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Both climate change and human activities are known to have induced changes to hydrology. Consequently, quantifying the net impact of human contribution to the streamfiow change is a challenge. In this paper, a decomposition method based on the Budyko hypothesis is used to quantify the climate (i.e., precipitation and potential evaporation change) and direct human impact on mean annual streamflow (MAS) for 413 watersheds in the contiguous United States. The data for annual precipitation, runoff, and potential evaporation are obtained from the international Model Parameter Estimation Experiment (MOPEX), which is often assumed to only include gauges unaffected by human interferences. The data are split into two periods (1948-1970 and 1971-2003) to quantify the change over time. Although climate is found to affect MAS more than direct human impact, the results show that assuming the MOPEX data set to be unaffected by human activities is far from realistic. Climate change causes increasing MAS in most watersheds, while the direct human-induced change is spatially heterogeneous in the contiguous United States, with strong regional patterns, e.g., human activities causing increased MAS in the Midwest and significantly decreased MAS in the High Plains. The climate- and human-induced changes are found to be more severe in arid regions, where water is limited. Comparing the results to a collection of independent data sets indicates that the estimated direct human impacts on MAS in this largely nonurban set of watersheds might be attributed to several human activities, such as cropland expansion, irrigation, and the construction of reservoirs. [ABSTRACT FROM AUTHOR]

Protecting water resources for expanding human enterprise while conserving valued natural habitat is among the greatest challenges of the 21st century. Global change processes such as climate change and intensive land use pose significant threats to water resources, particularly in arid regions where potential evapotranspiration far exceeds annual rainfall. Potentially compounding these shortages is the progressive expansion of non-native plant species in riparian areas along streams, canals and rivers in geographically arid regions. This paper sets out to identify when and where non-native riparian plant species are likely to have the highest potential impact on hydrologic fluxes of arid and semiarid river systems. We develop an ecophysiological framework that focuses on two main criteria: (1) examination of the physiological traits that promote non-native species establishment and persistence across environmental gradients, and (2) assessment of where and to what extent hydrologic fluxes are potentially altered by the establishment of introduced species at varying scales from individual plants, to small river reaches, to entire river basins. We highlight three non-native plant species that currently dominate southwestern United States riparian forests. These include tamarisk (Tamarix spp.), Russian olive (Eleagnus angustifolia), and Russian knapweed (Acroptilon repens). As with other recent reviews, we suspect that in many cases the removal of these, and other non-native species will have little or no impact on either streamflow volume or groundwater levels. However, we identify potential exceptions where the expansion of non-native plant species could have significant impact on ecohydrologic processes associated with southwestern United States river systems. Future research needs are outlined that will ultimately assist land managers and policy makers with restoration and conservation priorities to preserve water resources and valued riparian habitat given limited economic resources. [ABSTRACT FROM AUTHOR]

Virtual water flows are used to map the indirect water consumption connections implied by the supply chain of a city, region, or country. This information can be used to manage supply chains to achieve environmental policy objectives and mitigate environmental risks to critical supply chains. A limitation of prior work is that these flows are typically analyzed using monolayer networks, which ignores crucial intersectoral or interlayer couplings. Here, we use a multilayer network to account for such couplings when analyzing blue virtual water flows in the United States. Our multilayer network consists of 115 different regions (nodes), covering the entire conterminous United States; 41 coupled economic sectors (layers); and ∼2 × 107 possible links. To analyze the multilayer network, we focus on three fundamental network properties: topological connectivity, mesoscale structure, and node centrality. The network has a high connectivity, with each node being on average connected to roughly 2/3 of the network's nodes. Interlayer flows are a major driver of connectivity, representing ∼54% of all the network's connections. Five different groups of tightly connected nodes (communities) characterize the network. Each community represents a preferred spatial mode of long‐range virtual water interaction within the United States. We find that large (populous) cities have a stronger influence than small ones on network functioning because they attract and recirculate more virtual water through their supply chains. Our results also highlight differences between the multilayer and monolayer virtual water network, which overall show that the former provides a more realistic representation of virtual water flows. Plain Language Summary: Water is a critical input in the production of any product. The total water used in the production of a consumer product, including the water used in the production of the product's raw materials and component parts, is known as the embedded or virtual water. By tracing the water used in the production of a product along all its supply‐chain stages, we map the flow of virtual water between regions for the entire product economy of the United States. This mapping results in a multilayer network of virtual water flows. By analyzing this network, we identify five dominant spatial patterns of virtual water interactions in the United States. Each pattern represents a group of distant regions that strongly depend on each other for the provisioning of virtual water. One pattern shows that a substantial amount of water withdrawn on the Western United States is used to meet cities' demand for products on the Eastern seaboard. The identification of these patterns is a critical step in the design of strategies for making supply chains more resilient to water stress and reducing the impact of economic activity on water‐scarce regions. Key Points: The United States has five distinctive interregional patterns or spatial modes of long‐range virtual water interactionsLarge cities have a greater influence than small ones on network functioning because they act as bottlenecks in the flow of virtual waterMultilayer network provides a more realistic representation of virtual water flows than single‐layer network [ABSTRACT FROM AUTHOR]

Lakes are often defined by seasonal cycles. The seasonal timing, or phenology, of many lake processes are changing in response to human activities. However, long‐term records exist for few lakes, and extrapolating patterns observed in these lakes to entire landscapes is exceedingly difficult using the limited number of available in situ observations. Limited landscape‐level observations mean we do not know how common shifts in lake phenology are at macroscales. Here, we use a new remote sensing data set, LimnoSat‐US, to analyze U.S. summer lake color phenology between 1984 and 2020 across more than 26,000 lakes. Our results show that summer lake color seasonality can be generalized into five distinct phenology groups that follow well‐known patterns of phytoplankton succession. The frequency with which lakes transition from one phenology group to another is tied to lake and landscape level characteristics. Lakes with high inflows and low variation in their seasonal surface area are generally more stable, while lakes in areas with high interannual variations in climate and catchment population density show less stability. Our results reveal previously unexamined spatiotemporal patterns in lake seasonality and demonstrate the utility of LimnoSat‐US, which, with over 22 million remote sensing observations of lakes, creates novel opportunities to examine changing lake ecosystems at a national scale. Plain Language Summary: Lakes have seasonal cycles that result in yearly peaks in algal growth. The size and timing of these peak periods depends on the amount of nutrients available and the timing of key events such as freezing and thawing. Bluer lakes with little algae typically have one peak in the spring, while greener, high algae lakes can have multiple peaks or longer duration peaks that span the summer months. The timing and duration of these peaks manifest in changes to overall lake color. Here, we look at how these seasonal cycles changed in over 26,000 lakes across the United States between 1984 and 2020. We show that seasonal cycles are changing with distinct regional patterns. Specifically, lakes are generally moving toward stable blue states in the Pacific Northwest, while high latitude lakes in the Northeast are increasingly showing seasonal cycles associated with high algae waterbodies. Lakes at high elevations and in catchments with large year‐to‐year fluctuations in temperature and population density are most prone to changes in seasonal cycles over time. Key Points: Summer lake color phenology can be generalized into five distinct seasonal patterns of greening and blueing eventsSince the mid‐1990s, the number of lakes with color patterns corresponding to eutrophic waterbodies has been increasingWe observe these patterns using a new U.S. lake remote sensing data set that contains over 22 million lake observations [ABSTRACT FROM AUTHOR]

Understanding the dominant drivers of hydrological change is essential for water resources management. Watersheds in the United States are experiencing different types of changes (e.g., wet gets wetter and dry gets drier); however, few studies have analyzed what drivers are responsible for these changes, and how the dominant drivers vary over time and as a function of the climate/water regime and land cover. This study uses a time‐varying Budyko framework to quantify the relative importance of precipitation, potential evapotranspiration, and other factors (e.g., climate seasonality, agricultural drainage, and urbanization) in 889 watersheds in the contiguous United States from 1950 to 2009. Results show that watersheds that are getting wetter are primarily due to increases in precipitation. However, watersheds in dry climates that are getting drier are primarily due to other factors, while watersheds in wet climates that are getting drier are primarily due to precipitation. The drivers causing statistically significant streamflow trends vary depending on dominant land‐use types. Temporally, the increasing effects of other factors are more pronounced after the 1980s in the Midwest. The dominant drivers of streamflow in the United States are time‐varying instead of constant. This is consistent with non‐stationary patterns of streamflow. The time‐varying drivers provide information on the processes that are increasingly important and require the most attention in water resources management. Key Points: Dominant drivers of streamflow in the United States are shown to be time‐varying between 1950 and 2009Precipitation is the dominant driver in wet gets drier watersheds, while other factors are the dominant driver in dry gets drier watershedsPrecipitation is the dominant driver in reference watersheds, and other factors are the dominant driver in agricultural and urban watersheds [ABSTRACT FROM AUTHOR]

