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Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.

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At least 595 records · Page 33Linked to original sources
USGS2018

Direct observations of hydrologic exchange occurring with less‐mobile porosity and the development of anoxic microzones in sandy lakebed sediments

Quantifying coupled mobile/less‐mobile porosity dynamics is critical to the prediction of biogeochemical storage, release, and transformation processes in the zone where groundwater and surface water exchange. The recent development of fine‐scale geoelectrical monitoring paired with pore‐water sampling in groundwater systems enables direct characterization of hydrologic exchange between more‐ and less‐mobile porosity during tracer tests. We adapt this technique to sandy interface sediments at a groundwater flow‐through kettle lake. Tracer experiments were conducted within controlled‐head permeameters over a range of specified downward flow conditions over several days. Although the bed was predominantly composed of highly permeable sands and gravels, cobble inclusions created less‐mobile flow zones at the centimeter scale. Less‐mobile porosity fractions, residence times, and rates of exchange were inferred from paired bulk and fluid electrical conductivity data, without the need for inverse model calibration. The conservative solute experiments were paired with 15 NO 3 − and other reactive amendments, revealing anaerobic processes occurring at shallow sediment depths where pore‐water sampling indicated bulk‐oxic conditions. The average less‐mobile porosity residence times as evaluated with the geoelectrical method were on 1‐hr timescales, which appear to be biogeochemically important in the context of creating anoxic microzones within less‐mobile porosity of sandy interface sediments.

Water Resources Research↗
USGS2018

Responses of unimpaired flows, storage, and managed flows to scenarios of climate change in the San Francisco Bay-Delta watershed

Projections of meteorology downscaled from global climate model runs were used to drive a model of unimpaired hydrology of the Sacramento/San Joaquin watershed, which in turn drove models of operational responses and managed flows. Twenty daily climate change scenarios for water years 1980–2099 were evaluated with the goal of producing inflow boundary conditions for a watershed sediment model and for a hydrodynamical model of the San Francisco Bay-Delta estuary. The resulting time series of meteorology, snowpack, unimpaired flow, reservoir storage, and managed flow were analyzed for century-scale trends. In the Sacramento basin, which dominates Bay-Delta inflows, all 20 scenarios portrayed warming trends (with a mean of 4.1 °C) and most had precipitation increases (with a mean increase of 9%). Sacramento basin snowpack water equivalent declined sharply (by 89%), which was associated with a major shift toward earlier unimpaired runoff timing (33% more flow arriving prior to 1 April). Sacramento basin reservoirs showed large declines in end-of-September storage. Water-year averaged outflows increased for most scenarios for both unimpaired and impaired flows, and frequency of extremely high daily unimpaired and impaired flows increased (increases of 175% and 170%, respectively). Managed Delta inflows were projected to experience large increases in the wet season and declines in the dry season. Changes in management strategy and infrastructure can mitigate some of these changes, though to what degree is uncertain.

California↗
USGS2018

Time-dependent pore filling

Capillarity traps fluids in porous media during immiscible fluid displacement. Most field situations involve relatively long time scales, such as hydrocarbon migration into reservoirs, resource recovery, nonaqueous phase liquid remediation, geological CO 2 storage, and sediment‐atmosphere interactions. Yet laboratory studies and numerical simulations of capillary phenomena rarely consider the impact of time on these processes. We use time‐lapse microphotography to record the evolution of saturation in air‐ or hydrocarbon‐filled capillary tubes submerged in water to investigate long‐term pore filling phenomena beyond imbibition. Microphotographic sequences capture a lively pore filling history where various concurrent physical phenomena coexist. Dissolution and diffusion play a central role. Observations indicate preferential transport of the wetting liquid along corners, vapor condensation, capillary flow induced by asymmetrical interfaces, and interface pinning that defines the diffusion length. Other processes include internal snap‐offs, fluid redistribution, and changes in wettability as fluids dissolve into each other. Overall, the rate of pore filling is diffusion‐controlled for a given interfacial configuration; diffusive transport takes place at a constant rate for pinned interfaces and is proportional to the square root of time for free interfaces where the diffusion length increases with time.

