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The Middle East (ME), as an arid and semi-arid region, is prone to environmental risks and stresses, such as drought are inseparable phenomena of the region. In this study, an approach for identifying sustained vegetation cover (SVC) is suggested to identify the connection between SVC and drought. Normalized difference vegetation index (NDVI) and land surface temperature (LST) were used to filter zones of rich vegetation cover from poorly vegetated or non-vegetated regions of the ME. The change detection of vegetation cover was computed by the NDVI differencing technique, and the vegetation condition index (VCI) and normalized vegetation supply water index (NVSWI) were used to derive drought indices. The standardized precipitation index (SPI) and rainfall anomaly index (RAI) were used to monitor the intensity of meteorological drought events. A comparison of the estimates of vegetation change, remote sensing-based VCI, and meteorological drought indices revealed that the highest SVC is concurrent with the occurrence of drought. Moreover, it was found that the most severe meteorological drought and VCL-based drought condition occurred in 2008 and that the highest percentage of SVC was also obtained for this year. The results suggest the possibility of using the SVC instead of other spectral indices, such as the NDVI, VCI, NVSWI, and NVSWI, for the superior assessment and detection of environmental stresses such as drought.

Climate change can alter soil moisture with subsequent effects on water resources and vegetation growth. This study aims to understand the interactions and quantify the impact of climate change on soil moisture and vegetation in the Urmia Lake basin, Iran. The ERA-5 precipitation and temperature, GLDAS soil moisture, and MODIS NDVI monthly time series were used for 2001–2018. The MK test and Pearson correlation revealed the seasonal and monthly precipitation, and NDVI displayed insignificant trends, but the positive trend of temperature was observed in the cold season. At a depth of 0–10 cm, the monthly soil moisture trend indicated the highest negative trends occurring during April–May, while the lowest negative trends were in winter between December and January. Also, time-lagged (0, 1, and 2 months) correlation analysis showed soil moisture and climatic parameters of each month with short time-lagged (0 and 1 month) mostly presented significant correlations, but mid-time-lagged (2-months) correlations were not found significant with precipitation. Results showed that temperature played a more critical role than precipitation in soil moisture distribution within the study area. Investigating the impact of climate change on soil moisture by ensembles of AOGCM models under the different RCPs showed that soil moisture is influenced by temperature increasing.

In regional studies, reanalysis datasets can extend precipitation time series with insufficient observations. In the present study, the ERA5 precipitation dataset was compared to observational datasets from meteorological stations in nine different precipitation zones of Iran (0.125° × 0.125° grid box) for the period 2000–2018, and measurement criteria and skill detection criteria were applied to analyze the datasets. The results of the daily analysis revealed that the correlation between ERA5 and observed precipitation were larger than 0.5 at 90% of stations. Also, The daily standard relative bias indicated that precipitation was overestimated in zone 6. As detection criteria, the frequency bias index (FBI) and proportion correct (PC) showed that the ERA5 data could capture daily precipitation events. Correlation confidence comparisons between the ERA5 and observational time series at daily, monthly, and seasonal scales revealed that the correlation confidence was higher at monthly and seasonal scales. The standard relative bias results at monthly and seasonal scales followed the daily relative bias results, and most of the ERA5 underestimations during the summer belonged to zone 1 in the coastal area of the Caspian Sea with convective precipitation. In addition, some complex mountainous regions were associated with overestimated precipitation, especially in northwest Iran (zone 6) in different time scales.