As shallow aquifers become depleted or contaminated worldwide, use of deep aquifers will likely increase to meet growing water demands despite expensive drilling costs and well maintenance, and limited recharge to many of these deep systems. We discuss depletion of the Cambrian‐Ordovician sandstone aquifer, a deep bedrock aquifer that has been a major source of water in the Midwestern US for over 150 years. Using a participatory groundwater modeling approach with active stakeholder engagement, we developed a conceptual framework to assess risk to different sandstone units of the aquifer based on simulated heads and production well behavior (pumping levels and specific capacities). Over 320 m of static head decline has occurred since predevelopment in the deepest sandstone unit. Shallower units are actively being dewatered. Drawdown and head separation between sandstone units is even greater under pumping conditions and is most acute near a regional fault zone. Current depletion is prompting massive infrastructure changes to mitigate anticipated water supply shortages. Our results show that even with a substantial reduction in water use by 35% in 2030, many areas of the aquifer remain at High or Severe risk out to 2070. Head decline, as opposed to storage loss, is a critical metric for understanding depletion of deep bedrock aquifers. The participatory modeling process made clear that pumping levels at production wells should not be ignored in evaluations of aquifer sustainability and risk. Our conceptual risk framework could be applied to other deep bedrock aquifers undergoing depletion. Key Points: Over 320 m of head decline has occurred in the deep sandstone aquifer system underlying the United States' third largest metropolitan areaParticipatory modeling allows assessment of risk to groundwater supplies under pumping conditionsAssessment of deep aquifer sustainability requires understanding of multi‐aquifer wells, geologic barriers, and production well behavior [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]

Hydrometeorological flood generating processes (excess rain, short rain, long rain, snowmelt, and rain‐on‐snow) underpin our understanding of flood behavior. Knowledge about flood generating processes improves hydrological models, flood frequency analysis, estimation of climate change impact on floods, etc. Yet, not much is known about how climate and catchment attributes influence the spatial distribution of flood generating processes. This study aims to offer a comprehensive and structured approach to close this knowledge gap. We employ a large sample approach (671 catchments across the contiguous United States) and evaluate how catchment attributes and climate attributes influence the distribution of flood processes. We use two complementary approaches: A statistics‐based approach which compares attribute frequency distributions of different flood processes; and a random forest model in combination with an interpretable machine learning approach (accumulated local effects [ALE]). The ALE method has not been used often in hydrology, and it overcomes a significant obstacle in many statistical methods, the confounding effect of correlated catchment attributes. As expected, we find climate attributes (fraction of snow, aridity, precipitation seasonality, and mean precipitation) to be most influential on flood process distribution. However, the influence of catchment attributes varies both with flood generating process and climate type. We also find flood processes can be predicted for ungauged catchments with relatively high accuracy (R2 between 0.45 and 0.9). The implication of these findings is flood processes should be considered for future climate change impact studies, as the effect of changes in climate on flood characteristics varies between flood processes. Key Points: Flood generating processes are mostly influenced by climate attributesWhich attributes are influential varies between different flood processes and climate regionsMix of flood generating processes can be predicted for ungauged catchments with high accuracy [ABSTRACT FROM AUTHOR]

Future changes in the occurrence of flood events can be estimated using multi‐model ensembles to inform adaption and mitigation strategies. In the near future, these estimates could be used to guide the updating of exceedance probabilities for flood control design and water resources management. However, the estimate of return levels from ensemble experiments represents a challenge: model runs are affected by biases and uncertainties and by inconsistencies in simulated peak flows when compared with observed data. Moreover, extreme value distributions are generally fit to ensemble members individually and then averaged to obtain the ensemble fit with loss of information. To overcome these limitations, we propose a Bayesian hierarchical model for assessing changes in future peak flows, and the uncertainty coming from global climate, global impact models and their interaction. The model we propose allows use of all members of the ensemble at once for estimating changes in the parameters of an extreme value distribution from historical to future peak flows. The approach is applied to a set of grid‐cells in the eastern United States to the full and to a constrained version of the ensemble. We find that, while the dominant source of uncertainty in the changes varies across the domain, there is a consensus on a decrease in flood magnitudes toward the south. We conclude that projecting future flood magnitude under climate change remains elusive due to large uncertainty mostly coming from global models and from the intrinsic uncertain nature of extreme values. Key Points: A Bayesian hierarchical model is developed to assess the changes in future flood magnitude and quantify uncertainty in a single stepFuture flood magnitude at selected sites over the eastern United States decreases in the south of the domain with varying uncertaintyA constrained ensemble based on how well model runs replicate timing of observed peak flows yields similar results to the full ensemble [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]

Residence time distribution (RTD) is a critically important characteristic of groundwater flow systems; however, it cannot be measured directly. RTD can be inferred from tracer data with analytical models (few parameters) or with numerical models (many parameters). The second approach permits more variation in system properties but is used less frequently than the first because large‐scale numerical models can be resource intensive. Using a novel automated approach, a set of 115 inexpensive general simulation models (GSMs) was used to create RTD metrics (fraction of young groundwater, defined as <65 years old; mean travel time of young fraction; median travel time of old fraction; and mean path length). GSMs captured the general trends in measured tritium concentrations in 431 wells. Boosted Regression Tree metamodels were trained to predict these RTD metrics using available wall‐to‐wall hydrogeographic digital sets as explanatory features. The metamodels produced a three‐dimensional distribution of predictions throughout the glacial system that generally matched with the numerical model RTD metrics. In addition to the expected importance of aquifer thickness and recharge rate in predicting RTD metrics, two new data sets, Multi‐Order Hydrologic Position (MOHP) and hydrogeologic terrane were important predictors. These variables by themselves produced metamodels with Nash‐Sutcliffe efficiency close to the full metamodel. Metamodel predictions showed that the volume of young groundwater stored in the glaciated United States is about 6,000 km3, or about 0.5% of globally stored young groundwater. Plain Language Summary: Groundwater is an important source of water for people and ecosystems. The quality of groundwater depends partly on how long it has resided underground, but this is difficult to know. There are several methods to determine groundwater residence time, but they mostly do not apply over large regional aquifers. In this study, we used a novel method coupling statistical learning and numerical modeling to calculate groundwater residence time throughout the glaciated areas of the United States. The results reveal that most of the groundwater in the area is less than 65 years old and usually travels distances ranging from less than 1 km to about 30 km before discharging to streams, rivers, and lakes. Key Points: A novel method coupling statistical learning and numerical models is presentedThree‐dimensional continuous digital data sets of groundwater age metrics are presentedTwo novel data sets, Multi‐Order Hydrologic Position and hydrogeologic terrane, contain information about groundwater residence time [ABSTRACT FROM AUTHOR]

This study reports a new and significantly enhanced analysis of US flood hazard at 30 m spatial resolution. Specific improvements include updated hydrography data, new methods to determine channel depth, more rigorous flood frequency analysis, output downscaling to property tract level, and inclusion of the impact of local interventions in the flooding system. For the first time, we consider pluvial, fluvial, and coastal flood hazards within the same framework and provide projections for both current (rather than historic average) conditions and for future time periods centered on 2035 and 2050 under the RCP4.5 emissions pathway. Validation against high‐quality local models and the entire catalog of FEMA 1% annual probability flood maps yielded Critical Success Index values in the range 0.69–0.82. Significant improvements over a previous pluvial/fluvial model version are shown for high‐frequency events and coastal zones, along with minor improvements in areas where model performance was already good. The result is the first comprehensive and consistent national‐scale analysis of flood hazard for the conterminous US for both current and future conditions. Even though we consider a stabilization emissions scenario and a near‐future time horizon, we project clear patterns of changing flood hazard (3σ changes in 100 years inundated area of −3.8 to +16% at 1° scale), that are significant when considered as a proportion of the land area where human use is possible or in terms of the currently protected land area where the standard of flood defense protection may become compromised by this time. Plain Language Summary: We develop a method to estimate past, present, and future flood risk for all properties in the conterminous United States whether affected by river, coastal or rainfall flooding. The analysis accounts for variability within environmental factors including changes in sea level rise, hurricane intensity and landfall locations, precipitation patterns, and river discharge. We show that even for a conservative climate change trajectory we can expect locally significant changes in the land area at risk from floods by 2050, and by this time defenses protecting 2,200 km2 of land may be compromised. The complete dataset has been made available via a website (https://floodfactor.com/) created by the First Street Foundation in order to increase public awareness of the threat posed by flooding to safety and livelihoods. Key Points: First complete high‐resolution flood hazard analysis of conterminous US flood risk from all major sources (fluvial, pluvial, and coastal)In validation tests the model achieved Critical Success Index scores of 0.69–0.82, similar to many local custom‐built 2D modelsBy 2050, flood hazard increases for the Eastern seaboard and Western states, but decreases or changes little for the center and South‐West [ABSTRACT FROM AUTHOR]

The challenge of estimating design flood magnitude under climate change has led to the development of multiple approaches to long‐term flood projection: stationarity, informed‐parameter (composed of both trend informed and climate informed), and hydrologic simulation. This is the first study to compare these approaches across a large set of hydro‐climatologically diverse basins located throughout the contiguous United States, grouped into distinct clusters based on catchment and flood characteristics. The comparison is achieved using a split‐sample test conducted over the historic period; the climate (specifically, precipitation) informed and hydrologic simulation (specifically, Sacramento Soil Moisture Accounting model) approaches are forced with observations and downscaled general circulation model (GCM) simulations. The results provide a quantitative perspective on key long‐term flood projection issues. The precipitation informed approach can be informative for projections of regional change; the hydrologic simulation approach can be informative for direction of change for a single basin. When forced with GCM outputs, precipitation informed is both more accurate and more precise than hydrologic simulation, after accounting for model error. For hydrologic simulation, forcing with GCMs will add positive bias for basins in the Rocky Mountains and Southwest and will add negative bias with large uncertainty across GCMs for basins in the lower Midwest, the South, and the mid‐Atlantic seaboard. The results illustrate the need for continued improvement in long‐term flood projection approaches and for design paradigms incorporating uncertainty. Key Points: Long‐term flood projection approaches are compared across 233 U.S. river basins using split‐sample tests over the historic periodPrecipitation informed is useful for regional change projections; hydrologic simulation is useful for direction of change in a single basinGeneral circulation model outputs as forcing introduces less additional bias for precipitation informed compared to hydrologic simulation [ABSTRACT FROM AUTHOR]

