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Discusses several research on groundwater. "Methods to Determine Storativity of Infinite Confined Aquifers From a Recovery Test," by Djaouida Chenaf and Robert P. Chapuis; "Implications of Observed and Simulated Ambient Flow in Monitoring Wells," by Alper Elci, Fred J. Molz III and W. R. Waldrop; "Analytical Model for Aquifer Response Incorporating Distributed Stream Leakage," by Garey A. Fox, Paul DuChateau and Deana S. Durnford.

Among several hydrological processes, river flow is an essential parameter that is vital for different water resources engineering activities. Although several methodologies have been adopted over the literature for modeling river flow, the limitation still exists in modeling the river flow time series curve. In this research, a functional quantile autoregressive of order one model was developed to characterize the entire conditional distribution of the river flow time series curve. Based on the functional principal component analysis, the regression parameter function was estimated using a multivariate quantile regression framework. For this purpose, hourly scale river flow collected from three rivers in Australia (Mary River, Lockyer Valley, and Albert River) were used to evaluate the finite‐sample performance of the proposed methodology. A series of Monte‐Carlo experiments and historical data sets were examined at three stations. Further, uncertainty analysis was adopted for the methodology evaluation. Compared with the existing methods, the proposed model provides more robust forecasts for outlying observations, non‐Gaussian and heavy‐tailed error distribution, and heteroskedasticity. Also, the proposed model has the merit of predicting the intervals of future realizations of river flow time series at the central and non‐central locations. The results confirmed the potential for predicting the river flow time series curve with a high level of accuracy in comparison with the benchmark existing functional time series methods. Plain Language Summary: This paper proposes a functional quantile autoregressive model of order one, which is used to predict the entire distribution of the realizations of river flow time series curve. The proposed model allows modeling the conditional quantiles of the response variable as a function of its past values of it. The proposed method for historical river flow curves is an excellent alternative to existing mean regression methods at the 0.5 quantile level (median regression). Also, as an advantage over existing methods, it offers a more thorough explanation of the connection among previous and future realizations of river flow curves at various quantile levels, providing a more extensive understanding of the relationship. Moreover, this feature of the proposed method allows for the effortless generation of pointwise prediction intervals for future realizations of river flow curves. The numerical results obtained by Monte Carlo experiments and empirical data analyses exhibit that, compared with existing methods, the proposed method produces competitive or even better forecasting results. The results also indicate that the future realizations of the river flow measurements are well covered by the prediction intervals constructed by the proposed method. Key Points: Predicting the mean and extreme values of the river flow curve is important for various applications in water resources managementThe FQAR(1) allows predicting the entire distribution of future realizations of the river flow curve as a function of its past values of itNumerical results based on river flow measurements collected from the Australia Continent confirmed the potential of the FQAR(1) [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]

The runoff change in a large river is significant for hydrological event monitoring, such as floods and droughts. In this paper, we attempt to invert the river flow change based on the surface displacements observed by the continuous global navigation satellite system (GNSS) stations in the Wuhan section of the Yangtze River (WYR). We establish a two‐dimensional model accounting for deformation from a line load in an elastic half‐space to estimate the annual water load change from the GNSS data. The results show that the inverted line load from the annual maximum displacement change observed by the GNSS is Ne=2.30×108N·m−1 ${N}_{e}=2.30\times {10}^{8}\mathrm{N}\cdot {\mathrm{m}}^{-1}$, which is in good agreement with the river load from the hydrological station. This paper demonstrates a low‐cost and practical method to river flow research in local areas, which is also a creative application for the GNSS. Plain Language Summary: The runoff change in large rivers is an important element for flood and drought monitoring. Hydrological stations are used to monitor the water load change. Because of the high cost of construction and maintenance, there are few hydrological stations in most areas, and the unpredictable noise of hydrological stations influences the stability and precision of the results. We use the vertical displacements observed by the global navigation satellite system (GNSS) to estimate the river load and find that the results agree with the calculated water load change from hydrological stations. Thus, the GNSS can be an alternative for runoff change research, with low cost and high efficiency. In this study, we find a new application for the GNSS to water runoff change in local areas. Key Points: We propose a new method for estimate the river load change using the continuous global navigation satellite system (GNSS) dataA simple two‐dimensional line load on Elastic half‐space can invert the river load with considerable accuracy in local areasThe forward modeling prove that the modeled displacement agrees with the observed displacement from the GNSS [ABSTRACT FROM AUTHOR]

Sea level rise (SLR) will affect water levels and increase flood risk in river deltas. To adapt river deltas to SLR, various strategies can be followed. Many urbanised river deltas already have flood protection in place. Continuing a protection strategy under an increasing SLR, would mean higher embankments along the coast and rivers and possibly closing off the river mouths from the sea. However, closing of rivers will hamper the river flow. How to adapt river deltas and enabling rivers to discharge into the sea is a challenging question. This paper assesses impacts of SLR on flood risks in the Rhine‐Meuse Delta in the Netherlands in case the current protection strategy is continued and explores two alternative protection strategies: (1) a closed system with pumps and discharge sluices and (2) an open system in which rivers are diverted to less densely populated areas. The second alternative results in a more flexible river delta, which can accommodate larger SLR. The paper shows that a systems approach and using quantitative assessments of the implications of strategies is possible. This is needed to further assess the adaptation options, so we can anticipate and adapt when needed and avoid regret of decisions. [ABSTRACT FROM AUTHOR]

The article provides an overview of a study by P. S. Rand et al. that won the Robert L. Kendall Best Paper in the periodical "Transactions of the American Fisheries Society" in 2007. The researchers examined the potential effects of changing water discharges and warming water temperatures on adult sockeye salmon swimming upstream to spawn in the Fraser River, British Columbia. They have determined that declining river flows would offset the increased energy requirements of warmer water temperatures.

There is an extensive literature dealing with the interaction between groundwater and surface water, and this includes major reviews on baseflow, transmission losses, baseflow recession analysis, and broader aspects of low flows. Although these are mature topics in hydrology, they continue to attract strong interest, with hundreds of papers published in leading journals over the past 20 years. Our comprehensive review of the relevant literature focusses in detail on the use of linear and nonlinear models of recession discharge, and on transmission loss. From this review we identify three approaches to understanding recession, namely (a) theoretical approaches based on the Boussinesq equation, (b) applications of recession slope analysis, and (c) estimates of transmission loss by input–output water balance. We review the application of these techniques to ephemeral and humid areas and note the wide adoption of linear models despite most researchers accepting that the processes are nonlinear. Few papers address the characteristics of groundwater flow to ephemeral streams from unconfined aquifers. Although initially considered an ephemeral arid/semi‐arid stream feature, transmission loss occurs in streams in humid areas. Interestingly, we identify a significant omission in the literature, in that while it is common to observe nonlinear behavior in streamflow recessions, little explicit account has been given to the role of transmission loss which can be an important factor in identifying the structure of nonlinear recession models. This article is categorized under:Science of Water > Hydrological Processes [ABSTRACT FROM AUTHOR]

Tree rings have been central to the understanding of variability of flow of the Colorado River. Spurred by steadily declining flows after the 1920s, early tree‐ring research drew attention to the importance of climate variability to water supply by identifying episodes in the past that were even drier. Application of modern statistical methods to tree‐ring data later yielded a reconstruction of annual flows at Lees Ferry back to the early 1500s that highlighted the unprecedented wetness of the base period for the 1922 Colorado River Compact. That reconstruction served as the framework for a collection of papers in a 1995 special issue of Water Resources Bulletin on coping with severe sustained drought on the Colorado River. This retrospective paper reviews historical aspects of the dendrohydrology of the Colorado River, and the updates since 1995. A constantly expanding tree‐ring network has been subjected to an array of new statistical approaches to reconstruction. Climate change and increasing demand for water have meanwhile driven increased interest in the processing and presentation of reconstructions for optimal use in water resources planning and management. While highlighting the robustness of main findings of earlier studies, recent research yields improved estimates of magnitudes of flow anomalies, extends annual flows to more than 1200 years, and underscores unmatched drought duration in the medieval period. [ABSTRACT FROM AUTHOR]

