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Extreme flow conditions in river discharge have far-reaching environmental and economic consequences. The retention of surface water in lakes, wetlands, and floodplains can potentially moderate these extreme flows by modifying the timing, duration, and magnitude of flow generation. However, efforts to characterize the impact of surface water storage on river discharge have been limited in geographic extent. In this analysis, a suite of hydrologic signatures, quantifying components of watershed flow regimes, was calculated from daily discharge at 72 gaged watersheds across the conterminous United States. Random forest models were developed to explain variability in six hydrologic signatures related to flashiness and high and low flow conditions. In addition to traditionally considered variables such as climate, land cover, topography, and geology, a novel remote sensing (Sentinel-1 & 2) approach was used to study the contribution of surface water storage dynamics to each signature's variability. While climate variables explained much of the variability in the hydrologic signatures, models for five of the six signatures showed an improvement in explanatory power when landscape characteristics were added. Automated variable selection is part of the modeling process and can be indicative of the relative importance of certain variables over others. When all variables were considered, four of the six signature models selected remotely sensed inundation variables. The amount of semi-permanent and permanent floodplain inundation, for example, was both negatively correlated with, and showed the greatest variable importance for wet season flashiness. Further, increases in seasonal floodplain inundation were positively correlated with increases in peak flows. This suggests that the storage of surface water on floodplains is relevant to both flashiness and high flow signatures. In addition, spatial variability in the amount of semi-permanent and permanent non-floodplain water helped explain variability in the baseflow index. These findings suggest that watershed surface water storage dynamics explain a portion of streamflow signature variability. The results underscore the need for protection and restoration of surface water storage systems, such as wetlands, across watersheds. [ABSTRACT FROM AUTHOR]

Climate change is projected to impact river, lake, and wetland hydrology, with global implications for the condition and productivity of aquatic ecosystems. We integrated Sentinel‐1 and Sentinel‐2 based algorithms to track monthly surface water extent (2017–2021) for 32 sites across the central United States (U.S.). Median surface water extent was highly variable across sites, ranging from 3.9% to 45.1% of a site. To account for landscape‐based differences (e.g., water storage capacity, land use) in the response of surface water extents to meteorological conditions, individual statistical models were developed for each site. Future changes to climate were defined as the difference between 2006–2025 and 2061–2080 using MACA‐CMIP5 (MACAv2‐METDATA) Global Circulation Models. Time series of climate change adjusted surface water extents were projected. Annually, 19 of the 32 sites under RCP4.5 and 22 of the 32 sites under RCP8.5 were projected to show an average decline in surface water extent, with drying most consistent across the southeast central, southwest central, and midwest central U.S. Projected declines under surface water dry conditions at these sites suggest greater impacts of drought events are likely in the future. Projected changes were seasonally variable, with the greatest decline in surface water extent expected in summer and fall seasons. In contrast, many north central sites showed a projected increase in surface water in most seasons, relative to the 2017–2021 period, likely attributable to projected increases in winter and spring precipitation exceeding increases in projected temperature. Plain Language Summary: Climate change is expected to impact rivers, lakes, and wetlands. In this effort we used multiple satellites to track monthly surface water extent (2017–2021) for 32 sites across the central United States. The average amount of surface water was highly variable across sites. Individual statistical models, relating meteorological variables to surface water extent, were developed for each site. The models were then updated with climate change adjusted variables. Most sites were projected to show a decline in surface water extent, with drying most consistent across the southeast central, southwest central, and midwest central U.S. Projected declines under dry conditions at these sites suggest greater impacts of drought events are likely. Projected changes were seasonally variable, with the greatest decline in surface water extent expected in summer and fall seasons. In contrast, many north central sites showed a projected increase in surface water in most seasons, likely attributable to projected increases in winter and spring precipitation. Key Points: Surface water across the central U.S. responds to episodic, seasonal, and interannual variability in water availabilityMost sites showed projected declines in surface water extents under RCP4.5 and 8.5, peaking in summer‐fall and increasing drought impactsSelect sites, concentrated in the north central U.S., projected increases in surface water, associated with greater precipitation [ABSTRACT FROM AUTHOR]

Wildfires and housing development have increased since the 1990s, presenting unique challenges for wildfire management. However, it is unclear how the relative influences of housing growth and changing wildfire occurrence have altered risk to homes, or the potential for wildfire to threaten homes. We used a random forests model to predict burn probability in relation to weather variables at 1‐km resolution and monthly intervals from 1990 through 2019 in the Southern Rocky Mountains ecoregion. We quantified risk by combining the predicted burn probabilities with decadal housing density. We then compared the predicted burn probabilities and risk across the study area with observed values and quantified trends. Finally, we evaluated how housing growth and changes in burn probability influenced risk individually and combined. Fires burned 9055 km2 and exposed more than 8500 homes from 1990 to 2019. Observed burned area increased 632% from the 1990s to the 2000s, which combined with housing growth, resulted in a 1342% increase in homes exposed. Increases continued in the 2010s but at lower rates; burned area by 65% and exposure by 32%. The random forests model had excellent fit and high correlation with observations (AUC = 0.88 and r = 0.9). Observed values were within the 95% uncertainty interval for all years except 2016 (burned area) and 2000 (exposure). However, our model overpredicted in years with low observed burned area and underpredicted in years with high observed burned area. Overpredictions in risk resulted in lower rates of change in predicted risk compared with change in observed exposure. Increases in risk between the 1990s and 2000s were primarily due to warmer and drier weather conditions and secondarily because of housing growth. However, increases between the 2000s and 2010s were primarily due to housing growth. Our modeling approach identifies spatial and temporal patterns of wildfire potential and risk, which is critical information to guide decision‐making. Because the drivers behind risk shift over time, strategies to mitigate risk may need to account for multiple drivers simultaneously. [ABSTRACT FROM AUTHOR]