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High‐resolution water budget estimates benefit from modeling of human water management and satellite data assimilation (DA) in river basins with a large human footprint. Utilizing the Noah‐MP land surface model with dynamic vegetation growth and river routing, in combination with an irrigation module, Sentinel‐1 backscatter and snow depth retrievals, we produce a set of 0.7‐km2 water budget estimates of the Po river basin (Italy) for 2015–2023. The results demonstrate that irrigation modeling improves the seasonal soil moisture variation and summer streamflow at all gauges in the valley after withdrawal of irrigation water from the streamflow in postprocessing (12% error reduction relative to observed low summer streamflow), even if the basin‐wide irrigation amount is underestimated. Sentinel‐1 backscatter DA for soil moisture updating strongly interacts with irrigation modeling: when both are activated, the soil moisture updates are limited, and the simulated irrigation amounts are reduced. Backscatter DA systematically reduces soil moisture in the spring, which improves downstream spring streamflow. Assimilating Sentinel‐1 snow depth retrievals over the surrounding Alps and Apennines further improves spring streamflow in a complementary way (2% error reduction relative to observed high spring streamflow). Despite the seasonal improvements, irrigation modeling and Sentinel‐1 backscatter DA cannot significantly improve short‐term or interannual variations in soil moisture, irrigation modeling causes a systematically prolonged high vegetation productivity, and snow depth DA only impacts the deep snowpacks. This study helps advancing the design of digital water budget replicas for river basins. Plain Language Summary: Human activity takes place at the local scale. Fine‐scale estimates of the water distribution in river basins should therefore account for human water management, for example, by including irrigation simulation. It is also possible to correct model simulations by including detailed satellite information on water storage in the soil or snow. This paper reconstructs the water budget for the Po river basin in Italy at a 0.7‐km2 resolution for the years 2015–2023, using a combination of modeling and Sentinel‐1 satellite data. Explicitly including a simulation of irrigation in the Po valley and withdrawal of the irrigation water from the rivers is essential to improve the modeled streamflow in the summer. The Sentinel‐1 satellite data allow to improve soil moisture in the Po valley during the spring, and to update the snow in the mountains during the winter leading to improved spring streamflow simulations. Key Points: High‐resolution modeling of irrigation and river water withdrawal improves the summer streamflow estimates for the Po basinSnow and soil moisture updates via Sentinel‐1 data assimilation work in synergy to improve the spring streamflow estimatesJoint soil moisture data assimilation and irrigation modeling reduces both the soil moisture updates and irrigation events [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]

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]

Detailed, accurate information on flow patterns in river channels can improve understanding of habitat conditions, geomorphic processes, and potential hazards to help inform water management. Data describing flow patterns in river channels can be obtained efficiently via image‐based techniques that have become more widely used in recent years as the number of platforms for acquiring images has expanded and the number of algorithms for inferring velocities has grown. Image‐based techniques have been incorporated into various software packages, including the Toolbox for River Velocimetry using Images from Aircraft (TRiVIA). TRiVIA is a freely available, standalone computer program that provides a comprehensive workflow for performing particle image velocimetry (PIV)‐based analyses within a graphical interface. This paper summarizes major enhancements incorporated into the latest release of TRiVIA, version 2.1. For example, a new Tool for Input Parameter Selection (TIPS) provides guidance for specifying key inputs to the PIV algorithm by allowing users to explore relationships between flow velocity, pixel size, output vector spacing, and frame interval. Improved visualization capabilities include the ability to create streamlines and display PIV output on an interactive web map. The program now provides greater flexibility for importing field data in various formats and selecting which observations to use for accuracy assessment. The most substantial additions to TRiVIA 2.1 are the ability to integrate bathymetric information with image‐derived velocity estimates to calculate river discharge and to use images acquired from moving aircraft to efficiently map long segments of large rivers to support habitat assessment, contaminant transport studies, and a range of other applications. [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]

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]

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]

