Hydro–climatic variables play an essential role in assessing the long-term changes in streamflow in the snow-fed and glacier-fed rivers that are extremely vulnerable to climatic variations in the alpine mountainous regions. The trend and magnitudinal changes of hydro–climatic variables, such as temperature, precipitation, and streamflow, were determined by applying the non-parametric Mann–Kendall, modified Mann–Kendall, and Sen’s slope tests in the Kofarnihon River Basin in Central Asia. We also used Pettitt’s test to analyze the changes during the 1951–2012 and 1979–2012 time periods. This study revealed that the variations of climate variables have their significant spatial patterns and are strongly regulated by the altitude. From mountainous regions down to plain regions, the decadal temperature trends varied from −0.18 to 0.36 °C/decade and the variation of precipitation from −4.76 to −14.63 mm yr⁻¹ per decade. Considering the temporal variation, the temperature trends decreased in winter and significantly increased in spring, and the precipitation trends significantly decreased in spring but significantly increased in winter in the high-altitude areas. As consequence, total streamflow in headwater regions shows the obvious increase and clear seasonal variations. The mean monthly streamflow decreased in fall and winter and significantly increased in the spring and summer seasons which can be attributed to the influence of global warming on the rapid melting of snow and ice. Although the abrupt change points in air temperature and precipitation occurred around the 1970s and 1990s in the low-altitude areas and 2000s in the high-altitude areas during the 1951–2012 and 1979–2012 periods, the general trends of hydro–climatic variables keep consistent. This study benefits water resource management, socio–economic development, and sustainable agricultural planning in Tajikistan and its downstream countries.

Quantifying the relative contribution of climate change and anthropogenic activities to runoff alterations are essential for the sustainable management of water resources in Central Asian countries. In the Kofarnihon River Basin (KRB) in Central Asia, both changing climate conditions and anthropogenic activities are known to have caused changes to the hydrological cycle. Therefore, quantifying the net influence of anthropogenic contribution to the runoff changes is a challenge. This study applied the original and modified Mann–Kendall trend test, including the Sen’s slope test, Pettitt’s test, double cumulative curve, and elasticity methods. These methods were applied to determine the historical trends, magnitude changes and change points of the temperature, precipitation, potential evapotranspiration, and runoff from 1950 to 2016. In addition, the contributions of climate change and anthropogenic activities to runoff changes in the KRB were evaluated. The trend analysis showed a significant increasing trend in annual temperature and potential evapotranspiration, while the annual precipitation trend showed an insignificant decreasing trend during the 1950–2016 time period. The change point in runoff occurred in 1986 in the upstream region and 1991 in the downstream region. Further, the time series (1950–2016) is separated into the prior impacted period (1950–1986 and 1950–1991) and post impacted period (1987–2016 and 1992–2016) for the upstream and downstream regions, respectively. During the post impacted period, climate change and anthropogenic activities contributed to 87.96% and 12.04% in the upstream region and 7.53% and 92.47% in the downstream region of the KRB. The results showed that in runoff changes, the anthropogenic activities played a dominant role in the downstream (97.78%) and the climate change impacts played a dominant factor in the upstream region (87.96%). In the land-use type changes, the dominant role was played by construction land, which showed that the area from 248.63 km² in 1990 increased to 685.45 km² (175.69%) in 2015. These findings suggest that it is essential to adopt effective steps for the sustainable development of the ecological, hydrological, and social order in the KRB in Central Asia.

The analysis of precipitation, snow cover, and temperature based on measured data is important for many applications in hydrology, meteorology, climatology, disaster management, and human activities. In this study, we used long-term historical datasets from the Varzob River Basin (VRB) in Tajikistan to evaluate the trend and magnitudinal changes in temperature, precipitation, and snow cover area in the Anzob (upstream), Maykhura (midstream), and Hushyori (downstream) regions of the VRB using the original Mann–Kendall test, modified Mann–Kendall test, Sen’s slope test, and Pettitt test. The results revealed a decreasing trend in the mean monthly air temperature at Anzob station in the upstream region for all months except January, February, and December between 1960 and 2018 and 1991 to 2018. In each of the three regions, the mean annual temperature indicated a clear upward trend. Seasonal precipitation indicated a large increasing trend in January and February at the Anzob station from 1960 to 2018, but a significant downward trend in April in the upstream, midstream, and downstream regions between 1960 and 1990 and from 1991 to and 2018. In the VRB, almost all stations exhibited a downward trend in annual precipitation across all periods, whereas the upstream region showed a non-significant upward trend between 1960 and 1990. The monthly analysis of snow cover in the VRB based on ground data showed that the maximum increase in snow cover occurs in April at the Anzob station (178 cm) and in March at Maykhura (138 cm) and Hushyori stations (54 cm). The Mann–Kendall test, based on MODIS data, revealed that the monthly snow cover in the VRB increased in April and July while a decrease was recorded in February, September, November, and December from 2001 to 2022. The trend’s stable pattern was observed in March, May, August, and October.

