Challenges of modeling rainfall triggered landslides in a data-sparse region: A case study from the Western Ghats, India

Accurate rainfall estimates are required to forecast the spatio-temporal distribution of rain-triggered landslides. In this study, a comparison between rain gauge and satellite data for assessing landslide in data-sparse region, mountainous district Idukki, along Western Ghats southwestern India, is carried out. Global Precipitation Mission Integrated Multi-satellitE Retrievals GPM-Late (GPM IMERG-L) products were compared with measurements, it was found that observations underpredicting actual rainfall. A conditional merging algorithm applied develop product combines accuracy gauges spatial variability precipitation data. Correlation Coefficient (CC) Root Mean Squared Error (RMSE) used check performance process. An example from station least favorable statistics shows CC increasing 0.589 0.974 RMSE decreasing 65.22 20.01. case scenario considered evaluated prediction model by relying solely on sparse network. Rainfall thresholds computed both conditionally merged GPM IMERG-L differences indicated discrete, network would create false predictions. total 18.7% predictions only identified as true positives, while 60.7% overall false-negative rate, remaining false-positives. This pointed towards need having continuous accurate measurement efficient capturing