The response of annual runoff volume to sub‐annual climate warming is highly uncertain, and the governing mechanisms remain poorly understood, challenging adaptive water management. A typical exemplar is the Western United States, where climate models project substantially stronger warming in the warm season (April to September) than in the cool season (October to March). We investigate the asymmetrical responses of annual and seasonal streamflow changes to warm season and cool season warming for four regionally important basins in the Western United States using an ensemble of four land surface (hydrological) models. Our results show that (i) the general features of seasonal and annual streamflow responses to asymmetrical warming are consistent across models, although the magnitudes vary. The multi‐model mean shows annual runoff declining from 2.0% up to 7.5% under 3°C warm season warming, and from 2.2% up to 4.7% under 3°C cool season warming across the four basins. (ii) The asymmetry of the seasonal evapotranspiration sensitivity to temperature constrains the asymmetry of annual streamflow responses to seasonal warming; and (iii) basins with characteristics such as high ratios of warm to cool season gross incoming water, cooler summers, and colder winters have the strongest relative annual streamflow decreases for warm season warming relative to cool season warming. The pattern in (iii) is explained by the variation of evapotranspiration‐temperature sensitivity in response to a compound set of processes, including the enhanced rate of water holding capacity increase with warmer temperatures, temperature‐related snowmelt‐albedo feedback, and enhanced surface resistance with warmer temperatures. Key Points: Streamflow responses to warm versus cool season warming for four major Western United States basins are mostly consistent across four modelsEvapotranspiration to temperature sensitivity dominates the asymmetry of streamflow reductions under warm versus cool season warmingDifferences in the basins' runoff responses to seasonal warming are mainly associated with seasonal gross incoming water and temperature [ABSTRACT FROM AUTHOR]

Microwave brightness temperature observations from the NASA Soil Moisture Active Passive (SMAP) mission and gauge‐based precipitation data over the United States are used to reconstruct the soil water loss function and then historical (1979–2019) hydrological fluxes in the form of evapotranspiration (ET) and drainage (D) are quantified. Over the period of study, with the exception of snowy and hyper‐arid regions, we observe a correlation of R2 > 0.6 between SMAP‐precipitation derived drainage estimates and streamflow measurements from the U.S. Geological Survey (USGS). There is a bias between estimated drainage and USGS streamflow with an underestimation of about 1 mm day−1 in southwest United States to 3 mm day−1 in parts of the eastern United States. SMAP‐derived sensitivities of drainage and ET partitioning with respect to precipitation anomalies are also calculated. In parts of the Great Plains the drainage partitioning exhibits a near‐linear response, while in the southeast United States, the response is nonlinear. Partitioning also is examined for 6 four‐digit hydrologic unit basins wherein year‐to‐year variations in drainage partitioning are shown to be key mediators in translating precipitation anomalies into streamflow anomalies. Observation‐driven drainage and ET estimates are obtained without relying on full hydrologic and Land Surface Models (LSMs). This independence (isolation from model parameterization assumptions) provides a path toward using satellite‐derived landscape hydrological diagnostics to assess hydrologic models and LSMs as well as to guide their further development. Key Points: A large amount of hydrologic information is encoded within satellite remote sensing observations of soil moistureSMAP‐era soil moisture and precipitation information, without reliance on models, can characterize landscape hydrological partitioningHistorical remote sensing and precipitation‐based drainage estimates closely track streamflow measurements across the United States [ABSTRACT FROM AUTHOR]

Key Points: Groundwater withdrawals are not actively monitored in most places of the world at a scale necessary to implement sustainable solutionsVarious multitemporal remote sensing data are integrated into a machine learning framework to effectively predict groundwater withdrawalsThe results over the High Plains Aquifer, Kansas, USA, show that this approach is applicable to similar regions having sparse in situ data Effective monitoring of groundwater withdrawals is necessary to help mitigate the negative impacts of aquifer depletion. In this study, we develop a holistic approach that combines water balance components with a machine learning model to estimate groundwater withdrawals. We use both multitemporal satellite and modeled data from sensors that measure different components of the water balance and land use at varying spatial and temporal resolutions. These remote sensing products include evapotranspiration, precipitation, and land cover. Due to the inherent complexity of integrating these data sets and subsequently relating them to groundwater withdrawals using physical models, we apply random forests—a state of the art machine learning algorithm—to overcome such limitations. Here, we predict groundwater withdrawals per unit area over a highly monitored portion of the High Plains aquifer in the central United States at 5 km resolution for the Years 2002–2019. Our modeled withdrawals had high accuracy on both training and testing data sets (R2 ≈ 0.99 and R2 ≈ 0.93, respectively) during leave‐one‐out (year) cross validation with low mean absolute error (MAE) ≈ 4.31 mm and root‐mean‐square error (RMSE) ≈ 13.50 mm for the year 2014. Moreover, we found that even for the extreme drought year of 2012, we have a satisfactory test score (R2 ≈ 0.84) with MAE ≈ 9.72 mm and RMSE ≈ 24.17 mm. Therefore, the proposed machine learning approach should be applicable to similar regions for proactive water management practices. Plain Language Summary: Groundwater is an essential component of global water resources and is the largest source of Earth's liquid freshwater. It is extensively used for drinking water and to support global food production. Consequently, groundwater consumption has significantly increased owing to the pressing demands for water, food, and energy primarily driven by the increasing global population. Despite its critical role in the water‐food‐energy nexus, very few regions in the United States or elsewhere actively monitor their groundwater withdrawals (extraction or pumping) for implementing sustainable water management solutions. We develop a novel approach combining water balance components and land use measured using openly available remote sensing products with a machine learning model to estimate groundwater withdrawals. This framework automatically learns the interrelationships among these variables and groundwater withdrawals. Our study area is a portion of the High Plains Aquifer in Kansas (central United States) where overpumping has caused substantial groundwater storage loss. Also, a large amount of groundwater pumping data are available for validation. The results indicate good accuracy even for extreme drought years. Thus, this approach should be applicable to similar regions having sparsely or moderately available groundwater pumping data, enabling water managers to proactively implement sustainable solutions addressing water security issues. [ABSTRACT FROM AUTHOR]

Snowmelt‐driven floods result in large societal and economic impacts on local communities including infrastructure failures in the United States. However, the current U.S. government standard design precipitation maps are based on liquid precipitation data (e.g., National Oceanic and Atmospheric Administration's Precipitation‐Frequency Atlas 14; NOAA Atlas 14) with very limited guidance on snowmelt‐driven floods. In this study, we developed 25‐ and 100‐year return level design maps of snow water equivalent (SWE) and 1‐ and 7‐day snowmelt including precipitation events (e.g., rain‐on‐snow) using long‐term observation‐based gridded SWE developed by University of Arizona (UA) incorporating the national snow model product (SNOw Data Assimilation System; SNODAS) over the contiguous United States (CONUS). For the 44 U.S. states where the NOAA Atlas 14 maps are available, the design snowmelt values from this study exceed the standard design values in 23% of the total extent. The snowmelt values exceed the NOAA Atlas 14 design precipitation by up to 171 and 254 mm in the northeastern United States; 127 and 225 mm in the north central United States; and 191 and 425 mm in the western mountain United States for the 25‐ and 100‐year return levels, respectively. A comparison of 7‐day design snowmelt between with and without precipitation shows that including precipitation results in an average increase of 42 and 68 mm for 25‐ and 100‐year return levels, respectively, over snowmelt that do not include precipitation. The design snowmelt maps from this study complement the NOAA Atlas 14 design precipitation and provide additional guidance on infrastructure design for snowmelt‐driven floods in the CONUS. Plain Language Summary: Snow meltwater is a dominant driver of severe spring flooding in the north central and northeastern United States. Recent snowmelt floods, including rain‐on‐snow events, have resulted in large societal and economic impacts on local communities including infrastructure failures. The current U.S. government standard design precipitation maps are based on liquid precipitation data (e.g., NOAA Atlas 14 Volumes) with very limited guidance on snowmelt‐driven floods. We developed 25‐ and 100‐year return level design snow water equivalent (SWE) and snowmelt maps using the long‐term observation‐based gridded SWE incorporating the national snow model product over the contiguous United States. The design snowmelt values from this study exceed the Atlas 14 standard design values in 23% of the 44 U.S. states. In snow‐dominant regions, the magnitudes of design snowmelt are much higher than the Atlas 14 design with precipitation increasing up to 191 and 425 mm for 25‐ and 100‐year return levels, respectively. Results provide guidance in identifying areas and infrastructure vulnerable to snowmelt‐driven floods. Key Points: The 25‐ and 100‐year design extreme SWE and snowmelt maps were developed by GEV probability distribution using the UA and SNODAS SWEThe 7‐day snowmelt exceeds the NOAA Atlas 14 standard design precipitation in 23% of the CONUSOn average, the 25‐ and 100‐year 7‐day snowmelt with precipitation are 42 and 68 mm higher than those without precipitation, respectively [ABSTRACT FROM AUTHOR]

