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Accurate flood risk assessment requires a comprehensive understanding of flood sensitivity to regional drivers and climate factors. This paper presents the scaling of floods (duration, peak, volume) with geomorphologic characteristics of the basin (i.e., drainage area, slope, elevation) and precipitation patterns (rainfall accumulation, variability). Long‐term daily streamflow observations over the 20th and early 21st centuries from Hydro‐Climatic Data Network streamgages across the conterminous United States are used to create a flood event database based on their flood stage information. Antecedent daily rainfall accumulation and variability corresponding to these floods are computed using Global Historical Climatology Network daily data set. Two Bayesian scaling models are developed, and the spatial organization of scaling exponents is investigated. The baseline model quantifies the scaling of floods to geomorphologic characteristics. The dynamic model quantifies the scaling of floods to antecedent precipitation distribution which is further conditioned on geomorphologic characteristics. Results show that small and low‐elevation basins have a stronger response to antecedent rainfall distribution in amplifying flood peaks, while high‐elevation steeper basins have a lower response for flood duration and volume. The dynamic models demonstrate that there are significant variations in the flood scaling rates, with the largest rates up to 40% and 4.5% for flood duration, 64% and 44% for peak, and 98% and 40% for volume found across the Northeast, Coastal Southeast, and Northwest with intensifying rainfall accumulation and variability, respectively. This study advances flood predictions by better informing the flood attributes in the context of dynamical land‐atmosphere perturbations. Plain Language Summary: The relationships between floods, physical properties of river basins, and the spatio‐temporal distribution of precipitation define the regional flood scaling laws. Improved regional flood scaling models are required for updating flood risk scoring tools to help insurers and decision‐makers in the light of anthropogenic climate changes and natural variability of Earth systems. Two scaling models are developed here to examine how the physical properties of basins and precipitation patterns can inform flood attributes across the conterminous United States (US). Using long records of streamflow and precipitation observations, we identified different scaling relationships between the catchment factors, preceding precipitation, and the duration, peaks, and volume of floods. Larger basins located at higher elevations are found to have lower scaling rates with flood peaks when interacting with the preceding precipitation patterns. Overall, low‐elevation flat basins across the Eastern, Southern, and Northwestern US have a larger positive response to precipitation in amplifying flood attributes. These new scaling laws can help advance our understanding of controlling mechanisms for flash floods and mega‐floods, which is required for a dynamic flood risk assessment in a changing environment. Key Points: Two Bayesian scaling models are developed to understand the sensitivity of floods to geomorphologic characteristics and catchment rainfallLow‐elevation and flat basins with different sizes have greater scaling to rainfall in amplifying flood duration and volumesLow‐elevation and small basins with different steepness show larger sensitivity to rainfall distribution that enhances flood peaks [ABSTRACT FROM AUTHOR]

This paper is motivated to understand the multi‐temporal scale characteristics of Horton Index (HI), an indicator of catchment‐scale plant water use defined as the ratio of catchment vaporization (total evapotranspiration) to catchment wetting (precipitation minus surface runoff). Based on the generalized proportionality hypothesis, we first develop an analytical framework of HI applicable at multi‐temporal scales as a single function of ecological aridity index (EAI), defined as the ratio of potential evapotranspiration to catchment wetting. We successfully validate this framework at the long‐term, annual, and monthly scales at 343 natural catchments over the conterminous United States under varying climate and vegetation regimes. We show that vegetation plays an important role in regulating not only the partitioning of catchment wetting into vertical vaporization and horizontal baseflow, but also further partitioning of vaporization into initial and continuing components. Aided by this framework, we reveal emergent space‐time similarity patterns between HI's spatial (inter‐catchment, regional) and temporal (within‐catchment, inter and intra‐annual) variability. On the one hand, HI increases with EAI, both spatially and temporally, following similar trends predicted by the analytical framework. On the other hand, the slopes of HI ∼ EAI relationships, denoted as d (HI)/d (EAI), decreased with EAI both spatially and temporally and, again, following similar trends suggested by the analytical framework. These findings suggest the promising potential of Horton Index as a conceptual yet quantitative framework for improving understanding and modeling of multi‐scale ecological and hydrological processes and their interactions. Key Points: Derivation and validation of an analytical framework of Horton Index applicable over long‐term, annual, and monthly scales across biomesVegetation regulates catchment storage partitioning into vaporization and baseflow, then vaporization into initial and continuing componentsEmergent space‐time similarity patterns detected empirically and explained theoretically using the analytical framework [ABSTRACT FROM AUTHOR]

