Datasets for characterizing extreme events relevant to hydrologic design over the conterminous United States

Despite the close linkage between extreme floods and snowmelt, particularly through rain-on-snow (ROS), hydrologic infrastructure is mostly designed based on standard precipitation Intensity-Duration-Frequency curves (PREC-IDF) that neglect snow processes in runoff generation. For snow-dominated regions, such simplification could result in substantial errors in estimating extreme events and infrastructure design risk. To address this long-standing problem, we applied the Next Generation IDF (NG-IDF) technique to estimate design basis extreme events for different durations and return periods in the conterminous United States (CONUS) to distinctly represent the contribution of rain, snowmelt, and ROS events to the amount of water reaching the land surface. A suite of datasets were developed to characterize the magnitude, trend, seasonality, and dominant mechanism of extreme events for over 200,000 locations. Infrastructure design risk associated with the use of PREC-IDF was estimated. Accuracy of the model simulations used in the analyses was confirmed by long-term snow data at over 200 Snowpack Telemetry stations. The presented spatially continuous datasets are readily usable and instrumental for supporting site-specific infrastructure design. Measurement(s) gridded precipitation Technology Type(s) weather station Sample Characteristic - Environment flood center dot snowmelt center dot hydrological process Sample Characteristic - Location contiguous United States of America

Automated summary

This study used the Next Generation IDF (NG-IDF) technique to estimate extreme events in the conterminous United States (CONUS). The study differentiated the contributions of rainfall, snowmelt, and rain-on-snow (ROS) events to water reaching the land surface. By developing datasets and validating the model simulations with long-term snow data, the study provided valuable support for site-specific infrastructure design.