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The Surface Water and Ocean Topography (SWOT) mission will vastly expand measurements of global rivers, providing critical new data sets for both gaged and ungaged basins. SWOT discharge products (available approximately 1 year after launch) will provide discharge for all river that reaches wider than 100 m. In this paper, we describe how SWOT discharge produced and archived by the US and French space agencies will be computed from measurements of river water surface elevation, width, and slope and ancillary data, along with expected discharge accuracy. We present for the first time a complete estimate of the SWOT discharge uncertainty budget, with separate terms for random (standard error) and systematic (bias) uncertainty components in river discharge time series. We expect that discharge uncertainty will be less than 30% for two‐thirds of global reaches and will be dominated by bias. Separate river discharge estimates will combine both SWOT and in situ data; these "gage‐constrained" discharge estimates can be expected to have lower systematic uncertainty. Temporal variations in river discharge time series will be dominated by random error and are expected to be estimated within 15% for nearly all reaches, allowing accurate inference of event flow dynamics globally, including in ungaged basins. We believe this level of accuracy lays the groundwork for SWOT to enable breakthroughs in global hydrologic science. Plain Language Summary: The Surface Water and Ocean Topography (SWOT) satellite mission was launched on 15 December 2022. SWOT is designed to produce estimates of river discharge on many rivers where no in situ discharge measurements are currently available. This paper describes how SWOT discharge estimates will be created, and their expected accuracy. SWOT discharge will be estimated using simple flow laws that combine SWOT measurements of river water elevation above sea level, river width, and river slope, with ancillary data such as river bathymetry. We expect that discharge uncertainty will be less than 30% for two‐thirds of global reaches and will be dominated by a systematic bias. Temporal variations in river discharge time series are expected to be estimated within 15% for nearly all reaches, thus capturing the response of river discharge to rainfall and snowmelt events, including in basins that are currently ungaged, and providing a new capability for scientists to better track the flows of freshwater water through the Earth system. Key Points: The Surface Water and Ocean Topography satellite mission is designed to enable the estimation of discharge for global rivers wider than 100 mWhen unconstrained by in situ data, discharge uncertainty is expected to be <30% for most reaches, and to be dominated by systematic biasWe expect discharge temporal variations to be estimated to within 15% for nearly all reaches globally [ABSTRACT FROM AUTHOR]

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

The paper presents a thermodynamic basis for water balance partitioning at the catchment scale, through formulation of flux-force relationships for the constituent hydrological processes, leading to the derivation of optimality conditions that satisfy the principle of Maximum Entropy Production (MEP). Application of these optimality principles at three different time scales leads to the derivation of water balance equations that mimic widely used, empirical models, i.e., Budyko-type model over long-term scale, the 'abcd' model at monthly scale, and the SCS model at the event scale. The applicability of MEP in each case helps to draw connections between the water balances at the three different time scales, and to demonstrate a common thermodynamic basis for the otherwise empirical water balance models. In particular, it is concluded that the long time scale Budyko-type model and the event scale SCS model are both special cases of the monthly 'abcd' model. [ABSTRACT FROM AUTHOR]

Snow surveys in two Arctic watersheds located in Alaska, USA, provide 32 years of spatially distributed snow water equivalent (SWE) and snow depth observations. Annual snow surveys from the Imnavait Creek (20,036 measurements from 1985 to 2017) and Upper Kuparuk River (5,804 measurements from 1997 to 2017) watersheds were conducted to capture end‐of‐winter snow accumulation. The average end‐of‐winter SWE in the Upper Kuparuk River watershed (102 ± 29 mm) is consistently less than the Imnavait Creek watershed (130 ± 34 mm) during the common period of record (1997–2017). The average end‐of‐winter SWE in both watersheds indicates a positive trend. Comparison of SWE records with cumulative solid precipitation measured at the Imnaviat [sic] SNOTEL site highlights the undercatch of gauge precipitation and difference in long‐term trends. In this paper, we present a historic overview of data collection, discuss data accuracy, and point out advantages and limitations associated with ground‐based snow measurements in remote Arctic locations. As new methods and techniques of measuring SWE and solid precipitation become available, the presented data set will provide a historic perspective for new observations and will quantitatively relate current or future snow conditions to those that have occurred since the late twentieth century. Key Points: Repeated snow surveys (25,840 measurements over 32 years) are conducted in two Arctic watersheds for hydrologic applicationsAnnual snow water equivalent range is 73–207 mm, with average SWE of 124 ± 34 mm in the Imnavait Creek watershed (1985–2017)Annual snow water equivalent range is 51–169 mm with average SWE of 102 ± 29 mm in the Upper Kuparuk River watershed (1997–2017) [ABSTRACT FROM AUTHOR]

Integrated, process-based numerical models in hydrology are rapidly evolving, spurred by novel theories in mathematical physics, advances in computational methods, insights from laboratory and field experiments, and the need to better understand and predict the potential impacts of population, land use, and climate change on our water resources. At the catchment scale, these simulation models are commonly based on conservation principles for surface and subsurface water flow and solute transport (e.g., the Richards, shallow water, and advection-dispersion equations), and they require robust numerical techniques for their resolution. Traditional (and still open) challenges in developing reliable and efficient models are associated with heterogeneity and variability in parameters and state variables; nonlinearities and scale effects in process dynamics; and complex or poorly known boundary conditions and initial system states. As catchment modeling enters a highly interdisciplinary era, new challenges arise from the need to maintain physical and numerical consistency in the description of multiple processes that interact over a range of scales and across different compartments of an overall system. This paper first gives an historical overview (past 50 years) of some of the key developments in physically based hydrological modeling, emphasizing how the interplay between theory, experiments, and modeling has contributed to advancing the state of the art. The second part of the paper examines some outstanding problems in integrated catchment modeling from the perspective of recent developments in mathematical and computational science. [ABSTRACT FROM AUTHOR]

