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Accurate flood risk assessment requires a comprehensive understanding of flood sensitivity to regional drivers and climate factors. This paper presents the scaling of floods (duration, peak, volume) with geomorphologic characteristics of the basin (i.e., drainage area, slope, elevation) and precipitation patterns (rainfall accumulation, variability). Long‐term daily streamflow observations over the 20th and early 21st centuries from Hydro‐Climatic Data Network streamgages across the conterminous United States are used to create a flood event database based on their flood stage information. Antecedent daily rainfall accumulation and variability corresponding to these floods are computed using Global Historical Climatology Network daily data set. Two Bayesian scaling models are developed, and the spatial organization of scaling exponents is investigated. The baseline model quantifies the scaling of floods to geomorphologic characteristics. The dynamic model quantifies the scaling of floods to antecedent precipitation distribution which is further conditioned on geomorphologic characteristics. Results show that small and low‐elevation basins have a stronger response to antecedent rainfall distribution in amplifying flood peaks, while high‐elevation steeper basins have a lower response for flood duration and volume. The dynamic models demonstrate that there are significant variations in the flood scaling rates, with the largest rates up to 40% and 4.5% for flood duration, 64% and 44% for peak, and 98% and 40% for volume found across the Northeast, Coastal Southeast, and Northwest with intensifying rainfall accumulation and variability, respectively. This study advances flood predictions by better informing the flood attributes in the context of dynamical land‐atmosphere perturbations. Plain Language Summary: The relationships between floods, physical properties of river basins, and the spatio‐temporal distribution of precipitation define the regional flood scaling laws. Improved regional flood scaling models are required for updating flood risk scoring tools to help insurers and decision‐makers in the light of anthropogenic climate changes and natural variability of Earth systems. Two scaling models are developed here to examine how the physical properties of basins and precipitation patterns can inform flood attributes across the conterminous United States (US). Using long records of streamflow and precipitation observations, we identified different scaling relationships between the catchment factors, preceding precipitation, and the duration, peaks, and volume of floods. Larger basins located at higher elevations are found to have lower scaling rates with flood peaks when interacting with the preceding precipitation patterns. Overall, low‐elevation flat basins across the Eastern, Southern, and Northwestern US have a larger positive response to precipitation in amplifying flood attributes. These new scaling laws can help advance our understanding of controlling mechanisms for flash floods and mega‐floods, which is required for a dynamic flood risk assessment in a changing environment. Key Points: Two Bayesian scaling models are developed to understand the sensitivity of floods to geomorphologic characteristics and catchment rainfallLow‐elevation and flat basins with different sizes have greater scaling to rainfall in amplifying flood duration and volumesLow‐elevation and small basins with different steepness show larger sensitivity to rainfall distribution that enhances flood peaks [ABSTRACT FROM AUTHOR]

Occurrences of extreme events, especially floods, have become more frequent and severe in the recent past due to the global impacts of climate change. In this context, possibilities for generating a near‐accurate streamflow forecast at higher lead times, which could be utilized for developing a reliable flood warning system to minimize the effects of extreme events, are highly important. This paper aims to investigate the potential of a novel hybrid modeling framework that couples the random forest algorithm, particle filter, and the HBV model for improving the overall accuracy of forecasts at higher lead times through the dynamic error correction schematic. The new framework simulates an ensemble of streamflow for estimating uncertainty associated with the predictions and is applied across two snow‐fed Himalayan rivers: the Beas River in India and the Sunkoshi River in Nepal. Several statistical indices along with graphical performance indicators were used for assessing the accuracy of the model performance and associated uncertainty. The modeling framework achieved the Nash Sutcliffe Efficiency of 0.94 and 0.98 in calibration and 0.95 and 0.99 in validation for the Beas and Sunkoshi river basin respectively for a 7‐day ahead forecast. Thus, the proposed framework can be considered as a promising tool having reasonably good performance in forecasting streamflow at a higher lead time. Key Points: Hybrid hydrological model integrates process‐based model with machine learning algorithm through data assimilation techniqueDynamic error correction framework capable of improving the streamflow forecast at longer lead time is proposedOverall the developed framework improves the forecast accuracy along with quantifying the model prediction uncertainty [ABSTRACT FROM AUTHOR]

There is a lack of Ordinary Differential Equation (ODE) formulations in numerical hydrology, contributing to the lack of application of canned adaptive timestep solvers; hence the continued dominance of fixed (e.g., Euler) timestep techniques despite their fundamental problems. In this paper, we reformulate Dynamic-TOPMODEL into a constraint-handling ODE form and use MATLAB's advanced adaptive ODE-solvers to solve the resulting system of equations. For wider applicability, but based on existing research and/or first principles, we developed Generalized Multistep Dynamic TOPMODEL which includes: iso-basin spatial discretization, diffusion wave routing, depth-dependent overland flow velocity, relaxing the assumption of water-table parallelism to the ground surface, a power-law hydraulic conductivity profile, new unsaturated zone flux, and a reference frame adjustment. To demonstrate the model we calibrate it to a peat catchment case study, for which we also test sensitivity to spatial discretization. Our results suggest that (a) a five-fold improvement in model runtime can result from adaptive timestepping; (b) the additional iso-basin discretization layer, as a way to further constrain spatial information where needed, also improves performance; and (c) the common-practice arbitrary Topographic Index (TI) discretization substantially alters calibrated parameters. More objective and physically constrained (e.g., top-down) approaches to TI classification may be needed. [ABSTRACT FROM AUTHOR]

Key to model development is the selection of an appropriate representational system, including both the representation of what is observed (the data), and the formal mathematical structure used to construct the input-state-output mapping. These choices are critical, because they completely determine the questions we can ask, the nature of the analyses and inferences we can perform, and the answers we can obtain. Accordingly, a representation that is suitable for one kind of investigation might be limited in its ability to support some other kind. Arguably, how different representational approaches affect what we can learn from data is poorly understood. This paper explores three representational strategies as vehicles for understanding how catchment scale hydrological processes vary across hydro-geo-climatologically diverse Chile. Specifically, we test a lumped water-balance model (GR4J), a data-based dynamical systems model (LSTM), and a data-based regression tree model (Random Forest). Insights were obtained regarding system memory encoded in data, spatial transferability by use of surrogate attributes, and informational deficiencies of the data set that limit our ability to learn an adequate input-output relationship. As expected, each approach exhibits specific strengths, with LSTM providing the best characterization of dynamics, GR4J being the most robust under informationally deficient conditions, and Random Forest regression-tree method being most supportive of interpretation. Overall, the contrasting nature of the three approaches suggests the value of adopting a multi-representational framework to more fully extract information from the data and, by doing so, find information that better facilities the goals of robust prediction and improved understanding, ultimately supporting enhanced scientific discovery. [ABSTRACT FROM AUTHOR]

