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Stochastic hydrology produces ensembles of time series that represent plausible future streamflow to simulate and test the operation of water resource systems. A premise of stochastic hydrology is that ensembles should be statistically representative of what may occur in the future. In the past, the application of this premise has involved producing ensembles that are statistically equivalent to the observed or historical streamflow sequence. This requires a number of metrics or statistics that can be used to test statistical similarity. However, with climate change, the past may no longer be representative of the future. Ensembles to test future systems operations should recognize non‐stationarity and include time series representing expected changes. This poses challenges for their testing and validation. In this paper, we suggest an evidence‐based analysis in which streamflow ensembles, whether statistically similar to and representative of the past or a changing future, should be characterized and assessed using an extensive set of statistical metrics. We have assembled a broad set of metrics and applied them to annual streamflow in the Colorado River at Lees Ferry to illustrate the approach. We have also developed a tree‐based classification approach to categorize both ensembles and metrics. This approach provides a way to visualize and interpret differences between streamflow ensembles. The metrics presented, along with the classification, provide an analytical framework for characterizing and assessing the suitability of future streamflow ensembles, recognizing the presence of non‐stationarity. This contributes to better planning in large river basins, such as the Colorado, facing water supply shortages. Plain Language Summary: Long‐range water supply planning in many river basins requires an assessment of ensembles of plausible future streamflow time series used to simulate and test the operation of water resource systems. With climate change, and growing recognition that hydrologic processes are changing over time, the past may no longer be representative of the future. This poses challenges when using statistical metrics to test future streamflow ensembles. In this paper, we suggest an evidence‐based approach in which streamflow ensembles, whether statistically similar to and representative of the past or a changing future, should be characterized using an extensive set of statistical metrics. We have assembled a broad set of metrics and applied them to annual streamflow in the Colorado River at Lees Ferry to illustrate the approach. We have also developed an approach to categorize both ensembles and metrics. The metrics presented and the classification provide an analytical framework for characterizing and assessing the suitability of future streamflow ensembles for water resources system planning. The metrics and classification developed advance and contribute to better planning in large river basins facing water supply shortages. Key Points: Many ensembles representing plausible future streamflow are available for the Colorado River BasinMetrics are presented to provide an evidence‐based framework for evaluating these streamflow ensemblesA classification approach was developed to group similar ensembles and assess their suitability for planning in different future scenarios [ABSTRACT FROM AUTHOR]

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

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]

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]

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

It remains an open question whether the state of knowledge on bend hydrodynamics applies to sharp bends in the largest river systems on Earth. This paper reports a field investigation into the hydrodynamics in two consecutive sharp bends of the Middle Yangtze River (MYR), and demonstrates that the reduced‐order model of Blanckaert and de Vriend is able to resolve the secondary flow strength and continuous flow redistribution in rivers of this size to high accuracy. Consequently, the model has been validated over the entire range of scales. These two bends show similarities to those of smaller size in terms of bathymetry, flow redistribution, and secondary flow evolution. The transverse location of the core of maximum velocity along the bends lags behind the thalweg by approximately half a width. A center‐region cell of secondary flow develops over the outer bank near the apex, and gradually decays downstream until it completely vanishes at the bend exit. Both bends are mildly curved from the perspective of hydrodynamic modeling, with a weak interaction between the secondary flow and primary flow, although they are sharply curved from the perspective of geomorphology (width/radius > 0.5). A term‐by‐term analysis of the model indicates that the flow redistribution is primarily controlled by planimetric changes in curvature and by topographic steering, and is slightly influenced by the secondary flow. However, the secondary flow may indirectly affect the flow redistribution by conditioning the bed topography. Important knowledge gaps remain with respect to the coupling between the hydrodynamic and morphological processes. Key Points: Field measurements of 3D flow field in very large sharp bendsPatterns of mean and secondary flows are similar as in smaller systemsThe reduced‐order model can resolve the flow field over the entire range of scales [ABSTRACT FROM AUTHOR]

Evidence suggests that catchment state variables such as groundwater can exhibit multiyear trends. This means that their state may reflect not only recent climatic conditions but also climatic conditions in past years or even decades. Here we demonstrate that five commonly used conceptual "bucket" rainfall‐runoff models are unable to replicate multiyear trends exhibited by natural systems during the "Millennium Drought" in south‐east Australia. This causes an inability to extrapolate to different climatic conditions, leading to poor performance in split sample tests. Simulations are examined from five models applied in 38 catchments, then compared with groundwater data from 19 bores and Gravity Recovery and Climate Experiment data for two geographic regions. Whereas the groundwater and Gravity Recovery and Climate Experiment data decrease from high to low values gradually over the duration of the 13‐year drought, the model storages go from high to low values in a typical seasonal cycle. This is particularly the case in the drier, flatter catchments. Once the drought begins, there is little room for decline in the simulated storage, because the model "buckets" are already "emptying" on a seasonal basis. Since the effects of sustained dry conditions cannot accumulate within these models, we argue that they should not be used for runoff projections in a drying climate. Further research is required to (a) improve conceptual rainfall‐runoff models, (b) better understand circumstances in which multiyear trends in state variables occur, and (c) investigate links between these multiyear trends and changes in rainfall‐runoff relationships in the context of a changing climate. Key Points: Environmental state variables such as groundwater display multiyear trends in response to sustained climate anomaliesWe show that five commonly used conceptual "bucket" rainfall runoff models are unable to replicate such trendsBecause of this, the models fail to extrapolate realistically to different climatic conditions, compromising runoff projections Plain Language Summary: It is common in science to use a mental picture or metaphor that simplifies a complex phenomenon. A common metaphor used for a water supply catchment is that of a leaky bucket. When it rains, the bucket fills up; when it does not rain for a while, the bucket empties due to evaporation and water used by trees; and leaking water is like river flow. Computer models based on variants of this metaphor are common and can provide predictions of how much streamflow might occur under future scenarios. This paper explores limitations of the bucket metaphor and associated models. Recently, during a 13‐year drought in Australia, river catchments gradually started to dry up. With each passing year, the depth to groundwater increased gradually as the water used by trees was not replenished by rainfall. We compare this long, slow behavior to that of five commonly used "bucket" models. The models do not show the long, slow drying up—they only show the seasonal ups and downs, and their predictions of streamflow over the drought are poor. This is surprising, and it means we should choose our models carefully and seek out models that can simulate this behavior and its impact on streamflow. [ABSTRACT FROM AUTHOR]

River floods cause extensive losses to economy, ecology, and society throughout the world. They are driven by the space‐time structure of catchment rainfall, which is determined by large‐scale, or even global‐scale, atmospheric processes. The identification of coherent, large‐scale atmospheric circulation structures that determine the moisture transport and convergence associated with rainfall‐induced flooding can help improve its predictability and phenomenology. In this paper, we extend a methodology, used for the analysis of extreme rainfall events, to high streamflow events (HSEs). The approach combines multiple machine learning methods to link HSEs to atmospheric circulation patterns. An application to the German streamflow network using reanalysis data for the period 1960 to 2012 is presented. Daily streamflow from 166 gauges, homogeneously distributed across Germany, are used. Geopotential height fields and integrated vapor transport (IVT) are derived from reanalysis data. An unsupervised neural network, Self Organizing Maps, is applied to geopotential height to identify a finite number of circulation patterns (CPs). Event synchronization between CPs and HSEs is used to establish if they are linked or not. If they are linked, the Event Synchronization method computes the delay between the occurrence of a CP and a HSE. Finally, local logistic regression is used to estimate the probability of occurrence of a HSE, as function of CP and IVT. We demonstrate that our approach is very effective to evaluate HSE probability occurrence across Germany. Key Points: Atmospheric circulation patterns that generate high streamflow events in Germany are identifiedEvent synchronization is used to determine synchronization and delay between atmospheric circulation patterns and high streamflow eventsThe occurrence probability of high streamflow events is conditioned on atmospheric circulation patterns and integrated water vapor transport [ABSTRACT FROM AUTHOR]

Rainfall thresholds for streamflow generation are commonly mentioned in the literature, but studies rarely include methods for quantifying and comparing thresholds. This paper quantifies thresholds in ephemeral streams and evaluates how they are affected by rainfall and watershed properties. The study sites are in southern Arizona, USA; one is hyperarid and the other is semiarid. At both sites rainfall and streamflow were monitored in watersheds ranging from 10−3 to 102 km2. Streams flowed an average of 0–5 times per year in hyperarid watersheds and 3–11 times per year in semiarid watersheds. Although hyperarid sites had fewer flow events, their flow frequency (fraction of rain events causing flow) was higher than in semiarid sites for small (<1 km2) watersheds. At both locations flow frequency decreased with drainage area, but the decrease was steeper in hyperarid watersheds. Watershed mean 60‐min intensity thresholds ranged from 3–13 mm/hr in hyperarid watersheds and 7–16 mm/hr in semiarid watersheds. Higher runoff thresholds and lower flow frequencies in small semiarid watersheds likely relate to greater ground cover and soil development compared to the desert pavement and bedrock surfaces in hyperarid sites. The choice of rain data strongly influenced threshold values; single rain gauges were only adequate for threshold prediction in <1‐km2 watersheds, and incomplete rainfall data led to increases in thresholds with drainage area. We recommend using mean rainfall intensity over the drainage area for threshold analysis because this reduces apparent scale dependence in thresholds caused by incomplete rainfall information. Plain Language Summary: Ephemeral streams in deserts are usually dry, flowing only after heavy rains. Our goal was to determine how much rain is needed for these streams to flow. We studied streams in dry southwestern and in wetter southeastern Arizona, USA. The dry site has mostly bare rock across the landscape, whereas the wetter site has more vegetation, including shrubs, grasses, and oak. In the dry area, small streams flowed 3–5 times per year, and larger streams flowed 0–2 times per year. Streams required 3–13 mm of rain over 1 hr to trigger flow. The small streams flowed more frequently because rain falling on bare rock could rapidly reach the stream. Water was lost into the channel bed before reaching larger streams. In the wetter area, both small and large streams flowed 3–11 times per year. These streams required 7–16 mm of rain over 1 hr to trigger flow. This range of rainfall is slightly higher than for streams in the dry study area, likely because vegetation cover and soil development allow more rainfall to infiltrate into soil before reaching streams. Information on the amount of rainfall needed to trigger streamflow can help with issuing flash flood warnings for ephemeral streams. Key Points: Rainfall thresholds predict streamflow responses with high accuracy in small hyperarid and semiarid watershedsUsing insufficient rain data usually increases threshold values for larger watersheds, leading to apparent scale dependence in thresholdsDeclines in flow frequency and increases in thresholds with drainage area are steeper in hyperarid than in semiarid watersheds [ABSTRACT FROM AUTHOR]

