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Drought is a natural, global and recurring phenomenon caused by climatic anomalies and inevitable meteorological changes. Lake Urmia in northwestern Iran has been drying up in recent years mainly because of its management and operations. On the other hand, with decreasing rainfall, the process of the lake drying up has intensified and affected different aspects of people's lives, especially farmers. In this paper, regional drought was studied in the Urmia Lake Basin from an agricultural perspective in the period of 2001 to 2021 using different vegetation indices such as the Enhanced Vegetation Index (EVI), the Vegetation Condition Index (VCI), the Temperature Condition Index (TCI) and the Temperature Vegetation Dryness Index (TVDI) as well as by applying the temperature product and vegetation cover product of the MODIS sensor of the Terra satellite. The efficiency of the methods was evaluated. The results of the linear correlation between drought indices and cumulative precipitation values during 1 month, 2 months, 3 months, 5 months and 7 months before the acquisition of the image showed that the correlation of indices, especially of the TVDINDVI index with the cumulative rainfall of 4 months, showed that the precipitation is more frequent than with other time scales due to the delayed response of vegetation to rainfall. From 2001 to 2020, it was observed that the severity of annual drought decreased according to TVDI values. The area with heavy drought decreased from 4.45 to 1.8% and medium drought decreased from 43.49 to 33%. Also, the correlation coefficient between other vegetation indices and temperature vegetation drought indices (TVDINDVI) showed that TCI and TVDI values were negatively (r = −0.73) correlated. Generally, by observing the calculated TVDI maps, it was seen that this basin has not encountered heavy drought so far. The results showed that the Urmia Lake Basin experienced long-term light and medium drought conditions. On the other hand, by calculating the NDWI index, changes in the area of Lake Urmia were monitored and it was found that the lake surface started to decrease beginning in 2005 and reached its lowest level in 2015, which corresponds to the other droughts that occurred. Finally, by conducting a trend analysis of TVDI, it was observed that there is no specific trend in the drought time series so it can be seen that drought may be one of the causes of a sharp decline in the lake's water level, but not the only factor. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) governs the exchange of energy and water between the atmosphere and land surface. In situ measurements of SM are uneven in Iran. This knowledge gap can be filled using satellite- and model-based products. This study assessed the performance of SM products, including Soil Moisture Active Passive (SMAP), Advanced Microwave Scanning Radiometer (AMSR2), and Global Land Data Assimilation System (GLDAS) Catchment Land Surface Model (CLSM) against in situ observations considering the influence of soil texture, climate, and land cover over Lake Urmia Basin, which is the largest salt lake in Iran and the Middle East. In situ SM was measured over Lake Urmia Basin in the morning and afternoon using the time domain reflectometry (TDR) and oven drying and weighing techniques. Five statistical indicators, including correlation (R), absolute correlation (R(abs)), bias, root mean square error (RMSE), and unbiased root mean square error (ubRMSE), were applied. C-band AMSR2 products showed the best performance in grassland and croplands with the highest absolute correlation (0.63) and lowest average bias (−0.01). Among soil textures, SM products performed better in clay soils with the highest absolute correlation between C-band AMSR2 products and in situ observations (0.64) and low average bias and RMSE. Analyzing data based on climate, AMSR2 C1, and GLDAS products with the lowest average RMSE (0.08 m3m−3) and bias (0.01) and AMSR2 C2 with the absolute correlation of 0.6 showed the best performance in both temperate (Csa) and cold (Dsa) climate classes. For all classifications (land cover, soil texture, climate divisions), SMAP products reported the lowest average value of ubRMSE (0.03 m3m−3). The major contribution of the paper is finding the best SM products that can fill the gap in SM measurements data in Lake Urmia. In this analysis, the impacts of land cover, climate, and soil texture on the performance of products were considered. [ABSTRACT FROM AUTHOR]

