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Despite regularly being criticized, the Impact Factor (IF) remains a widely used journal analysis metric. In the Water Resources category of the Web of Science, a thorough investigation of the reliability of this metric is lacking. This study analyzed the citation records of the top 10 journals from 2002 through 2021 for the last two decades. Important to note, there are inconsistencies in the Web of Science database. Contradictory to many fields, few papers are uncited. However, the Simpson paradox applies. More specifically, on the one hand, the correlation between citations to papers 7 years post publication and 1 or 2 years post publication (which determine the IF) has increased, which supports this metric. On the other hand, many of the most highly cited papers at the time of the IF calculation are lowly cited 5 years afterward. Additionally, most currently very highly cited papers were relatively uncited when they were used in the IF calculation. This raises doubt about the usefulness of this metric. For a limited number of journals, papers from individual nations strongly influence the IF. Also, for a few journals, the IFs are strongly influenced by review papers, but leaving these out of the IF does change the top 3 and 10 of the rankings. The general conclusion is that IFs do contain some valuable information about the journals' citation impact, but that using them to rank journals is questionable practice. Plain Language Summary: Impact Factors (IFs) continue to be very frequently used to evaluate researchers' publication records. They are calculated in a very simplistic manner, and are essentially an attempt to summarize a very complicated issue (publication quality) into one single metric. This paper shows that care must be taken when applying this practice. Not only does the data set contain errors, there are a number of other reasons why these IFs are not reliable indicators of a journal's quality. Key Points: In many cases the calculation of the Impact Factor (IF) by the Web of Science is contradictory with their own dataAnother number of issues have been identified which raise doubt on the IF as a measure of journal qualityA number of recommendations for a better use of the literature databases have been made [ABSTRACT FROM AUTHOR]

Liu et al. (2020, https://doi.org/10.1029/2019WR026292) provide comments on two papers introducing the rescaled Complementary Relationship (CR) of evaporation. Four concerns regarding the rescaled CR were raised and are addressed herein. Specifically, it is shown here that (1) the rescaled CR is not simply an asymmetric advection-aridity model; (2) a discontinuity in the slope of the rescaled CR at the point where evaporation is zero is not problematic because that point represents a true minimum value; (3) a particular formulation of the rescaled CR presented in an earlier paper was not recommended by the present authors, but was simply intended to illustrate that a range of boundary conditions can be accommodated; and (4) while behavior of the model advocated by Liu et al. 2020) can be modified to match the lower limit introduced by the rescaled CR, there is little theoretical justification or practical utility for doing so. Inherent in several of the concerns seems to be a misconception held by the authors of the Comment regarding the nature of the lower limit in the rescaled CR. Once this misconception is addressed, none of the concerns represent a weakness in the rescaled CR concept. [ABSTRACT FROM AUTHOR]

Crago et al. published two papers in Water Resources Research in 2016 and 2018, respectively, which discussed recent generalized complementary evaporation relationships; they recommended that the lower boundary condition of the Generalized Nonlinear Advection-Aridity (GNAA) model proposed by Brutsaert (2015, https://doi.org/10.1002/2015WR017720) should be modified to y = 0 at x = xmin, where xmin-[0,1]. First, it is shown herein that the rescaled complementary relationship (CR) y = X, proposed by Crago et al., does not advance the analysis forward, but that instead the rescaling is tantamount to a reversion to an earlier linear and more traditional asymmetric complementary model, in which the parameter b is a function of xmin. In fact, at xmin = 0, y = X the rescaled formulation becomes physically unrealistic. The second part of this comment deals with the extended quartic form of GNAA model. The nature of the adjustable parameter c in this extended formulation is further explored, and it is shown that while its lower limit is -1, calibration with actual data can result in values in excess of 2. Such calibration allows then the estimation of xmin as a function of c, that is, xmin = f (c). This way, in addition to satisfying the original four boundary conditions, the extended GNAA model also satisfies the boundary condition of y = 0 at x = xmin. The physical variables affecting the nature of the parameter c and its relationship with Epo/Epmax will require further investigation. [ABSTRACT FROM AUTHOR]

The year 2015 marks the 50th anniversary of Water Resources Research (WRR), which was founded in 1965. More than 15,000 papers have been published in WRR since its inception, and these papers have been cited more than 430,000 times. The history of hydrology and the water sciences are also reflected in WRR, which has served as a premier publication outlet and instigator of scientific growth over the last 50 years. The legacy of WRR provides a strong scientific foundation for the hydrology community to rise to the challenges of sustainable water resources management in a future where dramatic environmental change and increasing human population are expected to stress the world's water resources from local to global scales. [ABSTRACT FROM AUTHOR]

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]

A paper has recently been published in Water Resources Research, which explored the influence of smallholder's perceptions regarding water availability on crop choice and water allocation through social‐hydrological modeling. The present paper attempts to discuss and comment on that paper. The authors have developed a social‐hydrological model, behind which some assumptions of the hydrological aspects (including the omission of deep percolation and the complete separation of transpiration and evaporation for the growing season and the off season) are not well justified. Further, a few parameter values seem to be mistakenly presented by the authors. Most importantly, the equation used for calculating the irrigation depth for crop 1 was incorrectly derived, which could lead to apparent biases in the simulated soil moisture, drought memories, and crop fractions. Hence, the key findings of Kuil et al. (2018, https://doi.org/10.1002/2017WR021420) might be associated with large uncertainties and should be interpreted with much caution. Key Points: The present paper attempts to discuss and comment on a paper recently published in Water Resources ResearchSome assumptions of the hydrological aspects within the social‐hydrological model are not well justifiedThe irrigation depth for crop 1 was incorrectly estimated, which could bring significant biases to the simulations [ABSTRACT FROM AUTHOR]

