As is already known, solar photovoltaic (PV) technology is a widely accepted technology for power generation worldwide. However, it is scientifically proven that its power output decreases with an increase in the temperature of the PV module. Such an important issue is controlled by adopting a number of cooling mechanisms for the PV module. The present experimental study assesses the effect of a fanless CPU heat pipe on the performance of a PV module. The experiment was conducted in June in real weather conditions in Yekaterinburg, Russian Federation. The comparative analysis of two PV panels (i.e., cooled, and uncooled) based on the electrical energy, exergy performance, economic, embodied energy and energy payback (5E) for the two systems is presented and discussed. The key results from the study are that the average temperature reduction from the cooling process is 6.72 °C. The average power for the cooled panel is 11.39 W against 9.73 W for the uncooled PV panel; this represents an increase of 1.66 W for the cooled module. Moreover, the average improvements in the electrical efficiency, and embodied energy recorded for a cooled PV panel 2.98%, and 438.52 kWh, respectively. Furthermore, the calculations of the levelized cost of energy (LCE) for the cooled PV panel indicate that it can range from 0.277–0.964 USD/kWh, while that for the uncooled PV panel also ranges from 0.205–0.698 USD/kWh based on the number of days of operation of the plant.

In this study, the performances of four satellite-based precipitation products (IMERG-V06 Final-Run, TRMM-3B42V7, SM2Rain-ASCAT, and PERSIANN-CDR) were assessed with reference to the measurements of in-situ gauges at daily, monthly, seasonal, and annual scales from 2010 to 2017, over the Hindu Kush Mountains of Pakistan. The products were evaluated over the entire domain and at point-to-pixel scales. Different evaluation indices (Correlation Coefficient (CC), Root Mean Square Error (RMSE), Bias, and relative Bias (rBias)) and categorical indices (False Alarm Ration (FAR), Critical Success Index (CSI), Success Ratio (SR), and Probability of Detection (POD)) were used to assess the performances of the products considered in this study. Our results indicated the following. (1) IMERG-V06 and PERSIANN capably tracked the spatio-temporal variation of precipitation over the studied region. (2) All satellite-based products were in better agreement with the reference data on the monthly scales than on daily time scales. (3) On seasonal scale, the precipitation detection skills of IMERG-V06 and PERSIANN-CDR were better than those of SM2Rain-ASCAT and TRMM-3B42V7. In all seasons, overall performance of IMERG-V06 and PERSIANN-CDR was better than TRMM-3B42V7 and SM2Rain-ASCAT. (4) However, all products were uncertain in detecting light and moderate precipitation events. Consequently, we recommend the use of IMERG-V06 and PERSIANN-CDR products for subsequent hydro-meteorological studies in the Hindu Kush range.

Millions of people in Uzbekistan, Turkmenistan, Tajikistan, and Kyrgyzstan are dependent on the freshwater supply of the Vakhsh River system. Sustainable management of the water resources of the Vakhsh River Basin (VRB) requires comprehensive assessment regarding future climate change and its implications for streamflow. In this study, we assessed the potential impacts of projected climate change scenarios on the streamflow in the VRB for two future periods (2022–2060 and 2061–2099). The probable changes in the regional climate system were assessed using the outputs of five global climate models (GCMs) under two representative concentration pathways (RCPs), RCP4.5 and RCP8.5. The probable streamflow was simulated using a semi-distributed hydrological model, namely the Soil and Water Assessment Tool (SWAT). Evidence of a significant increase in the annual average temperature by the end of the 21st century was found, ranging from 2.25 to 4.40 °C under RCP4.5 and from 4.40 to 6.60 °C under RCP8.5. The results of three GCMs indicated a decreasing tendency of annual average precipitation (from −1.7% to −16.0% under RCP4.5 and from −3.4% to −29.8% under RCP8.5). Under RCP8.5, two GCMs indicated an increase (from 2.3% to 5.3%) in the average annual precipitation by the end of 2099. The simulated results of the hydrological model reported an increasing tendency of average annual streamflow, from 17.5% to 52.3% under both RCPs, by the end of 2099. A shift in the peak flow month was also found, i.e., from July to June, under both RCPs. It is expected that in the future, median and high flows might increase, whereas low flow might decrease by the end of 2099. It is concluded that the future seasonal streamflow in the VRB are highly uncertain due to the probable alterations in temperature and precipitation. The findings of the present study could be useful for understanding the future hydrological behavior of the Vakhsh River, for the planning of sustainable regional irrigation systems in the downstream countries, i.e., Uzbekistan and Turkmenistan, and for the construction of hydropower plants in the upstream countries.