The construction and operation of large‐scale reservoirs alter the natural river flow regimes and destroy the original suitable reproduction and living conditions of aquatic organisms. Especially during the spawning season, the fluctuating water flow process is particularly important for fish reproduction. A bi‐objective optimization model considering both the environmental flow demand for fish spawning and the stability of hydropower output is established in this study and solved by one of the most widely used algorithms for multi‐objective problems, the Non‐Dominated Sorted Genetic Algorithm‐II. The derived Pareto Front can be used to guide reservoir operation during fish spawning seasons. The model proposed in this paper is applied to the Yangqu Reservoir, currently under construction on the upper Yellow River in China. The results show that the stability of hydropower output can still be improved by 4.80% to 10.56%, even if the environmental flow demand is taken into account. Even for the scheme preferring ecological recovery, the stability of hydropower output can be increased by more than 0.85%. Highlights: Fish spawning demand and hydropower stability are considered simultaneously.Derived Pareto Front can be used for reservoir operation in fish spawning seasons.Room still exists for improving hydropower stability meeting ecological demands. [ABSTRACT FROM AUTHOR]

3H enters the hydrologic cycle after oxidizing in the 3H1HO molecule and it constitutes a very useful tracer for hydrological studies. One of these applications is streamflow component separation, which provides useful information to understand the hydrological cycle. This application is based on the fact that the contents in precipitation (runoff) tend to be markedly higher than those in groundwater (baseflow) because of decreasing activity in the last as a consequence of radioactive decay. The main objective of this paper is to test 3H for hydrograph separation in sub‐tropical South America, where it is favoured by high values in precipitation. The catchment of the Quequén Grande River, in Argentina, was selected. Total flow in surface water is a mixing between the baseflow and the event flow portion; the separation was done in three sections of the drainage network, and the proportion of baseflow were 36%, 88% and 47%. [ABSTRACT FROM AUTHOR]

Non‐perennial river catchments are characterized by an ever‐changing spatial configuration of their flowing streams. A combination of empirical data and simplified analytical frameworks has been frequently used in the literature to analyse the co‐evolution of the total active stream length (L$$ L $$) and the catchment discharge at the outlet (Q$$ Q $$). However, despite the increasing availability of field data, understanding how runoff generation processes drive the spatio‐temporal dynamics of non‐perennial river reaches remains challenging. In this paper we use CATHY, an integrated surface–subsurface hydrological model (ISSHM), to investigate the impact of saturation‐excess (Dunnian) and infiltration‐excess (Hortonian) runoff generation on the stream network dynamics of two virtual catchments with spatially homogeneous subsurface properties but different morphology. The numerical simulations show that when surface runoff is triggered by saturation‐excess mechanisms, the subsurface domain is slowly saturated, and the stream network gradually expands upstream from the outlet towards the catchment divides. In these conditions, the specific inflow per unit contributing area is relatively uniform along the network, thereby implying that L$$ L $$ and Q$$ Q $$ display a monotonically increasing one‐to‐one relationship. On the other hand, infiltration‐excess mechanisms lead to more heterogeneous saturation patterns in the subsurface domain. In particular, during the wetting phase, Hortonian processes originate highly transient conditions and a non‐uniform spatial distribution of the specific inflow along the stream network. This is reflected by a hysteretic LQ$$ L(Q) $$ relation and a marked asymmetry between the wetting and drying phases of the event. The application of an ISSHM proved to be a useful tool to elucidate the processes that drive stream network expansion and retraction in non‐perennial rivers. [ABSTRACT FROM AUTHOR]

Groundwater (GW) is commonly conceptualized as causally linked to streamflow (SF). However, confirming where and how it occurs is challenging given the expense of experimental field monitoring. Therefore, hydrological modeling and water management often rely on expert knowledge to draw causality between SF and GW. This paper investigates the potential of convergent cross‐mapping (CCM) to identify causal interactions between SF and GW head. Widely used in ecology, CCM is a nonparametric method to identify causality in nonlinear dynamic systems. To apply CCM between variables the only required inputs are time‐series data (stream gauge and bore), so it may be an attractive alternative or complement to expensive field‐based studies of causality. Three upland catchments across different hydrogeologic settings and climatic conditions in Victoria, Australia, are adopted as case studies. The outputs of the method seem to largely agree with a priori perceptual understanding of the study areas and offered additional insights about hydrological processes. For instance, it suggested weaker SF‐GW interactions during and after the Millennium Drought than in the previous wet periods. However, we show that CCM limitations around seasonality, data sampling frequency, and long‐term trends could impact the variability and significance of causal links. Hence, care must be taken while physically interpreting the causal links suggested by CCM. Overall, this study shows that CCM can provide valuable causal information from common hydrological time‐series, which is relevant to a wide range of applications, but it should be used and interpreted with care and future research is needed. Plain Language Summary: It is common in hydrology to assume that the water below the ground (in aquifers) interacts with the rivers. However, it is challenging to identify where and how it occurs with the available data and financial resources. Therefore, we often use computer models that represent these interactions according to the knowledge of experts. This paper investigates the potential of convergent cross‐mapping (CCM), a method that is widely used in ecology, to identify these interactions. This method only requires time‐series data (e.g., streamflow and groundwater level data) and represents an attractive alternative or complement to expensive field‐based studies. The results of the analysis appear to agree with an initial understanding of the aquifer‐river interactions in three study areas in Victoria, Australia, and offered additional insights. For instance, it suggests weaker interactions during and after the Millennium Drought than in the previous wet periods. However, variability and significance of the causal links inferred by CCM can be due to some limitations of the method. Hence, care must be taken while physically interpreting the results. Overall, CCM can unlock valuable information from common data, which is relevant to hydrology, but it should be used and interpreted with care and future research is needed. Key Points: Convergent cross‐mapping (CCM) is a nonparametric method to identify causality in nonlinear dynamic systems that is widely used in ecologyCCM results suggest heterogeneous causal links that are largely consistent with perceptual understanding of processesCCM seems widely applicable in hydrology, but also has limitations and thus should be used and interpreted with care [ABSTRACT FROM AUTHOR]

The purpose of this paper is to describe the internal structure and composition of recently discovered subglacial landforms called "murtoos" in order to interpret their formative processes and depositional environment. So far, murtoos have only been reported from Finland and Sweden, but they probably exist in all areas covered by past ice sheets. Murtoos mainly occur in fields along subglacial meltwater routes or corridors in close relation to eskers and ribbed moraine tracts. Murtoos were excavated perpendicular to the long axes of the triangular murtoo heads in six locations. Murtoos were found to be composed of silt/clay‐poor, sandy and gravelly diamictons interbedded with sorted sediments, and are suggested to be produced by pulsed, highly sediment‐concentrated flows during weak glaciotectonic deformation, indicating effective pressure close to zero. Murtoos can be divided into three main depositional units: 1) the core, 2) the murtoo body and 3) the murtoo mantle. The initial deposition of murtoos took place in a network of low canals and conduits or cavities with fluctuating stream flow likely over 50 km from the ice margin. Murtoos reveal an increasing influence of subglacial meltwater flow in rapidly widening broad and low conduits with increasing sediment transport over short distances. The results of this work suggest that murtoos were formed time‐transgressively over yearly meltwater cycles in a semi‐distributed drainage system not recognised before, in which was high‐pressure porewater conditions with rapid mobilisation of subglacial saturated sediments, a critical factor in the development of semi‐efficient drainage. Murtoos are suggested as missing element between distributed and channelised drainage systems not included in current glaciohydrological models or even in the theoretical basis of glacial hydrology. [ABSTRACT FROM AUTHOR]