This paper studies how streamflow predictability varies with basin characteristics. We introduce an index of basin complexity that is based on a model of least statistical complexity that is needed to reliably predict daily streamflow of the basin. We then relate it with climate, vegetation and soil characteristics of the basin. Daily streamflow is modeled using k nearest neighbor model of lagged streamflow that predicts next time step streamflow based on the occurrences of similar streamflow events from the past. In order to calculate basin complexity, we identify difficult streamflow events of the basin and then use Vapnik‐Chervonenkis generalization theory, which trades off model performance with Vapnik‐Chervonenkis dimension (i.e., a measure of model complexity), to find a k nearest neighbor model of appropriate complexity for predicting a difficult streamflow event of the basin. The average of selected model complexities corresponding to difficult events is then defined as the basin's complexity. Basin complexity of 412 Model Parameter Estimation Experiment basins from continental United States are then related with its six basin characteristics. All the characteristics have been derived from the Model Parameter Estimation Experiment database to represent climate, vegetation and soil characteristics of the basins in a concise manner. Results find that more complex basins that are drier have less seasonal rainfall, vegetation with more storage capacity (i.e., smaller 5‐week Normalized Difference Vegetation Index gradient), and faster responsive soils. The results reaffirm prior observations that minimum complexity that is required to model a basin depends on its climate and landscape characteristics (e.g., complex models do not perform well in dry basins). Key Points: Basin complexity is defined in terms of difficulty in learning statistical patterns of daily streamflowRelationship between complexity of MOPEX basins and hydrological characteristics is analyzedMore complex basins that are drier have less seasonal rainfall, vegetation with more storage capacity and faster responsive soils [ABSTRACT FROM AUTHOR]

Recursive digital filters (RDFs) are widely used for estimating base flow from streamflow hydrographs, and various forms of RDFs have been developed based on different physical models. Numerical experiments have been used to objectively evaluate their performance, but they have not been sufficiently comprehensive to assess a wide range of RDFs. This paper extends these studies to understand the limitations of a generalized RDF method as a pathway for future field calibration. Two formalisms are presented to generalize most existing RDFs, allowing systematic tuning of their complexity. The RDFs with variable complexity are evaluated collectively in a synthetic setting, using modeled daily base flow produced by Li et al. (2014) from a range of synthetic catchments simulated with HydroGeoSphere. Our evaluation reveals that there are optimal RDF complexities in reproducing base flow simulations but shows that there is an inherent physical inconsistency within the RDF construction. Even under the idealized setting where true base flow data are available to calibrate the RDFs, there is persistent disagreement between true and estimated base flow over catchments with small base flow components, low saturated hydraulic conductivity of the soil and larger surface runoff. The simplest explanation is that low base flow 'signal' in the streamflow data is hard to distinguish, although more complex RDFs can improve upon the simpler Eckhardt filter at these catchments. [ABSTRACT FROM AUTHOR]

This paper explores the development and application of distributed hydrological models, focusing on the key decisions of how to discretize the landscape, which model structures to use in each landscape element, and how to link model parameters across multiple landscape elements. The case study considers the Attert catchment in Luxembourg-a 300 km2 mesoscale catchment with 10 nested subcatchments that exhibit clearly different streamflow dynamics. The research questions are investigated using conceptual models applied at hydrologic response unit (HRU) scales (1-4 HRUs) on 6 hourly time steps. Multiple model structures are hypothesized and implemented using the SUPERFLEX framework. Following calibration, space/time model transferability is tested using a split-sample approach, with evaluation criteria including streamflow prediction error metrics and hydrological signatures. Our results suggest that: (1) models using geology-based HRUs are more robust and capture the spatial variability of streamflow time series and signatures better than models using topography-based HRUs; this finding supports the hypothesis that, in the Attert, geology exerts a stronger control than topography on streamflow generation, (2) streamflow dynamics of different HRUs can be represented using distinct and remarkably simple model structures, which can be interpreted in terms of the perceived dominant hydrologic processes in each geology type, and (3) the same maximum root zone storage can be used across the three dominant geological units with no loss in model transferability; this finding suggests that the partitioning of water between streamflow and evaporation in the study area is largely independent of geology and can be used to improve model parsimony. The modeling methodology introduced in this study is general and can be used to advance our broader understanding and prediction of hydrological behavior, including the landscape characteristics that control hydrologic response, the dominant processes associated with different landscape types, and the spatial relations of catchment processes. [ABSTRACT FROM AUTHOR]

In this paper we combined a multimodel ensemble based on 15 regional climate models with a multicatchment approach to explore the hydrologic sensitivity of 14 neighboring and rather similar catchments to changing climate conditions. Current (1982-2010) and future (2062-2090) streamflow was simulated with the HBV model. A diagnostic approach was used, which considered major behavioral catchment functions by using hydrologically relevant signatures related to overall water balance, flow duration curves and hydrograph attributes. Projected increases in temperature and precipitation resulted in increased total available streamflow, with lower spring and summer flows, but substantially higher winter streamflow. Furthermore, significant changes in flow durations with lower chances of both high and low flows can be expected in boreal Sweden in the future. This overall trend in projected streamflow pattern changes was comparable among the analyzed catchments but the magnitude of change differed considerably. This suggests that catchments belonging to the same region can show distinctly different degrees of hydrological responses to the same external climate change signal. We reason that differences in spatially distributed physical catchment properties within catchments are not only of great importance for current streamflow behavior, but also play a major role in the sensitivity of catchments to changing climate conditions. [ABSTRACT FROM AUTHOR]

We combine experimental and modeling results from a headwater catchment at the Hubbard Brook Experimental Forest (HBEF), New Hampshire, USA, to explore the link between stream solute dynamics and water age. A theoretical framework based on water age dynamics, which represents a general basis for characterizing solute transport at the catchment scale, is here applied to conservative and weatheringderived solutes. Based on the available information about the hydrology of the site, an integrated transport model was developed and used to compute hydrochemical fluxes. The model was designed to reproduce the deuterium content of streamflow and allowed for the estimate of catchment water storage and dynamic travel time distributions (TTDs). The innovative contribution of this paper is the simulation of dissolved silicon and sodium concentration in streamflow, achieved by implementing first-order chemical kinetics based explicitly on dynamic TTD, thus upscaling local geochemical processes to catchment scale. Our results highlight the key role of water stored within the subsoil glacial material in both the short-term and long-term solute circulation. The travel time analysis provided an estimate of streamflow age distributions and their evolution in time related to catchment wetness conditions. The use of age information to reproduce a 14 year data set of silicon and sodium stream concentration shows that, at catchment scales, the dynamics of such geogenic solutes are mostly controlled by hydrologic drivers, which determine the contact times between the water and mineral interfaces. Justifications and limitations toward a general theory of reactive solute circulation at catchment scales are discussed. [ABSTRACT FROM AUTHOR]

This paper presents a spatially explicit model for hydrothermal response simulations of Alpine catchments, accounting for advective and nonadvective energy fluxes in stream networks characterized by arbitrary degrees of geomorphological complexity. The relevance of the work stems from the increasing scientific interest concerning the impacts of the warming climate on water resources management and temperature-controlled ecological processes. The description of the advective energy fluxes is cast in a travel time formulation of water and energy transport, resulting in a closed form solution for water temperature evolution in the soil compartment. The application to Alpine catchments hinges on the boundary conditions provided by the fully distributed and physically based snow model Alpine3D. The performance of the simulations is illustrated by comparing modeled and measured hydrographs and thermographs at the outlet of the Dischma catchment (45 km2) in the Swiss Alps. The Monte Carlo calibration shows that the model is robust and that a simultaneous fitting of streamflow and stream temperature reduces the uncertainty in the hydrological parameters estimation. The calibrated model also provides a good fit to the measurements in the validation period, suggesting that it could be employed for predictive applications, both for hydrological and ecological purposes. The temperature of the subsurface flow, as described by the proposed travel time formulation, proves warmer than the stream temperature during winter and colder during summer. Finally, the spatially explicit results of the model during snowmelt show a notable hydrothermal spatial variability in the river network, owing to the small spatial correlation of infiltration and meteorological forcings in Alpine regions. [ABSTRACT FROM AUTHOR]

The changes in climate and catchment properties have altered the hydrological processes significantly at different spatiotemporal scales around the world. In particular for finer time scales, changes in water storage, which has been commonly neglected for long‐term temporal scales, may play an important role on hydrological processes. Nevertheless, few studies addressed this question in modifying the Budyko framework, with most of them remaining a steady‐state assumption on catchment characteristics. Here, we derive new analytical formulas of unsteady‐state streamflow elasticity in a modified Budyko framework, incorporating both storage change and one specific parameter for catchment properties. We study 78 humid catchments in the USA with simulation data of daily hydrological processes from a probability‐distribution‐based hydrological model (the modified HyMOD). As indicated by results, the annual storage change ratio is linearly correlated with the annual aridity index, and this relationship can be used to estimate elasticity coefficients with our formulas. The estimated elasticity coefficients perform well in simulating the annual streamflow with the power‐law model. For different catchments, variability of the unsteady‐state elasticity is higher than that of the steady‐state elasticity. Unsteady‐state streamflow coefficients show significant linear correlation with catchment properties, such as the average slope, average elevation, and catchment area. This study provides a new analytical approach to investigate the interannual stability of catchments with varying climate and catchment properties. Plain Language Summary: With the warming of the climate and shortage of water resource, the importance of studying water cycle is increasingly recognized, especially at finer time scales. Therefore, it is necessary to study the response of hydrologic variables to changes in climate and catchment properties under unsteady‐state conditions. In this study, we develop a new Budyko‐based analytical method for hydrologic elasticity analysis on catchments bearing the changes in climate and catchment properties. Case studies are undertaken for 78 humid catchments in the USA. Results show that the annual streamflow is mostly sensitive to the change in precipitation and then to changes in catchment properties and potential evapotranspiration. Key Points: Analytical formulas of unsteady‐state elasticity incorporate the aridity index, storage change, and a catchment characteristic parameterEstimated elasticity coefficients in 78 humid catchments agree well with the power‐law model for the annual streamflowVariability of unsteady‐state elasticity in different catchments is larger in comparison to the steady‐state framework [ABSTRACT FROM AUTHOR]