The challenge of sustainable freshwater management requires identification and characterization of the underlying components and dynamic interactions within the coupled human-hydrology system. This paper builds a model that captures the dynamic water balance evolution and coupled human response within the Lake Toolibin catchment in West Australia's wheatbelt region. Two subcatchments in different parts of the landscape were selected to examine the key emergent properties of the coupled socio-hydrology system over a 100 year period, by analyzing the two-way feedbacks of land use management (human system feedback) and land degradation (natural system feedback). Using a relatively simple parameterization of community sensitivity to land degradation within the model, we identified positive and negative feedbacks, the presence of threshold behavior, time scale differences between fast and slow moving variables, differences in time lags resulting from disparate resistance levels of the natural system, and the degree of adaptive learning inherent in the human system. Specifically, the valley floor subcatchment transitioned through four phases-expansion, contraction, recession, and recovery-demonstrating a threshold shift in the human feedback after 60 years, while the upslope subcatchment appears to still be in the contraction phase, with no sign of reaching a threshold shift in 100 years. These results demonstrate that the model is capable of isolating the two-way feedbacks of the coupled system and has implications for resilience theory, suggesting that greater resistance in the underlying natural system counteracts the onset of a negative feedback loop and instigation of adaptive behaviors in the human system. [ABSTRACT FROM AUTHOR]

The hyporheic zone, defined by shallow subsurface pathways through river beds and banks beginning and ending at the river, is an integral and unique component of fluvial systems. It hosts myriad hydrologically controlled processes that are potentially coupled in complex ways. Understanding these processes and the connections between them is critical since these processes are not only important locally but integrate to impact increasingly larger scale biogeochemical functioning of the river corridor up to the river network scale. Thus, the hyporheic zone continues to be a growing research focus for many hydrologists for more than half the history of Water Resources Research. This manuscript partly summarizes the historical development of hyporheic zone hydrologic science as gleaned from papers published in Water Resources Research, from the birth of the concept of the hyporheic zone as a hydrologic black box (sometimes referred to as transient storage zone), to its adolescent years of being torn between occasionally competing research perspectives of interrogating the hyporheic zone from a surface or subsurface view, to its mature emergence as an interdisciplinary research field that employs the wide array of state-of-the-art tools available to the modern hydrologist. The field is vibrant and moving in the right direction of addressing critical fundamental and applied questions with no clear end in sight in its growth. There are exciting opportunities for scientists that are able to tightly link the allied fields of geology, geomorphology, hydrology, geochemistry, and ecology to tackle the many open problems in hyporheic zone science. [ABSTRACT FROM AUTHOR]

This paper examines catchments that are almost ungauged, i.e., catchments for which only a small number of point flow measurements are available. In these catchments, hydrologists may still need to simulate continuous streamflow time series using a rainfall-runoff model, and the methodology presented here allows using few point measurements for model parameterization. The method combines regional information (parameter sets of neighboring gauged stations) and local information (contributed by the point measurements) within a framework where the relative weight of each source of information is made dependent on the number of point measurements available. This approach is tested with two different hydrological models on a set of 609 catchments in France. The results show that on average a few flow measurements can significantly improve the simulation efficiency, and that 10 measurements can reduce the performance gap between the gauged and ungauged situations by more than 50%. The added value of regional information progressively decreases until being almost insignificant when sufficient flow measurements are available. Model parameters tend to come closer to the values obtained by calibration in fully gauged conditions as the number of point flow measurements increases. [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]

Detiding end-of-catchment flow data are an important step in determining the total volumes of freshwater (and associated pollutant loads) entering the ocean. We examine three approaches for separating freshwater and tidal flows from tidally affected data: (i) a simple low-pass Butterworth filter (BWF); (ii) a robust, harmonic analysis with Kalman smoothing (RoHAKS) which is a novel approach introduced in this paper; and (iii) dynamic harmonic regression (DHR). Using hydrographic data collected in the Logan River, Australia, over a period of 452 days, we judge the accuracy of the three methods based on three criteria: consistency of freshwater flows with upstream gauges; consistency of total discharge volumes with the raw data over the event; and minimal upstream flow. A simulation experiment shows that RoHAKS outperforms both BWF and DHR on a number of criteria. In addition, RoHAKS enjoys a computational advantage over DHR in speed and use of freely available software. [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]

This paper evaluates the use of a small number of generalist parameter sets as an alternative to classical calibration. Here parameter sets are considered generalist when they yield acceptable performance on a large number of catchments. We tested the genericity of an initial collection of 106 parameter sets sampled in the parameter space for the four-parameter GR4J rainfall-runoff model. A short list of 27 generalist parameter sets was obtained as a good compromise between model efficiency and length of the short list. A different data set was used for an independent evaluation of a calibration procedure, in which the search for an optimum parameter set is only allowed within this short list. In validation mode, the performance obtained is inferior to that of a classical calibration, but when the amount of data available for calibration is reduced, the generalist parameter sets become progressively more competitive, with better results for calibration series shorter than 1 year. [ABSTRACT FROM AUTHOR]

The location of a point on the landscape within a stream network (hydrologic position) can be an important predictive measure in hydrology. Hydrologic position is defined here by two metrics: lateral position and distance from stream to divide, both measured horizontally. Lateral position (dimensionless) is the relative position of a point between the stream and its watershed divide. Distance from stream to divide (units of length) is an indicator of position within a watershed: generally small near a confluence and generally large in headwater areas. Watersheds and watershed divides are defined here by Thiessen polygons rather than topographic divides. Lateral position and distance from stream to divide are also defined in the context of hydrologic order. Hydrologic order "n" is defined as the network of streams, and associated divides, of order n and higher. And given that a point can have different positions in different hydrologic orders the term multiorder hydrologic position (MOHP) is used to describe the ensemble of hydrologic positions. MOHP was mapped across the conterminous United States for nine hydrologic orders at a spatial resolution of 30 m (about 8.7 billion pixels). There are 18 metrics for each pixel. Four case studies are presented that use MOHP metrics as explanatory factors in random forest machine learning models. The case studies show that lower order MOHP metrics can serve as indicators of hydrologic process while higher‐order metrics serve as indicators of location. MOHP is shown to have utility as a predictor variable across a large range of scales (50,000 to 8,000,000 km2). Plain Language Summary: In hydrology, as in other endeavors, location matters. This study presents a new type of data that describes the location of a point on the landscape in the context of the network of streams that are present across the continental United States. The new data are presented as maps, and different patterns can be recognized in different areas of the United States. The patterns that can be seen also vary as one looks more or less closely at an area. The patterns that are present in these maps are shown to be useful for the purposes of mapping water resources. Key Points: Multiorder hydrologic position (lateral position and distance from stream to divide) quantifies the location of a point on the landscapeMultiorder hydrologic position is mapped for nine hydrologic orders at a 30‐m resolution for the conterminous United StatesMultiorder hydrologic position is used in machine learning models for the purposes of physiographic and hydrologic mapping [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]

