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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 performance of a filter paper sampling system for monitoring solute transport in unsaturated soils and tuffs was evaluated using numerical simulations. The sampling system consisted of a collection borehole and a sampling assembly designed specifically for the easy removal and replacement of a filter paper pad used to collect solution samples at locations throughout the borehole. Simulations were conducted to approximate an experimental setup at Busted Butte, Nevada, for two hypothetical soil formations: a Calico Hills tuff and a loamy sand. The purpose of the simulations was to evaluate any significant effects the sampling system might have on the prevailing flow and transport processes. The simulation results revealed that the overall disturbance of the system is a superposition of three major components caused by the installation and operation of the sampling device. Two of these components delayed the observed solute breakthroughs and hence increased the mean and variance of solute travel times, whereas the third disturbed the system by increasing the mean solute velocity. The analytical solution of the one-dimensional convective-dispersive equation in the fluid coordinate frame was introduced to approximate the system response at different levels of disturbance for a better understanding of the system dynamics. The general difficulties in interpreting flow and transport properties using field-measured data are illustrated. To better interpret field data, it is important to understand the limitations a measuring device may impose on data analysis. [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]

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]

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]

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

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

The 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]

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 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]

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]

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]