Urban drainage models (UDMs) are often used to manage urban flooding. However, these models generally involve many parameters to represent the underlying complex hydrodynamic processes. This results in significant challenges to achieving effective and robust model calibration especially with frequently limited observations, leading to unreliable model predictions. This paper makes the first attempt at UDM calibration using the Bayesian‐based Ensemble Smoother (ES) method. Three ES variants are considered, that is, the primary ES, the versions with multiple data assimilation (ES‐MDA) and iterative local update (ES‐ILU). Two synthetic cases and one real‐world application with up to 5,236 calibration parameters are tested. Results obtained show that: (a) both ES‐MDA and ES‐ILU can produce effective model calibration with ES‐ILU outperforming ES‐MDA in terms of both accuracy and uncertainty while ES exhibits limited performance; (b) for the real‐world case, both the ES‐MDA and ES‐ILU methods provide better calibration results than the best‐known solution manually obtained, (c) a minimum number of observations are required to enable an overall accurate model calibration (e.g., four and ten more monitoring sites are needed in the two synthetic cases); and (d) the model calibrated using an intense rainfall event is generally robust to make reliable predictions across different rainfall events while the model calibrated using less intense rainfall event does not perform well for more intense rainfall events. It was also found that ubiquitous parameter equifinality significantly hinders unique parameter identification even when overall accurate state estimates are obtained. This should be clearly understood in practical applications. Plain Language Summary: Urban floods have been a serious disaster worldwide. Urban drainage models (UDMs) have been widely used to facilitate flooding prevention and mitigation. However, a challenge associated with the UDMs is that a large number of parameters need to be specified and calibrated. While some optimization‐based or manual calibration methods are available, their efficiency or accuracy are often unsatisfactory. This can lead to unreliable predictions of the rainfall‐runoff process. To this end, this paper proposes the ensemble smoother (ES) methods to calibrate the UDMs. Benefits of these ES methods include the great efficiency, high accuracy and the identification of the uncertainty when calibrating model parameters. These conclusions are based on results of two synthetic cases and one real‐world UDM. Key Points: Ensemble smoothers are promisingly effective and robust methods to calibrate urban drainage modelsUbiquitous parameter equifinality hinders unique parameter identification, which is a concern for practical applicationsEnsemble smoothers are validated on a real‐world case, providing insights into how improved model performance can be achieved [ABSTRACT FROM AUTHOR]

The clarification of freezing in a soil‐water system is critical for assessing the formation of a freezing zone and liquid water flow. The supercooling phenomenon of soil pore solutions has been found during the freezing process, but the mechanism remains poorly understood. In this study, we propose a free energy function of soil‐water systems based on the Classical Nucleation Theory. The analytical solution of the critical nucleation problem of saline soil‐water system is obtained by combining the initial freezing temperature model and Pitzer activity coefficients model in electrolyte solutions. Then, the freezing‐thawing experiments of saline soil with various salt contents were conducted for verifying the analytical solution. The derived boundary nucleation rate is the quantitative solution of the critical condition for the supercooling. The findings suggested that the theory results agreed well with the experiment results. For the salt‐free soil‐water system, the critical maximum radius of supercooling was 7.15 nm. We compared eight classical ice‐water interfacial tension models, and the "Reinhardt & Doye" and "DeMott & Rogers" models showed excellent performance when using the new free energy theoretical framework to predict the crystallization nucleation rate of soil‐water systems. A positive correlation between the boundary nucleation rate and soil‐water potential is detected. According to the influencing factors, the boundary nucleation rate of soil‐water system can be divided into three zones: salt nature control zone (R > 100 μm), salt‐pore mixed control zone (100 μm > R > 100 nm), and pore size control zone (R < 100 nm). Plain Language Summary: Unfrozen water refers to the soil moisture that still exists in the form of liquid water in the soil at negative temperatures, which is critical for determining the material properties of frozen soil. The phenomenon of soil water not being able to freeze as soon as the temperature falls below the freezing temperature is referred to as "supercooling." However, the critical condition of supercooling is still unclear. In this paper, we developed a nucleation model that can be applied to saline soils based on the thermodynamic approach. Subsequently, the critical conditions were verified by freezing‐thawing tests of soil samples with different salt contents. We further optimized the model of this paper by selecting the best model for the intermediate variables. Quantitative relationships between the initiation condition of supercooling and soil properties were established. We also delineated pore size intervals for distinguishing differences in supercooling properties at different pore sizes. The proposed theoretical framework is developed from Classical Nucleation Theory, which may provide a theoretical reference for revealing the freezing‐thawing mechanism of soil‐water systems. Key Points: There is supercooling in the freezing process and not supercooling‐jump phenomenon in the melting processThe boundary nucleation rate is proportional to the soil water potentialThere exists a critical pore size for the non‐saline soil where supercooling‐jump occurs, and the value is at the 10 nm level (7.15 nm) [ABSTRACT FROM AUTHOR]

