Abstract
Hydrological models are based on the relationship between rainfall and discharge, which means that a poor representation of rainfall produces a poor streamflow result. Typically, a poor representation of rainfall input is produced by a gauge network that is not able to capture the rainfall event. The main objective of this study is to evaluate the impact of the mean areal rainfall on a modular rainfall-runoff model. These types of models are based on the divide-and-conquer approach and two specialized hydrological models for high and low regimes were built and then combined to form a committee of model that takes the strengths of both specialized models. The results show that the committee of models produces a reasonable reproduction of the observed flow for high and low flow regimes. Furthermore, a sensitivity analysis reveals that Ilopango and Jerusalem rainfall gauges are the most beneficial for discharge calculation since they appear in most of the rainfall subset that produces low Root Mean Square Error (RMSE) values. Conversely, the Puente Viejo and Panchimalco rainfall gauges are the least beneficial for the rainfall-runoff model since these gauges appear in most of the rainfall subset that produces high RMSE value.
Highlights
Floods problems in El-Salvador have been present since a long time ago
The aim of this study is to evaluate the impact of the mean areal rainfall on a modular model with a soft combination of local models in a catchment in El-Salvador that can reproduce high and low flow regimes
Secia. iEmng.o2f02u0s,i8n, gx FsOpRecPiEaElRizReEdVoIErWlocal models instead of only one single model is to reprod1u0 coef 17 accurate high and low flow regimes
Summary
Floods problems in El-Salvador have been present since a long time ago. According to Gierloff-Emden [1], a hurricane in 1934 produced urban and river floods due to 500 mm of rainfall in three days. Hurricane Fifi in 1974 produced flood damages in the coastal area of El-Salvador and Hurricane Mitch caused USD 388.1 million worth of economic losses, which accounts for 3.26% of the Gross Domestic Product (GDP) of the country [2]. In 2015, important rivers and reservoir levels in the country reached a critical state due to the El Niño phenomenon. This type of extreme event develops slowly and affects larger areas than flood conditions
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