Development of an NRCS curve number global dataset using the latest geospatial remote sensing data for worldwide hydrologic applications

Abstract

ABSTRACTThe United States Natural Resources Conservation Service (NRCS) Runoff Curve Number (CN) is the most widely used rainfall-runoff modeling method. The hydrologic soil group (HSG), land use/cover, hydrologic surface condition and antecedent moisture condition jointly determine the CN value. This study aims to update the previous global CN map using the recently available geospatial remote sensing data. Based on the conventional United States Department of Agriculture (USDA) and National Engineering Handbook Section 4 (NEH-4) standard lookup tables, the newly derived CN map utilizes the latest Moderate Resolution Imaging Spectroradiometer (MODIS) land cover and the Harmonized World Soil Database (HWSD) soil data to investigate the current flood potential of the global land surface. The results show that the new CN map outperforms the previous one in detecting China’s flood event locations with an improvement in the ratio of the averaged CN value for flood events to the averaged CN value for the whole land area of China. The new CN map reveals the effects of land surface change on hydrological conditions in the 2000s. Based on the Soil Moisture Active and Passive (SMAP) data and the gauge-combined Global Satellite Mapping of Precipitation (GSMaP) product, global surface runoff (April 2015-March 2016, 0.1°×0.1°) is estimated with the new CN map. The currently derived global CN dataset can be used in multi-scale hydrological applications, such as rainfall-runoff modeling and flood hazard risk assessment across continents.