Monitoring vegetation drought in the nine major river basins of China based on a new developed Vegetation Drought Condition Index

The effect of global climate change on vegetation growth is variable.Timely and effective monitoring of vegetation drought is crucial for understanding its dynamics and mitigation,and even regional protection of ecological environments.In this study,we constructed a new drought index(i.e.,Vegetation Drought Condition Index(VDCI))based on precipitation,potential evapotranspiration,soil moisture and Normalized Difference Vegetation Index(NDVI)data,to monitor vegetation drought in the nine major river basins(including the Songhua River and Liaohe River Basin,Haihe River Basin,Yellow River Basin,Huaihe River Basin,Yangtze River Basin,Southeast River Basin,Pearl River Basin,Southwest River Basin and Continental River Basin)in China at 1-month–12-month(T1–T12)time scales.We used the Pearson's correlation coefficients to assess the relationships between the drought indices(the developed VDCI and traditional drought indices including the Standardized Precipitation Evapotranspiration Index(SPEI),Standardized Soil Moisture Index(SSMI)and Self-calibrating Palmer Drought Severity Index(scPDSI))and the NDVI at T1–T12 time scales,and to estimate and compare the lag times of vegetation response to drought among different drought indices.The results showed that precipitation and potential evapotranspiration have positive and major influences on vegetation in the nine major river basins at T1–T6 time scales.Soil moisture shows a lower degree of negative influence on vegetation in different river basins at multiple time scales.Potential evapotranspiration shows a higher degree of positive influence on vegetation,and it acts as the primary influencing factor with higher area proportion at multiple time scales in different river basins.The VDCI has a stronger relationship with the NDVI in the Songhua River and Liaohe River Basin,Haihe River Basin,Yellow River Basin,Huaihe River Basin and Yangtze River Basin at T1–T4 time scales.In general,the VDCI is more sensitive(with shorter lag time of vegetation response to drought)than the traditional drought indices(SPEI,scPDSI and SSMI)in monitoring vegetation drought,and thus it could be applied to monitor short-term vegetation drought.The VDCI developed in the study can reveal the law of unclear mechanisms between vegetation and climate,and can be applied in other fields of vegetation drought monitoring with complex mechanisms.

Detecting Agro-Droughts in Southwest of China Using MODIS Satellite Data

The normalized difference vegetation index （NDVI） has proven to be typically employed to assess terrestrial vegetation conditions. However, one limitation of NDVI for drought monitoring is the apparent time lag between rainfall deficit and NDVI response. To better understand this relationship, time series NDVI （2000-2010） during the growing season in Sichuan Province and Chongqing City were analyzed. The vegetation condition index （VCI） was used to construct a new drought index, time-integrated vegetation condition index （TIVCI）, and was then compared with meteorological drought indices-standardized precipitation index （SPI）, a multiple-time scale meteorological-drought index based on precipitation, to examine the sensitivity on droughts. Our research findings indicate the followings： （1） farmland NDVI sensitivity to precipitation in study area has a time lag of 16-24 d, and it maximally responds to the temperature with a lag of about 16 d. （2） We applied the approach to Sichuan Province and Chongqing City for extreme drought monitoring in 2006 and 2003, and the results show that the monitoring results from TIVCI are closer to the published China agricultural statistical data than VCI. Compared to VCI, the best results from TIVCI3 were found with the relative errors of -4.5 and 6.36% in 2006 for drought affected area and drought disaster area respectively, and 5.11 and -5.95% in 2003. （3） Compared to VCI, TIVCI has better correlation with the SPI, which indicates the lag and cumulative effects of precipitation on vegetation. Our finding proved that TIVCI is an effective indicator of drought detection when the time lag effects between NDVI and climate factors are taken into consideration.

Research on the Summer Drought in Chongqing Using MODIS-EVI Time Series Data

MODIS-EVI time series data from 2000 to 2009 in Chongqing were selected for this study.By the use of best index slope extraction （BISE） method for cloud elimination,analysis on the change vector of EVI time series data were conducted to investigate the EVI response on drought; then,four typical regions were selected to study the relationship between precipitation,temperature and EVI when the sever drought occurred in 2006; finally,based on the time series of vegetation condition index （VCI） and precipitation abnormity percentage,the temporal and spatial distributions of drought were studied.The results showed that,the EVI value of the summer in 2006 was significantly lower than the average EVI at the corresponding period of the other years in Chongqing.In addition,summer drought occurred mainly during the hot and dry weather.Except the southeast area,most of the other regions in Chongqing were all under severe drought.

Spatiotemporal Drought Assessment over Sahelian Countries from 1985 to 2015

Due to infrequent rainfall, high temperatures, and degraded land, the Sahel region often suffers from droughts. The Sahel region is considered as one of the world’s driest and extreme environmental conditions. In order to assess spatiotemporal vulnerability of potential drought impacts, we used remote sensing and ground station data to evaluate drought conditions in the Sahel region from 1985 to 2015. The standard precipitation index(SPI), standard precipitation evapotranspiration index(SPEI), vegetation condition index(VCI) anomaly, along with socioeconomic indicators were performed. In addition, Pearson correlation coefficient(PCC) was computed between drought indices and three main crops(sorghum, millet, and maize) in the region to estimate the effects. The analysis showed that temperature increased by 0.78°C from 1985 to 2015, which had a significant impact on crop yield for sorghum, maize, and millet with a statistical significance value of P > 0.05. In the decade spanning 1994 to 2005 alone, the temperature increased by 0.57°C, which resulted in extreme drought in Algeria, Sudan, Chad, Nigeria, and Mauritania. For the effect of drought on crop production, high significance was noted on the SPI and SPEI-3 timescale: sorghum with SPI-3(r = 0.71) and SPEI-3(r = 0.65), millet with SPI-3(r = 0.61) and SPEI-3(r = 0.72), and maize with SPI-3(r =0.81) and SPEI-3(r = 0.65) during the study period. In the growing season, VCI anomaly had strong correlations with sorghum and millet(r = 0.67 and 0.75, respectively). A significant agreement was also noticed between the combined drought index(CDI) and vulnerability index(VI) in Burkina Faso(r =-0.676;P < 0.00), Mali(r =-0.768;P < 0.00), Mauritania(r = 0.843;P < 0.001), Niger(r =-0.625;P < 0.001), and Nigeria(r =-0.75;P < 0.005). The results show that the above indices are effective in assessing agricultural drought and its impact on crop production in the Sahel, and in identifying areas most affected by drought.