Distribution and change of freshwater resources is often simulated with global hydrological models. However, owing to process representation limitations and forcing data uncertainties, these model simulations have shortcomings. Combining them with observations via data assimilation, for example, with data from the Gravity Recovery and Climate Experiment (GRACE) mission or streamflow measured at in situ stations is considered to improve the realism of the simulations. We assimilate gridded total water storage anomaly (TWSA) from GRACE into the WaterGAP Global Hydrology Model (WGHM) over the Mississippi River basin via an Ensemble Kalman Filter. Our results agree with previous studies where assimilating GRACE observations nudges TWSA simulations closer to the observations, reducing the root mean square error (RMSE) by 21% compared to an uncalibrated model. However, simulations of streamflow show degeneration at more than 90% of all gauge stations for metrics such as RMSE and correlations; only the annual phase of simulated streamflow improves at half the stations. Therefore, for the first time, we instead assimilated streamflow observations into the WGHM, which improved simulated streamflow at up to nearly 80% of the stations, with normalized RMSE showing improvements of up to 0.1, while TWSA was well‐simulated in all metrics. Combining both approaches, that is, jointly assimilating GRACE‐derived TWSA and streamflow observations, leads to a trade‐off between a good fit of both variables albeit skewed to the GRACE observations. Overall, we speculate that our findings point to limitations of process representation in WGHM hindering consistent flux simulation from the storage history, especially in dry regions. Plain Language Summary: The distribution of freshwater on Earth can be simulated by hydrological models like the WaterGAP Global Hydrology Model (WGHM). Changes of the total water storages can also be derived from satellite gravimetry, for example, with the Gravity Recovery and Climate Experiment (GRACE) mission. Model and observations do not necessarily agree and are both prone to errors. We combined the model and observations over the Mississippi River basin taking the errors into account by applying a data assimilation technique. This led to a more realistic simulation of the total water storage changes. Since simulated streamflow was found to degenerate at nearly all gauge stations, we assimilated streamflow observations (instead of GRACE data) into the WGHM. This turned out to improve simulated streamflow at up to nearly 80% of the stations. The assimilation of both data sets (GRACE and streamflow) together leads to a compromise between the TWSA and the streamflow simulations, with the GRACE data affecting the simulations more than the streamflow data. Our findings are expected to aid reducing the weaknesses of the WGHM model equations and thus contribute to improved quantification of Earth's freshwater distribution in the future. Key Points: GRACE assimilation improves the WGHM storage representation, but degrades the simulated streamflow at up to 99% of the gauge stationsAssimilating streamflow data instead improves the streamflow simulations at up to 79% of the validation stationsJoint assimilation leads to a trade‐off between a good fit of simulated storages and streamflow albeit skewed to the GRACE observations [ABSTRACT FROM AUTHOR]

Antecedent hydrological conditions are recorded through the evolution of dissolved lithium isotope signatures (δ7 ${\delta }^{7}$Li) by juxtaposing two storm events in an upland watershed subject to a Mediterranean climate. Discharge and δ7 ${\delta }^{7}$Li are negatively correlated in both events, but mean δ7 ${\delta }^{7}$Li ratios and associated ranges of variation are distinct between them. We apply a previously developed reactive transport model (RTM) for the site to these event‐scale flow perturbations, but observed shifts in stream δ7 ${\delta }^{7}$Li are not reproduced. To reconcile the stability of the subsurface solute weathering profile with our observations of dynamic stream δ7 ${\delta }^{7}$Li signatures, we couple the RTM to a distribution of fluid transit times that evolve based on storm hydrographs. The approach guides appropriate flux‐weighting of fluid from the RTM over a range of flow path lengths, or equivalently fluid residence times. This flux‐weighted RTM approach accurately reproduces dynamic storm δ7 ${\delta }^{7}$Li‐discharge patterns distinguished by the antecedent conditions of the watershed. Plain Language Summary: Storm events often cause characteristic shifts in stream solute chemistry. Interpreting these signals offers insight into the water‐rock interactions occurring within watersheds. Here, we use lithium stable isotopes and reactive transport modeling to relate how long water spends in a catchment, or how deep water infiltrates through a catchment, to the extent of chemical weathering. We show that the first significant storm after a dry season exports more chemically evolved water, while a wet season storm releases less evolved, shallower, and younger water. Our results indicate that stream flow δ7 ${\delta }^{7}$Li in small watersheds offers a sensitive record of hydrological conditions prior to the storm, reflecting subtle shifts in the efficiency of the Critical Zone to generate, transport, and ultimately export solutes. Key Points: Stream lithium stable isotope ratios (δ7Li) recorded at high frequency over storm events are sensitive to antecedent conditionsA reactive transport model cannot produce observed shifts in stream chemistry through variations in flow rate aloneFlux‐weighting of model fluid outputs based on time‐varying fluid transit time distributions describes stream δ7Li over storm hydrographs [ABSTRACT FROM AUTHOR]