Satellite remote sensing has witnessed a global widespread vegetation greening since the 1980s. However, reliable observation-based quantitative knowledge on global greening remains obscure due to uncertainties in model simulations and the contribution of natural variability is largely unknown. Here, we revisit the attribution of global vegetation changes using the Time Series Segment and Residual Trend (TSS-RESTREND) method. Results showed global vegetation significantly greening over 40.6% of the vegetated grids, whereas vegetation significantly browning over 11.6% of the vegetated grids. The attribution results based on the TSS-RESTREND method show that CO₂ fertilization (CO₂) plays an influential role in vegetation changes over 61.4% of the global vegetated areas, followed by land use (LU, 23.5%), climate change (CC, 7.3%), and climate variability (CV, 1.5%). The vegetation greening can be largely attributed to CO₂ fertilization while the vegetation browning is mainly caused by LU. Meanwhile, we also identify positive impacts of LU and CC on vegetation change in arid regions but negative impacts in humid regions. Our findings indicate spatial heterogeneity in causes behind global vegetation changes, providing more detailed references for global vegetation modeling.

The variations of climate and water resources in the Buqtyrma River Basin (BRB), which is located at the cross-section of the Altai Mountains, Eurasian Steppe and Tian Shan Mountains, have a great significance for agriculture and ecosystems in the region. Changing climatic conditions will change the hydrological cycle in the whole basin. In this study, we examined the historical trends and change points of the climate and hydrological variables, the contributions of climate change and human activities to runoff changes, and the relative changes in the runoff to the precipitation and potential evapotranspiration from 1950 to 2015 by using the Mann–Kendall trend test, Pettitt test, double cumulative curve and elasticities methods. In addition, a multi-model ensemble (MME) of the six general circulation models (GCMs) for two future periods (2036–2065 and 2071–2100) was assessed to estimate the spatio-temporal variations in precipitation and temperature under two representative concentration pathways (RCPs 4.5 and 8.5) scenarios. Our study detected that the runoff change-point occurred in 1982. The impacts induced by climate change on runoff change were as follows—70% in the upstream, 62.11% in the midstream and 15.34% in the downstream area. The impacts of human activity on runoff change were greater in the downstream area (84.66%) than in the upstream and midstream areas. A continuously increasing trend was indicated regarding average annual temperature under RCP 4.5 (from 0.37 to 0.33 °C/decade) and under RCP 8.5 (from 0.50 to 0.61 °C/decade) during two future periods. Additionally, an increasing trend in predicted precipitation was exhibited under RCP 4.5 (13.6% and 19.9%) and under RCP 8.5 (10.5% and 18.1%) during both future periods. The results of the relative runoff changes to the predicted precipitation and potential evapotranspiration were expected to increase during two future time periods under RCP 4.5 (18.53% and 25.40%) and under RCP 8.5 (8.91% and 13.38%) relative to the base period. The present work can provide a reference for the utilization and management of regional water resources and for ecological environment protection.

Land cover change (LCC) and climate change (CC) impacts on streamflow in high elevated catchments are a great challenge to sustainable management and the development of water resources. This study evaluates the possible future impacts of both land cover and climate change on the streamflows in the Mohmand Dam catchment, Pakistan, by utilizing the semi-distributed hydrological model known as the Soil and Water Assessment Tool (SWAT), along with the latest Coupled Model Intercomparison Project phase 6 (CMIP6) dataset of different global climate models (GCMs). The downscaling of the precipitation and temperature data was performed by the CMhyd software. The downscaled precipitation and temperature projections from the best performing GCM, out of four GCMs, under two shared socioeconomic pathways (SSP2 and SSP5) and future land cover conditions were forced in a calibrated hydrological model (SWAT model). Compared to the baseline period (1990–2015), the outputs from the selected GCM indicated an increase in the average monthly precipitation, and the maximum and minimum temperature in the study area under both the SSP2 and SSP5 scenarios, by the end of the 21st century. It is expected that the increase in precipitation for the period 2016–2100 is 10.5% and 11.4% under the SSP2 and SSP5 scenarios, respectively. Simulated results from the SWAT model showed significant impacts from the projected climate and land cover changes on Mohmand Dam flows that include: (a) an increase in the overall mean annual flow ranging from 13.7% to 34.8%, whereas the mean monthly flows of June, July and August decreased, and (b) a shift in the peak flows in the Mohmand catchment from July to June. It is concluded that the projected climate changes can substantially influence the seasonality of flows at the Mohmand Dam site. Climate and land cover change impacts are significant, so project planners and managers must include CC and LCC impacts in the proposed operational strategy.