Data based mechanistic model for low flows: Implications for the effects of climate change

SummaryAnalysis of climate change scenarios indicates an increased probability of high rainfalls and related flooding. Climate change may also lead to an increase in rainfall variability and the increased probability of otherwise extreme events. This forces researchers to focus on both high and low flow models. In addition, changes in soil management and an increase in water demand due to growing industrial and domestic requirements are adding to water scarcity. Sustainable water management is not possible without a better understanding of water balance in the catchment and better modelling tools for low flows. Moreover, application to climate change requires the development of a simulation model. This paper aims to present a Data Based Mechanistic approach to flow modelling, with special emphasis on low flows. The model applies a Stochastic Transfer Function approach to log-transformed flow. In this respect, the new model has certain interesting similarities with TOPMODEL which assumes an exponential relationship between soil moisture storage and flow. The paper explores these similarities, gives a physical explanation of the model structure and estimates model predictive uncertainty. A physical interpretation of the model allows the ranges of its applicability to be stated. The approach is tested on two catchments in the UK.