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Global flood mapping has developed rapidly over the past decade, but previous approaches have limited scope, function, and accuracy. These limitations restrict the applicability and fundamental science questions that can be answered with existing model frameworks. Harnessing recently available data and modeling methods, this paper presents a new global ∼30 m resolution Global Flood Map (GFM) with complete coverage of fluvial, pluvial, and coastal perils, for any return period or climate scenario, including accounting for uncertainty. With an extensive compilation of global benchmark case studies—ranging from locally collected event water levels, to national inventories of engineering flood maps—we execute a comprehensive validation of the new GFM. For flood extent comparisons, we demonstrate that the GFM achieves a critical success index of ∼0.75. In the more discriminatory tests of flood water levels, the GFM deviates from observations by ∼0.6 m on average. Results indicating this level of global model fidelity are unprecedented in the literature. With an optimistic scenario of future warming (SSP1‐2.6), we show end‐of‐century global flood hazard (average annual inundation volume) increases are limited to 9% (likely range ‐6%–29%); this is within the likely climatological uncertainty of −8%–12% in the current hazard estimate. In contrast, pessimistic scenario (SSP5‐8.5) hazard changes emerge from the background noise in the 2040s, rising to a 49% (likely range of 7%–109%) increase by 2100. This work verifies the fitness‐for‐purpose of this new‐generation GFM for impact analyses with a variety of beneficial applications across policymaking, planning, and commercial risk assessment. Plain Language Summary: Computer models use a variety of data and physical equations to estimate the extent and depth of possible flood events. Global applications of these tools have been developed over the past decade, but they are not very good at simulating the behavior of real floods. In this paper, we address some key problems to make a global model that does a lot better than past ones. We apply new techniques to better understand how much water we need to put into the model for a given flood probability. This movement of water is simulated by the model over a more accurate map of the Earth's terrain than has been available previously, with river channels represented in a smarter way. We look at the projected changes in rainfall, river discharge, and sea levels for given levels of warming simulated by available climate models and adjust the probabilities of a given magnitude flood accordingly. The model results suggest that the effect of future climate change might be small relative to our ability to understand flood hazards today, but this depends heavily on how much carbon we emit in the coming decades. Key Points: New climate‐conditioned model framework represents fluvial, pluvial, and coastal flood hazards at high‐resolution globallyComprehensive validation studies suggest that the model is approaching local model skill in many casesEmissions reduction can hold flood hazards largely constant this century, though coastal flooding will increase drastically regardless [ABSTRACT FROM AUTHOR]

The UC Berkeley Random Walk Algorithm WaterMask from CYGNSS (Berkeley‐RWAWC) is a new data product designed to address the challenges of monitoring inundation in regions hindered by dense vegetation and cloud cover as is the case in most of the Tropics. The Cyclone Global Navigation Satellite System (CYGNSS) constellation provides data with a higher temporal repeat frequency compared to single‐satellite systems, offering the potential for generating moderate spatial resolution inundation maps with improved temporal resolution while having the capability to penetrate clouds and vegetation. This paper details the development of a computer vision algorithm for inundation mapping over the entire CYGNSS domain (37.4°N–37.4°S). The sole reliance on CYGNSS data sets our method apart in the field, highlighting CYGNSS's indication of water existence. Berkeley‐RWAWC provides monthly, low‐latency inundation maps starting in August 2018 and across the CYGNSS latitude range, with a spatial resolution of 0.01° × 0.01°. Here we present our workflow and parameterization strategy, alongside a comparative analysis with established surface water data sets (SWAMPS, WAD2M) in four regions: the Amazon Basin, the Pantanal, the Sudd, and the Indo‐Gangetic Plain. The comparisons reveal Berkeley‐RWAWC's enhanced capability to detect seasonal variations, demonstrating its usefulness in studying tropical wetland hydrology. We also discuss potential sources of uncertainty and reasons for variations in inundation retrievals. Berkeley‐RWAWC represents a valuable addition to environmental science, offering new insights into tropical wetland dynamics. Plain Language Summary: The UC Berkeley Random Walk Algorithm WaterMask from CYGNSS (Berkeley‐RWAWC) is a new data product developed to better monitor areas that are hard to observe due to thick vegetation and clouds, such as tropical regions. Using data from the Cyclone Global Navigation Satellite System (CYGNSS), an 8‐satellite constellation, Berkeley‐RWAWC has more frequent data collection compared to single‐satellite systems. This allows mapping of flooding or water accumulation with improved accuracy over time, even in clouds‐prone and overgrown areas. Berkeley‐RWAWC spans from 37.4° North to 37.4° South and consists of monthly inundation maps at approximately 1 km by 1 km resolution since August 2018. The method places the greatest emphasis on CYGNSS data indications of where is the water, making it different from others. In this paper, we explain how we made the maps, and compare them with other data sets in four different areas: the Amazon Basin, the Pantanal, the Sudd, and the Indo‐Gangetic Plain. Our comparisons show that Berkeley‐RWAWC is better at showing how water changes with the seasons, which is useful for understanding tropical wetland water cycles. Berkeley‐RWAWC is publicly available and can become an important new resource for studying our planet, especially in the study of patterns in tropical wetlands. Key Points: Vegetation and clouds can obstruct the view of waterbodies, making accurate, seasonal mapping difficultThis new CYGNSS‐based product combines L‐band microwaves with computer vision to produce quasi‐global monthly maps of waterbodiesThe product shows greater seasonal and interannual variability than other data sets for new insights into tropical hydrological processes [ABSTRACT FROM AUTHOR]

Extensive experiments have demonstrated that fluid flow in low‐permeability media deviates from Darcy's law at low pressure gradients, which is called pre‐Darcy flow. Although numerous pre‐Darcy flow models have been proposed, these models generally contain one or more empirical parameters with no clear physical meaning. In this paper, we present a simple unified model to describe pre‐Darcy flow in porous media by introducing the concept of loss permeability. The physical meaning of the loss permeability parameter and its relationship to permeability are analyzed. Based on the statistics of the loss permeability parameter results of 24 core samples, we found that there is a good positive correlation between the loss permeability parameter and the absolute permeability. A smaller loss permeability indicates a stronger fluid‐solid interaction and a stronger nonlinearity between the flow velocity and the pressure gradient. Taking a one‐dimensional linear unsteady flow of a slightly compressible fluid in a homogeneous porous medium as an example, we solve the pressure diffusion equation based on the proposed model using a finite difference method. Our results demonstrate that the rate of pressure propagation for pre‐Darcy flow is slower than that for Darcy flow for the entire observation period, which corrects previous conclusions. This study is highly important for improving the understanding of fluid flow in low‐permeability media. Plain Language Summary: Pre‐Darcy flow usually occurs in low‐permeability media when the pressure gradient is very low. As the pressure gradient increases, the flow gradually transitions from the pre‐Darcy flow to Darcy flow. For a long time, pre‐Darcy flow and Darcy flow were described using two relatively independent equations, which is highly inconvenient. Because it is very difficult to accurately determine the critical pressure gradient between the pre‐Darcy flow and the Darcy flow, especially for the fluid flow in natural low‐permeability reservoirs. How to perfectly unify the pre‐Darcy flow and the Darcy flow in a simple and easy‐to‐use model is a scientific problem that has been researched for decades. Although several unified models have been proposed in recent years, they all contain one or more empirical parameters with no clear physical meaning. In this study, we propose a simple unified model to describe the flow from the pre‐Darcy flow to the Darcy flow in a porous medium by introducing the concept of loss permeability. Then, we discuss the relationship between the loss permeability parameter and permeability based on a series of experimental data from published papers. Finally, we analyzed the pressure propagation feature for a one‐dimensional linear unsteady flow of a slightly compressible fluid in a homogeneous porous medium. Key Points: A simple unified model is proposed to describe the flow from pre‐Darcy flow to Darcy flow by introducing the concept of loss permeabilityThe physical meaning of the loss permeability parameter and its relationship to permeability are analyzedThe pressure propagation characteristics of pre‐Darcy flow and Darcy flow at the field scale are analyzed [ABSTRACT FROM AUTHOR]

Stochastic hydrology produces ensembles of time series that represent plausible future streamflow to simulate and test the operation of water resource systems. A premise of stochastic hydrology is that ensembles should be statistically representative of what may occur in the future. In the past, the application of this premise has involved producing ensembles that are statistically equivalent to the observed or historical streamflow sequence. This requires a number of metrics or statistics that can be used to test statistical similarity. However, with climate change, the past may no longer be representative of the future. Ensembles to test future systems operations should recognize non‐stationarity and include time series representing expected changes. This poses challenges for their testing and validation. In this paper, we suggest an evidence‐based analysis in which streamflow ensembles, whether statistically similar to and representative of the past or a changing future, should be characterized and assessed using an extensive set of statistical metrics. We have assembled a broad set of metrics and applied them to annual streamflow in the Colorado River at Lees Ferry to illustrate the approach. We have also developed a tree‐based classification approach to categorize both ensembles and metrics. This approach provides a way to visualize and interpret differences between streamflow ensembles. The metrics presented, along with the classification, provide an analytical framework for characterizing and assessing the suitability of future streamflow ensembles, recognizing the presence of non‐stationarity. This contributes to better planning in large river basins, such as the Colorado, facing water supply shortages. Plain Language Summary: Long‐range water supply planning in many river basins requires an assessment of ensembles of plausible future streamflow time series used to simulate and test the operation of water resource systems. With climate change, and growing recognition that hydrologic processes are changing over time, the past may no longer be representative of the future. This poses challenges when using statistical metrics to test future streamflow ensembles. In this paper, we suggest an evidence‐based approach in which streamflow ensembles, whether statistically similar to and representative of the past or a changing future, should be characterized using an extensive set of statistical metrics. We have assembled a broad set of metrics and applied them to annual streamflow in the Colorado River at Lees Ferry to illustrate the approach. We have also developed an approach to categorize both ensembles and metrics. The metrics presented and the classification provide an analytical framework for characterizing and assessing the suitability of future streamflow ensembles for water resources system planning. The metrics and classification developed advance and contribute to better planning in large river basins facing water supply shortages. Key Points: Many ensembles representing plausible future streamflow are available for the Colorado River BasinMetrics are presented to provide an evidence‐based framework for evaluating these streamflow ensemblesA classification approach was developed to group similar ensembles and assess their suitability for planning in different future scenarios [ABSTRACT FROM AUTHOR]

Forecasting flood and drought events requires accurate modeling tools. Hydraulic river models are based on estimates of riverbed geometry which are traditionally collected in situ. The novel Ice, Cloud and Land Elevation Satellite 2 [ICESat‐2] lidar altimetry mission with 6 simultaneous high‐resolution laser beams provides the opportunity to define river cross‐section geometries as well as observe water surface elevation [WSE] and water surface slope spatially resolved along the river chainage. This paper describes a method to utilize terrain altimetry and water surface slope estimates to define complete river geometries from ICESat‐2 data products, using the diffusive wave approximation to calculate depth in the submerged section not penetrated by the lidar. Exemplifying the method, cross‐sections are defined for a stretch of the Mekong River. Hydrodynamic model results of the stretch are compared with ICESat‐2 WSE estimates and in situ gauging station time series. Insights in river characteristics from satellite imagery and the ICESat‐2 slope estimates allow for fine‐tuning of the cross‐sections using spatially varying Manning numbers. The final model achieves a root mean square error against the ICESat‐2 WSE of 0.676 m and average Kling‐Gupta Efficiency against gauging station time series of 0.880. The method is limited by the diffusive wave approximation resulting in inaccurate cross‐section estimates in sections with supercritical flow or significant acceleration. Errors can be identified from ICESat‐2 WSE estimates and reduced with additional cross‐sections. Combined with hydrological models, the method will allow for cross‐section definition without in situ data. Plain Language Summary: The depth and width of the river channel are important factors when seeking to predict floods. To predict water level in a river, computer models for flood forecasting must be informed with river channel geometry at cross‐sections along the river. Such cross‐sections are traditionally measured in the field which is expensive and time consuming. This paper describes a method to estimate river cross‐sectional shape from land and water height measurements from the satellite ICESat‐2. When the satellite path crosses the river, the precise laser instrument onboard outlines the surface to indicate the river channel shape, but it does not penetrate the water. We found that it was possible to estimate the shape of the submerged part of the cross‐section using the water surface slope obtained from the satellite's unique instrument. Evaluating the method on a stretch of the Mekong River, we were able to model water level along the river with accuracy sufficient for flood forecasting. In some sections, where the flow speed changes quickly, a hydraulic model with the defined cross‐sections does not reproduce observed water levels. Using satellite crossings from days with steadier flow reduces the error. The method allows for building hydraulic river models without cross‐section surveys. Key Points: Land elevation and water surface slope estimates from the ICESat‐2 lidar altimetry mission are combined to define river cross‐sectionsRMSE of 0.676 m and KGE of 0.88 is achieved for a hydrodynamic model of a stretch of the Mekong River using the derived cross‐sectionsSections with significant flow acceleration lead to inaccurate cross‐sections, but errors are reduced with additional ICESat‐2 crossings [ABSTRACT FROM AUTHOR]