Water Resources Research↗
USGS2019

Winter precipitation and summer temperature predict lake water quality at macroscales

Climate change can have strong effects on aquatic ecosystems, including disrupting nutrient cycling and mediating processes that affect primary production. Past studies have been conducted mostly on individual or small groups of ecosystems, making it challenging to predict how future climate change will affect water quality at broad scales. We used a subcontinental-scale database to address three objectives: (1) identify which climate metrics best predict lake water quality, (2) examine whether climate influences different nutrient and productivity measures similarly, and (3) quantify the potential effects of a changing climate on lakes. We used climate data to predict lake water quality in ~11,000 north temperate lakes across 17 U.S. states. We developed a novel machine learning method that jointly models different measures of water quality using 48 climate metrics and accounts for properties inherent in macroscale data (e.g. , spatial autocorrelation). Our results suggest that climate metrics related to winter precipitation and summer temperature were strong predictors of lake nutrients and productivity. However, we found variation in the magnitude and direction of the relationship between climate and water quality. We predict that a likely future climate change scenario of warmer summer temperatures will lead to increased nutrient concentrations and algal biomass across lakes (median ~3%–9% increase), whereas increased winter precipitation will have highly variable effects. Our results emphasize the importance of heterogeneity in the response of individual ecosystems to climate and are a caution to extrapolating relationships across space.

Water Resources Research↗
USGS2018

Predicting geogenic arsenic in drinking water wells in glacial aquifers, north-central USA: Accounting for depth-dependent features

Chronic exposure to arsenic (As) via drinking groundwater is a human health concern worldwide. Probabilities of elevated geogenic As concentrations in groundwater were predicted in complex, glacial aquifers in Minnesota, north‐central USA, a region that commonly has elevated As concentrations in well water. Maps of elevated As hazard were created for depths typical of drinking water supply and with well construction attributes common for domestic wells. Conventional variables describing aquifer properties and materials, position on the hydrologic landscape, and soil geochemistry were among the most influential for predicting the probability of elevated As. We also found that certain well construction attributes were influential in predicting As hazard. Smaller distances between the top of the well screen and overlying aquitard (proximity) and shorter well screen lengths were each associated with higher probabilities of elevated As. Influential predictor variables, which are either mapped across the region or are well construction attributes, are proxies in the model for measurable physical or geochemical causes of elevated As (e.g., redox condition, till or aquifer sediment chemistry, and water chemistry), which are not mapped across the region. Our setting shares some important characteristics with deltaic and other high‐As aquifers in Southeast Asia: late Quaternary age, complex layering of coarse‐ and fine‐grained materials, low‐As sediment concentrations, and geochemical controls on As mobilization. Translating three‐dimensional geologic and geochemical understanding of As mobility to quantifiable variables for modeling with powerful, flexible statistical tools could improve predictions and help identify safer groundwater supply options in the USA, Southeast Asia, and elsewhere.

Minnesota↗
USGS2019

The dependence of hydroclimate projections in snow‐dominated regions of the western United States on the choice of statistically downscaled climate data

We assess monthly temperature and precipitation data produced by four statistically based techniques that were used to downscale general circulation models (GCMs) in the Climate Model Intercomparison Program Phase 5 (CMIP5) (Taylor et al., 2012). We drive a simple water-balance model with the downscaled data to demonstrate the effect of the methods on the cold season hydrology of three, snow dominated regions in the western U.S. Independent of substantial variation among the GCM simulations over the regions (maximum range of ~3.5 °C and 50% change in precipitation), the four methods produce disparate high resolution representations of the magnitude and spatial patterns of future temperature and precipitation simulated by the models that range for up to ~3 °C and 30% change in precipitation that propagate into the hydrologic simulations. Temperature-dependent snowfall, accumulation, and melt in the model are sensitive to how atmospheric lapse rates are applied in the gridded observations that are used to remove the bias in raw GCM temperatures. By the end of the century the same downscaling method (Bias Corrected Spatial Disaggregation) yields a loss of cold-season snowpack of 34% over the Greater Yellowstone Area under a constant lapse rate ( 6.5°C km-1), whereas spatially variable lapse rates nearly double the loss to 66%, highlighting the roll of both lapse rates and high elevation stations in the bias correction dataset. The two newest downscaling methods (Multivariate Adaptive Constructed Analogs and Localized Constructed Analogs) preserve the magnitude of change simulated GCMs better than the other methods and the produce comparable hydrologic projections. Because the downscaled data from the methods vary spatially and by GCM, the downscaled data should be evaluated carefully as part of the process of using downscaled climate products to drive hydrological models over the area of interest.