Ensemble climatological forecasts play a critical part in benchmarking the predictive performance of hydroclimatic forecasts. Accounting for the skewness and censoring characteristics of hydroclimatic variables, ensemble climatological forecasts can be generated by the log, Box‐Cox and log‐sinh transformations, by the combinations of the Bernoulli distribution with the Gaussian, Gamma, log‐normal, generalized extreme value, generalized logistic and Pearson type III distributions and by the non‐parametric resampling, empirical cumulative distribution function and kernel density estimation methods. This paper is concentrated on the reliability of the 12 types of ensemble climatological forecasts. Specifically, mathematical formulations are presented and large‐sample tests are devised to verify the forecast reliability for the Multi‐Source Weighted‐Ensemble Precipitation version 2 across the globe. Climatological forecasts of monthly precipitation over 18,425 grid cells are generated for 30 years under leave‐one‐year‐out cross validation, leading to 6,633,000 (12 × 18425 × 30) sets of ensemble climatological forecasts. The results point out that the reliability of climatological forecasts considerably varies across the 12 methods, particularly in regions with high hydroclimatic variability. One observation is that climatological forecasts tend to deviate from the distributions of observations when there is inadequate flexibility to fit precipitation data. Another observation is that ensemble spreads can be overly wide when there exist overfits of sample‐specific noises in cross validation. Through the tests of global precipitation, the robustness of the log‐sinh transformation and the Bernoulli‐Gamma distribution is highlighted. Overall, the investigations can serve as a guidance on the uses of transformations, distributions and non‐parametric methods in generating climatological forecasts. Plain Language Summary: Ensemble climatological forecasts have been extensively used as the benchmark to evaluate forecast skill. That is, forecasts generated by a certain forecasting model are skillful when they outperform climatological forecasts and otherwise they are not. In practice, ensemble climatological forecasts are generated by different methods, including the log, Box‐Cox and log‐sinh transformations, the combinations of the Bernoulli distribution with the Gaussian, Gamma, log‐normal, generalized extreme value, generalized logistic and Pearson type III distributions and the non‐parametric resampling, empirical cumulative distribution function and kernel density estimation methods. It is important to investigate pros and cons of different types of climatological forecasts. Focusing on the reliability, that is, statistical consistency between forecasts and observations, this paper has devised large‐sample tests of global monthly precipitation. The results show that owing to hydroclimatic variability, different types of climatological forecasts exhibit varying characteristics of reliability. On the one hand, climatological forecasts can deviate from observations when there is inadequate flexibility to fit precipitation data, especially for the Bernoulli‐Gaussian distribution. On the other hand, ensemble spreads can be too wide when there exist overfits of sample‐specific noises in cross validation. Among the 12 methods, the robustness of the log‐sinh transformation and the Bernoulli‐Gamma distribution is highlighted. Key Points: Climatological forecasts can be generated by using data transformations, statistical distributions and non‐parametric methodsThe reliability of climatological forecasts generated by different methods is shown to vary considerably in large‐sample testsThe robustness of log‐sinh transformation and Bernoulli‐Gamma distribution is illustrated through the tests of global precipitation [ABSTRACT FROM AUTHOR]

Despite the considerable social and economic benefits of hydropower dams, they can cause total dissolved gas (TDG) supersaturation downstream of hydropower dams, which can harm fish through gas bubble trauma (GBT) and cause issues with aquatic ecology. This paper systematically reviewed the generation and release mechanism of TDG supersaturation, including three stages of TDG generation and release, to broaden the research ideas of TDG supersaturation and encourage the construction of environmentally friendly dams and other hydropower projects. The factors affecting the generation and release were summarized from the three stages of TDG generation and release, including temperature, momentum difference at the water‐air interface, and effective depth of bubble arrival. The research methods were introduced from four aspects: theoretical methods, field observations, experiments, and numerical simulations. The advantages, disadvantages, and application scopes of different research methods were outlined. Finally, the abatement measures in the three stages of TDG generation and release were introduced from the engineering and non‐engineering perspectives, and the future research directions were pointed out from the four aspects of TDG supersaturation generation and release mechanism, research methods, abatement measures, and management methods, including in‐depth study of whether gas‐liquid transfer through bubble surfaces or free surfaces dominates, consideration of bubble coalescence and rupture to improve the accuracy of numerical methods in TDG supersaturation prediction, the combination of engineering and non‐engineering measures to optimize TDG supersaturation abatement measures and consideration of artificial intelligence technology to improve hydropower project management methods. Plain Language Summary: Generally, the dissolved amount of total dissolved gas (TDG) in water and that in the atmosphere is in balance, and the state at this time is called the phase equilibrium state. When the dissolved gas concentration in water is higher than the equilibrium concentration, TDG supersaturation will occur. TDG supersaturation often occurs in dam flood discharge, and its adverse effect is to harm fish through gas bubble trauma and cause issues with aquatic ecology. This paper systematically reviewed the generation and release mechanism of TDG supersaturation, including three stages of TDG generation and release, to broaden the research ideas of TDG supersaturation and encourage the construction of environmentally friendly dams and other hydropower projects. Key Points: The review covers the generation and release mechanism and abatement measures for total dissolved gas (TDG) supersaturationSummarize the factors affecting TDG supersaturationThe advantages, disadvantages, and application scopes of different research methods are highlighted [ABSTRACT FROM AUTHOR]

Numerical solution of the one‐dimensional Richards equation (RE) accurately partitions precipitation into infiltration and runoff in capillary dominated soils. However, its application sometimes requires significant computational effort and presents reliability challenges. The Green and Ampt (G&A) approach represents a conservative and efficient method to calculate one‐dimensional infiltration but is limited to deep, well drained, uniform, non‐layered soils for a single rainfall event. The original G&A model and subsequent advancements represent wetting fronts as discrete objects, rather than discretizing the model domain in space, yielding a computationally simpler model than the RE. This paper describes an extension of the G&A method into layered soils on a continuous basis that we call Layered Green and Ampt with Redistribution (LGAR). Assumptions employed in the derivation include: uniform soil hydraulic properties within layers, single‐valued capillary head at the interface between soil layers, and no influence of groundwater table on soil moisture. Wetting fronts advance due to the combined effects of gravity and wetting front capillary drive in a layered soil profile. Results of multi‐month continuous LGAR simulations of infiltration using forcing and soil hydraulic data from USDA SCAN sites are compared against infiltration calculated using the HYDRUS‐1D RE solver using standard metrics. The assumptions employed in deriving the LGAR method limit its application to situations where cumulative potential evapotranspiration is greater than cumulative precipitation, typical of arid or semi‐arid conditions. LGAR is a mass‐conservative, computationally efficient, reliable and reasonably accurate method for simulating infiltration over extended time periods compared to the numerical solution of the RE in layered soils in arid and semi‐arid regions. Plain Language Summary: In arid and semi‐arid regions the groundwater table is typically far below the land surface, and the process of infiltration divides rainfall into soil moisture and surface runoff. Previous extensions of the Green and Ampt (1911, https://doi.org/10.1017/s0021859600001441) infiltration method allow accurate calculation of infiltration into a layered soil for short time periods. This paper shows a novel extension that calculates infiltration in layered soils on a continuous basis using a Green and Ampt like method. This development represents an advance because it provides an accurate, mass‐conserving, computationally efficient and reliable method to calculate infiltration in arid and semi‐arid landscapes where stormflow generation is often dominated by layered soil properties. Key Points: The Richards equation (RE) is widely used to simulate infiltration but often presents computational challengesWe developed a Layered Green and Ampt with Redistribution (LGAR) approach that compares well against RE infiltration predictionsThe LGAR method is unconditionally stable with guaranteed mass conservation and is designed for semi‐arid and arid areas [ABSTRACT FROM AUTHOR]

