Search

We develop a spatio‐temporal geostatistical interpolation framework to estimate hypoxia extent (dissolved oxygen [DO] concentrations below 2 mg/L) with data from a network of DO loggers. The framework uses empirical orthogonal functions and Bayesian kriging to identify the spatially varying temporal pattern and estimate the distribution of hypoxia, including estimation uncertainty. A prototype web application is also developed in R. The framework is applied to analyze spatio‐temporal dynamics of DO in the central basin of Lake Erie in North America using data sampled from a logger network placed on the lake bottom during the summers of 2014, 2015, and 2016. Cross‐validation results demonstrate that the framework is capable of capturing the dynamic nature of bottom hypoxia over offshore areas, but nearshore areas have poor interpolation performance due to the impacts of complex physical processes such as seiche events. The findings showed that in the central basin, hypoxia started to emerge in early August of 2014, while in 2015 and 2016 hypoxia began in July. The peak hypoxia extent occurred in late September 2014, mid‐August 2015, and early September 2016. The prediction error of the overall spatial extent of hypoxia was as large as 25% of the interpolation area based on current logger deployment. Based on the cross‐validation and interpolation error, we suggest placing more loggers in nearshore areas to reduce prediction error near the margins of the hypoxic zone. Key Points: A new framework uses empirical orthogonal function, conditional simulation, and Bayesian kriging to estimate lake hypoxia extentIn Lake Erie, the approach captures the dynamic nature of bottom hypoxia in offshore areas, but more sensors are needed in nearshore areasOpen‐source R code and a web application are provided for researchers to build upon the research and explore other hypoxia datasets [ABSTRACT FROM AUTHOR]

The Huanghe once had a sediment flux of >1,000 Mt/yr, but this has decreased by ∼90% as its river sediment routing systems have undergone dramatic changes influenced by human activities such as dam construction. However, the way in which the sediment geochemistry of the river has responded to the altered sediment routing processes is not well known. This study investigates the sediment source‐to‐sink routing regime of the Huanghe River using Nd isotope fingerprinting. Four major source terranes, namely the Songpan‐Ganzi (SG) Block, Ordos Desert (OD), Chinese Loess Plateau (CLP) and North China Craton (NCC) are recognized according to their distinct Nd isotopes. The gradual downstream decrease in εNd values in sediments of the upper Huanghe indicates a decreasing contribution of material from the SG Block and a corresponding increase contribution of local underlying basement rocks, which is inferred to be related to sediment capture by a cascade of hydroelectric dams. A gradual increase in εNd from Yinchuan to Tongguan suggests an increasing contribution from the CLP under intense erosion. Relatively low εNd in the downstream sediments suggest a contribution from proximal NCC basement, consistent with the shift from deposition to erosion in the lower channel in recent years. The marked heterogeneity in Nd isotopes in the Huanghe sediments corresponds well to sediment source‐to‐sink processes in response to increasing human impacts. In a setting of global rivers facing strong anthropogenic impacts, the ways in which altered sediment routing systems affect river sediment geochemistry deserve more research attention. Plain Language Summary: The sediment flux of the Huanghe River (Yellow River) once exceeded 1,000 Mt/yr, but has decreased by ∼90% over the past 60 years owing mainly to the nature and intensity of human activity, during which river sediment transport processes have also changed. Here, we present Nd isotopic data for sediments from the Huanghe mainstream to investigate sediment provenances and the river sediment routing system under anthropogenic influences. Nd isotopes of potential source terrane materials allow the sediment provenances to be discriminated. The construction of cascade hydroelectric engineering projects in the upper reaches might have caused a marked decrease in sediment contribution from the Songpan–Ganzi Block, as evidenced from the gradual downstream decrease in sediment εNd in the upper reaches. The gradual increase in εNd values from Yinchuan to Tongguan suggests an increasing sediment contribution from the China Loess Plateau by intense erosion. However, relatively low εNd values in the lower reaches suggest an increased contribution of proximal sediment derived from basement of the North China Craton, with a shift from depositional to erosional processes in the lower reaches. The heterogeneity in Nd isotopes in the river sediments reveals sediment routing processes under the impact of anthropogenic activities and interventions. Key Points: Trace elements and Sr‐Nd isotopes are used to ascertain sediment provenance for modern Huanghe River in ChinaFour major source terranes are recognized and discriminated based on their unique Nd isotopic compositionsThe heterogeneity of Nd isotopes in the river sediments reveals the sediment routing regime under anthropogenic activity [ABSTRACT FROM AUTHOR]

The long‐term water balance of catchments is given by precipitation partitioned into either runoff or evaporation. Understanding precipitation partitioning controls is a critical focus of hydrology and water resources management. A useful theoretical framework that serves their understanding is the Budyko Framework. Our purpose is to understand how Budyko's n parameter is related to different controls and what is its relevance to precipitation partitioning. We investigated the relative importance of the dryness index and the Budyko parameter for precipitation partitioning, then applied partial correlation analysis and multivariate regressions to find out which were the principal partitioning controls. We focused our research in the central Appalachian mountains located in the eastern United States, considered as water towers to metropolitan areas in the eastern and mid‐western US (e.g., Pittsburgh, Washington DC), and selected a set of catchments characterized by minimal human disturbance and with large proportions of temperate forests. We found that climate controls such as mean annual temperature and fraction of precipitation falling in the form of snow exert a higher influence on partitioning than landscape controls (e.g., forest cover, Normalized Difference Vegetation Index, and slope). Thus, the importance of vegetation as a primary driver of partitioning could not be confirmed based on regional or basin‐wide characteristics. On the other hand, the influence of topography, and elevation in particular, was highly ranked as important. Our study highlights that partitioning controls could differ between basins in the same climate region, especially in a complex, mountainous topography setting. Key Points: Precipitation partitioning controls vary across the region due to complexity created by the eastern continental divideClimate controls, in particular temperature and fraction of precipitation falling as snow, were more important than landscape controlsAmong the landscape controls, elevation was the most influential to precipitation partitioning [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]

Despite reduction measures, nitrate and aluminum concentrations remain high in aquifers in northwestern Europe. To evaluate the effectiveness of groundwater protection policies, the long‐term fate of these contaminants in groundwater needs to be understood. The groundwater catchment of the Haren water works, NW Germany, was characterized hydrogeochemically in the late 1990s, which provides an opportunity to study the solute fronts over a two‐decade period and conduct a post‐audit of the predicted front movement. Results indicate that, despite a significant reduction of the atmospheric acid loads, the acidification of soil and groundwater at the forest site persists. Removal of sorbed aluminum is required to induce a noticeable improvement, which will take at least several decades. The unexpected appearance of nitrate at the site, caused by a land use change in 1998, highlights the need for long‐term monitoring. Core data at the agricultural site show that the denitrification front has moved very little between 1998 and 2017, in accordance with previous forecasts. Denitrification by‐products, mainly sulfate and nitrogen, have migrated from the upper into the lower aquifer. A reactive transport model demonstrated how the link between the regional groundwater flow, pyrite oxidation, and the temporal variability of the nitrate concentration in recharge water, as reconstructed from age tracers, result in the observed vertical distribution of sulfate and nitrogen. This study demonstrates how long‐term monitoring, aided by model‐based data interpretation, can be used to successfully study and predict the fate of contaminants in groundwater. Key Points: Position of reaction fronts for nitrate and acidification in aquifers checked after 20+ yearsAcidification of soil and groundwater still strong, despite reductions of atmospheric inputNitrate front has moved very slowly over last 20 years but denitrification products have migrated into lower aquifer [ABSTRACT FROM AUTHOR]

Climate change has been concerned as a trigger of hydrologic changes around the world. Budyko framework was widely used in hydrologic sensitivity analyses on the steady‐state water balance. The unsteady‐state responses, for example the inter‐annual hydrologic sensitivity, were not well investigated, especially for catchments influenced by croplands as that exist in Illinois in the United States. We propose a new hydrologic sensitivity framework for the unsteady‐state condition, together with a new hydrological model, to investigate the impact of croplands on the hydrologic sensitivity. In addition to the Budyko‐type relationship between the annual evapotranspiration ratio (E/P) and the aridity index (Ep/P), this framework incorporates the empirical relationship between the annual storage change ratio (ΔS/P) and Ep/P into sensitivity coefficients. The required data of annual E/P and ΔS/P could be obtained from observation and hydrological models. In particular, the bcd‐αK model is newly developed from the existing bcd model in considering croplands in Illinois, which includes only five parameters while capturing major hydrological processes for catchments with croplands. Results show that the annual E/P, ΔS/P and Q/P in 12 studied catchments have significant linear relationships with Ep/P. The annual runoff is much more sensitive to the change in precipitation than to the change in potential evapotranspiration. The traditional steady‐state analysis overestimates the sensitivity of the evapotranspiration to the change in the precipitation for the unsteady‐state condition and underestimates the variability of sensitivity coefficients among catchments. Plain Language Summary: With the changing climate, frequent disasters of floods and droughts, shortage of water resources, and water quality deterioration, the importance of studying water cycle is increasingly recognized. Moreover, human activities have seriously changed the processes of hydrological cycle. Therefore, it makes no sense to only study the natural water cycle in which intensive and extensive human activities occur, such as agricultural development. In this study, we develop a new method for hydrologic sensitivity analysis on catchments bearing the changes in climate and land use. Case studies are undertaken for 12 catchments in Illinois, USA. Results show that the annual runoff is much more sensitive to the change in the precipitation than to the change in the potential evapotranspiration. Key Points: Empirical relationships of multiple hydrologic ratios versus the aridity index are jointly used to estimate sensitive coefficientsA new hydrological model is developed to incorporate the impact of croplands with minimum additional parametersThe steady‐state analysis misestimates the sensitivity and underestimate the variability of sensitivity for the unsteady‐state condition [ABSTRACT FROM AUTHOR]