We investigate whether geomorphic signatures of permafrost are embedded in planforms of river meanders, and we inquire as to how physical factors unique to permafrost environments are able to affect their dynamics. By exploiting satellite imagery, a data set of 19 freely‐meandering Arctic rivers is compared against an independent data set of 23 freely‐meandering streams flowing through temperate and tropical regions. Suitable dimensionless metrics are defined to characterize morphometric properties of meanders in terms of the spatio‐temporal distribution of curvature and channel width. Results show the absence of marked contrasts in the amplitude of bend‐curvature between the two data set. Differently, we find a permafrost signature in the channel width response, which manifests itself through larger values of the average bend‐width and by peaks of width fluctuations. Field data suggest that permafrost meanders tend to widen for increasing bend sinuosity, likely promoting a shift of their morphodynamic regime as final cutoff is approached. Plain Language Summary: One of the most striking impacts of climate warming in the Arctic region is permafrost thaw. Arctic rivers typically flow through perennially‐frozen floodplains, thus they are particularly susceptible to ground thawing. In order to understand the response of Arctic rivers to climate variability, basic knowledge about key differences with respect to non‐permafrost streams is needed. Despite recent studies which have emphasized the slower yearly movement rates distinguishing Arctic streams, we still do not understand whether permafrost‐affected rivers show distinctive features in their morphology due to specific physical mechanisms. By exploiting satellite imagery, we show that permafrost leaves a signature in the shape of meandering Arctic rivers. Specifically, their average bend‐width increases as sinuosity develops, while the amplitude of width oscillations is larger than that displayed by their non‐permafrost kin. Key Points: Permafrost is found to leave a morphological signature in the spatio‐temporal signal of channel width at the bend scalePermafrost meander bends show larger amplitude of width oscillations and widen as sinuosity increasesBend curvature as a standalone indicator does not provide evidence of any significant permafrost‐fingerprint [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]

Arctic and Subarctic environments are among the most vulnerable regions to climate change. Increases in liquid precipitation and changes in snowmelt onset are cited as the main drivers of change in streamflow and water temperature patterns in some of the largest rivers of the Canadian Arctic. However, in spite of this evidence, there is still a lack of research on water temperature, particularly in the eastern Canadian Arctic. In this paper, we use the CEQUEAU hydrological‐water temperature model to derive consistent long‐term daily flow and stream temperature time series in Aux Mélèzes River, a non‐regulated basin (41 297 km2) in the eastern Canadian subarctic. The model was forced using reanalysis data from the fifth‐generation ECMWF atmospheric reanalyses (ERA5) from 1979 to 2020. We used water temperature derived from thermal infrared (TIR) images as reference data to calibrate CEQUEAU's water temperature model, with calibration performed using single‐site, multi‐site, and upscaling factors approaches. Our results indicate that the CEQUEAU model can simulate streamflow patterns in the river and shows excellent spatiotemporal performance with Kling‐Gupta Efficiency (KGE) metric >0.8. Using the best‐performing flow simulation as one of the inputs allowed us to produce synthetic daily water temperature time series throughout the basin, with the multi‐site calibration approach being the most accurate with root mean square errors (RMSE) <2.0°C. The validation of the water temperature simulations with a three‐year in situ data logger dataset yielded an RMSE = 1.38°C for the summer temperatures, highlighting the robustness of the calibrated parameters and the chosen calibration strategy. This research demonstrates the reliability of TIR imagery and ERA5 as sources of model calibration data in data‐sparse environments and underlines the CEQUEAU model as an assessment tool, opening the door to its use to assess climate change impact on the arctic regions of Canada. [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]