The paper uses information from eddy‐resolving simulations to characterize flow and turbulence around freshwater mussels at the organism scale. The focus is on the simplest case of a partially burrowed, isolated mussel aligned with the incoming flow in an open channel. The wake structure and the capacity of the flow to displace the mussel from the bed substrate and to induce local scour around the mussel are investigated as a function of the ratio between the height of the exposed part of the mussel, h, and the total mussel height, d, and as a function of the filtration velocity ratio (VR) between the incoming channel flow velocity, U0, and the mean velocity inside the excurrent siphon, Ue. As opposed to the flow past most surface‐mounted obstacles where the bed shear stresses are reduced in the wake of the body, the capacity of the flow to induce bed erosion behind isolated mussels aligned with the flow is relatively large because of strong downwelling motions inside the horizontal separated shear layers. These flow features are associated with the formation of counterrotating base vortices of unequal coherence that induce upwash behind the mussel. The total circulation of the base vortices increases with increasing h/d and VR. Though both symmetrical and antisymmetrical shedding are observed in the wake, the antisymmetrical mode dominates, and its strength increases with increasing VR. The nondimensional streamwise force acting on the emerged part of the mussel's shell increases with increasing h/d and VR. Finally, the paper discusses the effects of varying h/d and VR on the dynamics and dilution of the jet of filtered water originating in the excurrent siphon, which is important to understand how mussels affect mixing and water quality (e.g., nutrient availability and phytoplankton concentration) in natural streams. [ABSTRACT FROM AUTHOR]

Global flood models integrate flood maps of constant probability in space, ignoring the correlation between sites and thus potentially misestimating the risk posed by extreme events. Stochastic flood models alleviate this issue through the simulation of flood events with a realistic spatial structure, yet their proliferation at large scales has historically been inhibited by data quality and computer availability. In this paper, we show, for the first time, the efficacy of modeled river discharge reanalyses in the characterization of flood spatial dependence in the absence of a dense stream gauge network. While global hydrological models may show poor correspondence with absolute observed river flows, we find that the rate at which they can simulate the joint occurrence of relative flow exceedances at two given locations is broadly similar to when a gauge‐based statistical model is used. Evidenced over the United States, flood events simulated using observed gauge data from the U.S. Geological Survey versus those generated using modeled streamflows have similar (i) distributions of site‐to‐site correlation strength, (ii) relationships between event size and return period, and, importantly, (iii) loss distributions when incorporated into a continental‐scale flood risk model. Extremal dependence is generally quantified less accurately on larger rivers, in arid climates, in mountainous terrain, and for the rarest high‐magnitude events. However, local‐scale errors are shown to broadly cancel each other out when combined, producing an unbiased flood spatial dependence model. These findings suggest that building accurate stochastic flood models worldwide may no longer be a distant aspiration. Plain Language Summary: Global flood risk is commonly estimated through flood inundation maps with a defined probability of occurrence. These flood simulations have a key drawback in that they fail to capture the spatial patterns exhibited during real flood events, instead modeling the same probability of flooding on every river at once. Solutions which rely on networks of gauged river flow observations will necessarily break down in the majority of the world's regions which lack such a resource. In this paper, we use historic river flows simulated by global rainfall‐runoff models (rather than observed flows) into a statistical model which captures the spatial correlation of flow extremes. If we examine the relative flow exceedance probabilities from these hydrological models rather than the volumetric flow values, flood events are generated which exhibit similar characteristics to those when gauged flow observations are used. Crucially, the simulation‐ and observation‐generated flood events produce near‐identical losses to buildings in the United States. The implications of this are that true stochastic flood risk models, which account for spatial dependence, can proliferate globally via the generation of realistic flood event sets from hydrological models. Key Points: Large‐scale flood hazard models typically neglect to represent the spatial dependence of real flood eventsRelative flow exceedances simulated by the fusion of global hydrological with statistical models reproduce gauge‐driven flood event setsAt the continental scale, key characteristics of a flood risk model are indistinguishable when driven with observed versus modeled flow data [ABSTRACT FROM AUTHOR]

The Surface Water Ocean Topography (SWOT) mission will launch in early 2022 to provide the first global inventory of terrestrial surface water. Although SWOT is primarily a research mission with key science objectives in both the oceanography and hydrology domains, SWOT data are expected to have application potential to address many societal needs. To identify SWOT applications, prepare for the use of SWOT data, and quantify SWOT impacts prior to launch, realistic proxy SWOT observations with representative measurement errors are required. This paper provides a step‐by‐step description of two methods for deriving proxy SWOT water surface elevations (WSEs) from an Observing System Simulation Experiment (OSSE) using the Weather Research and Forecasting hydrological extension package (WRF‐Hydro). The first, a basic method, provides a simple and efficient way to sample WRF‐Hydro output according to the SWOT orbit and add random white noise to simulate measurement error, similar to many previous approaches. An alternate method using the Centre National d'Etudes Spatiales (CNES) Large‐Scale SWOT Hydrology Simulator accounts for additional sources of measurement error and produces output in formats comparable to that expected from official SWOT products. The basic method is ideal for river hydrology applications in which a full representation of SWOT measurement errors and spatial resolution is unnecessary, whereas the CNES simulator approach is better‐suited for more rigorous scientific studies that require a comprehensive error budget. Plain Language Summary: The Surface Water Ocean Topography (SWOT) mission is a multinational satellite mission that is expected to launch in 2022 to observe global rivers, lakes, reservoirs, and wetlands. As the first of its kind to measure inland water, SWOT is expected to address many societal needs. To identify SWOT applications, prepare for the use of SWOT data, and quantify SWOT impacts prior to launch, realistic sample SWOT observations are needed. This paper provides a step‐by‐step description for deriving proxy SWOT measurements using a hydrologic model and a SWOT observation simulator. Key Points: Two approaches for generating proxy SWOT water surface elevations from a hydrology modeling framework are providedProxy SWOT data are well‐suited for societal applications and scientific studies prior to and following launchThe Large‐Scale SWOT Hydrology Simulator provides realistic proxy SWOT data for error budget studies [ABSTRACT FROM AUTHOR]

The editors of Water Resources Research (WRR) express their appreciation to the reviewers of articles submitted to the journal in 2019. We are proud of the rigorous WRR review process, where reviewers habitually go out of their way to improve the caliber and impact of WRR papers. Of the 2,640 reviewers listed below for the 2,034 new papers and 1,129 revised papers submitted to the journal, 398 people reviewed three or more papers. On behalf of the journal and the authors, we thank all reviewers for their service and dedication to the scientific community. We look forward to the exciting advances that will be published in WRR in 2020. Key Points: The editors thank the 2019 peer reviewers [ABSTRACT FROM AUTHOR]

The forecast‐informed hydropower operation for mixed reservoir systems, which consist of parallel and cascade reservoirs, is of considerable importance in practice; however, this operation still lacks an analytical basis in theory. From the perspective of energy, this paper introduces the concept of "hydraulic potential energy" and mathematically derives the energy transformation formula for multi‐reservoir hydropower operation. Based on this formula and the rolling single‐period forecast, a maximum hydraulic potential energy model (E1 model) is proposed. The presented rigorous proofs demonstrate the deficiencies of the commonly considered objectives or principles of minimizing the outflow‐induced energy‐cost, while the objective function of the E1 model is demonstrated to be superior. If the constraints of power output and reservoir storage are nonbinding, the derived optimal spatial principle for hydropower operation is (1) to equalize the Relative Marginal Energy (RME) among reservoirs or (2) if this status is not feasible, to release water and generate hydropower first from the reservoirs that have the largest RME values. Considering the uncertainty of future inflow, the E1 model is extended to the two‐stage hydraulic potential energy model (E2 model). A case study of a hypothetical mixed three‐reservoir system demonstrates the superior performance of the E2 model compared with the conventional K‐value principle and the minimum energy‐cost model. This paper provides an innovative spatial principle and a practical model for the realization of forecast‐informed hydropower operation, which contributes to the optimization of the large‐scale energy market. Key Points: The energy transformation formula for multi‐reservoir hydropower operation is proved based on the concept of hydraulic potential energyThe theoretical Relative Marginal Energy principle is mathematically derived for the optimal spatial allocation of reservoir storagesThe hydraulic potential energy model is an effective tool to improve the forecast‐informed hydropower operation for mixed reservoir systems [ABSTRACT FROM AUTHOR]