Sediment transport is the main driver of the channel morphology and landscape evolution, with implications for chemical and biological river processes, and human‐related activities. Understanding the processes governing the relations between discharge and suspended sediments is essential for the management of river catchments and river networks. Here, we use the method of wavelet transformation to identify the time‐scale dependency of suspended sediment patterns concerning the temporally and spatially uneven transient processes of sediment production, accumulation, and transport. We analyze the temporal variation of concurrent discharge and suspended sediment fluxes for the Upper Changjiang (Yangtze River, China) at Pingshan station by using a long‐term database collected for over 50 years. Furthermore, we bridge the limitations of pure predictive models to learn from temporal data structures with the main purpose of identifying the mechanisms underpinning the suspended sediment patterns (e.g., climatic forces). Intraseasonal‐to‐seasonal, annual, and inter‐annual dominant time‐scales are thus identified. The short time‐scales are driven by the bi‐modal seasonal precipitation pattern specific to the climate of the region and provide a continuous supply of sediments to the river. The large time‐scales, controlled by high magnitude flow events and within‐reach sediment storage, display alternating periods of increasing and decreasing sediment fluxes; ultimately, they maintain the river channel within balance or within a moderate positive sediment accumulation process. This analysis and methodology help to understand temporal sediment dynamics, and ultimately to manage river catchments. Key Points: Distinct time‐scales of discharge and suspended sediment concentration (SSC) are identified and related qualitatively to seasonal, annual, and short (1.1–5 years) and long‐term (5.5–20 years) variabilitySimilar dominant time‐scales of variability for discharge and SSC are identified in the Upper Changjiang for a 50‐year study periodBi‐modal seasonal climatic pattern influence short‐term time‐scales whereas high magnitude flow events control intra‐annual time‐scales [ABSTRACT FROM AUTHOR]

The temporal dynamics of the land surface are commonly modeled by so‐called landscape evolution models, consisting of coupled partial differential equations accounting for the balance of sediment and water. An average sediment budget equation for a landscape evolution model in detachment‐limited condition is derived in terms of curvature‐dependent and curvature‐independent components. This highlights the role of plane curvature and spatial distribution of surface divergence/convergence on the average erosion rate. Performing a Reynolds‐type averaging of the original equations leads to a decomposition of the erosion into an unchannelized erosion sink and a channelization sediment flux. The latter is the derivative of a potential (referred to as "channelization sediment potential") that arises from the negative correlation of elevation and specific drainage area and closely resembles, in both form and behavior, the Reynolds stress in fluid turbulence. The negative elevation‐area covariance emerges as a statistical signature of surface channelization. Building on analogies between landscape channelization and fluid turbulence, these theoretical results may provide a path to estimate model parameters from observations and offer useful benchmarks for numerical simulations in the channelized regime. Key Points: The sediment budget equation is linked to the spatial distribution of surface convergenceThe erosion term is decomposed into an unchannelized erosion sink and a channelization sediment fluxThe channelization sediment flux is shown to arise from the negative correlation of elevation and drainage area [ABSTRACT FROM AUTHOR]

Undisturbed peatlands are effective carbon sinks and provide a variety of ecosystem services. However, anthropogenic disturbances, especially land drainage, strongly alter peat soil properties and jeopardize the benefits of peatlands. The effects of disturbances should therefore be assessed and predicted. To support accurate modeling, this study determined the physical and hydraulic properties of intact and disturbed peat samples collected from 59 sites (in total 3,073 samples) in Finland and Norway. The bulk density (BD), porosity, and specific yield (Sy) values obtained indicated that the top layer (0–30 cm depth) at agricultural and peat extraction sites was most affected by land use change. The BD in the top layer at agricultural, peat extraction, and forestry sites was 441%, 140%, and 92% higher, respectively, than that of intact peatlands. Porosity decreased with increased BD, but not linearly. Agricultural and peat extraction sites had the lowest saturated hydraulic conductivity, Sy, and porosity, and the highest BD of the land use options studied. The van Genuchten‐Mualem (vGM) soil water retention curve (SWRC) and hydraulic conductivity (K) models proved to be applicable for the peat soils tested, providing values of SWRC, K, and vGM‐parameters (α and n) for peat layers (top, middle and bottom) under different land uses. A decrease in peat soil water content of ≥10% reduced the unsaturated K values by two orders of magnitude. This unique data set can be used to improve hydrological modeling in peat‐dominated catchments and for fuller integration of peat soils into large‐scale hydrological models. Key Points: Land use such as agriculture and peat extraction alter the physical and hydraulic properties of the peat more strongly than other land usesThe top 30 cm peat depth was most affected by agriculture and peat extraction, as indicated by the bulk density, specific yield, and porosity valuesThe van Genuchten‐Mualem soil water retention model was applied successfully to different layers of peat under different land use [ABSTRACT FROM AUTHOR]

One class of descriptions of landscape evaporation is based on the principle that actual evaporation E and atmospheric evaporative demand Epa exhibit complementary behavior. A feature of some recent implementations of this approach is the need for the estimation of a free parameter, usually by calibration. In a different class of representations of landscape evaporation, several functional forms have been proposed in the past for the dependency of the mean annual evaporation precipitation ratio (E/P) on the mean annual aridity index (Emax/P)—the Schreiber‐Oldekop hypothesis, also known as the Budyko framework. While there is no general agreement in the literature on the optimal formulation of the "maximum possible evaporation" Emax, the functional forms of (E/P)=f(Emax/P) appear to be quite insensitive to its exact nature. This observation allows Emax to be equated with the atmospheric evaporative demand Epa, and this immediately leads to a blending of the mean annual evaporation precipitation ratio (E/P) with the complementary evaporation principle, and the prediction of its unknown free parameter. As this free parameter is found to be relatively insensitive to time scale, the complementary functions become not only calibration‐free at the mean annual time scale, but also applicable even at daily time scales. The results are shown to be applicable worldwide with experimental data from 524 catchment water balance set‐ups and 156 high quality eddy covariance flux stations. The present approach offers a practical tool for the prediction of daily evaporation using only routine meteorological inputs including air temperature, humidity, wind speed, net radiation, and long‐term average precipitation. Key Points: Blending of annual evaporation precipitation ratio with complementary evaporation principle led to the prediction of its unknown parameterThe method developed allows the complementary functions to become calibration‐free at the annual time scale and even at daily time scalesThe present approach offers a practical tool for the prediction of daily evaporation using only routine meteorological data [ABSTRACT FROM AUTHOR]

The world's water resources are continuously facing challenges in fulfilling the needs of increasing agricultural water demand with finite or diminishing resources. Therefore, it is important to quantify the amount of irrigation water required to attain sustainable yield at a local, regional, and global level, especially in arid and semi‐arid regions. This is mostly quantified by using agro‐hydrological or agricultural models. The advances in simulation models and several options incorporated in them allow catchment/site‐specific application of irrigation water to depict the field management practices undertaken by farmers. The objective of the present study is to provide a review of the simulation of irrigation water demand at catchment scale by agro‐hydrological and agricultural models. This study discusses the different types of models, their dimensions, and the hydrological and agricultural process models incorporated into them. Additionally, this review provides an overview of how irrigation can be scheduled, how water is applied, and from which sources irrigation water can be extracted by the considered models, taking horizontal hydrological connectivity into consideration. Adding to the model review, seven different fields of innovative case studies are covered. Many agricultural models have been applied in a regional context without simulating horizontal hydrological fluxes, but only a few hydrological catchment models provide full support of both irrigation and plant growth simulation, which are important for the simulation of future crop yield under different climatic and agricultural management scenarios. Key Points: The catchment scale is of high relevance for simulating irrigation in the context of water resources planning and managementOnly few catchment models provide a full integration of hydrology and plant growthThere is a growing number of catchment scale irrigation studies, many of which are related to climate change impact and adaptation [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]