Wildfires naturally occur in many landscapes, however they are undergoing rapid regime shifts. Despite the emphasis in the literature on the most severe hydrological responses to wildfire, there remains a knowledge gap on the thresholds of wildfire (i.e., burned area/drainage area ratio, BAR) required to initiate hydrological responses. We investigated hydrological changes in the Russian River Watershed (RRW) in California, a coastal, Mediterranean, drought‐prone, wildfire‐adapted ecosystem, following ten wildfires that burned 30% of the watershed. Our findings suggest that sub‐watersheds of the RRW have not burned beyond an intrinsic, unknown, threshold required to initiate change. Using paired watersheds, we examined spatiotemporal patterns of pre‐and‐post wildfire hydrology with a rainfall‐runoff hydrological model. Even though these successive wildfires burned 1%–50% of each sub‐watershed (1%–30% at moderate/high severity), we found little evidence of wildfire‐related shifts in hydrology. As a function of BAR, wildfire imposed limited effects on runoff ratios (runoff/precipitation) and runoff residuals (observations—model simulations). Our findings that post‐wildfire runoff enhancements asymptote beyond 30% burn indicate that when a watershed is burned beyond a certain threshold, the magnitude of the hydrologic response no longer increases. Drought and storm conditions explained much of the variability observed in streamflow, whereas wildfire explained only moderate variability in streamflow even when wildfire accounted for >45% BAR. While the BAR in the RRW was sufficiently beyond previously reported minimum disturbance thresholds (>20% burned forest), the lack of hydrological response is attributed to buffering effects of wildfire adaptation and drought factors that are unique to Mediterranean ecoregions. Plain Language Summary: Western United States water resources are vulnerable to changes caused by wildfires. While many studies indicate that streamflow may increase during post‐wildfire years, the compounding factors of drought and wildfire‐adapted landscapes challenge our ability to predict and prepare for coming changes in streamflow. Our current knowledge cannot answer the question "How much wildfire is required for river water flows to change?" Our study results show that drought can buffer the hydrological response to wildfire. While the drought can mitigate post‐wildfire flood potential, effects of the wildfire could emerge years later when high intensity rain events return. Key Points: Little evidence of wildfire‐related shifts in hydrology in drought‐prone Northern California coastal region having a Mediterranean climateWhen the percent of burned area increased beyond 30% of the watershed, the magnitude of the runoff response asymptotesPost‐wildfire hydrological variability did not extend outside of pre‐wildfire streamflow conditions [ABSTRACT FROM AUTHOR]

River flows change on timescales ranging from minutes to millennia. These vibrations in flow are tuned by diverse factors globally, for example, by dams suppressing multi‐day variability or vegetation attenuating flood peaks in some ecosystems. The relative importance of the physical, biological, and human factors influencing flow is an active area of research, as is the related question of finding a common language for describing overall flow regime. Here, we addressed both topics using a daily river discharge data set for over 3,000 stations across the globe from 1988 to 2016. We first studied similarities between common flow regime quantification methods, including traditional flow metrics, wavelets, and Fourier analysis. Across all these methods, the flow data showed low‐dimensional structure (i.e., simple and consistent patterns), suggesting that fundamental mechanisms are constraining flow regime. One such pattern was that day‐to‐day variability was negatively correlated with year‐to‐year variability. Additionally, the low‐dimensional structure in river flow data correlated closely with only a small number of catchment characteristics, including catchment area, precipitation, and temperature—but notably not biome, dam surface area, or number of dams. We discuss these findings in a framework intended to be accessible to the many communities engaged in river research and management, while stressing the importance of letting structure in data guide both mechanistic inference and interdisciplinary discussion. Key Points: We compared tools for describing streamflow timeseries, including streamflow metrics, wavelet, and Fourier analysisEach method indicated streamflow data are structured: variability at short timescales is negatively correlated with long timescalesGlobally, dams were less correlated with streamflow regime than catchment size and climate were [ABSTRACT FROM AUTHOR]

Many Victorian catchments experienced a rainfall‐runoff relationship (RRR) shift during the Millennium Drought (1997–2009). Less annual streamflow was generated from annual rainfall during the drought than would have been expected for the same rainfall before the drought. Changes in weather systems, such as cyclones, fronts, and thunderstorms, were also observed during this period. In this study, we assess the role that changes in weather systems played in the RRR shift. We find that catchments that experienced a RRR shift tended to receive less of their rainfall from frontal weather systems, and more of their rainfall from thunderstorm‐enhanced cyclone and cyclone‐front hybrid systems during the drought than catchments that showed little change in RRR. Overall, changes in the proportion of rainfall from weather systems alone explained up to 43% of the variance in the RRR shift between catchments. Including parameters that quantify catchment processes and physiographic features indicated that weather system changes are not the primary indicator of a RRR shift. Catchment mean slope, aridity and valley confinement, together with proportional rainfall change of cyclone‐thunderstorms and cyclone‐front‐thunderstorms, explained 60% of the variance in the RRR shift between catchments. Focusing on streamflow availability, we find the importance of different weather systems and catchment characteristics varies depending on warm or cool season, eastern or western Victoria, and before or during the drought. Following the drought, some catchments largely maintained the RRR shift that occurred during the drought, and also experienced less of their rainfall from fronts and cyclones compared to the other catchments. Key Points: Weather system changes in Victoria, Australia are partly related to a change in rainfall‐runoff relationship (RRR) during a decade‐long droughtLess rainfall from fronts and more rainfall from thunderstorm enhanced systems were an indicator of RRR changeInternal catchment characteristics were a greater indicator of RRR change than changes in weather systems [ABSTRACT FROM AUTHOR]

Seasonally snow‐covered catchments in the western United States supply water to growing populations as both annual snowmelt‐driven streamflow and multi‐year groundwater recharge. Although interannual variability in streamflow is driven largely by precipitation, runoff efficiency (the ratio of streamflow to precipitation) in individual catchments varies by 50% or more. Recent work suggests that interannual variability in groundwater storage, inferred from winter baseflow, is a primary control on runoff efficiency, highlighting a need to quantify both the time scales on which groundwater storage varies and the hydro‐climatic drivers of storage. Using over a century of daily stream discharge data from 10 seasonally snow‐covered catchments in northern Utah, we find that temporal variability in winter baseflow, an index of groundwater storage, measured from mean daily January discharge, exhibits a 2–5‐ and 12–15‐year periodicity, driven by regional precipitation patterns and snowmelt dynamics. Specifically, multiple linear regression (MLR) modeling using antecedent hydro‐climatic variables demonstrates that winter baseflow (groundwater storage) was positively related to 3–4 years of antecedent annual precipitation, negatively related to the previous year's mean annual temperature, and positively related to 1–4 antecedent years of snowmelt rate and duration. Because antecedent baseflow (groundwater storage) is strongly related to runoff efficiency, these results suggest that more frequent and longer droughts in a future climate will reduce surface water supplies faster than otherwise expected. More broadly, these results highlight the importance of including the influence of antecedent climate on groundwater storage when modeling and managing water supplies from seasonally snow‐covered catchments. Key Points: Interannual changes in groundwater storage inferred from baseflow in 10 headwater catchments exhibit coherent 2–5 and 12–15 year periodicityInterannual variability in groundwater storage is related to 1–4 years of antecedent precipitation (+) snowmelt rate (+) and temperature (−)Groundwater storage in warmer/drier catchments is related to longer periods of antecedent climate than cooler/wetter catchments [ABSTRACT FROM AUTHOR]

Reservoirs play a vital role in the supply and management of water resources and their operation can significantly alter downstream flow. Despite this, reservoirs are frequently excluded or poorly represented in large‐scale hydrological models, which can be partly attributed to a lack of open‐access data describing reservoir operations, inflow and storage. To help inform the development of reservoir operation schemes, we collate a suite of hydrological signatures designed to detect the impacts of reservoirs on the flow regime at large‐scales from downstream flow records only. By removing the need for pre‐and‐post‐reservoir flow timeseries (a requirement of many pre‐existing techniques), these signatures facilitate the assessment of flow alteration across a much wider range of catchments. To demonstrate their application, we calculate the signatures across Great Britain in 111 benchmark (i.e., near‐natural) catchments and 186 reservoir catchments (where at least one upstream reservoir is present). We find that abstractions from water resource reservoirs induce deficits in the water balance, and that pre‐defined flow releases (e.g., the compensation flow) reduce variability in the downstream flow duration curve and in intra‐annual low flows. By comparing signatures in benchmark and reservoir catchments, we define thresholds above which the influence of reservoirs can be distinguished from natural variability and identify 40 catchments significantly impacted by the presence of reservoirs. The signatures also provide insights into local reservoir operations, which can inform the development of tailored reservoir operation schemes, and identify locations where current modeling practices (which lack reservoir representation) will be insufficient. Plain Language Summary: Reservoirs play a vital role in the supply and management of water resources. However, reservoirs are frequently either excluded, or poorly represented in large‐scale river flow simulations due to a lack of open‐access data describing their operation. To help inform the representation of reservoirs, we use a suite of simple metrics to detect where and how rivers are being impacted by upstream reservoirs from records of downstream flow. We apply these metrics over hundreds of gauges across Great Britain and find that abstractions for water supply reduce the volume of water in rivers downstream of reservoirs, and that controlled water releases reduce downstream flow variability. The application of the metrics defined in this study enable us to gain insights into how upstream reservoirs are operated and will help to improve our ability to simulate and predict river flow under changing climate and water demand. Key Points: Hydrological signatures can be used to identify reservoir‐induced hydrological alteration using only downstream flow timeseriesApplication to 186 catchments across Great Britain finds reservoirs often induce losses in the water balance and reduce flow variabilitySignatures provide insights into reservoir operations which will help to design and evaluate new operation schemes in hydrological models [ABSTRACT FROM AUTHOR]

