Integration of the WRF Model With Fine-Scale Land Use Data to Simulate Extreme Rainfall Events

Urbanization results in drastic land alteration in which natural land cover is replaced by impermeable surfaces such as compacted soils, buildings and associated infrastructures. While the impact of urbanization on extreme rainfall is captured in satellite data to a great extent, its signal is frequently less obvious in station-level data. Also, the lack of local meteorological data hinders the development of adequate mitigation measures to reduce the impact of extreme rainfall scenarios. To regenerate the local meteorological data, numerical model-based simulations using global boundary conditions are required at finer Spatio-temporal scales. To this end, integrated land surface models which can provide the maximum likelihood of observed rainfall can be of great significance, especially in urban complexes. Weather Research Forecasting (WRF) model is one such numerical model that can lay down a framework to provide short-range weather forecasts by fixing site-specific physics-based parametrization schemes. This study demonstrates the application of the WRF model to provide building-scale weather forecasts based on the finer-scale Urban Canopy Model (UCM) and Local Climate Zonation (LCZ). The numerical modelling framework is set up for Bangalore city, India. Bangalore city is categorized as one of the major urban complexes with a total built-up area of 77.5%. The World Urban Database Access Portal Tool (WUDAPT), which is based on random forest classification of the ground truth training samples, is used to develop the LCZ database for the WRF model. A single-layer UCM is developed to indicate the importance of structural and aerial characteristics of static datasets with appropriate land features. WRF model runs are carried out based on global boundary conditions to provide a 24hr forecast with 3km and 1km spatial domain for the study area at an urban scale. The overall accuracy of 92% (for the built-up area) and 85% (for water bodies) is obtained for LCZs developed using the random forest classification in WUDAPT. In comparison to default configurations of WRF, the forecasts of WUDAPT-based LCZs have shown an improvement at both spatial and temporal scales. The bias (particularly the spatial shift) observed using the default WRF is reduced drastically, and the forecasts are well-matched with the observed Telemetric Rain Gauge (TRG) station rainfall datasets. Assessment of the maximum likelihood of extreme rainfall forecasts can provide a platform for the development of an integrated WRF hydrological configuration in the future. Such frameworks will be greatly beneficial for obtaining more accurate rainfall and flood forecasts.