Abstract
Summary The complex temporal heterogeneity of rainfall coupled with mountainous physiographic context makes a great challenge in the development of accurate short-term rainfall forecasts. This study aims to explore the effectiveness of multiple rainfall sources (gauge measurement, and radar and satellite products) for assimilation-based multi-sensor precipitation estimates and make multi-step-ahead rainfall forecasts based on the assimilated precipitation. Bias correction procedures for both radar and satellite precipitation products were first built, and the radar and satellite precipitation products were generated through the Quantitative Precipitation Estimation and Segregation Using Multiple Sensors (QPESUMS) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS), respectively. Next, the synthesized assimilated precipitation was obtained by merging three precipitation sources (gauges, radars and satellites) according to their individual weighting factors optimized by nonlinear search methods. Finally, the multi-step-ahead rainfall forecasting was carried out by using the adaptive network-based fuzzy inference system (ANFIS). The Shihmen Reservoir watershed in northern Taiwan was the study area, where 641 hourly data sets of thirteen historical typhoon events were collected. Results revealed that the bias adjustments in QPESUMS and PERSIANN-CCS products did improve the accuracy of these precipitation products (in particular, 30–60% improvement rates for the QPESUMS, in terms of RMSE), and the adjusted PERSIANN-CCS and QPESUMS individually provided about 10% and 24% contribution accordingly to the assimilated precipitation. As far as rainfall forecasting is concerned, the results demonstrated that the ANFIS fed with the assimilated precipitation provided reliable and stable forecasts with the correlation coefficients higher than 0.85 and 0.72 for one- and two-hour-ahead rainfall forecasting, respectively. The obtained forecasting results are very valuable information for the flood warning in the study watershed during typhoon periods.
Highlights
Rainfall is a key hydrological variable that links the atmosphere and land surface processes
This study aims at providing reliable and accurate short-term typhoon rainfall forecasts using artificial intelligent (AI) techniques based on the assimilation of satellite- and radar-derived rainfall estimations and ground gauge measurements
Data Sources (G: Gauge; R: Radar; S: Satellite) still capture the main trend and the variation of observations but the effect of time-lag occurred. These results demonstrate that the thirteen rainfall gauging stations contributed the most to the assimilated precipitation while both QPESUMS- and PERSIANN-CCS-derived precipitation products made certain contributions to the assimilated precipitation, which shows the effectiveness of merging multiple precipitation sources in improving the accuracy and reliability of rainfall forecasting
Summary
Rainfall is a key hydrological variable that links the atmosphere and land surface processes. The complex temporal heterogeneity of typhoon rainfall coupled with mountainous physiographic context makes the development of accurate forecasting reservoir inflow several hours ahead of time a great challenge. Typhoons are commonly coupled with heavy rainfall. The highest rainfall record of Typhoon Morakot was over 1000 mm/day in southern Taiwan in 2009. The inundation disaster occurred in most of this area and caused more than.
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