Data-based mechanistic rainfall-runoff modelling for a large monsoon dominated catchment in Thailand

Abstract

Hydrological modelling for water resource and flood management in large monsoon-dominated sub-tropicalcatchments has not been the subject of extensive research and it is not clear what the appropriate model structuresand data requirements may be. High degrees of seasonality, limited data availability, rapidly changinghydrological regimes as a result of land use change and climate variability and a lack of complete understandingof the details of the physical hydrology in these regimes and regions all contribute to this situation. This paperuses Data Based Mechanistic (DBM) modelling methods to explore the hydrology of the 3,853 km2 Mae Chaemcatchment in northern Thailand, where there is an unusually rich database of runoff and rainfall data. This is usedto examine the appropriate model structure and parameter values in DBM models and the effects of using theavailable rainfall and runoff data in a range of different ways. Rainfall data are area-weighted using Theissenpolygons, within which altitude adjustment is effected on the basis of evidence for an increase of about 0.5 mm ofrain per rainday for each 100 m increase in elevation above 1000 m, in the monsoon season only. The modelstructure suggests a second-order model and the parameter values seem to be rather stable when higher qualityrainfall data are used. Furthermore, it is possible to maintain reliable flow simulations by cascading a series ofrunoff prediction regression models that predict a downstream flow from an upstream flow and the incrementalrainfall between gauging stations. Key Words: Data-based mechanistic modelling; Monsoon; Rainfall-runoff modelling; Semi-distributed rainfalland runoff data; Thailand