Groundwater discharge zones connect aquifers to surface water, generating baseflow and serving as ecosystem control points across aquatic ecosystems. The influence of groundwater discharge on surface flow connectivity, fate and transport of contaminants and nutrients, and thermal habitat depends strongly on hydrologic characteristics such as the spatial distribution, age, and depth of source groundwater flow paths. Groundwater models have the potential to predict spatial discharge characteristics within river networks, but models are often not evaluated against these critical characteristics and model equifinality with respect to discharge processes is a known challenge. We quantify discharge characteristics across a suite of groundwater models with commonly used frameworks and calibration data. We developed a base model (MODFLOW‐NWT) for a 1,570‐km2 watershed in the northeastern United States and varied the calibration data, control of river‐aquifer exchange directionality, and resolution. Most models (n = 11 of 12) fit similarly to calibration metrics, but patterns in discharge location, flow path depth, and subsurface travel time varied substantially. We found (1) a 15% difference in the percent of discharge going to first‐order streams, (2) threefold variations in flow path depth, and (3) sevenfold variations in the subsurface travel times among the models. We recalibrated three models using a synthetic discharge location data set. Calibration with discharge location data reduced differences in simulated discharge characteristics, suggesting an approach to improved equifinality based on widespread field‐based mapping of discharge zones. Our work quantifying variation across common modeling approaches is an important step toward characterizing and improving predictions of groundwater discharge characteristics. Key Points: Spatial patterns of simulated groundwater discharge dynamics along river networks are largely unevaluated in most groundwater flow modelsDischarge zone source flow path depth, travel time, and network position varied greatly among models with similar calibration metricsEquifinality was reduced when knowledge of groundwater discharge patterns was included in model calibration [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]

The original paradigm of urban hydrology identified links between urbanization and changes in hydrologic processes. One of the prominent relationships identified was increased impervious surface coverage decreased subsurface recharge and subsequently low flows. This does not consider watershed‐specific biophysical or anthropogenic characteristics that could increase low flows, such as direct discharge from wastewater treatment plants and indirect recharge to groundwater from leaking pipes and infiltration‐based green infrastructure. Here, in an effort to sharpen this original paradigm, we take a statistical approach to evaluate relationships between natural and human‐impacted watershed characteristics and a suite of low flow metrics that describe mean annual low flow magnitudes and interannual variability in 54 watersheds over 15 years in the Piedmont physiographic region of the southeastern United States. We find that annual low flow magnitudes are most strongly correlated to biophysical watershed factors, including positive relationships with topography and precipitation and negative relationships with poorly drained soils. In contrast, interannual variability is positively correlated to land use characteristics, including forest, and negatively related to urbanization. Similarities in best fit models across long‐term average, wet, and dry years indicate a robustness of hydrologic drivers across watershed wetness conditions. Our results also showcase that while the original paradigm of urban hydrology may hold across traditional low flow metrics, such as baseflow index, extreme low flow drivers are complex and counterintuitive. Understanding these first‐order controls on baseflow magnitude and variability across an urbanization gradient is needed to conceptualize the impacts that continued, rapid urbanization will have on low flow dynamics. [ABSTRACT FROM AUTHOR]

While the physical processes governing groundwater flow are well understood, and the computational resources now exist for solving the governing equations in three dimensions over continental‐scale domains, there remains substantial uncertainty about the subsurface distribution of the properties that control groundwater flow and transport for much of the contiguous United States (CONUS). The transmissivity of the shallow subsurface is a key parameter for the simulation of water table position, shallow groundwater flow, and base‐flow discharge, but is not well‐characterized at large regional to continental scales. We used a process‐based inversion of CONUS‐extent groundwater information to generate national data sets of (a) the transmissivity of the shallow groundwater system, (b) the depth to the water table, (c) groundwater discharge as base‐flow, and (d) long‐term average water content in the unsaturated zone. CONUS‐extent coverage was developed in the form of 75 subdomain models, with the spatial distribution of long‐term average transmissivity for each subdomain model calibrated against water‐levels derived from U.S. Geological Survey (USGS) observation wells, NHDPlusV2 first‐order perennial streams, and National Wetlands Inventory (NWI) freshwater wetlands. Estimated transmissivities were lower in the western CONUS than the eastern CONUS, and across the CONUS both transmissivity and depth to water correlate with recharge, elevation, and topographic slope. These generated data sets provide spatially distributed, long‐term average estimates of subsurface properties and hydrological states that we anticipate will complement other environmental modeling efforts as explanatory variables, boundary conditions, or transport pathways. Plain Language Summary: We developed a simulation of the shallow groundwater system for the contiguous United States (CONUS) and compared it to large data sets of water table measurements in order to generate a map of surficial transmissivity, which is a property that controls the flow of water between its entrance into the groundwater system and its discharge to streams and rivers. Better maps of this property are one ingredient to improved understanding of how the landscape stores and releases water at large spatial scales. Key Points: Calibrated simulations of the shallow groundwater system were used to estimate the CONUS‐extent spatial distribution of transmissivityCorrelations were explored between the estimated transmissivities and recharge, elevation, and topographic slopeSimulated depth to water and groundwater discharge were used to derive unsaturated zone properties and long‐term average base‐flow on the NHDPlusV2 stream network [ABSTRACT FROM AUTHOR]

The multifaceted interface of plant roots, microbes, water, and soil can be considered a critical zone within the Critical Zone as it is host to many important dynamically linked processes, including the promotion of nutrient cycling through absorption and rhizodeposition, interaction and feedbacks with microorganisms and fungi, root‐facilitated hydraulic redistribution, and soil carbon dynamics. Such important processes in the Critical Zone have not been fully characterized and modeled in an ecohydrologic framework linking above‐ground natural and/or anthropogenic processes to below‐ground biogeochemical cycling. Specifically, the relation between root exudates and nutrient cycling remains an open challenge. Here we present the model REWT (Root Exudation in Watershed‐scale Transport) to demonstrate the systematic modeling of root exudation in an interconnected ecohydrologic framework. REWT incorporates an explicit dynamic root exudation transport model, nutrient absorption, and coupled microbial processes within the framework of a validated ecohydrologic model. Model simulations demonstrate the influence of root exudation of glucose, a polysaccharide that serves as fuel for microbes, and flavonoids, which can act as a biological nitrification inhibitor on microbial processes linked to soil carbon and nitrogen cycling. To demonstrate the capabilities of this theoretical framework, we parameterize REWT for corn and soybean crops in the Midwestern United States, and simulations indicate that rates of nitrification and respiration were substantially altered compared to model simulations in which root exudation was not explicitly included. This work demonstrates the importance of systematically incorporating root exudates into hydrobiogeochemical models and can serve to inform experimental design for active root zone processes. Plain Language Summary: Root exudation is the highly complex and variable process by which roots release carbon and other nutrients into the surrounding soil to influence microbial activity and biogeochemical conditions to their benefit. It is a process common to all types of plants and trees and has important implications for nutrient cycling and soil health. Here we develop a model, REWT (Root Exudation in Watershed‐scale Transport), which couples exudation with moisture transport and microbial transformation of nutrients in agricultural and natural soils in an effort to more accurately capture dynamic biogeochemical processes in the Critical Zone. We present REWT simulations for corn and soybean crops in the Midwestern United States. These demonstrate that explicitly incorporating root exudation into hydrobiogeochemical models can result in substantial increases or decreases of microbial biomass, rate of nitrification, and total nitrate leached by the soil system, in this case on the order of ±4%, up to 100% decrease, and up to 16% decrease, respectively. REWT simulations are not only promising for future work related to plant breeding and modeling of biogeochemical cycling in natural and agroecosystems; they also shed light on the need for more empirical work related to rhizosphere processes, particularly the measurement of root exudation rates. Key Points: Roots influence subsurface biogeochemical dynamics and mediate microbial dynamics and associated geochemical feedbacks via exudationRoot exudates both enhance microbial activity, such as through glucose production, and inhibit it, such as through production of flavonoidsHere, the highest sensitivity of exudation's effect on nutrient dynamics results from nitrification inhibition through flavonoid exudation [ABSTRACT FROM AUTHOR]

