Due to rising temperatures, rainfall patterns around the world are being affected, causing extreme precipitation events to become more frequent and intense, resulting in an increased probability of severe flash floods. Thailand is no exception to the increased risk of these hazards, which is why Early Warning Systems are being set up. Since flash floods occur within a few hours after the triggering precipitation event, timely and accurate precipitation observations are critical, to enable timely warning and evacuation of inhabitants to mitigate the risk. One solution for this is the use of rain radar, which provides rain data with high spatial and temporal resolutions.

An analysis of this technique was chosen to form the basis of this research in Northeastern Thailand's Lam Takhlong basin. The objective was to investigate the importance of using distributed precipitation data. In this research, the hydrological response of the catchment of interest was studied. The ability of the physically-based, conceptual and distributed CALEROS model to capture this response was assessed. Different modelling strategies and sources of precipitation input were analysed, and the additional value of using distributed precipitation data was determined. The examination of multiple hydrological characteristics in the study area shows great heterogeneity of the catchments response to precipitation. A clear differentiation can be made between the hydrological response of the upstream and downstream part of the catchment. Additionally, the results from the catchment characterisation indicate great spatial variability of the precipitation patterns in the study area. This is confirmed when using the CALEROS model to recreate the catchments hydrology.

Four modelling strategies were used, varying by spatial and temporal constancy. Calibration performed using uniform precipitation showed to be incapable of capturing the discharge trends in the catchment, failing to properly catch the observed peaks as well as the base flow. Runs performed using distributed precipitation maps obtained by Inverse Distance interpolation of rain gauge data showed significantly better results, adequately capturing the trend of the discharge observed in the catchment. Differences in parameterisation only had limited effect on the outcome, making the precipitation input the most important parameter. Runs using synthetic precipitation data demonstrated the importance of properly capturing the precipitation pattern and movement across the catchment, as it greatly influences the timing of occurring discharge peaks.

A comparison between rain gauge data and rain radar data shows that, although data quality of the rain gauges seems acceptable, rain gauges are not capable of properly capturing rainfall patterns, while rain radar does. Precipitation patterns were found to be the most crucial parameter for modelling of flash floods in the area of interest, signifying the importance of using rain radar data for accurate flash flood forecasting.