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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]

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]