Long-term climate is the first-order control on mean annual water balance, and vegetation and the interactions between climate seasonality and soil water storage change have also been found to play important roles. The purpose of this paper is to extend the Budyko hypothesis to the seasonal scale and to develop a model for interannual variability of seasonal evaporation and storage change. A seasonal aridity index is defined as the ratio of potential evaporation to effective precipitation, where effective precipitation is the difference between rainfall and storage change. Correspondingly, evaporation ratio is defined as the ratio of evaporation to effective precipitation. A modified Turc-Pike equation with a horizontal shift is proposed to model interannual variability of seasonal evaporation ratio as a function of seasonal aridity index, which includes rainfall seasonality and soil water change. The performance of the seasonal water balance model is evaluated for 277 watersheds in the United States. The 99% of wet seasons and 90% of dry seasons have Nash-Sutcliffe efficiency coefficients larger than 0.5. The developed seasonal model can be applied for constructing long-term evaporation and storage change data when rainfall, potential evaporation, and runoff observations are available. On the other hand, vegetation affects seasonal water balance by controlling both evaporation and soil moisture dynamics. The correlation between NDVI and evaporation is strong particularly in wet seasons. However, the correlation between NDVI and the seasonal model parameters is only strong in dry seasons. [ABSTRACT FROM AUTHOR]

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

Regionalization approaches are widely used in water resources engineering to identify hydrologically homogeneous groups of watersheds that are referred to as regions. Pooled information from sites (depicting watersheds) in a region forms the basis to estimate quantiles associated with hydrological extreme events at ungauged/sparsely gauged sites in the region. Conventional regionalization approaches can be effective when watersheds (data points) corresponding to different regions can be separated using straight lines or linear planes in the space of watershed related attributes. In this paper, a kernel-based Fuzzy c-means (KFCM) clustering approach is presented for use in situations where such linear separation of regions cannot be accomplished. The approach uses kernel-based functions to map the data points from the attribute space to a higher-dimensional space where they can be separated into regions by linear planes. A procedure to determine optimal number of regions with the KFCM approach is suggested. Further, formulations to estimate flood quantiles at ungauged sites with the approach are developed. Effectiveness of the approach is demonstrated through Monte-Carlo simulation experiments and a case study on watersheds in United States. Comparison of results with those based on conventional Fuzzy c-means clustering, Region-of-influence approach and a prior study indicate that KFCM approach outperforms the other approaches in forming regions that are closer to being statistically homogeneous and in estimating flood quantiles at ungauged sites. [ABSTRACT FROM AUTHOR]

Temporal shifts in precipitation and runoff regime curves appear throughout the continental United States, but differ from region to region. This paper explores these regime shifts by building upon a hydroclimatic classification system that partitions the United States into clusters of similarly behaved catchments using four simple hydroclimatic indicators. Hydroclimate data from over four hundred catchments over a 55 year period (belonging to the MOPEX data set) are analyzed to reveal how the indicators have shifted before and after 1970, before and after 1975, and before and after 1980. Statistically significant hydroclimatic changes in these indicators are explored qualitatively, suggesting which catchments today might resemble other catchments tomorrow. Thus, a preview of current locations in one class under future conditions is provided by observing existing locations of another class. The classification system structure enables organization of these data, allowing patterns of regime change to emerge without highly specified models at each individual site. Regional analyses explore changes in mean seasonal precipitation/runoff regimes, including shifts in the daily variability of precipitation and runoff. Additionally, changes in regime curves of minimum and maximum precipitation/runoff observations are analyzed and discussed. Results indicate that after 1980, classifications typically found in the southeastern quarter of the United States have expanded northward and westward. Regionally, the Midwest and Rocky Mountains seem to demonstrate more frequent, but less intense storms after 1980, while southeastern catchments receive much less water in the form of precipitation and runoff than in previous years. [ABSTRACT FROM AUTHOR]