Models of water resources systems are conceived to capture the underlying environmental dynamics occurring within watersheds. All such models can be regarded as working hypotheses, differing in the aspects of process representation and conceptualization. Most of the associated efforts in the water resources research community is dedicated to development of new models that perform well under specific atmospheric conditions and catchment properties. In this context, flexible modeling frameworks are gaining importance as they facilitate the model building process by providing the model building blocks, whereby the hydrologist is free to assemble the model for task at hand. Such flexible models have high degree of transferability, which in turn aid in progressing toward a unified hydrological theory at catchment scale. However, in cases without sufficient insights regarding a catchment characteristics and/or lack of expert's knowledge, one may have to try a large number of model configurations based on available model building blocks to construct an appropriate model for the catchment of interest. Undoubtedly, this may be time consuming and computationally intensive. This paper proposes a novel model building algorithm, which uses the full potential of flexible modeling frameworks by searching the model space and inferring suitable model configurations relying on machine learning. Proposed machine learning algorithm is based on evolutionary computation approach using genetic programming (GP). State‐of‐art GP applications in rainfall‐runoff modeling so far used the algorithm as a short‐term forecasting tool that generates an expected future time series very similar to neural networks application. In this case, the proposed algorithm develops a physically meaningful rainfall‐runoff model. Although at the moment we learn models using two flexible modeling frameworks (SUPERFLEX and FUSE), the model induction toolkit can be armed with any internal coherence building blocks. The model induction capabilities of the proposed framework have been evaluated on the Blackwater River basin, Alabama, United States. The model configurations evolved through the model induction toolkit are consistent with the fieldwork investigations and previously reported research findings. Key Points: This paper presents a novel machine learning algorithm, which is guided through the incorporation of existing hydrological knowledgeProposed machine learning algorithm is based on evolutionary computation approach using genetic programmingIn the present case, the building blocks of flexible hydrological modeling frameworks represent elements of the background knowledge [ABSTRACT FROM AUTHOR]

Fresh water—the bloodstream of the biosphere—is at the center of the planetary drama of the Anthropocene. Water fluxes and stores regulate the Earth's climate and are essential for thriving aquatic and terrestrial ecosystems, as well as water, food, and energy security. But the water cycle is also being modified by humans at an unprecedented scale and rate. A holistic understanding of freshwater's role for Earth system resilience and the detection and monitoring of anthropogenic water cycle modifications across scales is urgent, yet existing methods and frameworks are not well suited for this. In this paper we highlight four core Earth system functions of water (hydroclimatic regulation, hydroecological regulation, storage, and transport) and key related processes. Building on systems and resilience theory, we review the evidence of regional‐scale regime shifts and disruptions of the Earth system functions of water. We then propose a framework for detecting, monitoring, and establishing safe limits to water cycle modifications and identify four possible spatially explicit methods for their quantification. In sum, this paper presents an ambitious scientific and policy grand challenge that could substantially improve our understanding of the role of water in the Earth system and cross‐scale management of water cycle modifications that would be a complementary approach to existing water management tools. Plain language summary: Freshwater is crucially important for all life on Earth. There is abundant research and evidence on how different processes within the water cycle regulate climate and support ecosystems, and by extension, human societies. Humans are also a major force disturbing those processes and modifying the water cycle. These modifications include, for instance, surface water withdrawals, groundwater pumping, deforestation and other land cover change, and ice melt due to warming climate. As most previous research on human–water interactions focuses on understanding systems at smaller scales, such as a watershed or a nation, comprehensive understanding of what human modifications of the water cycle mean for the stability of the planet is still lacking. In this paper we propose a new framework for analysing and establishing limits to a variety of human modifications of the water cycle, to ensure that the stability of the Earth would not be compromised. We see this as an important and urgent scientific challenge that has the potential to substantially improve our understanding of the functioning of the Earth system and to inform local and global policy toward a more sustainable future. Key Points: Earth system resilience depends on an improved understanding and management of water cycle modificationsWe identify four key functions of freshwater in the Earth system and evidence of regional to global regime shifts and disruptionsThe water planetary boundary is a compelling framework to improve our understanding and management of water cycle modifications in the Earth system [ABSTRACT FROM AUTHOR]

The basis for all knowledge is “information” that we compile about the world, expressed through models that support understanding, prediction, and decision making. This overview paper provides a contextual basis for the four papers that make up the “debate series” compiled under the above title. We briefly introduce Information Theory, discuss how “information” can be considered to be both a “physical” quantity and a “probabilistic” basis for representing incompleteness in knowledge, discuss the core motivation for this debate series, and briefly summarize the major arguments advanced by each of the debate papers. Our purpose is to facilitate an understanding of how these papers are related and how they approach the debate series question from different perspectives, while pointing to future directions for research. Finally, we invite further discourse and debate to advance the understanding and prediction of natural system dynamics using Information Theory, including the assessment of its limitations and complementarity to existing physics and machine learning approaches. Ultimately, our goal is to press for the development of philosophical and methodological advances that will enable the Earth science community to address some of the compelling unsolved problems in our field. [ABSTRACT FROM AUTHOR]

A risk‐based decision‐making mechanism capable of accounting for uncertainty regarding local conditions is crucial to water resources management, regulation, and policy making. Despite the great potential of hydrogeological models in supporting water resources decisions, challenges remain due to the many sources of uncertainty as well as making and communicating decisions mindful of this uncertainty. This paper presents a framework that utilizes statistical hypothesis testing and an integrated approach to the planning of site characterization, modeling prediction, and decision making. Benefits of this framework include aggregated uncertainty quantification and risk evaluation, simplified communication of risk between stakeholders, and improved defensibility of decisions. The framework acknowledges that obtaining absolute certainty in decision making is impossible; rather, the framework provides a systematic way to make decisions in light of uncertainty and determine the amount of information required. In this manner, quantitative evaluation of a field campaign design is possible before data are collected, beginning from any knowledge state, which can be updated as more information becomes available. We discuss the limitations of this approach by the types of uncertainty that can be recognized and make suggestions for addressing the rest. This paper presents the framework in general and then demonstrates its application in a synthetic case study. Results indicate that the effectiveness of field campaigns depends not only on the environmental performance metric being predicted but also on the threshold value in decision‐making processes. The findings also demonstrate that improved parameter estimation does not necessarily lead to better decision making, thus reemphasizing the need for goal‐oriented characterization. Key Points: We present the risk‐based data acquisition design evaluation (RDADE) framework to integrate stochastic analyses with defensible decisionsWe find that improved parameter estimation does not always guarantee lower decision riskWhen dealing with extreme events with high consequences, it is advantageous to improve system resiliency in addition to modeling accuracy [ABSTRACT FROM AUTHOR]