Many rivers and streams are ungauged or poorly gauged and predicting streamflow in such watersheds is challenging. Although streamflow signals result from processes with different frequencies, they can be "sparse" or have a "lower‐dimensional" representation in a transformed feature space. In such cases, if this appropriate feature space can be identified from streamflow data in gauged watersheds by dimensionality reduction, streamflow in poorly gauged watersheds can be predicted with a few measurements taken. This study utilized this framework, named data‐driven sparse sensing (DSS), to predict daily‐scale streamflow in 543 watersheds across the contiguous United States. A tailored library of features was extracted from streamflow training data in watersheds within the same climatic region, and this feature space was used to reconstruct streamflow in poorly gauged watersheds and identify the optimal timings for measurement. Among different regions, streamflow in snowmelt‐dominated and baseflow‐dominated watersheds (e.g., Rocky Mountains) was more effectively predicted with fewer streamflow measurements taken. The prediction efficiency in some rainfall‐dominated regions, for example, New England and the Pacific coast, increased significantly with an increasing number of measurements. The spatial variability of prediction efficiency can be attributed to the process‐driven mechanisms and the dimensionality of watershed dynamics. Storage‐dominated systems are lower‐dimensional and more predictable than rainfall‐dominated systems. Measurements taken during periods with large streamflow magnitudes and/or variances are more informative and lead to better predictions. This study demonstrates that DSS can be an especially useful technique to integrate ground‐based measurements with remotely sensed data for streamflow prediction, sensor placement, and watershed classification. Plain Language Summary: Many rivers and stream reaches are ungauged or poorly gauged because streamflow measurement is costly and resource intensive. Predicting the streamflow time‐series in these ungauged or poorly gauged watersheds is still challenging. Here, we use a signal processing technique called data‐driven sparse sensing on a national‐scale streamflow data set across the contiguous United States. We predict streamflow time‐series in each watershed based on existing streamflow data in watersheds nearby, and explore the best times during the year for measuring streamflow. Our analysis shows that data‐driven sparse sensing is an effective tool to predict streamflow time‐series in poorly gauged watersheds based on very few streamflow measurements. The streamflow in watersheds with high snowmelt and high baseflow can be more easily predicted than in other watersheds. Our analysis also shows that the streamflow measurements taken during periods with large streamflow peaks and variances contain more information and are beneficial for making predictions. We conclude that data‐driven sparse sensing can be further used to classify watersheds and to identify the best locations for streamflow gauging. Key Points: We utilize data‐driven sparse sensing to predict daily streamflow and identify the optimal times for streamflow measurement across the contiguous United StatesStreamflow was more effectively predicted in watersheds dominated by snowmelt and baseflow than those dominated by rainfall and quickflowThe optimal sampling times for streamflow prediction by data‐driven sparse sensing are periods with large flow magnitudes and variances [ABSTRACT FROM AUTHOR]

Despite plentiful evidence of frozen ground effects on snowmelt infiltration from lab experiments at pedon scales, streamflow observations show a weaker or no effect in terms of timing and magnitude at larger scales. We aim to understand this conflicting phenomenon through modeling using the Noah land surface model with multi‐physics (MP; Noah‐MP) options and the Routing Application for Parallel computatIon of Discharge (RAPID) over the Mississippi River Basin. We conduct 16 experiments with combinations of two supercooled liquid water (SLW) parameterization schemes and four soil hydraulic property schemes in Noah‐MP driven by two gridded precipitation products of the North American Land Data Assimilation System (NLDAS) and the Integrated Multi‐satellitE Retrievals for GPM (IMERG) Final. We then use RAPID to route Noah‐MP modeled surface runoff and groundwater discharge to predict daily streamflow at 52 United States Geological Survey gauges from 2015 to 2019. A model with the highest permeability performs better than other schemes on daily streamflow predictions by 20%–57% throughout a water year and 29%–113% for the spring as measured by the mean Kling‐Gupta Efficiency of the 52 gauges. Different SLW schemes demonstrate negligible effects on streamflow predictions. Models forced by IMERG show a better prediction skill compared with those forced by NLDAS at most of the gauges. Both precipitation products confirm that a scheme of higher permeability yields more accurate streamflow predictions over frozen ground. Future models should represent preferential flows through macropore networks to improve the understanding of frozen soil effects on infiltration and discharge across scales. Plain Language Summary: Frozen ground presumably affects the discharge of snowmelt water into rivers during winter and spring due to the apparent effects of ice "blockage." The presence of ice in the soil affects the release of soil liquid water and the time to release the water to local streams and rivers through the effects of soil ice on water flow and capacity to hold snowmelt water. At present, it is not fully understood how the soil ice affects the soil's capability of holding and releasing liquid water to rivers at river‐basin to continental scales. We use a computer model to test competing hypotheses through combinations of optional schemes of water holding capacity and water flow. The modeling results over major sub‐basins in the Mississippi River show that a model with higher permeable frozen soil results in higher skill in streamflow predictions at river basin scales. This study highlights the need to represent water flow through macropores that may be formed due to ice expansion during freezing/thawing cycles. Key Points: Streamflow predictions are substantially sensitive to the choice of frozen soil hydraulic property parameterizationsIrrespective of precipitation product used, a scheme of higher frozen soil permeability yields more skillful streamflow predictionsStreamflow prediction improvement with a scheme of higher frozen soil permeability is pronounced in basins dominated by frozen ground [ABSTRACT FROM AUTHOR]

While bushfires are often regarded as a vital trigger that alters the partitioning of hydrological fluxes, their role in water balance changes remains poorly quantified, especially in regions where the impacts of frequent bushfires and climate variability overlap. Here, we estimated the fire‐induced water balance changes based on a modified paired catchment method that considers the partial effect of annual precipitation differences. In the application for eight forested catchments impacted by the 2009 Victorian Bushfires with multiple burned areas (12%–89%), we found that evapotranspiration declined by 33 ± 20 mm yr−1 (mean ± standard deviation) and streamflow increased by 68 ± 32 mm yr−1 during the post‐fire decade. For the interannual changes within this decade, the decline in evapotranspiration due to fires gradually recovered after the first year since bushfires, while the increase in streamflow mostly peaked in the second or third year and diminished in subsequent years. We surmised that such asynchronous responses of the two fluxes to bushfires occurred with the initial increase and the later decrease in terrestrial water storage. Averaged for the post‐fire decade, there seems to be an overall decline in terrestrial water storage for burned catchments relative to unburned catchments. Plain Language Summary: Apart from high temperature and burning biomass, bushfires can induce dramatic changes in plant water use and water yield in catchments during post‐fire years. Its impact on water resources is often compounded with climate change. In early 2009, large forest fires swept many parts of Victoria, Australia. Using a set of burned and unburned catchments and high‐resolution water balance data, we found that bushfires caused asynchronous decreases in evapotranspiration and increases in streamflow. The different magnitudes of the changes in the two fluxes may indicate a decline in terrestrial water storage for burned catchments when compared with that of unburned catchments. These findings advance our understanding of the role of bushfires on hydrological fluxes and terrestrial water storage in a changing climate. Key Points: Paired catchment method is modified for the use of detecting changes in catchment water balance, including water storage changeThe decreased evapotranspiration and the increased streamflow due to the 2009 Victorian Bushfires are temporally asynchronousThe water storage increase in a wetter climate may be less than expected for burned catchments [ABSTRACT FROM AUTHOR]

The Columbia River basin is a large transboundary basin located in the Pacific Northwest. The basin spans seven US states and one Canadian province, encompassing a diverse range of hydroclimates. Strong seasonality and complex topography are projected to give rise to spatially heterogeneous climate effects on unregulated streamflow. The basin's water resources are economically critical, and regulation across the domain is extensive. Many sensitivity studies have investigated climate impacts on the basin's naturalized hydrology; however, few have considered the large role of regulation. This study investigates where and when regulation affects projected changes in streamflow by comparing climate outcomes across 80‐member ensembles of unregulated and regulated streamflow projections at 75 sites across the basin. Unregulated streamflow projections are taken from an existing data set of climate projections derived from Coupled Model Intercomparison Project version 5 Global Climate Models. Regulated streamflow projections were modeled by the US Army Corps of Engineers and the US Bureau of Reclamation by using these unregulated flows as input to hydro‐regulation models that simulate operations based on current and historical water demands. Regulation dampens shifts in winter and summer streamflow volumes. Regulation generally attenuates changes in cool‐season high flow extremes but amplifies shifts in warm‐season and annual high flow extremes at historically snow‐dominant headwater reservoirs. Regulation reduces dry‐season low flow changes in headwater tributaries where regulation is large but elsewhere has little effect on changes in low flows. Results highlight the importance of accounting for water management in climate sensitivity analysis particularly in snow‐dominant basins. Key Points: Regulation dampens future winter and summer volume changes where the degree of upstream regulation is largeRegulation dampens cool‐season high flow extreme increases and amplifies warm‐season increases at snow‐dominant headwater basinsRegulation dampens low flow changes in tributaries with a large degree of upstream regulation but has little to no effect elsewhere [ABSTRACT FROM AUTHOR]

Afforestation to control soil erosion has been implemented throughout China over the past few decades. The long‐term hydrological effects, such as total water yield and baseflow, of this large‐scale anthropogenic activity remain unclear. Using six decades of hydrologic observations and remote sensing data, we explore the hydrological responses to forest expansion in four basins with contrasting climates across China. No significant change in runoff was found for the period 1970–2012 for the cold and dry Hailar River Basin in northeastern China. However, both forest expansion and reduced precipitation contributed to the runoff reduction after afforestation since the late 1990s. Similarly, afforestation and drying climate since the mid‐1990s induced a significant decrease in runoff for the Weihe River Basin in semi‐arid northwestern China. In contrast, the two wet basins in the humid southern China, Ganjiang River Basin and Dongjiang River Basin, showed insignificant changes in total runoff during their study periods. However, the baseflow in the winter dry seasons in these two watersheds significantly increased since the 1950s. Our results highlight the long‐term variable effects of forest expansion and local climatic variability on basin hydrology in different climatic regions. This study suggests that landuse change in the humid study watersheds did not cause dramatic change in river flow and that region‐specific afforestation policy should be considered to deal with forestation‐water quantity trade‐off. Conclusions from this study can help improve decision‐making for ecological restoration policies and water resource management in China and other countries where intensive afforestation efforts are taking place. Key Points: Both precipitation and afforestation explained reduction in runoff for dry basinsForest recovery did not result in significant changes in total runoff for two wet basinsDry season baseflow in two wet basins had an upward trend [ABSTRACT FROM AUTHOR]

The dynamic relationships between water flux and storage, together with the associated water ages and speed of hydrological responses (as proxies for velocity and celerity respectively) are fundamental to understanding how catchments react to hydroclimate perturbations, such as floods and droughts. Using results from a calibrated, tracer‐aided ecohydrological model (EcH2O‐iso) we analyzed the dynamics of storage‐flux‐age‐response time (RT) interactions at scales that resolve the internal heterogeneity of these non‐stationary relationships. EcH2O‐iso has previously shown an adequate representation of ecohydrological flux partitioning and storage dynamics (celerity), and water ages (velocity) over 11‐year at Demnitzer Millcreek catchment (DMC, 66 km2), a drought‐sensitive, lowland catchment in Germany. The 11‐year period had marked hydroclimatic contrasts facilitating the evaluation of flux‐storage‐age‐RT dynamics under different wetness anomalies. Our results show that the spatio–temporal variability of soil moisture and ecohydrological partitioning dynamics reflect both land use (especially forest cover) and distinct soil units (i.e., brown earth vs. podzolic soils). Spatial differences in RTs of storage were driven by rapid soil evaporation and transpiration responses to rainfall, which revealed a divergence of transpiration ages from RTs. RTs of groundwater and streamflow were fast (days), but mediation by soil water storage dynamics caused marked separation from water ages (years‐decades) of deeper flow paths. Analysis of RTs and ages revealed a degradation of process representation with coarsening model spatial resolution. This study uses novel analysis of the spatio‐temporal interactions of flux‐storage‐age‐RT from a model to understand the sensitivity and resilience of catchment functionality to hydroclimatic perturbations. Key Points: Spatio‐temporal variability of soil storage and ecohydrological partitioning was modulated by vegetation characteristicsTranspiration, groundwater, and streamflow response times were distinct from water ages, with spatial differences driven by vegetation unitsLower model resolution reduced spatial variability and increased the difference of catchment response and water age of fluxes and storages [ABSTRACT FROM AUTHOR]