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

This study explores multi‐scale variability in the relationship between turbidity (Tn) and flow (Q) in 162 watersheds across the contiguous United States. Sites are selected where Tn acts as a good surrogate for suspended sediment concentration. We use dynamic linear models (DLMs) to infer time‐varying parameters of Tn‐Q rating curves at each site, and calibrate a hyper‐parameter of the DLM model (δ $\delta $) to quantify the degree of dynamicity in the rating curve relationship. The DLM can capture dynamics in the Tn‐Q relationship at the resolution of the data (daily in this study), enabling an analysis of the dynamics across time scales. Regional multivariate regressions are used to identify physiographic features that relate to the magnitude of δ $\delta $ and spectral signatures in the DLM parameters across sites. Results show that watersheds in the Midwest and Pacific Northwest tend to exhibit more variable Tn‐Q relationships, while these relationships are more stable in watersheds in the humid east and Lower Mississippi River basin. Stream network complexity, soil composition, perennial snow coverage, saturation‐excess overland flow, and modifications to the stream network are associated with the dynamicity in the Tn‐Q relationship. DLM parameters exhibit cyclic behavior at sub‐monthly, sub‐annual, and annual time scales at sites across the country, with annual cycling associated with basin features that reflect watershed sediment availability and the erosive power of rivers. Overall, our analysis highlights significant multi‐scale variability in Tn‐Q relationships across the nation, with important implications for how sediment dynamics should be measured and managed at the watershed‐scale. Plain Language Summary: Effective sediment management often requires accurate sediment yield predictions and forecasts. However, the predictive skill of various models often declines in regions where the sediment transport processes are variable across timescales. This study aims to improve our understanding of variability in the relationship between turbidity (Tn) and flow (Q) through time, focusing on sites where turbidity is a good proxy for suspended sediment concentration. We use regression models with parameters that can vary over time to quantify the variability in the Tn‐Q relationship and to highlight the presence of seasonal patterns in transport processes. We then explore how watershed characteristics influence the variability and seasonality in the Tn‐Q relationship. Our results suggest that stream network complexity, soil composition, perennial snow coverage, saturation‐excess overland flow, and modifications to the stream network influence the overall variability in the Tn‐Q relationship, while the seasonality is mainly associated with basin features that reflect watershed sediment availability and the erosive power of rivers. The results of the study have important implications for how sediment dynamics should be addressed in watershed management studies. Key Points: The dynamicity of sediment transport processes is quantified using dynamic linear rating curve models of turbidity and flowThe degree of rating curve dynamicity varies spatially and is partially influenced by natural and anthropogenic physiographic featuresDynamic rating curves exhibit cyclic behavior at various timescales across the US, which is also partially explained by basin features [ABSTRACT FROM AUTHOR]

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

Regional flood frequency analysis (RFFA) is widely used to quantify flood risk at ungauged and sparsely gauged locations. There are minimal attempts to use partial duration series (PDS) for RFFA, though the use of PDS instead of widely used annual maximum series (AMS) can offer some advantages. This article contributes two novel random/regression forests (RFs)‐based methodologies, namely generalized pareto distribution (GPD)‐based distributional RFs (DRFs) and multivariate RFs (MVRFs), for RFFA with PDS. The RFs facilitate modeling interactions between predictors and their complex relationships with the predictands without explicitly specifying them. The DRFs and MVRFs comprise an ensemble of corresponding regression trees, each constructed by recursive binary partitioning of the feature space into meaningful segments. The proposed DRFs account for the sampling uncertainty of PDS in the partitioning and parameter estimation. In DRFs (MVRFs), quantile estimates for an ungauged site are obtained using maximum likelihood estimates (expected values) of GPD parameters corresponding to the segments to which the site belongs. The potential of DRFs and MVRFs relative to two recently proposed techniques (univariate RFs‐based quantile regression, generalized additive model based on GPD) is demonstrated through Monte‐Carlo simulation experiments and a study on 1,031 watersheds in the United States. The key features influencing scale and shape parameters of GPD fitted to PDS of the watersheds are identified as drainage area and 24‐hr rainfall intensity corresponding to 2‐year return period, respectively. Those identified for shape parameter differ from key features known based on analysis with AMS and generalized extreme value distribution. Key Points: Two new methods based on random forests (RFs) are proposed for regional flood frequency analysis with partial duration series (PDS)Proposed distributional RFs (DRFs) can account for sampling uncertainty in PDS and capture complex predictor‐predictand relationshipsDRFs appear effective in Monte‐Carlo simulations and study on the United States and yielded key features influencing PDS of floods [ABSTRACT FROM AUTHOR]

