Subarctic palsa mires are natural indicators of the status of permafrost in its sporadic distribution zone. Estimation of the rate of their thawing can become an auxiliary indicator to predict climate shifts. The formation, growth, and degradation of palsas are dynamic processes that depend on seasonal weather fluctuations and local environmental factors. Therefore, accurate forecasts of palsas conditions and related ecosystem shifts must be based on a broad set of attributes of palsas from different regions of the Northern Hemisphere. With this in mind, we studied two palsa mires sites on the Kola Peninsula, for which no thorough descriptions were previously available. The first site, Chavanga, is at the southern limit of the permafrost zone under unfavorable climatic conditions and is a collapsing relic. The second site, Ponoy, in contrast, is within the sporadic permafrost zone with relatively cold and dry conditions. Our dataset was created by combining several methods to produce detailed spatial models of permafrost for the studied palsa mires. We used 3D ground-penetrating radar (GPR) survey, UAV-based orthophoto maps, peat thermometry, time-domain reflectometry, and manual sampling. We developed two integrated geospatial models that describe the active layer, the configuration of the palsa frozen core, and its thermal state and identify the zones of the most intense thawing. These observations revealed a significant thermal effect of the groundwater flow and its critical role in the palsas segmentation and rapid collapse. We have investigated a regulating effect of micromorphological features of palsa mounds such as heights, slope, depressions, and mire mineral bed through groundwater drainage. As a result, two new scenarios for the palsa degradation process have been developed, emphasizing the influence of environmental factors on the permafrost condition.

Permafrost carbon could produce a positive climate feedback. Until now, the ecosystem carbon budgets in the permafrost regions remain uncertain. Moreover, the frequently used models have some limitations especially regarding to the freeze-thaw process. Herein, we improved the IBIS model by incorporating an unfrozen water scheme and by specifying the parameters to estimate the present and future carbon budget of different land cover types (desert steppe, steppe, meadow, and wet meadow) in the permafrost regions. Incorporating an unfrozen water scheme reduced the mean errors in the soil temperature and soil water content by 25.2%, and the specifying leaf area parameters reduced the errors in the net primary productivity (NPP) by 79.9%. Further, the simulation results showed that steppes are carbon sources (39.16 gC/m(2)/a) and the meadows are carbon sinks (-63.42 gC/m(2)/a ). Under the climate warming scenarios of RCP 2.6, RCP 6.0, and RCP 8.5, the desert steppe and alpine steppe would assimilated more carbon, while the meadow and wet meadow were projected to shift from carbon sinks to carbon sources in 2071-2100, implying that the land cover type plays an important role in simulating the source/sink effects of permafrost ecosystem carbon in the IBIS model. The results highlight the importance of unfrozen water to the soil hydrothermal regime and specific leaf area for the growth of alpine vegetation, and present new insights on the difference of the responses of various permafrost ecosystems to climate warming.

Flash floods represent a significant threat, triggering severe natural disasters and leading to extensive damage to properties and infrastructure, which in turn results in the loss of lives and significant economic damages. In this study, a comprehensive statistical approach was applied to future flood predictions in the coastal basin of North Al-Abatinah, Oman. In this context, the initial step involves analyzing eighteen General Circulation Models (GCMs) to identify the most suitable one. Subsequently, we assessed four CMIP6 scenarios for future rainfall analysis. Next, different Machine Learning (ML) algorithms were employed through H2O-AutoML to identify the best model for downscaling future rainfall predictions. Forty distribution functions were then fitted to the future daily rainfall, and the best-fit model was selected to project future Intensity-Duration-Frequency (IDF) curves. Finally, the Soil and Water Assessment Tool (SWAT) model was utilized with sub-daily time steps to make accurate flash flood predictions in the study area. The findings reveal that IITM-ESM is the most effective among GCM models. Additionally, the application of stacked ensemble ML model proved to be the most reliable in downscaling future rainfall. Furthermore, this study highlighted that floods entering urbanized areas could reach 20.33 and 20.70 m(3)/s under pessimistic scenarios during rainfall events with 100 and 200-year return periods, respectively. This hierarchical comprehensive approach provides reliable results by utilizing the most effective model at each step, offering in-depth insight into future flash flood prediction.

