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Located in the central part of the arid regions of Northwest China, the Heihe River Basin (HRB) plays an important role in wind prevention, sand fixation, and soil and water conservation as the second largest inland river basin. In the context of the warming and wetting climate observed in Northwest China, the situation of the ecological environment in the HRB is of significant concern. Using the data from meteorological observation stations, grid fusion and hydrological monitoring, this study analyzes the multi-scale climate changes in the HRB and their impacts on runoff. In addition, predictive models for runoff in the upper and middle reaches were developed using machine learning methods. The results indicate that the climate in the HRB has experienced an overall warming and wetting trend over the past 60 years. At the same time, there are clear regional variabilities in the climate changes. Precipitation shows decreasing trends in the northwestern part of the HRB, while it shows increases at rates higher than the regional average in the southeastern part. Moreover, the temperature increases are generally smaller in the upper reaches than those in the middle and lower reaches. Over the past 60 years, there has been a remarkable increase in runoff at the Yingluo Gorge (YL) hydrological station, which exhibits a distinct "single-peak" pattern in the variation of monthly runoff. The annual runoff volume at the YL (ZY) hydrological station is significantly correlated with the precipitation in the upper (middle) reaches, indicating the precipitation is the primary influencing factor determining the annual runoff. Temperature has a significant impact only on the runoff in the upper reaches, while its impact is not significant in the middle reaches. The models trained by the support vector machines and random forest models perform best in predicting the annual runoff and monthly runoff, respectively. This study can provide a scientific basis for environmental protection and sustainable development in the HRB. [ABSTRACT FROM AUTHOR]

Nature-based solutions (NbSs) are considered to form an innovative stormwater management approach that has living resolutions grounded in natural processes and structures. NbSs offer many other environmental benefits over traditional grey infrastructure, including reduced air pollution and climate change mitigation. This review predominantly centers on the hydrological aspect of NbSs and furnishes a condensed summary of the collective understanding about NbSs as an alternatives for stormwater management. In this study, which employed the CIMO (Context, Intervention, Mechanism, Outcome) framework, a corpus of 187 NbS-related publications (2000–2023) extracted from the Web of Science database were used, and we expounded upon the origins, objectives, and significance of NbSs in urban runoff and climate change, and the operational mechanisms of NbSs (including green roofs, permeable pavements, bioretention systems, and constructed wetlands), which are widely used in urban stormwater management, were also discussed. Additionally, the efficacy of NbSs in improving stormwater quality and quantity is discussed in depth in this study. In particular, the critical role of NbSs in reducing nutrients such as TSS, TN, TP, and COD and heavy metal pollutants such as Fe, Cu, Pb, and Zn is emphasized. Finally, the main barriers encountered in the promotion and application of NbSs in different countries and regions, including financial, technological and physical, regulatory, and public awareness, are listed, and future directions for improving and strategizing NbS implementation are proposed. This review gathered knowledge from diverse sources to provide an overview of NbSs, enhancing the comprehension of their mechanisms and applications. It underscores specific areas requiring future research attention. [ABSTRACT FROM AUTHOR]

This study aims to estimate the surface runoff and examine the impact of climate change on water resources in the Upper Kabul River Basin (UKRB). A hydrological model was developed using the Soil and Water Assessment Tool (SWAT) from 2009 to 2019. The monthly calibration was conducted on streamflow in six stations for the period from 2010 to 2016, and the results were validated from 2017 to 2018 based on available observed data. The hydrological sensitivity parameters were further prioritized using SWAT-CUP. The uncertainty of the model was analyzed by the 95% Prediction Uncertainty (95PPU). Future projections were analyzed for the 2040s (2030–2049) and 2090s (2080–2099) compared to the baseline period (1986–2005) under two representation concentration pathways (RCP4.5, RCP8.5). Four Regional Climate Models (RCMs) were bias-corrected using the linear scaling bias correction method. The modeling results exhibited a very reasonable fit between the estimated and observed runoff in different stations, with NS values ranging from 0.54 to 0.91 in the calibration period. The future mean annual surface runoff exhibited an increase in the 2040s and 2090s compared to the baseline under both RCPs of 4.5 and 8.5 due to an increase in annual precipitation. The annual precipitation is projected to increase by 5% in the 2040s, 1% in the 2090s under RCP4.5, and by 9% in the 2040s and 2% in the 2090s under RCP8.5. The future temperature is also projected to increase and consequently lead to earlier snowmelt, resulting in a shift in the seasonal runoff peak to earlier months in the UKRB. However, the shifts in the timing of runoff could lead to significant impacts on water availability and exacerbate the water stress in this region, decreasing in summer runoff and increasing in the winter and spring runoffs. The future annual evapotranspiration is projected to increase under both scenarios; however, decreases in annual snowfall, snowmelt, sublimation, and groundwater recharge are predicted in the UKRB. [ABSTRACT FROM AUTHOR]

Climate change is expected to significantly affect countries in Southern Europe and the Mediterranean Basin, causing higher-than-average temperature increases, considerable reductions in rainfall and water runoff, and extreme events such as heatwaves. These pose severe threats to local energy systems, requiring a reliable and quantitative risk analysis. A methodological approach is thus proposed which covers both energy supply and demand, utilizing the latest climate projections under different greenhouse gas emissions scenarios and an appropriate scale for each energy form. For energy supply technologies, risks are assessed through statistical regression models and/or mathematical equations correlating climatic parameters with energy productivity. To analyze climate risks for energy demand, bottom–up models were developed, integrating both behavioral and policy aspects which are often considered in a very limited way. The results show that climate change will mainly affect electricity generation from hydroelectric and thermal power plants, with variations depending on the plants' locations and uncertainties associated with precipitation and runoff changes. The climate risks for solar and wind energy were found to be low. Energy consumption will also be affected, but the range of risks depends on the ambition and the effectiveness of measures for upgrading the thermal performance of buildings and the intensity of climate change. [ABSTRACT FROM AUTHOR]

This study presents a framework to attribute river runoff variations to the combined effects of reservoir operations, land surface changes, and climate variability. We delineated the data into natural and impacted periods. For the natural period, an integrated Long Short-Term Memory and Random Forest model was developed to accurately simulate both mean and extreme runoff values, outperforming existing models. This model was then used to estimate runoff unaffected by human activities in the impacted period. Our findings indicate stable annual and wet season mean runoff, with a decrease in wet season maximums and an increase in dry season means, while extreme values remained largely unchanged. A Budyko framework incorporating reconstructed runoff revealed that rainfall and land surface changes are the predominant factors influencing runoff variations in wet and dry seasons, respectively, and land surface impacts become more pronounced during the impacted period for both seasons. Human activities dominate dry season runoff variation (93.9%), with climate change at 6.1%, while in the wet season, the split is 64.5% to 35.5%. Climate change and human activities have spontaneously led to reduced runoff during the wet season and increased runoff during the dry season. Only reservoir regulation is found to be linked to human-induced runoff changes, while the effects of land surface changes remain ambiguous. These insights underscore the growing influence of anthropogenic factors on hydrological extremes and quantify the role of reservoirs within the impacts of human activities on runoff. [ABSTRACT FROM AUTHOR]