Previously utilized techniques for analysis of flow velocity in ice-controlled zone are confirmed to be valid. Potential sources of error regarding the estimation of energy grade line slope are corrected considering the comments by the discussant. A modified version of the equation for composite roughness calculation originally introduced in the paper is presented. The revised new method then is tested against the other available methods and its accuracy is evaluated. Other assumptions and analyses presented in the paper are also tested and validated. [ABSTRACT FROM AUTHOR]

The turning point in the classic environmental Kuznets curve (EKC) fails to identify the pollution risk transfer effect. Based on the interactions of environmental regulations among local governments, this paper decomposes the relationship between sewage emissions and the economic growth depicted by the pollution risk transfer effect and proposes a spatial hyperbolic model to illustrate this relationship in local and other regions. The results show that the relationship between industrial sewage emissions and economic growth presents an inverted U‐shaped curve in the local region and a U‐shaped curve in other regions. The turning point of the inverted U‐shaped curve in the hyperbola lags behind that in the EKC, indicating that the turning point in the EKC is influenced by the pollution risk transfer effect. The turning point of the inverted U‐shaped curve lags behind that of the U‐shaped curve in the hyperbola, indicating that pollution transfer behavior has become the main choice under environmental regulation. Based on this conclusion, this paper suggests that the central government should optimize the assessment standards of local government achievements and should encourage enterprises to engage in technological innovation using market incentive environmental regulation. Key Points: Expounding on the dual effects of sewage emissions and economic growth driven by technological innovation and pollution risk transferExtending the classical model with a single curve into a hyperbolic model in the spatial dimensionClarifying the internal causality between sewage emissions and economic growth under environmental regulation [ABSTRACT FROM AUTHOR]

Although renewed interest in flow around groundwater circulation wells is a positive development, the multiple and unacknowledged republication of previously published results is not. This is what has happened in a recent paper published in Water Resources Research by Tu et al. (2020, https://doi.org/10.1029/2020WR027529). The paper repeats results that appeared in Ni et al. (2011, https://doi.org/10.1016/j.applthermaleng.2011.04.030) as well as in the lead authors' own earlier work: Tu et al. (2019, https://doi.org/10.1016/j.applthermaleng.2018.09.029). And all three papers fail to acknowledge that the same ideas and closely related solutions were published in this Journal much earlier by Kabala (1993, https://doi.org/10.1029/92WR01820) and Zlotnik and Ledder (1996, https://doi.org/10.1029/95WR03813). Key Points: Tu et al. (2020) conducted a superficial literature review for flow around groundwater circulation well and missed numerous relevant publications, including those that previously published their resultsAll results claimed as novel by Tu et al. (2020) have been published earlier [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]

A new measurement protocol, labeled Acoustic Mapping Velocimetry (AMV), has been successfully tested for in‐situ estimation of bedload transport features in sandy beds. The AMV has proven efficient in using the dune‐tracking method (DTM) for characterizing the bedform geometry and dynamics as well as for estimation of the rates of bedload transport. Given the novelty of the AMV protocol and its extensive reliance on multiple site‐specific assumptions and user‐defined parameters, a comparison of this emerging technique with other three non‐intrusive DTM‐based methods and analytical predictors is attempted in this paper. The comparison highlights that the AMV estimates are within 22% of the estimates with the other non‐intrusive protocols and up to 98% different from analytical predictions. The observed differences are related to the possible sources of uncertainty in the AMV workflows and to the means to reduce their impact on the targeted estimations. Key Points: Demonstrate Acoustic Mapping Velocimetry (AMV) feasibility for in‐situ measurements bedform dynamicsIdentify sources of uncertainty in the AMV workflow subcomponentsAMV characterization of bedform morpho‐dynamics is within 22% of the estimates made with two other non‐intrusive methods [ABSTRACT FROM AUTHOR]

The water planetary boundary (PB) quantifies a safe operating space for human water consumption and is understood as a control‐variable set at a safe distance from a selected threshold (or dangerous level) based on precautionary principles. The water PB is useful for guiding sustainable development, and various multiscale quantitative assessments of water PB have been conducted. To improve our understanding and guide future applications, we conduct a comprehensive review of all studies published since the concept's introduction. We identify a total of 288 relevant papers in the literature and perform a systematic review and meta‐analysis of 28 papers providing 488 data records at various spatial scales. The analyses mainly involve the relevant quantification indicators, quantification methods, comparisons of the water PB with the current state of water consumption at different scales, and the identification of associated problems. Finally, we propose 10 outlooks for important future research. This review indicates that the theoretical water‐PB framework, as an early‐stage research area, has gradually developed, despite the few, imperfect available methods. To address the limitations of previous studies, we propose two improvement frameworks for guiding future research: the further improvement of traditional popular approaches to quantifying the multiscale water PB within a consistent scientific framework and an improved framework for quantifying the water PB at various scales to obtain better‐fitting results for different research purposes and study areas. Based on this review, we point out some important issues that have received little attention, including the quantification of safe distances and the linkages between multiscale water PBs. Key Points: The first systematic review and meta‐analysis on the quantification of water planetary boundary at various spatial scales is developedExisting studies have developed three types of quantification indicators with corresponding quantification methodsWe proposed 10 outlooks and two future improvements with specific guidance for implementation [ABSTRACT FROM AUTHOR]