This paper presents a method to analyze and improve the set of equations constituting a rainfall‐runoff model structure based on a combination of a data assimilation algorithm and polynomial updates to the state equations. The method, which we have called "Data Assimilation Informed model Structure Improvement" (DAISI) is generic, modular, and demonstrated with an application to the GR2M model and 201 catchments in South‐East Australia. Our results show that the updated model generated with DAISI generally performed better for all metrics considered included Kling‐Gupta Efficiency, NSE on log transform flow and flow duration curve bias. In addition, the elasticity of modeled runoff to rainfall is higher in the updated model, which suggests that the structural changes could have a significant impact on climate change simulations. Finally, the DAISI diagnostic identified a reduced number of update configurations in the GR2M structure with distinct regional patterns in three sub‐regions of the modeling domain (Western Victoria, central region, and Northern New South Wales). These configurations correspond to specific polynomials of the state variables that could be used to improve equations in a revised model. Several potential improvements of DAISI are proposed including the use of additional observed variables such as actual evapotranspiration to better constrain internal model fluxes. Plain Language Summary: This paper presents a data‐driven method to improve rainfall‐runoff models used to generate future water resources scenario in climate change studies. The method, which we have called "Data Assimilation Informed model Structure Improvement" (DAISI) is generic, modular, and demonstrated with an application to monthly streamflow simulations over a large data set of catchments in South‐East Australia. Our results show that DAISI improves model performance for a wide range of metrics and increases the sensitivity of the model to climate inputs, which is critical in climate change scenarios. Finally, the improvements identified by DAISI take a simple mathematical form with distinct regional patterns in three sub‐regions of the study domain (Western Victoria, central region, and Northern New South Wales). Several improvements of DAISI are discussed including the inclusion of additional observed variables such as evapotranspiration to better constrain model simulations. Key Points: Data Assimilation Informed model Structure Improvement method diagnoses hydrological model structures by combining data assimilation with a polynomial update of state equationsThe method was applied to the GR2M rainfall‐runoff model with significantly improved streamflow simulations in 201 Australian catchmentsThe method identified updates to state equations with marked regional characteristics that could guide future improvement of GR2M [ABSTRACT FROM AUTHOR]

Distribution forecasts P over future quantities or events are routinely made in hydrology but usually traded for a (likelihood‐weighted) mean or median prediction to accommodate error measures or scoring functions such as the mean absolute error or mean squared error. Case in point is the so‐called KG efficiency (KGE) of Gupta et al. (2009, https://doi.org/10.1016/j.jhydrol.2009.08.003) and improvements thereof (Lamontagne et al., 2020, https://doi.org/10.1029/2020wr027101), which have rapidly gained popularity among hydrologists as alternative scoring functions to the commonly used Nash and Sutcliffe (1970, https://doi.org/10.1016/0022‐1694(70)90255‐6) efficiency, but are equally exclusive in how they quantify model performance using only single‐valued output of the quantities of interest. This point‐valued mapping necessarily implies a loss of information about model performance. This paper advocates the use of probabilistic watershed model training, evaluation and diagnostics. Distribution evaluation opens a mature literature on scoring rules whose strong statistical underpinning provides, as we will demonstrate, the theory, context and guidelines necessary for the development of robust information‐theoretically principled metrics for watershed signatures. These so‐called hydrograph functionals are scalar‐valued mappings of major behavioral watershed functions embodied in a strictly proper scoring rule. We discuss past developments that led to the current state‐of‐the‐art of distribution evaluation in hydrology and review scoring rules for dichotomous and categorical events, quantiles (intervals) and density forecasts. We are particularly concerned with elicitable functionals and scoring rule propriety, discuss the decomposition of scoring rules into a sharpness, reliability and entropy term and present diagnostically appealing strictly proper divergence scores of hydrograph functionals for flood frequency analysis, flow duration and recession curves. The usefulness and power of distribution‐based model evaluation and diagnostics by means of scoring rules is demonstrated on simple illustrative problems and discharge distributions simulated with watershed models using random sampling and Bayesian model averaging. The presented theory (a) enables a more complete evaluation of distribution forecasts, (b) offers a statistically principled means for watershed model training, evaluation, diagnostics and selection using hydrograph functionals and/or extreme events and (c) provides a universal framework for metric development of watershed signatures, promoting metric standardization and reproducibility. Plain Language Summary: The past decades have witnessed an unbridled growth in goodness‐of‐fit metrics of hydrologic models. These metrics may satisfy the needs of hydrologists but lack conforming theory and principles. This state of affairs (a) elicits improper model training and evaluation, (b) provokes and supports misguided inferences, (c) impedes statistically‐principled uncertainty quantification, metric standardization and development of universal model benchmarks and (d) obfuscates determination of whether the model has finished learning. What is more, most hydrologic model evaluation metrics in use today are rather exclusive in how they quantify model performance using only single‐valued simulated output of the quantities of interest. Predictive distributions derived from (quasi)‐Bayesian methods or ensembles are usually traded for a (likelihood‐weighted) mean or median prediction to accommodate error measures (scoring functions) such as the mean absolute error. This implies a large loss of information. This paper develops a distribution‐based approach to hydrologic model evaluation and diagnostics. Distribution evaluation opens the necessary theory and guidelines for development of robust information‐theoretically principled metrics of watershed signatures. These so‐called hydrograph functionals are scalar‐valued mappings of major behavioral watershed functions embodied in a strictly proper scoring rule. The hydrograph functionals offer a statistically principled means for hydrologic model evaluation, diagnostics and selection. Key Points: Scoring rules of hydrograph functionals provide an information‐theoretically principled means for watershed model training, evaluation, and diagnosticsWe present strictly proper (divergence) scores for flood frequency analysis, flow duration, and recession curvesPropriety and elicitability offer useful working paradigms for metric development of hydrograph functionals [ABSTRACT FROM AUTHOR]

Widely distributed in natural rivers and coasts, vegetation interacts with fluid flows and sediments in a variable and complicated manner. Such interactions make it difficult to predict associated drag forces during sediment transport. This paper investigates the drag coefficient for an emergent vegetated patch area under nonuniform flow and mobile bed conditions, based on an analytical model solving the momentum equation following our previous work (Zhang et al., 2020, https://doi.org/10.1029/2020WR027613). Emergent vegetation was modeled with rigid cylinders arranged in staggered arrays of different vegetation coverage ∅. Laboratory flume tests were conducted to measure variations in both the water and bed surfaces along a vegetated patch on a sand bed. Based on the experimental and theoretical analyses, a dimensionless drag model integrating both terms of flow properties and bed effects is proposed to predict the drag coefficient Cd over a mobile bed. The calculated values of Cd exhibit two different trends, that is, nonmonotonically or monotonically increasing along the streamwise direction, due to the combined effect of water surface gradient and bed slope. The morphodynamic response of the mobile bed to nonuniform flow manifests as an evolution in the bed slope within the vegetated patch. Ongoing scouring directs the flow's energy toward overcoming the rising Cd and bed slope, leading to a relatively stable stage with a low sediment transport rate. This study advances the existing understanding of the drag coefficient's role over a mobile bed within nonuniform flows. It also enhances the applicability of vegetation drag models in riverine restoration. Plain Language Summary: The drag exerted by vegetation on a riverbed dictates the sediment transport rate with important implications for river morphological evolution. Predicting vegetation drag in nonuniform flow based on the bed characteristics of mobile sand bed conditions poses both theoretical and practical challenges. The implications of this endeavor include the formulation of predictive models for drag and a deeper understanding of the influence of gradually varied flow conditions in rivers. Through both experimental and theoretical investigations, this paper reveals that the drag coefficient exhibits varying patterns along the streamwise direction within the vegetated patch over a mobile sand bed. These patterns manifest in two distinct forms: a steady increase or a parabolic shape, wherein the coefficient initially rises before subsequently decreasing. This contrasts with prior studies on fixed beds, where the drag coefficient consistently follows a parabolic distribution in the streamwise direction. The discrepancy is attributed to the distinct physical contributions of pressure, advection, and bed friction to the drag coefficient. This study provides valuable insights into the importance of flow nonuniformity on vegetation drag, aiding in the prediction of backwater profiles in vegetated flows over a mobile bed. Furthermore, it facilitates modifications to sediment transport within vegetated patches. Key Points: Vegetation drag in nonuniform flow over a mobile sand bed is explored using the momentum equationDrag coefficient in nonuniform flow over a mobile bed exhibits either a parabolic or a monotonic increase along the streamwise directionWater surface gradient and bed slope contribute to the flow nonuniformity, collectively influencing the variability of the drag coefficient [ABSTRACT FROM AUTHOR]

The article titled "Thank You to Our 2023 Reviewers" expresses gratitude to the individuals who reviewed manuscripts for the journal Water Resources Research (WRR) in 2023. The efforts of these reviewers have contributed to the high quality and impact of the journal's papers and the overall research in the field of water science. In total, 2,809 individuals provided 4,453 peer reviews, with 354 individuals reviewing three or more papers. The editors, associate editors, and authors of WRR sincerely thank all the reviewers and anticipate the publication of exciting advances in 2024. [Extracted from the article]

To confront the converging challenges of failing infrastructure, climate change, degraded water quality, and fewer undeveloped lands, many municipalities are establishing stormwater utilities (SWUs) to allocate a dedicated funding source to stormwater management (SWM) services. As a public service, creating a "fair" SWU by defining collective values that reflect community and municipal needs is crucial. However, the prevalent professional discourse surrounding SWUs often conflates "equity" with "economic efficiency" when they are two theoretically separate concepts, which obscure concerns raised by service beneficiaries. This paper unpacks ideas of fairness based on a systematic literature review that frames SWM financing in terms of economic efficiency and SWM services in terms of distributional, procedural, and structural equity. The distinction of which is important to set appropriate expectations between SWUs and beneficiaries. Plain Language Summary: Many municipalities are creating stormwater utilities to fund stormwater management (SWM) services in the face of infrastructure challenges, climate change, and degraded water quality. Creating a "fair" public service utility requires defining collective values that reflect community needs. However, current professional discourse tends to overuse and misuse the term "equity," which can muddle concerns about "fairness" raised by service beneficiaries. This review paper unpacks SWM financing and services in terms of economic efficiency and distributional, procedural, and structural equity. Key Points: The term "equity" is often misused in professional stormwater financing discourse to describe concepts of "economic efficiency"Appropriately defining economic efficiency and equity in stormwater management is crucial to setting public service expectationsDefining fairness in stormwater financing (economic efficiency) and services (equity) for a municipality largely depends on its context [ABSTRACT FROM AUTHOR]

Micro‐continuum models are versatile and powerful approaches for simulating coupled processes in two‐scale porous systems. Initially oriented for modeling static single‐phase flow in microtomography images with sub‐voxel porosity, the concept has been extended over the years to multi‐phase flow, reactive transport, and poromechanics. This paper introduces an integrated micro‐continuum framework to model coupled processes in porous media. It reviews state‐of‐the‐art models and discusses applications in geosciences including Digital Rock Physics with sub‐voxel porosity, moving fluid‐solid interface at the pore‐scale due to geochemical reactions, fracture‐matrix interactions, and solid deformation. Finally, the paper discusses future developments in micro‐continuum models. Key Points: Micro‐continuum models are hybrid‐scale approaches for solving flow and transport in porous mediaState of the art micro‐continuum models handle full Temperature‐Hydrodynamics‐Mechanics‐Chemistry coupling in unsaturated environmentsApplications include Digital Rock Physics with sub‐voxel porosity, pore‐scale reactive transport, and poromechanics [ABSTRACT FROM AUTHOR]