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]

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]

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]

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]

Several models have been developed with streamflow as a covariate for predicting daily pesticide concentrations in surface water systems. Among these models, the SEAWAVE‐QEX model has been proposed by the United States Environmental Protection Agency for regulatory assessments. In this paper, the model was modified to include alternative transformations of streamflow data, and to include no streamflow covariates. The predictive performance of the modified models was evaluated and compared with the original SEAWAVE‐QEX model using a high frequency sampling dataset that includes 9 sites with 10 years of data from the Atrazine Ecological Monitoring Program. Streamflow transformations evaluated included those in the SEAWAVE‐QEX model (short‐term and mid‐term flow anomalies), reduced models with only short‐term flow anomaly or without any flow covariates, normalized Box‐Cox transformation of flow, and combinations of normalized Box‐Cox and flow anomalies. Loglinear interpolation was also evaluated. The normalized Box‐Cox transformation provided best predictive performance and significantly better predictive performance than that of the SEAWAVE‐QEX model for a target quantity of regulatory interest, such as the maximum 1‐day rolling average (similarly for the maximum 60‐day rolling average, but not significantly so). The no‐flow covariate model was only slightly worse than Box‐Cox. Significant differences in predictive performance of the SEAWAVE‐QEX model were detected across sites. [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]

Sedimentary landforms on Earth and other planetary bodies are built through scour, transport, and deposition of sediment. Sediment connectivity refers to the hypothesis that pathways of sediment transport do not occur in isolation, but rather are mechanistically linked. In dryland river systems, one such example of sediment connectivity is the transport of fluvially deposited sediment by wind. However, predictive tools that can forecast fluvial‐aeolian sediment connectivity at meaningful scales are rare. Here we develop a suite of models for quantifying the availability of river‐sourced sediment for aeolian transport as a function of river flow, wind regime, and land cover across 168 km of the Colorado River in Grand Canyon, USA. We compare and validate these models using topographic changes observed over 10 years in a coupled river sandbar‐aeolian dunefield setting. The models provide a path forward for directly linking fluvial hydrology with the management and understanding of aeolian landscapes. Plain Language Summary: Landscapes on Earth are built from sediment, which is deposited by a variety of processes, especially water and wind. In rivers located in dry regions, sediment is deposited by flowing water during river floods. Once this sediment dries, it is often then moved by wind across the landscape. Although we frequently observe this sediment connectivity between water and wind, we are not currently able to predict when and where windblown transport of river‐deposited sediment may occur. We used maps of sand in Grand Canyon along 168 km of the Colorado River, along with records of river flow, wind speed, and vegetation cover, to develop four models that predict how much sediment is available for wind to move. We compared the results of these models to actual windblown sediment transport at a study site in Grand Canyon, finding that when models predicted more sand was available at the site, we also observed more landscape changes there which were due to wind. Our research makes it possible to understand how changes in river flows affect the amount of sediment moved by wind. Key Points: We developed four methods for modeling the extent of exposed, bare sand available for windblown transport in river valleysOur models employ data on river flow, land cover, wind velocity, and vegetation to predict exposed, bare sand extent at corridor scalesWhere models predict increased areal extent of bare sand, we also observe a greater volume of topographic changes driven by wind [ABSTRACT FROM AUTHOR]