Evaluating the seismic vulnerability of facades of historic masonry buildings is essential not only for their significant historical and heritage value, but also to evaluate the safety of this type of construction. This work applies a simplified methodology to assess the seismic vulnerability of the facade of masonry buildings in the historic center of Morelia, Michoac & aacute;n, M & eacute;xico. The historic center of Morelia was declared a World Cultural Heritage Site by UNESCO in 1991. On the facades, there is ornamentation with sculptural and vegetal decorative elements. The methodology involved conducting visual inspections to identify the location, type of structure, construction materials, doors, windows, balconies, cornices, ironwork, pediments, niches, and sculptures, among other characteristic elements of colonial architecture. The seismic demands were determined specifically for the city's historic center based on a recent seismic hazard assessment of Morelia. Based on the methodology and the compiled database, characterized vulnerability indices were defined for the different damage scenarios that buildings may present. Results indicate that earthquakes with intensities greater than VIII on the Modified Mercalli scale risk collapsing heritage masonry buildings' facades.

Understanding climate change and land use impacts is crucial for mitigating environmental degradation. This study assesses the environmental vulnerability of the Doce River Basin for 2050, considering future climate change and land use and land cover (LULC) scenarios. Factors including slope, elevation, relief dissection, precipitation, temperature, pedology, geology, urban distance, road distance, and LULC were evaluated using multicriteria analysis. Regional climate models Eta-HadGEM2-ES and Eta-MIROC5 under RCP 4.5 and RCP 8.5 emission scenarios were employed. The Land Change Modeler tool simulated 2050 LULC changes and hypothetical reforestation of legal reserve (RL) areas. Combining two climate and two LULC scenarios resulted in four future vulnerability scenarios. Projections indicate an over 300 mm reduction in average annual precipitation and an up to 2 degrees C temperature increase from 2020 to 2050. Scenario 4 (RCP 8.5 and LULC for 2050 with reforested RLs) showed the greatest basin area in the lowest vulnerability classes, while scenario 3 (RCP 4.5 and LULC for 2050) exhibited more high-vulnerability areas. Despite the projected relative improvement in environmental vulnerability by 2050 due to reduced rainfall, the complexity of associated relationships must be considered. These results contribute to mitigating environmental damage and adapting to future climatic conditions in the Doce River Basin.

Humidity is a basic and crucial meteorological indicator commonly measured in several forms, including specific humidity, relative humidity, and absolute humidity. These different forms can be inter-derived based on the saturation vapor pressure (SVP). In past decades, dozens of formulae have been developed to calculate the SVP with respect to, and in equilibrium with, liquid water and solid ice surfaces, but many prior studies use a single function for all temperature ranges, without considering the distinction between over the liquid water and ice surfaces. These different approaches can result in humidity estimates that may impact our understanding of surface-subsurface thermal-hydrological dynamics in cold regions. In this study, we compared the relative humidity (RH) downloaded and calculated from four data sources in Alaska based on five commonly used SVP formulas. These RHs, along with other meteorological indicators, were then used to drive physics-rich land surface models at a permafrost-affected site. We found that higher values of RH (up to 40 %) were obtained if the SVP was calculated with the over-ice formulation when air temperatures were below freezing, which could lead to a 30 % maximum difference in snow depths. The choice of whether to separately calculate the SVP over an ice surface in winter also produced a significant range (up to 0.2 m) in simulated annual maximum thaw depths. The sensitivity of seasonal thaw depth to the formulation of SVP increases with the rainfall rate and the height of above-ground ponded water, while it diminishes with warmer air temperatures. These results show that RH variations based on the calculation of SVP with or without over-ice calculation meaningfully impact physicallybased predictions of snow depth, sublimation, soil temperature, and active layer thickness. Under particular conditions, when severe flooding (inundation) and cool air temperatures are present, care should be taken to evaluate how humidity data is estimated for land surface and earth system modeling

Many studies have focused on elevation-dependent warming (EDW) across high mountains, but few studies have examined both EDW and LDW (latitude-dependent warming) on Antarctic warming. This study analyzed the Antarctic amplification (AnA) with respect to EDW and LDW under SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 from Coupled Model Intercomparison Project Phase 6 (CMIP6) during the period 2015-2100. The results show that the AnA appears under all socioeconomic scenarios, and the greatest signal appears in austral autumn. In the future, Antarctic warming is not only elevation-dependent, but also latitude-dependent. Generally, positive EDW of mean temperature (T-mean), maximum temperature (T-max) and minimum temperature (T-min) appear in the range of 1.0-4.5 km, and the corresponding altitudinal amplification trends are 0.012/0.012/0.011 (SSP1-2.6)- 0.064/0.065/0.053 (SSP5-8.5) degrees C decade(-1)center dot km(-1). Antarctic EDW demonstrates seasonal differences, and is strong in summer and autumn and weak in winter under SSP3-7.0 and SSP5-8.5. For T(mea)n, T-max and T-min, the feature of LDW is varies in different latitude ranges, and also shows seasonal differences. The strongest LDW signal appears in autumn, and the warming rate increases with increasing latitude at 64-79 degrees S under SSP1-2.6. The similar phenomenon can be observed at 68-87 degrees S in the other cases. Moreover, the latitude component contributes more to the warming of T-mean and T-max relative to the corresponding altitude component, which may relates to the much larger range of latitude (similar to 2600 km) than altitude (similar to 4.5 km) over Antarctica, while the EDW contributes more warming than LDW in the changes in T-min in austral summer. Moreover, surface downwelling longwave radiation, water vapor and latent heat flux are the potential factors influencing Antarctic EDW, and the variation in surface downwelling longwave radiation can also be considered as an important influencing factor for Antarctic LDW. Our results provide preliminary insights into EDW and LDW in Antarctica.