This paper proposes a method for reducing the environmental impact of sewer network (SN) overflows. The main objective of the paper is to minimize the wastewater quantity and the pollutant loads that overflow from the SN. The proposed algorithm to achieve this goal is Model Predictive Control using Particle Swarm Optimization as optimization method. It was tested in simulation using a simplified model of the network based on Benchmark Simulation Modelsewer as prediction model, and a forecasted influent. Three cases have been considered: (a) the fitness function is defined as the global yearly overflow volume calculated using equal weights for each tank; (b) the fitness function uses different weights for each tank depending on the medium loads and (c) integrating a penalty term related to the system state at the end of the prediction horizon in the previous fitness function. The simplified model determined a significant reduction of the integration time minimizing the optimization time. Key Points: Model Predictive Control is used to reduce pollution caused by sewer network (SN) overflowsSimplified SN flow model decreases the optimization time of the control algorithmPollutant loads are assessed by the control algorithm by weighting overflow volumes with values calculated based on offline measurements [ABSTRACT FROM AUTHOR]

The relationship between unfrozen water content and temperature, called as soil freezing characteristic curve (SFCC), is of importance for hydrologic, engineering, environmental issues related to frozen soil. The SFCC of saline soil is essentially a result of phase equilibrium of pore solution, which is similar but not identical to that of bulk solution. However, there is still a vacancy of study on the phase equilibrium of pore solution in frozen soil. In this study, image transformation was used to establish the relationship of phase equilibrium between bulk solution and pore solution, with four introduced parameters. Then, the new model of SFCC for saline soil was proposed based on the equivalent state of bulk solution with Pitzer model and SFCC of nonsaline soil. The model was validated by the experimental data from published articles and showed good performance in calculating SFCC of saline soils regardless of soil type, phase transition path, and soil initial water‐salt condition, and some advantages when compared to other three models. All the four introduced parameters have clear physical meanings and their relationships with soil type and initial salt concentration were discussed. Finally, the evolution of phase diagram from bulk solution to pore solution at icing stage was figured out. Further studies are needed for their relationship at salt crystallization stage. Shifting the research perspective from unfrozen water content to pore solution, this study gives a new approach to research of freezing characteristic of saline soil and could promote hydrological and engineering research in cold regions. Plain Language Summary: The freezing temperature of pure water declines in soil like in solution. There is a reason to believe that the chemical equilibrium of pore solution differs from that of bulk solution. The chemical equilibrium of pore solution, to a certain extent, determines the amount of unfrozen water content, which affects the thermal, hydraulic and mechanical properties of soil. Two phase transition stages have been found in some saline soils with high salt content, which does not appear in nonsaline soils. The relationship of chemical equilibrium between pore solution and bulk solution is the vital interface to apply mature results of chemical characteristics of bulk solution into research of freezing characteristic of saline soils. However, it is still ambiguous for most researchers ignore the soil matrix effect on chemical equilibrium of pore solution. This paper used the transformation of phase diagram from pore solution to bulk solution, sounds like coordinate conversion, to establish their relationship and proposed an intuitive model to calculate the unfrozen water content of saline soil with equivalent state of bulk solution. The performance of the proposed model is good for various saline soils. This study provides an interesting perspective for frozen soil research. Key Points: A soil freezing characteristic curve (SFCC) model for saline soil is proposed with the equivalent bulk solution and SFCC of nonsaline soilGood performance of the new model at predicting SFCC of saline soil both in ice‐eutectic and crystallization‐eutectic pathsThe evolution of phase diagram at icing stage from bulk solution to pore solution is discovered with the proposed model [ABSTRACT FROM AUTHOR]

Water insecurity poses a complex challenge for the western United States. Large populations are exposed and susceptible to physical and social factors that can leave them with precarious access to sufficient water supplies. Consideration of social issues by water managers can help ensure equitable supply. However, how social factors affect water insecurity conditions remains unclear. This paper reviews literature on how social vulnerability influences water insecurity in the western United States. Through a meta‐analysis, indicators measuring how dimensions of social vulnerability influence water insecurity were classified and hierarchical clustering was used to characterize the relationships among these vulnerability dimensions for the largest water‐users—the agricultural and municipal sectors. The study then assessed uncertainty associated with social vulnerability dimensions and their indicators. There is greatest evidence for the influence of demographic characteristics, socioeconomic status, and exposure. Indicators of these determinants were mainly significant and exacerbated conditions of water insecurity. Evidence for indicators of social dependence and special needs populations was limited, although studies assessing these factors showed significant agreement on their influence on water insecurity. Conceptual framings of social vulnerability and water security determined which indicators were measured, whereas studies of the water‐use sectors focused on differing associations of social vulnerability. These findings indicate the importance of recognizing the different contexts posed by water‐use sectors and diverse conceptual framings. Further, some determinants such as living conditions remain important but underexplored drivers of a community's experience of water insecurity. Understanding the uncertainty associated with these measures has implications to equitable decision making. Plain Language Summary: Water security ensures sustainable access to adequate quantities of safe water to sustain livelihoods and economic and social well‐being. Rapidly growing populations and changing climatic conditions make this a challenging task for the western United States. This study reviews previous academic literature to identify how they measured socioeconomic factors that impact water access, quality, and quantity for the largest water‐users ‐ the agricultural and municipal sectors ‐ in the western US. Which social factors were measured depended on which water‐use sector was studied, and whether the authors viewed vulnerability and water security as preexisting or emergent conditions. We found most studies measured characteristics of demographics, socioeconomics, or exposure. Characteristics of risk perception or health were studied less often, with less agreement on their relationship to conditions of water security. There are therefore different levels of surety associated with the available measures of social vulnerability to water insecurity. Water resources managers need to be aware of these differing amounts of surety in the measures used to assist their decision making. Our study explores what is known about different indicators of social vulnerability to water security. Further, we provide a framework comparing otherwise disparate research on the complex subject of water insecurity. Key Points: This study determines the influence and uncertainty associated with indicators of social vulnerability to water insecurityThere is substantial uncertainty about the strength and/or direction of many indicator relationships to water insecurity conditionsIntegrated studies are vital as conceptual framing of social vulnerability and water security define which indicators are measured [ABSTRACT FROM AUTHOR]

This paper introduces a novel methodology that integrates low‐ and high‐frequency pressure monitoring in the parts of the urban water distribution network characterized by the largest number of repairing operations. This integrated approach aims to pinpoint the source and characterize potentially dangerous pressure variations. The actual behavior of a real operational water distribution network is considered as an example to verify the effectiveness of the proposed methodology. The analysis of the acquired pressure signals allows for mapping frequent pressure surges and identifying their source in the functioning conditions of a big user. Notwithstanding these pressure variations are not overly large, according to the literature, their near‐continuous nature poses a risk of inducing pipe breaks, and then leakage, due to fatigue. The proposed method integrating routine low‐frequency measurements with targeted high‐frequency surveys provides a cost‐effective, proactive management strategy. Key Points: A method is proposed for detecting transient source in Urban Water Distribution NetworksLow‐frequency pressure monitoring initially identifies the most pressure‐stressed section in a high‐maintenance areaHigh‐frequency pressure monitoring allows localizing the transient source [ABSTRACT FROM AUTHOR]

The probabilistic prediction of monthly precipitation events, specifically more‐than‐normal (MN), normal (N), and less‐than‐normal (LN) events, holds significant importance in the field of water resource management. In this paper, we employ the Bayesian Joint Probability (BJP) modeling approach to generate monthly probabilistic forecasts for LN, N, and MN events in Northwest Iran. To model the joint distribution of observed precipitation and model precipitation, we propose the utilization of a log‐sinh transformed multivariate normal distribution. The prior distribution for the parameters of this multivariate normal distribution is established as the normal‐inverse‐Wishart, which is known to be the conjugate of the multivariate normal distribution. Bayesian inference of model parameters and associated uncertainties is conducted using STAN Markov Chain Monte Carlo (MCMC) sampling. The methodology is applied to the outputs of four North American Multi‐Model Ensemble (NMME) models, namely CanCM4i, CCSM4, NEMO, and NASA, spanning the period from 1982 to 2017 for a 4‐month forecast horizon. The accuracy of predictions is assessed through various statistical measures and graphical methods using a 12‐fold cross‐validation strategy. The results demonstrate that (a) BJP probabilistic predictions of precipitation consistently outperform raw predictions across all models, months, lead times. (b) The BJP‐NASA model exhibits superior discrimination and reliability compared to other models. (c) May and November show the least amount of discrimination. This observation may be attributed to the fact that these months serve as transitional periods in Iran. (d) Fall forecasts are the most reliable, while summer forecasts are the least reliable. Key Points: Bayesian joint probability (BJP) probabilistic predictions of precipitation have more Ranked Probability Skill Score (RPSS) than raw predictions for all models, months, and lead times, and generally outperform the raw predictionsThe BJP‐NASA models have better discrimination and higher reliability compared to othersSpring and fall have lower discrimination compared to summer and winter. On the other hand, summer is less reliable, and the fall is the most [ABSTRACT FROM AUTHOR]

It is challenging for decision‐makers (DMs) to deal with uncertainties in multi‐level agricultural water resource systems, where DMs independently make decisions but have different levels of power. In this paper, we model the multi‐level agricultural water resources system under deep uncertainties as a Stackelberg game, use multi‐level programming to solve equilibrium water allocation problems, and introduce robustness metrics into multi‐level programming to balance solution feasibility and model optimality within uncertain environments. The approach is applied to a shallow groundwater area with three decision levels, pursuing, from the top level to the bottom one, high food production, fair water allocation, and increased economic benefit. The model generated a series of optimal equilibrium solutions with different robustness degrees. DMs can choose "rational" solutions according to their acceptable costs, oriented robustness degree, expected objective values, and advance risk assessment of uncertainties. Among these solutions, we capture a critical point with high objective values and strong robustness, where DMs can accomplish both objective optimality and solution robustness with a low cost. The proposed approach in this study provides a posterior decision support to consider solution robustness while designing policies in multi‐level agricultural water resource systems under deep uncertainties. Key Points: An introduction of robust measure into multi‐level programming balances solution robustness and model optimalityThere is a critical point that decision makers can achieve high objective values and strong robustness with low costs [ABSTRACT FROM AUTHOR]

During rest periods, bedload tracers can be buried, while transport can move them to locations with different bed shear stresses or a different riverbed composition. This affects the mobility of the tracers compared to that of the bedload at the location where the tracers were seeded and has so far limited the explanatory power of field tracer studies on the virtual bedload velocity. This paper proposes a method to assess both the unsteady virtual tracer velocity and the unsteady virtual bedload velocity from field tracer studies. First, the virtual bedload velocity was conceptualized as the velocity of a relay run and contrasted with the velocity of the decelerating runs of bedload tracers. Then, a regression method for deriving the unsteady virtual velocity of bedload tracers was extended to account for tracer slowdown by including a corresponding function of the distance traveled. Finally, data from 65 bedload tracers in the Upper Drava River with very‐high‐frequency (VHF) transmitters were used for method testing. By linking the measured tracer mobility to the hydraulics and bed surface grain size near the seeding location, it was possible to determine the unsteady bedload velocity function as the unsteady tracer velocity function at a travel distance of zero. The tracer travels exhibited increasing slowdown effects with increasing tracer grain size, probably due to the dominant role of advection effects at the study site. The derivation of the bedload velocity ensures comparability to laboratory results and between tracer studies. Key Points: Gaps between laboratory and field are bridged by basing bedload tracer analyses on theories from laboratory and considering slowdownUnderstanding the bedload velocity as that of a relay run as opposed to that of individual tracer runs allowed deriving the bedload velocityThe derived formulas describe well the transport of bedload tracers with measured travel distances reaching more than 30 km [ABSTRACT FROM AUTHOR]

