The Himalayan foothills are highly prone to rainfall induced flash floods. This research focuses on the August 19-20, 2022 flash flood event in Song watershed of Doon valley, Uttarakhand caused significant damages to buildings and a road bridge. The study aims to assess the flood intensity through flood simulation in a semi-distributed hydrological model by utilizing rainfall data, land use and soil data. Further, the flood hydrographs generated through hydrological modelling were used to simulate hydrodynamic model to estimate flood depth. Pre and post-flood inundation assessments were conducted using PlanetScope and Sentinel-1 imagery. Furthermore, development activities on river courses were analyzed utilizing Google earth and Bing maps high resolution imagery. Cumulative rainfall observations revealed 344 mm rainfall in Rishikesh and 225 mm in Sahastradhara on 19-20 August for the 24 hrs, contributed in a peak flood discharge 2679 m(3)/s at the Rishikesh outlet. The simulated flood depth depicted 4.81 m flood depth at the damaged Thano-Bhogpur bridge. The PlanetScope satellite imagery showed 182 m expansion in the cross-sectional width of river at Maldevta after the flood. A 5.36 sq. km. flood area observed throughout the entire Song catchment in two days post event Sentinel-1 imagery. Analysis of high-resolution imageries revealed increasing development activities in floodplains of the catchment, which got affected by flood. The findings indicate urgent need of floodplain management by implementing comprehensive flood risk management plans including early warning systems, land-use regulations based on flood hazard zonation and flood resilient infrastructure to mitigate future flood exposure to society.

It is increasingly acknowledged that the acceleration of the global water cycle, largely driven by anthropogenic climate change, has a disproportionate impact on sub-daily and small-scale hydrological extreme events such as flash floods. These events occur thereby at local scales within minutes to hours, typically in response to high-intensity rainfall events associated with convective storms. In the present work, we show that by employing physically based representative hillslope models that resolve the main gradients controlling overland flow hydrology and hydraulics, we can get reliable simulations of flash flood response in small data-scarce catchments. To this end, we use climate reanalysis products and transfer soil parameters previously obtained for hydrological predictions in an experimental catchment in the same landscape. The inverted mass balance of flood reservoirs downstream is employed for model evaluation in these nearly ungauged basins. We show that our approach using representative hillslopes and climate data sets can provide reasonable uncalibrated estimates of the overland runoff response (flood magnitude, storm volume, and event runoff coefficients) in three of the four catchments considered. Given that flash floods typically occur at scales of a few km2 and in ungauged places, our results have implications for operational flash flood forecasting and open new avenues for using gradient resolving physically based models for the design of small and medium flood retention basins around the world. Flash floods have become increasingly common worldwide, with catastrophic damages to both human life and the economy. While the extent of global warming and climate change impacting these events is still under much debate, it is almost certain now that we need to be better equipped to understand and model these extremes to prevent and mitigate the possible risk to human life and infrastructure in a warming climate. To test, if we can use first principles derived from thermodynamic conservation laws and process based hydrological models for the same, we modeled flash flood response in four headwater catchments over Southern Germany using the concept of representative hillslope. Since the regions considered in our work are poorly gauged, we made use of global climate reanalysis products and parameter transfer from past experiments. The encouraging results obtained in predicting the flood magnitude and volume speak to the overall applicability of our approach. We are able to get decent uncalibrated predictions in three out of the four catchments considered with minimum computational effort. Understanding and managing the adverse impacts of such extreme hydroclimatic events remains one of the crucial hurdles facing humanity toward the sustainable development goals (SDG17) in this decade. Physically based representative hillslope models can be used for flash flood predictions in small data-scarce and rural catchments Climate reanalysis data enable the initialization of a process-based model, helping to reduce the uncertainties in estimating antecedent soil conditions Transfer of model parameters within the same hydrological landscape is feasible