Arizona, California, Colorado, Idaho, Montana,Neva↗
USGS2019

Temporal variability in nitrate – discharge relationships in large rivers as revealed by high frequency data

Little is known about temporal variability in nitrate concentration responses to changes in discharge on intraannual time scales in large rivers. To investigate this knowledge gap, we used a six‐year data set of daily surface water nitrate concentration and discharge averaged from near‐continuous monitoring at U.S. Geological Survey gaging stations on the Connecticut, Potomac, and Mississippi Rivers, three large rivers that contribute substantial nutrient pollution to important estuaries. Interannually, a comparison of nitrate concentration‐discharge (c‐Q) relationships between a traditional discrete grab sample data set and the near‐continuous data set revealed differing c‐Q slopes, which suggests that sample frequency can impact how we ultimately characterize hydrologic systems. Intraannually, we conducted correlation analyses over 30‐day windows to isolate the strength and direction of monthly c‐Q relationships. Monthly c‐Q slopes in the Potomac were positive (enrichment/mobilization response) in summer and fall and negative (dilution response) and weakly chemostatic (nonsignificant near‐zero c‐Q slope) in winter and spring, respectively. The Connecticut displayed a dilution response year‐round, except summer when it was weakly chemostatic. Mississippi c‐Q slopes were weakly chemostatic in all seasons and showed inconsistent responses to discharge fluctuations. The c‐Q dynamics in the Potomac and Connecticut were correlated ( R > 0.3) to river temperature, flow percentile, and calendar day. Minimal correlation in the Mississippi suggests that the large basin area coupled with spatiotemporally variable anthropogenic forcings from substantial land use development created stochastic short‐term c‐Q relationships. Additional work using high‐frequency sensors across large river networks can improve our understanding of spatial source input dynamics in these natural‐human coupled systems.

Water Resources Research↗
USGS2019

Remote sensing of river bathymetry: Evaluating a range of sensors, platforms, and algorithms on the upper Sacramento River, California, USA

Remote sensing has become an increasingly viable tool for characterizing fluvial systems. In this study, we used field measurements from a 1.6 km reach of the upper Sacramento River, CA, to evaluate the potential of mapping water depths from a range of platforms, sensors, and depth retrieval methods. Field measurements of water column optical properties also were compared to similar data sets from other rivers to provide context for our results. We considered field spectra, a multispectral satellite image, hyperspectral data collected from conventional and unmanned aircraft, and a bathymetric LiDAR and applied a generalized version of Optimal Band Ratio Analysis (OBRA) and the K nearest neighbors regression (KNN) machine learning algorithm. Linear, quadratic, exponential, power, and lowess OBRA models enabled more flexible curve-fitting in calibrating spectrally based quantities to depth; an exponential formulation avoided artifacts associated with other model types. KNN increased observed vs. predicted R2 values, particularly for the satellite image; we also found that pre-processing of satellite images was unnecessary and that a basic data product could be used for depth retrieval. Bathymetric LiDAR was highly accurate and precise in shallow water, but a lack of bottom returns from areas greater than 2 m deep resulted in large gaps in coverage. The maximum detectable depth imposes an important constraint on fluvial remote sensing and a hybrid approach combined with field surveys of deep areas might be a more realistic operational strategy for bathymetric mapping. Future work will focus on scaling up from short reaches to long river segments.

California↗
USGS2019

Soil physical, hydraulic, and thermal properties in interior Alaska, USA: Implications for hydrologic response to thawing permafrost conditions

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 dataset. 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, 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.

Alaska↗
USGS2019

Salinity yield modeling of the Upper Colorado River Basin using 30-meter resolution soil maps and random forests

Salinity loading in the Upper Colorado River Basin (UCRB) costs local economies upwards of $300 million US dollars annually. Salinity source models have generally included coarse spatial data to represent non‐agriculture sources. We developed new predictive soil property and cover maps at 30 m resolution to improve source representation in salinity modeling. Salinity loading erosion risk indices were also created based on soil properties, remotely sensed bare ground exposure, and topographic factors to examine potential surface soil erosion drivers. These new maps and data from previous SPARROW models were related to recently updated records of salinity at 309 stream gauges in the UCRB using random forest regressions. Resulting salinity yield predictions indicate more diffuse salinity sources, with slightly higher yields in more arid portions of the UCRB, and less overall load coming from irrigated agricultural sources. Model simulations still indicate irrigation to be the major human source of salinity (661,000 Mg, or 12%), but also suggest that 75,000 Mg (1.4%) of annual salinity in the UCRB is coming from areas with excessive exposed bare ground in high elevation mountain areas. Model inputs allow for field scale screening of locations that could be targeted for salinity control projects. Results confirm recent studies indicating limited surface erosional influence on salinity loading in UCRB surface waters, but impacts of monsoonal runoff events are still not fully understood, particularly in drylands. The study highlights the utility of new predictive soil maps and machine learning for environmental modeling.