Surface water storage in small yet abundant landscape depressions—including wetlands and other small waterbodies—is largely disregarded in conventional hydrologic modeling practices. No quantitative evidence exists of how their exclusion may lead to potentially inaccurate model projections and understanding of hydrologic dynamics across the world's major river basins. To fill this knowledge gap, we developed the first‐ever major river basin‐scale modeling approach integrating surface depressions and focusing on the 450,000‐km2 Upper Mississippi River Basin (UMRB) in the United States. We applied a novel topography‐based algorithm to estimate areas and volumes of ~455,000 surface depressions (>1 ha) across the UMRB (in addition to lakes and reservoirs) and subsequently aggregated their effects per subbasin. Compared to a "no depression" conventional model, our depression‐integrated model (a) improved streamflow simulation accuracy with increasing upstream abundance of depression storage, (b) significantly altered the spatial patterns and magnitudes of water yields across 315,000 km2 (70%) of the basin area, and (c) provided realistic spatial distributions of rootzone wetness conditions corresponding to satellite‐based data. Results further suggest that storage capacity (i.e., volume) alone does not fully explain depressions' cumulative effects on landscape hydrologic responses. Local (i.e., subbasin level) climatic and geophysical drivers and downstream flowpath‐regulating structures (e.g., reservoirs and dams) influence the extent to which depression storage volume in a subbasin causes hydrologic effects. With these new insights, our study supports the integration of surface depression storage and thereby catalyzes a reassessment of current hydrological modeling and management practices for basin‐scale studies. Key Points: We present the first hydrologic model incorporating surface depression and wetland water storage in one of the world's major river basinsSurface depressions, often overlooked in conventional large‐scale hydrologic modeling, significantly alter landscape water yieldsCumulative hydrologic effects of surface depression storage on downstream waters depend on a combination of local drivers [ABSTRACT FROM AUTHOR]

Land subsidence caused by groundwater extraction has numerous negative consequences, such as loss of groundwater storage and damage to infrastructure. Understanding the magnitude, timing, and locations of land subsidence, as well as the mechanisms driving it, is crucial to implementing mitigation strategies, yet the complex, nonlinear processes causing subsidence are difficult to quantify. Physical models relating groundwater flux to aquifer compaction exist but require substantial hydrological data sets and are time consuming to calibrate. Land deformation can be measured using interferometric synthetic aperture radar (InSAR) and GPS, but the former is computationally expensive to estimate at scale and is subject to tropospheric and ionospheric errors, and the latter leaves many temporal and spatial gaps. In this study, we apply for the first time a machine learning approach that quantifies the relationships of various widely available input data, including evapotranspiration, land use, and sediment thickness, with land subsidence. We apply this method over the Western United States and estimate that from 2015 to 2016, ~2.0 km3/yr of groundwater storage was lost due to groundwater pumping‐induced compaction of sediments. Subsidence is concentrated in the Central Valley of California, and the state of California accounts for 75% of total subsidence in the Western United States. Other significant areas of subsidence occur in cultivated regions of the Basin and Range province. This study demonstrates that widely available ancillary data can be used to estimate subsidence at a larger scale than has been previously possible. Key Points: Remote sensing and geological data sets are integrated to estimate land subsidence due to groundwater extractionThe estimates are integrated using a machine learning method called random forestsUsing this method we estimate that from 2015 to 2016, ~2.0 km3/yr of groundwater storage was lost due to land subsidence in the western United States [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]

Seasonal thermal stratification in reservoirs changes the thermal regime of regulated river systems as well as stream temperature responses to climate change. Cold releases from the reservoir hypolimnion can depress downstream river temperature during warm seasons. Recent large‐scale climate change studies on stream temperature have largely ignored reservoir thermal stratification. In this study, we used established models to develop a framework which considers water demand and reservoir regulation with thermal stratification and applied this model framework to the southeastern United States. About half of all 271 reservoirs in our study area retain strong thermal stratification by the 2080s (2070–2099) under RCP8.5 even as median residence times decrease to 60 days from 69 days in the historic period (1979–2010). Reservoir impacts on downstream temperatures become slightly weaker in the future because of higher air temperature and stronger solar radiation. We defined a "cooling potential" to quantify the thermal energy that a water body can absorb before exceeding a water temperature threshold. In the future, higher river temperatures will reduce the cooling potential for all river segments, but more so for river segments minimally impacted by thermal stratification. Reservoir impacts on cooling potential remain strong for river segments downstream of reservoirs with strong thermal stratification. We conducted a sensitivity analysis to evaluate the robustness of our findings to errors in the hydrological simulations. Although river segments subject to reservoir regulation are more sensitive to errors in hydrology than those without regulation impacts, our overall findings do not materially change due to these errors. Plain Language Summary: River temperature is influenced by both climate change and human activities, especially dam regulation. Large dams impound deep reservoirs where only the top layer is warmed while the bottom stays cold. This phenomenon, with colder, heavier water at the bottom and warmer, lighter water on top, is known as thermal stratification. Large reservoirs usually release water from the bottom and thus cool downstream river temperature in the summer. In this study, we use computer models to simulate river flow and temperature while considering the influence of reservoir regulation and thermal stratification. Thermal stratification will strengthen for about half of the 271 reservoirs in the southeastern United States, but the cold outflow warms up faster due to global warming. Environmental agencies regulate maximum allowable river temperature, constraining a river's ability to provide cooling water for power plants. The cool release from the bottom layer provides more downstream cooling potential, that is, the energy that a river can absorb before exceeding a water temperature threshold, than would be possible in an unregulated river. In the future, the cooling potential will decrease as the climate warms, especially in unregulated rivers. Although our models are subject to errors, these errors do not change our overall findings. Key Points: Summer stream temperatures in regulated rivers are lower than those in unregulated rivers, but they are more sensitive to climate changeReservoirs with long residence times will be more stratified in the future, but the effects on tailwater temperature will dissipate fasterSimulated summer stream temperatures in regulated rivers are more sensitive to errors in hydrology than those in unregulated rivers [ABSTRACT FROM AUTHOR]

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

We have assembled a comprehensive and publicly accessible U.S. Geological Survey (USGS) streamflow measurement data set, called HYDRoSWOT, from a USGS National Water Information System archive of acoustic Doppler current profiler river discharge measurements collected from a wide range of rivers throughout the United States. The data set provides a wealth of information on the range of hydraulic characteristics of river cross sections in the United States. Preliminary exploration of the data set, filtered for quality control, indicates that rivers tend toward consistent and predictable forms as discharge increases. The ratio of maximum-to-mean depth is highly predictable and is remarkably consistent across all river sizes and discharges. Distributions of hydraulic characteristics provide a large-scale perspective on the general hydraulic characteristics of rivers. The data set affords the opportunity to analyze hydraulic relations for individual rivers as a function of stage, geomorphic setting, and energy environments and, combined with additional information contained in this data set, might yield predictive relations that could help constrain and parameterize river hydraulic models. [ABSTRACT FROM AUTHOR]

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

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

The 14 September 2015 Hildale, Utah, storm resulted in 20 flash flood fatalities, making it the most deadly natural disaster in Utah history; it is the quintessential example of the "paroxysmal precipitation of the desert". The measured peak discharge from Maxwell Canyon at a drainage area of 5.3 km2 was 266 m3/s, a value that exceeds envelope curve peaks for Utah. The 14 September 2015 flash flood reflects features common to other major flash flood events in the region, as well as unique features. The flood was produced by a hailstorm that was moving rapidly from southwest to northeast and intensified as it interacted with complex terrain. Polarimetric radar observations show that the storm exhibited striking temporal variability, with the Maxwell Canyon tributary of Short Creek and a small portion of the East Fork Virgin River basin experiencing extreme precipitation. Periods of extreme rainfall rates for the 14 September 2015 storm are characterized by KDP signatures of extreme rainfall in polarimetric radar measurements. Similar KDP signatures characterized multiple storms that have produced record and near‐record flood peaks in Colorado Plateau watersheds. The climatology of monsoon thunderstorms that produce flash floods exhibits striking spatial heterogeneities in storm occurrence and motion. The hydroclimatology of flash flooding in arid/semiarid watersheds of the southwestern United States exhibits relatively weak dependence on drainage basin area. Large flood peaks over a broad range of basin scales can be produced by small thunderstorms like the 14 September 2015 Hildale Storm, which pass close to the outlet. Key Points: The 14 September 2015 Hildale storm was a hailstorm that produced catastrophic flooding in southern UtahThe climatology of monsoon thunderstorms that produce flash floods in the southwestern United States exhibits large spatial heterogeneityRecord flood peaks over a wide range of basin scales are produced by "small," monsoon storms that pass close to the basin outlet [ABSTRACT FROM AUTHOR]

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

Satellite estimates of inland water quality have the potential to vastly expand our ability to observe and monitor the dynamics of large water bodies. For almost 50 years, we have been able to remotely sense key water quality constituents like total suspended sediment, dissolved organic carbon, chlorophyll a, and Secchi disk depth. Nonetheless, remote sensing of water quality is poorly integrated into inland water sciences, in part due to a lack of publicly available training data and a perception that remote estimates are unreliable. Remote sensing models of water quality can be improved by training and validation on larger data sets of coincident field and satellite observations, here called matchups. To facilitate model development and deeper integration of remote sensing into inland water science, we have built AquaSat, the largest such matchup data set ever assembled. AquaSat contains more than 600,000 matchups, covering 1984–2019, of ground‐based total suspended sediment, dissolved organic carbon, chlorophyll a, and SDDSecchi disk depth measurements paired with spectral reflectance from Landsat 5, 7, and 8 collected within ±1 day of each other. To build AquaSat, we developed open source tools in R and Python and applied them to existing public data sets covering the contiguous United States, including the Water Quality Portal, LAGOS‐NE, and the Landsat archive. In addition to publishing the data set, we are also publishing our full code architecture to facilitate expanding and improving AquaSat. We anticipate that this work will help make remote sensing of inland water accessible to more hydrologists, ecologists, and limnologists while facilitating novel data‐driven approaches to monitoring and understanding critical water resources at large spatiotemporal scales. Key Points: AquaSat contains ∼600,000 paired observations of water quality and Landsat reflectance, the largest such matchup data setMatchups capture diverse water bodies across the USA for 1984–2019; we see clear water quality/reflectance relationshipsAquaSat and open source code developed here will enable better development of models for remote sensing of water quality [ABSTRACT FROM AUTHOR]