Wildfire substantially alters aquatic ecosystems by inducing moderate to catastrophic physical and chemical changes. However, the relations of environmental and watershed variables that drive those effects are complex. We present a Driver‐Factor‐Stressor‐Effect (DFSE) conceptual framework to assess the current state of the science related to post‐wildfire water‐quality. We reviewed 64 peer‐reviewed papers using the DFSE framework to identify drivers, factors, stressors, and effects associated with each study. A total of five drivers were identified and ranked according to their frequency of occurrence in the literature: atmospheric processes > fire characteristics > ecologic processes and characteristics > land surface characteristics > soil characteristics. Commonly reported stressors include increased nutrients, runoff, and sediment transport. Furthermore, although several different factors have been used at least once to explain water‐quality effects, relatively few factors outside of precipitation and fire characteristics are frequently studied. We identified several gaps indicating the need for long‐term monitoring, multi‐factor studies, consideration of organic contaminants, consideration of groundwater, and inclusion of soil characteristics. This assessment expands on other reviews and meta‐analyses by exploring causal linkages between influential variables and overall effects in post‐wildfire watersheds. Information gathered from our assessment and the framework itself can be used to inform future monitoring plans and as a guide for modeling efforts focused on better understanding specific processes or to mitigate potential risks of post‐wildfire water quality. Plain Language Summary: Wildfires impact the hydrology and water quality of surface waters that drain burned areas. However, the environmental and watershed factors that drive post‐wildfire water quality are not well understood. We conducted a scoping review of peer‐reviewed publications to assess the current state of the science and identify knowledge gaps. Using the data gathered from the review, we developed a conceptual framework to identify factors that most frequently explain post‐wildfire impacts, the resulting stressors (i.e., changes in the system), and the effects associated with the observed stressors. Precipitation and burn severity are most frequently related to post‐wildfire impacts. Factors such as soil characteristics are studied relatively infrequently. Effects include impaired water quality, increased drinking water treatment, and increased streamflow. Our analysis identified several gaps that can be filled by long‐term monitoring, conducting studies in geographic regions affected by wildfire but where little data exists, expanding current observations to better understand shifts in baseflow contributions, and expanding chemical analyses to include organic contaminants. Key Points: Precipitation and burn severity are studied more often than soil and land characteristics to explain post‐wildfire water qualityIncreased streamflow and impaired water quality are the most documented effects of wildfire, though other effects can be importantKnowledge gaps include long‐term monitoring, multi‐factor studies, and consideration of groundwater and organic contaminants [ABSTRACT FROM AUTHOR]

Selecting an appropriate model for a catchment is challenging, and choosing an inappropriate model can yield unreliable results. The Automatic Model Structure Identification (AMSI) method simultaneously calibrates model structural choices and model parameters, which reduces the workload of comparing different models. In this study we benchmark AMSI's capabilities in two ways, using 12 hydro‐climatically diverse Model Parameter Estimation Experiment catchments. First, we calibrate parameter values for 7,488 different model structures and test AMSI's ability to find the best‐performing models in this set. Second, we compare the performance of these 7,488 models and AMSI's selection to the performance of 45 commonly used, structurally more diverse, conceptual models. In both cases, we quantify model accuracy (through the Kling‐Gupta Efficiency) and model adequacy (through various hydrologic signatures). AMSI effectively identifies high‐accuracy models among the 7,488 options, with Kling‐Gupta‐Efficiency scores comparable to the best among the 45 models. However, model adequacy remains poor for the accurate models, regardless of the selection method. In nine of the tested catchments, none of the most accurate models replicate observed signatures with less than 50% errors; in the remaining three catchments, only a handful of models do so. This paper thus provides strong empirical evidence that relying on aggregated efficiency metrics is unlikely to result in hydrologically adequate models, no matter how the models themselves are selected. Nevertheless, AMSI has been shown to effectively search the model hypothesis space it was given. Combined with an improved calibration approach it can therefore offer new ways to address the challenges of model structure selection. Key Points: Automatic Model Structure Identification (AMSI) reliably identifies the best‐performing model structures in a large model hypothesis spaceCommonly used models have no distinct advantage over AMSI‐derived ones, neither in Kling‐Gupta‐Efficiency (KGE) performance nor in signature performanceUsing KGE as objective function showed little value for identifying suitable model structures and therefore for AMSI [ABSTRACT FROM AUTHOR]

Extreme precipitation is increasing the risk of dam breaks and formation occurring debris dams. Accurate prediction of dam‐break wave propagation is critical to disaster emergency management. Intense bed‐load transport by dam‐break floods can result in a dramatic change of topography, which in turn may affect flood propagation. However, only a very few studies have investigated the thin intense bed‐load layer under dam‐break floods. In this paper, a meshless two‐phase mathematical model is utilized to examine the water velocity, sediment velocity and volumetric fraction, and bed‐load transport flux as well as energy dissipation in bed‐load layer. The model is applied to simulate two‐ and three‐dimensional laboratory experiments of dam‐break wave over erodible beds. For the two‐dimensional experiment, the relative root mean square errors in computed water surface are all below 3.60% and those in profiles of bed‐load layer and static bed are mostly below 13.40%. For the three‐dimensional case, the relative error in computed highest water level is lower than 5.9%. Sediment stream‐wise velocity in bed‐load layer follows a power‐law vertical distribution while sediment volumetric fraction decreases linearly upwards. Accordingly, a formulation of the vertical distribution of bed‐load transport flux, contradictory to the parabolic law in existing studies, is proposed. Most of the water mechanical energy transferred to the sediment is dissipated due to the shear stress in the intense bed‐load layer while only a limit part is kept by the sediment grains. Energy dissipation due to sediment shear stress dominates the consumption of total mechanical energy in the two‐phase system. Key Points: The thin intense bed‐load layer transported by dam‐break floods is numerically examined using a meshless two‐phase mathematical modelA power‐law profile of sediment streamwise velocity and a linear one of sediment volumetric fraction in the thin bed‐load layer are proposedA new formulation of the sediment transport flux in the bed‐load layer is proposed [ABSTRACT FROM AUTHOR]

The maximum information entropy production model (MaxEnt), the relative humidity at equilibrium approach (ETRHEQ), and the Surface Flux Equilibrium model (SFE) are three recently developed models to estimate evapotranspiration. Although the connection between ETRHEQ and SFE is evident, no attempts have been made to investigate the congruence, distinctions, or potential complementarity between the two models and MaxEnt. Our mathematical analysis demonstrates that minimizing the vertical variance of RH in ETRHEQ is equivalent to minimizing the dissipation function of energy fluxes in MaxEnt, under the assumption of the same eddy diffusivity of heat and water vapor and with a specific expression for the ratio between the thermal inertia terms for H and LE. The connection between ETRHEQ, SFE, and MaxEnt is independent of Monin‐Obukhov similarity theory (MOST)'s extremum solution, and MOST's extreme solution can be viewed as equivalent to introducing a constant correction factor to account for atmospheric stability. While ETRHEQ and MaxEnt can be united within a single hydrometeorological framework, they diverge in their approaches to modeling evapotranspiration, particularly in how they address the roles of vegetation and land surface heterogeneity. More importantly, the unified framework suggests that turbulence fluxes within the atmospheric boundary layer adhere to the principles of maximum information entropy production. The way in which dissipation, along with its associated entropy production, is established using information entropy theory deviates from traditional thermodynamic entropy formulations. Exploring the connection between thermodynamic and information entropy and developing proper formulations of dissipation for energy fluxes presents an appealing avenue for prospective research. Plain Language Summary: This paper seeks to establish a common theory for explaining the effectiveness of the maximum information entropy production model (MaxEnt), the relative humidity at equilibrium approach (ETRHEQ), and the Surface Flux Equilibrium model (SFE) in estimating evapotranspiration over a wide range of conditions. It uncovers that under reasonable assumptions, ETRHEQ's method in terms of minimizing the vertical variance in relative humidity is equivalent to MaxEnt's approach to minimizing dissipation. The united theory suggests that the movement of air and energy in the lower atmosphere follows the principles of maximum information entropy production, a concept that is a bit different from traditional ideas of entropy in thermodynamics. Looking into how thermodynamic entropy and information entropy are connected and figuring out the right way to calculate dissipation and entropy production for energy flux could lead to new insights in atmospheric science and land‐atmosphere interactions. Key Points: The study presented a pure theorical analysis to unifying MaxEnt, ETRHEQ, and SFE within the same hydrometeorological frameworkMinimizing the vertical variance of RH in ETRHEQ is equivalent to minimizing the dissipation function of energy fluxes in MaxEntThe connection between MaxEnt, ETRHEQ, and SFE is independent of Monin‐Obukhov similarity theory (MOST)'s extremum solution [ABSTRACT FROM AUTHOR]

The soil freezing characteristic curve (SFCC) plays a crucial role in investigating the soil freezing‐thawing process. Due to the challenges associated with measuring the SFCC, there is a shortage of high‐quality or rigorous test results with sufficient metadata to be effectively used for applications. Current researchers typically conduct freezing tests to measure the SFCC and assume a singular SFCC when studying the freezing‐thawing process of soils, although limited studies indicated that there is a hysteresis during the freezing and thawing process. In this paper, a series of freezing‐thawing tests were performed to assess the SFCC, utilizing a precise nuclear magnetic resonance apparatus. The test results reveal a hysteresis between the SFCC obtained from the freezing process and that from the thawing process. Through analyzing the test results, the hysteresis mechanism of the SFCC is attributed to supercooling. Supercooling inhibits initial pore ice formation during freezing, causing a drastic liquid water‐ice phase change once supercooling ends. Despite being considered closely related, the hysteresis of the SFCC differs from the soil water characteristic curve (SWCC), and the models used to simulate the hysteresis of SWCC cannot directly be used. To address the impact of supercooling on soil freezing‐thawing hysteresis, a novel theoretical model is proposed. Comparisons between the measured and predicted results affirm the validity of the proposed model. Plain Language Summary: Understanding the freezing and thawing behavior of soils is critical for construction in cold regions. The soil freezing characteristic curve (SFCC), which describes the relationship between temperature and unfrozen water content, is essential for characterizing soil behavior during freeze‐thaw cycles. However, measuring SFCCs for both freezing and thawing presents significant challenges, often resulting in simplifications and incomplete data in many studies. In this research, we conducted freezing‐thawing tests using precise technology called nuclear magnetic resonance to examine the SFCC. We found a hysteresis between the SFCC during freezing and thawing, primarily attributed to supercooling, where the soil remains liquid below the freezing temperature. Supercooling delays initial ice formation, causing a rapid transition from liquid water to ice once it ceases. Importantly, the SFCC hysteresis differs significantly from the drying‐wetting hysteresis in the soil water characteristic curve. To address this, we propose a novel model considering the impact of supercooling on soil freezing‐thawing hysteresis. The proposed model fits well with the measured data and outperforms existing models. This study introduces a new understanding and a reliable model for soil freezing‐thawing process, contributing to better comprehension of frozen soil phase changes. Key Points: Supercooling is the primary cause of freezing‐thawing hysteresisThe hysteresis of the soil freezing characteristic curve differs substantively from that of the soil water characteristic curveThe proposed model can address the impact of supercooling on soil freezing‐thawing hysteresis [ABSTRACT FROM AUTHOR]

In mountainous watersheds, floodplain sediments are typically characterized by gravel bed layers capped by an overlying soil unit that serves as a hotspot for biogeochemical reactivity. However, the influence of soil biogeochemistry on gravel bed underflow composition remains unclear, especially during hydrological transitions that alter the vertical connectivity between overlaying soils and the underlying gravel bed. This study investigates these dynamics by measuring hydraulic gradients and water compositions over three hydrological years in a typical mountainous, low‐order stream floodplain in the Upper Colorado River Basin. Results indicate that the timing of hydrological conditions strongly influences the vertical exchanges that control water quality. Specifically, during flooding events such as beaver ponding, that induce downward flushing of the soil, anoxic conditions prevalent in the biogeochemically active soil are transferred downstream via gravel bed underflow. Conversely, snowmelt and drought conditions increase oxic conditions in the gravel bed due to diminished hydrological connectivity with the overlying soil. To compare water quality response to hydrological transitions across similar floodplain environments, we propose a conceptual model that quantifies the inundation‐induced flushing of soil porewater to measure solute exchange efficiency with the gravel bed solute convergence efficiency (SCE). This model provides a framework for quantifying biogeochemical processes in hydrological underflow systems, which is critical for water and elemental budgets in these globally important mountainous ecosystems. Plain Language Summary: Mountains are important sources of freshwater for humans and ecosystems. They are however increasingly impacted by climate change. In this paper, we investigate how changes in water availability (droughts, snowmelt, inundations) can cascade into changes in water quality (concentrations of dissolved chemical elements). We show that in mountain valleys, the zone of contact between the soil and the underlying gravel bed aquifer is important for water quality. Chemical elements from the soil can be flushed down into the gravel bed aquifer, then transported by groundwater to the stream. Chemical elements from the soil can also react with chemical elements present in the aquifer. Our research can be used to better predict the water and elemental budgets in these important mountainous systems. Key Points: Soil / gravel bed connectivity in floodplains is important for water qualitySnowmelt and drought both reduce soil / gravel bed connectivityBeaver ponding increases downward flushing to gravel bed unit [ABSTRACT FROM AUTHOR]

