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Post-fire flooding and debris flows are often triggered by increased overland flow resulting from wildfire impacts on soil infiltration capacity and surface roughness. Increasing wildfire activity and intensification of precipitation with climate change make improving understanding of post-fire overland flow a particularly pertinent task. Hydrologic signatures, which are metrics that summarize the hydrologic regime of watersheds using rainfall and runoff time series, can be calculated for large samples of watersheds relatively easily to understand post-fire hydrologic processes. We demonstrate that signatures designed specifically for overland flow reflect changes to overland flow processes with wildfire that align with previous case studies on burned watersheds. For example, signatures suggest increases in infiltration-excess overland flow and decrease in saturation-excess overland flow in the first and second years after wildfire in the majority of watersheds examined. We show that climate, watershed and wildfire attributes can predict either post-fire signatures of overland flow or changes in signature values with wildfire using machine learning. Normalized difference vegetation index (NDVI), air temperature, amount of developed/undeveloped land, soil thickness and clay content were the most used predictors by well-performing machine learning models. Signatures of overland flow provide a streamlined approach for characterizing and understanding post-fire overland flow, which is beneficial for watershed managers who must rapidly assess and mitigate the risk of post-fire hydrologic hazards after wildfire occurs. 

Abstract Freshwater salinization of rivers is occurring across the globe because of nonpoint source loading of salts from anthropogenic activities such as agriculture, urbanization, and resource extraction that accelerate weathering and release salts. Multidecadal trends in river salinity are well characterized, yet our understanding of annual regimes of salinity in rivers draining diverse central and western U.S. landscapes and their associated catchment attributes is limited. We classified annual salinity regimes in 242 stream locations through dynamic time warping and fuzzy c‐medoids clustering of salinity time series. We found two dominant regimes in salinity characterized by an annualsummer–fall peakorspring decline. Using random forest regression, we found that precipitation amount, stream slope, and soil salinity were the most important predictors of salinity regime classification. Advancing our understanding of salinity regimes in rivers will improve our ability to predict and mitigate the effects of salinization in freshwater ecosystems through management interventions.