Arizona, Colorado, New Mexico, Utah, Wyoming↗
USGS2020

Deposition potential and flow-response dynamics of emergent sandbars in a braided river

Sandbars are ubiquitous in sandy‐braided rivers throughout the world. In the Great Plains of the United States, recovery and expansion of emergent sandbar habitat (ESH) has been a priority in lowland rivers where the natural extent of sandbars has been degraded. Recovery efforts are aimed at protection of populations of the interior least tern ( Sterna antillarum ) and piping plover ( Charadrius melodus ). But quantitative observations of deposition and erosion dynamics of populations of sandbars across long segments of rivers are rare. We present a three‐part case study which used Bayesian regression models to examine relations between hydrology, channel morphology, and ESH responses in the Platte River, eastern Nebraska. Logistic regression indicates presence of ESH is positively related to the Parker, (1976) stability criterion and a gradient in sediment transport mode, and negatively related to presence of vegetation. Hierarchical linear regression modeling shows direct coupling between sandbar top‐surface height and formative flood magnitude, but the gap between formative flood stage and sandbar top‐surface increases with increasing discharge. Finally, linear regression modeling of sandbar erosion demonstrates rates of ESH erosion are on the order of 10 −1 ha/day during high‐flow periods and 10 −2 during low‐flow periods, but sandbar persistence is largely a function of sandbar starting size. The collective observations highlight the importance of large floods (>3‐year recurrence) in creating very large sandbars that persist as high‐quality ESH over periods of years whereas lower‐magnitude, more‐frequent flood events create lower‐quality ESH that typically does not persist into the following nesting season.

Nebraska↗
USGS2019

Morphological computation of dune evolution with equilibrium and non-equilibrium sediment-transport models

This paper presents an exploratory study that comprises the implementation and comparison of different approaches and parameterization of sediment transport mechanisms in a process-based morphological model for simulating river dunes. The purpose of this study was to assess the underlying physical processes associated with sediment transport and dune evolution simulated by the model with two different bedload transport models: a non-equilibrium approach considering saltation distance and an equilibrium approach with the incorporation of the effect of the local bed slope. The advancement of this study is that it comprised detailed analysis of the bed shear stress and sediment transport over a dune during time varying flows, comparing both transport models and revealing distinctive transient features of bedload transport and dune evolution. We also improved a non-equilibrium transport model by incorporating a formulation for sediment saltation distance as a length-scale. Using both sediment transport approaches, the morphological model was applied to replicate a large-scale field experiment. Notably, the model yielded the quasi-equilibrium dune feature reasonably well using either sediment-transport formulation. However, detailed analysis of simulated spatial and temporal features of sediment transport and the dune evolution process were found to be noticeably different. Even though both sediment transport mechanisms appear to work adequately at a basic level, the major difference between the two approaches is the underlying transport process over the dunes and time-scale of the dune evolution process.

Water Resources Research↗
USGS2019

Fishway entrance gate experiments with adult American Shad

The goal of this multiyear study was to examine how changes to an upstream fishway entrance impacted the passage rate of adult American shad ( Alosa sapidissima ). We evaluated a total of nine treatment conditions that consisted of three fishway entrance gate types and three submergence depths (i.e., the water surface elevation of the tailwater relative to the height of the gate crest). Approximately 2,000 wild, actively migrating shad participated in one of the 64 total trials (6–8 trials per treatment) that were conducted for this experiment. The three fishway entrance gate types were the vertical gate (the most common entrance gate type used in fishways), the overshot gate (less common in fishways), and the reversed overshot gate (novel to fishways). The submergence depths ranged from 30.5 to 91.4 cm above the gate crest. Increases to the submergence depth were shown to be the most influential predictor variable of passage time, followed by gate type and river temperature. The reversed overshot and overshot gates outperformed the vertical gate with the best performance occurring for the newly introduced reversed overshot gate type. The results of this study provide guidance on methods to improve fishway attraction and entry rates to numerous state and federal resource agencies and the hydropower industry.