The Surface Water and Ocean Topography (SWOT) mission will vastly expand measurements of global rivers, providing critical new data sets for both gaged and ungaged basins. SWOT discharge products (available approximately 1 year after launch) will provide discharge for all river that reaches wider than 100 m. In this paper, we describe how SWOT discharge produced and archived by the US and French space agencies will be computed from measurements of river water surface elevation, width, and slope and ancillary data, along with expected discharge accuracy. We present for the first time a complete estimate of the SWOT discharge uncertainty budget, with separate terms for random (standard error) and systematic (bias) uncertainty components in river discharge time series. We expect that discharge uncertainty will be less than 30% for two‐thirds of global reaches and will be dominated by bias. Separate river discharge estimates will combine both SWOT and in situ data; these "gage‐constrained" discharge estimates can be expected to have lower systematic uncertainty. Temporal variations in river discharge time series will be dominated by random error and are expected to be estimated within 15% for nearly all reaches, allowing accurate inference of event flow dynamics globally, including in ungaged basins. We believe this level of accuracy lays the groundwork for SWOT to enable breakthroughs in global hydrologic science. Plain Language Summary: The Surface Water and Ocean Topography (SWOT) satellite mission was launched on 15 December 2022. SWOT is designed to produce estimates of river discharge on many rivers where no in situ discharge measurements are currently available. This paper describes how SWOT discharge estimates will be created, and their expected accuracy. SWOT discharge will be estimated using simple flow laws that combine SWOT measurements of river water elevation above sea level, river width, and river slope, with ancillary data such as river bathymetry. We expect that discharge uncertainty will be less than 30% for two‐thirds of global reaches and will be dominated by a systematic bias. Temporal variations in river discharge time series are expected to be estimated within 15% for nearly all reaches, thus capturing the response of river discharge to rainfall and snowmelt events, including in basins that are currently ungaged, and providing a new capability for scientists to better track the flows of freshwater water through the Earth system. Key Points: The Surface Water and Ocean Topography satellite mission is designed to enable the estimation of discharge for global rivers wider than 100 mWhen unconstrained by in situ data, discharge uncertainty is expected to be <30% for most reaches, and to be dominated by systematic biasWe expect discharge temporal variations to be estimated to within 15% for nearly all reaches globally [ABSTRACT FROM AUTHOR]

Editors of several journals in the field of hydrology met during the Assembly of the International Association of Hydrological Sciences-IAHS (within the Assembly of the International Union of Geodesy and Geophysics-IUGG) in Prague in June 2015. This event was a follow-up of a similar meeting held in July 2013 in Gothenburg (as reported by Blöschl et al. [2014]). These meetings enable the group of editors to review the current status of the journals and the publication process, and share thoughts on future strategies. Journals were represented in the 2015 meeting through their editors, as shown in the list of authors. The main points on fostering innovation and improving impact assessment in journal publications in hydrology are communicated in this joint editorial published in the above journals. [ABSTRACT FROM AUTHOR]

Improving the long‐term energy production performance of geothermal reservoirs can be accomplished by optimizing field development and management plans. Reliable prediction models, however, are needed to evaluate and optimize the performance of the underlying reservoirs under various operation and development strategies. In traditional frameworks, physics‐based simulation models are used to predict the energy production performance of geothermal reservoirs. However, detailed simulation models are not trivial to construct, require a reliable description of the reservoir conditions and properties, and entail high computational complexity. Data‐driven predictive models can offer an efficient alternative for use in optimization workflows. This paper presents an optimization framework for net power generation in geothermal reservoirs using a variant of the recurrent neural network (RNN) as a data‐driven predictive model. The RNN architecture is developed and trained to replace the simulation model for computationally efficient prediction of the objective function and its gradients with respect to the well control variables. The net power generation performance of the field is optimized by automatically adjusting the mass flow rate of production and injection wells over 12 years, using a gradient‐based local search algorithm. Two field‐scale examples are presented to investigate the performance of the developed data‐driven prediction and optimization framework. The prediction and optimization results from the RNN model are evaluated through comparison with the results obtained by using a numerical simulation model of a real geothermal reservoir. Plain Language Summary: The paper proposes the use of recurrent neural network (RNN) architectures for capturing the dynamics of historical well response data as a function of input control variables. A trained RNN is then used as an efficient input‐output dynamical model for optimization of energy recovery from geothermal reservoirs. Results from time‐consuming simulation‐based and fast RNN prediction models are presented and evaluated to compare the optimization strategies of the two approaches, indicating their consistency. The results suggest that RNN can be used as an efficient dynamic prediction tool for decision support and management of geothermal reservoir operations and development. Key Points: An efficient workflow is developed for long‐term optimization of energy recovery from geothermal reservoirs using deep learning‐based proxy models (a specific design of the recurrent neural network (RNN) is used in this paper)Using a limited set simulated data for training the RNN model improves its extrapolation power and leads to reliable long‐term predictionsDuring training RNN captures the dynamics in well response data and uses it to predict long‐term net energy production under different well control settings (e.g., mass flow rates) [ABSTRACT FROM AUTHOR]