Concentration‐discharge (C‐Q) relationships are frequently used to understand the controls on material export from watersheds. These analyses often use a log‐log power‐law function (C = aQb) to determine the relationship between C and Q. Use of the power‐law in C‐Q analyses dates to two seminal papers by Francis Hall (1970, https://doi.org/10.1029/WR006i003p00845) and Francis Hall (1971, https://doi.org/10.1029/WR007i003p00591), where he compared six increasingly complex hydrological models, concluding the power‐law had the greatest explanatory power. Hall's analyses and conclusions, however, were based on a limited data set, with assumptions regarding water volume and storage, and used simple model selection criteria. While the power‐law is applied widely, it has not been rigorously tested and evaluated in over 50 years. We reexamined Hall's original models across time scales using 8 years of high‐frequency and weekly specific conductance data and evaluated model performance using more sophisticated model selection criteria. While we found the power‐law analysis remains one of the best performing models, other models performed equally as well including the log‐linear functional form. Model performance was similar at the sub‐daily to weekly scale but varied with sampling method. More complex models performed poorly relative to simpler models and tended to underpredict concentration at flow extremes due to constraints in fitting model parameters to the observed data. While we conclude, based on the data analyzed here, that the power‐law remains a suitable model for C‐Q analyses, opportunities exist to refine and differentiate among C‐Q models based on underlying assumptions of data distribution, recession analyses, and for applying models to reactive solutes. Plain Language Summary: Understanding how solute concentrations respond to changes in river flow remains a fundamental challenge in water resources science. Evaluating the relationship between solute concentrations and river flow (or discharge) can provide insight into how watersheds are structured and how they function. Concentration‐discharge (C‐Q) relationships can be used to estimate the rate of material export from terrestrial landscapes to surface waters. Many C‐Q analyses use a power‐law analysis and model to determine the response of C to variation in Q; yet this model and its embedded assumptions have not been rigorously tested following the development of water quality sensors that provide high‐frequency data. Here we revisit eight mathematical models originally developed by Hall (1970, https://doi.org/10.1029/WR006i003p00845) and Hall (1971, https://doi.org/10.1029/WR007i003p00591) that were initially evaluated with only 36 data points. We reexamine Hall's models using eight years of 15‐min specific conductivity data and find that while the power‐law model still is one of the best models to use in the evaluation of C‐Q relationships. And overall, simpler models outperform more complex models. We discuss many of the assumptions, such as constant load, that underpin C‐Q analyses to demonstrate that future studies could further parameterize C‐Q analyses for more insight on the mechanisms driving solute‐discharge relationships. Key Points: We re‐evaluate equations proposed by Francis Hall to assess concentration‐discharge (C‐Q) relationships using newly available long‐term and high‐frequency data setsAcross time steps we find that log‐log and log‐linear models perform equally well to describe C‐Q relationshipsParametrization of storage‐discharge relationships via recession analyses provides additional insight to C‐Q relationships [ABSTRACT FROM AUTHOR]

The need for and the use of different data assimilation techniques to improve the quality of streamflow forecast is now well established. In this paper, the goal is to demonstrate the power of a new class of methods known as the forward sensitivity method (FSM) which is based on the temporal evolution of model sensitivities with respect to the control variables consisting of initial conditions and parameters. FSM operates in two phases: the first phase provides a simple algorithm for placing observations at or near where the square of forward sensitivities attains their maximum values. Using only this selected subset of observations in a weighted least squares method, the second phase then provides an estimate of the unknown elements of the control variables. In this paper, FSM based assimilation is applied to a simple class of two parameter model in a medium‐sized agriculture dominant watershed lying in the Krishna River Basin, India. Four assimilation scenarios were tested to determine the effect of assimilating only sensitive observations as well as the impact of temporally evolving initial condition sensitivity. Sensitivity results showed that observations during the monsoon time alone are enough for assimilation purposes, which has helped in reducing the computational time greatly. Assimilation and forecast results also indicated that the scenarios which assimilated only sensitive observations are better in estimating daily streamflow. From the obtained results, it is concluded that FSM based assimilation has significant potential to improve the streamflow simulations, especially in places where data availability remains a major challenge. Key Points: The potential of assimilating only sensitive soil moisture observations on streamflow forecast was demonstrated using a novel forward sensitivity method (FSM) methodImpact of temporally evolving sensitivity of initial condition on hydrological model performance during assimilation was determinedThe sensitivity‐based soil moisture assimilation was effective up to 45 lead days during streamflow forecast [ABSTRACT FROM AUTHOR]

Hydrologic variability can present severe financial challenges for organizations that rely on water for the provision of services, such as water utilities and hydropower producers. While recent decades have seen rapid growth in decision‐support innovations aimed at helping utilities manage hydrologic uncertainty for multiple objectives, support for managing the related financial risks remains limited. However, the mathematical similarities between multi‐objective reservoir control and financial risk management suggest that the two problems can be approached in a similar manner. This paper demonstrates the utility of Evolutionary Multi‐Objective Direct Policy Search for developing adaptive policies for managing the drought‐related financial risk faced by a hydropower producer. These policies dynamically balance a portfolio, consisting of snowpack‐based financial hedging contracts, cash reserves, and debt, based on evolving system conditions. Performance is quantified based on four conflicting objectives, representing the classic tradeoff between "risk" and "return" in addition to decision‐makers' unique preferences toward different risk management instruments. The dynamic policies identified here significantly outperform static management formulations that are more typically employed for financial risk applications in the water resources literature. Additionally, this paper combines visual analytics and information theoretic sensitivity analysis to improve understanding about how different candidate policies achieve their comparative advantages through differences in how they adapt to real‐time information. The methodology presented in this paper should be applicable to any organization subject to financial risk stemming from hydrology or other environmental variables (e.g., wind speed, insolation), including electric utilities, water utilities, agricultural producers, and renewable energy developers. Key Points: Reservoir control and financial risk management share a common multi‐objective decision structure and can be optimized using similar methodsEvolutionary Multi‐Objective Direct Policy Search is used to develop financial risk management policies for a hydropower producerInformation theoretic sensitivity analysis and visual analytics are used to build intuition about how policies adapt to changing conditions [ABSTRACT FROM AUTHOR]

In this paper, we analyse the uncertainty and parameter sensitivity of a conceptual water quality model, based on a travel time distribution (TTD) approach, simulating electrical conductivity (EC) in the Duck River, Northwest Tasmania, Australia for a 2‐year period. Dynamic TTDs of stream water were estimated using the StorAge Selection (SAS) approach, which was coupled with two alternate methods to model stream water EC: (1) a solute‐balance approach and (2) a water age‐based approach. Uncertainty analysis using the Differential Evaluation Adoptive Metropolis (DREAM) algorithm showed that: 1. parameter uncertainty was a small contribution to the overall uncertainty; 2. most uncertainty was related to input data uncertainty and model structure; 3. slightly lower total error was obtained in the water age‐based model than the solute‐balance model; 4. using time‐variant SAS functions reduced the model uncertainty markedly, which likely reflects the effect of dynamic hydrological conditions over the year affecting the relative importance of different flow pathways over time. Model parameter sensitivity analysis using the Variogram Analysis of Response Surfaces (VARS‐TOOL) framework found that parameters directly related to the EC concentration were most sensitive. In the solute‐balance model, the rainfall concentration Crain and in the age‐based model, the parameter controlling the rate of change of EC with age (λ) were the most sensitive parameter. Model parameters controlling the age mixes of both evapotranspiration and streamflow water fluxes (i.e., the SAS function parameters) were influential for the solute‐balance model. Little change in parameter sensitivity over time was found for the age‐based concentration relationship; however, the parameter sensitivity was quite dynamic over time for the solute‐balance approach. The overarching outcomes provide water quality modellers, engineers and managers greater insight into catchment functioning and its dependence on hydrological conditions. [ABSTRACT FROM AUTHOR]