Denitrification in the hyporheic zone (HZ) of river corridors is crucial to removing excess nitrogen in rivers from anthropogenic activities. However, previous modeling studies of the effectiveness of river corridors in removing excess nitrogen via denitrification were often limited to the reach‐scale and low‐order stream watersheds. We developed a basin‐scale river corridor model for the Columbia River Basin with random forest models to identify the dominant factors associated with the spatial variation of HZ denitrification. Our modeling results suggest that the combined effects of hydrologic variability in reaches and substrate availability influenced by land use are associated with the spatial variability of modeled HZ denitrification at the basin scale. Hyporheic exchange flux can explain most of spatial variation of denitrification amounts in reaches of different sizes, while among the reaches affected by different land uses, the combination of hyporheic exchange flux and stream dissolved organic carbon (DOC) concentration can explain the denitrification differences. Also, we can generalize that the most influential watershed and channel variables controlling denitrification variation are channel morphology parameters (median grain size (D50), stream slope), climate (annual precipitation and evapotranspiration), and stream DOC‐related parameters (percent of shrub area). The modeling framework in our study can serve as a valuable tool to identify the limiting factors in removing excess nitrogen pollution in large river basins where direct measurement is often infeasible. Key Points: Hyporheic exchange flux controls the spatial variation of denitrification across reaches with different sizes and land usesThe combination of hyporheic exchange flux and stream dissolved organic carbon (DOC) explains the differences in denitrification for different land use streamsD50, stream slope, precipitation, evapotranspiration, and shrub area can explain most of the spatial variability in denitrification [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]

Land Surface Models (LSMs) are key components of Earth System Models, which the Intergovernmental Panel on Climate Change relies on in many of their studies. However, these models either neglect or oversimplify basin‐scale hydrological processes that produce the land surface water balance. Bringing the dominant physical processes from the local scale up to the LSMs presents a significant challenge for improving the models. This article presents an upscaling (or bottom‐up) approach to identify the basin‐scale driving variables that need to be exported into LSMs. The approach is developed on the Little Washita Watershed (OK, USA) using 20‐year hydrology (1993–2013). A physically based 3D model built with HydroGeoSphere first produces a reference simulation. An equivalent hillslope model is then able to capture both 3D simulated water balance and local water table dynamics with reasonable accuracy. The physical analysis of the water balance in the different hillslope compartments leads to the identification of two driving variables: seepage face extension and water table slope. The two variables are then implemented in a conceptual model. Results show a good capacity of this model to capture the water balance of hillslopes having different lengths and slopes. Moreover, the model is able to capture the water balance of the reference simulation with reasonable accuracy. The proposed approach thus reduces the 3D watershed model to a two‐variable conceptual model that constitutes a basis for developing an improved LSM hydrology. Key Points: An upscaling approach reduces model dimensionality from 3D watershed to 2D equivalent hillslope, and then to a two‐hydrologic‐variable modelThe seepage face extension and the water table slope are identified as two key variablesThe approach is validated on the 20‐year hydrology (1993–2013) of the Little Washita Watershed (Ok, USA) [ABSTRACT FROM AUTHOR]

Climate change and other disturbances significantly impact hydrogeochemical exports from mountainous headwater catchments such as the Upper Colorado River Basin. Developing a mechanistic understanding of how the physical and chemical processes interact in time and space in an integrated manner is key to quantifying the future impacts of such disturbances. The hydrogeochemical response of a mountainous catchment in the 2010–2019 period is evaluated quantitatively using a high‐resolution model that simulates integrated hydrology, and transport and reactions for selected solutes and minerals. The model assumes that pyrite is present only at depth while calcite is distributed uniformly, and captures the observed C‐Q reasonably well. Distinct C‐Q dynamics are observed in an average (WY16), a wet (WY17), and a dry (WY18) water year. The model also quantifies the water fraction from surface, shallow and deep groundwater compartments using tracers, and suggests greater groundwater contributions to peak stream discharge in the dry WY18. Results demonstrate that calcium concentrations do not change significantly from year to year, while sulfate shows significant temporal variability. Pyrite dissolution is affected by the changing hydrological drivers where it is enhanced in the dry WY18; calcite dissolution supplements calcium dilution under high flow conditions. The model simulates the reaction hotspots controlled by hydrological conditions, and the spatially‐resolved results show that higher soil saturation and less snowpack occur earlier on the south‐facing side than on the north‐facing side. This is a first‐of‐its‐kind demonstration of a model that integrates hydrologic processes, including evapotranspiration, and reactive transport to enable a predictive understanding of hydrogeochemical exports. Plain Language Summary: Climate change significantly impacts water resources, including water quantity and quality, particularly in mountainous headwater catchments such as the Upper Colorado River Basin that are key for water supply in downstream regions of the western U.S. In this work, we used a mathematical model to quantify the movement of water and chemical species under changing weather and climate conditions. Changing rainfall and early snowmelt affects both the volume of water and the amount of minerals that dissolve, impacting water quality observed at the gauged stations downstream. Consistent with the available measurements, the three‐dimensional model makes it possible to understand how the watershed's topography and the flow of water in the streams and the groundwater interact in time and in space. In particular, we found that the mineral composition with depth explains in part the changes in observed concentrations between dry and wet periods and that the north‐ and south‐facing slopes of the river valley contribute differently to these concentrations. These effects are relatively small in this study but larger climate variability could enhance them. Key Points: Multi‐year calibration to discharge and chemical exports enables reasonable prediction of watershed hydro‐geochemical responseThe role of vertical mineral heterogeneity is captured by differences in the C‐Q response in the wet and dry yearsA heterogeneous landscape response is also observable but is diluted in terms of aggregate C‐Q response [ABSTRACT FROM AUTHOR]

