Abstract
The recent droughts that have occurred in different parts of Ethiopia are generally linked to fluctuations in atmospheric and ocean circulations. Understanding these large-scale phenomena that play a crucial role in vegetation productivity in Ethiopia is important. In view of this, several techniques and datasets were analyzed to study the spatio–temporal variability of vegetation in response to a changing climate. In this study, 18 years (2001–2018) of Moderate Resolution Imaging Spectroscopy (MODIS) Terra/Aqua, normalized difference vegetation index (NDVI), land surface temperature (LST), Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) daily precipitation, and the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) soil moisture datasets were processed. Pixel-based Mann–Kendall trend analysis and the Vegetation Condition Index (VCI) were used to assess the drought patterns during the cropping season. Results indicate that the central highlands and northwestern part of Ethiopia, which have land cover dominated by cropland, had experienced decreasing precipitation and NDVI trends. About 52.8% of the pixels showed a decreasing precipitation trend, of which the significant decreasing trends focused on the central and low land areas. Also, 41.67% of the pixels showed a decreasing NDVI trend, especially in major parts of the northwestern region of Ethiopia. Based on the trend test and VCI analysis, significant countrywide droughts occurred during the El Niño 2009 and 2015 years. Furthermore, the Pearson correlation coefficient analysis assures that the low NDVI was mainly attributed to the low precipitation and water availability in the soils. This study provides valuable information in identifying the locations with the potential concern of drought and planning for immediate action of relief measures. Furthermore, this paper presents the results of the first attempt to apply a recently developed index, the Normalized Difference Latent Heat Index (NDLI), to monitor drought conditions. The results show that the NDLI has a high correlation with NDVI (r = 0.96), precipitation (r = 0.81), soil moisture (r = 0.73), and LST (r = −0.67). NDLI successfully captures the historical droughts and shows a notable correlation with the climatic variables. The analysis shows that using the radiances of green, red, and short wave infrared (SWIR), a simplified crop monitoring model with satisfactory accuracy and easiness can be developed.
Highlights
In the era of climate change, there is a continuous need to thoroughly assess vulnerabilities caused by complex environmental, ecological, and anthropogenic factors
A multiple linear regression model with k predictor variables x1, x2, . . . , xk and a response can be written as: y = β0 + β1x1 + β2x2 + · · · + βkxk + ε where i = 1, 2, . . . , k, and ε is the residual terms of the model, which tries to minimize, y is the dependent variable in this case normalized difference vegetation index (NDVI), xi represents the independent variables, β0 is the intercept, and β1, β2, · · ·, βk are the coefficients of xi
The years 2001, 2005, 2006, 2007, 2013, 2016, and 2018 reflected near-normal NDVI throughout most of the rain-fed agriculture regions. These results are coherent with the findings of previous studies in indicating the onset, spatial, and temporal dynamics of agricultural drought in Ethiopia [18]
Summary
In the era of climate change, there is a continuous need to thoroughly assess vulnerabilities caused by complex environmental, ecological, and anthropogenic factors. CHIRPS is a 30+ year quasi-global rainfall dataset combining satellite observations from the Climate Prediction Center (CPC) and the National Climate Forecast System version 2 (CFSv2) and in situ precipitation observations [35,37] It is widely used in Ethiopia for drought monitoring [38]. The monthly soil moisture (0–10 cm) was generated from the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) dataset, developed to assist food security assessments in data-sparse developing countries [50] This is a natural tool to monitor drought conditions and was accessed from https://earlywarning.usgs.gov/fews/product/308. MEI time series from Jan. 2001 to Dec. 2018 Monthly DMI time series from Jan. 2001 to Dec. 2018
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