Fires increasingly impact forested watersheds, with uncertain water resources impacts. While research has revealed higher peak flows, longer‐term yields may increase or decrease following fire, and the mechanisms regulating post‐fire streamflow are little explored. Hydrologic response to disturbance is poorly understood in the Lower Colorado River Basin (LCRB), where snowmelt often occurs before the growing season. Here, we quantify annual streamflow changes following what have been, before 2020, two of the largest wildfires in the modern history of the contiguous United States. We evaluate nine nested watersheds with >50 years records within the Salt River Basin to evaluate fire impact over ranges of elevation, climate, vegetation, burned area, and spatial scale. We employ double‐mass comparison of paired watersheds, pre‐ and post‐fire runoff ratio comparison, multiple linear regression of climate and fire, and time‐trend analysis. Precipitation and streamflow are decoupled during dry periods; therefore we conduct separate change detection for wet and dry periods. Post‐fire summer streamflow increased by 24%–38% at all elevations. While winter/spring streamflow remained constant in the highest, coldest headwaters, winter flows declined in lower‐elevation headwaters. As a result, basin annual streamflow declined. These results support emerging understanding that warm semiarid watersheds respond differently to disturbance than well‐studied, colder watersheds. Asynchrony between winter snowmelt and summer evaporative demand is likely important when considering long‐term impacts of forest management and disturbance on water supply in the LCRB. Plain Language Summary: Wildfire is increasingly common and severe in many of the forested watersheds important for water supplies. Following fire, there is an increased risk of short‐term flooding. However, we do not understand how wildfire changes the amount of water flowing out of a watershed over multiple years. Although wildfire leaves fewer trees to take up water, it also destroys the shade from sun and wind which protects snowpack and soil moisture from evaporation. Here, we made side‐by‐side comparisons and before‐after comparisons to determine wildfire impacts on the multiyear streamflow from nine watersheds of the Salt River Basin in Arizona. We found that streamflow increased in summer. While the much larger winter/spring streamflow did not change much at high elevations, it declined in lower‐elevation watersheds following fire. One reason for this difference might be that at high elevation, the snow melts at the start of the summer growing season, when trees are likely to take up the water. Wildfire reduces trees and thereby increases streamflow. At lower elevations, snow melts much earlier in the year, when trees are not active, making the water savings from burned forests less important. These results suggest that lower, warmer forested watersheds may produce less streamflow following wildfire. Key Points: Summer streamflow increased in headwaters at all elevations following fireDominant winter/spring streamflow was unchanged in higher/colder headwaters but decreased in lower/warmer headwatersClimatological asynchrony of snowmelt and transpiration in warmer watersheds may reduce streamflow benefits of fire [ABSTRACT FROM AUTHOR]

Streamflow in Australia's northern rivers has been steadily increasing since the 1970s, most likely due to increased intensity in the Indo‐Australian monsoon. However, because of limited data availability, it is hard to assess this recent trend and therefore contextualize potential future climatic changes. In this study, we used a network of 63 precipitation‐sensitive tree‐ring chronologies from the Indo‐Australian and Asian monsoon regions to reconstruct streamflow in the Daly catchment in the Northern Territory of Australia from 1413 to 2005 CE. We used a novel wavelet‐based method to transform the variance structure of the tree‐ring chronologies to better match the hydroclimate prior to reconstruction with a hierarchical Bayesian regression model. Our streamflow reconstruction accounts for 72%–78% of the variance in the instrumental period and closely matches both historical flood events and independent proxy records, increasing confidence in its validity. We find that while streamflow has been increasing since the 1800s, the most recent 40‐year period is unprecedented in the last ∼600 years. Comparison to an independent coral‐based streamflow record shows regional coherency in this trend. Extreme high flows were found to be linked to La Niña events, but we found no significant relationship between streamflow and El Niño events, or streamflow and other regional climatic drivers. More work is therefore needed to understand the drivers of the recent streamflow increase, but, regardless of the cause, water managers should be aware of the paleoclimatic context before making decisions on water allocations. Plain Language Summary: Large‐scale agricultural development has been proposed for the Daly catchment in the Northern Territory of Australia. Since the start of record keeping in the Daly catchment in the 1970s, streamflow has been steadily increasing, most likely due to increases in Australian monsoon rainfall. However, because of the limited amount of data, it is hard to assess whether this recent increase in streamflow is unusual, part of a longer trend, or a natural cycle in monsoon rainfall. In this study, we used rainfall‐sensitive tree growth and a statistical model to reconstruct Daly River streamflow over the past 592 years. Our streamflow reconstruction closely matches known past flood events, increasing confidence in its validity. We find that streamflow has increased since the 1800s but that the most recent 40‐year period is unprecedented in the last 600 years. More work is needed to understand the drivers of this increased streamflow but regardless of the cause, water managers should be aware of the Daly River's history before making decisions on allocating water to agriculture or other large users. Key Points: 592‐year annual streamflow reconstruction of the Daly River Catchment, Australia, derived using a tree‐ring networkWavelet‐based techniques to transform tree ring spectral characteristics improve the reconstruction abilityHigh streamflow in the twentieth Century is regionally coherent and unprecedented in the preceding five centuries [ABSTRACT FROM AUTHOR]

We present a novel approach for modeling streamflow in ungauged catchments. Because of their widespread availability and global coverage, remotely sensed data provide an attractive alternative to supplement the absence of streamflow data in hydrological model calibration. One observable signal holds particular appeal; the satellite‐derived calibration ratio‐measurement (C/M ratio) has been widely studied as a direct measurement of streamflow because of its physical relationship to streamflow and demonstrated correlation with in situ streamflow. This study identifies the challenges in calibrating a hydrological model using a satellite‐derived C/M ratio, presenting a rationale designed to account for the limitations that these data pose. A new Bayesian calibration approach is developed that uses the surrogate streamflow derived from the C/M ratio in place of direct streamflow observations. We assess and demonstrate our approach for three Australian Hydrologic Reference Stations, which can be considered free from anthropogenic effects, with distinct attributes. The results indicate the competency of the proposed approach, showing model performance with 0.54 ∼ 0.78 Nash‐Sutcliffe efficiency values, with the uncertainties in the model calibration quantified via Markov Chain Monte Carlo sampling. Overall, our study finds the new model calibration promising for predictions in ungauged basins (PUB), as the global data coverage of satellite data and the suitability of the approach suggest significant improvements over traditional approaches to PUB. Plain Language Summary: We present a new approach to predict streamflow using satellite data and a hydrological model. Remotely sensed data provide an attractive alternative to address the absence of streamflow data in hydrological model calibration. One observable signal holds particular appeal; the satellite‐derived calibration ratio‐measurement (C/M ratio) has been widely studied as a direct measurement of streamflow because of its physical relationship to streamflow and demonstrated correlation with in situ streamflow. This study evaluates the hydrological model calibration using the satellite‐derived C/M ratio by assessing the results from three selected catchments. The results prove the competency of the proposed approach with a good statistical fit. The uncertainties in the proposed calibration method are also analyzed. Overall, the results indicate that the proposed model calibration method using satellite data is promising for undertaking predictions in the absence of ground data. Key Points: A Bayesian approach that couples a surrogate streamflow model and a hydrological model is proposed to estimate streamflow in ungauged basinsThe approach allows high‐flow prediction using dimensionless satellite retrievals and an estimated value of mean streamflowThe uncertainties associated with the hydrological model and satellite data are evaluated [ABSTRACT FROM AUTHOR]

New tools are needed to evaluate the impacts of short‐term hydropower regulation practices on downstream river systems and to progress towards sustainable river‐flow management. As hydropower is increasingly being used to balance the energy load deficit caused by other less flexible sources, sub‐daily flow conditions across many regulated river (RR) systems are changing. To address this, we used wavelet analyses to quantify the discharge variability in RRs and categorized the level of variability based on the conditions in natural free‐flowing rivers. The presented framework used the definition of fluvial connectivity (Grill et al., 2019) to identify free‐flowing rivers used in the study. We tested the developed framework in 12 different RRs in Finland and found higher overall averaged subdaily variations, with up to 20 times larger variability than natural conditions. A large, highly regulated Finnish river system was found to have the highest sub‐daily variations in winter, while smaller RRs with lower levels of regulation the highest variations in summer. The proposed framework offers a novel tool for sustainable river management and can be easily applied to various rivers and regions globally. It had flexibility to analyze sub‐daily variations in desired seasonal or other ecologically sensitive periods. Key Points: An approach is presented to characterize high‐frequency flow variability induced by hydropeakingCSI index values are used to distinguish free flowing rivers (FFRs)At the largest RRs, the levels of sub‐daily variability were 10 times or higher than (FFR) threshold value in the winter season [ABSTRACT FROM AUTHOR]