This paper explores the relationship between channel network configurations and hydrograph sensitivity to storm kinematics with different storm speeds, storm directions, and storm sizes. A synthetic circular catchment is introduced, in order to prevent bias due to interaction between storm directions and catchment geometry. The drainage network of the test catchment is simulated with Gibbs' model for a given network configuration (β). The peak response of the catchment is investigated with different configurations of drainage network, combined with different conditions of storm kinematics. The results show that the relationship between storm kinematics and the peak discharge response is dependent on the network configuration. The network configuration indicates the network efficiency in terms of the total drainage time of a network. The resonance condition can be defined for a 2-D drainage network as the inverse of the averaged total sum of flow distance. The results show that the storm kinematics that produces the maximum peak discharge depends on the network configuration because the resonance condition changes with the network configuration. The investigation of 12 catchments in the Chicago area indicates that urban drainage networks, typically, are highly efficient but can also be inefficient. The results illustrate that more inefficient networks (networks with lower β) are less sensitive to rainstorm movement and produce lower peak discharge, compared with efficient networks. In contrast, an efficient network produces higher peak discharges and is more sensitive to storm kinematics, compared with an inefficient network. [ABSTRACT FROM AUTHOR]

This paper applies a four-objective calibration strategy focusing on peak flows, low flows, water balance, and flashiness to 392 model parameter estimation experiment (MOPEX) watersheds across the United States. Our analysis explores the influence of model structure by analyzing how the multiobjective calibration trade-offs for two conceptual hydrologic models, the Hydrology Model (HYMOD) and the Hydrologiska Byråns Vattenbalansavdelning (HBV) model, compare for each of the 392 catchments. Our results demonstrate that for modern multiobjective calibration frameworks to identify any meaningful measure of model structural failure, users must be able to carefully control the precision by which they evaluate their trade-offs. Our study demonstrates that the concept of epsilon-dominance provides an effective means of attaining bounded and meaningful hydrologic model calibration trade-offs. When analyzed at an appropriate precision, we found that meaningful multiobjective trade-offs are far less frequent than prior literature has suggested. However, when trade-offs do exist at a meaningful precision, they have significant value for supporting hydrologic model selection, distinguishing core model deficiencies, and identifying hydroclimatic regions where hydrologic model prediction is highly challenging. [ABSTRACT FROM AUTHOR]

This paper synthesizes a 3-year collaborative effort to characterize the biogeochemical and hydrological features of intensively managed agricultural catchments by combining data analysis, modeling, and preliminary hypothesis testing. The specific focus was on the Midwestern region of the United States. The results suggest that: (1) water management, specifically the homogenization of evapotranspiration losses driven by mono-cultural vegetation cover, and the homogenization of runoff generation driven by artificial drainage, has created engineered, predictable hydrologic systems; (2) nutrient and pesticide management, specifically their regular applications have created two kinds of biogeochemical export regimes: chemostatic (low variability in concentration as exhibited by nitrate) and episodic (high variability in concentration as exhibited by pesticides); (3) coupled mass-balance models for water and solutes reproduce these two regimes as a function of chemical rate constants. Phosphorus transport regimes were found to be episodic at smaller spatial scales, but chemostatic at larger scales. Chemostatic response dominates in transport-limited catchments that have internal sources of the solute to buffer the periodicity in episodic mputs, while episodic response dominates in source-limited catchments. The shift from episodic nitrate export in pristine catchnients to chemostatic regimes in managed watersheds was attributed to legacy stores of nitrogen (built from continued fertilizer applications) that buffer interannual variations in biogeochemical processing. Fast degradation kinetics of pesticides prevents the build-up of legacy sources, and leads to episodic export. Analytical expressions were derived for the probability density functions of solute delivery ratio as a function of the stochastics of rainfall-runoff events and biogeochemical controls. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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]