Infrastructure deterioration threatens urban water security and the pursuit of Sustainable Development Goal 6 (SDG 6, "water for all"). One billion urban residents worldwide already face intermittent piped water access, making their public water supply unreliable and unequal. Thus, effective investments in water infrastructure improvements are critical. In a recent WRR paper, Jeuland et al. (2023, https://doi.org/10.1029/2022WR033897) evaluate the impacts of a large infrastructure improvement project on various water security metrics and assess the challenges of such an investigation. They apply a rigorous difference‐in‐difference approach to data from a US$275 million investment in upgrades to water supply and sewage networks in the Zarqa governorate in arid Jordan, analyzing its effects on households, businesses, farms, and the water utility. The authors find a range of moderate improvements to water access and re‐use metrics, including a significant reduction in reported shortages. Key water security indicators related to access intermittency, however, such as the supply duration per day and the expenditure on water deliveries by tanker trucks, saw little to no improvement. The findings reveal that the efforts required to overcome the insecurity and inequity of intermittent public water supply systems might be considerably larger than expected. This suggests an urgent need to further enhance both the accuracy of impact evaluation methods and the effectiveness of infrastructure investments to support the pursuit of SDG 6. Plain Language Summary: One billion people in cities worldwide experience frequent interruptions in their tap water supply. Improving unreliable and inefficient public water supply networks is critical to cope with growing water scarcity. How do we know if these improvements are effective? A recent WRR paper by Jeuland et al. (2023, https://doi.org/10.1029/2022WR033897) reveals how difficult it is to determine this. This commentary discusses what their findings mean for efforts to make public water supply reliable and equitable. The authors are only able to confirm moderate benefits for a large‐scale water infrastructure investment. This suggests that the challenge to achieve secure water supply for all is greater than expected. Key Points: This commentary discusses the findings of an impact evaluation study for a large‐scale investment in water infrastructure improvementsThe study finds only limited impacts of the investment regarding public water supply intermittency despite a suitable setting and approachThe study's findings suggest that overcoming public water supply intermittency poses an unexpectedly large challenge [ABSTRACT FROM AUTHOR]

Determining the minimum supercooling temperature (Tsc) of unsaturated saline soils is the key scientific problem in cold‐region agricultural and engineering, and theoretical models with good predictability are urgently needed. This study establishes a framework for pore solution freezing nucleation in unsaturated saline soils, the chemical potential energy balance equation was established by determining phase transition driving force and potential energy barrier for the formation of stable crystal embryos. Finally, the physical model was constructed. By citing 46 sets of data from different researchers, the validity of the prediction results of this paper's model under different soil types and cooling rates is demonstrated, and the microscopic mechanism of supercooling occurring in the pore solution of positive permafrost is elucidated. The analysis found that electrolyte action restricts the free diffusion of molecules and promotes nucleation to occur, so that a maximum value of Tsc exists during the growth of salinity (in NaCl saline soils occurs at a solution concentration of 0.26 mol/L). Too fast cooling rate will lead to a lag in crystallization time and an increase in supercooling. The combined water content determines the Tsc when rc < 1 μm (pore size), and the effect of changes in water content and cooling rate on supercooling is more significant in small pores; the electrolyte concentration determines the Tsc when rc > 1 μm, Tsc‐max from concentration changes will be more significant in large pores. This study can provide theoretical reserves for controlling the freezing process of soils, studying the mechanism of freezing‐thawing hysteresis, and improving the related numerical calculation models. Plain Language Summary: Permafrost and seasonal permafrost cover more than 50% of land area in the world. The engineering problems caused by freezing‐thawing cycles seriously affect engineering safety and people's well‐being in many countries. Therefore, an in‐depth study of the soil freezing‐thawing cycle is necessary. The water in the soil pore space remains sometimes in liquid state when it is below the freezing temperature. This phenomenon is usually referred to as supercooling. There is still a lack of quantitative studies on the minimum temperature that can be reached by this phenomenon. In this paper, a model that can reasonably predict the minimum subcooling temperature of soil is developed based on thermodynamic methods. The model is verified to be of good accuracy by means of measured data from different researchers. Subsequently, the supercooling characteristics of the soil are discussed based on the model and the microscopic mechanism of the gradual development of freezing in the supercooling state is described. This study will help to recognize and control the soil freezing process in engineering and agricultural construction, and serve to protect engineering structures. Key Points: A physical model is proposed to quantify the supercooling degree of unsaturated saline soilsA maximum value of soil minimum supercooling temperature will occur with increasing salt concentrationToo fast cooling rate will lead to a lag in crystallization time and an increase in supercooling [ABSTRACT FROM AUTHOR]

Various multivariate statistical techniques (MST) can provide valuable insights into water quality variability. Despite numerous studies in which these methods have been used, their potential has not been fully exploited. This paper presents an improved approach to better understand the hydrodynamics of karst systems. The integrated application of hierarchical cluster and principal component analysis in combination with factor analysis allowed the construction of an advanced multivariate chemograph. The analytical procedure was applied in a binary karst aquifer known for its complex hydrodynamics and mixing of water with similar hydrochemical composition. In addition, the study area provides access to an integral groundwater flow system (ponor‐cave‐spring) and offers extensive prior hydrogeological knowledge. The approach allowed reduction and discrimination of the main parameters affecting water quality characteristics. Their identification enabled recognition of three predominant recharge components: (a) stored water impact with Cl and electrical conductivity, (b) sinking stream impact with turbidity and bacteria composition and (c) karst aquifer impact with Ca/Mg ratio as principal parameters. The results supported innovative characterization of the dominant processes and isolation of temporal hydrodynamic phases of individual monitoring points within the aquifer system. On this basis, a spatio‐temporal conceptual model was developed and the hydrodynamic behavior of the main springs was revealed. The applied methodology demonstrated to be useful in ascertaining functioning of a complex karst system under flood event conditions. Plain Language Summary: Karst aquifer systems contain important water resources. The quality of karst springs can deteriorate significantly after rain events, but it is difficult to distinguish how water flows and mixes in the subsurface, especially in large and complex systems. Statistical methods are powerful tools for studying these issues, but most common approaches are inadequate in some cases to reveal the origin of the water and its fate. In this paper, we present an approach in which we combined different statistical methods to explain the dynamics of water flow based on the physicochemical and microbiological properties of water. The application of these methods led to the discrimination of parameters most useful for a reliable interpretation of statistical results, such as turbidity, bacteria, Cl, EC, and Ca/Mg, and to the construction of an advanced diagram that we called a multivariate chemograph. This diagram allowed us to see where the water was coming from at any given time to our monitoring points, which allowed us to construct a detailed explanation of water flow dynamics in space and time. Our contribution is important to better predict the fate of contaminants in karst underground and to develop an early warning system for better water supply management. Key Points: A new approach to study and explain hydrodynamics of karst aquifers was developedIt offers an innovative solution to distinguish influential monitoring parametersMultivariate chemographs allowed spatio‐temporal detection of recharge phases [ABSTRACT FROM AUTHOR]