At‐many‐stations hydraulic geometry (AMHG) has provided a novel way to understand river network development, simulate water flow, and retrieve river discharge in data‐scared regions. Based on in‐situ measurements of six major rivers originating from the Qinghai‐Tibet Plateau (QTP), this study verifies the existence of AMHG relations along the rivers and explores AMHG relations for cross sections that are not located in the same river reach. The mainstreams and tributaries of the studied rivers in the southern and the eastern portions of the QTP have satisfactory AMHG relation strengths with R2 > 0.9 for over 60% of the relations. For cross sections located in the same stream order or within a certain range of contributing area (CA), approximately 60% (9/15) and 53% (8/15) of the AMHG relations have an R2 > 0.6. AMHG strength increases with increasing stream order and CA; this finding reflects the increasing coherence and maturity of the river networks associated with the geomorphic shaping power of increased discharge. Width‐AMHG intercepts are larger than those of depth‐AMHG and velocity‐AMHG for all stream orders and CAs. Most of the congruent hydraulics generated from cross sections located in middle‐scale rivers (orders 7–8) are within the observed range. Congruent hydraulics generally increase with an increase in in‐situ measured hydraulics when the stream order or the CA increases. The AMHG relations existing among cross sections that are not located in the same reach, which is named as cross channel AMHG, indicate linear variability of cross‐sectional geometric and hydraulic similarities in the same stream order or within a certain CA range. The results break the watershed divide boundary control on AMHG and have the potential to provide background knowledge for discharge estimation in mountain rivers located in the QTP. Key Points: Cross sections across river reach show strong AMHG relationsAMHG strength increases with the increased stream order and contributing areaLow b values contribute to congruent hydraulics outside the observed range only for cross sections located in higher‐order streams [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]

Meandering river floodplains often contain intermittently flooded complex channel networks. Many questions remain as to the pervasiveness, function, and evolution of these floodplain channels. In this present work, we analyzed size‐specific sediment transport potential and assessed whether the channelized floodplain of the meandering East Fork White River near Seymour, Indiana is on a net erosional or depositional trajectory. We applied a two‐dimensional hydrodynamic model and used simulated model results to estimate the largest sediment size that can be moved in suspension and as bedload at various flows for grain size classes between 4 µm and 64 mm. We developed a probabilistic method that integrates the largest sediment size that can be moved at various flows to compute an effective grain size, which we compared to measured field data. Results show that the river is capable of supplying sand to the floodplain and these floodplain channels can transport sand in suspension and gravel as bedload. This suggests that sediment supplied from the river could be transported as bedload in floodplain channels. These floodplain channels are supply limited under the current hydrologic regime and the grain size distribution of the bed surface is set by the flow conditions; thus, these floodplain channels are net erosional. Finally, our proposed method of probabilistically integrating the largest sediment size that can be moved at various flows can be used to predict the upper end of the grain size distribution in suspension and in bed material, which is applicable to floodplains as well as coastal areas. Key Points: We investigate the potential of various sizes of sediment to move over a complex topographic floodplain surface at different flows.We compute an effective sediment size moved in suspension and as bedload by integrating the largest sediment size moved for all flows.We compare our effective sediment size with field data and conclude unvegetated floodplain channels are on a net erosional trajectory. [ABSTRACT FROM AUTHOR]

Salinity in the Colorado River Basin causes an estimated $300 to $400 million per year in economic damages in the United States. To inform and improve salinity‐control efforts, this study quantifies long‐term trends in salinity (dissolved solids) across the Upper Colorado River Basin (UCRB), including time periods prior to the construction of large dams and preceding the implementation of salinity‐control projects. Weighted Regressions on Time, Discharge, and Season was used with data sets of dissolved‐solids and specific‐conductance measurements, collected as early as 1929, to evaluate long‐term trends in dissolved‐solids loads and concentrations in streams from 1929 to 2019 (n = 14). Results indicate that large, widespread, and sustained downward trends in dissolved‐solids concentrations and loads occurred over the last 50–90 years. For 12 of the 14 stream sites with significant downward change, median declines of −38% (range of −14% to −57%) and −40% (range of −9 to −65%) were observed for flow‐normalized concentration and load, respectively. Steepest rates of decline occurred from 1980 to 2000, coincident with the initiation of salinity‐control efforts in the 1980s. However, there was a consistent slowing or reversing of downward trends after 2000 even though salinity‐control efforts continued. Significant decreases in salinity occurred as early as the 1940s at some streams, indicating that, in addition to salinity‐control projects, changes in land cover, land use, and/or climate substantially affect salinity transport in the UCRB. Observed dissolved‐solids trends are likely the result of changes to watershed‐related processes, not due to changes in the streamflow regime. Plain Language Summary: Salinity, or dissolved salt, in the Colorado River Basin causes an estimated $300 to $400 million per year in economic damages in the United States. The Colorado River Basin Salinity Control Program implements and manages projects to reduce salinity, investing millions of dollars per year in improved irrigation systems, vegetation recovery, and other mitigation strategies. To inform and improve mitigation efforts, there is a need to better understand changes in salinity that occurred prior to the implementation of salinity‐control projects in the 1980s. This study uses decades of water‐quality measurements, collected as early as 1929, to explore salinity trends in the Upper Colorado River Basin from 1929 to 2019. Findings indicate that large, widespread, and sustained downward trends in salinity occurred over the last 50–90 years. Further, the timing and amount of salinity reductions suggest changes in land cover, land use, and/or climate, in addition to salinity control, substantially affect how salinity is transported to streams in the basin. Identifying the causes of dropping salinity levels will be important for water managers in the basin so they can anticipate future changes in salinity, develop more efficient salinity‐control practices, and capitalize on natural processes that reduce salinity. Key Points: Substantial and widespread declines in dissolved‐solids loads and concentrations were observed across the Upper Colorado River Basin during the 20th centuryMajority of the downward trends are estimated to be the result of watershed‐related processes, not changes in the streamflow regimeDecreases in the 1940s indicate watershed processes other than salinity control substantially affected dissolved‐solids transport [ABSTRACT FROM AUTHOR]

Descriptions of runoff generation processes continue to grow, helping to reveal complexities and hydrologic behavior across a wide range of environments and scales. But to date, there has been little grouping of these process facts. Here, we discuss how the "fill‐and‐spill" concept can provide a framework to group event‐based runoff generation processes. The fill‐and‐spill concept describes where vertical and lateral additions of water to a landscape unit are placed into storage (the fill)—and only when this storage reaches a critical level (the spill), and other storages are filled and become connected, does a previously infeasible (but subsequently important) outflow pathway become activated. We show that fill‐and‐spill can be observed at a range of scales and propose that future fieldwork should first define the scale of interest and then evaluate what is filling‐and‐spilling at that scale. Such an approach may be helpful for those instrumenting and modeling new hillslopes or catchments because it provides a structured way to develop perceptual models for runoff generation and to group behaviors at different sites and scales. Key Points: Runoff is at the scale of the beholder. We need to move from a notion of uniqueness of place to uniqueness of scaleFill‐and‐spill, together with its components is common to all event runoff systemsFill‐and‐spill as a framework is perhaps a guide for field hydrologists on what to measure, in what order and why [ABSTRACT FROM AUTHOR]

There is a critical knowledge gap about how glacier retreat in remote and rapidly warming tropical montane watersheds will impact solute export, which has implications for downstream geochemical cycling and ecological function. Because tropical glacierized watersheds are often uniquely characterized by year‐round ablation, upslope vegetation migration, and significant groundwater flow, baseline understanding is needed of how spatiotemporal variables within these watersheds control outlet hydrochemistry. We implemented a recently developed reactive transport watershed model, BioRT‐Flux‐PIHM, for a sub‐humid glacierized watershed in the Ecuadorian Andes with young volcanic soils and fractured bedrock. We found a unique simulated concentration and discharge (C‐Q) pattern that was mostly chemostatic but superimposed by dilution episodes. The chemostatic background was attributed to large simulated contributions of groundwater (subsurface lateral flow) to streamflow, of which a notable fraction (37%) comprised infiltrated ice‐melt. Relatively constant concentrations were further maintained in the model because times and locations of lower mineral surface wetting and dissolution were offset by concentrating effects of greater evapotranspiration. Ice‐melt did not all infiltrate in simulations, especially during large precipitation events, when high surface runoff contributions to discharge triggered dilution episodes. In a model scenario without ice‐melt, major ion concentrations, including Na+, Ca2+, and Mg2+, became more strongly chemostatic and higher, but weathering rates decreased, attenuating export by 23%. We expect this reduction to be exacerbated by higher evapotranspiration and drier conditions with expanded vegetation. This work brings to light the importance of subsurface meltwater flow, ecohydrological variability, and interactions between melt and precipitation for controlling hydrochemical processes in tropical watersheds with rapidly retreating glaciers. Key Points: Integrated hydrologic model with reactive transport can probe spatiotemporally variable hydrochemical processes in data‐sparse watershedUnique C‐Q pattern due to infiltrated ice‐melt, weathering with groundwater, vegetation‐driven ET, and melt + precipitation dilution eventsAs glaciers fully retreat, concentrations increase and become more chemostatic, but solute export decreases due to lower stream discharge [ABSTRACT FROM AUTHOR]

