In this paper, we used data from 42 soil temperature observation sites in permafrost regions throughout the Northern Hemisphere to analyze the characteristics and variability in soil temperature. The observation data were used to evaluate soil temperature simulations at different depths from 10 CMIP6 models in the permafrost region of the Northern Hemisphere. The results showed that the annual average soil temperature in the permafrost regions in the Northern Hemisphere gradually decreased with increasing latitude, and the soil temperature gradually decreased with depth. The average soil temperatures at different depths were mainly concentrated around 0 degrees C. The 10 CMIP6 models performed well in simulating soil temperature, but most models tended to underestimate temperatures compared to the measured values. Overall, the CESM2 model yielded the best simulation results, whereas the CNRM-CM6-1 model performed the worst. The change trends in annual average soil temperature across the 42 sites ranged from -0.17 degrees C/10a to 0.41 degrees C/10a from 1900 to 2014, the closer to the Arctic, the faster the soil warming rate. The rate of soil temperature change also varied at different depths between 1900-2014 and 1980-2014. The rate of soil temperature change from 1980 to 2014 was approximately three times greater than that from 1900 to 2014.

The current water environment carrying capacity assessment method has a single assessment index and does not constrain the scope of assessment. It is not possible to adaptively assess the water environment carrying capacity layer by layer. In order to solve this problem, in this paper. we propose an adaptive assessment method of urban water environment carrying capacity based on water quality target constraints. This method constructs a new evaluation index system for water environment carrying capacity, which takes water resources and environment, water pollution control, and economic carrying capacity as the criteria, and takes water quality status, pollution discharge, technology management. economic development, and social development as the constraint target layer, and takes the total wastewater discharge, industrial water consumption, and urbanization level as the constraint index layer. Two methods of structural entropy weight and mean square error decision are introduced to realize the adaptive joint weight assignment evaluation of the reference layer and the target layer. Through experimental analysis, the assessed area has a good water environment carrying capacity and foundation, and the overall water environment carrying capacity of the study area from 2016 to 2019 was on the rise.

Introduction: More than 16% of the total electricity used worldwide is met by hydropower, having local to regional environmental consequences. With positive indicators that energy is becoming more broadly available and sustainable, the world is moving closer to achieving Sustainable Development Goal 7 (SDG 7). Pakistan became the first nation to include the Sustainable Development Goals (SDGs) in its national development strategy.Methodology: The current study sought to investigate the structural limits of Environmental Impact Assessment (EIA) guidelines for hydropower development in Pakistan. The study included the document review of the EIA reports about hydropower projects in Pakistan, scientific questionnaires from decision-makers, and public consultation.Results and Discussion: The document evaluates that an adequate mechanism is available, and donors like the Asian Development Bank and World Bank observe the implementation process of EIA in Pakistan. However, a comprehensive analysis of the EIA system found several things that could be improved, not only in the institutional framework but also in actual implementation and practices. More than 20% of respondent decision-makers disagreed with the compliance of the current Institutional Framework with EIA guidelines, and 25% think that the existing guidelines followed in Pakistan are not aligned with international standards and practices for Hydropower in actual practice. EIA has a limited impact on decision-making due to insufficient technical and financial resources.Recommendations: There should be a think tank with experts who can meet the needs of present and future epochs. The public should be communicated with and educated about EIA. For better efficiency, the officers and decision-makers should be trained internationally, i.e., the Water and Power Development Authority (WAPDA).

Classifying a given landscape on the basis of its susceptibility to surface processes is a standard procedure in low to mid-latitudes. Conversely, these procedures have hardly been explored in periglacial regions. However, global warming is radically changing this situation and will change it even more in the future. For this reason, un-derstanding the spatial and temporal dynamics of geomorphological processes in peri-arctic environments can be crucial to make informed decisions in such unstable environments and shed light on what changes may follow at lower latitudes. For this reason, here we explored the use of data-driven models capable of recognizing locations prone to develop retrogressive thaw slumps (RTSs) and/or active layer detachments (ALDs). These are cryo-spheric hazards induced by permafrost degradation, and their development can negatively affect human set-tlements or infrastructure, change the sediment budget and release greenhouse gases. Specifically, we test a binomial Generalized Additive Modeling structure to estimate the probability of RST and ALD occurrences in the North sector of the Alaskan territory. The results we obtain show that our binary classifiers can accurately recognize locations prone to RTS and ALD, in a number of goodness-of-fit (AUCRTS = 0.83; AUCALD = 0.86), random cross-validation (mean AUCRTS = 0.82; mean AUCALD = 0.86), and spatial cross-validation (mean AUCRTS = 0.74; mean AUCALD = 0.80) routines. Overall, our analytical protocol has been implemented to build an open-source tool scripted in Python where all the operational steps are automatized for anyone to replicate the same experiment. Our protocol allows one to access cloud-stored information, pre-process it, and download it locally to be integrated for spatial predictive purposes.