In this study, the applicability of three gridded datasets was evaluated (Climatic Research Unit (CRU) Time Series (TS) 3.1, “Asian Precipitation—Highly Resolved Observational Data Integration Toward the Evaluation of Water Resources” (APHRODITE)_V1101, and the climate forecast system reanalysis dataset (CFSR)) in different combinations against observational data for predicting the hydrology of the Upper Vakhsh River Basin (UVRB) in Central Asia. Water balance components were computed, the results calibrated with the SUFI-2 approach using the calibration of soil and water assessment tool models (SWAT–CUP) program, and the performance of the model was evaluated. Streamflow simulation using the SWAT model in the UVRB was more sensitive to five parameters (ALPHA_BF, SOL_BD, CN2, CH_K2, and RCHRG_DP). The simulation for calibration, validation, and overall scales showed an acceptable correlation between the observed and simulated monthly streamflow for all combination datasets. The coefficient of determination (R²) and Nash–Sutcliffe efficiency (NSE) showed “excellent” and “good” values for all datasets. Based on the R² and NSE from the “excellent” down to “good” datasets, the values were 0.91 and 0.92 using the observational datasets, CRU TS3.1 (0.90 and 0.90), APHRODITE_V1101+CRU TS3.1 (0.74 and 0.76), APHRODITE_V1101+CFSR (0.72 and 0.78), and CFSR (0.67 and 0.74) for the overall scale (1982–2006). The mean annual evapotranspiration values from the UVRB were about 9.93% (APHRODITE_V1101+CFSR), 25.52% (APHRODITE_V1101+CRU TS3.1), 2.9% (CFSR), 21.08% (CRU TS3.1), and 27.28% (observational datasets) of annual precipitation (186.3 mm, 315.7 mm, 72.1 mm, 256.4 mm, and 299.7 mm, out of 1875.9 mm, 1236.9 mm, 2479 mm, 1215.9 mm, and 1098.5 mm). The contributions of the snowmelt to annual runoff were about 81.06% (APHRODITE_V1101+CFSR), 63.12% (APHRODITE_V1101+CRU TS3.1), 82.79% (CFSR), 81.66% (CRU TS3.1), and 67.67% (observational datasets), and the contributions of rain to the annual flow were about 18.94%, 36.88%, 17.21%, 18.34%, and 32.33%, respectively, for the overall scale. We found that gridded climate datasets can be used as an alternative source for hydrological modeling in the Upper Vakhsh River Basin in Central Asia, especially in scarce-observation regions. Water balance components, simulated by the SWAT model, provided a baseline understanding of the hydrological processes through which water management issues can be dealt with in the basin.

Cryospheric and ecological studies become very complicated due to the absence of observed data, particularly in the mountainous regions of Central Asia. Performance analysis of Satellite-Based Precipitation Datasets (SBPD) is very critical before their direct hydro-climatic applications. This study assessed the ground validation of four SBPDs (IMERG, TRMM, PERSIANN-CDR, and PERSIANN-CSS). From January 2000 to December 2013, all SBPD data were analyzed on daily, monthly, seasonal (winter, spring, summer, autumn), and annual scales at the entire spatial domain and point-to-pixel scale. The performance of SBPD was analyzed by using evaluation indices (root mean square error (RMSE), correlation coefficient (CC), bias, and relative bias (r-Bias)) along with categorical indices (false alarm ratio (FAR), probability of detection (POD), success ratio (SR), and critical success index (CSI). Results revealed that: (1) IMERG’s spatiotemporal tracking ability is better as compared to other datasets with appropriate ranges (CC > 0.8 and r-BIAS (±10)). The performance of all SBPDs is more capable on a monthly scale as compared to a daily scale. (2) In terms of POD, the IMERG outperformed all other SBPD on daily and seasonal scales. All SBPD showed underestimations in the summer season, and PERSIANN-CCS showed the most significant underestimation (−70). Moreover, the IMERG signposted the most satisfactory performance in all seasons. (3) All SBPD showed better performance in capturing the light precipitation events as indicated by the Probability Density Function (PDF%). Moreover, the performance of PERSIANN-CDR and TRMM is acceptable at low topography; the performance of PERSIANN-CCS is very poor in diverse topographical and climatic conditions over Tajikistan. Therefore, we advocate the use of daily, monthly, and seasonal estimations of IMERG precipitation product for hydro-climatic applications over the mountainous domain of Central Asia.

The ground validation of satellite-based precipitation products (SPPs) is very important for their hydroclimatic application. This study evaluated the performance assessment of four soil moisture-based SPPs (SM2Rain, SM2Rain- ASCAT, SM2Rain-CCI, and GPM-SM2Rain). All data of SPPs were compared with 64 weather stations in Pakistan from January 2005 to December 2020. All SPPs estimations were evaluated on daily, monthly, seasonal, and yearly scales, over the whole spatial domain, and at point-to-pixel scale. Widely used evaluation indices (root mean square error (RMSE), correlation coefficient (CC), bias, and relative bias (rBias)) along with categorical indices (false alarm ratio (FAR), probability of detection (POD), success ratio (SR), and critical success index (CSI) were evaluated for performance analysis. The results of our study signposted that: (1) On a monthly scale, all SPPs estimations were in better agreement with gauge estimations as compared to daily scales. Moreover, SM2Rain and GPM-SM2Rain products accurately traced the spatio-temporal variability with CC >0.7 and rBIAS within the acceptable range (±10) of the whole country. (2) On a seasonal scale (spring, summer, winter, and autumn), GPM-SM2Rain performed more satisfactorily as compared to all other SPPs. (3) All SPPs performed better at capturing light precipitation events, as indicated by the Probability Density Function (PDF); however, in the summer season, all SPPs displayed considerable over/underestimates with respect to PDF (%). Moreover, GPM-SM2RAIN beat all other SPPs in terms of probability of detection. Consequently, we suggest the daily and monthly use of GPM-SM2Rain and SM2Rain for hydro climate applications in a semi-arid climate zone (Pakistan).