Extreme precipitation and consequent floods are some of California's most damaging natural disasters, but they are also critical to the state's water supply. This motivates the need to better understand the long‐term variability of these events across the region. This study examines the possibility of reconstructing extreme precipitation occurrences in the Sacramento River Watershed (SRW) of Northern California using a network of tree‐ring based moisture proxies across the Western US. We first develop a gridded reconstruction of the cold‐season standardized precipitation index (SPI) west of 100°W. We then develop an annual index of regional extreme precipitation occurrences in the SRW and use elastic net regression to relate that index to the gridded, tree‐ring based SPI. These regressions, built using SPI data across the SRW only and again across a broader region of the Western US, are used to develop reconstructions of interannual variability in extreme precipitation frequency back to 1400 CE. The SPI reconstruction is skillful across much of the West, including the Sacramento Valley and Central Oregon. The reconstructed SPI also captures historical interannual variations in extreme SRW precipitation, although individual events may be under‐ or over‐estimated. The reconstructions show more SRW extremes from 1580 to 1700 and 1850 to 1915, a dearth of extremes prior to 1550, and a 2–8 year oscillation after 1550. Using tree‐ring proxies beyond the SRW often dampens the reconstructed extremes, but these data occasionally help to identify known extreme events. Overall, reconstructions of SRW extreme precipitation can help to understand better the historic variability of these events. Key Points: Tree‐ring based moisture proxies were used to reconstruct extreme precipitation in the Sacramento River Watershed back to 1400 CETree‐ring based proxies in the Intermountain West can improve reconstructions in some years, but can also dampen the magnitude of extremesExtreme precipitation was prevalent from 1580 to 1700 and 1850 to 1915, very rare from 1400 to 1550, and exhibited a 2–8 year oscillation after 1550 [ABSTRACT FROM AUTHOR]

Addressing uncertainty in sediment predictions due to land use/land cover (LULC) change could better inform the selection of sediment management options for reservoir sustainability. We used the Nam Kong catchment of the Mekong River Basin in Southern Laos, with two hydropower dams in series, to understand the implications of LULC change uncertainty for catchment‐level and reservoir‐level sediment management options. The catchment‐level sediment management options of terracing, vegetative filter strips, and no tillage were evaluated applying the Soil and Water Assessment Tool (SWAT). The reservoir‐level sediment management option of flushing was assessed using the Sediment Simulation Screening Python Model (PySedSim). Costs of sediment management options were assessed via the economic value of the loss in hydropower production and the avoided cost of dredging. Our results suggest that LULC projections resulted in high variability in loss of reservoir capacity and cost of sediment management. Terracing was found to be the best catchment‐level management option at decreasing both the magnitude and variability in loss of reservoir storage for both dams, but it was also the most expensive option. Flushing was also effective in reducing sedimentation, but it was less economically beneficial compared to catchment‐level sediment management options. Combinations of catchment‐level and reservoir‐level management strategies, however, can be effective in reducing the magnitude and variability in loss of reservoir storage and associated costs in response to LULC change uncertainty. Key Points: Costs of sediment management options are linked to the economic value of the loss in hydropower production and the avoided cost of dredgingThe sediment management strategy best at increasing the life span of a reservoir may not be the most cost‐effective option to implementIntegration of suitable sediment management options can reduce the amount and variability in reservoir storage loss and associated cost [ABSTRACT FROM AUTHOR]

We study a watershed containing an old stream valley at the Kaiwi Coast on Oʻahu, Hawaiʻi. The study site has undergone significant hydrogeological changes throughout its history, and at present displays complex interactions of a variety of geological formations. We highlight an innovative and efficient combination of two hydrogeophysical methodologies for studying groundwater systems: ambient noise surface wave tomography (ANSWT), and self‐potential (SP) measurements. We collected (1) 5 days of continuous seismic data, using a total of 54 autonomous seismic nodes, exploiting both ocean wave and traffic noise sources and (2) 386 SP measurements with an average 20 m spacing, in the same area. We showcase how these two data sets complement each other in a joint‐interpretation framework. The SP data display the distribution of groundwater in the region, as well as zones of higher infiltration and/or flow rates. Using ANSWT, we identify the structural contact between the older Koʻolau basalt formation and the younger post‐erosional Honolulu volcanics, as well as the distribution of sedimentary valley fill and coastal deposits. The joint interpretation of the SP and seismic data clearly illustrates that groundwater flow occurs in the identified paleo‐channels at the erosional surface of the basaltic bedrock. Furthermore, it highlights that the basaltic bedrock of the ridges likely forms an important groundwater flow unit. Some flow may occur in the shallower valley fill material. Complementing SP data with seismic data enables us to place groundwater flow inferences, as made from SP data, at depth. Key Points: Self‐potential (SP) and seismic ambient noise data were collected at an old stream valley on Oʻahu, HawaiʻiThe main groundwater flow likely occurs in basaltic bedrock and deeper parts of identified paleo‐channelsComplementing SP data with seismic data enables us to place the groundwater flow inferences at depth [ABSTRACT FROM AUTHOR]

The value of streamflow forecasts to inform water infrastructure operations has been extensively studied. Yet, their value in informing infrastructure design is still unexplored. In this work, we investigate how dam design is shaped by information feedbacks supporting the implementation of flexible operating policies informed by streamflow forecasts to enable the design of less costly reservoirs relative to alternatives that do not rely on forecast information. Our approach initially explores the maximum potential gain attainable by searching and using the most valuable forecast information and lead time. We then analyze the results? sensitivities relative to existing and synthetic biased forecasts. We demonstrate our approach through an ex post analysis of the Kariba Dam in the Zambezi River Basin. Results show that informing dam design with perfect forecasts enables attaining the same hydropower production of the existing dam, while reducing infrastructure size and associated capital costs by 20%. A forecast‐informed operation of the existing system can instead facilitate an annual average increase of 60 GWh in hydropower production. This finding, extrapolated to the new planned dams in the basin, suggests that forecast informed policies could yield power production benefits equal to 75% of the current annual electricity consumption of the Zambian agricultural sector. The use of biased forecasts substantially reduces this gain, showing that the ESP forecasts value is marginal and that informed infrastructure designs are particularly vulnerable to forecast overestimation. Advancing information feedbacks may therefore become a valuable asset for the ongoing hydropower expansion in the basin. Key Points: The value of using streamflow forecasts in informing joint dam design and operation is explored for the first timeThe use of perfect seasonal streamflow forecasts could enable a 20% reduction in capital costs for large reservoirsThe value of forecast information changes remarkably with dam size and operational trade‐offs [ABSTRACT FROM AUTHOR]

Remote sensing has gained attention as a novel source of primary information for estimating river discharge, and the Mass‐conserved Flow Law Inversion (McFLI) approach has successfully estimated river discharge in ungauged basins solely from optical satellite data. However, McFLI currently suffers from two major drawbacks: (1) existing optical satellites lead to temporally and spatially sparse discharge estimates and (2) because of the assumptions required, McFLI cannot guarantee downstream flow continuity. Hydrological modeling has neither drawback, yet model accuracy is frequently limited by a lack of discharge observations. We therefore combine McFLI and models in a data assimilation framework applicable globally. We establish a daily "ungauged" baseline model for 28,998 reaches of the Missouri river basin forced by recently published global runoff data, which we do not calibrate. We estimate discharge via McFLI using ∼1 million width measurements made from 12,000 Landsat scenes and assimilate McFLI into the model before validating at 403 USGS gauges. Results show that assimilated discharges did not impair already accurate baseline flows and achieved median improvements of 28% normalized root mean square error, 0.50 Nash–Sutcliffe efficiency (NSE), and 0.23 Kling–Gupta efficiency where baseline performance was poor (defined as baseline negative NSE, 225/403 reaches). We ultimately improved flows at 92% of these originally poorly modeled gauges, even though Landsat images only provide McFLI discharges at 1.5% of reaches and 26% of simulated days. Our results suggest that the combination of McFLI and state‐of‐the‐art hydrology models can improve flow estimations in ungauged basins globally. Key Points: ∼1 million satellite‐derived river widths are used to estimate river discharge via the BAM algorithmDaily basin‐wide discharge estimation is improved by addition of remote sensingOur approach is a potential direct analog for future SWOT observations [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]