New remote sensing technologies, and the meter‐scale geospatial data they create, now allow for detailed urban landscape characterization, thereby advancing grid‐based hydrodynamic models. However, using a uniform fine grid over urban catchments generally result in dense grids and can lead to prohibitive computational costs. Moreover, an inability to see below the water surface and measure river bathymetry in most terrain remote sensing can severely impact local‐scale river hydraulics calculations given the significant volume of water conveyed by the channel. This paper introduces a super grid channel model which allow river channels with any width above that of the grid resolution to be simulated in 1D manner. As an extension of a previous subgrid model, this integration facilitates a seamless transition between subgrid and super grid channels, accommodating situations where channel width may surpass or fall below the grid resolution. The key contribution is the integration of the novel 1D channel representation with a nonuniform structured 2D floodplain hydrodynamic model and then coding this for application on GPU. Compared with the previous pure 2D nonuniform structured approaches, the new model presents an efficient compromise for riverine urban flooding where we are less concerned about fine‐scale details of in‐channel flow. Three tests reveal that the proposed model maintains accuracy but with significantly reduced computational cost. By leveraging GPU architectures, a ∼10× speedup compared to CPU computations is achieved, and a typical 6‐day urban flooding problem (domain size 1.42 km2) at 1 m resolution can be achieved within 10 hr on a single 8 GB GPU. Key Points: A nonuniform structured grid with a super grid river channel model is implemented on GPU for efficient meter‐scale urban flood modelingAs an extension of the subgrid model approach, this integration allows a seamless transition between subgrid and super grid channelsA ∼10× speedup compared to CPU computations is achieved by leveraging GPU architectures [ABSTRACT FROM AUTHOR]

The hydraulic resistance of groynes is an important factor in the determination of design flood water levels on rivers and the assessment of how much these levels are lowered by modifying the groynes. In standard one‐ or two‐dimensional numerical hydrodynamic models for flood risk management, groynes are commonly represented as subgrid features with a local energy loss according to a weir formula. We tested this representation by using a two‐dimensional horizontal mesh at various groyne submergence degrees by comparing the results with those of flume experiments. We also compared the results with simulations using different 2D and 3D approaches on finer grids that incorporate groynes in the bed topography. In one of the two tested 3D models, complete Reynolds‐averaged Navier‐Stokes equations were solved. The second tested 3D model was constructed simpler by assuming hydrostatic pressure distribution in the vertical direction. We employed Delft3D software in construction and execution of all models. One of the 3D models did predict the hydraulic resistance at low submergence better than the standard model, but it slightly underestimated the resistance at higher submergences. Despite differences in flow characteristics, weirs and groynes were found to produce similar flow resistances for the same height and boundary conditions. Simulations of groyne modifications showed that hydraulic resistance decreased nonlinearly with groyne lowering and streamlining. Plain Language Summary: Groynes are used for river training. Their positive effects include riverbank stabilization, improvement of navigability and prevention of ice jams. However, during floods they become submerged and increase the flood water depths by blocking the flow and increasing turbulence. This may lead to severe outcomes. Floods are among the most fatal disasters that affect the globe. Even an increase of flood water depths by some centimeters may cause disastrous outcomes. Engineers generally resort to approximate solutions for adding the effects of groynes into hydraulic flood models for long river reaches. In this paper, we assess the capabilities of these approximate models as well as those of more simplified and more advanced models. Insights were sought to help flood modelers for better prediction of flood water levels. Our study shows that the most widely used groyne resistance model leaves room for further development, despite demonstrated capabilities. Key Points: Characterizing the effect of groynes on river flow in two‐dimensional hydrodynamic models by using weir formulas was evaluatedAlternative methods to implement groynes into two‐ and three‐dimensional river models were proposed, and their reliability was testedThe performances of two of the available modeling options in simulating the groyne modifications were assessed [ABSTRACT FROM AUTHOR]

When less water is available, hydropower turbines are less efficient, or have to stop altogether. This reality is often neglected in recent work on the planning and operations of hydropower systems, despite widespread expected increases in drought intensity, frequency and duration. This paper is the first to integrate variable‐efficiency turbines into a hydropower plant design framework that accounts for design optimization as well as deep uncertainty in climatic and socio‐economic variables. Specifically, this framework focuses on leveraging multi‐objective robust decision making for the financially robust design of run‐of‐river hydropower plants, whose output is highly sensitive to flow variability. Application to five plants in Türkiye challenges two key design assumptions, use of net present value as a design objective and use of identical turbines. Instead, maximizing the benefit‐cost ratio yields plants with better financial viability over a range of plausible futures. They tend to have smaller capacity, and feature a small turbine that is well‐adapted to low‐flow periods. Another key insight is that socio‐economic uncertainties have as much or even more impact on robustness than climate conditions. In fact, these uncertainties have the potential to make many small hydropower projects too risky to build. Our findings are of considerable practical relevance at a time where 140 GW of unexploited small hydropower potential could help power the energy transition. They also highlight the need for similar research in reservoir‐based plants, considering over 3,000 such plants planned or in construction worldwide. Key Points: Traditional approaches to hydropower planning need to be revisited to account for the impact of a variable climate on turbine efficiencyMaximizing the benefit cost ratio rather than the net present value promotes smaller designs, better adapted to a drought‐prone worldSocio‐economic and climatic factors are both crucial to design financial robustness [ABSTRACT FROM AUTHOR]

Previous studies have shown that regional deep learning (DL) models can improve runoff prediction by leveraging large hydrological datasets. However, training a DL regional model using all data without screening may degrade local performance. This study focuses on constructing enhanced local models through the utilization of cross‐basin data. To this end, we propose an approach that employs a novel training strategy to optimize DL model training for specific basins. The approach measures the impact of any one basin's gradient on the loss of the basin of interest, providing insights into the relationships between different basins. The approach was validated using 531 basins from the CAMELS dataset. Results suggest that local performance degradation is a common occurrence in regional models, and imbalanced data are likely to result in a specific pattern dominating the entire regional model. In comparison to a regional model simply trained with all basins, the median Nash‐Sutcliffe efficiency (NSE) for our models is 0.031 higher. In particular, the increase in NSE can exceed 0.2 for some dry basins. Our findings indicate that this novel DL strategy can significantly improve model performance in specific basins using large hydrological datasets, while mitigating local performance loss. Plain Language Summary: In the realm of deep learning, incorporating more data into the training process typically results in a more potent model. Conventionally, large datasets have been employed to train regional models with the intention of predicting rainfall‐runoff processes across all basins. However, such regional models often encounter performance degradation when applied to local basins. This degradation can be attributed to the extraction of overly general features, leading to a loss of specificity. In this paper, our objective is to harness information from a large dataset to establish a more robust local model. We have introduced a method that autonomously learns the similarities in rainfall‐runoff behavior among basins. Subsequently, we utilize this learned similarity to selectively choose data that proves advantageous for training the local model. Our results demonstrate that this strategy can significantly enhance runoff prediction, particularly in arid basins. Key Points: Training a DL regional model using all data without screening may degrade local performanceProper selection of training data is crucial for enhancing DL model training for individual basins, especially arid basinsData from different basins might act as mutual noise during the training process [ABSTRACT FROM AUTHOR]

Non‐Newtonian fluid flow within porous media, exemplified by polymer remediation of contaminated groundwater/aquifer systems, presents complex challenges due to the fluids' complex rheological behavior within 3D tortuous pore structures. This paper introduces a pore‐scale flow simulator based on the OpenFOAM open‐source library, designed to model shear‐thinning flow within porous media. Leveraging this developed solver, extensive pore‐scale flow simulations were conducted on μ‐CT images of various real and synthetic porous media with varying complexity for both power‐law and Cross‐fluid models. We focused on the macroscale‐averaged deviation between bulk viscosity and the in‐situ viscosity, commonly denoted by a shift factor. We provided an in‐depth evaluation of the shift factor's dependency on the fluid's rheological attributes and the rock's pore space complexity. The least‐squares fitted values of the shift factor fell in the range of 1.6–9.5. Notably, the most pronounced shift factor emerged for extreme flow behavior indices. Our findings highlight not just the critical role of rheological parameters, but also demonstrate how the shift factor fluctuates based on tortuosity, characteristic pore length, and the cementation exponent. In particular, less porous/permeable systems with smaller characteristic pore lengths exhibited larger shift factors due to higher variations of shear rate and local viscosity in narrower flow paths. Additionally, the shift factor increased as rock became more tortuous and heterogeneous. The introduced pore‐scale simulation proves instrumental in determining the macroscopic averaged shift factor. This, in consequence, is vital for precise computations of viscosity and pressure drop when dealing with non‐Newtonian fluid flow in porous media. Key Points: The pore‐scale flow simulation allowed an accurate evaluation of macroscopic deviation between bulk viscosity and the in‐situ viscosityThe fluid's rheology exhibited stronger impacts on the dynamics of non‐Newtonian flow in porous media than pore structure descriptorsThe porous medium viscosity decreased by increasing the complexity of the pore space of the rock [ABSTRACT FROM AUTHOR]

Currently, climate change is considered as an important factor affecting nutrient loads introduced through riverine systems into the Baltic Sea. Although the prospect of a large increase in pollution has long seemed very real, it still does not translate into planning of effective remedial actions. One of the factors limiting the development of such activities is the scale of simulations, focusing generally on catchment outlet profiles. To fill this gap and enable a step forward in understanding responses toward future predictions in a higher resolution scale (subcatchment), we assessed nutrient load contribution using calculation profiles localized along a main watercourse and its tributaries. To track spatial and seasonal changes of total nitrogen and phosphorus for the Wełna River (central Poland), we used climate change data and the SWAT model. Having at our disposal a catchment model with a good performance we could follow not only total load changes in particular subcatchments, but also track localization of the pollution sources and their direct impact on load estimations. Our results showed an increase of the loads, especially from the agricultural land use type, up to 34% for TN and 85% for TP, in the most extreme scenario. Moreover, forest areas have been noted as highly reactive to climate change, and through their localisation are able to distinctly alter nutrient outflow. Finally, the contribution of urban areas should be further investigated since the dynamics of nitrogen and phosphorus release from impervious surfaces is noticeably different here than from the other diffuse sources. Plain Language Summary: This paper describes how climate change will affect the amount of nutrients in a small river catchment in the Baltic Sea region. While it is known that climate change can increase nutrient loads, effective actions to prevent them are still lacking. The "big picture" based on a whole catchment is still poor in terms of finding nutrient vulnerable areas. In this research, we looked at a more detailed scale to see where the nutrients are coming from and how they are changing over time. We used computer modeling to show that the amount of nutrients coming from agriculture, forests, and city areas will increase due to climate change. Overall, the amount of nitrogen can raise by 34% and the amount of phosphorus by 85%. Our results can be the basis for making decisions regarding actions aimed at improving the condition of surface waters and counteracting climate change effects. Key Points: Spatio‐temporal trends of nutrient loads have been tracked in a Baltic Sea region catchment under climate change scenariosClimate change suggests a potential load increase from the whole catchment by 34% for total nitrogen and 85% for total phosphorusOutputs from individual nutrient sources could grow by even 187% for total nitrogen and 302% for total phosphorus [ABSTRACT FROM AUTHOR]

For nearly three decades, satellite radar altimetry has provided measurements of the water surface elevation (WSE) of rivers. These observations can be used to calculate the water surface slope (WSS), which is an essential parameter for estimating flow velocity and river discharge. In this study, we calculate a non time‐varying high‐resolution WSS of 11 Polish rivers based on multi‐mission altimetry observations from 11 satellites in the period from 1994 to 2023. The proposed approach is based on a weighted least squares adjustment with an additional Laplace condition and an a priori gradient condition. The processing is divided into river sections not interrupted by dams and reservoirs. After proper determination of the WSE for each river kilometer (bin), the WSS between adjacent bins is calculated. To assess the accuracy of the estimated WSS, it is compared with slopes between gauge stations, which are referenced to a common vertical datum. Such gauge stations are available for 8 studied rivers. The root mean squared error (RMSE) ranges from 4 mm/km to 77 mm/km, with an average of 27 mm/km. However, the mean RMSE decreases to 11 mm/km when the 2 mountain rivers are excluded. The WSS accuracies are also compared with slope data sets based on digital elevation models, ICESat‐2 altimetry, and lidar. For 6 rivers the estimated WSS shows the highest accuracy. The improvement was particularly significant for mountain rivers. The proposed approach allows an accurate, non time‐varying high‐resolution WSS even for small and medium‐sized rivers and can be applied to almost any river worldwide. Plain Language Summary: The Water Surface Slope (WSS) of a river is a measure of how steeply it flows downstream. This value affects the velocity of the water and also the force with which the water erodes the river bed. WSS is calculated by dividing the difference between two water surface elevations (WSE) by the length of the river section between these points. In this paper, we determine the non time‐varying WSS on almost every kilometer of 11 Polish rivers. For this purpose, we used almost 30 years of satellite altimetry measurements, which provide information about the height of the water surface at a given place and time. After filtering and mathematical adjustment of these measurements, we determined the WSE and WSS on almost every kilometer of the studied rivers. We compared our results with the average gradients between neighboring water level gauge stations, and for most rivers we obtained very small errors. Compared to other sources of WSS data, our method showed the highest accuracy. The results presented in this work are the first such accurate and spatially dense WSS information of Polish rivers. Moreover, the proposed method allows the determination of WSS on almost any river in the world. Key Points: High‐resolution non time‐varying water surface slope (WSS) for 11 Polish rivers have been determined using cross‐calibrated multi‐mission altimetry measurementsFor 8 rivers studied, we obtained a mean root mean square error of 27 mm/km, which decreases to 11 mm/km if 2 mountain rivers are excludedFor 6 rivers, the estimated WSS showed the highest accuracy compared to WSS data sets based on digital elevation models, ICESat‐2, or lidar [ABSTRACT FROM AUTHOR]