It is necessary to evaluate the impacts of climate change and human activities on vegetation dynamics. The purpose of this study is to assess the linkage of vegetation cover and climate fluctuations and distinguish the contribution of climate fluctuations and human activities on vegetation and its vulnerability in Namak Lake basin, Iran. For this purpose, changes in the enhanced vegetation index (EVI) in association with standardized precipitation evapotranspiration index (SPEI) were analyzed. The roles of climate fluctuations and human activities on vegetation vulnerability were then assessed in various land use land cover (LULC) classes using the residual analysis and probability of vegetation vulnerability index (PVVI). The EVI and land surface temperature (LST) maps for the period 2001–2019 were obtained from MOD13A2 and MOD11A2 products of MODIS, respectively. The results indicated that vegetation cover was mainly dependent on short-term climatic changes and their correlation decreased with increasing time scale of SPEI. It reflected that short-term water availability was vital for vegetation growth. Also, the sparse vegetation cover was mainly more vulnerable to climate fluctuations. Residual analysis showed that the vegetation dynamics was intensively attributed to the climate fluctuations, so that climate fluctuations affected vegetation cover in 78.94% of the basin, while 15.58% was affected by human activities and 5.48% was affected by both factors. The value of PVVI in the regions affected by climatic change was the highest (55.99); in the regions affected by human activities, it was lower (50.40); and in the regions affected by both factors, it was between the other two numbers (50.94). Therefore, climate fluctuations and human activities were both driving factors affecting vegetation covers over time in Namak Lake basin. [ABSTRACT FROM AUTHOR]

Previous studies depicted that the global gridded hydroclimatic products mostly lack precision and are inconsistent throughout the real-world water cycle. The current study evaluates the efficiency of the eight streamflow datasets (FLDAS, GLDAS 2.1, GLDAS 2.0, ERA5, TERRA, ERA5_Land, MERRA2, and GRUN) in two large-scale watersheds with different climate conditions (Karkhe River basin in Iran and Rio Itapecuru River basin in Brazil). To correct the products via different statistical metrics, two tuning procedures (including scale factors and the network-based Muskingum method) have been used. Based on the results showing the paramount impacts on the correction procedures of the products, GRUN, ERA_land, and GLDAS 2 perform the best accuracy in the Karkhe River basin; however, the worst product in the watershed is GLDAS 2.1. In the Rio Itapecuru River basin, MERRA2 and TERRA have the best and worst performance, respectively. In the first watershed, the KGE and TaylorS in GLDAS 2.1 improved by 0.85 and 0.23 in the case of correction using scale factor, and these statistics also significantly increased using coupled scale factor and Muskingum routing methods by 0.84 and 0.21, respectively. In the second watershed, these statistics increased by 9.16 and 0.56 in the worst case using scale factors; these metrics also levelled up by 9.32 and 0.62 via coupled scale factor and Muskingum routing methods. In addition, it appeared that the corrected products could better simulate the streamflow in terms of oscillation time series and the extreme temporal values of the watersheds. [ABSTRACT FROM AUTHOR]

Landuse and climate change are the two main dynamics significantly affecting watershed hydrology. Adequate knowledge about how these changes will alter the hydrologic regime provides valuable information for future water resources planning and management. The current study attempts to analyze the joint consequences of these dynamics on the future hydrological response of the Gorganroud watershed in northern Iran. For landuse, the integrated Markov Chain analysis and Multi-Layer Perceptron Neural Network (MC-MLPNN) algorithm were used to obtain landuse for 2030 and 2050. For climate, we developed future scenarios based on the downscaled data from MPI-ESM-MR for 2021–2040 and 2041–2060. Soil and Water Assessment Tool (SWAT) was used to simulate watershed hydrology. We found that (1) from 1986 to 2050, agriculture and rangeland are likely to expand by 18.6% and 10.7% of the total watershed area, respectively, at the expense of forest covers. (2) Temperature and precipitation are expected to increase by 1.3 °C and 2.5 °C and by 31.7% and 27.1% for Representative Concentration Pathway 8.5 (RCP8.5) during 2021–2040 and 2041–2060, respectively, compared to the baseline of 1976–1995. (3) We found that the integrated landuse and climate change will likely increase annual evapotranspiration during 2021–2040 and 2041–2060 by 113.9% and 11.4%, lateral flow by 14.6% and 7%, baseflow by 166.7% and 77.2%, surface runoff by 54.2% and 41%, water yield by 50.5% and 35.9%, and streamflow by 48.6% and 32.1% for RCP8.5 compared to the baseline of 1976–1995. [ABSTRACT FROM AUTHOR]