TRCN100, High-Resolution Gridded Curve Numbers of Türkiye to Use in Hydrological Models

Accurate estimation of runoff is essential for hydrological modeling, flood prediction, and water resource management. However, existing curve number (CN) data sets have limitations due to their relatively coarse resolution and reliance on generalized global data sets. To address these limitations, this study presents the first high-resolution TRCN100 (Türkiye Curve Number at 100 m resolution) data set, which includes detailed soil maps and land use data for Türkiye. The soil conservation service (SCS) method, developed by the United States Department of Agriculture (USDA), is widely applied to establish the rainfall-runoff relationship, with one of its key inputs being the CN. The CN can be calculated from land use/cover and soil data, and one study has already provided globally gridded CN data sets with an approximate 250-m resolution (GCN250). In this study, for the first time, both hydrologic soil group (HSG) and CN values at 100 m resolution (TRCN100) were generated for Türkiye by using detailed soil maps based on field measurements along with the Coordination of Information on the Environment (CORINE) land use/cover map under average antecedent runoff conditions (ARCII). TRCN100, derived from detailed field-based soil data, is digitally produced and available for use throughout the country, unlike previous local data sets. A comparative analysis of GCN250 and TRCN100 data sets was conducted across 25 river basins in Türkiye, revealing average differences of approximately +6% and -7% in cases with positive and negative differences, respectively. Also, runoff simulations produced using TRCN100 and GCN250 were compared with the global land data assimilation system (GLDAS), showing that TRCN100 performed better, with 21 out of 25 basins achieving higher Nash-Sutcliffe efficiency (NSE) scores. The results suggest that TRCN100, derived from higher resolution ground-based data, offers significant improvements for use in hydrological model studies for Türkiye. The development of this data set covering the whole country is crucial as it fills a significant gap in hydrological studies. The improved resolution and accuracy make it a valuable resource for flood risk assessment, watershed management, and hydrological modeling studies. Additionally, an interface was developed to retrieve TRCN100 values for the user-defined areas in Türkiye.