Establishing Depth Duration Frequency Curves and Critical Rainfall Thresholds Using Satellite Rainfall Estimates in Data-Scarce Regions: A Case Study of Jammu and Kashmir

Abstract

Climate change has significantly altered rainfall regimes, resulting in recurrent cycles of flooding and drought. Effective flood mitigation and agricultural management now require timely, real-time precipitation alerts. Historically, rainfall has been monitored using ground-based precipitation gauges; however, the spatial density of these stations is low, and their coverage is geographically limited. In recent years, satellite-derived rainfall estimates with enhanced accuracy have become widely available, providing comprehensive coverage across the entire globe. This open-source data offers a superior alternative to the sparsely distributed and often incomplete ground-based gauge records. In this study, rainfall observations from the Global Precipitation Measurement (GPM) Mission and the Tropical Rainfall Measurement Mission (TRMM) were utilized, spanning seventeen years. These datasets enable a more robust assessment of rainfall variability and improve the reliability of hydrological and agricultural analyses.Evaluation of satellite data shows the good results where  R² values greater than 0.774 for both GPM and TRMM data sets. The other statistical evaluations such as ME, MAE, RMSE, PBias, NSE and r were under acceptable range for both data sets The Generalized Extreme Value (GEV) distribution was used in this study to carry out the statistical analysis. This method helps capture the behaviour of extreme rainfall events more accurately than traditional techniques. Depth–Duration–Frequency (DDF) curves were then developed for selected districts of Kashmir across four time intervals 3 hours, 6 hours, 12 hours, and 24 hours. These curves provide valuable insights for planning, flood risk assessment, and infrastructure design.