The National Weather Service (NWS) Office of Water Prediction (OWP), in conjunction with the National Center for Atmospheric Research and the NWS National Centers for Environmental Prediction (NCEP) implemented version 2.1 of the National Water Model (NWM) into operations in April of 2021. As with the initial version implemented in 2016, NWM v2.1 is an hourly cycling analysis and forecast system that provides streamflow guidance for millions of river reaches and other hydrologic information on high‐resolution grids. The NWM provides complementary hydrologic guidance at current NWS river forecast locations and significantly expands guidance coverage and water budget information in underserved locations. It produces a full range of hydrologic fields, which can be leveraged by a broad cross section of stakeholders ranging from the emergency responder and water resource communities, to transportation, energy, recreation and agriculture interests, to other water‐oriented applications in the government, academic and private sectors. Version 2.1 of the NWM represents the fifth major version upgrade and more than doubles simulation skill with respect to hourly streamflow correlation, Nash Sutcliffe Efficiency, and bias reduction, over its original inception in 2016. This paper will discuss the driving factors underpinning the creation of the NWM, provide a brief overview of the model configuration and performance, and discuss future efforts to improve NWM components and services. [ABSTRACT FROM AUTHOR]

Baseflow was proven to be the most unpredictable component of streamflow through various research. However, the recent method for estimating baseflow is due to the development of theoretical and computational techniques. This paper attempted to develop a fully automated baseflow separation system based on a recursive digital filter with an optimization algorithm for the single separation. Most of the previous baseflow separation methods use a single set of a parameter and BFImax (the maximum value of baseflow index), which can underestimate or overestimate the baseflow; however, the system developed in this study estimates multiple optimized a parameters using seasonality and flow conditions and uses them for BFImax calculation and baseflow separation. This system derived baseflow results in better understanding of watershed and streamflow tendency characteristics. This study developed a Web‐based Hydrograph Analysis Tool 2020 with a user‐friendly interface and new separation method regarding the seasonality and flow conditions with a fully automated python module to optimize a parameters and BFImax. The application to the two area show diverse parameter sets and different baseflow according to seasonality and flow conditions representing the flow characteristics. This study could be a fundamental tool for detailed watershed management decisions regarding water security in the dry season or environmental water for aquatic ecosystems. [ABSTRACT FROM AUTHOR]

Extended hydrometric (water level and flow) records are presented for eight Irish catchments subject to arterial drainage. The procedures employed to collect and process historical data, extend flow records and compile key metadata and information about each gauging station are described. Procedures are developed to handle data quality issues related to hydrometric practices and equipment malfunction and to quality assure rescued data using quality codes that complement modern hydrometric practices. The workflow developed will assist other hydrometric data rescue efforts and minimize subjectivity during the rescue process. The newly extended records represent the longest continuous river flow series available in Ireland, extending to the commencement of formal hydrometric monitoring in the country in 1940. The resultant data sets add 150 years of daily data across eight stations and will provide a key new resource for hydrological studies into the impacts of arterial drainage and flow nonstationarity. [ABSTRACT FROM AUTHOR]

In water resource studies, long‐term measurements of river streamflow are essential. They allow us to observe trends and natural cycles and are prerequisites for hydraulic and hydrology models. This paper presents a new application of the stage‐discharge rating curve model introduced by Maghrebi et al. (2016) to estimate continuous streamflow along the Gono River, Japan. The proposed method, named single stage‐discharge (SSD) method, needs only one observed data to estimate the continuous streamflow. However, other similar methods require more than one observational data to fit the curve. The results of the discharge estimation by the SSD are compared with the improved fluvial acoustic tomography system (FATS), conventional rating curve (RC), and flow‐area rating curve (FARC). Some statistical indicators, such as the coefficient of determination (R2), root mean square error (RMSE), percent bias (PBAIS), mean absolute error (MAE), and Kling‐Gupta efficiency (KGE), are used to assess the performance of the proposed model. ADCP data are used as a benchmark for comparing four studied models. As a result of the comparison, the SSD method outperformed of FATS method. Also, the three studied RC methods were highly accurate at estimating streamflow if all observed data were used in calibration. However, if the observed data in calibration was reduced, the SSD method by R2 = 0.99, RMSE = 2.83 (m3/s), PBIAS = 0.715(%), MAE = 2.30 (m3/s), and KGE = 0.972 showed the best performance compared to other methods. It can be summarized that the SSD method is the feasible method in the data‐scarce region and delivers a strong potential for streamflow estimation. [ABSTRACT FROM AUTHOR]

Groundwater exploitations in semi‐arid regions result in significant declines of groundwater levels and streamflow and even trigger seasonal streamflow drying up, which may bring adverse impacts on riparian ecosystems. It is critical to explore future streamflow changes caused by variable climate scenarios and groundwater exploitation intensities; however, a comprehensive assessment of the streamflow drying up frequency remains lacking. Furthermore, the thresholds of controlling factors beyond which streamflow drying up occurs have rarely been explored. Here we develop an integrated surface water‐groundwater model for the Taoer River Basin (TRB), a typical semi‐arid watershed in northeast China. We apply the model to multiple future scenarios to reveal the frequency of potential streamflow drying up and explore the thresholds of controlling factors of streamflow drying up based on a decision tree method. We find that the groundwater level in the lower region of the TRB (LTRB) is hard to recover to the riverbed elevation and the streamflow drying up frequency is still high unless the groundwater exploitation is reduced by 50%. However, this leads to a competition between the ecological and economic objectives. Our results also show that the streamflow drying up frequency in the LTRB is very sensitive to the upstream water inflow that can be regulated through the reservoir operation or inter‐basin water diversion, indicating the importance of a basin scale strategic management. The findings of this paper can help policy makers sustainably exploit water resources in semi‐arid regions. [ABSTRACT FROM AUTHOR]

The basic structure of the scientific method-at least in its idealized form-is widely championed as a recipe for scientific progress, but the day-to-day practice may be different. Here, we explore the spectrum of current practice in hypothesis formulation and testing in hydrology, based on a random sample of recent research papers. This analysis suggests that in hydrology, as in other fields, hypothesis formulation and testing rarely correspond to the idealized model of the scientific method. Practices such as 'p-hacking' or 'HARKing' (Hypothesizing After the Results are Known) are major obstacles to more rigorous hypothesis testing in hydrology, along with the well-known problem of confirmation bias-the tendency to value and trust confirmations more than refutations-among both researchers and reviewers. Nonetheless, as several examples illustrate, hypothesis tests have played an essential role in spurring major advances in hydrological theory. Hypothesis testing is not the only recipe for scientific progress, however. Exploratory research, driven by innovations in measurement and observation, has also underlain many key advances. Further improvements in observation and measurement will be vital to both exploratory research and hypothesis testing, and thus to advancing the science of hydrology. [ABSTRACT FROM AUTHOR]