The local‐inertial approximations of the shallow water equations (SWEs) have been used for flood forecasting at larger spatial scales owing to the improved computational efficiency and similar accuracy compared to the full 2D SWEs. With the availability of high‐resolution elevation data, the complex terrain of urban areas with various small‐scale features is represented well. Even for a local‐inertial model, utilizing such high‐resolution elevation data in flood simulations of urbanized areas increases the computational cost. A subgrid‐based local‐inertial formulation that permits large numerical grid size for computations while preserving the within‐grid topography is proposed to circumvent this. The subgrid topography can be incorporated into the coarse numerical grid computations by estimating the hydraulic properties, namely, volume and face area, based on water surface elevation variations of the associated high‐resolution terrain. The pre‐stored hydraulic properties are then used to dynamically update the hydraulic variables during the execution of the local‐inertial model. Idealized and real‐world test cases were simulated to illustrate the advantages of the proposed model. The proposed subgrid model performs better in capturing flood depth around subgrid‐scale features such as streets, highways, minor canals, etc., than the simple grid‐averaged local‐inertial models of the same grid size. The proposed model is faster than the existing local‐inertial model (e.g., LISFLOOD‐FP) (∼21–34 times) and the full 2D model (e.g., HEC‐RAS 2D) (∼361–660 times) of similar accuracy in the slow‐rising flood applications. Thus, the subgrid local‐inertial model holds promise in real‐time flood inundation forecasting, resolving smaller urban features. Plain Language Summary: Quick and accurate flood forecasting is crucial in highly populated urban areas with several small terrain features like roads, streets, highways, canals, etc. The simulation of flood depth and extent can be made faster by using simplified equations or dividing the domain into larger grids. The equations will be solved over each grid, generating flood depth and flow. However, opting for a large grid will not capture smaller within‐grid terrain elements. Therefore, this paper proposes a novel subgrid‐based local‐inertial model that uses simplified equations and operates at a large grid while still representing the within‐grid terrain. The subgrid‐inertial model simulates flooding around the smaller features of urban areas with less time than finer grid full 2D models. Meanwhile, the existing local‐inertial models operating at large grids often fail to resolve urban terrain features. Thus, using limited computing facilities, the proposed subgrid‐inertial model is highly suited for real‐time riverine flood forecasting in urbanized floodplains. Key Points: A subgrid‐based local‐inertial formulation that makes computations at coarse grids while capturing the within‐grid terrain is proposedIt is faster than a full 2D model and can resolve complex urban terrain (streets, canals, roads) where existing coarse grid models failIt improves mass flux estimation of coarse grids and holds promise in real‐time forecasting of slow‐rising urban floods [ABSTRACT FROM AUTHOR]

Although several laboratory studies on the propagation of bed load pulses were carried out in the last decades, most studies neglect grain‐size‐specific aspects or use invasive measurement techniques. To remedy the situation, we present a novel, time‐efficient and non‐destructive laboratory technique to investigate grain‐size‐specific transport characteristics of bed load pulses. The method consists of a through‐water, high‐resolution image acquisition followed by the application of a supervised color classification algorithm (Gaussian Maximum Likelihood Classification). The analyzed bed load pulse consisted of five different grain size classes of dyed quartz sand and gravel, each having a unique color. The initial experimental bed was uni‐colored and contained the same size fractions as the augmented pulse. Quality assessment based on a confusion matrix approach and basic random sampling showed a high classification performance. By statistically analyzing the temporal and spatial color distribution of the experimental reach, characteristic parameters to describe the propagation behavior were determined. The bed load pulse presented in the application example initially showed strong deviations in the grain‐size‐specific advection and dispersion, and advection proved to be predominant in the transport process. Plain Language Summary: In the present paper we introduce a novel laboratory method to investigate the grain‐size‐specific transport behavior of bed load pulses. A bedload pulse is a sudden increase in sand and gravel moving along the bottom of a river. We supplied a multi‐colored sediment input representing the downstream propagating pulse on a uni‐color experimental bed which contained the same size fractions than the pulse. Here, five different colors, each indicating a specific grain size fraction, were used as input. By automatically detecting the transported grains due to its color, we were able to analyze the grain‐size‐specific spreading and transport characteristics of the pulse. Therefore, we used high‐resolution photographs of our bed surface and applied a classification algorithm termed Gaussian Maximum Likelihood Classification. A quality assessment revealed a high classification accuracy of the used method. During the experiment, coarser fractions initially showed higher transport velocities. The transport process was dominated by a downstream shift (advection) of the plume rather than by the longitudinal spreading (dispersion). Key Points: A novel image‐based, non‐destructive laboratory method to investigate grain‐size‐specific transport of bed load pulses is presentedIn addition to bed load pulse studies, the proposed method has high potential for other applications in sediment researchThe pulse in the application example evolves by a combination of advection and dispersion with a predominant advective component [ABSTRACT FROM AUTHOR]

In this paper, a model is presented for modeling saturated granular‐liquid free‐surface flows, in which the volume‐averaged mixture bulk velocity is employed to derive the balance equations for the mass and momentum of mixture flow. Additionally, an evolution equation of the slip velocity between granular‐and liquid constituents is derived to describe the separation between these constituents. The frictional‐collisional constitutive relation for granular‐constituent is employed to determine the stress due to particles interaction. The governing equations for mixture flows are numerically solved by a finite difference two‐step projection method. The volume of fluid (VOF) method is employed to track the free surface of the mixture flow in the present numerical model. Good agreements between numerical results and experimental data are observed by modeling the dam‐break process of granular‐liquid mixture flow, dam‐break waves over the saturated erodible beds and surge waves induced by submarine landslides along an inclined plane. Furthermore, the difference between the volume‐averaged mixture bulk velocity and mass‐averaged mixture bulk velocity is found to vary as the instinct density ratio of granular‐constituent and liquid‐constituent and the volumetric concentration ns of the granular‐constituent, and the evolution in the slip velocity during the process of the settlement of sediments is numerically analyzed. Plain Language Summary: In this study, we developed a mathematical model for saturated granular‐liquid free‐surface flows, in which the volume‐averaged mixture bulk velocity is employed to describe the balance equations for the mass and momentum of the mixture flow of granular‐liquid flows and the evolution equation of the slip velocity between granular‐constituent and liquid‐constituent is derived to describe the separation between the granular‐constituent and liquid‐constituent. The stress due to the interaction of particles is determined based on the frictional‐collisional constitutive relations. The finite difference two‐step projection method is employed to numerically solve the governing equations and the volume of fluid (VOF) method is employed to track the free surface of the mixture flow in the present numerical model. Good agreements between numerical results and experimental data are observed. Finally, the role of slip velocity on the dynamics of granular‐liquid flows is analyzed. Key Points: A novel mathematical model for saturated granular‐liquid free‐surface flows is presentedGood agreements between the numerical results and experimental data are observedThe evolution of slip velocity plays a pivotal role in the dynamics of granular‐liquid flows [ABSTRACT FROM AUTHOR]

In recent years, as a result of emerging renewable energy markets, several developed regions have already launched Real‐Time Pricing (RTP) strategies for electricity markets. Establishing optimal pump operation for water companies in RTP electricity markets presents a challenging problem. In a RTP market, both positive and negative electricity prices are possible. These negative prices create economically attractive opportunities for Water Distribution System (WDS) to dispatch their energy consumption. On the other hand, excessively high prices may put WDS at risk of supply disruptions and reduced service levels. However, the continuous development of wind power and photovoltaics results in more volatile and unpredictable fluctuations in the price of renewable energy. The risk arising from uncertainty in electricity prices can lead to a significant increase in actual costs. To address this issue, this paper develops an a posteriori random forest (AP‐RF) approach to forecast the probability density function of electricity prices for the next day and provide a risk‐constrained pump scheduling method toward RTP electricity market. The experimental results demonstrate that the developed method effectively addresses the issue of increased costs caused by inaccurate electricity price forecasting. Plain Language Summary: With the emergence of renewable energy markets in recent years, several developed regions have introduced Real‐Time Pricing (RTP) strategies for their electricity markets. This has created a difficult challenge for water companies seeking to establish the optimal pump operation in RTP markets. This study investigates the use of a risk‐constrained optimization scheduling approach for water distribution networks to mitigate the risks associated with inaccurate real‐time electricity price forecasting. Our proposed method is designed to reduce the costs associated with inaccurate electricity price prediction. Key Points: A robust pump scheduling approach toward real‐time electricity price market is developedDeveloping a posteriori random forest algorithm to predict the probability density function of Real‐time electricity priceOptimal scheduling with risk constraints is an effective approach to mitigating the risks associated with inaccurate electricity forecasting [ABSTRACT FROM AUTHOR]

This study proposes a methodology for developing deterioration models and predicting the service lives of vertical assets of urban water systems (i.e., water storage tanks and pumping stations) using regression analysis. The main factors contributing to the deterioration of these assets are analyzed. Simple and multiple linear regression models of average and maximum deterioration are calculated for 22 water storage tanks and 17 wastewater pumping stations. Data on a set of four water storage tanks are used to validate the developed deterioration models. Service life prediction is carried out using the developed models and considering two maximum deterioration levels: the maximum recommended and admissible deterioration levels. Two water storage tanks are further studied to illustrate and discuss the effect of maintenance and rehabilitation interventions on asset service life by comparing the asset deterioration before and after the interventions. Results include simple linear regression models of average and maximum deterioration indices as a function of asset age and multiple linear regression models that incorporate other physical, operational and environmental factors. The results show that simple linear regression models of asset deterioration show a better predictive power than multiple regression models. Despite the higher data variability of multiple regression models, these models allow to include the random process of asset deterioration, through the calculation of the standard deviation. This study also shows that periodic interventions are a preferable maintenance and rehabilitation strategy over major sporadic rehabilitation interventions since it allows to maintain assets in good condition and to extend their service life almost indefinitely. Plain Language Summary: Urban water assets are continuously deteriorating and more investments are necessary to maintain adequate levels of service. However, investment budgets are often limited and appropriate deterioration models and reliably predicted service lives are essential for planning and scheduling maintenance actions. This paper develops deterioration models for water storage tanks and wastewater pumping stations based on the identification and classification of anomalies through visual inspection. Additionally, service lives (i.e., the period from the installation until the asset or its components fulfill the service requirements) were obtained and compared with reference values. Finally, the effect of maintenance actions and rehabilitation interventions on the service life of vertical assets was discussed. In order to maintain a good asset condition and extend its service life quasi‐indefinitely, periodic and well‐established interventions are a preferable maintenance and rehabilitation strategy over major sporadic rehabilitation interventions. Key Points: Physical, operational and environmental factors of asset deterioration are discussed and deterioration models are summarizedPredicted service lives are obtained and the effect of maintenance and rehabilitation interventions on asset service life are analyzedThe methodology for service life prediction can be applied to other water assets [ABSTRACT FROM AUTHOR]