Debris flows and floods generated by rainfall runoff occur in rocky mountainous landscapes and burned steeplands. Flow type is commonly identified post‐event through interpretation of depositional structures, but these may be poorly preserved or misinterpreted. Prior research indicates that discharge magnitude is commonly amplified in debris flows relative to floods due to volumetric bulking and increased frictional resistance. Here, we use this flow amplification to develop a metric (Q*) to separate debris flows from floods based on the ratio of observed peak discharge to the theoretical maximum water discharge from rainfall runoff. We compile 642 observations of floods and debris flows and demonstrate that Q* distinguishes flow type to ∼92% accuracy. Q* allows for accurate identification of debris flows through simple channel cross‐section surveys rather than through qualitative interpretation of deposits, and therefore should increase the performance of models and engineered structures that require accurate flow‐type observations. Plain Language Summary: Steep channels in rocky landscapes and recently burned areas carry a wide range of flow types, ranging from water floods to highly destructive debris flows composed of rock, sediment, other incidental debris, and water. Typically, the flow type that recently occurred in a channel is identified by interpreting the structure of deposits left behind by the flow. However, these deposits can be challenging to interpret, provided they are preserved at all. Prior research has shown that the shift in flow physics occurring in debris flows has the effect of amplifying the magnitude of the flow beyond that which is possible in floods. In this study, we take advantage of this flow amplification to develop a quantitative metric that allows for flow type to be identified without interpreting depositional structures. We demonstrate the metric to be highly accurate. This methodology should increase the accuracy of predictive models trained on such observations, which can help to better understand where these hazards exist and protect communities and infrastructure at risk. Key Points: To distinguish debris flows from floods, a metric Q* equal to the ratio of peak discharge to rainfall‐runoff discharge is developedQ* separates floods (Q* ≤ 1) from debris flows (Q* > 1) to ∼92% accuracy and is robust to uncertainties in the key parametersThe Q* metric should reduce misidentification of flow type and increase the performance of empirical‐hazard models and hazard mitigation [ABSTRACT FROM AUTHOR]

In low‐lying coastal regions, the joint occurrence of high river flow and high water levels can cause coastal flooding with substantial economic and social implications. Recent studies over Canada's coasts have shown that neglecting the interdependency between flood drivers can underestimate the risk of flooding by up to 50%. However, to date, such interdependency has not been investigated for the coasts of the St. Lawrence River, Estuary and Gulf system (StL), where Sea Level Rise (SLR), along with intensified river peaks, are already threatening these communities. In this study, a copula‐based bivariate frequency analysis was applied to quantify the likelihood of occurrence of flooding events under dependent and independent assumptions, for 26 sites along the StL. Furthermore, to quantify the impact of anthropogenic climate change, the joint return period in historical period was compared with that of projected SLR associated with RCP 8.5 for the year 2100. Results show that (a) the independence assumption can underestimate the likelihood of occurrence of flooding event in the Fluvial Section of the StL by up to 30 times and (b) the SLR can increase the likelihood of occurrence of flooding event by up to 50 times in the Estuary and the Gulf and by up to 5 times in the Fluvial Section of the StL. This study highlights the need for explicit consideration of the dependence between flood drivers and of SLR in the delineation of flood maps along the coast of the St. Lawrence. Plain Language Summary: Coastal communities are at risk of flooding from both high river streamflow and high coastal water levels at the same time. However, to date, this kind of compound event has not been investigated for the coasts of the StL. As such, in this study, extreme return levels were computed at 26 sites along the St. Lawrence River, Estuary and Gulf system by taking the probability of occurrence of both high peak flow and high coastal water levels, as well as the probability of their joint occurrence. Furthermore, the impact of climate change was estimated by incorporating an increase in sea level as projected by high emission scenarios. Results show that joint occurrence of high river flow and water levels is likely to happen in the Fluvial Section of the St. Lawrence River, but less so elsewhere, and that sea level rise will have a severe impact on likelihood of occurrence of flooding events in the Estuary and the Gulf, but limited impact along the Fluvial Section. This study highlights the need to take both flood drivers, their interaction with each other, as well as the impacts of sea level rise into consideration in the delineation of flood maps located along the coast of StL. Key Points: Bivariate Frequency Analysis of extreme water level and river flows of 26 river outlets along the coast of the St. Lawrence River under independent and dependent assumption between flood driversThe assumption of independence can underestimate the likelihood of occurrence by up to 30 times in the Fluvial SectionSea level rise increases the likelihood flooding up to 5 and 50 times in the Fluvial and Estuary/Gulf of St. Lawrence River [ABSTRACT FROM AUTHOR]