Flood inundation forecasts from hydrodynamic models can help with flood preparedness, but uncertainty in the inputs and parameters can lead to erroneous flood inundation estimates. However, Synthetic aperture radar (SAR)‐based flood extent information can be used to constrain such model forecasts through data assimilation thus making them more accurate. Since high‐resolution SAR satellites can only provide partial coverage for medium to large catchments, it is expedient to evaluate the combination of observation footprint, timing, and frequency which can lead to maximum forecast improvements. Consequently, multiple spatiotemporal SAR‐based flood extent assimilation scenarios have been simulated here to identify the optimum observation design for improved flood inundation forecasts. A mutual information‐based particle filter was implemented in a synthetic setup for the 2011 flood event in the Clarence Catchment, Australia, to combine SAR‐based flood extents with the hydraulic model LISFLOOD‐FP. The open loop ensemble was forced using uncertain inflows and the impact of assimilating flood extents in morphologically homogenous river reaches was evaluated for different first visit and revisit scenarios. Results revealed that the optimum temporal acquisition strategy strongly depends on reach morphology and flood wave arrival timing. Further, it was found that a single image at the right time could improve the 8‐days forecast by ∼95% when assimilated at reaches with large flat floodplains but limited tidal influence, while in reaches with narrow valleys over 10 images were needed to achieve the same outcome. Experiments such as the one presented here can therefore inform targeted observation strategies to ensure cost effective flood monitoring and maximize the forecast accuracy resulting from flood extent assimilation. Plain Language Summary: Satellite observations of flood inundation have the potential to increase the reliability and accuracy of flood forecasts, thereby contributing to improved flood resilience of vulnerable populations. However, new generation high‐resolution satellites can only observe small portions of large river systems river during a flood. Since, the model‐data integration methods used to combine flood forecasting models with satellite data are sensitive to the observation coverage, timing, and frequency best case scenarios can be constructed to obtain maximum improvements in accuracy. This study investigated the possibility of designing targeted observation strategies that can lead to more accurate flood forecasts after the model‐data integration. Synthetic experiments were used to simulate multiple different image acquisition scenarios and assess the impacts on flood forecast accuracy. Results indicate that the location and timing of the images is more important than the revisit interval. Findings from this study can therefore be used to inform future satellite acquisitions, to ensure more cost effective flood monitoring from space leading to more reliable flood inundation forecasts. Key Points: The potential of targeted observations for optimal flood inundation forecast improvements from flood extent assimilation is demonstratedImpact of assimilating images in morphologically uniform reaches at different times, on channel, and floodplain water depth quantifiedAssimilating a single ideal flood extent could lead to persistent improvements comparable to assimilating multiple sub‐optimal images [ABSTRACT FROM AUTHOR]

The ecosystem function of vegetation to attenuate export of nutrients is of substantial importance for securing water quality. This ecosystem function is at risk of deterioration due to an increasing risk of large‐scale forest dieback under climate change. The present study explores the response of the nitrogen (N) cycle of a forest catchment in the Bavarian Forest National Park, Germany, in the face of a severe bark beetle (Ips typographus Linnaeus) outbreak and resulting large‐scale forest dieback using top‐down statistical‐mechanistic modeling. Outbreaks of bark beetle killed the dominant tree species Norway spruce (Picea abies (L.) H.Karst.) in stands accounting for 55% of the catchment area. A Bayesian hierarchical model that predicts daily stream NO3 concentration (C) over three decades with discharge (Q) and temperature (T) (C‐Q‐T relationship) outperformed alternative statistical models. A catchment model was subsequently developed to explain the C‐Q‐T relationship in top‐down fashion. Annually varying parameter estimates provide mechanistic interpretations of the catchment processes. Release of NO3 from decaying litter after the dieback was tracked by an increase of the nutrient input parameter cs0. The slope of C‐T relation was near zero during this period, suggesting that the nutrient release was beyond the regulating capacity of the vegetation and soils. Within a decade after the dieback, the released N was flushed out and nutrient retention capacity was restored with the regrowth of the vegetation. Key Points: Pulse of nitrate export from a forest catchment in response to bark beetle infestation followed by recovery of nutrient retention capacityTop‐down, data‐driven Bayesian hierarchical model assists mechanistic interpretation of hydrochemical processesConcentration‐discharge‐temperature relationship is shaped by spatial heterogeneity of nutrient and seasonality of biogeochemical reactions [ABSTRACT FROM AUTHOR]

In this study, the hydrological dynamics of the Congo River Basin (CRB) have been analyzed. This is achieved using multiangular and dual‐polarization passive L‐band microwave signal from the Soil Moisture and Ocean Salinity (SMOS) satellite to estimate water surface extent dynamics from 2010 to 2017. The results provide new insights into the poorly characterized wetlands, peatlands, and floodplains dynamics of the CRB. We found that the mean flooded area for the entire CRB is equal to 89,408 ± 20,623 km2 corresponding to 2.39% of the entire basin. The results were compared with three land cover maps (European Space Agency‐Climate Change Initiative [ESA‐CCI], International Geosphere‐Biosphere Programme [IGBP], and Global Surface Water Occurrence [GSWO]) and the SWAMPS global dynamic water surfaces product. More inland waters were detected than the four previous products, except along the rivers. Floods and droughts during the last 10 years were also depicted. The knowledge about the CRB hydrological behavior was improved by analyzing the date of maximum floods, the time lag in days between precipitation, water surface extent, and river water height at the outlet of the nine major Congo subbasin. A lag of 67 ± 3 days between rainfall and inundated areas was found in the Upper Congo (r = 0.89). In the Kasai subbasin, no time lag between rainfall and inundation was found (r = 0.86). The contribution of each floodplain to the Congo discharge was also evaluated. In the future, the fusion of the current surface waters with water heights from the SWOT mission can provide further information to the water volumes of the CRB floodplains. Plain Language Summary: The Congo River Basin (CRB) has a vast flooded area, that is poorly characterized. We developed new flood extent maps for the CRB based on satellite data in the L‐band spectral band. This enabled an unprecedented insight into water dynamics of the wetlands, peatlands, and floodplains of the CRB through the detection of the dates of maximum floods, the lag with the rainfall peaks, and the duration of floods even in vegetated areas. Key Points: New knowledge of the Congo River Basin hydrology for the last decade through a new temporal and spatial dynamic water surface extent dataThe water extent in the Congo River Basin ranges from 78,602 to 99,225 km2 between 2011 and 2017The lags between rainfall and flood in the nine Congo subbasins has a high variability ranging from 0 to 67 days [ABSTRACT FROM AUTHOR]

Cataclysmic eruption of Mount St. Helens (USA) in 1980 reset 30 km of upper North Fork Toutle River (NFTR) valley to a zero‐state fluvial condition. Consequently, a new channel system evolved. Initially, a range of streamflows eroded channels (tens of meters incision, hundreds of meters widening) and transported immense sediment loads. Now, single, large‐magnitude, or multiple moderate‐magnitude events are needed to accomplish substantial channel modification. Three large floods (two ≥100‐year events; one ∼10–25‐year event along lower Toutle River) from 1996 to 2015 indicate flood effectiveness in this environment is affected by both geomorphic and environmental factors. The largest and smallest of these floods (February 1996, November 2006) transported the most sediment by single floods since 1982; erosion and sediment transport by an ∼100‐year flood in December 2015 was not exceptional. Strong coupling between NFTR and its tall corridor banks, local geologic and hydraulic conditions promoting threshold erosion, event sequencing, and possibly a longitudinal gradient in stream power are important factors affecting event effectiveness on channel modification. In addition, environmental factors have also been influential, as variations in snowpack, storm trajectories and rainfall distributions, and episodic mobilization of debris flows have also influenced geomorphic response. Other factors such as vegetation anchoring, strong channel–hillside coupling, disparities between flood frequencies and perturbation relaxation times, and large variations in flood duration do not appear to be critical influences. Climate forecasts for warmer temperatures and a shift from snowfall to rainfall at high elevations may promote further acute geomorphic responses. Key Points: Three large floods affected an evolving, cataclysmically disturbed river system over a 20‐year span and produced varied geomorphic responsesEvent responses were influenced by storm character, snowpack cover, geomorphic process, and local hydraulic and geologic conditionsSingle, large‐magnitude, or multiple moderate‐magnitude events are currently needed to accomplish substantial channel modification [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]

Climate change impacts on hydroclimatology are becoming increasingly apparent around the world. It is unknown how annual variations in precipitation and air temperature alter the model‐inferred importance of hydrological processes and how this varies across watersheds. To examine this, we used parsimonious rainfall‐runoff model and applied time‐varying sensitivity analysis across 30 Californian watersheds for a 33‐year period (1981–2014). We calculated annual total order sensitivity indices for five performance metrics: Kling‐Gupta Efficiency (KGE) and model error in simulating hydrologic signatures (runoff ratio, slope of flow duration curve, baseflow index, and timing of streamflow centroid). Sensitivity of hydrological signatures to the parameters differed by signature, while parameter importance with respect to KGE was much more spatially and temporally variable. Variations in parameter sensitivity with respect to KGE were either correlated with air temperature (snow‐dominated sites in the Sierra Nevada) or precipitation (lower elevation sites < 1,300 m). Across error metrics and signatures, parameter sensitivity strongly differed between wet and dry years for a subset of our study sites. While parameter importance varied through time, parameter sensitivity variations across watersheds were much more pronounced. This suggests that parameter controls on model performance are much more a reflection of watershed properties as opposed to being dominantly shaped by shifts in precipitation and air temperature. These findings emphasize the importance of understanding simulated watershed responses to fluctuating annual conditions, as this conceptual knowledge is necessary to anticipate similarities and differences in response across even relatively proximal watersheds in the face of growing extreme conditions. Plain Language Summary: Computer models are useful tools that can be used to analyze what inputs matter most to ensuring that model output is similar to observed conditions. In order to understand how very wet years versus very dry years impact which model inputs (called parameters) are most important to matching good predictions, we simulated streamflow using a simple mathematical model many thousands of times. We used these outputs to determine how variations in predictions of streamflow (output from the model) are related to variations in model parameters, to identify which parameters are most important for ensuring reasonable model predictions. We focused on watersheds that are close together, but that may experience different average conditions and interannual variability. We found that which parameters are important varied across different watersheds and also varied with total annual precipitation and watershed elevation. In addition, we found that drought impacted the importance of processes in the model, especially in mountainous watersheds. Our results emphasize the importance of understanding how watersheds respond to varying annual conditions, as this type of knowledge is essential to anticipate the future of streamflow availability and inform the management style that needs to be implemented to predict water availability in these systems. Key Points: Annual time‐varying sensitivity analysis revealed unique patterns of parameter importance across 21 proximal study watershedsLow elevation parameter importance to Kling‐Gupta Efficiency varied with precipitation, while shifts at high elevations varied with elevationParameter sensitivity to select hydrological signatures was generally static through time, despite annual variations in observed values [ABSTRACT FROM AUTHOR]