Uncertainty arising from climate change poses a central challenge to the long‐term performance of many engineered water systems. Water supply infrastructure projects can leverage different types of flexibility, in planning, design, or operations, to adapt infrastructure systems in response to climate change over time. Both flexible planning and design enable future capacity expansion if‐and‐when needed, with flexible design proactively incorporating physical design changes that enable retrofits. All three forms of flexibility have not previously been analyzed together to explicitly assess their relative value in mitigating cost and water supply reliability risk. In this paper, we propose a new framework to evaluate combinations of flexible planning, design, and operations. We develop a nested stochastic dynamic optimization approach that jointly optimizes dam development and operating policies under dynamic climate uncertainty. We demonstrate this approach on a reservoir project near Mombasa, Kenya. Our results find that flexible operations have the greatest potential to reduce costs. Flexible design and flexible planning can amplify the value of flexible operations under higher discounting scenarios and when initial infrastructure capacities are undersized. This approach provides insight on the climate change and techno‐economic conditions under which flexible planning, design, and operations can be best leveraged individually or in combination to reduce climate change uncertainty risks in water supply infrastructure projects. Key Points: We develop a framework to compare the value of combinations of flexibility in water supply infrastructure planning, design, and operationsFlexible operations are more effective than flexible planning or design in addressing climate uncertainty in water‐limited environmentsFlexible planning and design are more useful when initial reservoir capacity is low, discounting is high, or the cost of shortages is high [ABSTRACT FROM AUTHOR]

With increasing demands for precise water resource management, there is a growing need for advanced techniques in mapping water bodies. The currently deployed satellites provide complementary data that are either of high spatial or high temporal resolutions. As a result, there is a clear trade‐off between space and time when considering a single data source. For the efficient monitoring of multiple environmental resources, various Earth science applications need data at high spatial and temporal resolutions. To address this need, many data fusion methods have been described in the literature, that rely on combining data snapshots from multiple sources. Traditional methods face limitations due to sensitivity to atmospheric disturbances and other environmental factors, resulting in noise, outliers, and missing data. This paper introduces Hydrological Generative Adversarial Network (Hydro‐GAN), a novel machine learning‐based method that utilizes modified GANs to enhance boundary accuracy when mapping low‐resolution MODIS data to high‐resolution Landsat‐8 images. We propose a new non‐saturating loss function for the Hydro‐GAN generator, which maximizes the log of discriminator probabilities to promote stable updates and aid convergence. By focusing on reducing squared differences between real and synthetic images, our approach enhances training stability and overall performance. We specifically focus on mapping water bodies using MODIS and Landsat‐8 imagery due to their relevance in water resource management tasks. Our experimental results demonstrate the effectiveness of Hydro‐GAN in generating high‐resolution water body maps, outperforming traditional methods in terms of boundary accuracy and overall quality. Plain Language Summary: This study addresses the imperative challenges of water resource management, including coastal zone oversight, detecting sea border shifts due to rising waters, and erosion tracking. Satellite data currently offers a choice between high spatial detail with infrequent updates or lower spatial detail with more frequent updates, presenting a trade‐off between data precision and frequency. To efficiently monitor environmental resources like water bodies, we require data with both high spatial detail and frequent updates. To meet this need, we introduce the Hydrological Generative Adversarial Network, a novel machine learning tool that enhances data clarity, particularly in outlining water bodies. In testing, we employed images from the Moderate Resolution Imaging Spectroradiometer satellite, providing less detailed images, and the Land Remote‐Sensing Satellite, offering highly detailed imagery. In essence, this study enhances water resource management by effectively combining data from multiple sources, even in adverse conditions, potentially advancing environmental protection and management efforts. Key Points: Remote sensing data augmentation for improving the accuracy of environmental assessments can be achieved using adversarial networksHigh spatiotemporal resolution of water bodies data enhances the precision of their areal forecastingShape and areal accuracies play an important role for efficient spatiotemporal data interpolation [ABSTRACT FROM AUTHOR]

Coupled simulations of surface and variably saturated subsurface flow, termed integrated hydrologic models (IHMs), can provide powerful insights into the complex dynamics of watersheds. The system of governing equations solved by an IHM is non‐linear, making them a significant computational burden and challenging to accurately parameterize. Consequently, a large fraction of the IHM studies to date have been "numerical hypothesis testing" studies, but, as parallel computing continues to improve, IHMs are approaching the point where they might also be useful as predictive tools. For this to become reality, the predictive uncertainty of such highly parameterized simulations must be considered. However, uncertainty is seldom considered in the IHM literature, likely due to the long runtimes of the complex simulations. The questions considered herein are how much uncertainty is there in an IHM for a common watershed simulation scenario, and how likely is it that any one realization of a system will give the same relative change as any other due to a perturbation in recharge? A stochastic ensemble of 250 permeability field realizations was used to show that uncertainty in a high‐mountain headwaters systems is dominated by the subsurface. Recharge perturbation scenarios echo these results, but the uncertainty of changes in streamflow or groundwater pressure heads were significantly smaller than the uncertainty in their base‐case values. The main finding is that IHMs do provide confident, predictive estimates of relative changes in watersheds, even when uncertainty in specific simulation outputs may be high. Plain Language Summary: Watershed simulations that consider stream‐groundwater interactions require the specification of lots of parameters to define the model but there is often little data to base those parameters on. Consequently, simulation results have uncertainty and if it is not quantified then any predictions made by these models have no "plus‐or‐minus" factors, giving an illusion of high confidence. The catch is that quantifying uncertainty is computationally expensive, which is why it is seldom done, yet at some point the uncertainty must be explored if these models are to be used for predictions. The work in this paper assesses the variability of predictions for a "typical" watershed simulation under three different rainfall scenarios using an ensemble of 250 random versions of the system. The results show that groundwater has higher variability than surface flows, meaning the latter is simulated with higher confidence. However, an important finding is that the relative changes due to the changes in the rainfall were generally consistent across the ensemble. The main point is that even with limited confidence in what an output will be, the relative change of that output (e.g., a 10% increase in recharge increases streamflow by 10%) can likely be inferred with much greater confidence. Key Points: Ensemble simulations of coupled surface‐subsurface flows in a high mountain watershed were performed using the numerical model ParFlowUncertainty throughout the simulation domain was significant and was dominated by the subsurface domainRelative changes in the surface‐ and groundwater values due to a perturbation had smaller confidence intervals than the base‐case values [ABSTRACT FROM AUTHOR]

In this Reply, we address the concerns of Knoben and Clark (2023, https://doi.org/10.1029/2022WR034294) or KC23 that "the assumptions needed to effectively use difference‐based variance estimation methods are not always met by hourly streamflow records." There should be little doubt that the assumptions of our difference‐based estimator will sometimes be violated in hourly streamflow records but the results from de Oliveira and Vrugt (2022, https://doi.org/10.1029/2022wr032263) and confirmed by the findings in our Reply show that such violations are sporadic enough not to affect much the error variance estimates. Snowmelt as pointed out by KC23 (https://doi.org/10.1029/2022WR034294) may not have received sufficient attention in our paper, yet their 365‐day record is simply not long enough to demonstrate bias of our discharge error variance estimates (and their dependence on flow level). This would require analysis of a much longer, multi‐year, streamflow record of a snowmelt‐driven watershed. The snowmelt catchment analyzed in dOV22 (https://doi.org/10.1029/2022wr032263) did not demonstrate bias in the discharge error variance estimates. We also provide additional clarification on the interpretation of the variance estimates obtained with the nonparametric estimator, and discuss the main issues in the test case presented in Knoben and Clark (2023, https://doi.org/10.1029/2022WR034294)). Key Points: We address the concerns of Knoben and Clark (2023) on the use of the nonparametric estimator with hourly discharge dataWe provide further clarification on the interpretation of the variance estimates obtained with the nonparametric estimator [ABSTRACT FROM AUTHOR]

The intermittent water supply (IWS) of water distribution systems is a common practice caused by the imbalance between available water resources and customers' demands. Transition to the continuous water supply (CWS) is desirable to reduce quality, equity distribution, and pipe integrity issues but is often practically unachievable in the short or medium term. The World Bank funded a laboratory activity at the Water Engineering laboratory of the University of Perugia, Italy, to investigate how remedies can be introduced to mitigate the impacts of IWS and preserve pipe integrity, facilitating the transition from IWS to CWS. The results of the tests with different feeding conditions, downstream outflows, and air valve dimensions and locations are described and discussed in this paper, culminating in typical interventions that can be implemented to reduce, if not completely eliminate, the undesirable consequences. Plain Language Summary: Almost one billion people worldwide are estimated to receive water intermittently, especially in low‐ and middle‐income countries. Intermittent supply has a significant impact on the water quality, equity of water distribution among users, accuracy of the customer meters, and the structural condition of the pipes. Water distribution systems are not usually designed for such an operating approach, and interventions are required to preserve the pipe integrity, particularly in the transition to a continuous water supply. The World Bank funded a laboratory investigation to assess in a controlled environment the causes of the harmful consequences and the remedies that can be introduced to alleviate their impact. This study is of importance not only in preserving existing networks in the transition to a continuous supply situation but also in ensuring that the large sums being invested in new infrastructure will not be wasted. Key Points: Transition from intermittent to continuous water supply of water distribution systems could be unachievableA laboratory investigation of effects and remedies that can be introduced to mitigate the impacts of intermittent water supply is presentedAir release valve design and location and control of the upstream conditions are crucial and must be properly analyzed [ABSTRACT FROM AUTHOR]

This paper focuses on deriving new approximate analytical solutions in wedge‐shaped aquifers. The proposed methodology is applicable to any type of aquifer namely, leaky, confined and unconfined, under both steady state and transient flow conditions. By applying the method of images and separating the flow field into sections using physical arguments, approximate analytical expressions are obtained for the drawdown function, which in contrast to the conventional theory, are applicable to any arbitrary wedge angle. Moreover, the solutions fully observe the boundary conditions, while they preserve the continuity of the drawdown, which can be calculated directly at any point of the flow field. Nevertheless, comparison of the results of the new approximate analytical solutions to numerical ones, has been considered necessary to check their validity. MODFLOW, a well‐known numerical tool is used to calculate the numerical results. The discrepancies between the numerical results and those of the approximate analytical solution are negligible. The main advantages of the proposed methodology are the following: (a) The model needs only finite number of terms compared to conventional analytical and numerical solutions that involve infinite series, (b) The computational load is low, so it can be easily used in conjunction with meta‐heuristic algorithms to solve groundwater resources optimization problems, (c) Stream depletion rate can be calculated rather accurately and (d) The method is applicable to related flow problems. Key Points: New functions for drawdown calculation in wedge‐shaped aquifers are presentedThe proposed functions are suitable to be used in conjuction with meta‐heuristic algorithms to solve groundwater optimization problemsConvergence of the results obtained from the set of functions and numerical methods (MODFLOW) point out the validity of the proposed solutions [ABSTRACT FROM AUTHOR]

Stochastic methods have been typically used for the design and operations of hydraulic infrastructure. They allow decision makers to evaluate existing or new infrastructure under different possible scenarios, giving them the flexibility and tools needed in decision making. In this paper, we present a novel stochastic streamflow simulation approach able to replicate both temporal and spatial dependencies from the original data in a multi‐site basin context. The proposed model is a multi‐site extension of the modified Fractional Gaussian Noise (mFGN) model which is well‐known to be efficient to maintain periodic correlation for several time lags, but presents shortcomings in preserving the spatial correlation. Our method, called Weighted‐mFGN (WmFGN), incorporates spatial dependency into streamflows simulated with mFGN by relying on the Cholesky decomposition of the spatial correlation matrix of the historical streamflow records. As the order in which the decomposition steps are performed (temporal then spatial, or vice‐versa) affects the performance in terms of preserving the temporal and spatial correlation, our method searches for an optimal convex combination of the resulting correlation matrices. The result is a Pareto‐curve that indicates the optimal weights of the convex combination depending on the importance given by the user to spatial and temporal correlations. The model is applied to a number of river basins in Chile, where the results show that the WmFGN approach maintains the qualities of the single‐site mFGN, while significantly improving spatial correlation. Key Points: Weighted modified Fractional Gaussian Noise model improves spatial correlations without significantly sacrificing temporal correlationThe method searches for an optimal convex combination of the spatial and temporal correlation matrices according on the user's priorityOur results in several Chilean basins demonstrate that Weighted‐modified Fractional Gaussian Noise (WmFGN) represents a significant improvement over the mFGN in preserving correlations [ABSTRACT FROM AUTHOR]

