Modelling for science, for a better future - some recent outcomes

Urban flooding affects millions of people in Indian major cities during past decades and is expected to be severe in near future under the warming climate scenario. Rapid urbanization and demography changes in the Bengaluru city in the past has led to significant hydrological and environmental stress contributing to the severity of urban flooding. The whole Bengaluru city (Urban as well as Rural) consists of four watershed valley and here, we have chosen Koramangala-Challaghatta (KC) valley as our study area which is known for its low-lying topography and frequent flooding events. In this study, we selected four Extreme Rainfall Events (EREs), occurred on 07 September 2015, 1 June 2016, 15 August 2017, and 24 September 2018, for simulation using the Storm Water Management Model (SWMM). The digital elevation model (DEM) is examined to understand the topography of high and low-lying areas to assess the flood impacts of EREs in various regions. Based on the frequency distribution, the 99th percentile of daily rainfall intensity (39.5 mm/day) is considered as the threshold for defining EREs in the study region, and long-term rainfall data (1998–2024) revealed a clear increasing trend in frequency of EREs. Results indicate that rainfall exceeding 30 mm/day leads to flooding in more than half of the area in KC Valley, with flash floods occurring when rainfall surpasses 55 mm/day. The most flood-prone locations are those near historical natural lakes, where urban encroachment has diminished natural flood detention capacity. Evaluation using multiple statistical metrics demonstrates reliable performance of SWMM in flood simulation, particularly for high-intensity rainfall events. The bias in simulated stormwater discharge ranges from + 4 to −4 m³/s, indicating useful skill of the model. The reliable methodological framework to tackle hydrometeorological challenges presented in this study can be handy tools for designing integrated flood management strategies for urban cities.

Quantifying the variability of hydro-meteorological variables and various land-surface processes in response to the diverse sources of uncertainties arising from the structure of hydrological models, model parameters, calibration procedures, and schemes related to the various physical process representations is a significant scientific endeavour with important implications in hydrology. Sensitivity analysis is the only plausible way to provide valuable insights into how different parameters influence model outputs and helps evaluate the rationality of each model parameter. Hence, the present study employs sensitivity analysis for comprehensive evaluation of the impact of an enhanced hydrological parameterized and newly developed WRF-Hydro model towards the simulation of hydro-meteorological variables, in terms of model parameter uncertainty and structural variability. The study is carried out in the vicinity of the Krishna River basin in India with a sub grid-scale of 200 m. We evaluate the model's fidelity to accurately simulate streamflow and key hydrometeorological variables, including Evapotranspiration, soil moisture, and land heat and moisture fluxes, through a series of sensitivity experiments. These experiments quantify the impact of uncertainties related to land surface model parameterization, thresholds of model parameters, and structural configurations of the WRF-Hydro framework. The characteristics of the simulations thus generated have been validated against in-situ gauge data and remote sensing-based multi-platform observations, revealing a significant improvement in the simulation accuracy, with correlation coefficients exceeding 0.9 and 0.7 for soil moisture and soil temperature, respectively. Our findings show marked enhancement in the model’s predictive accuracy. By identifying the configurations with superior performance, this study provides practical insight for selecting optimal model parameterizations, thereby contributing to improving the hydro-meteorological forecasting frameworks and supporting better water management practices in regions with limited data availability.