Including spatial distribution in a data-driven rainfall-runoff model to improve reservoir inflow forecasting in Taiwan
Journal
Hydrological Processes
Journal Volume
28
Journal Issue
3
Pages
1055-1070
Date Issued
2014
Author(s)
Abstract
Multi-step ahead inflow forecasting has a critical role to play in reservoir operation and management in Taiwan during typhoons as statutory legislation requires a minimum of 3-h warning to be issued before any reservoir releases are made. However, the complex spatial and temporal heterogeneity of typhoon rainfall, coupled with a remote and mountainous physiographic context, makes the development of real-time rainfall-runoff models that can accurately predict reservoir inflow several hours ahead of time challenging. Consequently, there is an urgent, operational requirement for models that can enhance reservoir inflow prediction at forecast horizons of more than 3h. In this paper, we develop a novel semi-distributed, data-driven, rainfall-runoff model for the Shihmen catchment, north Taiwan. A suite of Adaptive Network-based Fuzzy Inference System solutions is created using various combinations of autoregressive, spatially lumped radar and point-based rain gauge predictors. Different levels of spatially aggregated radar-derived rainfall data are used to generate 4, 8 and 12 sub-catchment input drivers. In general, the semi-distributed radar rainfall models outperform their less complex counterparts in predictions of reservoir inflow at lead times greater than 3h. Performance is found to be optimal when spatial aggregation is restricted to four sub-catchments, with up to 30% improvements in the performance over lumped and point-based models being evident at 5-h lead times. The potential benefits of applying semi-distributed, data-driven models in reservoir inflow modelling specifically, and hydrological modelling more generally, are thus demonstrated. © 2012 John Wiley & Sons, Ltd.
SDGs
Type
journal article