Transient‐based leak/burst detection has received more and more attentions in the operation and management of water distribution networks. However, with the increase in pipe network complexity, it is hard to detect and locate the potential pipe defects and water losses efficiently. Recently, we proposed an efficient leak/burst localization strategy in single pipelines based on the forward and backward transient analysis, and in this work, this effective approach is customized for the more realistic branched (tree‐shaped) pipe networks. Unlike traditional transient‐based methods which rely on tedious optimization procedures, the proposed method uses a mismatch property between forward and backward analyses of transient signals for the pipe burst detection during the water supply process. Compared with other developed methods, this method can directly calculate the defect location, so it is expected to have high efficiency and wide applicability. To fulfill this goal, a framework for burst detection in a tree‐shaped pipe network is developed in this paper. Using the mass conservation and energy relations at the branched junctions, the forward and backward transient analysis is extended from one pipe to another until the burst is found and then located in the network. Both experimental and numerical tests show the effectiveness of the proposed method, and further analysis has shown that the method is also valid for leak detection in both transient and steady conditions, confirming the robustness and practicality of this proposed method. At last, this paper discusses the influence of transient sources and the potential combination of the method with other methods. Key Points: Burst detection in tree‐shape pipe networks using forward and backward transient analysisAn effective and efficient framework for burst identification, and localization based on pipe end pressure transientsSuccessful burst detection in pipe networks under steady and unsteady flow states [ABSTRACT FROM AUTHOR]

Simple models continue to be important for continental‐scale floodwater depth mapping due to the prohibitively expensive cost of calibrating and applying hydrodynamic models. This paper investigates the accuracy of three simple models for floodwater depth estimation from remote sensing derived water extent and/or Digital Elevation Models (DEMs) in semiarid regions. The three models are Height Above Nearest Drainage (HAND; Nobre et al., 2011, https://doi.org/10.1016/j.jhydrol.2011.03.051), Teng Vaze Dutta (TVD; Teng et al., 2013, http://hdl.handle.net/102.100.100/97033?index=1), and Floodwater Depth Estimation Tool (FwDET; Cohen, Brakenridge, et al., 2018, https://doi.org/10.1111/1752-1688.12609). The model accuracy and nature of errors are established using industry's best practice hydrodynamic models as benchmarks in three regions in eastern Australia. The overall results show that FwDET tends to underestimate (by 0.32 m at 50th percentile) while HAND and TVD overestimate floodwater depth for almost all floods (by 0.97 and 0.98 m, respectively). We quantify how switching DEM from 5 m LiDAR to national or global data sets DEM‐H (Gallant et al., 2011, https://ecat.ga.gov.au/geonetwork/srv/eng/catalog.search#/metadata/72759), MERIT (Yamazaki et al., 2019, https://doi.org/10.1029/2019WR024873), or FABDEM (Hawker et al., 2022, https://doi.org/10.1088/1748-9326/ac4d4f) can affect different models differently; and we evaluate model performance against reach geomorphology and magnitude of flood events. The findings emphasize the importance of choosing a model that is fit for the intended application. By describing the applicability, advantages, and limitations of these models, this paper assists practitioners to choose the most appropriate model based on characteristics of their study area, type of problems they try to solve, and data availability. Plain Language Summary: We have assessed the performance of three simple models for estimating floodwater depth by evaluating their accuracy against different Digital Elevation Model inputs, characteristics of study area, and magnitude of flood events. Our results emphasize that selection of an appropriate model depends on the circumstances. This study provides recommendations for future studies that enhance remote sensing‐based flood maps with water depth, making use of the growing number of publicly available remote sensing water extent and digital elevation data sets globally. All the methods have been coded in Python and are freely available at https://github.com/csiro-hydroinformatics/water-depth-estimation. Key Points: We assessed three simple models for estimating floodwater depth by comparing their results with those of hydrodynamic modelsThe model performance was evaluated against reach geomorphology, magnitude of flood events, and different Digital Elevation Model inputsThe results emphasize that a model should be chosen for the intended application. Guidelines provided for possible applications [ABSTRACT FROM AUTHOR]

A previous paper proposed an accurate and efficient matched‐field processing method for leak localization in looped pipe networks based on the known discharge and pressure head (i.e., hydraulic properties) at all internal nodes. However, the computation of these internal hydraulic properties from boundary measurements is an ill‐conditioned inverse problem: for a small perturbation in the boundary measurements there is a huge change in the internal hydraulic properties. We prove and clarify the invalidity of the classic Tikhonov regularization for this ill‐conditioned problem. As an alternative, this paper derives the theoretical upper bounds of noise‐induced errors, based on which a threshold is determined for identifying ill‐conditioned frequencies. The leak localization is implemented by discarding wave information at all ill‐conditioned frequencies. This results in an efficient and robust algorithm for leak localization in noisy environments. Numerical experiments demonstrate that an unknown leak anywhere in looped pipe networks can be accurately localized by the proposed algorithm. Key Points: The invalidity of the classic Tikhonov regularization for ill‐conditioned problems of the previous paper is proved and clarifiedAs an alternative, the theoretical upper bounds of amplified noise are derived, which are used for identifying ill‐conditioned frequenciesAccurate and robust leak localization is realized in noisy environments by discarding wave information at all ill‐conditioned frequencies [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]

