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.

Floods are the leading cause of natural disaster damages in the United States, with billions of dollars incurred every year in the form of government payouts, property damages, and agricultural losses. The Federal Emergency Management Agency oversees the delineation of floodplains to mitigate damages, but disparities exist between locations designated as high risk and where flood damages occur due to land use and climate changes and incomplete floodplain mapping. We harnessed publicly available geospatial datasets and random forest algorithms to analyze the spatial distribution and underlying drivers of flood damage probability (FDP) caused by excessive rainfall and overflowing water bodies across the conterminous United States. From this, we produced the first spatially complete map of FDP for the nation, along with spatially explicit standard errors for four selected cities. We trained models using the locations of historical reported flood damage events (n = 71 434) and a suite of geospatial predictors (e.g. flood severity, climate, socio-economic exposure, topographic variables, soil properties, and hydrologic characteristics). We developed independent models for each hydrologic unit code level 2 watershed and generated a FDP for each 100 m pixel. Our model classified damage or no damage with an average area under the curve accuracy of 0.75; however, model performance varied by environmental conditions, with certain land cover classes (e.g. forest) resulting in higher error rates than others (e.g. wetlands). Our results identified FDP hotspots across multiple spatial and regional scales, with high probabilities common in both inland and coastal regions. The highest flood damage probabilities tended to be in areas of low elevation, in close proximity to streams, with extreme precipitation, and with high urban road density. Given rapid environmental changes, our study demonstrates an efficient approach for updating FDP estimates across the nation.

Accurate assessment of global river flows and stores is critical for informing water management practices, but current estimates of global river flows exhibit substantial spread and estimates of river stores remain sparse. Estimates of river flows and stores are hampered by uncertainties in land runoff, an unobserved quantity that provides water input to rivers. Here we leverage global river flow observations and an ensemble of land surface models to generate a globally gauge-corrected monthly river flow and storage dataset. We estimate a global river storage mean (± monthly variability) of 2,246 ± 505 km³ and a global continental flow of 37,411 ± 7,816 km³ yr⁻¹. Our global river water storage time series demonstrates that flow wave residence time is a fundamental driver that can double or halve river water stores and their variability. We also reconcile the wide range in previous estimates of monthly variability in global river flows. We identify previously underappreciated freshwater sources to the ocean from the Maritime Continent (Indonesia, Malaysia and Papua New Guinea) amounting to 1.6 times the Congo River and illustrate our capability of detecting severe anthropogenic water withdrawals.