摘要
This study evaluates the high-resolution satellite estimated long-term precipitation data for monitor-ing the drought condition over the Lake Victoria Basin(LVB)from 1984 to 2020.Standardized Precipitation Indices(SPI)were used to capture the short,medium and long-term meteorological drought conditions at multiple time scales(i.e.3,6,and 12).For these,the following two primaries Quantitative Precipitation Estimation(QPEs)products were employed-1)Climate Hazards group Infra-Red Precipitation with Station(CHIRPS),and 2)the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network-Climate Data Record(PERSIANN-CDR).This dataset was compared based on the observation data obtained from the Climate Research Unit(CRU)over the nine selected regions surrounding lake basins.The performance of these two QPEs products was evaluated using seven statistical metrics.The findings of this study indicate that the CHIRPS and PERSIANN-CDR datasets could capture the behavior of drought magnitude based on the time scale of SPI-3,SPI-6,SPI-12.The results indicate that 2012 and 2017 are significant severe drought years in the recent decade over LVB.However,the CHIRPS datasets provide good agreement(Correlation Coefficient(CC)=0.65)with observation,whereas PERSIANN-CDR present satisfactory results(CC=0.54).In addition,Hurst(H)exponent was used to predict the future drought trend and found that the CHIRPS performed well to predict the degree of drought trend.Therefore,this study considers the CHIRPS product for near-real-time drought monitoring and PERSIANN-CDR for historical drought assessment.Moreover,the outcome from the H values is greater than 0.5,which indicates the future drought trend would be decreased over LVB.These results are useful for developing the strategies for drought hazards and water resource management in LVB.
基金
Integrated management for sustainable utilization of water resources in East Africa great Lakes basin and the project commissioned by National Key R&D program of China[grant number 2018YFE0105900].