In the spring of 1818, ice avalanches from the Giétro Glacier created an ice dam, which in turn formed a glacial lake in the Drance Valley (Canton of Valais, Switzerland). Today, its maximum volume is estimated to have been 25×106 m3. Cantonal authorities commissioned an engineer named Ignaz Venetz to mitigate the risk of the ice dam's failure. He supervised the construction of a tunnel through which a large volume of water was drained as the lake rose (9×106 m3 according to his estimates and 11×106 m3 according to our model). After 2.5 days of slow drainage, the ice dam failed on 16 June 1818 and caused major flooding in the Drance Valley up to 40 km downstream, resulting in about 40 deaths. Venetz's lake monitoring notes, numerous testimonies gathered in the disaster's aftermath, and our field survey have made it possible to collect a wealth of information on this event, which is one of the world's major documented glacial lake outburst floods. Reconstructing major outburst floods remains challenging because not only do they involve enormous volumes of water spreading over long distances but they are also associated with additional physical processes such as massive erosion; intense transport of ice, sediment, and debris; and damage to vegetation and buildings. This paper attempts to reconstruct the 1818 Giétro flood by focusing on its water component. We develop a simple model to estimate the initial hydrograph during the slow drainage and failure phases. The flood's features are deduced by solving the shallow‐water equations numerically. The computational framework involves six free parameters, of which five are constrained by physical considerations. Using iterative manual parameter adjustments, we matched the numerical simulations to the historical data. We found that the peak discharge was close to 14,500 m3/s, the flood's front velocity was about 6 m/s, and flow depth varied considerably along the River Drance's bed (from 30 m just downstream of the ice dam to 2 m on the alluvial fan, 24 km west of the dam). To achieve a good agreement between computations and historical data, we had to select a high value for the Manning friction coefficient n (with n as large as 0.08 s/m1/3). As the Drance Valley is narrow, high flow resistance caused the flood's leading edge to behave like a plug, moving at a fairly constant velocity, with little dependence on what happened behind it. This result may explain why a simple flood routing model is able to reproduce the flood's features, because in an Alpine valley, a lateral spreading of the water volume is limited. Plain Language Summary: Every year, natural and man‐made dams fail and cause flooding. For public authorities, estimating the risk posed by dams is essential to good risk management. Efficient computational tools are required for analyzing flood risk. Testing these tools is an important step toward ensuring their reliability and performance. Knowledge of major historical floods makes it possible, in principle, to benchmark models, but because historical data are often incomplete and fraught with potential inaccuracies, validation is seldom satisfactory. Here we present one of the few major historical floods for which information on flood initiation and propagation is available and detailed: the Giétro flood. This flood occurred in June 1818 and devastated the Drance Valley in Switzerland. In the spring of that year, ice avalanches blocked the valley floor and formed a glacial lake, whose volume is today estimated at 25×106 m3. The local authorities initiated protection works: A tunnel was drilled through the ice dam, and about half of the stored water volume was drained in 2.5 days. On 16 June 1818, the dam failed suddenly because of significant erosion at its base; this caused a major flood. This paper presents a numerical model for estimating flow rates, velocities, and depths during the dam drainage and flood flow phases. The numerical results agree well with historical data. The flood reconstruction shows that relatively simple models can be used to estimate the effects of a major flood with good accuracy. Key Points: The 1818 Giétro flood was one of the world's major glacial lake outburst floods in historical timesWe apply a simple computational framework to reconstruct the initial hydrograph and flood motionFlow resistance was significant, presumably because of intense sediment and debris transport [ABSTRACT FROM AUTHOR]

Crowdsourcing 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 crowdsourcing management for rainfall monitoring. Specifically, we model the allocation of incentives (quantitatively measurable and limited rewards) among crowdsourcing 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 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 trade‐off 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 crowdsourcing rainfall monitoring. The proposed crowdsourcing method is beneficial for a wide range of applications that require rainfall data with fine resolution, such as storm water management and water availability and biomass assessment for food and energy crops. Key Points: A modeling framework is developed for managing crowdsourcing rainfall monitoring based on budget and participants' behavior and locationsThe spatial distribution of participants is found as a key factor affecting the best design of reward allocation policiesTrade‐off between performance and cost is provided for several reward allocation policies [ABSTRACT FROM AUTHOR]

Hydraulic connections between a river and an adjacent aquifer are controlled by the river resistance and aquifer diffusivity. In this paper, we derive a spectral solution linking the power spectrum of river stage fluctuations to that of the hydraulic head of a confined aquifer by means of a physical scaling factor. The physical scaling factor represents an algebraic expression of the river resistance and aquifer diffusivity and is included in an exact spectral solution derived herein. Statistical measures of the aquifer diffusivity and river resistance are provided by fitting the solution versus observed groundwater hydraulic head obtained at several distances and/or frequencies. At a study site in the middle reach of the Yangtze River and downstream of the Three Gorges Dam in China, we find systematic damping of the hydraulic head variations with distance from the river, which follows a fractal pattern driven by the river stage. In general, the estimated parameters are consistent with results reported in the literature, which supports the validity of the proposed spectral approach, although the paper discusses advantages and limitations due to application conditions. Plain Language Summary: The characterization of hydraulic connections between a river and an adjacent aquifer requires knowledge of two critical parameters: the river resistance and the aquifer diffusivity. In this paper, we derive a spectral solution to link the power spectrum of river stage fluctuations to that of the groundwater hydraulic head in a confined aquifer and show how such solutions can be used to simultaneously assess the two critical parameters. Parameters estimated from the field case located in the middle reach of the Yangtze River and downstream of the Three Gorges Dam in China are consistent with results reported in the literature. Application conditions, advantages, and limitations of this spectral approach are also discussed. Key Points: The power spectrum of the hydraulic head is linked to that of the river stage by a scaling factorRiver resistance and aquifer diffusivity are analyzed in spectral formsTheoretical developments are applied to a field case study of the Yangtze River, China [ABSTRACT FROM AUTHOR]

The coupled hydrology and mechanics of soft porous materials (such as clays, hydrogels, membranes, and biofilms) is an important research area in several fields, including water and energy technologies as well as biomedical engineering. Well‐established models based on poromechanics theory exist for describing these coupled properties, but these models are not adapted to describe systems with more than one characteristic length scale, that is, systems that contain both macropores and micropores. In this paper, we expand upon the well‐known Darcy‐Brinkman formulation of fluid flow in two‐scale porous media to develop a "Darcy‐Brinkman‐Biot" formulation: a general coupled system of equations that approximates the Navier‐Stokes equations in fluid‐filled macropores and resembles the equations for poroelasticity in microporous regions. We parameterized and validated our model for systems that contain either plastic (swelling clay) or elastic microporous regions. In particular, we used our model to predict the permeability of an idealized siliciclastic sedimentary rock as a function of pore water salinity and clay content. Predicted permeability values are well described by a single parametric relation between permeability and clay volume fraction that agrees with existing experimental data sets. Our novel formulation captures the coupled hydro‐chemo‐mechanical properties of sedimentary rocks and other deformable porous media in a manner that can be readily implemented within the framework of Digital Rock Physics. Plain Language Summary: Knowledge of how fluids flow through porous materials has significant implications for the design and operation of batteries, aquifers, oil rigs, and biomedical devices. Even though scientists have been successful in creating computer models that capture fluid flow through rigid porous media, it has been very challenging to create models that can model flow through deformable porous media. In this paper, we describe a new model that can predict flow through and around deformable porous media. We derived this model by superimposing separate conventional fluid‐flow and solid‐deformation models into a single simulation through a technique called volume averaging. We then connected the two models using Newton's Third Law, meaning that when fluids flow through a porous solid they "push" the solid, and whenever a solid resists fluid movement or deforms, it "pushes" the fluid in turn. The resulting model can capture complex multiscale, multiphysics phenomena such as the impact of clay swelling (think of an expanding sponge absorbing water) on the ability of fluids to flow through soils or sedimentary rock formations. Given the model's generality and its successful verification presented in this paper, we believe that it can help understand important phenomena in the fields of water and energy resources. Key Points: We developed a novel approach to model coupled fluid flow and solid mechanics in porous media with two characteristic length scalesThe model was validated against data on flow and chemically driven deformation in viscoplastic and elastic solidsSedimentary rock permeability is predicted to have an error function dependence on the volume fraction occupied by microporous clay [ABSTRACT FROM AUTHOR]