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]

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]

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]

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]

Abstract: Fallout radionuclides (FRNs) and their ratios, such as Beryllium‐7 (7Be) and excess Lead‐210 (210Pbxs), have been used to determine suspended sediment source and age in catchments. These models are based on numerous assumptions, for example, that channel deposition of FRNs from precipitation is negligible in comparison to their delivery to the channel from land surface erosion during individual storm events. We test this assumption using a mass balance approach during eight storms from summer 2011 to fall 2012 in a mid‐Atlantic United States piedmont region watershed with mixed land use. Event peak discharge and storm type corresponded to the importance of direct channel FRN deposition from precipitation. During relatively low discharge summer thunderstorms, with minimal overland flow, less than 1% of 7Be and 210Pbxs flux deposited on the watershed exits the watershed associated with suspended sediment. The majority but not all deposited on the stream channel exits the watershed associated with suspended sediment (60% of 7Be and 80% of 210Pbxs). Here precipitation and throughfall onto the wetted channel area can be responsible for any FRN newly associated with suspended sediment, as opposed to landscape surface erosion. Furthermore, FRNs can be stored with sediments in the channel between events. Events with higher discharges, including hurricanes, show the opposite pattern—FRN flux associated with suspended sediment exported from the reach is greater than channel FRN wet deposition, suggesting net erosion from the watershed landscape and/or stored material during these types of storms. [ABSTRACT FROM AUTHOR]

With climate warming, many regions are experiencing changes in snow accumulation and persistence. These changes are known to affect streamflow volume, but the magnitude of the effect varies between regions. This research evaluates whether variables derived from remotely sensed snow cover can be used to estimate annual streamflow at the small watershed scale across the western U.S., a region with a wide range of climate types. We compared snow cover variables derived from MODIS, snow persistence (SP), and snow season (SS), to more commonly utilized metrics, snow fraction (fraction of precipitation falling as snow, SF), and peak snow water equivalent (SWE). Each variable represents different information about snow, and this comparison assesses similarities and differences between the snow metrics. Next, we evaluated how two snow variables, SP and SWE, related to annual streamflow (Q) for 119 USGS reference watersheds and examined whether these relationships varied for wet/warm (precipitation surplus) and dry/cold (precipitation deficit) watersheds. Results showed high correlations between all snow variables, but the slopes of these relationships differed between climates, with wet/warm watersheds displaying lower SF and higher SWE for the same SP. In dry/cold watersheds, both SP and SNODAS SWE correlated with Q spatially across all watersheds and over time within individual watersheds. We conclude that SP can be used to map spatial patterns of annual streamflow generation in dry/cold parts of the region. Applying this approach to the Upper Colorado River Basin demonstrates that 50% of streamflow comes from areas >3,000 masl. If the relationship between SP and Q is similar in other dry/cold regions, this approach could be used to estimate annual streamflow in ungauged basins. [ABSTRACT FROM AUTHOR]

The reply addresses concerns raised by Belmont et al. (2016) on Gupta et al. (2015) through additional analysis of streamflow versus precipitation relationships for the Whetstone and the Redwood Rivers and with data on available soil moisture in prechange and postchange periods in the Cottonwood River watershed. [ABSTRACT FROM AUTHOR]

Changes in streamflow are an important area of ongoing research in the hydrologic sciences. To better understand spatial patterns in past changes in streamflow, we examined relationships between watershed-scale spatial characteristics and trends in streamflow. Trends in streamflow were identified by analyzing mean daily flow observations between 1940 and 2009 from 967 U.S. Geological Survey stream gages. Results indicated that streamflow across the continental U.S., as a whole, increased while becoming less extreme between 1940 and 2009. However, substantial departures from the continental U.S. (CONUS) scale pattern occurred at the regional scale, including increased annual maxima, decreased annual minima, overall drying trends, and changes in streamflow variability. A subset of watersheds belonging to a reference data set exhibited significantly smaller trend magnitudes than those observed in nonreference watersheds. Boosted regression tree models were applied to examine the influence of watershed characteristics on streamflow trend magnitudes at both the CONUS and regional scale. Geographic location was found to be of particular importance at the CONUS scale while local variability in hydroclimate and topography tended to have a strong influence on regional-scale patterns in streamflow trends. This methodology facilitates detailed, data-driven analyses of how the characteristics of individual watersheds interact with large-scale hydroclimate forces to influence how changes in streamflow manifest. [ABSTRACT FROM AUTHOR]

