The Study of Optimal Spatial-temporal Information in Rainfall-runoff Modelling
Date Issued
2016
Date
2016
Author(s)
Tsai, Meng-Jung
Abstract
Accurate forecasting of extreme rainfall event and its corresponding flow is still a challenging issue for most of hydrologists. Due to unique topographic feature and weather pattern in Taiwan, this issue is even more critical. Thus, there is an urgent need to develop an accurate forecasting of rainfall and discharge. The major aims of this study are two-folds. First, the study is to compare various rainfall products, such as rain-gauge measurement, radar rainfall, and rainfall estimation from satellite imagery, and evaluate the ability of merging different combiuations of rainfall products to improve rainfall forecasting using an artificial neural network model. Secondly, different approaches for spatio-temporal lumping of radar rainfall are proposed here to evaluate the rainfall-runoff relationship using a data driven model for inflow forecasting in Shihmen reservoir. In this study, ground measurements and a radar rainfall dataset (QPESUMS) provided by Center Weather Beural (CWB) and a satellite-based rainfall dataset (PERSIANN-CCS) are collected. A BP model was developed to calibrate the estimation errors of the QPESUMS and PERSIANN-CCS, respectively. After calibration, a Genetic Algorithm (GA) is applied to merge these three rainfall datasets. The merged rainfall is further used as the input of an ANFIS model for rainfall forecasting at 1 and 2 hours horizons. The results showed that the BP effectively reduces the estimation error of QPESUMS dataset while only limit improvement can be made for PERSIANN-CCS. The reason for this may due to the PERSIANN-CSS was developed for Continental-scale climate modeling and may not be able applied directly to an island-scale climte pattern in Taiwan. After merged by GA, the merged rainfall has very high correlation with actural rainfall and has best performance for rainfall forecasting. With a better understanding of these rainfall products, the next focus of this study is to evealute inflow forecasting using proper rainfall dataset. Flood forecasting is an extremely crucial non-structural approach for real-time reservoir operation in Taiwan due to its unique topographical features and heterogeneous typhoon patterns. As a result of steep slope and short rivers in Taiwan, a flash flood occurs typically within few hours and reservoirs could easily and quickly be filled up with mass inflow in a typhoon event. Such conditions make real-time reservoir operation very challenging and reveal an urgent need for efficient and accurate multi-step-ahead inflow forecasting models. This study utilizes different rainfall datasets, such as rain-gauges and QPESUMS, and inflow data to evaluate the rainfall-runoff relationship. The spatial-lumping of QPESUMS is based on terrain analysis using DEM data and aggregrates the catchment into 1, 4, 8, and 12 sub-catchments. Six input strategies (S1, S2, S3(n), n= 1,4,8,12) were designed for a ANFIS model to forecast inflow of Shihmen reservoir at 1 to 5 hours horizons. From correlation analysis, it reveals that the time of concentration is about 5-7 hours in the catchment. For one hour forecasting, there is no significant difference between 6 strategies; while for 3 hours horizon, the improvement of using radar dataset is quite clear than using gauge-based rainfall. The spatial lumping to 4 sub-catchments has optimal performance in long-term, longer than 3 hours, inflow forecasting. These results suggest that using point-based ground measurements fails to catch spatial information in the catchment and leads to poor results of inflow forecasting; while simple spatial aggregration, 4 sub-catchments, of radar rainfall is more suitable for ANFIS model than complex spatial aggregration, 12 sub-catchments. The above inflow forecasting results may be further improved by using a non-linear spatio-temporal lumping approach. Here, the spatial lumping method based on terrain analysis using only DEM is replaced by a non-linear clustering method, Self-Orgnizied Map (SOM) using DEM and radar rainfall of typhoon events. The linear correlation analysis is replaced by a 2-staged Gamm test approach which is proposed in this study and is an efficient method to select non-trival input combination for ANFIS forecasting model. This novel spatio-temporal lumping method is termed as SOM+2-staged GT. There are several advantages in flow forecasting. First, it has best forecasting results for 3 and 4 hours horizons with correlation coefficient (CC) as high as 0.94 and coefficient of efficience (CE) as 0.88. Secondly, for one hour ahead forecasting, there is no time-lag between estimated and observed inflow peak; while for 3 and 4 hours horizons the time-lags are typically less than 2 hours. This is a remarked improvement in an inflow forecasting model based on ANFIS. From the perspective of end-users (or decision makers), this study suggested a confidence level of inflow forecasting using a pre-determined threshold of forecasting error. The confidence level of forecasts is presented by the percentage of forecast errors that fall within the designed error threshold.
Subjects
Artificial Neural Networks
rainfall forecasting model
inflow forecasting model
data merging
2-staged Gamma test
realiability analysis
SDGs
Type
thesis
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