Nonpoint source urban nutrient loading into streams and receiving water bodies is widely recognized as a major environmental management challenge. A dominant research and management paradigm assumes that loading primarily derives from elevated stormwater. However, baseflow can account for a large portion of total loading, especially for low development intensity watersheds which comprise the largest urban areas. We investigated the sources and drivers of nonpoint source baseflow nitrogen loading across 27 headwater catchments in the urbanized Piedmont region of North Carolina, USA. Nitrate isotopes, predictors of concentration‐discharge (CQ) slopes, and predictors of mean annual total dissolved nitrogen (TDN) loading suggest that wastewater was a major baseflow nonpoint source of nitrogen across developed catchments likely contributing 61% of nitrate loading from septic served catchments and 49% from sewer served catchments. Our findings suggest that subsurface TDN was abundant, loading was largely transport limited, and the hydrogeomorphic position of sanitary infrastructure strongly influences transport. We developed an empirical model showing catchment loading increased with the topographic wetness index of sanitary sewer location, convergent sloping land area, parcel density, and residual agricultural landcover (R2 = 0.78). We extended this model to the study region's 1,436 developed small (0.3–20.8 sq km) catchments. We estimated up to 92.7% of nonpoint source baseflow TDN loading comes from low and medium development intensity catchments, and sanitary infrastructure in wet areas of the landscape accounts for 39% of regional baseflow loading. Our research indicates that managing baseflow loading will require addressing lower development intensity catchments and sanitary infrastructure. Plain Language Summary: Nutrient loading from developed landscapes into streams and receiving water bodies is widely recognized as a major environmental management challenge. The dominant research and management paradigm is that loading primarily derives from elevated stormwater. However, baseflow periods between storms can account for a large portion of total loading, especially for low development intensity watersheds which are most common. We investigated the sources and transport mechanisms of nonpoint source baseflow nitrogen loading across 26 developed catchments and one primarily forested catchment in the urbanized Piedmont region of North Carolina. We found that wastewater likely contributed 57% of nonpoint source baseflow nitrate loading across developed catchments served by both septic and sewer systems. Our findings suggest that subsurface nitrogen was abundant in developed watersheds such that increased baseflow resulted in higher nitrogen loading and often higher concentrations. Loading increased with metrics of the predicted wetness of sanitary infrastructure location, landforms which promotes subsurface flow, parcel density, and residual agricultural landcover. We used these predictors to model nitrogen loading regionally and estimated up to 92.7% of nonpoint source baseflow TDN loading comes from low, and medium development intensity catchments, although urban management efforts are often concentrated within high development intensity catchments. Key Points: Wastewater was a major source of nonpoint source baseflow N loading across developed catchments served by septic and sewer systemsBaseflow loading was transport limited and largely controlled by the position of sanitary infrastructure within the terrestrial flow fieldExtensive lower intensity development was estimated to produce more baseflow N loading regionally than high intensity development [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]

Artificial levees are anthropogenic structures designed to hydrologically disconnect rivers from floodplains. The extent of artificial levees in the contiguous United States (CONUS) is unknown. To better estimate the distribution of artificial levees, we tested several different geomorphic, land cover, and spatial variables developed from the National Elevation Dataset, the National Land Cover database, and the National Hydrology Dataset HR Plus. We used known levee locations from the National Levee Database as training data. We tested machine learning and general logistic models' ability to detect artificial levees in a 100‐year hydrogeomorphic floodplain of seven geographically diverse 8‐digit HUC basins. Random forest models outperformed other models in predicting the location of levees using variables representing geomorphic attributes, land cover, and distance from streams ranging in size between stream order one through six. To demonstrate the ability of our approach to detect unknown levees, we conducted a leave‐one‐out cross‐validation in the lower Mississippi Basin using approximately 1,100 artificial levees. This approach detected known levees constituting 94% of the total levee length in the basin. Scaling up to the CONUS, we applied a high performing (overall accuracy of 97%) random forest model using land cover and stream order variables. We detected 182,213 km of potential levees, mostly along streams of order 2–6 in the Mississippi and Missouri River Basins, indicating that the national levee database contains 20.4% of levee length. Potential levees and those documented in the national levee database modify 2% of the total length of streams in the contiguous United States. Plain Language Summary: There has been a lot of research exploring how humans have impacted river systems to include impacts from dams and roads at national and global scales. However, the study of artificial levees has been limited to local studies due to incomplete databases of levee location. Artificial levees are linear dirt mounds, built next to rivers to stop flooding of property built on floodplains. Because the location of every artificial levee is not known in the US, we do not know to what extent floodplains have been separated from their rivers. To ameliorate this, our study explores different methods to detect unknown levees and applies an algorithm that is 97% accurate to the contiguous U.S. Most levees are built within floodplains, so we limited our study to the 100‐year floodplain. We tested different types of algorithms in a limited study with variables explaining levee shape, human land cover, and distance from streams of different sizes. Then, we applied the most effective models to the contiguous U.S. We located over 182,000 km of potential levees, potential areas with artificial levees not identified in the NLD, most of which were built on smaller streams and concentrated in the Mississippi and Missouri River basins. Key Points: An incomplete database of artificial levees inhibits the study of artificial levee impacts to floodplain extent in the contiguous USADifferent methods of detecting artificial levees are tested in case study basins and in the contiguous USAThis study detects 182,000 km of potential levees, along smaller streams in the Mississippi and Missouri Basins [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]