The physical river characteristics datasets described here provide spatially consistent information to support hydrological and inundation modelling at a 1 km × 1 km resolution across the United Kingdom, on the British National Grid. The datasets of physical river characteristics provide gridded datasets (outflow drainage directions, catchment areas, widths of bankfull rivers and depths of bankfull rivers) and a comma‐separated table of NRFA (National River Flow Archive) gauging station locations. These datasets are derived from a range of sources: outflow drainage directions, catchment area and bankfull river widths are derived from existing higher resolution datasets, whereas bankfull river depths were harder to source and instead are derived from sparse historical measurements. The new gridded datasets provide a derived value for each UK land cell on the British National Grid (BNG). The comma‐separated NRFA gauging station locations table provides the most appropriate locations of 1,499 river flow gauging stations on the 1 km resolution grids, together with the approximate error in the 1 km × 1 km gridded delineation of the upstream catchment area. This article explains how UK‐wide 1‐km grids of these variables were estimated, their format and how to use them. The data are available from the Environmental Information Data Centre (EIDC). [ABSTRACT FROM AUTHOR]

Glaciers and ice streams flowing over sediment beds commonly have a layer of ice‐rich debris adhered to their base, known as a "frozen fringe," but its impact on basal friction is unknown. We simulated basal slip over granular beds with a cryogenic ring shear device while ice infiltrated the bed to grow a fringe, and measured the frictional response under different effective stresses and slip speeds. Frictional resistance increased with increasing slip speed until it plateaued at the frictional strength of the till, closely resembling the regularized Coulomb slip law associated with clean ice over deformable beds. We hypothesize that this arises from deformation in a previously unidentified zone of weakly frozen sediments at the fringe's base, which is highly sensitive to temperature and stress gradients. We show how a rheologic model for ice‐rich debris coupled with the thermomechanics of fringe growth can account for the regularized Coulomb behavior. Plain Language Summary: Many glaciers move by sliding over sediment beds. As the glacier flows downslope, ice can infiltrate the underlying sediments, forming a layer of ice‐rich debris attached at the glacier's base. We investigated how this frozen fringe impacts glacier motion by simulating glacier slip in a cold‐room facility with a specialized ring shear device. We recreated glacier conditions, sliding ice over granular beds to form the fringe, and then assessed how frictional resistance at the slip interface varied under different stresses and ice speeds. We found that frozen fringe influences the relationship between ice speed and frictional resistance, known as the "slip law." As ice speed increased, basal friction increased to a threshold that matches the strength of the ice‐free sediment bed—mirroring the "regularized Coulomb slip law" inferred for clean ice over soft beds. We attribute this behavior to deformation in a weakly frozen zone at the base of the frozen fringe and show how this behavior can be incorporated into existing parameterizations of glacier slip. Key Points: Ring shear experiments show frozen fringe alters basal slip dynamics for soft‐bedded glaciersDeformation in a zone of weakly frozen sediments within the fringe leads to a regularized Coulomb slip response [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]

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]

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]

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]

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]

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]