Mountain‐block recharge (MBR) is the subsurface inflow of groundwater to lowland aquifers from adjacent mountains. MBR can be a major component of recharge but remains difficult to characterize and quantify due to limited hydrogeologic, climatic, and other data in the mountain block and at the mountain front. The number of MBR‐related studies has increased dramatically in the 15 years since the last review of the topic was conducted by Wilson and Guan (2004), generating important advancements. We review this recent body of literature, summarize current understanding of factors controlling MBR, and provide recommendations for future research priorities. Prior to 2004, most MBR studies were performed in the southwestern United States. Since then, numerous studies have detected and quantified MBR in basins around the world, typically estimating MBR to be 5–50% of basin‐fill aquifer recharge. Theoretical studies using generic numerical modeling domains have revealed fundamental hydrogeologic and topographic controls on the amount of MBR and where it originates within the mountain block. Several mountain‐focused hydrogeologic studies have confirmed the widespread existence of mountain bedrock aquifers hosting considerable groundwater flow and, in some cases, identified the occurrence of interbasin flow leaving headwater catchments in the subsurface—both of which are required for MBR to occur. Future MBR research should focus on the collection of high‐priority data (e.g., subsurface data near the mountain front and within the mountain block) and the development of sophisticated coupled models calibrated to multiple data types to best constrain MBR and predict how it may change in response to climate warming. Key Points: Mountain‐block recharge confirmed as important source of recharge to basin aquifers in a variety of climatic and geologic settings globallyRecent work advanced understanding of fundamental controls on mountain‐block recharge and somewhat improved methods for characterizationFuture research should aim to acquire subsurface data in mountain block and at the mountain front [ABSTRACT FROM AUTHOR]

Based on a process‐level modeling of the rain‐on‐snow (ROS) events in the period of 1950 to 2013 and in a warmer climate, we quantify the historical and future runoff contribution from ROS to extreme floods and the source of runoff and snowmelt in large ROS events within the conterminous United States (CONUS). We find that the regions impacted most heavily by ROS include the West Coast, the major mountain ranges of the western interior, the Upper Midwest, the Northeast, and the lower Appalachians. While 70% of extreme (upper 0.1%) runoff events in these regions have some contribution from ROS, the runoff generated during these ROS events accounts for less than 10% of the total extreme flood runoff; the much larger fraction of extreme runoff is from either intense rainfall or clear‐sky snowmelt. Rainfall is the dominant source of runoff in ROS events along the West Coast and over the west‐facing slopes of the Cascades and Sierra Nevada, while snowmelt dominates ROS runoff in the other regions in the CONUS. Net radiation dominates the snowmelt during ROS in the high mountains in the West, while net radiation and turbulent heat flux are equally dominant in the rest of CONUS. Historically, the role of ROS in streamflow extremes is most significant in midelevation areas, but this "significant influence zone" will shift to higher elevations in a warmer future. The future ROS frequency changes exert a first order control on the future change of the runoff contribution from ROS to extreme floods. Plain Language Summary: Rain‐on‐snow (ROS) occurs during periods when liquid precipitation falls on a pre‐existing snowpack. The intense rainfall and the rapid snowmelt during large ROS events have a long history of contributing to floods that lead to costly property damages and even the loss of life. For example, the 2017 Oroville Dam crisis that caused damages exceeding $1B was caused in part by ROS flooding. Through an analysis of long‐term ROS and flood records over the conterminous United States, we evaluate the contribution of ROS to past flooding, and how ROS's contribution to floods will change in a warmer future. Historically, ROS has contributed to many historical flood events across the conterminous United States, especially at low to intermediate elevations (between 1,000m and 1,500 m). However, its overall contribution to the total flood runoff mostly is modest. Radiative energy, primarily the longwave radiation emitted from the moist atmosphere, collectively explains the most snowmelt during ROS. In a warmer future, the contribution of ROS to floods runoff will generally increase at high elevations (above 2,000 m) in the Western United States and will mostly decrease in the rest of the conterminous United States. Key Points: Rain on snow affects many flood events across the conterminous United States, but its contribution to total flood runoff mostly is modestRain‐on‐snow frequency exerts a first‐order control on the role of rain on snow in hydrologic extremesNet radiation is overall the most dominant energy source for the snowmelt during rain on snow in most of the conterminous United States [ABSTRACT FROM AUTHOR]

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

Snow albedo is a dominant control on snowmelt in many parts of the world. An empirical albedo decay equation, developed over 60 years ago, is still used in snowmelt models. Several empirical snow albedo models developed since show wide spread in results. Remotely sensed snow albedos have been used in a few studies, but validations are scarce because of the difficulty in making accurate in situ measurements. Reconstruction of snow water equivalent (SWE), where the snowpack is built in reverse, is especially sensitive to albedo. We present two new contributions: (1) an updated albedo model where grain size and light absorbing particle content are solved for simultaneously and (2) multiyear comparisons of remotely sensed and in situ albedo measurements from three high‐altitude sites in the western United States. Our remotely sensed albedos show 4 to 6% RMSE and negligible bias. In comparison, empirical albedo decay models, which require extensive in situ measurements, show RMSE values of 7 to 17% with biases of −6 to −14%. We examine the sensitivity of SWE reconstructions to albedo error at two sites. With no simulated error in albedo, reconstructed SWE had MAE values of 7 to 13% and 5–6% bias. The accuracy actually improved with some simulated added error, likely because of a fundamental bias in the reconstruction approach. Conversely, the best age‐based decay model showed an 18–20% MAE and bias in reconstructed SWE. We conclude that remotely sensed albedos where available are superior to age‐based approaches in all aspects except simplicity. Key Points: An updated broadband snow albedo model and statistical fit are presented for the western U.S. mountainsRemotely sensed snow albedo from MODIS shows 4–6% RMSE with no bias validated by measurements over 1,600 days at three high‐altitude sitesReconstructed SWE with 5–11% RMSE and 0–3% bias is achieved using remotely sensed albedo [ABSTRACT FROM AUTHOR]

Vulnerability‐based frameworks are increasingly used to better understand water system performance under climate change. This work advances the use of stochastic weather generators for climate vulnerability assessments that simulate weather based on patterns of regional atmospheric flow (i.e., weather regimes) conditioned on global‐scale climate features. The model is semiparametric by design and includes (1) a nonhomogeneous Markov chain for weather regime simulation; (2) block bootstrapping and a Gaussian copula for multivariate, multisite weather simulation; and (3) modules to impose thermodynamic and dynamical climate change, including Clausius‐Clapeyron precipitation scaling, elevation‐dependent warming, and shifting dynamics of the El Niño–Southern Oscillation (ENSO). In this way, the model can be used to evaluate climate impacts on water systems based on hypotheses of dynamic and thermodynamic climate change. The model is developed and tested for cold‐season climate in the Tuolumne River Basin in California but is broadly applicable across the western United States. Results show that eight weather regimes exert strong influences over local climate in the Tuolumne Basin. Model simulations adequately preserve many of the historical statistics for precipitation and temperature across sites, including the mean, variance, skew, and extreme values. Annual precipitation and temperature are somewhat underdispersed, and precipitation spell statistics are negatively biased by 1‐2 days. For simulations of future climate, the model can generate a range of Clausius‐Clapeyron scaling relationships and modes of elevation‐dependent warming. Model simulations also suggest a muted response of Tuolumne climate to changes in ENSO variability. Key Points: A weather‐regime stochastic weather generator is presented to support process‐informed climate vulnerability assessments of water systemsClimate change perturbations can be specified separately for thermodynamic and dynamical processesThe model is developed to be broadly applicable across the western United States in the cold season [ABSTRACT FROM AUTHOR]

We develop multioutput neural network models to predict flow‐duration curves (FDCs) in 9,203 ungaged locations in the Southeastern United States for six decades between 1950 and 2009. The model architecture contains multiple response variables in the output layer that correspond to individual quantiles along the FDC. During training, predictions are made for each quantile, and a combined loss function is used for back propagation and parameter updating. The loss function accounts for the covariance between the quantiles and generates physically consistent outputs (i.e., monotonically increasing quantiles with increasing nonexceedance probabilities). We use neural network dropout to generate posterior‐predictive distributions for FDCs and test model performance under cross validation. Finally, we demonstrate how local surrogate models, via the Local Interpretable Model‐agnostic Explanations method, can be used to infer the relation between basin characteristics and the predicted FDCs. Results suggest that multioutput neural network models can learn the monotonic relations between adjacent quantiles on an FDC; they result in better predictions than single‐output neural network models that predict each quantile independently, and basin characteristics are most useful for predicting smaller quantiles, whereas bias terms from neighboring quantiles are most informative for predicting higher quantiles. Key Points: Multioutput neural networks (MNNs) generate monotonically increasing flow‐duration curvesMonte Carlo dropout captures uncertainty for estimating streamflow quantilesLocal surrogate models approximate how the MNN is using basin characteristics for each observation [ABSTRACT FROM AUTHOR]

