Spatiotemporal evolution of curve number (CN) and its variations in the Ichu River Basin (2017-2023) - Ivan Arturo Ayala Bizarro (PANGAEA, 2025)
This dataset presents the spatiotemporal evolution of the Curve Number (CN) in the experimental Ichu River basin, located in the central Peruvian Andes, from 2017 to 2023. CN maps were generated by integrating satellite data (Sentinel-2) and soil models (SoilGrid), applying the GPCN10 methodology for automated CN value assignment. Additionally, annual comparative analyses highlight variations in the basin extquotesingles hydrological response, linked to both land-use changes and climate variability. The dataset includes graphical comparisons of CN differences between consecutive years (e.g., 2017 vs. 2018), revealing areas with significant changes in soil infiltration capacity. Furthermore, change class maps identify regions undergoing land degradation (due to urbanization or deforestation) and areas showing improvements in water retention, associated with conservation practices such as reforestation. These data provide a foundation for assessing the impact of land-use changes on surface runoff and contribute to sustainable water resource management strategies in the region. This dataset contains the spatiotemporal evolution of Curve Number (CN) in the Ichu River Basin (2017–2023), including: - Annual CN distribution maps at 10-meter resolution. - Direct pixel-by-pixel CN differences between consecutive years. - Change class maps identifying areas with significant land-use variations. All data were derived using Sentinel-2 imagery, SoilGrid datasets, and the GPCN10 methodology. These datasets are intended to support hydrological modeling, land-use change assessments, and climate impact analysis in Andean watersheds. — References cited in this dataset are related to the sources and methodologies applied during data processing. Specifically: - Sentinel-2 10m Land Use/Land Cover Time Series (2022) provided land cover information used for CN classification. - SoilGrids250m (Hengl et al., 2017) supplied soil property data essential for determining CN values. - GCN250 dataset (Jaafar et al., 2019) served as a reference for hydrological curve number modeling. These references supported the estimation of spatiotemporal CN variations but do not represent direct input datasets within the processed files.