Water Resources Research↗
USGS2019

Physical controls on salmon redd site selection in restored reaches of a regulated, gravel-bed river

Large‐scale river restoration programs have emerged recently as a tool for improving spawning habitat for native salmonids in highly altered river ecosystems. Few studies have quantified the extent to which restored habitat is utilized by salmonids, which habitat features influence redd site selection, or the persistence of restored habitat over time. We investigated fall‐run Chinook salmon spawning site utilization and measured and modeled corresponding habitat characteristics in two restored reaches: a reach of channel and floodplain enhancement completed in 2013 and a reconfigured channel and floodplain constructed in 2002. Redd surveys demonstrated that both restoration projects supported a high density of salmon redds, 3 and 14 years following restoration. Salmon redds were constructed in coarse gravel substrates located in areas of high sediment mobility, as determined by measurements of gravel friction angles and a grain entrainment model. Salmon redds were located near transitions between pool‐riffle bedforms in regions of high predicted hyporheic flows. Habitat quality (quantified as a function of stream hydraulics) and hyporheic flow were both strong predictors of redd occurrence, though the relative roles of these variables differed between sites. Our findings indicate that physical controls on redd site selection in restored channels were similar to those reported for natural channels elsewhere. Our results further highlight that in addition to traditional habitat criteria (e.g., water depth, velocity, and substrate size), quantifying sediment texture and mobility, as well as intragravel flow, provides a more complete understanding of the ecological benefits provided by river restoration projects.

Water Resources Research↗
USGS2019

Prediction and inference of flow-duration curves using multi-output neural networks

We develop multi-output neural network models (MNNs) to predict flow-duration curves (FDCs) in 9,203 ungaged locations in the Southeastern United States for six decades between 1950-2009. 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 surrgotate models, via the Local Interpretable Model-agnostic Explanations (LIME) method, can be used to infer the relation between basin characteristics and the predicted FDCs. Results suggest that MNNs 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.

Water Resources Research↗
USGS2019

Streamflow reconstruction in the Upper Missouri River Basin using a novel Bayesian network model

A Bayesian model that uses the spatial dependence induced by the river network topology, and the leading principal components of regional tree-ring chronologies for paleo-streamflow reconstruction is presented. In any river basin, a convergent, dendritic network of tributaries comes together to form the main stem of a river. Consequently, it is natural to think of a spatial Markov process that recognizes this topological structure to develop a spatially consistent basin-scale streamflow reconstruction model that uses the information in streamflow and tree-ring chronology data to inform the reconstructed flows, while maintaining the space-time correlation structure of flows that is critical for water resource assessments and management. Given historical data from multiple streamflow gauges along a river, their tributaries in a watershed, and regional tree-ring chronologies, the model is fit and used to simultaneously reconstruct the full network of paleo-streamflow at all gauges in the basin progressing upstream to downstream along the river. The spatial network structure allows a substantial reduction in the uncertainty associated with paleo-streamflow as one proceeds downstream in the network and the spatial dependence structure increases the information content. Our application to eighteen streamflow gauges in the Upper Missouri River Basin shows that the mean adjusted-R2 for the basin is approximately 0.5 with good overall cross-validated skill as measured by five different skill metrics. A comparison with the traditional principal components regression shows that the spatial Bayesian model offers improvements, as downstream gauges are informed by the reconstruction of the upstream gauges, as well as the tree-ring chronologies.

Idaho, Montana, Wyoming↗
USGS2019

Process-guided deep learning predictions of lake water temperature

The rapid growth of data in water resources has created new opportunities to accelerate knowledge discovery with the use of advanced deep learning tools. Hybrid models that integrate theory with state‐of‐the art empirical techniques have the potential to improve predictions while remaining true to physical laws. This paper evaluates the Process‐Guided Deep Learning (PGDL) hybrid modeling framework with a use‐case of predicting depth‐specific lake water temperatures. The PGDL model has three primary components: a deep learning model with temporal awareness (long short‐term memory recurrence), theory‐based feedback (model penalties for violating conversation of energy), and model pretraining to initialize the network with synthetic data (water temperature predictions from a process‐based model). In situ water temperatures were used to train the PGDL model, a deep learning (DL) model, and a process‐based (PB) model. Model performance was evaluated in various conditions, including when training data were sparse and when predictions were made outside of the range in the training data set. The PGDL model performance (as measured by root‐mean‐square error (RMSE)) was superior to DL and PB for two detailed study lakes, but only when pretraining data included greater variability than the training period. The PGDL model also performed well when extended to 68 lakes, with a median RMSE of 1.65 °C during the test period (DL: 1.78 °C, PB: 2.03 °C; in a small number of lakes PB or DL models were more accurate). This case‐study demonstrates that integrating scientific knowledge into deep learning tools shows promise for improving predictions of many important environmental variables.

Minnesota, Wisconsin↗
USGS2019

Advances in quantifying streamflow variability across continental scales: 1. Identifying natural and anthropogenic controlling factors in the USA using a spatially explicit modeling method

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.

conterminous United States↗