Understanding the expansion and contraction dynamics of flowing drainage networks is important for many research fields like ecology, hydrology, and biogeochemistry. This study analyzes for the first time the network shrinking and dry down in two seasonally dry hot‐summer Mediterranean catchments (overall area 1.15 km2) using a comprehensive approach based on monitoring and modeling of the flowing network. A field campaign consisting of 19 subweekly visual surveys was carried out in the early summer of 2019. These observations were used to calibrate and validate an integrated model aimed to estimate the time evolution of the total flowing drainage network length based on meteorological drivers and define the position of the stretches with flowing water based on topographic and geological information. We used a statistical model to describe the observed variations in the total flowing length based on the accumulated difference between antecedent precipitation and evapotranspiration. The study emphasizes the relevant role of evapotranspiration in the seasonal network contraction. Then, we modeled spatial patterns of the flowing channels using an empirical approach based on topographic data, achieving satisfactory performances. Nevertheless, the performance further increased when site‐specific geological information was integrated into the model, leading to accuracies up to 92% for cell‐by‐cell comparisons. The proposed methodology, which combines meteorological, topographic, and geological information in a sequential manner, was able to accurately represent the space‐time dynamics of the flowing drainage network in the study area, proving to be an effective and flexible tool for investigating network dynamics in temporary streams. Key Points: For the first time, the network shrinking and dry down in two seasonally dry hot‐summer Mediterranean headwater catchments are analyzedNetwork length dynamics depend on the difference between antecedent accumulated rainfall and evapotranspirationThe modeling of the flowing network spatial patterns based on topographic and geological information achieved accuracies up to 92% [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]

Watersheds have served as one of our most basic units of organization in hydrology for over 300 years (Dooge, 1988, https://doi.org/10.1080/02626668809491223; McDonnell, 2017, https://doi.org/10.1038/ngeo2964; Perrault, 1674, https://www.abebooks.com/first-edition/lorigine-fontaines-Perrault-Pierre-Petit-Imprimeur/21599664536/bd). With growing interest in groundwater-surface water interactions and subsurface flow paths, hydrologists are increasingly looking deeper. But the dialog between surface water hydrologists and groundwater hydrologists is still embryonic, and many basic questions are yet to be posed, let alone answered. One key question is: where is the bottom of a watershed? Knowing where to draw the bottom boundary has not yet been fully addressed in the literature, and how to define the watershed "bottom" is a fraught question. There is large variability across physical and conceptual models regarding how to implement a watershed bottom, and what counts as "deep" varies markedly in different communities. In this commentary, we seek to initiate a dialog on existing approaches to defining the bottom of the watershed. We briefly review the current literature describing how different communities typically frame the answer of just how deep we should look and identify situations where deep flow paths are key to developing realistic conceptual models of watershed systems. We then review the common conceptual approaches used to delineate the watershed lower boundary. Finally, we highlight opportunities to trigger this potential research area at the interface of catchment hydrology and hydrogeology. [ABSTRACT FROM AUTHOR]

Pronounced climate warming over the arctic‐subarctic regions has lead to profound hydrological changes including intensified river flow, but how soil frost controls aquifer discharge remains poorly understood. This study quantifies the relationship between freezing temperature and baseflow in winter. Analyses show that the traditional reservoir models are unable to reproduce the observed baseflow variations. By incorporating a freezing temperature function in the reservoir models, the model performances are largely improved. It indicates the dominant role of freezing temperature in controlling the aquifer discharge through reducing the watershed conductivity and liquid (active) water content. The results for the Albany watershed in Canada show that the watershed lump conductivity decreases by half when air temperature accumulates to −172 °C·day from winter start and in extremely cold years, it could decrease by more than 85%. With this relationship, a climate warming of +1, +2, and +4 °C would suggest an increase of 7.7%, 16.7%, and 41.0% in conductivity or 6.8%, 14.7%, and 35.0% in winter discharge, respectively. The study provides an important link between climate warming and aquifer discharge in cold regions. The results could be particularly useful for developing process‐based models, estimating baseflow variations, and assessing climate change impact on cold region hydrology. Plain Language Summary: Pronounced climate warming over the arctic‐subarctic regions has lead to intensified river flow, but how soil frost controls aquifer discharge remains poorly understood. This study reveals the relationship between freezing temperature and baseflow in winter. The results show that freezing temperature reduces baseflow through decreasing hydraulic conductivity by more than 85% in extremely cold years for the Albany watershed in Canada. As such, a climate warming of +1, +2, and +4 °C could result in an increase in winter aquifer discharge of 6.8%, 14.7%, and 35.0%, respectively. The temperature‐baseflow relationship found in this study provides new insight for cold region climate change impact and water resources studies. Key Points: Freezing temperature is the dominant factor controlling cold region winter baseflowSoil frost reduces baseflow by reducing hydraulic conductivity and active water contentClimate warming could significantly increase winter river flow in cold regions [ABSTRACT FROM AUTHOR]

Understanding controls of P movement through watersheds are essential for improved landscape management in intensively managed regions. Here, we analyze observational data from 104 gaged river sites and 176 nongaged river sites within agriculturally dominated watersheds of Minnesota, USA, to understand the role of landscape features, land use practices, climate variability, and biogeochemical processes in total, dissolved and particulate P dynamics at daily to annual scales. Our analyses demonstrate that factors mediating P concentration‐discharge relationships varied greatly across watersheds and included near‐channel sediment sources, lake and wetland interception, assimilation by algal P, and artificial land drainage. The majority of gaged sites exhibited mobilizing behavior for all forms of P at event (i.e., daily) timescales and chemostatic behavior at annual timescales. The large majority of watershed P export (>70%, on average) occurred during high flow conditions, suggesting that more frequent large storm events arising from climate change will drive increased P losses from agricultural watersheds without substantial management changes. We found that P export could be dominated by dissolved P, particulate P, or an even mix of the two forms, depending on watershed attributes. Implementation of management practices to control P losses must be guided by understanding of how local landscapes interact with current and future climate conditions. Managing for both dissolved and particulate P is required to reduce overall P load in many agricultural watersheds. Plain Language Summary: When phosphorus from farm fertilizer, eroded soil, and septic waste enters our water, it leads to problems like toxic algae blooms, fish kills, and contaminated drinking supplies. In this study, we examine how phosphorus travels through streams and rivers of farmed areas. In the past, soil lost from farm fields was considered the biggest contributor to phosphorus pollution in agricultural areas, but our study shows that phosphorus originating from fertilizer stores in the soil and from crop residue, as well as from soil eroded from sensitive ravines and bluffs, contributes strongly to the total amount of phosphorus pollution in agricultural rivers. We also found that most phosphorus leaves farmed watersheds during the very highest river flows. Increased frequency of large storms due to climate chaos will therefore likely worsen water quality in areas that are heavily loaded with phosphorus from farm fertilizers. Protecting water in agricultural watersheds will require knowledge of the local landscape along with strategies to address (1) drivers of climate chaos, (2) reduction in the highest river flows, and (3) ongoing inputs and legacy stores of phosphorus that are readily transported across land and water. Key Points: Specific places, processes, and land use practices mediate P concentration‐discharge relationships within agricultural watershedsDissolved and particulate P often contribute equally to total P loads, underscoring the importance of strategies to reduce both formsThe majority of P export occurs during large storms, indicating strong intersecting roles of climate and agriculture in driving P losses [ABSTRACT FROM AUTHOR]