Understanding future river flood risk is a prerequisite for developing climate change adaptation strategies and enhancing disaster resilience. Previous flood risk assessments can barely take into account future changes of fine‐scale hydroclimatic characteristics and hardly quantify multivariate interactions among flood variables, thereby resulting in an unreliable assessment of flood risk. In this study, for the first time, we develop probabilistic projections of multidimensional river flood risks at a convection‐permitting scale through the Weather Research and Forecasting (WRF) climate simulations with 4‐km horizontal grid spacing. Vine copula has been widely used to assess the multidimensional dependence structure of hydroclimate variables, but the commonly used frequentist approach may fail to identify the correct vine model and to obtain the uncertainty interval. Thus, a Bayesian vine copula approach is proposed to explicitly address the multidimensional dependence of flood characteristics (i.e., flood peak, volume, and duration) and underlying uncertainties. The proposed approach enables a robust assessment of return periods of future floods for Guadalupe and Mission river basins located in South Texas of the United States. Our findings reveal that the South Texas region is projected to experience more flood events with longer duration and greater discharge volume. The flood peak, however, will not necessarily increase even though precipitation extremes are expected to become more frequent. The projected flood return periods over the Guadalupe river basin do not show an obvious increase while the Mission river basin is projected to face a dramatic increase in flood risk with exposed to 100‐year and even severer floods nearly every 2 years, on average, when considering the combined effects of flood peak, volume, and duration. Key Points: Probabilistic projections of multidimensional flood risks were developed at a convection‐permitting scaleHigh‐resolution climate projections with the 4‐km horizontal grid spacing were developed using the convection‐permitting Weather Research and Forecasting modelBayesian vine copula approach was proposed to uncover potential interactions among flood variables and associated uncertainties [ABSTRACT FROM AUTHOR]

Representation of subsurface hydrology in global land surface models has been advanced but outstanding challenges and opportunities remain, especially in better simulating lateral groundwater flow and aquifer pumping for irrigation. This study improves the representation of groundwater in the latest version of the Community Land Model (version 5) by implementing a prognostic groundwater module that accounts for lateral groundwater flow, aquifer pumping, and conjunctive use of groundwater and surface water for irrigation. In particular, we introduce—for the first time—explicit representation of steady‐state well equation in large‐scale hydrological modeling. Simulations are conducted at ∼5 km resolution over the conterminous US. Groundwater level, terrestrial water storage (TWS), and river discharge are evaluated over the major US aquifers and river basins using in situ observations of groundwater and river discharge and the TWS inferred from the Gravity Recovery and Climate Experiment (GRACE) satellites. The implementation of groundwater pumping results in significant improvements in simulation of spatiotemporal water‐level change over the US High Plains and Central Valley Aquifer (CVA) and that of TWS over the CVA. Improvements in river discharge over the major US river basins are also achieved but are not substantial. Furthermore, the representation of pumping leads to certain overestimation in TWS trend compared to GRACE data in regions with high groundwater withdrawals. The consideration of lateral groundwater flow provides improvements in the subsurface response to pumping and natural wet and dry cycles, particularly at the grid cell level. These results provide a basis for improved groundwater modeling over large scales. Key Points: A new prognostic groundwater model is implemented into the latest version of the Community Land Model (CLM5)The new model accounts for lateral groundwater flow and conjunctive use of groundwater and surface water for irrigationSignificant improvements are achieved in simulating the spatiotemporal dynamics of water level over the heavily exploited US aquifers [ABSTRACT FROM AUTHOR]

River temperature exerts a critical control on habitat for aquatic biota. As the climate warms in eastern Canada, threats to habitats of cold‐water species will increase, underpinning the necessity to develop an understanding of landscape‐scale, thermal regimes of flowing waters. We assessed the performance of spatial statistical network (SSN) models of river temperature using high‐resolution thermal infrared imagery (0.6 m) and LiDAR (1 m) compared to NASA's Shuttle Radar Topography Mission (SRTM—30 m) topographic data and interrogate LiDAR derived fine‐scale models (3 ha) to describe groundwater connectivity to surface waters in catchments with shallow overburden and varied bedrock geology. LiDAR improved model performance in a catchment underlain by a homogeneous, high hydraulic conductance bedrock (Cains River) but did not improve model performance in a catchment with heterogeneous bedrock and variable hydraulic conductance (North Pole Stream). We hypothesize that differences in bedrock conductance modified topographic controls on subsurface flows and discharge patterns to the rivers and thus produced the mixed performance of the SSN models. At finer scales, river reaches in steep valleys incising high conductance bedrock produced groundwater discharge, which was absent in incised valleys with low conductance bedrock. These findings indicate that while topography exerts an important control on landscape‐scale hydrological processes, geologic setting is a similarly important influence on hydrological processes. We suggest the inclusion of a third dimension of spatial autocorrelation, representative of the vertical plane that captures the geologic setting, would broaden the geographic applicability of spatial statistical models for river temperature studies. Key Points: Spatial statistical river network models had mixed success in predicting river temperature in differing geologic and topographic settingsValley incision in shallow, high hydraulic conductance bedrock facilitated groundwater discharge associated with cool river temperaturesValley incision in shallow, low hydraulic conductance bedrock limited groundwater discharge and was associated with warm river temperatures [ABSTRACT FROM AUTHOR]

In cold agricultural regions, seasonal snowmelt over frozen soils provides the primary source of runoff and transports large nutrient loads downstream. The postglacial landscape of the Canadian Prairies and Northern Plains of the United States creates challenges for hydrological and water quality modeling. Here, the application of conventional hydrological models is problematic because of cold regions hydrological and chemical processes, the lack of fluvially eroded drainage systems, large noncontributing areas to streamflow and level topography. A new hydrodynamic model was developed to diagnose overland flow from snowmelt in this situation. The model was used to calculate the effect of variable contributing areas on (1) hydrological connectivity and the development of (2) tipping points in streamflow generation and (3) predominant chemical transport pathways. The agricultural Steppler Basin in Manitoba, Canada, was used to evaluate the model and diagnose snowmelt runoff. Relationships were established between contributing area and (1) snowmelt runoff intensity, (2) seasonal snowmelt volumes and duration, and (3) inundated, active and connected areas. Variations in the contributing area depended on terrain and snowmelt characteristics including wind redistribution of snow. Predictors of hydrological response and the size of the contributing area were developed which can be used in larger scale hydrological models of similar regions Key Points: Seasonally frozen soils, level topography, and poorly defined basin drainage networks create challenges for modeling in prairie regionsRelationships were established between contributing area and (1) snowmelt intensity, volume, and duration and (2) inundated and connected areaA new model gives novel insights into the spatiotemporal patterns of overland flow, variable contributing areas, and transport pathways [ABSTRACT FROM AUTHOR]

Time‐variable transit time distributions (TTDs) have been utilized as a tool to understand how catchments transmit water. However, most of the existing TTD estimation methods require to impose certain structures on those TTDs a priori, which could lead to misinterpreting data. We present a data‐based method to estimate time‐variable TTDs without imposing their structure a priori. The core of the method is the use of a revised flow‐weighted time, where TTDs do not reflect variable external forcings directly. The functional forms of the TTDs are much simpler in flow‐weighted time, compared to those in calendar time, and this allows for easier estimation of TTDs. Dynamic (state‐dependent) multiple linear regression methods were applied to estimate the time‐variable TTDs in flow‐weighted time, which can eventually be transformed back to calendar time. The method performs well in a proof‐of‐concept demonstration with synthetic data sets. We also discuss potential generalizations of the proposed method. Key Points: We outline a data‐based method to estimate transit time distributions (TTDs) without imposing their structure a prioriWe use a (dynamic) multiple linear regression method to estimate (time‐variable) TTDs in flow‐weighted timeIn a proof‐of‐concept demonstration, the method estimates TTDs close to the actual TTDs [ABSTRACT FROM AUTHOR]