As drought involves diverse natural and human‐made processes in hydrologic cycles, it is necessary to track a process‐specific variable for efficient drought risk management. In this study, we employed a state‐of‐the‐art formulation of the generalized complementary relationship (GCR) to estimate terrestrial evapotranspiration (ET). The GCR ET estimates were used to assess historical droughts over the contiguous United States (CONUS) in relation to long‐term natural fluctuations. Results showed that the GCR provided strong performance in reproducing water balance ET estimates and correlations to the El Niño–Southern Oscillation, when compared to observation‐driven estimates and land surface models. For 1982‐2011, the GCR yielded better performance than land surface models even with estimated radiation inputs and no wind speed variations. The Standardized Evapotranspiration Deficit Index (SEDI) based on the GCR provided a consistent drought assessment with precipitation‐based drought identification and remotely sensed vegetation conditions. The SEDI seems to capture the heat wave‐driven flash drought together with water deficit‐driven droughts. In the CONUS, the La Niña‐like conditions were likely to trigger extensive vegetation droughts with extreme severity. This study found the three long‐term environmental factors that affect the general trend of ET deficits over the CONUS, which are the Atlantic Multidecadal Oscillation, the mean air temperature, and the Pacific Decadal Oscillation. Especially, the Atlantic Oscillation seems to be the most significant predictor that could explain the long‐term variation of vegetation droughts over the CONUS. This study highlights that the GCR‐based SEDI could be useful for monitoring and assessment of drought risks at a continental scale. Key Points: The generalized complementary principle showed strong performance in estimating evapotranspiration over the contiguous United StatesDroughts identified by evapotranspiration deficits agreed with remote sensing vegetation conditions and historical recordsFluctuations of sea surface temperatures explain principal components of evapotranspiration deficits significantly [ABSTRACT FROM AUTHOR]

In a recent article in this journal, Luby, Polasky, and Swackhamer come to the provocative conclusion that urban water prices in the United States are "cheaper when drier." They also argue that utilities fail to provide affordable water and that they charge less for "additional" use compared to "essential" use. We challenge these claims. While the authors correctly point out the many challenges that utilities face in supplying affordable water while meeting conservation and revenue goals, there are serious flaws in their measures of price, scarcity, and affordability. These measurement problems lead the authors to incorrect conclusions about water pricing in the United States. Using improved measures of price and water scarcity, we find little statistical relationship between the marginal price of water/sewer services and scarcity. Our findings cast doubt on Luby et al.'s findings and point to important avenues for future research. Key Points: Contrary to a recent study's claims, there is little statistical relationship between water scarcity and pricing for conservationContrary to a recent study's claims, water for essential use is not more expensive than for nonessential use in water‐scarce regionsFuture research should use larger samples and better measurement to investigate the relationships between scarcity and prices [ABSTRACT FROM AUTHOR]

The Truckee/Carson Basin, like other semiarid basins in the western United States, faces challenges to water management and planning under a changing climate. We analyzed tree‐ring data, along with instrumental climatic and hydrologic records, to provide a perspective on extreme drought in the 21st century. Drought indices highlighted a recent increase in the average duration of hydroclimatic episodes: in the new millennium average duration was 74% longer for the 24‐month Standardized Precipitation Index (SPI‐24) and 62% longer for the Palmer Drought Severity Index (PDSI) than in the previous century. Average snow water equivalent (SWE) declined 7% per decade from 1965 to 2018. The 2012‐2015 drought, in particular, stood out for its intensity and expression in snowpack, streamflow, and drought indices. Likely because of recent warming, this 4‐year drought event had a very low likelihood based on observed Carson River flows from the first half of the 20th century. A 501‐year tree‐ring reconstruction (1500‐2000 CE) of average water‐year streamflow for the Carson River indicated that positive (wet) spells had slightly longer duration (mean of 2.7 years and range from 1 to 10 years) than negative (dry) intervals (mean of 2.4 years and range from 1 to 9 years). The early 1900s pluvial, that is, 1905‐1911 in this record, was the third strongest episode in the entire reconstruction. The driest years were 1580 and 1934, both well‐known widespread and severe droughts in the western United States. Noise‐added reconstructions suggest that 2012‐2015, while not unique in the 401 years prior to the start of the Carson River gaged flows in 1901, was a less than one‐in‐a‐century event. Key Points: Increasing drought duration on the Carson River has characterized the transition from the first to the second millenniumTree‐ring chronologies show that the 2012‐2015 drought on the Carson River is a less than one‐in‐a‐century eventRecent warming has exacerbated the hydrologic impact of 21st century precipitation droughts on the Carson River [ABSTRACT FROM AUTHOR]

The majority of annual sediment flux is transported during storm events in many watersheds across the world. Using X‐ray diffraction, we analyzed the mineralogy of grab samples of suspended sediment during different stages of storm hydrographs in the Southern Piedmont. Mineralogy of suspended sediment changes drastically from quartz‐dominated during the rising limb to clay dominated during the late falling limb/baseflow. Changes in mineralogy can shed insight into turbidity relationships, suspended sediment sources, energy versus supply‐limited sediment transport, and other suspended sediment parameters such as anion exchange capacity and trace element chemistry. An unexpected key finding, confirmed by X‐ray diffraction and electron microscopy, is that both kaolinite and quartz are primarily transported as discrete crystalline minerals of different size classes in our watersheds; this contrasts with existing scientific literature stating that in most fluvial systems suspended sediment is transported primarily as composite particles composed of a heterogeneous mix of all particle sizes. Our findings also support existing literature that turbidity can be a good proxy for elements such as P, which are preferentially adsorbed onto iron oxide coatings thus in situ turbidity probes have great potential to provide relatively inexpensive estimates of P flux when calibrated for specific watersheds. Key Points: Mineralogy of suspended sediment is dominated by quartz during the rising limb of the hydrograph and kaolinite during the late falling limbComposite particles are negligible in stormflow, per analysis of suspended sediment via X‐ray diffraction and electron microscopyTurbidity shows a nonlinear relationship with suspended sediment and a linear relationship with total phosphorus in the Southern Piedmont [ABSTRACT FROM AUTHOR]

Boreal forest regions are a focal point for investigations of coupled water and biogeochemical fluxes in response to wildfire disturbances, climate warming, and permafrost thaw. Soil hydraulic, physical, and thermal property measurements for mineral soils in permafrost regions are limited, despite substantial influences on cryohydrogeologic model results. This work expands mineral soil property quantification in cold regions through soil characterization from the discontinuous permafrost zone of interior Alaska, USA. Values extend beyond the range of prior measurement magnitudes in analogous regions, highlighting the importance of this data set. Rocky and silty upland soil landscape classifications and wildfire disturbance provided guiding frameworks for the sampling and analysis for potential implications for the hydrologic response to thawing permafrost. Bulk density (ρb), soil organic matter, soil‐particle size distributions (sand, silt, and gravel fractions), and soil hydraulic properties of van Genuchten parameters alpha and N had moderate evidence of differences between silty and rocky classifications. Burned and unburned sites had only moderate evidence of differences for silt fraction. Field‐saturated hydraulic conductivity (Kfs) was more variable at burned sites compared to unburned sites, which corresponded to observations of greater rooting depths at burned sites and observations of root paths in soil cores for Kfs measurement. Soil thermal properties suggested that gravel content may reduce the accuracy of commonly used estimation methods for thermal conductivity. This work provides soil parameter constraints necessary for hypothesis testing and site‐specific prediction with cryohydrogeologic models to examine controls on active layer and permafrost dynamics in upland boreal forests. Key Points: Physical and hydraulic property magnitudes in mineral soils extended beyond ranges from prior work in permafrost regions in interior AlaskaMineral soil thermal properties were substantially affected by gravel content, which is not included in common texture‐based estimationThis provides soil parameter constraints for hypothesis testing and prediction for active layer and permafrost dynamics in upland boreal forests [ABSTRACT FROM AUTHOR]

Incentive‐based policies, such as the cap‐and‐trade system, have been shown to be useful in the context of groundwater management. This study compares the performance of a groundwater market with water quotas when assumptions of perfect information are violated due to climate change and hydrogeologic heterogeneity and explores how changes in future climate affect market performance. A subbasin of the Republican River Basin, overlying the Ogallala aquifer in the High Plains of the United States, is used as a case study. Building on a previously developed model, a multiagent system model simulating a groundwater market is developed where self‐interested agents can trade water use permits to maximize individual benefits subject to irrigation and land constraints. This economic model is coupled with a calibrated physically based groundwater model for the study region that allows for an evaluation of streamflow depletion impacts, which has been the focus of management efforts in the basin. Results show that trading of permits between farmers results in increased economic benefits and, in some cases, reduced environmental violations. However, the benefits of a groundwater market are distributed unequally resulting in "winners" and "losers" across the system. Future changes in climate are shown to significantly influence farmers willingness to pay for groundwater and thus increase the variation in groundwater price and pumping. These findings emphasize the importance of addressing hydroclimatologic variability and change in the design of groundwater markets. Key Points: Heterogeneous economic and environmental benefits of groundwater trading are quantified and spatially illustratedClimate change increases the price elasticity of groundwater and leads to increased variation in groundwater price and pumpingGroundwater overdraft is sensitive to changes in both precipitation and temperature, with a higher sensitivity to changes in precipitation [ABSTRACT FROM AUTHOR]

