Soil salinity negatively affects plant growth and leads to soil degradation. Saline lands result in low agricultural productivity, affecting the well-being of farmers and the economic situation in the region. The prediction of soil salinization dynamics plays a crucial role in sustainable development of agricultural regions, in preserving the ecosystems, and in improving irrigation management practices. Accurate information through monitoring and evaluating the changes in soil salinity is essential for the development of strategies for agriculture productivity and efficient soil management. As part of an ex-ante analysis, we presented a comprehensive statistical framework for predicting soil salinity dynamics using the Homogeneity test and linear regression model. The framework was operationalized in the context of the Khorezm region of Uzbekistan, which suffers from high levels of soil salinity. The soil salinity trends and levels were projected under the impact of climate change from 2021 to 2050 and 2051 to 2100. The results show that the slightly saline soils would generally decrease (from 55.4% in 2050 to 52.4% by 2100 based on the homogeneity test; from 55.9% in 2050 to 54.5% by 2100 according to the linear regression model), but moderately saline soils would increase (from 31.2% in 2050 to 32.5% by 2100 based on the homogeneity test; from 31.2% in 2050 to 32.4% by 2100 according to the linear regression model). Moreover, highly saline soils would increase (from 13.4% in 2050 to 15.1% by 2100 based on the homogeneity test; from 12.9% in 2050 to 13.1% by 2100 according to the linear regression model). The results of this study provide an understanding that soil salinity depends on climate change and help the government to better plan future management strategies for the region.

Global climate change can have a significant impact on the development and sustainability of agricultural production. Climate scenarios indicate that an expected increase in air temperature in semiarid Uzbekistan can lead to an increase in evapotranspiration from agricultural fields, an increase in irrigation water requirements, and a deterioration in the ameliorative status of irrigated lands. The long-term mismanagement of irrigation practices and poor conditions of drainage infrastructure have led to an increase in the water table and its salinization level in the northwestern part of Uzbekistan. This article presents the results of an analysis of the amelioration of irrigated lands in the Khorezm region of Uzbekistan as well as the modeling of the dynamics of water table depths and salinity levels using the Mann–Kendall trend test and linear regression model. The study estimated the water table depths and salinity dynamics under the impact of climate change during 2020–2050 and 2050–2100. The results show that the water table depths in the region would generally decrease (from 1.72 m in 2050 to 1.77 m by 2100 based on the Mann–Kendall trend test; from 1.75 m in 2050 to 1.79 m by 2100 according to the linear regression model), but its salinity level would increase (from 1.72 g·L⁻¹ in 2050 to 1.85 g·L⁻¹ by 2100 based on the Mann–Kendall trend test; from 1.97 g·L⁻¹ in 2050 to 2.1 g·L⁻¹ by 2100 according to the linear regression model). The results of the study provide insights into the groundwater response to climate change and assist authorities in better planning management strategies for the region.

The relationship between the soil surface temperature and groundwater level is complex and influenced by various factors. As the soil surface temperature increases, water evaporates quickly from the soil, which can lead to a decrease in the groundwater level. In this study, we analyzed the impact of soil surface temperature on changes in the groundwater level in the Bukhara region of Uzbekistan using data from 1991 to 2020. The Bukhara region experiences regular water shortages, increased soil salinization, and inefficient energy in lift-irrigated areas, which is a typical constellation of challenges to the water–energy–food–environment (WEFE) nexus. The soil surface temperature data were collected from the Hydrometeorological Service Agency, whereas groundwater level data were obtained from the database of the Amelioration Expedition under the Amu-Bukhara Basin Irrigation Systems Authority. We used linear regression analysis and Analysis of Variance (ANOVA) tests to establish the significance of the relationship between the soil surface temperature and groundwater level, as well as the impact of the location of the groundwater level measurements. The results indicate that the model was a good fit to the data, and both the intercept and the soil surface temperature were significant factors that affected groundwater level. The results further suggest that the strength of the relationship between solar radiation and soil surface temperature is very high, with a correlation coefficient of 0.840. This means that when solar radiation increases, soil surface temperature also tends to increase. The analysis also showed that 53.5% of the changes in groundwater level were observed by the regression model, indicating a moderately correlated relationship between the groundwater level and soil surface temperature. Finally, higher solar radiation leads to higher soil surface temperature and higher evapotranspiration rates, which can lead to a decrease in groundwater level. As a result, we observe that the soil surface temperature determines changes in the groundwater level in the study region.