Preferential flow (PF)‐dominated soil structure is often considered a unique system consisting of micropores and macropores and thus supposed to provide dual‐pore filtering effects on hydrological signals, through which smoothing effects are likely to be stronger for matrix flow and weaker for PF via macropores. By using time series of hydrological signals (precipitation, canopy interception, throughfall, soil moisture, evapotranspiration, water storage in soil and groundwater, and catchment discharge) propagating through the Shale Hills Catchments and representative soil series, the filtering effects of the catchment and soil profiles were tested through the wavelet analysis. Typical filtering effects, that is, the characteristic of the role of soil and the groundwater table as a buffer, were observed from the wavelet spectrum, gradually smoothing off the precipitation signal. The hypothesized dual‐pore style filtering effects of the soil profile were also confirmed through the coherence spectra and phase differences, rendering them applicable for possible use as "fingerprints" of PF to infer subsurface flow features. We found that PF dominates the catchment's discharge response at the scales from 3 to 12 days, which contributes to the catchment discharge mainly as subsurface lateral flow at upper or middle soil horizons. Through subsurface PF pathways, even the hilltop is likely hydrologically connected to the valley floor, building connections with or making contributions to the catchment discharge. This study highlights the potential of wavelet analysis for retrieving and characterizing subsurface flow processes based on the revealed dual‐pore filtering effects of the soil system. Although some limitations and uncertainties still exist, we believe that wavelet methods provide a highly potential but underexplored approach to studying subsurface hydrology. Key Points: The dual‐pore filtering effects were confirmed in a small forested catchmentLateral preferential flow dominated the discharge response at scales of 3–12 daysWavelet analysis complements current methods of studying subsurface flow features [ABSTRACT FROM AUTHOR]

River runoff is estimated as a water budget residual using Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage time series, ERA5 reanalysis data, and precipitation observations for January 2003 through December 2015 for the Obidos upstream drainage basin and for the entire Amazon basin. Estimated runoff based on the water budget agrees remarkably well with in situ gauge observations at Obidos, especially at seasonal time scales, with nearly perfect phase agreement but slightly larger seasonal amplitude. The discrepancy in the seasonal amplitude may be attributed to underestimation of river gauge runoff during the wet season when water overflows the riverbanks. The ERA5 model appears to overestimate long‐term mean evapotranspiration in the Amazon by ~2 cm/month based on comparisons with precipitation and runoff observations. Using precipitation data based on satellites and gauge observations relative to gauge observations alone improved agreement between water budget runoff estimates and in situ runoff observations. Seasonal variations in ERA5‐simulated runoff are about twice as large as those from in situ observations and show a large phase lag as well. Water budget‐based runoff for the entire Amazon (~7,200 km3 averaged over the 13‐year period) is significantly larger than observed runoff (~5,700 km3) at Obidos and notably larger than previous estimates for the entire Amazon. These differences may be partly related to submarine runoff from the Amazon basin that cannot be captured by surface gauges. Key Points: We provide a new estimate of seasonal and yearly river runoff changes for the Amazon basin using the water budget closure methodThe water budget closure estimates remarkably agree well with in situ observations in the Amazon basin at seasonal scalesWe have demonstrated that water budget closure method has the potentials to detect submarine runoff from the Amazon basin [ABSTRACT FROM AUTHOR]

Prior to their application in a land surface data assimilation (DA) system, remotely sensed (RS) soil moisture (SM) products are typically rescaled to remove systematic differences with respect to comparable SM estimates obtained from a land surface model (LSM). This preprocessing of RS SM data—commonly referred to as bias correction—implicitly discards spatial information present in the RS SM retrievals. Here, based on dense SM network measurements obtained within the Huai River Basin of China, we demonstrate that L‐band Soil Moisture Active Passive (SMAP) L3 surface SM retrievals provide a better representation of spatial variability in time‐averaged SM fields than a LSM—suggesting that, as typically applied, bias correction is neglecting important spatial information present in RS SM products. To maximally use RS information, we propose an alternative bias correction approach for SM DA that provides the benefits of traditional rescaling while also improving modeled SM spatial patterns. Specifically, the regional mean of SMAP SM is first corrected using modeled SM of the Variable Infiltration Capacity (VIC) model. Then, two key VIC soil parameters (EXPT and BULKD) are calibrated to match the relative subregional spatial variability captured by the SMAP SM product. Results show that our model calibration method successfully improves VIC SM spatial patterns and pixel‐wise time series. Furthermore, these SM improvements translate into enhanced VIC streamflow estimates. Overall, results suggest that the proposed bias correction framework can improve current land surface DA systems by maximally utilizing spatial information contained in RS SM products. Key Points: Spatial information contained in the SMAP L3 SM product is used for VIC model calibrationThe model calibration improves VIC SM spatial patterns for the surface and second soil layersThe model calibration also enhances VIC streamflow estimates [ABSTRACT FROM AUTHOR]

Drought impacts on the society and environment are influenced by how droughts propagate through the hydrologic cycle; however, the physical processes involved in drought propagation are not well understood. In this study, a physically based hydrologic model is used to understand the drought propagation mechanisms and their controlling factors in multiple watersheds selected from different regions of the contiguous United States (CONUS). The characteristics of hydrologic droughts are found to be significantly different in three regions of the CONUS: (1) western U.S. watersheds have long and intense streamflow droughts, (2) Great Plains and southwest U.S. watersheds have low intensity and duration of streamflow droughts, and (3) eastern U.S. watersheds have short but intense streamflow droughts. This spatial pattern of hydrologic drought characteristics is found to coincide with the pattern of climate properties. Highly seasonal recharge driven by winter precipitation or snowmelt leads to longer streamflow droughts in the western United States; whereas humid climate with low seasonality in the eastern United States leads to shorter streamflow droughts. Furthermore, the storage‐discharge relationship is identified as a key watershed property which controls the intensity of hydrologic droughts. Higher sensitivity of baseflow to watershed storage in the western and the eastern U.S. watersheds leads to more intense streamflow droughts in these regions. In the Great Plains and southwest United States, watersheds have a lower sensitivity of baseflow to catchment storage which, combined with the highly arid climate, leads to low variability in baseflow, and in turn results in low intensity of streamflow droughts in the region. [ABSTRACT FROM AUTHOR]

Because remote sensing (RS) data are spatially and temporally explicit and available across the globe, they have the potential to be used for predicting runoff in ungauged catchments and poorly gauged regions, a challenging area of research in hydrology. There is potential to use remotely sensed data for calibrating hydrological models in regions with limited streamflow gauges. This study conducts a comprehensive investigation on how to incorporate gridded remotely sensed evapotranspiration (AET) and water storage data for constraining hydrological model calibration in order to predict daily and monthly runoff in 30 catchments in the Yalong River basin in China. To this end, seven RS data calibration schemes are explored and compared to direct calibration against observed runoff and traditional regionalization using spatial proximity to predict runoff in ungauged catchments. The results show that using bias‐corrected remotely sensed AET (bias‐corrected PML‐AET data) for constraining model calibration performs much better than using the raw remotely sensed AET data (nonbias‐corrected AET obtained from PML model estimate). Using the bias‐corrected PML‐AET data in a gridded way is much better than using lumped data and outperforms the traditional regionalization approach especially in headwater and large catchments. Combining the bias‐corrected PML‐AET and GRACE water storage data performs similarly to using the bias‐corrected PML‐AET data only. This study demonstrates that there is great potential in using bias‐corrected RS‐AET data to calibrating hydrological models (without the need for gauged streamflow data) to estimate daily and monthly runoff time series in ungauged catchments and sparsely gauged regions. Key Points: Using bias‐corrected remote sensing data to calibrate hydrological model shows great potential especially in ungauged catchmentsCompared to raw PML‐AET, bias‐corrected PML‐AET improves runoff prediction noticeably and adding GRACE shows limited benefitGridded application performs better than lumped catchment modeling application for maximizing the benefit from the spatial PML‐AET data [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]

It has long been assumed that over a sufficiently long period of time, changes in catchment water storage (ΔS) are a relatively minor term compared to other fluxes and can be neglected in the catchment water balance equation. However, the validity of this fundamental assumption has rarely been tested, and the associated uncertainties in water balance calculations remain unknown. Here, we use long‐term (1982–2011) observations of monthly streamflow (Q) and precipitation (P) for 1,057 global unimpaired catchments, combined with four independent evapotranspiration (E) estimates to infer ΔS and to provide a global assessment of the steady‐state assumption in catchment water balance calculations. Results show that when the threshold for steady state is set to 5% of the mean monthly P, ~70% of the catchments attain steady state within 10 years while ~6% of the catchments fail to reach a steady state even after 30 years. The time needed for a catchment to reach steady state (τs) shows a close relationship with climatic aridity and vegetation coverage, with arid/semiarid and sparsely vegetated catchments generally having a longer τs. Additionally, increasing snowfall fraction also increases τs. The imbalance (ewb) caused by ignoring ΔS decreases as averaging period for water balance calculations increases as expected. For a typical 10‐year averaging period, ewb accounts for ~7% of P in arid, but that decreases to ~3% of P in humid catchments. These results suggest that catchment properties should be considered when applying the steady‐state assumption and call for caution when ignoring ΔS in arid/semiarid regions. Key Points: The steady‐state assumption in water balance calculation and associated errors across global catchments are assessedArid/semiarid catchments require a longer time to reach an approximate steady state than humid/subhumid catchmentsImbalance caused by ignoring storage change accounts for 7% (3%) of precipitation in arid (humid) catchments for a typical 10‐year period [ABSTRACT FROM AUTHOR]