Streamflow forecasting over gauged and ungauged basins play a vital role in water resources planning, especially under the changing climate. Increased availability of large sample hydrology data sets, together with recent advances in deep learning techniques, has presented new opportunities to explore temporal and spatial patterns in hydrological signatures for improving streamflow forecasting. The purpose of this study is to adapt and benchmark several state‐of‐the‐art graph neural network (GNN) architectures, including ChebNet, Graph Convolutional Network (GCN), and GraphWaveNet, for end‐to‐end graph learning. We explicitly represent river basins as nodes in a graph, learn the spatiotemporal nodal dependencies, and then use the learned relations to predict streamflow simultaneously across all nodes in the graph. The efficacy of the developed GNN models is investigated using the Catchment Attributes and MEteorology for Large‐sample Studies (CAMELS) data set under two settings, fixed graph topology (transductive learning), and variable graph topology (inductive learning), with the latter applicable to prediction in ungauged basins (PUB). Results indicate that GNNs are generally robust and computationally efficient, achieving similar or better performance than a baseline model trained using the long short‐term memory (LSTM) network. Further analyses are conducted to interpret the graph learning process at the edge and node levels and to investigate the effect of different model configurations. We conclude that graph learning constitutes a viable machine learning‐based method for aggregating spatiotemporal information from a multitude of sources for streamflow forecasting Plain Language Summary: Streamflow forecasting represents a long‐standing problem in water resources management. Only a small fraction of river segments around the world are gauged. Hydrologists typically rely on other readily available catchment attributes (e.g., elevation, slope, climatology, and vegetation) to establish hydrological similarity between gauged and ungauged basins, and then "transfer" the information through a model to predict flow in ungauged basins. This work explores the use of graph neural networks (GNNs) to perform end‐to‐end streamflow forecasting for both gauged and ungauged basins. GNN is a type of machine learning algorithm that uses nodes and edges to represent physical entities. Specifically, GNNs provide an effective means for representing and learning unstructured data sets (e.g., data sets from monitoring networks). We formulated GNN models to perform end‐to‐end spatiotemporal learning, which generates streamflow forecast at all basins. We demonstrated the feasibility of using GNN for prediction at ungauged basins, which remains one of the challenging problems in hydrology. Our systematic benchmarking results, including a large number of sensitivity studies, show that GNN models performed well on a large sample hydrology data set. An Explainable AI technique is used to interpret the learned results. Key Points: The increasing volume and type of hydrological data calls for a more effective and automated way of extracting spatiotemporal informationThis study developed and benchmarked several graph neural network (GNN) models to learn the spatiotemporal dependencies in a large data setOur results suggest that GNN provides an effective and robust alternative for aggregating spatiotemporal information from multiple sources [ABSTRACT FROM AUTHOR]

Hydrologic functions of catchments are intrinsically diverse and defined as the ways catchments partition, store, and drain rainfall and snowmelt. Large‐sample hydrology (LSH) uses existing datasets of catchments to derive generalizable conclusions on hydrologic behaviors. LSH has the potential to synthesize the diversity of catchment hydrologic functions, allowing a robust extrapolation of streamflow generation mechanisms to poorly gauged regions. However, the descriptors of hydrologic functions, required to synthesize and extrapolate, have not been developed in LSH methodologies. This has potentially resulted in unexpectedly small associations between catchments' physical features and streamflow characteristics as well as poor predictability of the shape of streamflow hydrographs, as shown in recent LSH studies. Here, we propose three dimensionless indices—which directly quantify hydrologic functions—based on how interactions between a catchment's climatic and physical attributes construct catchment functions. Using climatic and physical data as well as long‐term streamflow observations at hundreds of gauged catchments across the United States and Canada, our results depict that the use of interactive functional indices as catchment descriptors improves the performance of LSH methodologies (hierarchical clustering, multivariate regression, and random forests) in identifying hydrologic similarities in shape‐based streamflow signatures among catchments, and in predicting shape‐based streamflow signatures in poorly gauged regions. This research highlights the importance of physical interpretability of LSH models and showcases the development of parsimonious and process‐based catchment‐scale frameworks, allowing the analysis of globally available catchment data to progress the generalizable understanding of catchment hydrologic functions and streamflow generation mechanisms. Key Points: Inclusion of interactive functional indices, developed independent of streamflow data, improves performance of hierarchical clusteringFunctional indices improve regression and random forests accuracy in extrapolating the predictions of shape‐based streamflow signaturesClimatic information could sufficiently explain (dis)similarity in magnitude‐based, but not in shape‐based, streamflow signatures [ABSTRACT FROM AUTHOR]

In this study, we perform a data assimilation (DA) experiment on a very large number (>700) of small‐ and medium‐scale (150–10,000 km2) European catchments to assess the impact of satellite soil moisture (SM) DA on streamflow simulations for different climatic and hydrologic conditions. In the experiment, Climate Change Initiative SM active, passive and combined products are assimilated over a time period 2003–2016 via an Ensemble Kalman Filter (EnKF). The results show that, on average, the assimilation of the three products provides relatively small improvements as compared to analogous open loop (OL) results (i.e., with an increase on median Kling‐Gupta Efficiency equal to 0.0048, 0.0033, and 0.0022 [−] for the active, the passive, and the combined products, respectively). OL performance itself is found to be a significant driver of the assimilation results: greater improvements are observed in catchments with poor OL streamflow predictions and inaccurate precipitation estimates. The remotely sensed product accuracy also emerges as relevant for assimilation efficiency, while factors affecting SM retrievals such as vegetation density, topographic complexity and basin area are found to have only a limited impact on the spatial pattern of performance. Small and detrimental effects of SM assimilation are observed over fully humid catchments and at high latitudes where pre‐storm soil moisture has reduced control on runoff generation as well as in basins where the hydrological model contains structural limitations. Key Points: The added value of satellite soil moisture assimilation on runoff simulations is assessed within 700 European catchmentsThe assimilation of the European Space Agency Climate Change Initiative, satellite soil moisture products show limited improvements, with the active product providing the best resultsThe impact of open loop model performance, remotely sensed data accuracy, climate conditions and catchment characteristics are evaluated [ABSTRACT FROM AUTHOR]

Despite a multitude of small catchment studies, we lack a deep understanding of how variations in critical zone architecture lead to variations in hydrologic states and fluxes. This study characterizes hydrologic dynamics of 15 catchments of the U.S. Critical Zone Observatory (CZO) network where we hypothesized that our understanding of subsurface structure would illuminate patterns of hydrologic partitioning. The CZOs collect data sets that characterize the physical, chemical, and biological architecture of the subsurface, while also monitoring hydrologic fluxes such as streamflow, precipitation, and evapotranspiration. For the first time, we collate time series of hydrologic variables across the CZO network and begin the process of examining hydrologic signatures across sites. We find that catchments with low baseflow indices and high runoff sensitivity to storage receive most of their precipitation as rain and contain clay‐rich regolith profiles, prominent argillic horizons, and/or anthropogenic modifications. In contrast, sites with high baseflow indices and low runoff sensitivity to storage receive the majority of precipitation as snow and have more permeable regolith profiles. The seasonal variability of water balance components is a key control on the dynamic range of hydraulically connected water in the critical zone. These findings lead us to posit that water balance partitioning and streamflow hydraulics are linked through the coevolution of critical zone architecture but that much work remains to parse these controls out quantitatively. Key Points: Quantitative signatures of hydrologic function show variations across the U.S. Critical Zone Observatory networkDynamic water storage range is correlated with hydroclimatic seasonality of water inputsVariations in catchment sensitivity and baseflow index point toward underlying structural controls [ABSTRACT FROM AUTHOR]

A catchment's hydrological response is controlled by climatic forcing and by the landscape through which water moves. Yet when we compare large samples of catchments, we often find climate to be the only good predictor of the hydrological response and a lot of variability is left unexplained. This contradicts extensive evidence from field and regional studies which shows the importance of catchment form (e.g., geology) on catchment hydrological processes, particularly on baseflow processes. We hypothesize that this is due to limitations in (a) the catchment attributes we use to inform our analyses and (b) the hydrological signatures we use to describe the hydrological response. To test these hypotheses, we use a large sample of catchment data across the contiguous United States. By reviewing literature from several U.S. regions, we show that region‐specific knowledge is underutilized in large sample studies. To organize the findings from these regions, we propose and apply a framework based on standardized perceptual models. Informed by these perceptual models, we use both available and newly calculated catchment attributes to show that baseflow signature predictions can be improved regionally. Multiple baseflow signatures are needed to better distinguish between different baseflow sources, such as the subsurface, surface water bodies, and snow. We conclude with pointing at potential future directions and argue that we should aim at a more systematic and hydrologically motivated selection of catchment attributes and hydrological signatures. Plain Language Summary: River flow dynamics are influenced by climate and by the landscape through which a river flows. However, when we investigate many river catchments using large‐scale data sets such as global maps, we often cannot find a link between river flow dynamics and landscape characteristics (e.g., geology). We show (a) that such maps are often too general and do not describe aspects relevant for river flow dynamics and (b) that we need to pay more attention to the metrics we use to quantify river flow dynamics. There is a wealth of information contained in articles and data sets focusing on the regional scale which we can and should make use of. Since such information is often very specific to a certain region, we propose a conceptual framework that facilitates the use of regional knowledge for comparison between different river catchments. Key Points: Region‐specific hydro(geo)logical knowledge is underutilized in large sample hydrologyMultiple baseflow signatures are needed to better distinguish between different baseflow sourcesWe propose and apply a framework based on standardized perceptual models to organize findings from hydrologically diverse regions [ABSTRACT FROM AUTHOR]

In spite of the importance of stream network dynamics for hydrology, ecology, and biogeochemistry, there is limited availability of analytical tools suitable for characterizing the temporal variability of the active fraction of river networks. To fill this gap, we introduce the concept of Stream Length Duration Curve (SLDC), the inverse of the exceedance probability of the total length of active streams. SLDCs summarize efficiently the effect of hydrological variability on the length of the flowing streams under a variety of settings. A set of stochastic network models is developed to link the features of the local hydrological status of the network nodes with the shape of the SLDC. We show that the mean network length is dictated by the mean persistency of the nodes, whereas the shape of the SLDC is driven by the spatial distribution of the local persistencies and their network‐scale spatial correlation. Ten field surveys performed in 2018 were used to estimate the empirical SLDC of the Valfredda river (Italy), which was found to be steep and regular—indicating a pronounced sensitivity of the active stream length to the underlying hydrological conditions. Available observations also suggest that the activation of temporary reaches during network expansion is hierarchical, from the most to the least persistent stretches. Under these circumstances, the SLDC corresponds to the spatial Cumulative Distribution Function of the nodes persistencies. The study provides a sound theoretical basis for the analyses of network dynamics in temporary rivers. Key Points: The concept of Stream Length Duration Curve is formalized and applied to a real‐world case studyStochastic models are developed to link the Stream Length Duration Curve to the network structure and local hydrological propertiesIn the Valfredda catchment (Italy), the activation of temporary reaches follows a hierarchical order [ABSTRACT FROM AUTHOR]

