Expansion of arid lands due to climate change, particularly in water stressed regions of the world can have severe implications on the economy and people’s livelihoods. The spatiotemporal trends in aridity, the shift of land from lower to higher arid classes and the effect of this shift on different land uses in Syria have been evaluated in this study for the period 1951–2010 using high-resolution monthly climate data of the Terrestrial Hydrology Research Group of Princeton University. The trends in rainfall, temperature and potential evapotranspiration were also evaluated to understand the causes of aridity shifts. The results revealed an expansion of aridity in Syria during 1951–1980 compared to 1981–2010. About 6.21% of semi-arid land was observed to shift to arid class and 5.91% dry-subhumid land to semi-arid land between the two periods. Analysis of results revealed that the decrease in rainfall is the major cause of increasing aridity in Syria. About 28.3% of agriculture land located in the north and the northwest was found to shift from humid to dry-subhumid or dry-subhumid to semi-arid. Analysis of results revealed that the shifting of drylands mostly occurred in the northern agricultural areas of Syria. The land productivity and irrigation needs can be severely affected by increasing aridity which may affect food security and the economy of the country.

In this study, the uncertainty in runoff simulations using hydrological models was quantified based on the selection of five evaluation metrics and calibration data length. The calibration data length was considered to vary from 1 to 11 years, and runoff analysis was performed using a soil and water assessment tool (SWAT). SWAT parameter optimization was then performed using R-SWAT. The results show that the uncertainty was lower when using a calibration data length of five to seven years, with seven years achieving the lowest uncertainty. Runoff simulations using a calibration data length of more than seven years yielded higher uncertainty overall but lower uncertainty for extreme runoff simulations compared to parameters with less than five years of calibration data. Different uncertainty evaluation metrics show different levels of uncertainty, which means it is necessary to consider multiple evaluation metrics rather than relying on any one single metric. Among the evaluation metrics, the Nash–Sutcliffe model efficiency coefficient (NSE) and normalized root-mean-squared error (NRMSE) had large uncertainties at short calibration data lengths, whereas the Kling–Gupta efficiency (KGE) and Percent Bias (Pbias) had large uncertainties at long calibration data lengths.

Climate change has significantly increased human thermal stress, particularly in tropical regions, exacerbating associated risks and consequences, such as heat-related illnesses, decreased workability, and economic losses. Understanding the changes in human thermal stress and its drivers is crucial to identify adaptation measures. This study aims to assess various meteorological variables’ spatial and seasonal impact on Wet Bulb Globe Temperature (WBGT), an indicator of human thermal stress, in Peninsular Malaysia. The Liljegren method is used to estimate WBGT using ERA5 hourly data from 1959 to the present. The trends in WBGT and its influencing factors are evaluated using a modified Mann-Kendall test to determine the region’s primary driver of WBGT change. The results indicate that air temperature influences WBGT the most, accounting for nearly 60% of the variation. Solar radiation contributes between 20% and 30% in different seasons. Relative humidity, zenith, and wind speed have relatively lesser impacts, ranging from −5% to 20%. Air temperature has the highest influence in the northern areas (>60%) and the lowest in the coastal regions (40%). On the other hand, solar radiation has the highest influence in the southern areas (20–40%) and the least in the north. The study also reveals a significant annual increase in temperature across all seasons, ranging from 0.06 to 0.24 °C. This rapid temperature rise in the study area region has led to a substantial increase in WBGT. The higher increase in WBGT occurred in the coastal regions, particularly densely populated western coastal regions, indicating potential implications for public health. These findings provide valuable insights into the factors driving WBGT and emphasize the importance of considering air temperature as a key variable when assessing heat stress.

An approach is proposed in the present study to estimate the soil erosion in data-scarce Kokcha subbasin in Afghanistan. The Revised Universal Soil Loss Equation (RUSLE) model is used to estimate soil erosion. The satellite-based data are used to obtain the RUSLE factors. The results show that the slight (71.34%) and moderate (25.46%) erosion are dominated in the basin. In contrast, the high erosion (0.01%) is insignificant in the study area. The highest amount of erosion is observed in Rangeland (52.2%) followed by rainfed agriculture (15.1%) and barren land (9.8%) while a little or no erosion is found in areas with fruit trees, forest and shrubs, and irrigated agriculture land. The highest soil erosion was observed in summer (June–August) due to snow melting from high mountains. The spatial distribution of soil erosion revealed higher risk in foothills and degraded lands. It is expected that the methodology presented in this study for estimation of spatial and seasonal variability soil erosion in a remote mountainous river basin can be replicated in other similar regions for management of soil, agriculture, and water resources.

A study has been conducted for projection of monthly rainfall in Baghdad of Iraq using a General Circulation Models (GCM) of Coupled Model Intercomparison Project Phase 5 (CMIP5) under four representative concentration pathways (RCP) scenarios namely RCP2.6, RCP4.5, RCP6.0 and RCP8.5. For this purpose, monthly gridded precipitation datasets produced by the centre for climatic research, University of Delaware (UDel) and GCM BCC-CSM1-1 simulated precipitation data at 46 grid points surrounding Baghdad were used. The statistical downscaling models were developed using Support Vector Machine (SVM) and Random Forest (RF). The performance of downscaling model assessed using different statistical measures showed that SVM could simulate historical rainfall in the region very well. Projection of rainfall using SVM revealed that rainfall at Baghdad will change in the range of 3.5% to -6.2% in the end of this century.