Hydropeaking is known for its adverse impacts on river ecosystems. However, the implementation of mitigation measures is still largely pending due to conflicting priorities of ecology and economics, which require scenario building to assess trade‐offs. Therefore, widely applicable and standardized tools are needed to analyze hydropeaking hydrology in affected rivers to expedite mitigation efforts. Here, we present a novel empirical approach—PeakTrace—that can (a) detect and follow source‐specific hydropeaking waves in the downstream direction by using multiple hydrographs and (b) describe how to flow metrics of hydropeaking waves change along a river's course. In detail, PeakTrace first identifies associated flow events and then models translation and retention processes between neighboring hydrographs. Finally, the models can be combined to establish a non‐linear hydropower plant‐specific model. We demonstrate the PeakTrace method's usability in 16 Austrian case studies. The results underline the high performance of PeakTrace, describing the longitudinal development of flow metrics with high model accuracy up to 25 km or more. Ecologically‐relevant metrics, such as rate of change or amplitude, decrease with distance from the hydropower outlet regarding down‐ramping events; the same pattern can be observed for up‐ramping events too, except for the rate of change for which an intensity increase may be observed, probably due to slope and the roughness difference between base flow and peak flow. Overall, this paper underlines the usability of PeakTrace as a basis to assess hydropower plant‐specific hydro‐ecological impacts and evaluate hydropeaking mitigation measures, especially by incorporating critical flow thresholds of river biota and life stages. [ABSTRACT FROM AUTHOR]

Although hydraulic groundwater (GW) theory has been recognized as a promising tool for understanding the role of the aquifer(s) in the surface‐subsurface hydrologic cycle, the integrated modeling community still lacks a proper hydrologic structure to apply the well‐studied theory to large‐scale hydrologic predictions. This study aims to present a novel hydrologic structure that enables the Boussinesq equation‐based depiction of the bidirectional stream‐hillslope processes for applying hydraulic GW theory to large‐scale model configurations. We integrated the BE3S's (Hong et al., 2020, https://doi.org/10.1029/2020wr027571) representation scheme of the catchment‐scale stream‐hillslope continuum into the National Water Model (NWM) and applied the modified NWM (i.e., the NWM‐BE3S) to three major basins in Texas (i.e., the Trinity, Brazos, and Colorado River basins). Since the NWM currently relies on a single reservoir model for baseflow simulation, we used the Boussinesq aquifer as an alternative subsurface hydrology routine and evaluated its predictive skill and efficacy. We identified that the implemented Boussinesq aquifer(s) in the NWM‐BE3S yielded noticeable improvements in predicting streamflow for aquifers that exhibited higher nonlinearities in the observed recessions. The varying degree of improvements in streamflow predictions per the recession nonlinearities demonstrated not only (a) the algorithmic enhancement of subsurface hydrology (physics) but also (b) the applicability of the Boussinesq theory‐based depiction of the stream‐hillslope two‐way continuum. We diagnosed each stream's state based on the bidirectional stream‐hillslope exchanges and identified the dominant processes (i.e., river infiltration or baseflow) that were represented spatially in the NWM‐BE3S. Plain Language Summary: Streamflow is a critical land hydrologic component to manage water resources and the health of the associated ecosystem. While the water cycle between the stream and hillslope is the key process to accurately simulating streamflow, most currently used hydrologic/land surface models lack a theoretical basis to characterize the catchment‐scale groundwater to depict the stream‐hillslope water cycle. In this paper, we presented a new structure NWM‐BE3S that enables the Boussinesq approximation‐based characterization of the catchment(s) for improving the predictability of streamflow. The NWM‐BE3S was developed by integrating a recent numerical scheme BE3S (Hong et al., 2020, https://doi.org/10.1029/2020wr027571) into the WRF‐Hydro National Water Model (NWM) configuration as an alternative subsurface routing routine. The NWM‐BE3S was tested against streamflow observations from three major basins in Texas (i.e., the Trinity, Brazos, and Colorado River basins) to ensure the applicability of the Boussinesq‐based stream‐hillslope continuum scheme. We identified theory‐consistent improvements in the streamflow predictions from the NWM‐BE3S framework compared to the original NWM. Based on the two‐way fluxes modeled from the hydraulically continuous catchment(s) in the NWM‐BE3S, moreover, about 10% of the river reaches in the three basins were identified as losing streams during the evaluation period. Key Points: The Boussinesq theory‐based stream‐hillslope two‐way hydrologic interactions were newly introduced in the National Water Model (NWM)The Boussinesq aquifer yielded improved streamflow predictions than the single bucket model as the nonlinearity of recession increasesGaining or losing reaches in three Texas major basins showed improved predictions using the modified NWM [ABSTRACT FROM AUTHOR]

Hydrologic signatures are quantitative metrics that describe a streamflow time series. Examples include annual maximum flow, baseflow index and recession shape descriptors. In this paper, we use machine learning (ML) to learn encodings that are optimal ML equivalents of hydrologic signatures, and that are derived directly from the data. We compare the learned signatures to classical signatures, interpret their meaning, and use them to build rainfall‐runoff models in otherwise ungauged watersheds. Our model has an encoder–decoder structure. The encoder is a convolutional neural net mapping historical flow and climate data to a low‐dimensional vector encoding, analogous to hydrological signatures. The decoder structure includes stores and fluxes similar to a classical hydrologic model. For each timestep, the decoder uses current climate data, watershed attributes and the encoding to predict coefficients that distribute precipitation between stores and store outflow coefficients. The model is trained end‐to‐end on the U.S. CAMELS watershed data set to minimize streamflow error. We show that learned signatures can extract new information from streamflow series, because using learned signatures as input to the process‐informed model improves prediction accuracy over benchmark configurations that use classical signatures or no signatures. We interpret learned signatures by correlation with classical signatures, and by using sensitivity analysis to assess their impact on modeled store dynamics. Learned signatures are spatially correlated and relate to streamflow dynamics including seasonality, high and low extremes, baseflow and recessions. We conclude that process‐informed ML models and other applications using hydrologic signatures may benefit from replacing expert‐selected signatures with learned signatures. Key Points: We built an encoder–decoder network to learn an optimal equivalent of hydrologic signaturesUsing learned signatures as input to a process‐informed ML model improves prediction accuracy over benchmark configurationsWe interpret learned signatures by correlation with classical signatures and by sensitivity analysis of their impact on model store dynamics [ABSTRACT FROM AUTHOR]

Intermittent and ephemeral streams in dryland environments support diverse assemblages of aquatic and terrestrial life. Understanding when and where water flows provide insights into the availability of water, its response to external controlling factors, and potential sensitivity to climate change and a host of human activities. Knowledge regarding the timing of drying/wetting cycles can also be useful to map critical habitats for species and ecosystems that rely on these temporary water sources. However, identifying the locations and monitoring the timing of streamflow and channel sediment moisture remains a challenging endeavor. In this paper, we analyzed daily conductivity from 37 sensors distributed along 10 streams across an arid mountain front in Arizona (United States) to assess spatiotemporal patterns in flow permanence, defined as the timing and extent of water in streams. Conductivity sensors provide information on surface flow and sediment moisture, supporting a stream classification based on seasonal flow dynamics. Our results provide insight into flow responses to seasonal rainfall, highlighting stream reaches very reactive to rainfall versus those demonstrating more stable streamflow. The strength of stream responses to precipitation are explored in the context of surficial geology. In summary, conductivity data can be used to map potential stream habitat for water‐dependent species in both space and time, while also providing the basis upon which sensitivity to ongoing climate change can be evaluated. [ABSTRACT FROM AUTHOR]