Extensive research has evaluated virtual water trade, the water embodied in traded commodities. However, relatively little research has examined virtual water storage or the water embodied in stored commodities. Just as in physical hydrology, both flows and stocks of virtual water resources must be considered to obtain an accurate representation of the system. Here we address the following question: How much water can be virtually stored in grain storage in the United States? To address this question, we employ a data‐intensive approach, in which a variety of government databases on agricultural production and grain storage capacities are combined with modeled estimates of grain crop water use. We determine the virtual water storage capacity (VWSC) in grain silos, map the spatial distribution of VWSC, calculate contributions from irrigation and rainwater sources, and assess changes in VWSC over time. We find that 728 km3 of water could be stored as grain in the United States, with roughly 86% coming from precipitation. National VWSC capacities were 777 km3 in 2002, 681 km3 in 2007, and 728 km3 in 2012. This represents a 6% decline in VWSC over the full 10‐year period, mostly attributable to increased water productivity. VWSC represents 62% of U.S. dam storage and accounts for 75–97% of precipitation receipts to agricultural areas, depending on the year. This work enhances our understanding of the food‐water nexus, will enable virtual water trade models to incorporate temporal dynamics, and can be used to better understand the buffering capacity of infrastructure to climate shocks. Key Points: Just as in physical hydrology, both flows and stocks of virtual water resources must be consideredApproximately 728 km3 of water can be virtually stored as grain in the United States, with roughly 86% coming from precipitationVirtual water storage capacity represents roughly 62% of normal U.S. dam storage or 75‐97% of precipitation receipts [ABSTRACT FROM AUTHOR]

This article presents a novel approach to couple a deterministic four‐dimensional variational (4DVAR) assimilation method with the particle filter (PF) ensemble data assimilation system, to produce a robust approach for dual‐state‐parameter estimation. In our proposed method, the Hybrid Ensemble and Variational Data Assimilation framework for Environmental systems (HEAVEN), we characterize the model structural uncertainty in addition to model parameter and input uncertainties. The sequential PF is formulated within the 4DVAR system to design a computationally efficient feedback mechanism throughout the assimilation period. In this framework, the 4DVAR optimization produces the maximum a posteriori estimate of state variables at the beginning of the assimilation window without the need to develop the adjoint of the forecast model. The 4DVAR solution is then perturbed by a newly defined prior error covariance matrix to generate an initial condition ensemble for the PF system to provide more accurate and reliable posterior distributions within the same assimilation window. The prior error covariance matrix is updated from one cycle to another over the main assimilation period to account for model structural uncertainty resulting in an improved estimation of posterior distribution. The premise of the presented approach is that it (1) accounts for all sources of uncertainties involved in hydrologic predictions, (2) uses a small ensemble size, and (3) precludes the particle degeneracy and sample impoverishment. The proposed method is applied on a nonlinear hydrologic model and the effectiveness, robustness, and reliability of the method is demonstrated for several river basins across the United States. Key Points: A joint sequential and variational data assimilation method was developed for superior and robust dual‐state‐parameter estimationThe proposed HEAVEN approach accounts for all sources of uncertainties involved in model predictionsThe effectiveness and usefulness of HEAVEN was evaluated by both deterministic and probabilistic measures [ABSTRACT FROM AUTHOR]

We examine the relative contributions of root zone soil moisture (SM) and groundwater (GW) storage to trends and interannual variability in total water storage (TWS) from essentially the entire Gravity Recovery and Climate Experiment (GRACE) record (2003–2016). Our study region is the Eastern United States where GW generally is shallow, and we use observations and simulations from a suite of land surface models (LSMs) where observations are not available. We first consider Illinois, which has a long‐term network of SM sensors and observation wells. We assessed the uncertainties in GRACE TWS in comparison with the observation‐based TWS using observed SM, well‐derived GW, and model‐reconstructed snow water equivalent. We evaluated LSM SM compared with observations over Illinois; generally good agreement encouraged us to use the LSM‐ensemble average SM, well‐derived GW, and Snow Data Assimilation System snow water equivalent over the entire Eastern United States to examine the relative contribution of storage components to GRACE TWS over this larger domain. Taken together, the three components explained 2%–85% of interannual variability in GRACE TWS across the 2‐digit Hydrologic Unit Code regions. Root zone SM was the dominant contributor to the interannual variability (but not trends) of GRACE TWS over much of the Eastern United States. We also compared three independent approaches to estimating GW: well data, baseflow observations, and GRACE. Despite much smaller magnitudes of variation, baseflow‐derived GW correlated more highly with well‐derived GW over the eastern‐most and Upper Mississippi regions, while GRACE‐derived GW matched better with well‐derived GW over the remaining regions. Key Points: Over the Eastern United States, soil moisture accounts for most of the interannual variability (but not trends) of terrestrial water storageModels that do not represent groundwater underestimate interannual variations in terrestrial water storage by about one third [ABSTRACT FROM AUTHOR]

Precipitation process is generally considered to be poorly represented in numerical weather/climate models. Statistical downscaling (SD) methods, which relate precipitation with model resolved dynamics, often provide more accurate precipitation estimates compared to model's raw precipitation products. We introduce the convolutional neural network model to foster this aspect of SD for daily precipitation prediction. Specifically, we restrict the predictors to the variables that are directly resolved by discretizing the atmospheric dynamics equations. In this sense, our model works as an alternative to the existing precipitation‐related parameterization schemes for numerical precipitation estimation. We train the model to learn precipitation‐related dynamical features from the surrounding dynamical fields by optimizing a hierarchical set of spatial convolution kernels. We test the model at 14 geogrid points across the contiguous United States. Results show that provided with enough data, precipitation estimates from the convolutional neural network model outperform the reanalysis precipitation products, as well as SD products using linear regression, nearest neighbor, random forest, or fully connected deep neural network. Evaluation for the test set suggests that the improvements can be seamlessly transferred to numerical weather modeling for improving precipitation prediction. Based on the default network, we examine the impact of the network architectures on model performance. Also, we offer simple visualization and analyzing approaches to interpret the models and their results. Our study contributes to the following two aspects: First, we offer a novel approach to enhance numerical precipitation estimation; second, the proposed model provides important implications for improving precipitation‐related parameterization schemes using a data‐driven approach. Plain Language Summary: The precipitation process is not well simulated in numerical weather models, since it takes place at the scales beyond the resolution of current models. We develop a statistical model using deep learning technique to improve the estimation of precipitation in numerical weather models. Key Points: We offer a novel approach to enhance numerical precipitation estimation using deep convolutional neural network (CNN)The model provides important implications for improving precipitation‐related parameterization schemes using a data‐driven approachThe CNN model outperforms existing precipitation statistical downscaling approaches by learning dominant spatial dynamics features [ABSTRACT FROM AUTHOR]

We assess monthly temperature and precipitation data derived by six statistically downscaled data sets for 14 general circulation models (GCMs) from the Climate Model Intercomparison Program Phase 5. We use a simple monthly water balance model to quantify and decompose uncertainties associated with the GCMs and statistical techniques in projections for four snow‐dominated regions in the western United States. The end‐of‐century projections from GCMs exhibit substantial variation in change over the regions (temperature range of 2.8–8.0 °C and precipitation range of −22–31%). The six downscaled data sets exhibit disparate high‐resolution representations of the magnitude and spatial patterns of future temperature (up to 2.2 °C) and precipitation (up to 30%) for a common GCM. Two data sets derived by the same downscaling method (Multivariate Adaptive Constructed Analogs) produce median losses of snow water equivalent over the Upper Colorado of 51% and 81%. The principal causes of the differences among the downscaled projections are related to the gridded observations used to bias correct the historical GCM output. Specifically, (1) whether a fixed atmospheric lapse rate (−6.5 °C/km) or a spatially and temporally varying lapse rate is used to extrapolate lower elevation observations to high‐elevations and (2) whether high‐elevation station data (e.g., SNOTEL) are included in the observations. The GCM projections are the largest source of uncertainty in the monthly water balance model simulations; however, the differences among seasonal projections produced by downscaled data sets in some regions highlight the need for careful evaluation of the statistically downscaled data in climate impact studies. Plain Language Summary: Climate change information simulated by global climate models is downscaled using statistical methods to translate spatially coarse regional projections to finer resolutions that are desirable to researchers and managers to assess local climate impacts. Several statistical downscaling methods have been developed over the past 15 years, resulting in multiple data sets derived by different methods. We apply a simple hydrology model to demonstrate how the differences among these data sets result in disparate projections of snow loss and future changes in runoff in the western United States and provide guidance to users on how to select and evaluate the appropriate data set for their needs. Key Points: Application of a water balance model reveals differences among statistically downscaled data sets result in varying future projectionsThe choice of bias correction data set is a major factor in the simulation of high‐elevation snow and the amount of snow loss in the futureThe spread among GCMs is the largest source of uncertainty in projections, but differences attributed to downscaled data can be substantial [ABSTRACT FROM AUTHOR]