The thawing of permafrost in the Arctic has led to an increase in coastal land loss, flooding, and ground subsidence, seriously threatening civil infrastructure and coastal communities. However, a lack of tools for synthetic hazard assessment of the Arctic coast has hindered effective response measures. We developed a holistic framework, the Arctic Coastal Hazard Index (ACHI), to assess the vulnerability of Arctic coasts to permafrost thawing, coastal erosion, and coastal flooding. We quantified the coastal permafrost thaw potential (PTP) through regional assessment of thaw subsidence using ground settlement index. The calculations of the ground settlement index involve utilizing projections of permafrost conditions, including future regional mean annual ground temperature, active layer thickness, and talik thickness. The predicted thaw subsidence was validated through a comparison with observed long-term subsidence data. The ACHI incorporates the PTP into seven physical and ecological variables for coastal hazard assessment: shoreline type, habitat, relief, wind exposure, wave exposure, surge potential, and sea-level rise. The coastal hazard assessment was conducted for each 1 km2 coastline of North Slope Borough, Alaska in the 2060s under the Representative Concentration Pathway 4.5 and 8.5 forcing scenarios. The areas that are prone to coastal hazards were identified by mapping the distribution pattern of the ACHI. The calculated coastal hazards potential was subjected to validation by comparing it with the observed and historical long-term coastal erosion mean rates. This framework for Arctic coastal assessment may assist policy and decision-making for adaptation, mitigation strategies, and civil infrastructure planning.

In this paper, we used data from 42 soil temperature observation sites in permafrost regions throughout the Northern Hemisphere to analyze the characteristics and variability in soil temperature. The observation data were used to evaluate soil temperature simulations at different depths from 10 CMIP6 models in the permafrost region of the Northern Hemisphere. The results showed that the annual average soil temperature in the permafrost regions in the Northern Hemisphere gradually decreased with increasing latitude, and the soil temperature gradually decreased with depth. The average soil temperatures at different depths were mainly concentrated around 0 degrees C. The 10 CMIP6 models performed well in simulating soil temperature, but most models tended to underestimate temperatures compared to the measured values. Overall, the CESM2 model yielded the best simulation results, whereas the CNRM-CM6-1 model performed the worst. The change trends in annual average soil temperature across the 42 sites ranged from -0.17 degrees C/10a to 0.41 degrees C/10a from 1900 to 2014, the closer to the Arctic, the faster the soil warming rate. The rate of soil temperature change also varied at different depths between 1900-2014 and 1980-2014. The rate of soil temperature change from 1980 to 2014 was approximately three times greater than that from 1900 to 2014.

Rapid atmospheric warming changes the thermal conditions of permafrost over the Northern Hemisphere (NH), including ground temperature warming and ground ice thawing. This warming and thawing of ice-rich permafrost damages existing infrastructure and poses a threat to sustainable development. Bearing capacity (BC) loss and ground subsidence (GS) due to permafrost thawing are two major risks to the infrastructure and key indexes for risk assessment. However, current information on the BC and GS is too coarse, restricted to the Arctic, and scarce for future periods. The aim of this study was to address these gaps by presenting spatial data on the BC and GS for current and future periods across the NH at a resolution of 1 km. A machine learning-based approach was developed to simulate permafrost thermal dynamics under four climate scenarios (SSPs 1-2.6, 2-4.5, 3-7.0, and 5-8.5). The associated changes in the BC and GS were estimated based on changes in the permafrost temperature at or near the depth of zero annual amplitude (MAGT) and active-layer thickness (ALT). The results indicate a continuous increase in MAGT and ALT by 2.3 degrees C (SSPs1-2.6) to 7.6 degrees C (SSPs5-8.5) and 16.0 cm (SSPs1-2.6) to 51.0 cm (SSPs5-8.5), respectively, at the end of the 21ts century. This permafrost degradation will lead to a high potential BC loss of 37.8% (SSPs1-2.6) to 40.2% (SSPs5-8.5) on average over 2041-2060, and up to 60.5% (SSPs1-2.6) to 92.2% (SSPs5-8.5) in 2081-2100. The produced average GS is approximately 1.0 cm in 2021-2040, and further up to 1.5 cm (SSPs1-2.6) to 4.7 cm (SSPs5-8.5) in 2081-2100, with notable variations across the permafrost region. These forecasts provide new opportunities to assess future permafrost changes and associated risks and costs with climate warming.