A reliable understanding of linkages between meteorological, hydrological and agricultural droughts (MD, HD, and AD respectively) is crucial to building resilience and planning for future climate changes. Despite Australia being prone to severe droughts, lagtimes of propagation (and recovery), from meteorological to hydrological and agricultural droughts across its large hydroclimatic regions, are poorly understood. Therefore, we investigate the characteristics of drought propagation and recovery time lags for droughts of four timescales and a combination of drought onset and cessation criteria in 407 unregulated catchments within six major precipitation zones across the country. We find that the propagation and recovery lags depend on climatic conditions, drought criteria and timescales. The median of catchment average propagation times from MD to HD across Australia varied from 0.8 to 1.7 months for 1‐month timescales, increasing to 2.2–5.0 months for 12‐month timescales. The corresponding recovery lagtimes were 1.3–3.7 and 1.7–7.0 months respectively. Similarly, the median of catchment average propagation times from MD to AD ranged from 0.8 to 1.9 months for 1‐month timescales, increasing to 0.6–5.0 months for 12‐month. The corresponding recovery lagtimes were 0.7–2.8 and 0.3–8.7 months respectively. For droughts of smaller timescales, propagation and recovery lags are linearly correlated with recovery lagtimes consistently greater than propagation times. However, as the timescale increases, these relationships weaken suggesting effects of other catchment attributes (e.g., groundwater contributions) on lag relationships. Plain Language Summary: The primary focus of the research is to investigate the time delay between the occurrence of the lack of precipitation (meteorological droughts) and its subsequent impact on river flow (hydrological drought) and soil moisture (agricultural drought). Understanding these delays is crucial for drought planning and management. This study uses observed precipitation, river flow, and satellite‐based soil moisture data spanning over 40 years across six major precipitation zones within Australia. The delays between drought types (meteorological, hydrological, and agricultural) vary depending on the specific location within Australia. The criteria used to define the onset and end of droughts, as well as the drought duration, also influence the observed delays. The average delay from meteorological to hydrological or agricultural droughts increases with longer timescales. For example, hydrological droughts measured on 1‐month timescale have shorter delays (0.8–1.7 months), while 12‐month droughts have longer delays (2.2–5 months). Similarly, shorter droughts have shorter recovery times, while longer droughts have longer recovery times. For shorter timescale droughts, there is a clear relationship between the delays with end delays consistently greater than the start delays. However, as droughts become larger and more prolonged, the relationship weakens, suggesting the influence of other catchment attributes. Key Points: Drought propagation and recovery behaviors for a large sample of catchments under varied climatic zones within Australia are investigatedPropagation and recovery lag relationships are well‐defined for shorter droughts but are increasingly indefinable for longer droughtsPropagation (TP) and recovery lags (TR) depend on drought severity with shorter TP to milder droughts and longer TR to milder droughts [ABSTRACT FROM AUTHOR]

Measurement of planktonic chlorophyll‐a—a proxy for algal biomass—in rivers may represent local production or algae transported from upstream, confounding understanding of algal bloom development in flowing waters. We modeled 3 years of chlorophyll‐a transport through a 394‐km portion of the Illinois River and found that although algal biomass is longitudinally widespread, most net production occurs at river control points in the upper reaches (up to 3.7 Mg chlorophyll‐a y−1 km−1). Up to 69% of the algal biomass in the upper river was a result of within‐reach production, with the remainder recruited from headwaters and tributaries. High chlorophyll‐a measured farther downstream was largely because of transport from source‐area control points, with substantial net losses of algal biomass occurring in the lower river. Modeling the often‐overlooked river transport component is necessary to characterize where, when, and why planktonic algae grow and predict how far and fast they move downstream. Plain Language Summary: Planktonic algae in rivers may accumulate during periods of high productivity stimulated by favorable light, temperature, nutrient, and flow conditions, which can disrupt ecological processes and affect human uses including recreation and drinking water supply. Planktonic algae observed in rivers may occur because of local growth or transport from upstream source areas. Therefore, considering both local and upstream conditions may improve early warnings of potentially harmful blooms. Along a 394‐km stretch of the Illinois River, we found that most of the algae grew in the upper reaches and was then transported to downstream reaches, contributing to potential downstream harms such as excessive turbidity, organic carbon, biological oxygen demand, and algal toxins. We demonstrate how the often‐overlooked river transport component can be quantified to better identify where, when, and why algae grow in river networks. Key Points: Planktonic algal biomass is pervasive in the Illinois River, yet production is favored at certain locations and timesMost planktonic algal biomass was produced in upper‐reach control points that supplied downstream areasTransport analysis using local and upstream data improves understanding of river algal blooms [ABSTRACT FROM AUTHOR]