A data set containing hydrometeorological and hydroclimatological data for 3,540 watersheds in North America is described. The data set contains four main parts: (a) observed hydrometeorological data including daily streamflow observations, precipitation, minimum temperature, and maximum temperature; (b) 20 bias‐corrected climate model projections for two Representative Concentration Pathway (RCP) scenarios and five bias correction methods; (c) hydrological model calibration parameters and simulated streamflow for 4 hydrological models, 2 objective functions, and 10 calibration parameter sets for the reference period; and (d) hydrological simulations for each of the combinations of the abovementioned elements of the climate change impact study chain, for a total of 16,000 combinations. The data set also contains simulations and bias‐corrected climate for 30‐year horizons corresponding to 1.5°C and 2°C temperature increases for a subset of the climate models, for an additional 8,000 combinations. All simulations in the reference period are also provided. Fifty‐one precomputed hydrological indices are made available for each simulation. Overall, 2.89 × 1012 years of simulations are classified, analyzed, compressed, and made available for all researchers. This data set can be used to evaluate the uncertainty of various components in the impact study chain, to establish relationships between catchment properties and hydrological response to climate change, and to evaluate the spatial distribution of hydrological change according to a multitude of hydrological indices. Key Points: A data set containing hydrometeorological observations and hydroclimatological simulations for 3,540 watersheds in North America is presentedThe data set is the first to provide large‐sample hydrological simulations and indices in the context of climate change impacts on hydrologyThis data set can be used for multiple purposes including impact assessment, uncertainty, and hydrological process analysis over large scales [ABSTRACT FROM AUTHOR]

Recession analysis is widely used for characterizing aquifer and basin properties based on the falling limb of the hydrograph. However, recession analysis using streamflow discharge requires a relationship (the rating curve) between simultaneous measurements of water height, h, and discharge, Q, across a wide range of flows, which is expensive to obtain, and changes in time. We leverage the relationship between h and Q (typical power law) to perform recession analysis using h directly, thus permitting identification of transient flow regimes, where only h is available. Recession analysis evaluates the rate of change in discharge, −dQ/dt, as a function of discharge, Q, in a bilogarithmic plot where the slope, b, contains information about aquifer characteristics. While values of b are not conserved when replacing Q with h, we find that the variability in values of b within and between events is captured in recession analysis for both Q and h. For example, when considering individual recessions from a large number of watersheds, a change from a smaller b at high discharge/stage to a larger b at lower discharge/stage occurs in most watersheds and suggests a transition to a more long‐lasting, and less drought‐sensitive, baseflow regime. With the advent of low‐cost reliable pressure loggers, as well as satellites that provide global reporting of river stage, recession analysis using water height expands the number of river systems where recession analysis can be conducted and provides the potential for insights into the variability of watershed drainage characteristics without the need for a discharge record. Key Points: Using water height in place of discharge for recession analysis preserves general characteristics of the recession curveAn analytical expression equating the power law exponents of water height and discharge is given when the rating curve is a power lawThe ratio of power law exponents for individual recessions at high and low water heights can identify a transition in streamflow regimes [ABSTRACT FROM AUTHOR]

Recent technological advances in representation of processes in numerical climate models have led to skillful predictions, which can consequently increase the confidence of hydrological predictions and usability of hydroclimatic services. Given that many water‐related stakeholders are affected by seasonal hydrological variations, there is a need to manage such variations to their advantage through better understanding of the drivers that influence hydrological predictability. Here we analyze the seasonal forecasts of streamflow volumes across about 35,400 basins in Europe, which lie along a strong gradient in terms of climatology, scale, and hydrological regime. We then link the seasonal volumetric errors to various physiographic‐hydroclimatic descriptors and meteorological biases in order to identify the key drivers controlling predictability. Streamflow volumes over Europe are well predicted, yet with some geographic and seasonal variability; however, the predictability deteriorates with increasing lead time particularly in the winter months. Nevertheless, we show that the forecast quality is well correlated to a set of descriptors, which vary depending on the initialization month. The forecast quality of seasonal streamflow volumes is strongly dependent on the basin's hydrological regime, with limited predictability in relatively flashy basins. On the contrary, snow and/or baseflow dominated regions with long recessions show high streamflow predictability. Finally, climatology and precipitation forecast biases are also related to streamflow predictability, highlighting the importance of developing robust bias adjustment methods. Overall, this investigation shows that the seasonal streamflow predictability can be clustered, and hence regionalized, based on a priori knowledge of local hydroclimatic conditions. Plain Language Summary: Hydrological information for the months ahead is of great value to existing decision‐making practices, particularly to those affected by the vagaries of the climate and who would benefit from better understanding and managing climate‐related risks. Currently, there is limited knowledge of the factors controlling the quality of the seasonal streamflow forecasts. We analyze such forecasts over Europe and link their predictability to basin descriptors and meteorological biases. This allows the identification of the key drivers along a strong hydroclimatic gradient. The seasonal streamflow predictability varies geographically and seasonally with acceptable values for the first lead months. Predictability deteriorates with increasing lead time particularly in the winter months. The hydrological regime is strongly linked to the forecast quality, with quickly reacting basins showing low values. Basin climatology and precipitation forecast biases are also related to the predictability of streamflow. Key Points: Forecast quality of seasonal streamflow volume varies geographically and seasonally, while streamflow predictability can be regionalizedStreamflow predictability is strongly dependent on the basin's hydrological regime, climatology, and precipitation forecast biasesPredictability is higher in river systems of long streamflow memory than in systems immediately responding to the precipitation signal [ABSTRACT FROM AUTHOR]

The impacts of warming temperatures and declining snowpack on seasonal water yields in the Missouri River Headwaters are not well understood, revealing a gap in our understanding of regional hydroclimate and drivers of streamflow within the Upper Missouri River basin. This study presents the first annually resolved tree‐ring reconstruction of spring precipitation for the Missouri River Headwaters. This reconstruction along with existing tree‐ring reconstructions of 1 April snow water equivalence (SWE) and water year streamflow are used to detect variable influences of winter and spring precipitation on streamflow over past centuries, and relative to the modern period. The results suggest that spring precipitation has been a more consistent influence on water year streamflow in the Missouri River Headwaters over past centuries than winter snowpack. The strong relationship between 1 April SWE values and water year streamflow in the Missouri River Headwaters observed over much of the 20th century is not found to be a consistent feature of these multicentury paleorecords. Instead, the climatic influences on streamflow within the Missouri River Headwaters are likely more variable than 20th‐century instrumental records indicate. Key Points: Skillful reconstructions of precipitation, streamflow, and snowpack can be generated and used to examine hydroclimatic relationships over past centuriesThe influence of snowpack and spring precipitation on water year streamflow has varied over past centuriesMissouri River Headwaters streamflow appears to have been more consistently influenced by spring rainfall over past centuries than by snowpack [ABSTRACT FROM AUTHOR]

Riparian lowlands act as interfaces between the streams and upland areas. This study identified and quantified local flow paths in four transects of a 26 ha Danish riparian lowland in a glacial till landscape. The riparian lowland was fed by drain water from the upland agricultural drainage catchments. Precipitation, stream stage, and drainage discharge into the riparian lowland were measured continuously, while groundwater hydraulic heads were measured in piezometer pipes twice per month. A water balance model was developed to quantify water fluxes leaving the riparian lowland area via evapotranspiration, leakage to a deeper aquifer, and via groundwater flow, drain flow, and overland flow to the adjacent stream. Overland flow originating from the tile drains was the main flow path in all four transects, and also fluxes to the stream via groundwater or lowland tile drains were significant in some subareas. The presence of a secondary tile drainage network within parts of the riparian lowland reduced overland flow and increased interaction with the riparian lowland soils. Area‐normalized fluxes varied greatly between transects, largely reflecting variations in hydraulic loading rate (ratio of water input rate to the area of the receiving riparian lowland). This, combined with the significance of groundwater flow and riparian lowland tile drain flow in some of the investigated transects, revealed a heterogeneous distribution of flow paths within the small headwater lowland. The rate of overland flow was highly correlated to the hydraulic loading rate, which in turn was dominated by the drainage discharge rate at the hillslope boundary. Key Points: Overland flow, bypassing the riparian lowland, is produced when the hydraulic loading exceeds riparian lowland infiltrationMagnitudes of overland flow are correlated with the ratio of catchment area to riparian lowland areaRiparian lowland drains reduce overland flow and increase water residence times [ABSTRACT FROM AUTHOR]

Streamflow generation in mountain watersheds is strongly influenced by snow accumulation and melt, and multiple studies have found that snow loss leads to earlier snowmelt timing and declines in annual streamflow. However, hydrologic responses to snow loss are heterogeneous, and not all areas experience streamflow declines. This research examines whether streamflow generation is different for rainfall versus snowmelt inputs. We compiled a sample of 57 small U.S. Geological Survey watersheds in the western United States containing a Natural Resource Conservation Service Snow Telemetry site and having ratios of mean annual peak snow water equivalent to precipitation ratios >0.25. Daily streamflow was separated into quickflow and baseflow using a digital filter, and quickflow was then divided into quickflow response intervals using thresholds in quickflow slope. Each quickflow response interval was categorized by its fraction of input from snowmelt. Most sites exhibited two streamflow generation peaks each year, with one peak in the winter when runoff efficiency is greatest, and the second in the spring during peak snowmelt input. On average, study watersheds were dominated by snowmelt inputs (70%), and snowmelt and mixed inputs usually generated greater streamflow than rainfall because of higher inputs and longer durations. However, rainfall produced high streamflow generation in winter, when watersheds have their highest runoff efficiency (81%) across all input types. We demonstrate that while snowmelt is important for streamflow generation due to high input over long periods, increases in rain and mixed input during wet winter periods can countervail tendencies for reduced streamflow with declining snowpacks. Key Points: Quickflow response intervals (QRIs) are subannual hydrograph responses suitable for analyzing both snowmelt and rainfall‐dominated streamflowSnowmelt contributions to streamflow in western watersheds are lower than previous estimates, with most QRIs originating from mixed rain and snow inputSnowmelt and mixed rain and snow QRIs produce more streamflow than rainfall‐dominated QRIs in most watersheds, except those with high winter rain and snowmelt input [ABSTRACT FROM AUTHOR]

The precise estimation of process effects in hydrological models requires applying models to large scales with extensive spatial variability in controlling factors. Despite progress in large‐scale applications of hydrological models in conterminous United States (CONUS) river basins, spatial constraints in model parameters have prevented the interbasin sharing of data, complicating quantification of process effects and limiting the accuracy of model predictions and uncertainties. Hierarchical Bayesian methods enable data sharing between basins and the identification of the causes of model uncertainties, which can improve model accuracy and interpretability; however, computational inefficiencies have been an obstacle to their large‐scale application. We used a new generation of Bayesian methods to develop a hierarchical version of a previous hybrid (statistical‐mechanistic) SPAtially Referenced Regression On Watershed attributes model of long‐term mean annual streamflow in the CONUS. We identified hierarchical (regional) variations in model coefficients and uncertainties and evaluated their effects on model accuracy and interpretability across diverse environments in 16 major CONUS regions. Hierarchical coefficients significantly improved spatial accuracy of model predictions, with the largest improvements in humid eastern regions, where uncertainties were approximately one third of those in arid western regions. Half of the coefficients varied regionally, with the largest variations in coefficients associated with water losses in streams and reservoirs. Our unraveling of the causes of model uncertainties identified a small latent process component of runoff that varies inversely with river size in most CONUS regions. Our study advances the use of hierarchical Bayesian methods to improve the predictive capabilities of hydrological models. Key Points: Use of hierarchical Bayesian methods with a spatially explicit statistical model improves accuracy of streamflow prediction across the United StatesThe study identifies regionally varying model coefficients for natural and human controls on water transport and losses in watershedsHierarchical models show promise for reducing uncertainties in transfers of hydrological information from gauged to ungauged watersheds [ABSTRACT FROM AUTHOR]