The footprint of catchment properties on water flow is reflected into hydrological signals, such as stream discharge. Here we demonstrate that it is possible to constrain catchment properties from the spectral analysis of hydrological signals but only when an appropriate transfer function (TF) is used for interpretation. We show that the appropriate theoretical TF, newly derived, is the only one to robustly describe a large diversity of experimental TFs that could be encountered in nature, because it entails the role of diffuse overflow and flow through the vadose zone, which have never been considered in spectral approaches before. The properties that may be estimated are the characteristic time scales of transfer in each compartment (surface, vadose zone, and aquifer) and the flow partitioning between surface and subsurface. We validate our approach by comparing the new and previous theoretical TFs to experimental TFs generated by a physically based distributed hydrological model, for a wide range of properties. The results confirm that without the use of the new TF, the interpretation of observed spectra may often lead to severe misestimations of catchment properties. The potential of the new TF to constrain catchment characteristics is exemplified by analyzing real hydrological signals from two watersheds with distinct behaviors. We finally discuss the broad implications of our findings and how they may contribute to a variety of topics in hydrology, thereby opening the way to a more widespread and robust use of spectral analysis to describe hydrosystems from effective rainfall, river discharge, and piezometric data. Plain Language Summary: Time series of hydrological variables, such as river discharge rates or groundwater levels, are very different from the rainfall signal that feeds continental hydrosystems. In addition, different watersheds undergoing similar climatic conditions do not produce similar hydrological responses. Therefore, deciphering how a climatic signal is altered and retranscribed into hydrological signals is a difficult task. Nevertheless, understanding how hydrosystems react to different time scales of climatic forcing remains an important challenge for water management, as it is key to predicting extreme hydrological events (severe droughts and floods). Here we develop and validate a new approach to study the impact of various hydrological compartments (such as surface waters or subsurface waters) on climatic signal transformation. We use a spectral domain approach, meaning that river discharge, for instance, is not seen as function of time but rather as a function of the various elementary frenquencies that compose the signal. We demonstrate that our new approach favors the interpretation of data in the spectral domain and thereby enhances comprehension of flow processes in response to different time scales of climatic inputs. Key Points: Hydrological responses to climatic forcing can be described more efficiently by new transfer functions (TFs)The new TFs account for flow in three key hydrosystem compartments (surface, vadose zone, and aquifer) to interpret data in the spectral domainThe use of previously derived TFs may lead to severe misestimation of catchment dynamics and properties, like aquifer hydraulic diffusivity [ABSTRACT FROM AUTHOR]

Hydrological signatures are now used for a wide range of purposes, including catchment classification, process exploration, and hydrological model calibration. The recent boost in the popularity and number of signatures has however not been accompanied by the development of clear guidance on signature selection. Here we propose that exploring the predictability of signatures in space provides important insights into their drivers and their sensitivity to data uncertainties and is hence useful for signature selection. We use three complementary approaches to compare and rank 15 commonly used signatures, which we evaluate in 600+ U.S. catchments from the Catchment Attributes and MEteorology for Large‐sample Studies (CAMELS) data set. First, we employ machine learning (random forests) to explore how attributes characterizing the climatic conditions, topography, land cover, soil, and geology influence (or not) the signatures. Second, we use simulations of the Sacramento Soil Moisture Accounting model to benchmark the random forest predictions. Third, we take advantage of the large sample of CAMELS catchments to characterize the spatial autocorrelation (using Moran's I) of the signature field. These three approaches lead to remarkably similar rankings of the signatures. We show (i) that signatures with the noisiest spatial pattern tend to be poorly captured by hydrological simulations, (ii) that their relationship to catchments attributes are elusive (in particular they are not well explained by climatic indices), and (iii) that they are particularly sensitive to discharge uncertainties. We suggest that a better understanding of the drivers of hydrological signatures and a better characterization of their uncertainties would increase their value in hydrological studies. Key Points: We used machine learning and a hydrological model to simulate 15 hydrological signatures over 600+ catchments in the United StatesThe predictability of the signatures is highly correlated with the smoothness of their spatial pattern, which we quantified using Moran's IPoorly predicted signatures vary abruptly in space, they are particularly sensitive to streamflow errors, and their links to catchment attributes are elusive [ABSTRACT FROM AUTHOR]