The thermal stability of permafrost under complex environment (climate scenarios, permafrost types and regional air temperatures) directly affects the long-term service performance of highway or railway. This study uses a large amount of valuable soil temperature monitoring and simulation data to examine the stability of typical crushed-rock embankments (CREs) along Qinghai-Tibet Railway, which is located in the permafrost re-gion on the Roof of the World. Firstly, a novel numerical model for CREs considering a complex heat transfer environment is established and verified. Then, alteration characteristics of recent-term thermal regime of permafrost across time and space under three CREs are revealed based on a decade of field monitoring data. Finally, long-term thermal regime of permafrost under three CREs under complex environment is analyzed by numerical simulation. Results include: 1) Soil temperature near the ground surface under the three CREs shows a decreasing trend, whereas the overall temperature around the deep permafrost increases over time under a recent climate warming. 2) The warming rate of permafrost under CREs rises with the acceleration of climate scenarios and regional air temperature and the decrease of regional ground temperatures. 3) U-shaped crushed-rock embankment is the most suitable CRE for managing complex environment, especially when the mean annual ground temperature is 0 similar to -1 degrees C or climate scenarios are RCP 2.6 and RCP 4.5. 4) Transition from low-temperature permafrost to warm permafrost is a warning signal of permafrost degradation under climate warming. 5) With an increase of permafrost degradation rate (the thawing and warming rates of permafrost), embankment stability becomes worse. These findings will not only serve as a scientific basis for the embankment damage prevention of the Qinghai-Tibet Railway, but also provide important technical supports for the successful building of infrastructure in permafrost regions under complex environment.

For two Austrian regions (Amstetten South and P & ouml;llauer valley), climate data from the periods 1961-1990 and 1991-2020 were analyzed and scenarios for +2 degrees C and +3 degrees C global warming (global warming level) were calculated in order to find out which changes relevant to fruit growing can be expected due to global warming. The comparison of the periods 1961-1990 and 1991-2020 already showed relevant changes that will continue to intensify in both scenarios: higher temperatures and less severe frosts in winter, longer growing seasons and an earlier start to vegetation at all altitudes. Late frosts in spring are becoming less frequent, but due to the earlier start of vegetation at the same time, the risk of frost damage - especially in April - remains and may even increase in some areas. The higher temperatures lead to a reduction in the climatic water balance, particularly in summer and at lower altitudes; in dry years, heat and drought stress are to be expected. In the lower altitudes of the two regions, where extensive orchards have had their main distribution up to now, they will come under increasing pressure, particularly on soils with low water retention capacity. Due to warmer summers and winters and longer growing seasons, the climate that is favorable for many types of fruit is increasingly shifting to higher altitudes that were previously less suitable and less commonly used for fruit growing. The risks and uncertainties for fruit production will increase considerably if the temperature rises by +2 degrees C, and at +3 degrees C, traditional forms of cultivation could be at risk. Active climate protection that limits global warming to below +2 degrees C is therefore essential to ensure a future perspective for extensive fruit orchards in Austria.

This paper presents a framework to assess the vulnerability of the electrical power grid (EPG) to extreme weather events. The paper presents a methodology based on the Extra-Trees classifier and historical weather data to identify the EPG assets that are most likely to be affected in future extreme weather conditions under various climate change scenarios. The developed methodology considers the EPG different asset classes (lines, towers, poles, transformers, substations.) and identifies the weather parameters that are most relevant to their vulnerability. The paper presents results concerning wind speed, wind gusts, soil type, and altitude, which are used to train a model that predicts the probability of an asset being damaged based on the future weather parameters. The methodology was developed has been applied to a dataset of historical events in Portugal, from the major Portuguese DSO, thus assessing the future vulnerability of the EPG under three different scenarios of climate change. The developed methodology is a successful tool, that would not only help prevent occurrences of faults/failures in the Electrical Power Grid and its recovery from these occurrences, but also to have a better perception of a geographically safe future expansion of infrastructures. In this way it contributes to a continuous, non-faulty EPG operation, fulfilling society's demands by generating maps that identify the most vulnerable areas for each future climate scenario.