In companion paper 1, the SAtellite Water Cycle (SAWC) satellite data set integration approach was presented. SAWC accounts for (1) the closure of the water budget at the sub‐basin scale by (2) using upstream/downstream dependencies. Here, the SAWC database is used to reconstruct a missing water component. The total water storage change (dS) can be reconstructed prior to the Gravity Recovery and Climate Experiment (GRACE) period. In terms of seasonal and interannual variations, SAWC provides long‐term dS estimates that are highly consistent with the GRACE estimate (2002–2015). When compared to the extent of inundation, correlation of anomalies reaches 0.6 over sub‐basins presenting large floodplain. A strong relationship (correlation of 0.7) exists between the monthly dS and the El Niño index. The river discharge R can also be estimated using the SAWC approach during the GRACE period. A scheme was developed to estimate R continuously along the river through the indirect interpolation of discharge station measurements by using satellite estimates of dS, precipitation P, and evaporation E. These R estimates along the river were evaluated with 80 independent measurements and had a correlation of 0.86 and a Kling‐Gupta efficiency index of 0.71. A comprehensive monthly river discharge estimate was proposed for the first time across the Amazon basin over 13 years (2002–2015) at a 0.25° resolution. The SAWC framework is a pure observation tool for integrating a large range of satellite estimates, potentially facilitating the assimilation of such data into land surface models. Plain Language Summary: In companion paper 1, the SAtellite Water Cycle (SAWC) methodology was presented. SAWC accounts for the conservation of the water mass at a regional scale to improve estimates of the water components. Here, our estimates are used to reconstruct a missing water component: the water storage change in the basin was reconstructed from 1980, and a strong relationship was found with the El Niño. On the other hand, we estimated the river discharge at any point along the river using discharge station measurement indirectly interpolating based on other water component estimates. These discharge estimates along the river were evaluated with 80 independent measurements showing good results. These comprehensive monthly river discharge estimates are proposed for the first time across the Amazon basin for a 13‐year period (2002–2015) at 0.25° resolution. The SAWC framework is a pure observation tool for integrating a large range of observations, potentially facilitating the synergy between models and observations. Key Points: Mass‐conserved reconstruction of dS allows to obtain accurate and longer temporal series compatible with climatological studiesRiver discharge reconstruction at 0.25° along the river is for the first time provided from indirect measurements without any models [ABSTRACT FROM AUTHOR]

The popular probability integral transform (PIT) uniform plot presents informative empirical illustrations of five types of ensemble forecasts, that is, reliable, under‐confident, over‐confident, negatively biased and positively biased. This paper has built a novel two‐stage framework upon the PIT uniform plot to quantitatively examine the forecast attributes of bias and reliability. The first stage utilizes the test statistic on bias to examine whether the mean of PIT values is equal to the theoretical mean of standard uniform distribution. Then, the second stage uses the test statistic on reliability to examine whether the mean squared deviation from the theoretical mean is equal to the theoretical variance of standard uniform distribution. Therefore, by using the two‐tailed bootstrap hypothesis testing, the first stage identifies unbiased ensemble forecasts, negatively biased forecasts and positively biased forecasts; the second stage focuses on unbiased ensemble forecasts to furthermore identify reliable forecasts, under‐confident forecasts and over‐confident forecasts. Numerical experiments are devised for the National Centers for Environmental Prediction's Climate Forecast System version 2 ensemble forecasts of global precipitation. The results highlight the existence of various shapes of the PIT uniform plots. Due to extreme values of observed precipitation, the PIT uniform plots in some cases can substantially deviate from the 1:1 line even though the mean and variance of ensemble forecasts are respectively in accordance with the mean and variance of observations. Nevertheless, the two‐stage framework along with the two test statistics serves as a robust tool for the verification of ensemble hydroclimatic forecasts. Plain Language Summary: Ensemble hydroclimatic forecasts provide useful information for water resources planning and management. The probability integral transform (PIT) uniform plot presents informative visualizations of five types of ensemble forecasts, that is, reliable, under‐confident, over‐confident, negatively biased and positively biased. While the use of PIT uniform plot is for individual grid cells, this paper proposes a novel two‐stage framework along with two test statistics for the examinations of forecast bias and reliability at the global scale. Specifically, the test of bias in the first stage identifies unbiased, negatively biased and positively biased ensemble forecasts; and then, the test of reliability in the second stage is applied to unbiased ensemble forecasts so as to identify reliable, under‐confident and over‐confident ensemble forecasts. Numerical experiments are devised to investigate the bias and reliability for ensemble global precipitation forecasts generated by the National Centers for Environmental Prediction's Climate Forecast System version 2. The five types are effectively identified for raw ensemble forecasts and also for post‐processed forecasts generated by the linear scaling, quantile mapping and Gamma‐Gaussian models. The results demonstrate the effectiveness of the two‐stage framework and highlight that forecast post‐processing can play an important part to provide more useful information for decision‐making. Key Points: The probability integral transform uniform plots facilitate the characterization of bias and reliability for five types of ensemble hydroclimatic forecastsA two‐stage framework along with two non‐parametric test statistics is developed to identify the five types of ensemble forecastsThe forecast attributes of bias and reliability are effectively evaluated for the National Centers for Environmental Prediction's Climate Forecast System version 2 ensemble forecasts of global precipitation [ABSTRACT FROM AUTHOR]