Patterns of interannual variability of the annual water balance are explored using data from 190 MOPEX catchments across the continental U.S. This analysis has led to the derivation of a quantitative, dimensionless, Budyko-type framework to characterize the observed interannual variability of annual water balances. The resulting model is expressed in terms of a humidity index that measures the competition between water and energy availability at the annual time scale, and a similarity parameter ( α) that captures the net effects of other short-term climate features and local landscape characteristics. This application of the model to the 190 study catchments revealed the existence of space-time symmetry between spatial (between-catchment) variability and general trends in the temporal (between-year) variability of the annual water balances. The MOPEX study catchments were classified into eight similar catchment groups on the basis of magnitudes of the similarity parameter α. Interesting regional trends of α across the continental U.S. were brought out through identification of similarities between the spatial positions of the catchment groups with the mapping of distinctive ecoregions that implicitly take into account common climatic and vegetation characteristics. In this context, this study has introduced a deep sense of similarity that is evident in observed space-time variability of water balances that also reflect the codependence and coevolution of climate and landscape properties. [ABSTRACT FROM AUTHOR]

The landscape of the coastal plain of the southeastern United States is rapidly changing due to urbanization and climate-change-related impacts. In addition to the forecasted population increase, this region could experience significant changes in precipitation patterns making watershed management very challenging. In order to establish baseline data, storm-event flow pathways were studied in three lower coastal plain (LCP) forested lowland watersheds of the southeastern United States between 2010 and 2011. Two of the watersheds had clay loam subsoils while the third had sandy soils throughout the profile. Stream flow and water samples from water-table wells, piezometers, lysimeters, and rain gauges were analyzed for ion concentrations; ion trends were assessed using principal components analysis; and chemical hydrograph separation was performed for nine storm events using end-member mixing analysis. End-members consisted of lower concentration rainwater; a near-stream source (riparian or streambed groundwater); and a distant or deep groundwater source. Storm-event stream water on the clayey sites was composed primarily of rainwater (45-67% by volume) and shallow groundwater (21-55%), with small inputs from deep (below the clay-rich soil horizon) groundwater (0.2-21%). At the sandy site, a greater proportion was groundwater (56-61%), with smaller inputs from rainwater (28-33%) and soil water (6-16%). Dry antecedent soil moisture (ASM) conditions and larger storms resulted in greater rainwater contribution at the clayey sites. Shallow groundwater was an important contributor even in dry ASM conditions, perhaps due to the high specific retention of the soils. The results from this study will inform researchers about stormwater routing in forested, shallow water table watersheds and provide land managers with baseline data as they plan stormwater mitigation practices. [ABSTRACT FROM AUTHOR]

Establishing an adequate hydrometric network to provide accurate and reliable continuous flow information for various users remains a major challenge. This includes the design of new networks or the evaluation of existing networks. This study proposes a combined regionalization and dual entropy-multiobjective optimization (CRDEMO) method for determining minimum network that meets the World Meteorology Organization (WMO) standards, which is considered herein an optimal minimum network. A regionalization approach is used to estimate flows in potential locations for new additional stations, and a dual entropy-multiobjective optimization approach is used to identify optimal trade-offs between the maximum possible information content and the minimum shared information among the stations. The method was examined in two Canadian River basins, namely, the St. John River and St. Lawrence River basins. The St. John River basin has a higher network density compared to the St. Lawrence River basin. Results of the analysis indicate that the St. Lawrence River basin requires a high number of new additional stations to meet the WMO minimum network density. It was also determined that existing stations do not have a significant influence in determining the locations of new stations in the St. Lawrence River basin. Conversely, however, in the St. John River basin, existing stations have significant influence on the determination of locations of new stations due to the higher number of existing stations. Overall, the CRDEMO technique is shown to be robust for designing optimal minimum hydrometric networks and can be a useful tool for evaluating current and proposed networks. [ABSTRACT FROM AUTHOR]

