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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]

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]

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]

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]

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]