Prescribed fire is an important tool for rangeland management in sage‐steppe ecosystems, yet the long‐term effects of this practice on soil hydraulic properties are not well known. We explore interactions among site geomorphology, soil organic carbon (SOC) soil N, soil water repellency (SWR), and plant community type on infiltration properties before fire and 8 years thereafter in a semiarid research watershed. The objective was to assess the sustainability of rangeland burning in sage‐steppe ecosystems. Many types of measurements were made in three plant communities to identify how differences in soil hydraulic properties are related to differences in plant cover and soil texture and to determine relationships among SOC, SWR, soil water contact angle, and infiltration properties. Measurements were made on transects in burned and unburned catchments. We found that severity and occurrence of surface SWR were substantially reduced 8 years after a fire within the area originally covered with mountain big sagebrush, where the fire intensity was greatest. Surface SWR was lowest in the sparsely vegetated low sagebrush, where SOC was also lowest. Unsaturated hydraulic conductivity (Kh) increased in each vegetation type over the 8‐year period after burning and was not directly related to SWR. Spatial variability in Kh was primarily controlled by soil texture, whereas differences in sorptivity (S) were controlled by SWR and aridity. SOC is not well correlated to soil surface SWR. The decadal scale changes in Kh and associations between S and site characteristics indicate forms of resilience to fire across a moisture gradient. Plain Language Summary: Wildfire that removes vegetation can alter soil surfaces and result in erosion. Vegetation succession patterns in grazed lands can accelerate the rangeland fire cycle and alter soil properties including infiltration and organic carbon storage. This study investigated evolving interactions between soil properties and rangeland plant ecosystems from prefire conditions until 8‐year postfire to clarify the changes in soil infiltration capacity. This information is intended to guide land managers who use prescribed fires to manage rangelands to limit the incidence of catastrophic fires and erosion. We found that the severity and presence of surface soil water repellency were greatly reduced 8 years after treatment in the Aspen stand, where the surface was manually burned. Although postfire decreases in soil water repellency were modest at in the big mountain sagebrush, the postfire infiltration capacity was at least twice as great as prior measurements. This indicates a positive long‐term effect of prescribed fire control of big mountain sagebrush at the study site. Key Points: Geomorphologic setting and microclimate influence local soil properties including texture, moisture, organic carbon, water repellency, and sorptivitySoils below discontinuous tree and shrub canopies were less water repellent after fire, and both unsaturated hydraulic conductivity and sorptivity tests indicated resilienceDifferent soil water repellency tests including water drop penetration time, contact angle, and negative sorptivity reveal different manifestations of soil organic carbon across vegetation types and soil texture [ABSTRACT FROM AUTHOR]

Forested catchments in Central Panama can produce more base flow during the dry season compared to pasture catchments—the so‐called "forest sponge effect." During rainfall events, peak storm runoff rates and storm runoff coefficients can be lower for forested catchments than pasture catchments, even when they have similar topographic characteristics, underlying geology, and soil texture. The internal mechanism of these differences in hydrological response due to land use is yet to be fully understood. A distributed model explicitly simulating preferential flow paths (PFPs), which is referred to as "PFPMod," is used to explain the hydrological response caused by land use using data from three catchments with distinct land covers in Central Panama. Input parameters of forest and pasture land covers were identified using field observations and literature values. Multiple satisfactory objective criteria demonstrate that the two end‐member land cover parameter sets are adequate to explain the observed difference in dry‐season base flow and storm runoff coefficients. Field measurements of matrix infiltrability using soil cores and plot‐scale infiltration capacity enabled estimating the number of vertical macropores that fully penetrate the root zone. Model simulation results demonstrate that fast drainage through lateral PFPs in the early wet season and high flow in vertical PFPs to recharge deep groundwater in the late wet season contribute to the observed differences in peak storm runoff and the "forest sponge effect" during the dry season. This study provided insights to the mechanism by which reforestation may help to restore ecosystem services and water security in tropical settings. Key Points: Fast lateral drainage in early wet season and high vertical groundwater recharge in late wet season explain land use effects on runoffCore‐scale infiltrability and plot‐scale infiltration capacity allow estimating a number of vertical macropores that fully penetrate root zoneA physically based model allows causal attribution in land use/land cover effects to preferential flow [ABSTRACT FROM AUTHOR]

Abstract: The spatial correlation of daily streamflows represents a statistical index encapsulating the similarity between hydrographs at two arbitrary catchment outlets. In this work, a process‐based analytical framework is utilized to investigate the hydrological drivers of streamflow spatial correlation through an extensive application to 78 pairs of stream gauges belonging to 13 unregulated catchments in the eastern United States. The analysis provides insight on how the observed heterogeneity of the physical processes that control flow dynamics ultimately affect streamflow correlation and spatial patterns of flow regimes. Despite the variability of recession properties across the study catchments, the impact of heterogeneous drainage rates on the streamflow spatial correlation is overwhelmed by the spatial variability of frequency and intensity of effective rainfall events. Overall, model performances are satisfactory, with root mean square errors between modeled and observed streamflow spatial correlation below 10% in most cases. We also propose a method for estimating streamflow correlation in the absence of discharge data, which proves useful to predict streamflow regimes in ungauged areas. The method consists in setting a minimum threshold on the modeled flow correlation to individuate hydrologically similar sites. Catchment outlets that are most correlated ( ρ > 0.9) are found to be characterized by analogous streamflow distributions across a broad range of flow regimes. [ABSTRACT FROM AUTHOR]

Transport of fine-grained sediment from unpaved forest roads into streams is a concern due to the potential negative effects of additional suspended sediment on aquatic ecosystems. Here we compared turbidity and suspended sediment concentration (SSC) dynamics in five nonfish bearing coastal Oregon streams above and below road crossings, during three consecutive time periods ('before', 'after road construction/improvement', and 'after forest harvest and hauling'). We hypothesized that the combined effects of road construction/improvement and the hauling following forest harvest would increase turbidity and SSC in these streams. We tested whether the differences between paired samples from above and below road crossing exceeded various biological thresholds, using literature values of biological responses to increases in SSC and turbidity. Overall, we found minimal increases of both turbidity and SSC after road improvement, forest harvest, and hauling. Because flow is often used as a surrogate for turbidity or SSC we examined these relationships using data from locations above road crossings that were unaffected by roads or forest harvest and hauling. In addition, we examined the association between turbidity and SSC for these background locations. We found a positive, but in some cases weak association between flow and turbidity, and between flow and SSC; the relationship between turbidity and SSC was more robust, but also inconsistent among sites over time. In these low order streams, the concentrations and transport of suspended sediment seems to be highly influenced by the variability of local conditions. Our study provides an expanded understanding of current forest road management practice effects on fine-grained sediment in streams and introduces alternative metrics using multiple thresholds to evaluate potential indicators of biological relevance. [ABSTRACT FROM AUTHOR]