Despite considerable progress in hydrological modeling, challenges remain in the interpretation and accurate transfer of hydrological information across watersheds and scales. In the conterminous United States (CONUS), these limitations are related to spatial inconsistencies and constraints in hydrological model structures, including a lack of spatially explicit process components (streams, reservoirs, and watershed development) and restricted estimation of model parameters across watersheds. Collectively, such limitations can impede identification of the causes of streamflow variations across the diversity of watershed sizes and land uses in the CONUS and contribute to model imprecision and spatial inconsistencies in prediction uncertainties. We addressed these concerns with a new approach, the first hybrid (statistical‐mechanistic) SPARROW (SPAtially Referenced Regression On Watershed attributes) model of long‐term mean annual streamflow, applied across diverse environmental settings of the CONUS. The hybrid model coupled previous catchment‐scale (1 km) water balance predictions of "natural" unit area runoff, which are inclusive of major water cycling processes, with additional explanatory variables (e.g., soils, vegetation, land use, topography, water losses in streams, and reservoirs) that account for the effects of natural and cultural water supply and demand processes that operate over large spatial scales and explain streamflow variability across CONUS river basins. Accounting for these statistically unique effects, including a nonlinear surface area‐dependent scaling of water loss in river networks, significantly improved the accuracy of mean streamflow predictions in CONUS basins. Our hybrid modeling approach provides new methods for transferring hydrological information to ungauged locations in river networks, especially those in larger and more culturally diverse CONUS watersheds. Key Points: Integration of water balance methods with a spatially explicit statistical model improves accuracy of streamflow prediction across the USAStudy identifies large‐scale natural and human controls on water delivery from the land to river networks and quantifies in‐stream lossesApproach improves understanding and parameterization of large‐scale hydrological processes and scaling properties to support prediction [ABSTRACT FROM AUTHOR]

Flow‐duration curve (FDC) based streamflow estimation methods involve estimating an FDC at an ungaged or partially gaged location and using the time series of nonexceedance probabilities estimated from donor streamgage sites to generate estimates of streamflow. We develop a mathematical framework to illustrate the connection between copulas and prior FDC‐based approaches. The performance of copula methods is compared to several other streamflow estimation methods using a decade of daily streamflow data from 74 sites located within two river basins in the southeast United States with different climate characteristics and physiographic properties. We show that copula approaches: (1) outperform other methods in the limiting case of perfect information with regard to the rank‐based correlation structure and FDCs across the gaging network; (2) provide a hedge against poor performance when donor information becomes sparser and less informative; (3) outperform other methods when used for partially gaged sites with several years of available data; and (4) remain a competitive albeit nondominating method for ungaged sites and partially gaged sites with limited data when realistic error is introduced in the estimation of FDCs and correlations across the gaging network. Key Points: Flow‐duration curve streamflow estimation methods (e.g., QPPQ) are formalized as copula modelsCopulas can flexibly model dependencies between streamflow at different sitesCopulas approaches allow for a statistically rigorous calculation of conditional variances [ABSTRACT FROM AUTHOR]

Catchment travel times integrate the multitude of hydrological flow processes and provide insights into catchment functioning. StorAge Selection (SAS) functions describe how residence times of water in storage are related to travel times of water in catchment outflows. As such, SAS functions are useful to summarize transport processes in catchments and are ideal to simulate catchment outflows and the concentrations of various solutes and tracers. Recent studies suggested that using one probability distribution function (pdf) for SAS functions may not account for all transport processes in various hydrological systems. In this study we introduced a composite streamflow SAS function defined as a weighted sum of several pdfs (called components), using a uniform pdf for the youngest water and two gamma pdfs for the oldest water. The novel parameterization of this SAS function is nonlinear with respect to catchment storage and to a proxy of storage variations. This composite streamflow SAS function was needed to obtain realistic simulations of the complex high‐resolution (subdaily) stream deuterium (δ2H) dynamics measured for 2 years in the Weierbach, a forested headwater catchment in Luxembourg, whereas various SAS functions using only one component (i.e., pdf) failed. The three components of the composite streamflow SAS function confirmed the superposition of streamflow generation mechanisms with contrasting travel times suggested by previous experimental and modeling studies. Our work suggests that in some catchments, composite SAS functions may offer a more realistic perspective on transport processes derived from high‐resolution tracer data than simpler SAS functions. Plain Language Summary: The age of stream water is the time it takes rainfall to reach the stream. Stream water age depends on how water flows through natural landscapes. It provides essential information because it represents the time water spends in contact with soils and rocks, which influences stream chemistry. Recent studies showed that the current mathematical models describing stream water ages cannot completely explain the observed variations of certain hydrological tracers in the stream. These tracers are the water itself, or solutes transported with water. In this study we propose an adaptation of the models describing stream water ages that better corresponds to such observations in a small stream located in a forest in Luxembourg. We suggest that considering that the streamflow is derived from a multitude of superimposed flow mechanisms is crucial for a better description of stream water ages and for better interpretations of the hydrological tracers measured in the streams. Key Points: We introduced composite StorAge Selection functions defined as a weighted sum of probability distribution functions (i.e., components)A composite streamflow StorAge Selection function was necessary to simulate the complex high resolution δ2H dynamics of the streamThe components of this StorAge Selection function confirmed the hypothesized superposition of streamflow generation processes [ABSTRACT FROM AUTHOR]

Synthetic daily streamflow generation requires a critical understanding of the underlying dynamics resulting from the inherent temporal asymmetry (time irreversibility) in the rising and falling limbs of the hydrograph. Most models considering the time irreversibility deal with single‐site streamflow simulation. Addressing intersite dependencies is, however, crucial for interconnected stream networks. This study presents a time irreversibility dynamics‐based multisite streamflow generating framework to simulate concurrent streamflow sequences at multiple sites in a basin. This framework explicitly takes into account the spatial correlation and time irreversible dynamics of streamflow. To demonstrate the utility and applicability, a few selected streamflow gauging stations in the Godavari River Basin, located in southern India, are considered. The proposed framework shows the ability to adequately generate multisite simulations capturing at‐site statistics as well as intersite correlations for the case study. Furthermore, the approach ensures that the simulated flow values are not merely resampled from the historical data, uses the physical features of the hydrograph, and shows variability beyond that observed in the historical sequence. Such a rich variety of streamflow sequences can help water managers to investigate how existing water resources systems on interconnected stream networks will operate in scenarios which have not been observed in the historical record. Key Points: A three‐stage algorithm for generating concurrent multisite streamflow is proposedTime irreversible dynamics of hydrologic processes at daily scale are accounted forThe algorithm results in simulated sequences that capture variability beyond that represented in the historical streamflow record [ABSTRACT FROM AUTHOR]

Conventional basin‐by‐basin approaches to calibrate hydrologic models are limited to gauged basins and typically result in spatially discontinuous parameter fields. Moreover, the consequent low calibration density in space falls seriously behind the need from present‐day applications like high resolution river hydrodynamic modeling. In this study we calibrated three key parameters of the Variable Infiltration Capacity (VIC) model at every 1/8° grid‐cell using machine learning‐based maps of four streamflow characteristics for the conterminous United States (CONUS), with a total of 52,663 grid‐cells. This new calibration approach, as an alternative to parameter regionalization, applied to ungauged regions too. A key difference made here is that we tried to regionalize physical variables (streamflow characteristics) instead of model parameters whose behavior may often be less well understood. The resulting parameter fields no longer presented any spatial discontinuities and the patterns corresponded well with climate characteristics, such as aridity and runoff ratio. The calibrated parameters were evaluated against observed streamflow from 704/648 (calibration/validation period) small‐to‐medium‐sized catchments used to derive the streamflow characteristics, 3941/3809 (calibration/validation period) small‐to‐medium‐sized catchments not used to derive the streamflow characteristics as well as five large basins. Comparisons indicated marked improvements in bias and Nash‐Sutcliffe efficiency. Model performance was still poor in arid and semiarid regions, which is mostly due to both model structural and forcing deficiencies. Although the performance gain was limited by the relative small number of parameters to calibrate, the study and results here served as a proof‐of‐concept for a new promising approach for fine‐scale hydrologic model calibrations. Key Points: Novel method proposed for hydrologic model calibration using machine learning‐based maps of streamflow characteristics1/8° grid‐by‐grid calibration resulting in parameters with good spatial coherenceCalibrated parameters substantially improved model performance in runoff prediction [ABSTRACT FROM AUTHOR]

Streamflow simulation of the headwater catchment of the Yellow River basin (HCYRB) in China is important for water resources management of the Yellow River basin. A statistical‐dynamical model, combining regular vine copulas with an optimization method for structure estimation, is presented with an application for simulating the monthly streamflow with local climate drivers at HCYRB. Local climate drivers for streamflow in every month are analyzed using rank‐based correlation. Precipitation, evaporation, and temperature generally show strong associations with streamflow. Winter streamflows relate to total precipitation of the wet season and total evaporation of October and November, while unfrozen‐month streamflows are correlated with evaporation and precipitation of current month and previous 1 month in the wet season. Both canonical vine and D‐vine copulas are applied to develop different conditional quantile functions for streamflows in different months with their dynamical covariates. The covariates are selected from historical streamflows and climate drivers with appropriate lags using partial correlations. The optimal vine trees are selected using the sequential maximum spanning tree algorithm with the weight based on both dependence and goodness of fit. The model demonstrates higher skill than existing vine‐based models and the seasonal autoregressive integrated moving average model. The enhanced skill of the hybrid statistical‐dynamical model comes from an improved capability of capturing nonlinear correlation and tail dependence of streamflow and climate drivers with the optimization of vine structure selection. The model provides an effective advance to enhance water resources planning and management for HCYRB and the whole basin. Key Points: The associations of streamflow in every month and different climate indices in Headwater Catchment of the Yellow River Basin are analyzedThe sequential maximum spanning tree optimization method is proposed for vine‐based monthly streamflow simulation with multiple predictorsThe model is verified skillful for its capability of capturing nonlinearity and tail dependence with the optimization of vine structures [ABSTRACT FROM AUTHOR]