Drought has affected much of the western United States since about the year 2000, including the Upper Colorado River Basin (UCRB). Using a time series of UCRB streamflow derived from a tree‐ring based reconstruction of UCRB streamflow for the years 1 CE through 1905 CE, together with naturalized UCRB streamflow values for 1906 CE through 2021 CE, we identify 51 drought events, including the 2000–2021 drought. Although the recent 2000–2021 drought has been relatively severe, it is not the most severe of the UCRB drought events we identified. Results also indicate that natural variability combined with projected climate warming can result in UCRB drought events that are more severe than any drought since 1 CE. Plain Language Summary: A long and severe drought has affected much of the western United States since about the year 2000 CE, including the Upper Colorado River Basin (UCRB). Comparing this drought to past UCRB droughts (during 1 CE through 2021 CE), we find that the 2000–2021 drought is not the most severe UCRB drought. The results also suggest that natural variability combined with projected climate warming could result in UCRB drought events that are more severe than any drought since 1 CE. Key Points: We identify 51 drought events for the years 1 CE through 2021 CE for the Upper Colorado River Basin (UCRB)Twelve of the 51 UCRB droughts we identify were more severe than the 2000–2021 droughtNatural variability and/or a 1°C temperature increase can trigger UCRB droughts that are more severe than the 2000–2021 drought [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]

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]

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]

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]

Concerns about streamflow depletions due to groundwater pumping have stimulated the development of various analytical and numerical approaches to quantifying such depletions. Absent among the analytical approaches are those applicable to problems involving a three‐dimensional aquifer with a partially penetrating production well. The U.S. Geological Survey program WTAQ can be used to simulate water‐level declines in a three‐dimensional aquifer without a stream. However, by applying the principles of superposition, WTAQ is used to simulate temporal streamflow depletions due to pumping from a partially penetrating well in a homogeneous, three‐dimensional, horizontally infinite, water‐table aquifer with an arbitrarily located infinitesimally thin stream positioned vertically at the water table. Article Impact Statement: The paper extends methods available for quantifying stream depletions owing to pumping from a well. [ABSTRACT FROM AUTHOR]

Coastal water level measurements represent one of the earliest geophysical measurements and allow an assessment of historical sea level rise and trends in tides, river flow and storm surge. However, recovery and digitization of archival tidal records have been much less widespread and systematic than, for example meteorological records. In this contribution, we discuss data rescue efforts and lessons learned in France, the United States and the United Kingdom, countries with early and extensive tide gauge networks by the mid‐19th century. We highlight the importance of (a) cataloguing the historical gauge records, as a first step towards locating them; (b) locating data in archives, and then recovering and saving data by any means necessary, including photographs and scanning; (c) obtaining metadata, including both quantitative survey records, gauge checks and clock data, but also qualitative records such as gauge notes, letters and reports; and (d) quantitative statistical analysis of data and datum quality, using both standard data‐entry checks but also tools that leverage the unique predictability of tide measurements. Methods for digitizing original analogue records are also discussed, including semi‐automatic, computer‐based methods of digitizing tidal charts (marigrams). Although the current best practice is described, future improvements are desirable and needed to make the more than estimated 10,000 station years of unused, undigitized records available to the scientific community. [ABSTRACT FROM AUTHOR]