In frequency analysis of annual maximum flood series (AMFS), it is mechanism‐based and significant to incorporate the information of the underlying "ordinary" daily streamflow events for improving accuracy of flood risk estimation and appropriate management. In previous studies related to flood nonstationarity, the classical norming constants method (C‐NCM) has been used to derive statistical parameters of annual maximum flood distribution from daily streamflow distribution. However, C‐NCM does not flexibly consider the feasible range of the scale parameter of annual maximum flood distributions, which can result in the inability to provide sufficient and reliable models to fit AMFS. This paper aims to investigate the potential of two alternative norming constants methods in fitting annual maximum floods, namely the Hall's norming constants method (H‐NCM) and the Fisher and Tippetts' norming constants method (FT‐NCM) respectively. A comparative study of these three methods was carried out using hydrological streamflow series of 77 stations in the Yangtze and Yellow River basins, China. The results found that H‐NCM outperforms both C‐NCM and FT‐NCM for the stations with relatively low‐skewness coefficient of AMFS. Therefore, H‐NCM is recommended to prioritize for practical applications when considering daily streamflow rather than small samples of flood events. Furthermore, if the skewness coefficient exceeds Cs,Gumbel≈1.14, the overall modelling performance of these three methods is significantly deteriorated. This can provide some reference for the applicability of the norming constants method in flood frequency analysis. [ABSTRACT FROM AUTHOR]

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

A large literature on the sensitivity of streamflow response to climate variations has emerged over the past several decades, but the underlying mechanisms are not fully understood. The usual approaches to this problem have been simple and do not fully address its complexity, which involves the individual and joint effects of large‐scale climate modes and smaller‐scale weather features on streamflow volumes, the presence of persistence in these time series and their effects on antecedent conditions. Ongoing improvements in observational and reanalysis datasets and online access allow for better quantification of the connections between streamflow response, climate variability modes and regional weather features. The purpose of this paper is to determine whether Bayesian networks can be used to better identify key factors and their associated pathways leading to streamflow generation. A Markov blanket approach is described and illustrated using monthly streamflow series recorded at eight gauging stations within or immediately adjacent to the coastal region of eastern mainland Australia. The method is compared and contrasted with conventional multiple linear regression with discussion around this type of approach as part of a growing interest in machine learning applications. One example is used to illustrate the application of both approaches to a streamflow series exhibiting strong non‐stationarity. Results for the other streamflow series are included in a concise synthesis. Overall, the results suggest that Bayesian networks have several desirable features in terms of transparency, interpretability and explanatory insight. The findings from this study lend support to the use of Bayesian networks for modelling connections between streamflow volume and the variability of climate and regional weather. This improved understanding of the key controls of streamflow variability is intended to help address growing needs around informing social, cultural, economic and ecological aspects of water planning and management. [ABSTRACT FROM AUTHOR]

The National Oceanic and Atmospheric Administration (NOAA)'s National Water Model (NWM) provides analyses and predictions of hydrologic variables relevant to drought monitoring and forecasts at fine time and space scales (hourly, 0.25–1 km). We present results exploring the potential for NWM soil moisture and streamflow analyses to inform operational drought monitoring. Both agricultural and hydrologic drought monitoring rely either explicitly or implicitly on an accurate representation of anomalous soil moisture values. Much of our analysis focuses on comparisons of soil moisture anomalies in the NWM to those from in‐situ observations. To establish benchmarks for NWM soil moisture skill, we also include other gridded data sets currently used to inform the US Drought Monitor, specifically those from the North American Land Data Assimilation System phase 2 (NLDAS‐2) land surface models. We then compare NWM streamflow low flows with ∼500 stream gauges from the United States Geological Survey (USGS) Hydro‐Climatic Data Network of undisturbed basins. The NWM soil moisture simulation's skill parallels that from NLDAS‐2. The accuracy of drought condition identification from NWM streamflow exceeds that based on soil moisture as determined by Critical Success Index scores for extreme dry percentiles. Different meteorological forcings are used in the operational NWM cycles than those used in this retrospective analysis. This forcing disconnect, together with concerns about current‐generation land surface model soil moisture‐transport schemes, inhibit its current operational use for drought monitoring. Plain Language Summary: The National Oceanic and Atmospheric Administration's National Water Model offers output relevant for drought monitoring. This paper evaluates the National Water Model soil moisture and streamflow with drought applications in mind, and compares those evaluations to other modeling tools currently used to inform drought monitoring. We find the model's ability to estimate anomalous low flows exceeds its ability to estimate anomalously dry soils, and discuss its current potential to inform operational drought monitoring. Key Points: Retrospective NOAA National Water Model soil moisture and streamflow were evaluated for drought‐monitoring applicationsThe National Water Model was comparable in skill to land model guidance currently used to inform the US drought monitorThe National Water Model's operational forcing strategy currently limits its application for real‐time drought monitoring [ABSTRACT FROM AUTHOR]

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

This paper introduces a community‐accessible dataset comprising daily hydroclimatic variables (precipitation, temperature, and streamflow) observed in 50 catchments in Sweden (median size of 1019 km2). The dataset covers a 60‐year period (1961–2020) and includes information on geographical location, landcover, soil classes, hydrologic signatures, and regulation for each catchment. Data were collected from various sources, such as the Swedish Meteorological and Hydrological Institute, the Swedish Geological Survey, and several Copernicus products provided by the European Environment Agency. The compiled, spatially‐matched, and processed data are publicly available online through the Swedish National Data Service (https://snd.se/en), contributing a new region to the collection of existing CAMELS (Catchment Attributes and Meteorology for Large‐sample Studies) datasets. The CAMELS‐SE dataset spans a wide range of hydroclimatic, topographic, and environmental catchment properties, making it a valuable resource for researchers and practitioners to study hydrological processes, climate dynamics, environmental impacts, and sustainable water management strategies in Nordic regions. [ABSTRACT FROM AUTHOR]

The Indus River dolphin (Platanista minor) is a severely threatened species of freshwater dolphin that occurs only in the lower Indus River system of Pakistan and India. The dolphin's range has declined by 80% since the 1870s, and total species abundance is estimated as approximately 2000 individuals.In 2007, a remnant population of Indus dolphins was discovered above Harike Barrage, in the Beas River in India, 600 km away from all other individuals of its species. This paper provides an overview of the conservation status of Indus dolphins in the Beas River, details the threats they face, and suggests priorities for their conservation and management.Between 2011 and 2022, 40 dolphin direct count surveys were conducted. Indus dolphins occur only in the lower third of the Beas River and reported counts have been from one to eight individuals. The data do not indicate an increase in abundance, and instead suggest a potential decrease; however, sightings of calves continue to be reported annually indicating reproduction is still taking place.Threats to the Beas River dolphin population include accidental entanglement in fishing gear, pollution, escapement downstream of Harike barrage, altered and depleted river flow regimes' and the effects of a very small population size. Urgent conservation measures are essential if this small, but important satellite population is to persist.Recommended management actions include the complete removal of fishing nets from dolphin habitat, reducing pollution, ensuring adequate river discharge to sustain aquatic ecology including dolphins, evaluating and monitoring dolphin movement through Harike barrage and into canals, and engaging riverside communities to protect dolphins. In addition, the possibility of conservation translocations to supplement this population with individuals from larger healthy populations elsewhere in the range of the species should be explored. [ABSTRACT FROM AUTHOR]

