Wang Xiaobo, Wang Shaoqiang, Cui Huijuan
Key Laboratory of Ecosystem Network Observation and Modeling, Chinese Academy of Sciences, Beijing 100101, China.
Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.
Entropy (Basel). 2019 Jul 25;21(8):722. doi: 10.3390/e21080722.
Reliable streamflow and flood-affected area forecasting is vital for flood control and risk assessment in the Brahmaputra River basin. Based on the satellite remote sensing from four observation sites and ground observation at the Bahadurabad station, the Burg entropy spectral analysis (BESA), the configurational entropy spectral analysis (CESA), maximum likelihood (MLE), ordinary least squares (OLS), and the Yule-Walker (YW) method were developed for the spectral analysis and flood-season streamflow forecasting in the basin. The results indicated that the BESA model had a great advantage in the streamflow forecasting compared with the CESA and other traditional methods. Taking 20% as the allowable error, the forecast passing rate of the BESA model trained by the remote sensing data can reach 93% in flood seasons during 2003-2017, which was significantly higher than that trained by observed streamflow series at the Bahadurabad station. Furthermore, the segmented flood-affected area function with the input of the streamflow forecasted by the BESA model was able to forecast the annual trend of the flood-affected area of rice and tea but needed further improvement in extreme rainfall years. This paper provides a better flood-season streamflow forecasting method for the Brahmaputra River basin, which has the potential to be coupled with hydrological process models to enhance the forecasting accuracy.
可靠的径流和洪水影响面积预测对于布拉马普特拉河流域的防洪和风险评估至关重要。基于四个观测站点的卫星遥感数据和巴哈杜拉巴德站的地面观测数据，开发了伯格熵谱分析（BESA）、构型熵谱分析（CESA）、最大似然法（MLE）、普通最小二乘法（OLS）和尤尔-沃克（YW）方法，用于该流域的谱分析和汛期径流预测。结果表明，与CESA和其他传统方法相比，BESA模型在径流预测方面具有很大优势。以20%作为允许误差，利用遥感数据训练的BESA模型在2003 - 2017年汛期的预测通过率可达93%，显著高于利用巴哈杜拉巴德站实测径流序列训练的模型。此外，以BESA模型预测的径流为输入的分段洪水影响面积函数能够预测水稻和茶叶受洪水影响面积的年变化趋势，但在极端降雨年份还需要进一步改进。本文为布拉马普特拉河流域提供了一种更好的汛期径流预测方法，该方法有可能与水文过程模型相结合以提高预测精度。
