Climate change and urbanization causes the increasing challenges of flooding in urban watersheds. Even the rivers identified as non-vulnerable are causing catastrophic damage due to heavy flooding. So, several satellite and radar-based precipitation data are considered to study the watersheds with no gauge station or need recent precipitation data. Weather Radar (NEXRAD)arch, the accuracy of satellite-based precipitation data, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks - Climate Data Record (PERSIANN-CDR), and radar-based precipitation data, Next Generation Weather Radar (NEXRAD), is evaluated in rainfall-runoff simulation considering Hydrological Engineering Centre-Hydrologic Modeling System (HEC-HMS) and Personal Computer Storm Water Management Model (PCSWMM), respectively.The primary research proposes a framework for modeling the rainfall-runoff process using PERSIANN-CDR and a floodplain map in an ungauged urban watershed. The one-dimensional Hydrologic Engineering Centre-River Analysis System (HEC-RAS) model generates a flood inundation map for the pertinent flooding occurrences from the acquired peak hydrograph, providing a quantifiable display of the inundation extent percentage. The second research uses the PCSWMMs to show the extent of flooding. It also employs the compromise programming method (CPM) to rank the most critical sub-catchments based on three parameters: slope, surface area, and impervious area. Three low-impact development (LID) strategies over the watershed determine the best flood management option. Therefore, the overall study presents a comprehensive framework for flood management in urban watersheds that integrates satellite precipitation data, hydrologic modeling, and LID strategies. The framework can provide an accurate flood-prone zone and help prioritize critical sub-catchments for flood management options. The study proposes using HEC-HMS and PCSWMM models to simulate and analyze interactions between rainfall, runoff, and the extent of the flood zone. Furthermore, LID can be applied to reduce flooding in urban watersheds. Overall, the framework can be helpful for policymakers and system managers to build the watershed's resilience during catastrophic flooding events caused by climate change and urbanization.

Availability of high-quality data is a major problem for climate and hydrological studies, especially in basins with complex topography where gauge network is typically limited and unevenly distributed. This study investigates the performance of 14 precipitation products – seven satellite-based (SPPs), two gauge-based (GPPs) and five reanalysis products (RPPs) – against ground observations (1998–2007) in the transboundary Jhelum River basin (33 397 km2). Among the seven SPPs (bias corrected), five demonstrate a significantly high correlation coefficient (CC > 0.7) with observed rainfall. However, most of the products tend to underestimate the seasonal precipitation amount, particularly in winter and spring. Likewise, Asian Precipitation – Highly Resolved Observational Data Integration Towards Evaluation of water resources APHRODITE (GPPs) and Japanese 55-year Reanalysis JRA-55 (RPPs) are the best-performing products in daily streamflow predictions, with Nash-Sutcliffe efficiency values of 0.68 and 0.62, whilst MSWEP (Multi-Source Weighted-Ensemble Precipitation), AgMERRA (Climate Forcing Dataset for Agricultural Modeling) and CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) have also good potential in flow prediction. Generally, our results indicate that APHRODITE and JRA-55 could be used as alternative sources of precipitation data in the Himalayas region.