Identification of flood seasonality using an entropy-based method

Abstract

Identifying flood seasonality is critical in hydrologic applications as well as water resources management. We develop an entropy-based method (EBM) for identifying flood seasonality and partitioning the entire flood season into multiple sub-seasons. The performance of the proposed EBM is evaluated using a Monte Carlo simulation test and compared with current methods. The Three Gorges Reservoir (TGR) basin in the Yangtze River is selected as a case study to test the applicability of the proposed method. Results of Monte Carlo simulation test demonstrate that the EBM performs better than the probability change-point method and the improved relative frequency method with less bias and higher efficiency. The case study results illustrate that the EBM can appropriately divide the entire flood season of the TGR into pre-flood season (from June 1st to June 20th), main-flood season (from June 21th to September 10th) and post-flood season (from September 11th to September 30th). The flood limited water levels (FLWL) in these three sub-seasons can then be derived, which are 150 m, 145 m and 149 m, respectively. Compared with conventional operation rule, the seasonal FLWL scheme can generate more hydropower (0.93 billion KWh) annually with a reliability of 99.86%. Therefore, it is meaningful to divide the entire flood season into three sub-seasons and apply seasonal FLWL for TGR operation.