Hydrologic simulation approach for El Nino Southern Oscillation (ENSO)-affected watershed with limited raingauge stations

摘要

Since the performance of hydrological models relies on numerous factors, the selection of an appropriate modeling approach for hydrological study has always been a crucial issue. The major objective of this research is to demonstrate that data-driven models such as the Adaptive Neuro-Fuzzy Inference system (ANFIS) are more suitable in a region where spatially distributed precipitation datasets are not available. Since precipitation has a teleconnection with the El Nino Southern Oscillation (ENSO) in different parts of the world, the sea surface temperatures (SSTs) and sea level pressures (SLPs) of the equatorial Pacific can be expected to act as surrogates for the precipitation if there are insufficient raingauge stations in the watershed. Moreover, in contrast to conceptual and physically-based models, data driven models can incorporate SST and SLP in their input vectors, and hence additional forcing of SST with precipitation has been experimented with in past studies. Therefore, our second objective is to test whether the additional forcing of SST and SLP will improve the hydrologic simulation. For this, various ANFIS models for the winter season were developed considering 10 raingauge stations situated at various locations in the watershed. Rainfall from each raingauge station was considered in the ANFIS model one at a time with and without SST/SLP. The results show that the performance of the ANFIS model improved with the additional fusion of SST and SLP, especially when a raingauge station from a remote location was considered. However, this improvement was observed when the analysis was primarily focused on the winter season which is a period with a strong ENSO signal.