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...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.展开更多
Background Mara River Basin is an ecologically fragile area in East Africa,with a pattern of alternating wet and dry seasons shaped by periodic precipitation.Considering the regional biological traits and climatic cha...Background Mara River Basin is an ecologically fragile area in East Africa,with a pattern of alternating wet and dry seasons shaped by periodic precipitation.Considering the regional biological traits and climatic change,the vegetation’s response to seasonal variation is complicated and frequently characterized by time lags.This study analyzed the variation of the Normalized Difference Vegetation Index(NDVI)and investigated its time lag to precipitation at the monthly scale.NDVI characteristic peaks were proposed from the perspective of seasonal mechanisms and were quantified to assess the lag effect.Results The results showed that the Anomaly Vegetation Index could identify low precipitation in 2006,2009,and 2017.The NDVI showed an increasing trend in 75%of areas of the basin,while showed a decreased significance in 3.5%of areas,mainly in savannas.As to the time lag,the 1-month lag effect dominated most months,and the spatiotemporal disparities were noticeable.Another method considering the alternations of wet and dry seasons found that the time lag was approximately 30 days.Based on the time distribution of NDVI characteristic peaks,the average time lag was 35.5 days and increased with the range of seasons.Conclusions The findings confirmed an increasing trend of NDVI in most regions from 2001 to 2020,while the trends were most obvious in the downstream related to human activities.The results could reflect the time lag of NDVI response to precipitation,and the 1-month lag effect dominated in most months with spatial heterogeneity.Four NDVI characteristic peaks were found to be efficient indicators to assess the seasonal characteristics and had a great potential to quantify vegetation variation.展开更多
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].
文摘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supported by the National Key R&D Program of China[Grant Number 2018YFE0105900].
文摘Background Mara River Basin is an ecologically fragile area in East Africa,with a pattern of alternating wet and dry seasons shaped by periodic precipitation.Considering the regional biological traits and climatic change,the vegetation’s response to seasonal variation is complicated and frequently characterized by time lags.This study analyzed the variation of the Normalized Difference Vegetation Index(NDVI)and investigated its time lag to precipitation at the monthly scale.NDVI characteristic peaks were proposed from the perspective of seasonal mechanisms and were quantified to assess the lag effect.Results The results showed that the Anomaly Vegetation Index could identify low precipitation in 2006,2009,and 2017.The NDVI showed an increasing trend in 75%of areas of the basin,while showed a decreased significance in 3.5%of areas,mainly in savannas.As to the time lag,the 1-month lag effect dominated most months,and the spatiotemporal disparities were noticeable.Another method considering the alternations of wet and dry seasons found that the time lag was approximately 30 days.Based on the time distribution of NDVI characteristic peaks,the average time lag was 35.5 days and increased with the range of seasons.Conclusions The findings confirmed an increasing trend of NDVI in most regions from 2001 to 2020,while the trends were most obvious in the downstream related to human activities.The results could reflect the time lag of NDVI response to precipitation,and the 1-month lag effect dominated in most months with spatial heterogeneity.Four NDVI characteristic peaks were found to be efficient indicators to assess the seasonal characteristics and had a great potential to quantify vegetation variation.