This paper conducts a multi‐instrument analysis of a latitudinal plasma density peak at the middle latitudes during the early recovery phase of the April 2023 geomagnetic storm. The total electron content (TEC), peak density of the F layer, and the in situ plasma density from Swarm and Defense Meteorological Satellite Program (DMSP) satellites all capture this peak feature. This narrow latitudinal peak structure appeared around 50°N and extended from 40°E to 150°E in longitude with a prolonged duration of about 8 hr from sunset to midnight. This mid‐latitude peak reveals a noticeable equatorward motion and a slight westward shift. According to the plasma composition observations from DMSP satellites, this peak structure shows an O+ ions dominance, which means that this peak is more likely to be formed by an internal rather than an external source from the plasmasphere. Meanwhile, the middle latitude Fabry–Perot interferometer (FPI) observed strong equatorward thermospheric winds, and the peak height of the F layer presented a visible elevation, which suggests that the equatorward wind lifting caused the plasma density enhancement. Besides, the O/N2 ratio significantly decreased at lower and middle latitudes, and ion drift observations showed a distinct subauroral westward channel. Based on these simultaneous measurements, this structure's sharp equatorward and poleward boundaries might be related to the O/N2 ratio change and the subauroral polarization stream (SAPS) flow separately. Plain Language Summary: The ionospheric response to geomagnetic storm disturbances exhibits diverse plasma density structures. During the early recovery phase of the April 2023 geomagnetic storm, a distinct latitudinal plasma density peak is observed at middle latitudes. The formation of the mid‐latitude plasma density peak structure is not settled yet, even though some homologous structures have been reported. This study investigates the spatial and temporal features of this mid‐latitude peak structure and the dominant ion composition. The investigation is achieved by utilizing the total electron content, F layer parameters, and in situ plasma observations. Furthermore, the observations of neutral wind, thermospheric composition, and ion drift observations show a good correlation with the spatial‐temporal characteristics of this peak structure. These clues suggest that the formation of the mid‐latitude peak structure during the April 2023 geomagnetic storm may be attributed to a combined effect involving equatorward neutral winds, thermospheric composition change, and subauroral polarization stream flow. Key Points: A mid‐latitude peak is present in total electron content, F layer peak density, and in situ plasma density during the storm recovery phaseThe mid‐latitude peak covers 40°–150°E in longitude with a noticeable equatorward motion and slight westward shiftThe peak structure may be associated with the thermospheric equatorward wind, composition change, and subauroral polarization stream [ABSTRACT FROM AUTHOR]

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

A key issue in the legal case before the International Court of Justice between Bolivia and Chile concerned artificial channels in the headwater wetlands of the Silala River in Bolivia, and their impact on the river and groundwater flow rates across the international border into Chile. In pleadings before the Court, Bolivia had claimed up to 40% changes in river flow, whereas Chile had maintained that these were much smaller. This paper reviews construction of a numerical groundwater model to quantify this impact, based on the hydrogeological conceptual model presented earlier in this volume. The groundwater model domain covers the Silala River topographical catchment, plus three adjacent closed (endorheic) topographical catchments to the northeast, which together form the groundwater catchment of the Silala River. The geometry was based on a 3D geological model, constructed based on knowledge gained from several geological mapping investigations. Surface water flows were simulated within a margin of error less than the range of uncertainty associated with the flow measurements. The resulting calibrated groundwater model was used to simulate scenarios corresponding to (a) the removal of channels in the Bolivian wetlands and (b) backfilling of the channels and a small increase in ground elevation (postulated by Bolivia due to long term peat accumulation). The results of these scenarios indicate that the measures would result in a small reduction to the surface water flow over the border to Chile; for Scenario A less than 1% change in surface flows, and for Scenario B less than 3%. This article is categorized under:Human Water > Rights to WaterScience of Water > Hydrological ProcessesHuman Water > Water Governance [ABSTRACT FROM AUTHOR]

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

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

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

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

Prompted by field observation of an aufeis‐induced channel avulsion along the Hula Hula River in June 2021, we use measurements of channel migration zone width along 15 rivers flowing north across the Arctic coastal plain in Alaska, USA. We differentiated sites with aufeis that covered ≥1 km2 in early summer during the period 2017–2021 from sites without such aufeis formation. All but four of the 28 sites with aufeis have widths greater than the 95% confidence interval and 20 sites fall outside of the 95% prediction interval for channel width based on drainage area. Pairwise comparison indicates that the population of aufeis sites have significantly wider channel migration zones (p < 0.0001) than non‐aufeis sites after accounting for drainage area. Seasonal aufeis facilitates lateral channel migration and associated heterogeneity. Loss of aufeis under warming climate may reduce habitat diversity in these river corridors. Plain Language Summary: Aufeis forms in Arctic river corridors when groundwater springs continue to provide water to the surface after the river is covered by ice. The resulting shelves of ice can be up to 3 m thick and extend across the channel and floodplain. As this ice slowly melts in early summer, it can divert river flow from the active channel onto the floodplain, creating new channels. This paper examines the resulting width of the floodplain across which active channel migration occurs. Comparing sites with and without aufeis, we find that sites with aufeis are significantly wider. Loss of aufeis with warming climate will reduce the habitat diversity of Arctic rivers. Key Points: Aufeis that persists into summer on rivers of the Alaskan Arctic coastal plain can force channel avulsionSites with repeated aufeis in successive years have wider channel migration zonesLoss of aufeis as climate warms may result in narrower and less diverse river corridors [ABSTRACT FROM AUTHOR]

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

Hydrologic models are simplified representations of natural hydrologic systems. Since these models rely on assumptions and simplifications to capture some aspects of hydrological processes, calibration of parameters is unavoidable. However, utilizing the philosophy of a recent modelling framework proposed by Bahremand (2016), we show how calibration of most model parameters can be avoided by allocating or presetting these parameters utilizing knowledge gained from sensitivity analyses, field observations and a priori specifications as a part of a parameter allocation procedure. This paper details the simulation of daily river flow of the Shemshak‐Roudak watershed performed using the Python version of the WetSpa model. The WetSpa‐Python model is a distributed model of hydrological processes applied at the watershed scale. The model was applied to the Shemshak‐Roudak watershed of Iran with parameter allocation. Model calibration involved only two parameters. Straightforward methods were proposed for allocating model parameters, including three baseflow‐related parameters and the determination of maximum active groundwater storage using a mass curve technique. Also, the Budyko curve was used to constrain a correction factor for potential evapotranspiration. The WetSpa‐Python model was extended to include the influence of snowmelt. A failure to include snow in the hydrological processes of the WetSpa‐Python model creates a significant discrepancy between the observed and simulated hydrographs during the spring. The results of daily simulations for 12 years (2002–2014) are in good agreement with observations of discharge (Kling‐Gupta Efficiency = 0.84). These results demonstrate that it is feasible to simulate hydrographs with limited calibration given a knowledge of hydrological processes and an understanding of relationships between catchment characteristics and model parameters. [ABSTRACT FROM AUTHOR]

Runoff prediction in ungauged and scarcely gauged catchments is a key research field in surface water hydrology. There have been numerous studies before and since the launch of the predictions in ungauged basins (PUB) initiative by the International Association of Hydrological Sciences in 2003. This study critically reviews and assesses the decadal progress in the regionalization of hydrological modeling, which is the major tool for PUB, from 2000 to 2019. This paper found that the journal publications have noticeably increased in terms of PUB in the past 7 years, and research countries have been expanded dramatically since 2013. The regionalization methods are grouped into three categories including similarity‐based, regression‐based, and hydrological signature‐based. There are more detailed researches focusing on the interdisciplinary and profound improvement of each regionalization method. Namely, tremendous efforts have been made and lots of improvements have been carried out in the parameterization domain for the post‐PUB period. However, there is still plenty of room to improve the prediction capability in data‐sparse regions (e.g., further verification and proof of multi‐modeling adaptation and uncertainties description). This paper also discusses possible research directions in the future, including PUB in a changing environment and better utilization of multi‐source remote‐sensing information. This article is categorized under:Science of Water > Science of Water [ABSTRACT FROM AUTHOR]