Information about catchment-scale groundwater dynamics is necessary to understand how catchments store and release water and why water quantity and quality varies in streams. However, groundwater level monitoring is often restricted to a limited number of sites. Knowledge of the factors that determine similarity between monitoring sites can be used to predict catchment-scale groundwater storage and connectivity of different runoff source areas. We used distance-based and correlation-based similarity measures to quantify the spatial and temporal differences in shallow groundwater similarity for 51 monitoring sites in a Swiss prealpine catchment. The 41 months long time series were preprocessed using Dynamic Time-Warping and a Flow-corrected Time Transformation to account for small timing differences and bias toward low-flow periods. The mean distance-based groundwater similarity was correlated to topographic indices, such as upslope contributing area, topographic wetness index, and local slope. Correlation-based similarity was less related to landscape position but instead revealed differences between seasons. Analysis of variance and partial Mantel tests showed that landscape position, represented by the topographic wetness index, explained 52% of the variability in mean distance-based groundwater similarity, while spatial distance, represented by the Euclidean distance, explained only 5%. The variability in distance-based similarity and correlation-based similarity between groundwater and streamflow time series was significantly larger for midslope locations than for other landscape positions. This suggests that groundwater dynamics at these midslope sites, which are important to understand runoff source areas and hydrological connectivity at the catchment scale, are most difficult to predict. [ABSTRACT FROM AUTHOR]

We analyze flash flooding in small urban watersheds, with special focus on the roles of rainfall variability, antecedent soil moisture, and urban storm water management infrastructure in storm event hydrologic response. Our results are based on empirical analyses of high-resolution rainfall and discharge observations over Harry's Brook watershed in Princeton, New Jersey, during 2005-2006, as well as numerical experiments with the Gridded Surface Subsurface Hydrologic Analysis (GSSHA) model. We focus on two subwatersheds of Harry's Brook, a 1.1 km2 subwatershed which was developed prior to modern storm water management regulations, and a 0.5 km2 subwatershed with an extensive network of storm water detention ponds. The watershed developed prior to modern storm water regulations is an 'end-member' in urban flood response, exhibiting a frequency of flood peaks (with unit discharge exceeding 1 m3 s−1 km−2) that is comparable to the 'flashiest' watersheds in the conterminous U.S. Observational analyses show that variability in storm event water balance is strongly linked to peak rain rates at time intervals of less than 30 min and only weakly linked to antecedent soil moisture conditions. Peak discharge for both the 1.1 and 0.5 km2 subwatersheds are strongly correlated with rainfall rate averaged over 1-30 min. Hydrologic modeling analyses indicate that the sensitivity of storm event hydrologic response to spatial rainfall variability decreases with storm intensity. Temporal rainfall variability is relatively more important than spatial rainfall variability in representing urban flood response, especially for extreme storm events. [ABSTRACT FROM AUTHOR]

Traditional approaches aiming at protecting surface waters from the negative impacts of forestry often focus on retaining fixed width buffer zones around waterways. While this method is relatively simple to design and implement, it has been criticized for ignoring the spatial heterogeneity of biogeochemical processes and biodiversity in the riparian zone. Alternatively, a variable width buffer zone adapted to site-specific hydrological conditions has been suggested to improve the protection of biogeochemical and ecological functions of the riparian zone. However, little is known about the monetary value of maintaining hydrologically adapted buffer zones compared to the traditionally used fixed width ones. In this study, we created a hydrologically adapted buffer zone by identifying wet areas and groundwater discharge hotspots in the riparian zone. The opportunity cost of the hydrologically adapted riparian buffer zones was then compared to that of the fixed width zones in a meso-scale boreal catchment to determine the most economical option of designing riparian buffers. The results show that hydrologically adapted buffer zones were cheaper per hectare than the fixed width ones when comparing the total cost. This was because the hydrologically adapted buffers included more wetlands and low productive forest areas than the fixed widths. As such, the hydrologically adapted buffer zones allows more effective protection of the parts of the riparian zones that are ecologically and biogeochemically important and more sensitive to disturbances without forest landowners incurring any additional cost than fixed width buffers. [ABSTRACT FROM AUTHOR]

In the past, hydrologic modeling of surface water resources has mainly focused on simulating the hydrologic cycle at local to regional catchment modeling domains. There now exists a level of maturity among the catchment, global water security, and land surface modeling communities such that these communities are converging toward continental domain hydrologic models. This commentary, written from a catchment hydrology community perspective, provides a review of progress in each community toward this achievement, identifies common challenges the communities face, and details immediate and specific areas in which these communities can mutually benefit one another from the convergence of their research perspectives. Those include: (1) creating new incentives and infrastructure to report and share model inputs, outputs, and parameters in data services and open access, machine-independent formats for model replication or reanalysis; (2) ensuring that hydrologic models have: sufficient complexity to represent the dominant physical processes and adequate representation of anthropogenic impacts on the terrestrial water cycle, a process-based approach to model parameter estimation, and appropriate parameterizations to represent large-scale fluxes and scaling behavior; (3) maintaining a balance between model complexity and data availability as well as uncertainties; and (4) quantifying and communicating significant advancements toward these modeling goals. [ABSTRACT FROM AUTHOR]

Initial conditions have been shown to have a strong effect on outputs of surface water models, but their impact on integrated hydrologic models is not well documented. We investigated the effects of initial conditions on an integrated hydrologic model of a 5632 km2 domain in the northeastern U.S. Simulations were run for the year 1980 using four initial conditions spanning a range of average depth to water table, including 1 m ('wet'), 3m, 5m, and 7 m ('dry') below land surface. Model outputs showed significant effects of initial conditions on basin-averaged variables such as subsurface storage, surface storage, and surface runoff, with the greatest impact observed on surface storage and runoff. Effects of initial conditions were related to meteorological conditions, with precipitation reducing the effects of initial conditions on surface storage and runoff. Additionally, feedbacks between soil moisture and land-energy fluxes affected the impacts of initial conditions: higher temperatures magnified the differences in storage, recharge, and discharge among the four initial-condition scenarios. Ten year recursive runs were conducted for the wet and dry scenarios. Spin-up times varied by model components and were considerably smaller for land-surface states and fluxes. Spin-up for dry initial conditions was slower than for wet initial conditions, indicating longer system memory for dry initial conditions. These variations in persistence of initial conditions should be taken into consideration when designing model initialization approaches. More broadly, this behavior is indicative of increased persistence of the effects of dry years as opposed to wet years in hydrologic systems. [ABSTRACT FROM AUTHOR]