Flood modellling approaches for large lowland tropical catchments.

Flooding is increasing in tropical regions, where millions of people are at risk, and challenges exist in providing reliable predictions and warnings. This research responds to this challenge by identifying and applying physics-based and data-based hydrological modelling approaches for large-scale flood modelling in lowland tropical regions. First, a distributed hydrological model was developed to accurately represent catchment conditions and processes in the model. Second, empirical data from nested catchments were analysed using statistical scaling relationships to complement the accuracy of peak discharge estimates. Finally, the effects of uncertainty propagation and interactions were quantified to increase the reliability of model results. The research was conducted in the Grijalva catchment area (57 958 km²) southeast of Mexico. A large-scale model with a 2 x 2 km grid cell resolution was developed using the SHETRAN hydrological model and run enforced with 3-hour input rainfall data. Geostatistical techniques were used to quantify and reduce errors in input data, and all diverted flows were accounted for to optimise simulations. For the first time, the application of the Scaling theory of floods was applied in the study area to improve the estimation of peak discharge. A Monte Carlo technique was used to propagate and quantify rainfall and parameter uncertainties through a coupled hydrologic and hydraulic model and into model results. Although the model under-predicted the magnitude of peak discharge, calibration results showed satisfactory model performance (NSCE = 0.72, CC = 0.74, Bias = –0.44% and RMSE 139.56 mm) and validation results were good (NSCE = 0.56, CC = 0.60, Bias = –6.3% and RMSE 62.59 mm). A statistical log-log relationship between intercepts (α) and peak discharge, from the smallest nested catchment, was used to complement the simulation of peak discharge magnitudes. It was observed that given rainfall uncertainties of ±71%, ranging from 63 to 73%; the model generates discharge with uncertainties of ± 46%, ranging from 45 to 49% and errors of ±46% ranging from 45 to 46%. The propagated uncertainties resulted in flood inundation extents of ±4.34 km² varying from 1.66 to 7.02 km² Thus, flood modelling in large tropical regions can be achieved by optimally integrating several datasets with the best combination of the model parameter, input and output datasets based on uncertainty and error quantification and removal approaches.