Flash-floods are recurrent natural hazards producing substantial impacts to the human communities. The increase in the frequency and magnitude of flash-floods is associated with the last decades of climate change at a global scale as well as changes affecting the Land Use/Land cover (LULC) characteristics. We employ Machine Learning technniques (Random Forest (RF), Multi layer Perceptron based Artificial Neural Network (MLP-ANN) and Geographical Information System (GIS) framework to deduce the spatial patterns of relationship between the flash-flood potential changes across the study area and the changes in the modified LULC during 2000-2019. High resolution Digital Elevation Model (DEM) and daily precipitation from AgERA5 will be utilized to detect the spatial variability of flash floods through the modified Founier Index. Identifying the probable changes in specific roughness coefficient (through the modified LULC), a geospatial risk based (flash flood potential of high, moderate and low categories) indices will be developed to assess future hydrological hazards.

Spatio-temporal variability of the terrestrial hydrological processes (land heat and water storage anomalies) has important implications in the climate predictability through their effects on surface energy and water fluxes. The changes in seasonal precipitation patterns associated with the Indian Summer Monsoon can alter the hydrological processes; for a given catchment, which in turn can influence the exchange of water and energy at the land surface-atmosphere interface. Hence the reliable prediction of the basin-scale water cycle components in a physically based high-resolution hydrological model equipped with sophisticated Land Surface Models (LSMs) is of prime requirement. The modern LSMs can provide detailed representations of important biophysical, biogeochemical and hydrological processes of varying spatial and temporal scales by incorporating the necessary feedbacks between the land and the atmosphere. When coupled to a physically based fully distributed hydrological model, it can affect the soil moisture patterns means of recycling the surface and sub-surface runoff (lateral terrestrial flow). However, despite the role of lateral terrestrial hydrological processes for the improved simulation of soil moistures, the sensitivity studies involving the land surface and sub-surface feedbacks are less pronounced especially for a tropical humid region with complex physiographic settings (presence of complex topography) under monsoon regimes (strong synoptic forcings). Therefore, in the present study, we examined a process based diagnosis regarding the role of the lateral flow on the terrestrial hydrological processes (Evapotranspiration, surface and sub-surface runoff, stream flow) and surface energy fluxes (latent heat, sensible heat) by using a multi-configured modeling framework of offline WRF-Hydro with Noah-Multi parameterizations (MP) LSM to enable systematic evaluation of the multiple physical parameterizations of hydrologic process representation; the validation has been done with the reanalysis dataset, a remotely sensed product and ground based observations.