Overview
Flooding in the Kathmandu Valley has intensified due to rapid urbanization, river encroachment, inadequate drainage infrastructure, and climate-driven rainfall extremes. This research evaluates present and future flood hazards using a Rain-on-Grid (RoG) based 2D flood modeling framework in HEC-RAS 2D, combined with CMIP6 climate projections.
Research Details
- Topic: Flood hazard assessment under climate change
- Study Area: Kathmandu Valley (Upper Bagmati River Basin)
- Modeling: HEC-RAS 2D (Rain-on-Grid)
- Climate Scenarios: CMIP6 • SSP245 & SSP585
- Bias Correction: Quantile Mapping (precipitation)
- Events: 50-year & 100-year rainfall-based flood simulations
Areas of Work
My research activities covered three main areas:
1) Study Area & Dataset Preparation
The study focuses on the Kathmandu Valley in central Nepal within the Upper Bagmati River Basin. Major rivers and tributaries include Bagmati, Bishnumati, Dhobikhola, Manohara, Hanumante, Balkhu, and Nakhu.
- Terrain: ALOS/AW3D-based DEM preparation for 2D flow domain
- LULC: Land cover data for surface roughness representation
- Soils: Hydrologic soil groups for infiltration modeling
2) RoG-based HEC-RAS 2D Flood Modeling
A Rain-on-Grid (RoG) approach was used to represent spatially distributed rainfall over the 2D domain. The workflow includes model setup, parameterization, simulation of design rainfall events, and post-processing of flood hazard outputs.
- Approach: Direct rainfall input over 2D domain
- Outputs: Flood depth, velocity, and inundation extent
3) Climate Scenario Analysis & Validation
Climate-driven rainfall inputs were derived from CMIP6 projections under SSP245 and SSP585. Model simulations were evaluated using observed discharge data from the Khokana hydrological station to assess model realism and response consistency.
Key Findings
The final outputs include flood inundation extents, depth and velocity hazard layers, and scenario-based comparisons for baseline, SSP245, and SSP585 conditions under 50-year and 100-year rainfall events. These results highlight flood-prone hotspots and support risk-informed urban and water management planning.