Urbanization impacts ecosystem functions and services by fundamentally altering the balances between precipitation, water yield (Q), and evapotranspiration (ET) in watersheds. Accurate quantification of future hydrologic impacts is essential for national urban planning and watershed management decision making. We hypothesize that "hydrologic impacts of urbanization are not created equal" as a result of the large spatial variability in climate and land use/land cover change (LULCC). A monthly water balance model was validated and applied to quantify the hydrologic responses of 81,900 12‐digit Hydrologic Unit Code (HUC) watersheds to historical and projected LULC in 2000, 2010, 2050, and 2100 in the conterminous United States (CONUS). Stepwise regression and Geographically Weighted Regression models were used to identify key factors controlling the spatially varied hydrologic impacts across CONUS. Although the simulated impact of future urbanization on mean change in water yield (ΔQ) was small at the national level, significant changes (ΔQ > 50 mm/year) were found in 1,046 and 3,747 watersheds by 2050 and 2100, respectively. Hydrologic responses varied spatially and were more pronounced in the eastern United States. Overall, the impacts of urbanization on water yield were influenced by local climate, previous LULC characteristics, and the magnitude of changes in land use and impervious surfaces. The continued increase in impervious surface, especially in previously urbanized watersheds, and background precipitation contributed most to future ΔQ through both increase in direct runoff and reduction in ET. Effective national‐scale integrated watershed management strategies must consider local climatic and LULC conditions to minimize negative hydrologic impacts of urbanization. Key Points: Future watershed hydrologic impacts of urbanization vary dramatically across the United StatesHydrologic responses to urbanization were influenced by local climate, previous land covers, and change in land imperviousnessStrategies to minimize impacts of urbanization must consider local climatic and land cover conditions [ABSTRACT FROM AUTHOR]

Projections of potential impacts of climate change and groundwater abstraction on gaining and losing streams, particularly in ephemeral river basins exhibiting sporadic and intricate flux exchanges, have remained largely unexplored. To fill this gap, we propose a promising modeling scheme based on the new fully integrated hydrological model SWAT‐MODFLOW‐NWT, calibrated and validated for 1978–2012, to quantify the intertwined surface‐groundwater interactions under a conjuncture of three climatic emission scenarios (RCP 2.6, 4.5 and 8.5) and two groundwater pumping variants: "pumping" (extending current groundwater utilization into the future) and "nonpumping" (assuming a complete cease of pumping in the future). By forcing the integrated model with future downscaled climate predictors of CanESM2 under the aforementioned RCPs for three time slices up to year 2100, projections of various water resources components for the Gharehsoo River Basin (GRB), in northwestern Iran were made. Results demonstrate that because of a general decrease of future precipitation, though with ups and downs across the total projection period, most of the surface and ‐subsurface budget quantities and fluxes are substantially affected. In particular, future groundwater discharge (baseflow) to the gaining streams will be more influenced by the "pumping" variant (increasing and decreasing for "nonpumping" and "pumping", respectively) than the concentrated groundwater recharge from the losing streams (decreasing and increasing for "nonpumping" and "pumping", respectively). Future water yield and groundwater storage will also diminish and, surprisingly, this cannot be alleviated by future "nonpumping", indicating the groundwater overutilization is the compelling reason for the future water scarcity in the GRB, rather than climate change alone. Plain Language Summary: Understanding climate change impacts on surface‐groundwater interactions, accommodating nearly all of water resources components of a watershed, is of paramount importance in devising effective strategies to alleviate the adverse impacts of climate change. This is particularly important in semiarid regions where the surface water scarcity has markedly increased the dependency on groundwater resources which, in turn, has highly affected the interaction between these two. We have provided an innovative scheme to assess and project climate change impacts on water resources of a basin and, in particularly, on gaining and losing streams at different temporal and spatial scales. In addition, the net impacts of climate change impacts on the quantity of the water resources, compared with the groundwater overutilization, can be quantified using the proposed methodological approach. Our findings importantly highlight that the groundwater overutilization is the major reason, rather than climate change impacts, for extreme groundwater storage depletion in the Gharehsoo River Basin, in northwestern Iran. As a result, groundwater discharge to the river network will diminish tremendously under the changing climate projected for the region. Key Points: A promising integrated hydrological modeling approach is proposed to assess the future effects of climate change and groundwater pumpingGaining streams will be more impacted by the climate change scenarios and pumping variants than losing streamsGroundwater overutilization turns out to be the major reason for the extreme groundwater storage drawdown in the aquifer of the Gharehsoo River Basin, Iran [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]

Heterogeneity in the hyporheic zone or near‐field geology can impart a threshold effect on groundwater‐surface water (GW‐SW) exchange. Variations in the texture of riverbed sediments and lithologic variations in adjacent and underlying geology are examples of common heterogeneities. Hydrologic interaction with these heterogeneities leads to distinct types of "behavior" that "switch" when surface water or groundwater levels rise above or fall below the interface of the layers of differing lithology. A dynamic water balance/nonlinear reservoir model incorporating threshold effects was developed for a perched phreatic aquifer–river system. Four conceptualizations of the system were modeled, each of which simulates a perched aquifer as a dynamical system that receives recharge from the riverbank and loses water to an underlying regional aquifer, using combinations of zero, one, or two thresholds representing layered heterogeneity in riverbank and/or aquifer lithology. Application of the model code was demonstrated at a location in the Lower Mississippi River Valley, USA. Models were run using hourly river gage measurements, calibrated to a 382‐day period of corresponding measurements in a nearby well, and further assessed for a 3.5‐yr period representing varied hydrologic conditions. The best performance was demonstrated by the model incorporating threshold effects, which elucidated four modes of GW‐SW system behavior controlled by both riverbank (riverbed hydraulic conductivity) and aquifer (hydraulic conductivity and storage coefficient) properties. The dynamical system modeling approach incorporates the salient hydrologic processes of a GW‐SW system with layered heterogeneity. Based upon fundamental mass conservation concepts, the simple dynamic water balance/nonlinear reservoir model has broad applicability to many hydrogeologic settings. Plain Language Summary: Interest in the role of rivers as a source of recharge to aquifers has grown as many regions worldwide have experienced groundwater depletion. To better manage our groundwater resources, an improved understanding of how rivers and aquifers are interconnected is needed. Sediment is often deposited in layers with different lithology, forming fine‐grained layers such as clay or coarse‐grained layers such as sand. When a river cuts through an aquifer with layered lithology or has a riverbed composed of layers of different lithology, the change in lithology causes the flow of water between the river and aquifer to change. A threshold effect results when the water level in the river or aquifer rises above or falls below the boundary between these layers. A simple model based on fundamental water balance concepts was developed to investigate threshold effects at a site on the Big Sunflower River in the Lower Mississippi River Valley. Model results show that when the river or aquifer water level is above a threshold, much more water flows out the river and into the aquifer than when either water level is below a threshold. The model can be applied to other river and aquifer systems. Key Points: Developed lumped parameter nonlinear reservoir model of water exchange between a perched phreatic aquifer and a riverLayered heterogeneity in riverbank or aquifer lithology imparts a hydrogeologic threshold effectA perched phreatic aquifer–river system behaves as a dynamical system where thresholds cause nonlinear interactions between river stage and groundwater level [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]

This study proposes a new process‐based framework to characterize and classify runoff events of various magnitudes occurring in a wide range of catchments. The framework uses dimensionless indicators that characterize space–time dynamics of precipitation events and their spatial interaction with antecedent catchment states, described as snow cover, distribution of frozen soils, and soil moisture content. A rigorous uncertainty analysis showed that the developed indicators are robust and regionally consistent. Relying on covariance‐ and ratio‐based indicators leads to reduced classification uncertainty compared to commonly used (event‐based) indicators based on absolute values of metrics such as duration, volume, and intensity of precipitation events. The event typology derived from the proposed framework is able to stratify events that exhibit distinct hydrograph dynamics even if streamflow is not directly used for classification. The derived typology is therefore able to capture first‐order controls of event runoff response in a wide variety of catchments. Application of this typology to about 180,000 runoff events observed in 392 German catchments revealed six distinct regions with homogeneous event type frequency that match well regions with similar behavior in terms of runoff response identified in Germany. The detected seasonal pattern of event type occurrence is regionally consistent and agrees well with the seasonality of hydroclimatic conditions. The proposed framework can be a useful tool for comparative analyses of regional differences and similarities of runoff generation processes at catchment scale and their possible spatial and temporal evolution. Key Points: A new process‐based framework for characterizing runoff events of diverse sizes is proposed and applied to a wide set of catchmentsNovel indicators combining the space–time dynamics of event precipitation and catchment state are used for classificationThe derived event typology captures distinct shapes of event hydrographs even if streamflow data are not directly used for classification [ABSTRACT FROM AUTHOR]