sediment because of excessive transport of terrigenous humic substances from catchment areas into the river. Such terrigenous humic substances enhanced stabilization by creating enhanced electrostatic repulsion via adsorption onto the sediment surface. Findings from this research provide a better insight into the highly complex behaviors of and interactions between OM and suspended sediment in natural water environments.

Description: Abstract Automated, reliable cloud masks over snow‐covered terrain would improve the retrieval of snow properties from multispectral satellite sensors. The U.S. Geological Survey and NASA chose the currently operational cloud masks based on global performance across diverse land cover types. This study assesses errors in these cloud masks over snow‐covered, midlatitude mountains. We use 26 Landsat 8 scenes with manually delineated cloud, snow, and land cover to assess the performance of two cloud masks: CFMask for the Landsat 8 OLI and the cloud mask that ships with Moderate‐Resolution Imaging Spectroradiometer (MODIS) surface reflectance products MOD09GA and MYD09GA. The overall precision and recall of CFMask over snow‐covered terrain are 0.70 and 0.86; the MOD09GA cloud mask precision and recall are 0.17 and 0.72. A plausible reason for poorer performance of cloud masks over snow lies in the potential similarity between multispectral signatures of snow and cloud pixels in three situations: (1) Snow at high elevation is bright enough in the “cirrus” bands (Landsat band 9 or MODIS band 26) to be classified as cirrus. (2) Reflectances of “dark” clouds in shortwave infrared (SWIR) bands are bracketed by snow spectra in those wavelengths. (3) Snow as part of a fractional mixture in a pixel with soils sometimes produces “bright SWIR” pixels that look like clouds. Improvement in snow‐cloud discrimination in these cases will require more information than just the spectrum of the sensor's bands or will require deployment of a spaceborne imaging spectrometer, which could discriminate between snow and cloud under conditions where a multispectral sensor might not.

Description: No abstract is available for this article.

Description: Abstract Predicting the proportion of the water year a given stream will remain at or above various flow thresholds is critically important for making sound water management decisions. Flow duration curves (FDCs) succinctly capture this information using all data available over some historical period, while annual flow duration curves (AFDCs) instead use data from each individual water year. Analyzing the population of AFDCs, and in particular the tails of this distribution, can allow water managers to better prepare for years with extreme streamflow conditions. However, long time series of observations are necessary to capture interannual streamflow variations and are problematic to obtain in rapidly changing and poorly gauged catchments. By incorporating a process‐based model to construct AFDCs based on daily rainfall statistics and flow recession characteristics, the proposed approach is a first step toward addressing this challenge. Results indicate that prediction performance varies substantially across flow quantiles and that the current model fails to properly capture the interannual variability of low flows. Numerical analyses attributed these errors to nonlinearity in storage‐discharge relation, rather than cross‐scale streamflow correlations and non‐Poissonian rainfall, explaining the origin of commonly observed heavy‐tailed behavior in low flow quantiles. We present a case study on hydroelectric power generation, showing that faithfully capturing both interannual streamflow variability and recession nonlinearity has important implications for installation profitability.

Description: Abstract The snow cover dynamics of High Mountain Asia are usually assessed at spatial resolutions of 250 m or greater (e.g., MODIS), but this scale is too coarse to clearly represent the rugged topography common to the region. Higher‐resolution measurement of snow‐covered area often results in biased sampling due to cloud‐cover and deep shadows. We therefore develop an NDSI‐based workflow to delineate snowlines from Landsat TM/ETM+ imagery, and apply it to the upper Langtang Valley in Nepal, processing 194 scenes spanning 1999 to 2013. For each scene, we determine the spatial distribution of snowline altitudes (SLA) with respect to aspect and across 6 sub‐catchments. Our results show that the mean SLA exhibits distinct seasonal behavior based on aspect and sub‐catchment position. We find that SLA dynamics respond to spatial and seasonal tradeoffs in precipitation, temperature and solar radiation, which act as primary controls. We identify two SLA spatial gradients, which we attribute to the effect of spatially‐variable precipitation. Our results also reveal that aspect‐related SLA differences vary seasonally and are influenced by solar radiation. In terms of seasonal dominant controls, we demonstrate that the snowline is controlled by snow precipitation in winter, melt in pre‐monsoon, a combination of both in post‐monsoon, and temperature in monsoon, explaining to a large extent the spatial and seasonal variability of the SLA in the upper Langtang Valley. We conclude that, while SLA and SCA are complementary metrics, the SLA has a strong potential for understanding local‐scale snow cover dynamics, and their controlling mechanisms.

Description: Abstract Field tracer experiments were conducted to examine tracer transport properties in a fracture‐dominated crystalline rock mass at the Grimsel Test Site, Switzerland. In the experiments reported here, both the DNA nanotracers and solute dye tracers were simultaneously injected. We compare the transport of DNA nanotracers to solute dye tracers by performing temporal moment analysis on the recorded tracer breakthrough curves (BTCs) and estimate the swept volumes and flow geometries. The DNA nanotracers, approximately 166nm in diameter, are observed to travel at a higher average velocity than the solutes, but with lower mass recoveries, lower swept volumes, and less dispersion. Moreover, size exclusion and potentially, particle density effects, are observed during the transport of the DNA nanotracers. Compared to solute tracers, the greatest strength of DNA nanotracers is the demonstrated zero signal interference of background noise during repeat or multi‐tracer tests. This work provides encouraging results in advancing the use of DNA nanotracers in hydrogeological applications, for example, during contaminant transport investigations or geothermal reservoir characterization.

Description: Abstract Floating treatment wetlands (FTWs) are efficient at wastewater treatment; however, data and physical models describing water flow through them remain limited. A two‐domain model is proposed dividing the flow region into an upper part characterizing the flow through suspended vegetation and an inner part describing the vegetation‐free zone. The suspended vegetation domain is represented as a porous medium characterized by constant permeability thereby allowing Biot's Law to be used to describe the mean velocity and stress profiles. The flow in the inner part is bounded by asymmetric stresses arising from interactions with the suspended vegetated (porous) base and solid channel bed. An asymmetric eddy viscosity model is employed to derive an integral expression for the shear stress and the mean velocity profiles in this inner layer. The solution features an asymmetric shear stress index that reflects two different roughness conditions over the vegetation‐induced auxiliary bed and the physical channel bed. A phenomenological model is then presented to explain this index. An expression for the penetration depth into the porous medium defined by 10% of the maximum shear stress is also derived. The predicted shear stress profile, local mean velocity profile, and bulk velocity agree with the limited experiments published in the literature.