Pressure measurement within each boundary pipe of a tree network has been the prerequisite to the formulation of its factorized transient wave propagation model, which is essential for developing a robust and efficient leak localization algorithm. However, the accessibility of all boundary pipes cannot be always guaranteed in underground pipe networks. The inaccessibility of any one boundary pipe for pressure sensor placement would result in the complete invalidity of this elegant algorithm. To address this problem, this paper reformulates and modifies the factorized wave propagation model, which allows the leak localization algorithm to be implemented without the strict accessibility requirement of all boundary pipes. This is realized by linearizing and factorizing the point transfer matrices of boundary pipes based on the small leak approximation. Numerical experiments demonstrate that the modified algorithm can accurately localize a single leak and give a good estimation of multiple co‐existing leaks in a tree network. Moreover, the modified algorithm is verified by recent laboratory experiments conducted in a tree network of viscoelastic pipes, where a leak is successfully localized. Key Points: A modified transient wave model is formulated in a general tree pipe network, where the leak location and size are factorizedAn efficient and robust leak localization method can be implemented without the strict accessibility requirements of all boundary pipesNumerical and laboratory experiments confirm that a leak anywhere in a tree network can be localized accurately by the proposed algorithm [ABSTRACT FROM AUTHOR]

This paper introduces a method for determining spatially‐distributed, 2‐D bedload rates using repeat, high‐resolution surveys of the bed topography. As opposed to existing methods, bedform parameters and bedload rates are computed from bed elevation profiles interpolated along the local bedform velocities. The bedform velocity fields are computed applying Large‐Scale Particle Image Velocimetry, initially developed for surface velocity measurements, to pairs of successive Digital Elevation Models (DEMs). The bathymetry data are interpolated along the direction of each bedform velocity and the mean height of the closest bedform is computed. The dune shape factor is also evaluated along each bedform direction of travel. The local bedload fluxes can be computed by multiplying the bedform velocity by its mean height averaged over the successive two DEMs, and they can be time‐averaged over a series of DEM pairs. This method is applied to a high‐resolution acoustical survey of an approximately 300 m long by 40 m wide reach of the Colorado River in Grand Canyon upstream from Diamond Creek, USA. The repeat period was about 6–10 min and bed elevation was interpolated every 0.25 m. The obtained results provide insight to the spatial and temporal variability of bedload rates, bedform parameters and bedload fluxes through cross‐sections. The method can be applied to other repeated acoustical surveys of river reaches provided that the space and time resolutions are high enough to capture the local movement of bedforms. Key Points: We introduce a method for mapping bedload fluxes using repeat, high‐resolution acoustical bathymetric surveys of a river reachThe method combines cross‐correlation velocimetry and local analysis of individual bedforms along their direction of travelApplication to the Colorado River in Grand Canyon provides a proof‐of‐concept and shows the spatio‐temporal variability of local bedload [ABSTRACT FROM AUTHOR]

In this paper, we introduce a relatively simple data‐driven method for the representation of the uncertainty in daily discharge records. The proposed method relies only on hourly discharge data and takes advantage of a nonparametric difference‐based estimator in the characterization of random errors in discharge time series. We illustrate with corrupted streamflow data that the nonparametric estimator provides an accurate characterization of the nature (homoscedastic or heteroscedastic) and magnitude of these errors. In addition, we demonstrate the practical usefulness of the estimator using discharge time series of 500+ watersheds of the Catchment Attributes and MEteorology for Large‐sample Studies data set. This analysis reveals that the magnitude of errors of aleatory nature in the investigated discharge records is rather small (less than 3% for 80% of the records). We then combine the effect of random errors and measurement frequency into a daily variance estimate, which serves as input to a streamflow generation approach. This procedure produces replicates of the discharge record which portray accurately the assigned streamflow uncertainty, preserve key statistical properties of the discharge record and are hydrologically realistic. The proposed method facilitates Bayesian analysis and supports tasks such as model diagnostics, data assimilation, uncertainty quantification and regionalization. Key Points: We present a model‐agnostic method for the generation of replicates of discharge recordsThe nonparametric estimator provides an accurate estimate of random errors, which for the investigated discharge records are rather smallThe replicates portray accurately the assigned streamflow uncertainty and preserve statistical and hydrologic properties [ABSTRACT FROM AUTHOR]

Changes in capillary water morphology in a cluster of three wet long cylindrical grains as reported in Mielniczuk and Hueckel (2022, https://doi.org/10.1029/2022wr031938) affect capillary forces evolving during drying. The paper focuses on a stable motion of menisci (air/liquid interfaces) and their unstable (Haines) jumps. The meniscus instabilities were shown in the past to be a trigger of soil drying‐cracking. The forces considered are Laplace pressure and surface tension resultants deduced from the menisci imaged every 10 s. The Laplace pressure for cylindrical clusters depends on a single curvature of the meniscus. Several known criteria the capillary body instability are examined using the experimental data. An extended Gibbs criterion points to a source of the instability. The instability is lost when the meniscus approaches the throat between the grains and when the balance is lost between the changes of forces of the Laplace pressure and of the surface tension acting at the meniscus. The presence of the surface tension forces in the instability considerations in addition to the Laplace pressure distinguishes Gibbs criterion from other criteria. Gibbs meniscus energy estimate also provides an inertia force arising to maintain the linear momentum balance for the meniscus, and a duration of the jump. Key Points: The Laplace pressure shows symmetry loss, pressure gradientsStability loss precedes meniscus accelerationGibbs meniscus instability explains jump mechanism [ABSTRACT FROM AUTHOR]

Flood events are the most commonly occurring natural disaster, with over 5 million properties at risk in the UK alone. Changes in the global climate are expected to increase the frequency and magnitude of flood events. Flood hazard assessments, using climate projections as input, guide policy decisions and engineering projects to reduce the impact of large return period events. Probabilistic flood modeling is required to take into account uncertainties in climate model projections. However, the dichotomous relationship between probabilistic modeling, computational cost and model resolution limits the applicability of such techniques. This paper examines improvements to traditional Monte Carlo methods using Latin hypercube sampling (LHS) and Multi‐level Monte Carlo (MLMC) to quantify the uncertainty in flood extent resulting from input hydrograph uncertainty. The results demonstrate that MLMC is a more efficient modeling strategy than current methods (i.e., traditional Monte Carlo) with high resolution outputs produced in less time than previously possible. The novel application of MLMC technique to three Scottish case studies, demonstrating a variety of river characteristics, domain sizes and computational costs, using a high resolution 5 m grid resulted in a 99.2% reduction in computational cost compared to traditional Monte Carlo methods and up to 2.3 times speedup over Latin Hypercube Sampling. Key Points: Multi‐level Monte Carlo (MLMC) reduces the computational cost by 99.2% over traditional full Monte Carlo; tested across a range of case studiesProbabilistic flood hazard assessment accuracy can be increased by incorporating higher resolution models with minimal increase in costsMLMC allows realistic uncertainty quantification at previously unfeasible 5 m grid resolution, yielding converged estimates of flooded area [ABSTRACT FROM AUTHOR]