Statistical models relating anthropogenic modifications of the watershed landscape to alterations in streamflow are often plagued by heterogeneity in background watershed conditions and coinciding trends in climate that can complicate the interpretation of clear statistical relationships. This study introduces the use of panel regression as a modeling approach that can better accommodate basin heterogeneity and identify more robust signals between anthropogenic impacts on the watershed landscape and hydrologic response. Panel regression techniques pool multidimensional data recorded across individuals (i.e., watersheds) and through time to better characterize within- and between-variability in the hydrologic data set. The separate attribution of variability to both time and space dimensions enables the method to better identify response characteristics that are generalizable across watersheds. This study introduces in detail two broad classes of panel regression models (the fixed effects and random effects models) that may be particularly useful in the context of identifying and quantifying the impact of human-induced landscape alterations on hydrologic response. The technique is presented in a case study relating watershed urbanization to changes in the annual runoff coefficient for 19 watersheds in the Northeast United States. Comparisons are made against more standard cross-sectional and time-based regression analyses. The results show that the estimated relationship between urbanization and the annual runoff coefficient in this region is highly dependent on the dimensions of data examined (space or time) and model structure selected, with the most sophisticated and appropriate model suggesting that no significant relationship can be detected. [ABSTRACT FROM AUTHOR]

A long-term streamflow discharge database has been developed by the U.S. Department of Agriculture (USDA) Agricultural Research Service (ARS) Pasture Systems and Watershed Management Research Unit to support intensive hydrologic and water quality research within WE-38, a 7.3 km2 experimental subwatershed of Mahantango Creek Watershed located in east central Pennsylvania and draining to the Susquehanna River. Daily streamflow discharge data were collected at the outflow of WE-38 from 1968 to 2007, producing a 40 year record of streamflow. Data are available on USDA ARS's Sustaining the Earth's Watersheds-Agricultural Research Data System (STEWARDS) Web site. [ABSTRACT FROM AUTHOR]

The accurate specification of observing and/or modeling error statistics presents a remaining challenge to the successful implementation of many land data assimilation systems. Recent work has developed adaptive filtering approaches that address this issue. However, such approaches possess a number of known weaknesses, including a required assumption of serially uncorrelated error in assimilated observations. Recent validation results for remotely sensed surface soil moisture retrievals call this assumption into question. Here we propose and test an alternative system for tuning a soil moisture data assimilation system, which is robust to the presence of autocorrelated observing error. The approach is based on the application of a triple collocation approach to estimate the error variance of remotely sensed surface soil moisture retrievals. Using this estimate, the variance of assumed modeling perturbations is tuned until normalized filtering innovations have a temporal variance of one. Real data results over three highly instrumented watershed sites in the United States demonstrate that this approach is superior to a classical tuning strategy based on removing the serial autocorrelation in Kalman filtering innovations and nearly as accurate as a calibrated Colored Kalman filter in which autocorrelated observing errors are treated optimally. [ABSTRACT FROM AUTHOR]

Forty-five water years (1962-2006) of carefully measured temperature, precipitation, snow, and streamflow data for valley bottom, midelevation, and high-elevation sites within the Reynolds Creek Experimental Watershed, located in the state of Idaho, United States, were analyzed to evaluate the extent and magnitude of the impact of climate warming on the hydrology and related resources in the interior northwestern United States. This analysis shows significant trends of increasing temperature at all elevations, with larger increases in daily minimum than daily maximum. The proportion of snow to rain has decreased at all elevations, with the largest and most significant decreases at midelevations and low elevations. Maximum seasonal snow water equivalent has decreased at all elevations, again with the most significant decreases at lower elevations, where the length of the snow season has decreased by nearly a month. All trends show a significant elevation gradient in either timing or magnitude. Though interannual variability is large, there has been no significant change in water year total precipitation or streamflow. Streamflow shows a seasonal shift, stronger at high elevations and delayed at lower elevations, to larger winter and early spring flows and reduced late spring and summer flows. [ABSTRACT FROM AUTHOR]