Environmental Impact Assessment (EIA) became mandatory in Pakistan in 1983 with the passage of the Pakistan Environmental Protection Ordinance. The Sustainable Development Goals were incorporated into Pakistan's national development strategy, making it the first country in history to do so. The study is based on evaluating the mitigation strategies and environmental impact assessment at the Gulpur Hydropower Project (HPP), Kotli, AJK, which uses the Poonch River's water resources to generate power and has a design capacity of 100 MW using the EIA documentation of Gulpur HPP. In addition to making additional observations and reviewing the literature, the study looked at Mira Power Limited's EIA reports. The possible effects, as well as the Government's and MPL's mitigating actions, were examined by the authors. EIA procedures at the Gulpur HPP considered several laws, including the Pakistan Environmental Protection Agency, AJK Wildlife Ordinance of 2013, the Land Acquisition Act of 1894, and Laws Regulating Flow Releases for Hydropower Projects. Projects using hydropower in delicate areas carry a high risk. Given the thorough analysis of the hazards in this instance, it is evident that the EIA had a significant impact on the project's design. The authors concluded that there are no negative environmental effects of the construction of hydropower projects in the concerned area and that all potential effects and compensation were handled legally and efficiently. The study suggested that all hydropower projects in Pakistan undertake environmental impact assessments. Evaluating the mitigation strategies and environmental impact assessment at the Gulpur Hydropower Project.EIA procedures at the Gulpur HPP considered several laws, including the Pakistan Environmental Protection Agency.The development of hydropower projects in the affected area had no negative environmental effects, and any potential effects or compensation were handled lawfully and effectively.

Snow plays an important role in catastrophic weather, climate change, and water recycling. In order to analyze the ability of different land surface models to simulate snow depth in China, we used atmospheric forcing data from the China Meteorological Administration (CMA) Land Data Assimilation System (CLDAS) to drive the CLM3.5 (the Community Land Model version 3.5), Noah (NCEP, OSU, Air Force and Office of Hydrology Land Surface Model), and Noah-MP (the community Noah land surface model with multi-parameterization options) land surface models. We also used 2380 daily snow-depth site observations of CMA to analyze the simulation effects of different models on the snow depth in China and different regions during the periods of snow accumulation and snowmelt from 2015 to 2019. The results show that CLM3.5, Noah, and Noah-MP can simulate the spatial distribution of the snow depth in China, but there are some differences between the models. In particular, the snow depth and snow cover simulated by CLM3.5 are lower than those simulated by Noah and Noah-MP in Northwest China and the Tibetan Plateau. From the overall quantitative assessment results for China, the snow depth simulated by CLM3.5 is underestimated, while that simulated by Noah is overestimated. Noah-MP has the best overall performance; for example, the biases of the three models during the snow-accumulation periods are -0.22 cm, 0.27 cm, and 0.15 cm, respectively. Furthermore, the three models perform differently in the three snowpack regions of Northeast China, Northwest China, and the Tibetan Plateau; Noah-MP has the best snow-depth performance in Northeast China, while CLM3.5 has the best snow-depth performance in the Tibetan Plateau region. Noah-MP performs best in the snow-accumulation period, and Noah performs best in the snowmelt period for Northwest China. In conclusion, no single model can perform optimally for snow simulations in different regions of China and at different times of the year, and the multi-model integration of snow may be an effective way to obtain high-quality snow simulation results. So this study provides some scientific references for the spatiotemporal evolution of snow in the context of climate change, monitoring and analysis of snow, the study of land surface models for snow, and the sustainable development and utilization of snow resources in China and other regions.

Study RegionThe Naryn River Basin, KyrgyzstanStudy FocusWe investigate the impacts of climate change in the basin based on two families of General Circulation Models (GCMs) using the hydrological model SWAT. The forcing datasets are the widely used ISIMIP2 (I2) and the newly derived ISIMIP3 (I3) data which refer to the 5th and 6th stage of the Coupled Model Intercomparison Project (CMIP). Due to notable differences in the forcing we evaluate their impacts on various hydrological components of the basin, such as discharge, evapotranspiration (ETA) and soil moisture (SM). Besides, a partial correlation (PC) analysis is used to assess the meteorological controls of the basin with special emphasize on the SM-ETA coupling. New Hydrological Insights for the RegionAgreement in the basin's projections is found, such as discharge shifts towards an earlier peak flow of one month, significant SM reductions and ETA increases. I3 temperature projections exceed their previous estimates and show an increase in precipitation, which differs from I2. However, the mitigating effects do not lead to an improvement in the region's susceptibility to soil moisture deficits. The PC study reveals enhanced water-limited conditions expressed as positive SM-ETA feedback under I2 and I3, albeit slightly weaker under I3.