Reliable urban flood modeling is highly demanded in emergency response, risk management, and urban planning related to urban flooding. In this paper, the Storm Water Management Model (SWMM) is adapted to simulate urban rainfall‐runoff and pipe drainage processes within the Dominant river tracing‐Routing Integrated with VIC Environment (DRIVE) model which accounts for natural river basin runoff generation and routing processes. The integrated DRIVE‐SWMM model (referred to as DRIVE‐Urban) allows to explicitly delineate the mass‐energy interactions between urban drainage system (e.g., pipes and dikes) and river networks. This presents a further step model development for accurate urban flooding prediction which is lacking in existing urban flood models and traditional hydrological models. The validity of the DRIVE‐Urban model is evaluated for three case studies in Haikou City, China, with camera observations of street inundation during typhoon landfalls and heavy rainfall events. The results show that the DRIVE‐Urban model successfully captures 62%, 69%, and 77% of the total observed inundated road‐sections for the three cases respectively. The third case study with severe flooding situation shows that the DRIVE‐Urban performance is further improved when given reliable river and tidal level information, indicating the importance of integrating river‐basin with urban hydrological and hydraulic modeling. Plain Language Summary: This study mainly focuses on the development of an urban flood model (Dominant river tracing‐Routing Integrated with VIC Environment, DRIVE‐Urban), which couples the river‐basin and urban hydrological modeling, with a great potential for large‐scale real‐time urban flood monitoring and forecasting through the current Global Flood Monitoring System. Two inundation algorithms are developed within the DRIVE‐Urban model. One is for the river inundation caused urban flooding, and the other one is for the urban inundation caused by manhole overflow or excessive runoff drainage over the city land surface. Multi‐source observations including the closed‐circuit television video, citizen photos and municipal reports are collected for model validation. The results show that the DRIVE‐Urban model simulates well the different type of urban inundations at the 5‐m high spatial resolution with a promising hit success (i.e., POD >60%) according to three case studies. In particular, the results for the typhoon Kalmaegi case show that the model performance can be improved significantly by coupling the upstream river flow simulation with the urban runoff‐routing process, which validates our strategy of including the upstream river‐basin hydrological process in the urban flood modeling. Key Points: The Dominant river tracing‐Routing Integrated with VIC Environment (DRIVE)‐Urban flood model is developed by coupling river basin and urban hydrological and hydraulic processTwo 2D inundation algorithms are implemented within the model to simulate the manhole inundating and river overflow flooding respectivelyThe validity of the DRIVE‐Urban model is well established through three case studies with observations during typhoon and rainfall events [ABSTRACT FROM AUTHOR]

Low‐elevation coastal areas are increasingly vulnerable to seawater flooding as sea levels rise and the frequency and intensity of large storms increase with climate change. Seawater flooding can lead to the salinization of fresh coastal aquifers by vertical saltwater intrusion (SWI). Vertical SWI is often overlooked in coastal zone threat assessments despite the risk it poses to critical freshwater resources and salt‐intolerant ecosystems that sustain coastal populations. This review synthesizes field and modeling approaches for investigating vertical SWI and the practical and theoretical understanding of salinization and flushing processes obtained from prior studies. The synthesis explores complex vertical SWI dynamics that are influenced by density‐dependent flow and oceanic, hydrologic, geologic, climatic, and anthropogenic forcings acting on coastal aquifers across spatial and temporal scales. Key knowledge gaps, management challenges, and research opportunities are identified to help advance our understanding of the vulnerability of fresh coastal groundwater. Past modeling studies often focus on idealized aquifer systems, and thus future work could consider more diverse geologic, climatic, and topographic environments. Concurrent field and modeling programs should be sustained over time to capture interactions between physical processes, repeated salinization and flushing events, and delayed aquifer responses. Finally, this review highlights the need for improved coordination and knowledge translation across disciplines (e.g., coastal engineering, hydrogeology, oceanography, social science) to gain a more holistic understanding of vertical SWI. There also needs to be more education of communities, policy makers, and managers to motivate societal action to address coastal groundwater vulnerability in a changing climate. Plain Language Summary: Along the world's coastlines, seawater flooding on the land surface by storms and high tide events can drive the downward infiltration of seawater into coastal aquifers that are an important source of fresh groundwater. Rising sea levels and the intensification of coastal storms are expected to exacerbate seawater flooding events globally. The salinization of fresh groundwater along the coast following floods is often "out of sight, out of mind," but this process threatens freshwater resources that sustain dense coastal human populations and valuable, sensitive ecosystems. This paper reviews prior studies that have applied monitoring and/or computer modeling methods to better understand processes and controls on vertical saltwater intrusion. Our review details how surface flood dynamics, groundwater processes, environmental characteristics, and human activities combine to create complex patterns of salinization and flushing over space and time. From our synthesis we identify future research needs, including long‐term, globally dispersed monitoring and collaboration across scientific disciplines. We further emphasize the need for communication with local stakeholders about this topic to motivate informed management and research initiatives to understand and reduce the vulnerability of coastal fresh groundwater resources in a changing climate. Key Points: Vertical saltwater intrusion following coastal floods threatens fresh groundwater in an age of rising seas and intensifying stormsWe review salinization and flushing processes identified with field and modeling methods and identify future research opportunitiesKnowledge gaps can be addressed through multidisciplinary studies to advance science and inform water resources management and policy [ABSTRACT FROM AUTHOR]

Multivariate data assimilation (DA), a novel way to couple big data with land surface models, was extensively employed in forecasting‐reanalyzing systems (FRSs), for example, ECMWF and GLDAS. Meanwhile, most (distributed) hydrological models, like soil and water assessment tool (SWAT), have not been equipped with straightforward ways to link to DA algorithms. Therefore, it is one of the main barriers to utilizing such hydrological models in FRSs. This paper deals with multivariate DA into SWAT (DA‐SWAT), which is complicated since the original model does not provide full access to the models' initial conditions (ICs) at the hydrologic response unit (HRU) scale. The preceding DA‐SWAT works commonly used an integrated approach in which the DA and SWAT codes were implemented in the same programming environment. We discuss how this approach complicates and prevents the application of DA‐SWAT in multivariate, multimodel, and multisensor systems. Accordingly, we proposed a new approach for DA‐SWAT by which SWAT can be perfectly linked with any DA algorithm of interest coded in any desired programming environment. Our framework utilizes input/output text files to access ICs and to link DA with SWAT. Moreover, we designed some univariate and multivariate scenarios for assimilating in situ streamflow measurement and MODIS's snow cover fraction (SCF) data, which has not yet been focused on in the SWAT calibration context. Results show that compared to the univariate assimilation of streamflow (SCF), the multivariate assimilation mitigates the equifinality problem and more accurately estimates SCF (streamflow) by improving NS and PBIAS measures with the differences of 0.4 (0.86), 12% (64%), respectively. Key Points: We propose a new approach to link SWAT with data assimilation (DA) algorithms based on source code modificationSnow cover fraction (SCF) data of MODIS are assimilated to estimate HRU‐scaled snow‐related parameters of SWATBivariate streamflow and MODIS SCF data assimilation mitigates the equifinality problem [ABSTRACT FROM AUTHOR]

