Changes in the frequency or intensity of rainfall due to climate always affect the conservation of soil resources, which leads to land degradation. The importance of assessing past and future climate differences plays an important role in future planning in relation to climate change. The spatiotemporal variability of erosivity depending on precipitation using the rainfall erosivity (R) of Universal Soil Loss Equation under the global circulation model (GCM) scenarios in the Chirchik–Akhangaran Basin (CHAB), which is in the northeastern part of the Republic of Uzbekistan, was statistically downscaled by using the delta method in Representative Concentration Pathways (RCPs) 4.5 and 8.5 during the periods of the 2030s, 2050s and 2070s. The (R) was used to determine the erosivity of precipitation, and the Revised Universal Soil Loss Equation (RUSLE) itself determined the effects of changes in erosivity. Ten weather station observational data points for the period from 1990 to 2016 were used to validate the global circulation models (GCMs) and erosion model. The assessment results showed an increase in precipitation from the baseline by an average of 11.8%, 14.1% and 16.3% for all models by 2030, 2050 and 2070, respectively, while at the same time, soil loss increased in parallel with precipitation by 17.1%, 20.5 % and 23.3%, respectively, in certain scenarios. The highest rainfall was observed for the models ACCESS1–3 and CanESM2 on both RCPs and periods, while more intense rainfall was the main reason for the increase in the spatial and temporal erosion activity of the rainfall-runoff. This study is a useful reference for improving soil conservation, preventing water erosion and ensuring the future sustainability of agricultural products, as well as improving the operational management and planning of agriculture.

The expansion of urban areas due to population increase and economic expansion creates demand and depletes natural resources, thereby causing land use changes in the main cities. This study focuses on land cover datasets to characterize impervious surface (urban area) expansion in select cities from 1993 to 2017, using supervised classification maximum likelihood techniques and by quantifying impervious surfaces. The results indicate an increasing trend in the impervious surface area by 35% in Bishkek, 75% in Osh, and 15% in Jalal-Abad. The overall accuracy (OA) for the image classification of two different datasets for the three cities was between 82% and 93%, and the kappa coefficients (KCs) were approximately 77% and 91%. The Landsat images with other supplementary data showed positive urban growth in all of the cities. The GDP, industrial growth, and urban population growth were driving factors of impervious surface sprawl in these cities from 1993 to 2017.Landscape Expansion Index (LEI) results also provided good evidence for the change of impervious surfaces during the study period. The results emphasize the idea of applying future planning and sustainable urban development procedures for sustainable use of natural resources and their management, which will increase life quality in urban areas and environments.

The main objective of this study is to investigate alternative climate data sources for long-term hydrological modeling. To accomplish this goal, one weather station data set (WSD) and three grid-based data sets including three types of precipitation data and two types of temperature data were selected according to their spatial and temporal details. An accuracy assessment of the grid-based data sets was performed using WSD. Then, the performances of corrected data combination and non-corrected grid-based precipitation and temperature data combinations from multiple sources on simulating river flow in the upstream portion of the Amu Darya River Basin (ADRB) were analyzed using a Soil and Water Assessment Tool (SWAT) model. The results of the accuracy assessments indicated that all the grid-based data sets underestimated precipitation. The Asian Precipitation Highly Resolved Observational Data Integration Towards the Evaluation of Water Resources (APHRODITE) precipitation data provided the highest accuracy (correlation coefficients (CF) > 0.89, root mean square error (RMSE) < 41.6 mm), followed by the CRUNCEP reanalysis data (a combination of the CRU TS.3.2 data and the National Centers for Environmental Prediction (NCEP) reanalysis data) (CF > 0.5, RMSE < 58.1 mm) and Princeton’s Global Meteorological Forcing Dataset (PGMFD) precipitation data (CF > 0.46, RMSE < 62.8 mm). The PGMFD temperature data exhibited a higher accuracy (CF > 0.98, RMSE < 7.1 °C) than the CRUNCEP temperature data (CF > 0.97, RMSE < 4.9 °C). In terms of the simulation performance, the corrected APHRODITE precipitation and PGMFD temperature data provided the best performance. The CF and Nash-Sutcliffe (NSE) coefficients in the calibration and validation periods were 0.96 and 0.92 and 0.93 and 0.83, respectively. In addition, the combinations of PGMFD temperature data and APHRODITE, PGMFD and CRUNCEP precipitation data produced good results, with NSE ≥ 0.70 and CF ≥ 0.89. The combination of CRUNCEP temperature data and APHRODITE precipitation produced a satisfactory result, with NSE = 0.58 and CF = 0.82. The combinations of CRUNCEP temperature data and PGMFD and CRUNCEP precipitation data produced poor results.