Operator choices, both in acquiring the video and data and in processing them, can be a prominent source of error in image‐based velocimetry methods applied to river discharge measurements. The Large Scale Particle Image Velocimetry (LSPIV) is known to be sensitive to the parameters and computation choices set by the user, but no systematic comparisons with discharge references or intercomparisons have been conducted yet to evaluate this operator effect in LSPIV. In this paper, an analysis of a video gauging intercomparison, the Video Globe Challenge 2020, is proposed to evaluate such operator effect. The analysis is based on the gauging reports of the 15 to 23 participants using the Fudaa‐LSPIV software and intents to identify the most sensitive parameters for the eight videos. The analysis highlighted the significant impact of the time interval, the grid points and the filters on the LSPIV discharge measurements. These parameters are often inter‐dependent and should be correctly set together to strongly reduce the discharge errors. Based on the results, several automated tools were proposed to reduce the operator effect. These tools consist of several parameter assistants to automatically set the orthorectification resolution, the grid and the time interval, and of a sequence of systematic and automatic filters to ensure reliable velocity measurements used for discharge estimation. The application of the assisted LSPIV workflow using the proposed tools leads to significant improvements of the discharge measurements with strong reductions of the inter‐participant variability. On the eight videos, the mean interquartile range of the discharge errors is reduced from 17% to 5% and the mean discharge bias is reduced from −9% to 1% with the assisted LSPIV workflow. The remaining inter‐participant variability is mainly due to the user‐defined surface velocity coefficient α. Key Points: Video‐based river discharge measurements are sensitive to both measuring conditions and user‐defined parameters and optionsThe sensitivity of Large Scale Particle Image Velocimetry discharge computations to operator choices is quantified through a video streamgauging intercomparisonProposed automatic settings and spurious velocity filters efficiently reduce discharge biases and inter‐operator variability [ABSTRACT FROM AUTHOR]

Water distribution systems (WDSs) are critical infrastructure used to convey water from sources to consumers. The mathematical framework governing the distribution of flows and heads in extended period simulations of WDSs lends itself to application in a wide range of optimization problems. Applying the classical mixed integer linear programming (MILP) approach to model WDSs hydraulics within an optimization framework can contribute to higher solution accuracy with lower computational effort. However, adapting WDSs models to conform to a MILP formulation has proven challenging because of the intrinsic non‐linearity of system hydraulics and the complexity associated with modeling hydraulic devices that influence the state of the WDS. This paper introduces MILPNet, an adjustable framework for WDSs that can be used to build and solve an extensive array of MILP optimization problems. MILPNet includes constraints that represent the mass balance and energy conservation equations, hydraulic devices, control rules, and status checks. To conform to MILP structure, MILPNet employs piece‐wise linear approximation and integer programming. MILPNet was implemented and tested using Gurobi Python API. Modeling accuracy was shown to be comparable to EPANET, a public domain software for hydraulic modeling, and sensitivity analyses were conducted to examine the impacts of the modeling assumptions on the performance of MILPNet. Additionally, application of the framework was demonstrated using pump scheduling optimization examples in single and rolling horizon scenarios. Our results show that MILPNet can facilitate the construction and solution of optimization problems for a range of applications in WDSs operations. Key Points: A mixed‐integer linear programming framework (MILPNet) for formulating and solving water distribution system optimization problems is presentedMILPNet models system dynamics, hydraulic devices, control rules, and status checks and is flexible to adding more devices and conditionsThe optimization model can be generated from a.INP file and case‐specific objectives and constraints can be specified via Python interface [ABSTRACT FROM AUTHOR]

Whole lake residence time has been associated with various water quality parameters, including harmful algal blooms. Despite observations of spatial variability in commonly measured lake water quality parameters, little attention is given to the spatial variability of residence time in lakes. In this paper we use water age as a surrogate for residence time and we examine its spatial and temporal distribution in 10 bays of varying size in Lake George, New York (USA). Using a validated hydrodynamic model against observations of water temperature and water currents, and using simulated water age, we show that the average residence time in most of the bays is less than 3 days. Timeseries of bay‐average water age shows that it can sharply decrease within 1 day due to a strong wind event. The average spatial distribution is shown to be non‐uniform, with only a small section of the bottom layer of the bays having a substantially greater age, which may be more than 1 week in certain bays. Snapshots of water age transects indicate that strong wind events substantially change the vertical distribution of water age in some bays, even to the extent of inverting the distribution. The substantial decreases of water age in the bays were associated with the shallowing and deepening of the thermocline. Our results highlight how variations in water residence times within lakes could introduce substantial variation in water quality attributes. Whole lake residence times may serve as a poor proxy to understand the dynamics of water masses, especially in large and morphologically complex waterbodies. Key Points: Simulation of water age with a validated hydrodynamic model against temperature and currentsAverage water age (surrogate to residence time) in bays is smaller than 6 daysSpatial distribution of water age within bays is not uniform and strong wind events lead to lower water age within 1 day [ABSTRACT FROM AUTHOR]

Groundwater pumping from wells, together with water uses such as agricultural irrigation have been converting formerly open groundwater basins into closed systems that accumulate total dissolved solids (TDS). This process of anthropogenic basin closure and salinization (ABCSal) would appear to pose a threat to groundwater sustainability that is at least as formidable as groundwater overdraft and contamination from the surface, yet has been little explored. Models of groundwater flow and solute transport herein show that groundwater basin openness itself should be considered a primary determinant of sustainability. Results show that groundwater basin closure is a threshold condition that sets the aquifer system on a path of increasing salinity that can only be halted by opening the basin. Further, the magnitude of groundwater pumping and degree of basin closure significantly influence the spatial distribution of salinity. In open basins, salinity approaches dynamic equilibrium over long‐term conditions. Stratification of higher‐TDS groundwater overlying lower‐TDS groundwater occurs below farmlands whose irrigation‐supplying wells are impacted by irrigation return flow from upstream farmlands, and act to redistribute relatively saline groundwater to the land surface. More intensive pumping leads to groundwater basin closure and more vertically‐oriented groundwater flow toward pumping wells. TDS retainment in the basin and repeated well capture, re‐distribution as irrigation water, and evapoconcentration lead to progressive salinization. Regardless of basin closure status, fresh recharge protects nearby downstream portions of the basin from salinization, indicating that managing or limiting the spread of contaminated groundwater may be achieved via managed aquifer recharge of good quality water. Plain Language Summary: This paper presents hydrologic basin openness, the degree to which inflow of groundwater is balanced by non‐evaporative outflow, as a new criteria for groundwater sustainability. Water use practices such as irrigation and groundwater pumping have in many cases been reducing groundwater basin openness, promoting accumulation of dissolved intrabasin salts. State‐of‐the‐art but simple groundwater models demonstrate the spatiotemporal dynamics of this Anthropogenic Basin Closure and groundwater Salinization (ABCSal) process. Simulations show that significant salinization with total dissolved solids concentration exceeding 1,000 to 6,000 mg/L can occur in large portions of a basin within two to six centuries. Strength of pumping and the degree of basin closure significantly influence spatial extent and organization of zones with different salinities. Structured salinization zones and relatively low salinity levels downstream of fresh recharge areas indicate viable water management strategies (e.g., managed aquifer recharge of good quality water) for coping with ABCSal consequences. However, maintaining sufficient groundwater basin openness is required to avoid ABCSal, necessitating a different paradigm of integrated water resources management with much greater emphasis on subsurface storage of water and more modern and intensive monitoring of the groundwater system state to ensure a sustainable evolution trajectory of both groundwater quantity and quality. Key Points: Groundwater pumping and irrigation cause progressive groundwater salinization that can be halted only by maintaining enough basin opennessGroundwater development strength influences groundwater flow pattern and salt load, and ultimately, the salinization pattern and intensityDistinct zones of different salinity levels establish under open and closed basin status [ABSTRACT FROM AUTHOR]

Future water availability is threatened by changes in both climate and water demand. Water rights with differing priorities are an important foundation of demand‐side tools (e.g., buyback, water pricing, and water market) to improve water use efficiency and reduce water scarcity, especially in highly regulated river systems. This paper assesses the impact of climate change on water yields from carryover storage with dual‐priority (high/low) water rights allocation systems using a simple and rapid analytical method. The method characterizes reservoir inflows using readily available flow characteristics (annual mean and Cv). We evaluate this method against a water resource simulation model in the Goulburn River basin, Australia. In general, our analytical "dual‐priority" Gould‐Dincer model reproduces water allocation estimates from the simulation model. We further demonstrate this method across 12 Australian catchments to investigate the climate change impact on "dual‐priority" water rights yield at the catchment scale. The hydrological projections show decreasing mean annual runoff and increasing annual runoff variability, except for some catchments in northern Australia. Water yield for high‐priority water rights (HPWRs) and low‐priority water rights (LPWRs) decreases for most catchments except for some catchments in northern Australia. South Dandalup in the 2070s (RCP8.5) shows the largest percentage decrease in HPWR and LPWR yield (about −53.53% and −56.81%, respectively). Our results show that changes in mean annual inflow have a more significant influence on water yield of HPWR and LPWR than Cv. Overall, the simple method provides a rapid assessment of water yields with "dual‐priority" water rights which is applicable across multiple sites at regional or even global scale. Key Points: A simple analytical method is used to evaluate the impact of climate change on the yield of "dual‐priority" water rightsWater yield for both high‐ and low‐priority water rights generally decreases under future climate in AustraliaFuture change in mean annual inflow has a more significant influence on water yield than change in annual flow variability [ABSTRACT FROM AUTHOR]

Optimization of spatially consistent parameter fields is believed to increase the robustness of parameter estimation and its transferability to ungauged basins. The current paper extends previous multi‐objective and transferability studies by exploring the value of both multi‐basin and spatial pattern calibration of distributed hydrologic models as compared to single‐basin and single‐objective model calibrations, with respect to tradeoffs, performance and transferability. The mesoscale Hydrological Model (mHM) is used across six large central European basins. Model simulations are evaluated against streamflow observations at the basin outlets and remotely sensed evapotranspiration patterns. Several model validation experiments are performed through combinations of single‐ (temporal evaluation through discharge) and multi‐objective (temporal and spatial evaluation through discharge and spatial evapotranspiration patterns) calibrations with holdout experiments saving alternating basins for model evaluation. The study shows that there are very minimal tradeoffs between spatial and temporal performance objectives and that a joint calibration of multiple basins using multiple objective functions provides the most robust estimations of parameter fields that perform better when transferred to ungauged basins. The study indicates that particularly the multi‐basin calibration approach is key for robust parametrizations, and that the addition of an objective function tailored for matching spatial patterns of ET fields alters the spatial parameter fields while significantly improving the spatial pattern performance without any tradeoffs with discharge performance. In light of model equifinality, the minimal tradeoff between spatial and temporal performance shows that adding spatial pattern evaluation to the traditional temporal evaluation of hydrological models can assist in identifying optimal parameter sets. Plain Language Summary: Hydrological models typically require local observations of river flow to calibrate the models and test their predictive capability. This limits the possibility for predictions in ungauged basins. This study used holdout tests to investigate the robustness of hydrological predictions for ungauged basins. Particularly we investigate how adding more basins and observed spatial patterns of evapotranspiration in the calibration of these models impact this robustness and transferability of model parameters to basins not used for calibration. Results show that transferability and spatial consistency of parameters increase when adding more basins and spatial pattern observations. Key Points: Clear improvements in simulated spatial patterns can be achieved with very limited tradeoff with discharge performanceMulti‐basin and spatial pattern‐based calibration improve parameter realism and transferability to ungauged basins [ABSTRACT FROM AUTHOR]

River networks are not steady blue lines drawn in a map, since they continuously change their shape and extent in response to climatic drivers. Therefore, the flowing length of rivers (L) and the corresponding catchment‐scale streamflow (Qsur) co‐evolve dynamically. This paper analyzes the relationship between the wet channel length and the streamflow of a river basin, formulating a general analytical model that includes the case of temporarily dry outlets. In particular, the framework relaxes the common assumption that when the discharge at the outlet tends to zero the upstream flowing length approaches zero. Different analytical expressions for the L(Qsur) law are derived for the cases of (a) a perennial outlet; (b) a non‐perennial outlet that dries out only when the whole network is dry; and (c) a temporarily dry outlet, that experiences surface flow for less time than other network nodes. In all cases, the shape of the L(Qsur) relationship is controlled by the distribution of the specific subsurface discharge capacity along the network. For temporarily dry outlets, however, the relation between L and Qsur might depend on an unknown shifting factor. Three real‐world examples are presented to demonstrate the flexibility and the robustness of the theory. Our results indicate that the whole shape of the L(Qsur) relation might not be empirically observable if a significant fraction of the network is perennial or some reaches in the network experience surface flow for longer than the discharge gauging station. The study provides a basis for integrating empirical L(Qsur) data gathered in diverse sites. Key Points: Active network length (L) versus catchment discharge (Qsur) relations are extended to temporarily dry outletsAll the L(Qsur) relations represent translations/truncations of the spatial cumulative distribution of the specific subsurface capacityThe L(Qsur) law is not fully visible if the outlet is less persistent than other network nodes (as in a spring‐fed stream drying upward) [ABSTRACT FROM AUTHOR]