Arsenic, a neurotoxin and carcinogen, is a legacy contaminant in the sediments of many urban lakes and poses health risks to aquatic ecosystems and lake users. Arsenic uptake into the aquatic food web is enhanced in shallow, polymictic lakes compared to deep, seasonally stratified lakes. We present the results of a 17‐month field study in Lake Killarney, a shallow, urban lake in Federal Way, Washington, USA, which examines the physical and biogeochemical mechanisms controlling arsenic mobilization and transport from sediment into lake waters, a prerequisite for arsenic uptake into the food web. In Lake Killarney, arsenic mobilization and transport into bottom waters occurred only when stratified conditions and elevated temperatures facilitated deoxygenation of bottom waters. Frequency of lake mixing varied seasonally and controlled the vertical distribution of arsenic in the water column. Convective mixing was the main contributor to elevated vertical turbulent intensity in the water column during periods of high arsenic mobilization, and thus to the upwards transport of arsenic from bottom waters. Maximum near‐surface arsenic occurred when the lakebed sediment temperature was elevated and the water column was overturning frequently. This work clarifies the mechanisms that contribute to vertical arsenic transport in shallow lakes and provides a basis for identifying contaminated systems with the physical and biogeochemical conditions that promote transport of arsenic into near‐surface water. Plain Language Summary: Arsenic, a toxic pollutant found in many urban lakes, can get taken up into the food web and make its way into fish and other animals consumed by humans. Arsenic has a larger impact on the food web in shallow lakes compared to deep lakes. To understand why this is the case, we carried out a 17‐month field study in Lake Killarney, a shallow, urban lake in Washington, USA. In this paper, we examine the processes that control the movement of arsenic from the bottom to the surface of contaminated lakes. We found that more arsenic moved out of lake sediments into bottom waters when the lakebed sediment was warm and the lake was separated into layers of different density. The frequency of lake mixing changed throughout the year and controlled how arsenic was distributed in the lake water. Highest arsenic concentrations in lake surface water occurred when the lakebed sediment temperature was elevated and the lake mixed frequently. This work clarifies the mechanisms that contribute to arsenic transport in shallow lakes and provides information needed to identify systems with conditions that lead to high levels of surface water arsenic. Key Points: In polymictic, temperate lakes, stratification and high temperatures are needed to promote anoxia and arsenic release in bottom waterConvective mixing is a key mechanism transporting arsenic from lake bottom waters to surface waters inhabited by oxygen‐requiring biotaArsenic release in shallow, temperate lakes overlaps with high primary producer growth and leads to arsenic accumulation in the food web [ABSTRACT FROM AUTHOR]

This paper deals with the issue of the consistency of water distribution network (WDN) modeling concerning the nature of demand for the optimal water quality sensor placement. In this regard, a biobjective optimization for the sensor placement is carried out by minimizing the contamination extension or the contaminated population in the trade‐off with the number of sensors installed, by considering two alternative demand modeling conditions, that is, smooth and pulsed demand, respectively. The real layout of the two Italian WDNs is considered for the application of the methodology. The results show that for a given placement of sensors, the contamination extension always increases when the pulsed demand is considered. Furthermore, optimal sensor positions are affected by the pulsed demand especially in the case of highly looped WDN layouts and when the extent of contamination is used as objective function. Key Points: Nodal demand modeling in water distribution networks influences the fate of contaminants moving through the systemLayout and performance of a water quality sensor system differ if a stochastic bottom‐up approach is used for the demand modelingThe extent of the influence of a different demand modeling depends on the monitoring target and on the water distribution network topology [ABSTRACT FROM AUTHOR]

Salt accumulations in aquifers significantly affect and transform the conjunctive use of surface water and groundwater supporting irrigated agriculture. Salt accumulates in aquifers under many semi‐arid irrigated lands where pumping has lowered water levels enough to prevent drainage of saline groundwater from the basin. This paper provides new insights into optimal conjunctive management of groundwater pumping, recharge, surface water, and cropping patterns with groundwater salinity and hydrologic variability in an irrigated semi‐arid region, such as California's western San Joaquin Valley, reducing agricultural crop yields and revenues. A two‐stage stochastic quadratic model explores this problem to prescribe economically optimal crop mix and conjunctive water operation policies over a 10‐year period with probabilistic annual surface water availability, considering groundwater salinity's harm to crop yields. At low groundwater salinity, agricultural conjunctive use usually pumps most groundwater in drier years, supplied by additional recharge in wetter years. In contrast, at higher groundwater salinity, optimal conjunctive use pumps less in drier years while pumping more in wetter years, when more surface water allows more dilution of saltier groundwater. Reduced pumping in drier years substantially reduces a region's ability to support higher‐value perennial crops and reduces or eliminates lower‐value annual crops in dry years. Artificial recharge with fresh surface water in wetter years can have economic value from slowing groundwater salination which allows more groundwater use in drier years. Key Points: A two‐stage stochastic quadratic model estimates the economic impact of groundwater salinity on agriculture, especially with perennial cropsEconomically optimized groundwater management fundamentally changes with groundwater salinationArtificial recharge for aquifers with severe salt accumulation can reduce both loss of agricultural production and groundwater salinity [ABSTRACT FROM AUTHOR]

Hydrodynamic dispersion in flow through porous media is an essential phenomenon in many geosystems. While dispersion in flow of Newtonian fluids is relatively well‐understood, many subsurface applications, such as groundwater remediation, use the flooding system with non‐Newtonian fluids, such as polymer oxidants. Despite its significance, however, very limited studies have been carried out focusing on hydrodynamic dispersion in flow of non‐Newtonian fluids. The present study addresses a fundamental question regarding how solute transport in flow of non‐Newtonian fluids in porous media differs from the Newtonian limit. We report on the results of an extensive pore‐scale study of the upscaling of advection‐diffusion in flow of a non‐Newtonian, shear thinning fluid through disordered and spatially‐correlated porous media, using an advanced GPU‐based pore‐scale simulator in order to delineate the effects of the fluid's rheology, dynamics of fluid flow, and pore‐scale spatial correlations on hydrodynamic dispersion. The simulations indicate a surprising non‐monotonic relation between the injection rate (or injection velocity) and the dispersivity. While dispersivity in Newtonian fluid flow in porous media is constant in the flow regime that we study, we find, however, that the dispersivity in flow of non‐Newtonian fluids is shear‐dependent. This highlights the gap in the existing theories of transport through porous materials for non‐Newtonian fluids, which can lead to erroneous estimates of dispersion coefficients in porous materials when the fluid's rheology cannot be represented by Newtonian mechanics. Plain Language Summary: Except for some fluids, such as water and air, as well as straight‐chain hydrocarbons, which are well represented by Newtonian rheology, most fluids in nature and used in industrial processes are non‐Newtonian. Examples are polymeric solutions, magma or lava, drilling fluids, and cement pastes. Even the flow of water in small pores may follow non‐Newtonian rheology. In addition, many natural and industrial processes involve the non‐isothermal flow of non‐Newtonian fluids. Despite their significance, however, the current understanding of the behavior of solute and/or heat transport in flow of non‐Newtonian fluids in porous media is highly incomplete. This paper demonstrates how the dispersion coefficient and fingering, induced by heat/solute transport during flow of a shear thinning fluid through porous materials are affected by the rheology of the fluid, as well as the morphology of the pore space. Key Points: For non‐Newtonian fluids, dispersivity cannot be defined only as a function of porous mediumDispersivity for non‐Newtonian fluids exhibits non‐monotonic dependence on the shear rateThe shear thinning rheology and the correlated pore‐size distribution enhance fingering in transport [ABSTRACT FROM AUTHOR]