River streamflow monitoring is currently facing a transformation due to the emerging of new innovative technologies. Fixed and mobile measuring systems are capable of quantifying surface flow velocities and discharges, relying on video acquisitions. This camera‐gauging framework is sensitive to what the camera can "observe" but also to field circumstances such as challenging weather conditions, river background transparency, transiting seeding characteristics, among others. This short communication paper introduces the novel idea of optimizing image velocimetry techniques selecting the most informative sequence of frames within the available video. The selection of the optimal frame window is based on two reasonable criteria: (a) the maximization of the number of frames, subject to (b) the minimization of the recently introduced dimensionless seeding distribution index (SDI). SDI combines seeding characteristics such as seeding density and spatial clustering of tracers, which are used as a proxy to enhance the reliability of image velocimetry techniques. Two field case studies were considered as a proof‐of‐concept of the proposed framework, on which seeding metrics were estimated and averaged in time to select the proper application window. The selected frames were analysed using LSPIV to estimate the surface flow velocities and river discharge. Results highlighted that the proposed framework might lead to a significant error reduction. In particular, the computed discharge errors, at the optimal portion of the footage, were about 0.40% and 0.12% for each case study, respectively. These values were lower than those obtained, considering all frames available. [ABSTRACT FROM AUTHOR]

Hydrology teaching currently relies upon educators' background, requiring a change in training future professionals to manage water resources to address climate change, among other issues. In the teaching experience described in this paper, traditional lectures in a postgraduate civil engineering master's degree were replaced by the development and assessment of a lumped hydrological model implemented into an Excel spreadsheet. Although the primary activity evaluated the long‐term impacts of climate change on streamflow in a watershed, the students were required to address several specific issues such as calibration and validation processes, goodness‐of‐fit metrics, uncertainties of parameters, and sensitivity analysis. The learning experience's efficacy was assessed by conducting two surveys comparing the participants' knowledge before and after the exercise. The results revealed that 92.3% of the students considered that their hydrological skills had improved significantly following the exercise. Furthermore, the acquisition of hydrological modeling concepts was satisfactorily appreciated by all the participants, 97.6% of whom considered it useful or highly useful. Using a spreadsheet as a complementary tool in hydrology teaching increases student participation and motivation provided it is a contemporary and appealing issue, and the teacher clearly defines, monitors, and follows up on the class objectives. [ABSTRACT FROM AUTHOR]

The phenomenon of fluidization of the bed sediment of the hyporheic zone involves the production of dynamic suspension of solid particles in a stream of water flowing in the direction opposite to gravity. Fluidization may be permanent; then, it becomes a spring or a headwater zone, where groundwater discharge rate is almost unchanged within an entire hydrological year. But it can also occur periodically, depending on the changing hydrodynamic conditions. In such cases, the fluidization of the bed sediment of the hyporheic zone may disturb its functioning by changing the conditions of surface water and groundwater exchange. The disturbances affect hydrodynamic and hydrochemical processes connected with water exchange, but first of all, the living conditions of hyporheic zone flora and fauna. Therefore, the phenomenon of fluidization is significant for broadly understood ecology of the hyporheic zone. The paper presents the analysis of conditions of fluidization occurrence depending on the main hydrodynamic parameters that may affect this process. The authors analysed the influence of the difference in water level between the drained aquifer and the river, the hydraulic conductivity and anisotropy of the aquifer, the vertical hydraulic conductivity in the hyporheic zone, thickness of the hyporheic zone, and the width of the river on the possibility of fluidization of the bed sediment of the hyporheic zone. The range of variability of particular parameters corresponds to the actual variability of those parameters in natural conditions. It was proved that the fluidization of the bed sediment of the hyporheic zone mostly depends on: water level difference, hydraulic conductivity of the aquifer, and value of vertical hydraulic conductivity in the hyporheic zone. The other parameters have little influence on this process. [ABSTRACT FROM AUTHOR]

Streamflow prediction is a long‐standing hydrologic problem. Development of models for streamflow prediction often requires incorporation of catchment physical descriptors to characterize the associated complex hydrological processes. Across different scales of catchments, these physical descriptors also allow models to extrapolate hydrologic information from one catchment to others, a process referred to as "regionalization". Recently, in gauged basin scenarios, deep learning models have been shown to achieve state of the art regionalization performance by building a global hydrologic model. These models predict streamflow given catchment physical descriptors and weather forcing data. However, these physical descriptors are by their nature uncertain, sometimes incomplete, or even unavailable in certain cases, which limits the applicability of this approach. In this paper, we show that by assigning a vector of random values as a surrogate for catchment physical descriptors, we can achieve robust regionalization performance under a gauged prediction scenario. Our results show that the deep learning model using our proposed random vector approach achieves a predictive performance comparable to that of the model using actual physical descriptors. The random vector approach yields robust performance under different data sparsity scenarios and deep learning model selections. Furthermore, based on the use of random vectors, high‐dimensional characterization improves regionalization performance in gauged basin scenario when physical descriptors are uncertain, or insufficient. Key Points: We propose a random vector strategy to serve as a surrogate to physical descriptors for regionalization under gauged scenariosThe deep learning model using random vectors yields comparable performance to the model using physical descriptors in gauged scenariosHigh‐dimensional characterization helps distinguish catchments and thus helps catchments learn from each other [ABSTRACT FROM AUTHOR]

Despite long‐standing efforts, hydrologists still lack robust tools for calibrating land surface model (LSM) streamflow estimates within ungauged basins. Using surface soil moisture estimates from the Soil Moisture Active Passive Level 4 Soil Moisture (L4_SM) product, precipitation observations, and streamflow gauge measurements for 617 medium‐scale (200–10,000 km2) basins in the contiguous United States, we measure the temporal (Spearman) rank correlation between antecedent (i.e., pre‐storm) surface soil moisture (ASM) and the storm‐scale runoff coefficient (RC; the fraction of storm‐scale precipitation accumulation converted into streamflow). In humid and semi‐humid basins, this rank correlation is shown to be sufficiently strong to allow for the substitution of storm‐scale RC observations (available only in basins that are both lightly regulated and gauged) with high‐quality ASM values (available quasi‐globally from L4_SM) in streamflow calibration procedures. Using this principle, we define a new, basin‐wise LSM streamflow calibration approach based on L4_SM alone and successfully apply it to identify LSM configurations that produce a high rank correlation with observed RC. However, since the approach cannot detect RC bias, it is less successful in identifying LSM configurations with low mean‐absolute error. Plain Language Summary: Accurately forecasting the fraction of rainfall that runs off into streams, as opposed to infiltrates into the soil, is critical for flash‐flood prediction, water‐resource monitoring, and tracking the transport of nutrients from agricultural fields into local waterways. Such forecasting is typically performed by hydrologic models that attempt to represent the physical processes responsible for surface runoff generation. However, to provide accurate streamflow forecasts, these models typically need to be calibrated against actual streamflow observations. This is problematic given the relatively poor, and declining, global availability of stream gauges. This paper presents a novel model calibration strategy that uses soil moisture from remote sensing and numerical modeling in place of streamflow observations during calibration. This transition has significant practical advantages because, unlike streamflow observations, the soil moisture data are continuously available across space. Our results demonstrate that this new approach can significantly improve hydrologic models within humid and semi‐humid basins lacking sufficient ground‐based instrumentation for traditional streamflow calibration. Key Points: Hydrologic models still perform poorly in ungauged basinsIn relative humid areas, the correlation between pre‐storm soil moisture and the storm‐scale runoff coefficient approaches unitySuch strong correlation can be exploited to improve the precision of runoff coefficient estimates provided by models in ungaged basins [ABSTRACT FROM AUTHOR]