Study region: The study focuses on the Indus River Basin and southern Pakistan, severely affected by flooding in 2022. Study focus: This study assessed how land surface temperature, snow cover, soil moisture, and precipitation contributed to the deluge of 2022. This study mainly investigated MODIS-AIRS land surface temperature, MODIS snow cover (NDSI), SMAP soil moisture, and GPM IMERG precipitation accumulation. Furthermore, different flood visualization and mapping techniques were applied to delineate the flood extent map using Landsat 8-9, Sentinel-2 MSI, and Sentinel-1 SAR data. New hydrological insights for the region: The region experienced some of the most anomalous climatic events in 2022, such as prolonged heatwaves as observed with higher-than-average land surface temperatures and subsequent rapid decline in snow cover extent during the spring, increased soil moisture followed by an abnormal amount of extreme monsoon precipitation in the summer. The upper subbasins experienced more than 8 degrees C in positive temperature anomaly, indicating a warmer climate in spring. Subsequently, the snow cover declined by more than 25 % in the upper subbasins. Further, higher surface soil moisture values (> 0.3 m3/m3) were observed in the basin during the spring due to the rapid snow and ice melt. Furthermore, the basin received more than 200 mm of rainfall compared to the long-term average rainfall of about 98 mm, translating to about 300 % more rainfall than usual in July and August. The analysis helps understand the spatial and temporal variability within the basin and facilitates the understanding of factors and their intricate connections contributing to flooding.

Influenced by a warm and humid climate, the permafrost on the Qinghai-Tibet Plateau is undergoing significant degradation, leading to the occurrence of extensive thermokarst landforms. Among the most typical landforms in permafrost areas is thaw slump. This study, based on three periods of data from keyhole images of 1968-1970, the fractional images of 2006-2009 and the Gaofen (GF) images of 2018-2019, combined with field surveys for validation, investigates the distribution characteristics and spatiotemporal variation trends of thaw slumps in the Hoh Xil area and evaluates the susceptibility to thaw slumping in this area. The results from 1968 to 2019 indicate a threefold increase in the number and a twofold increase in total area of thaw slumps. Approximately 70% of the thaw slumps had areas less than 2 x 104 m2. When divided into a grid of 3 km x 3 km, about 1.3% (128 grids) of the Hoh Xil region experienced thaw slumping from 1968 to 1970, while 4.4% (420 grids) showed such occurrences from 2018 to 2019. According to the simulation results obtained using the informativeness method, the area classified as very highly susceptible to thaw slumping covers approximately 26% of the Hoh Xil area, while the highly susceptible area covers about 36%. In the Hoh Xil, 61% of the thaw slump areas had an annual warming rate ranging from 0.18 to 0.25 degrees C/10a, with 70% of the thaw slump areas experiencing a precipitation increase rate exceeding 12 mm/10a. Future assessments of thaw slump development suggest a possible minimum of 41 and a maximum of 405 thaw slumps occurrences annually in the Hoh Xil region. Under rapidly changing climatic conditions, apart from environmental risks, there also exist substantial potential risks associated with thaw slumping, such as the triggering of large-scale landslides and debris flows. Therefore, it is imperative to conduct simulated assessments of thaw slumping throughout the entire plateau to address regional risks in the future.

Hydrologically-induced landslides are ubiquitous natural hazards in the Himalayas, posing severe threat to human life and infrastructure. Yet, landslide assessment in the Himalayas is extremely challenging partly due to complex and drastically changing climate conditions. Here we establish a mechanistic hydromechanical landslide modeling framework that incorporates the impacts of key water fluxes and stocks on landslide triggering and risk evolution in mountain systems, accounting for potential climate change conditions for the period 1991-2100. In the drainage basin of the largest river in the northern Himalayas- the Yarlung Zangbo River Basin (YZRB), we estimate that rainfall, glacier/snow melt and permafrost thaw contribute similar to 38.4%, 28.8%, and 32.8% to landslides, respectively, for the period 1991-2019. Future climate change will likely exacerbate landslide triggering primarily due to increasing rainfall, whereas the contribution of glacier/snow melt decreases owing to deglaciation and snow cover loss. The total Gross Domestic Productivity risk is projected to increase continuously throughout the 21st century, while the risk to population shows a general declining trend. The results yield novel insights into the climatic controls on landslide evolution and provide useful guidance for disaster risk management and resilience building under future climate change in the Himalayas.

In the context of global warming, landscapes with ice-rich permafrost, such as the Qinghai-Tibet Plateau (QTP), are highly vulnerable. The expansion of thermokarst lakes erodes the surrounding land, leading to collapses of various scales and posing a threat to nearby infrastructure and the environment. Assessing the susceptibility of thermokarst lakes in remote, data-scarce areas remains a challenging task. In this study, Landsat imagery and human-computer interaction were employed to improve the accuracy of thermokarst lake classification. The study also identified the key factors influencing the occurrence of thermokarst lakes, including the lake density, soil moisture (SM), slope, vegetation, snow cover, ground temperature, precipitation, and permafrost stability (PS). The results indicate that the most susceptible areas cover 19.02% of the QTP's permafrost region, primarily located in southwestern Qinghai, northeastern Tibet, and the Hoh Xil region. This study provides a framework for mapping the spatial distribution of thermokarst lakes and contributes to understanding the impact of climate change on the QTP.

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.

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.