Abstract. In steep wildfire-burned terrains, intense rainfall can produce large runoff that can trigger highly destructive debris flows. However, the ability to accurately characterize and forecast debris flow susceptibility in burned terrains using physics-based tools remains limited. Here, we augment the Weather Research and Forecasting Hydrological modeling system (WRF-Hydro) to simulate both overland and channelized flows and assess postfire debris flow susceptibility over a regional domain. We perform hindcast simulations using high-resolution weather-radar-derived precipitation and reanalysis data to drive non-burned baseline and burn scar sensitivity experiments. Our simulations focus on January 2021 when an atmospheric river triggered numerous debris flows within a wildfire burn scar in Big Sur – one of which destroyed California's famous Highway 1. Compared to the baseline, our burn scar simulation yields dramatic increases in total and peak discharge and shorter lags between rainfall onset and peak discharge, consistent with streamflow observations at nearby US Geological Survey (USGS) streamflow gage sites. For the 404 catchments located in the simulated burn scar area, median catchment-area-normalized peak discharge increases by ∼ 450 % compared to the baseline. Catchments with anomalously high catchment-area-normalized peak discharge correspond well with post-event field-based and remotely sensed debris flow observations. We suggest that our regional postfire debris flow susceptibility analysis demonstrates WRF-Hydro as a compelling new physics-based tool whose utility could be further extended via coupling to sediment erosion and transport models and/or ensemble-based operational weather forecasts. Given the high-fidelity performance of our augmented version of WRF-Hydro, as well as its potential usage in probabilistic hazard forecasts, we argue for its continued development and application in postfire hydrologic and natural hazard assessments.

Accurate soil moisture and streamflow data are an aspirational need of many hydrologically-relevant fields. Model simulated soil moisture and streamflow hold promise but numerical models require calibration prior to application to ensure sufficient model performance. Manual or automated calibration methods require iterative model runs and hence are computationally expensive. In this study, we leverage the Soil Survey Geographic (SSURGO) database and the probability mapping of SSURGO (POLARIS) to help constrain soil parameter uncertainties in the Weather Research and Forecasting Hydrological modeling system (WRF-Hydro) over a central California domain. After calibration, WRF-Hydro soil moisture exhibits increased correlation coefficients (r), reduced biases, and increased Kling-Gupta Efficiencies (KGEs) across seven in-situ soil moisture observing stations. Compared to four well-established soil moisture datasets including Soil Moisture Active Passive Level 4 data and three Phase 2 North American Land Data Assimilation System land surface models, our POLARIS-calibrated WRF-Hydro produces the highest mean KGE (0.67) across the seven stations. More importantly, WRF-Hydro streamflow fidelity also increases especially in the case where the model domain is set up with an SSURGO-informed total soil thickness. Both the magnitude and timing of peak flow events are better captured, r increases across nine United States Geological Survey stream gages, and the mean Nash-Sutcliffe Efficiency across seven of the nine gages increases from 0.19 in default WRF-Hydro to 0.63 after calibration. Our soil data-informed calibration approach, which is transferable to other spatially-distributed hydrological models, uses open-access data and non-iterative steps to improve model performance and is thus operationally and computationally attractive.

Accurate soil moisture and streamflow data are an aspirational need of many hydrologically‐relevant fields. Model simulated soil moisture and streamflow hold promise but models require validation prior to application. Calibration methods are commonly used to improve model fidelity but misrepresentation of the true dynamics remains a challenge. In this study, we leverage soil parameter estimates from the Soil Survey Geographic (SSURGO) database and the probability mapping of SSURGO (POLARIS) to improve the representation of hydrologic processes in the Weather Research and Forecasting Hydrological modeling system (WRF‐Hydro) over a central California domain. Our results show WRF‐Hydro soil moisture exhibits increased correlation coefficients (r), reduced biases, and increased Kling‐Gupta Efficiencies (KGEs) across seven in‐situ soil moisture observing stations after updating the model’s soil parameters according to POLARIS. Compared to four well‐established soil moisture datasets including Soil Moisture Active Passive data and three Phase 2 North American Land Data Assimilation System land surface models, our POLARIS‐adjusted WRF‐Hydro simulations produce the highest mean KGE (0.69) across the seven stations. More importantly, WRF‐Hydro streamflow fidelity also increases, especially in the case where the model domain is set up with SSURGO‐informed total soil thickness. The magnitude and timing of peak flow events are better captured, r increases across nine United States Geological Survey stream gages, and the mean KGE across seven of the nine gages increases from 0.12 to 0.66. Our pre‐calibration parameter estimate approach, which is transferable to other spatially‐distributed hydrological models, can substantially improve a model’s performance, helping reduce calibration efforts and computational costs.