Attributing nitrogen export to specific land use within heterogeneous catchments remains a challenge due to the spatio‐temporal variability in conditions influencing the mobilization and fate of nitrogen species. This study demonstrates that the measurement of dual stable isotopes of nitrate, taken along with routine tributary measurement of nitrogen in nitrate (NO3−‐N) and ammonium (NH4+‐N), aids in apportioning sources of the overall nitrogen load during wet periods. An inverse modeling technique was developed to estimate the land use‐specific export rates of NO3−‐N and NH4+‐N from the Caboolture River Catchment in Queensland, Australia. Measurements of nitrogen in streamflow at 51 locations during six sampling campaigns (May 2012 to April 2013) were made along with catchment geospatial data that was used to disaggregate sub‐catchments into six land use fractions. A hydrological model was applied to compute the runoff from each fraction and water routing through the stream network. This data was used within a nitrogen mixing model with inclusion δ15NNO3 and δ18ONO3. The land uses specific export rate was computed inversely as the posterior of a Bayesian interference applied to the model. During higher rainfall periods when export rates were highest, the main land use exporting nitrogen was wetland (110 g/ha/day NO3−‐N, 27 g/ha/day NH4+‐N) resulted from mineralization and nitrification of organic N, followed by urban (16 g/ha/day NO3−‐N, 2.3 g/ha/day NH4+‐N). The advantage of using the dual isotopes in conjunction with the nitrogen concentration data was demonstrated by reduced uncertainty in the computed rates during the higher rainfall periods, relative to calculations without δ15NNO3 and δ18ONO3. Plain Language Summary: Attributing nitrogen export to specific land use types within heterogeneous catchments is difficult. This paper demonstrates the use of an inverse modeling technique, dissolved inorganic nitrogen concentration data, a dual stable isotope measurement data, and geospatial data to estimate the land use‐specific export rates of dissolved inorganic nitrogen from six land use types in the Caboolture River Catchment in Queensland, Australia. The results show that during higher rainfall periods when export rates were highest, the main source of nitrogen was found to be wetland (110 g/ha/day NO3−‐N and 27 g/ha/day NH4+‐N), followed by urban (16 g/ha/day NO3‐N and 2.3 g/ha/day NH4+‐N). Key Points: An inverse modeling technique was developed to estimate the land use‐specific export rate of nitrogen using isotopes signaturesThe model was embedded within a Bayesian framework to assess the predictive uncertainty with and without inclusion of dual isotopes of nitrateDuring wet periods, when nitrogen export rates were highest, the predictive uncertainty was reduced when the model included dual isotopes of nitrate [ABSTRACT FROM AUTHOR]

In recent years, remote sensing satellites, including Soil Moisture Active Passive (SMAP), continuously monitor SM of the earth. However, the spatial resolution of the remotely sensed SM is insufficient for some hydrological applications. In this paper, a data‐driven model is proposed based on a hybrid approach called artificial neural network kriging (ANNK) to capture the interaction between SM and ancillary data (AD), including normalized difference vegetation index (NDVI), altitude, slope, Antecedent Moisture Condition, AMC (5‐day cumulative precipitation), and daily air temperature (DAT)/land surface temperature (LST). By co‐regionalization of the estimated SM from the ANNK and SMAP 36‐km product, SM in any part of the study area is estimated more accurately and on a finer resolution (1‐km). Finally, through a linear relationship between in‐situ SM with the up‐to‐date AD including AMC (previous 5‐day cumulative precipitation), LST or DAT, slope, NDVI, and the downscaled satellite SM, the soil moisture estimates are adjusted by also adding an error term. Estimation of the final SM product in terms of the Unbiased Root Mean Square Error of less than 0.04 m3/m3 meets the accuracy requirements of the SMAP SM retrieval. Moreover, the results of SM validation show that the proposed technique has a potential for real‐time SM estimations in developing areas. Key Points: A combined data‐driven geostatistically based model is developed for uncertain downscaling Soil Moisture Active Passive (SMAP) soil moisture (SM) data utilizing ancillary data (AD)Proposing a platform for uncertain SM estimation with 5‐day antecedent moisture condition that utilizes land cover and an error termA remarkable estimation of 1 km downscaled SM data by estimating missing data in 36 km SMAP readings using cokriging and AD [ABSTRACT FROM AUTHOR]

Predictions of hydrologic variables across the entire water cycle have significant value for water resources management as well as downstream applications such as ecosystem and water quality modeling. Recently, purely data‐driven deep learning models like long short‐term memory (LSTM) showed seemingly insurmountable performance in modeling rainfall runoff and other geoscientific variables, yet they cannot predict untrained physical variables and remain challenging to interpret. Here, we show that differentiable, learnable, process‐based models (called δ models here) can approach the performance level of LSTM for the intensively observed variable (streamflow) with regionalized parameterization. We use a simple hydrologic model HBV as the backbone and use embedded neural networks, which can only be trained in a differentiable programming framework, to parameterize, enhance, or replace the process‐based model's modules. Without using an ensemble or post‐processor, δ models can obtain a median Nash‐Sutcliffe efficiency of 0.732 for 671 basins across the USA for the Daymet forcing data set, compared to 0.748 from a state‐of‐the‐art LSTM model with the same setup. For another forcing data set, the difference is even smaller: 0.715 versus 0.722. Meanwhile, the resulting learnable process‐based models can output a full set of untrained variables, for example, soil and groundwater storage, snowpack, evapotranspiration, and baseflow, and can later be constrained by their observations. Both simulated evapotranspiration and fraction of discharge from baseflow agreed decently with alternative estimates. The general framework can work with models with various process complexity and opens up the path for learning physics from big data. Plain Language Summary: Recently, deep neural networks like long short‐term memory (LSTM) have received a lot of attention for producing high‐accuracy simulations in hydrology, but they do not respect physical laws and remain difficult to understand. However, what if you can have a model with similar accuracy, but with clarity about physical processes? What if at the same time the model respects physical laws like mass conservation and produces interpretable outputs (like soil moisture, groundwater storage, evapotranspiration and baseflow), with which you can tell a whole story to stakeholders? What if the same framework allows you to ask precise questions about different parts of hydrology and re‐examine your understanding of some parts of the physical system or check if your past equations are correct? This paper delivers a system that achieves these grand goals and opens many avenues for further exploration. Key Points: Differentiable (δ) hydrologic models show that process clarity and a performance approaching deep learning (DL) can be both attainedUnlike DL, δ models can output untrained physical variables, which agreed well with alternative estimatesDynamic parameterization has a moderate impact but is needed to narrow the gap to DL, suggesting that current model states are inadequate [ABSTRACT FROM AUTHOR]