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The present paper aims to study the Wachau Valley in Austria as a representative Cultural Landscape under threat from extreme hydrometeorological hazards linked to climate change. The primary objective is to investigate the impacts and assess the vulnerability associated with the events of heavy rain and flooding. The methodology employed consists of an investigation of recorded past events impacting the Wachau; a vulnerability ranking system; a climate time series analysis based on earth observation products; and future hazard maps at territorial level, developed with outputs from regional and global climate models. The investigation we carried out provides a vulnerability assessment of two terraced areas with a surface of about 10,000 m2 in total, characterized by the presence of dry stone walls, with different state of conservation in the Municipality of Krems (Wachau). In addition, climate projections at territorial level for the extreme climate indices R20mm, R95pTOT, and R×5day—selected for investigating the likelihood of increases/decreases in events of heavy rain and large basin flooding—are provided, with a spatial resolution of ~12 km for the near and far future (2021–2050; 2071–2100) under stabilizing (RCP 4.5) and pessimistic (RCP 8.5) scenarios. The results indicate a general increase for the three indices in the studied areas during the far future under the pessimistic scenario, suggesting a heightened risk of heavy rain and flooding. These findings aim to inform policymakers and decision-makers in their development of strategies for safeguarding cultural heritage. Furthermore, they serve to assist local stakeholders in enhancing their understanding of prioritizing interventions related to preparedness, emergency response, and recovery. [ABSTRACT FROM AUTHOR]

Rainfall erosivity, which signifies the inherent susceptibility of soil erosion induced by precipitation, plays a fundamental role in formulating a comprehensive soil loss equation (RUSLE). It stands as a crucial determinant among the foundational factors considered in a comprehensive soil loss equation's establishment. Nonetheless, the prediction and quantification of future alterations in rainfall erosivity under the influence of global warming have been relatively limited. In this study, climate change was widely evaluated and 10 preferred global climate models in the Loess Plateau were selected by using the data sets of 27 models simulating climate change and the CN05.1 data set provided by the latest CMIP6. The monthly precipitation forecast data were obtained by using the delta downscaling method. Combined with trend analysis, significance test, and coefficient of variation, the annual rainfall erosivity during 1961–2100 under four SSP scenarios was analyzed and predicted. Among the 27 GCM models used in this paper, the most suitable climate models for simulating monthly precipitation in the Loess Plateau were CMCC-CM2-SR5, CMCC-ESM2, TaiESM1, EC-Earth3, EC-Earth-Veg-LR, INM-CM4-8, CAS-ESM2-0, EC-Earth-Veg, ACCESS-ESM1-5, and IPSL-CM6A-LR. In comparison to the base period (1961–1990), during the historical period (1961–2014), the average annual rainfall erosivity on the Loess Plateau amounted to 1259.64 MJ·mm·hm−2·h−1·a−1, showing an insignificant downward trend. In the northwest of Ningxia, Yulin City and Yanan City showed a significant upward trend. In the future period (2015–2100), the annual rainfall erosivity continues to constantly change and increase. The potential average increase in rainfall erosivity is about 13.48–25.86%. In terms of spatial distribution, most areas showed an increasing trend. Among these regions, the majority of encompassed areas within Shanxi Province, central Shaanxi, and Inner Mongolia increased greatly, which was not conducive to soil and water conservation and ecological environment construction. This study offers a scientific reference for the projected future erosivity characteristics of the Loess Plateau. [ABSTRACT FROM AUTHOR]

In recent years, global climate change causes more extreme precipitation events in Lancang-Mekong River Basin (Lancang-Mekong Basin). It leads to the increase of rainstorm and flood risk, which poses a threat to the flood control safety of Lancang-Mekong Basin. Based on the precipitation data of four global climate models of CMIP6, this paper selected six indexes, including PRCPTOT and R10, to study the change characteristics of the extreme precipitation indexes in the Lancang-Mekong Basin during 1980–2020 and predicted the development trend of the extreme precipitation indexes in the Lancang-Mekong Basin during 2021–2050. The results show that from 1980 to 2020, the extreme precipitation events in regions I and IV showed an increasing trend, while those in regions II and III showed a decreasing trend. From 2021 to 2050, the extreme precipitation events in all regions of the Lancang-Mekong River show an upward trend, and most indexes increased compared with the historical period. The extreme precipitation indexes in most regions increases the most under the scenario of SSP5-8.5, while the increase is small under the scenario of SSP2-4.5; The six extreme precipitation indexes selected in the paper can better reflect the trend of extreme precipitation events and floods. The trend of extreme precipitation indexes in the Lancang-Mekong River basin in the future is more significant, which indicates that it may be affected more frequently by extreme precipitation events and floods. [ABSTRACT FROM AUTHOR]

In recent decades, climate change has become a critical global issue with far-reaching consequences for regional climates and ecosystems. While regional climate models provide valuable information, there is a growing need for high-resolution simulations to assess local impacts. This paper addresses this gap by presenting the first simulation of a 3 km convection-permitting (CP) scenario simulation for Bulgaria. The main aim of this study is to assess different precipitation indices and their future changes for Bulgaria under the Representative Concentration Pathway 8.5 (RCP8.5) scenario following the Coordinated Regional Climate Downscaling Experiment Flagship Pilot Study protocol. The simulations are evaluated against high-resolution observations. We downscale Coupled Model Intercomparison Project 5 Global Climate Model (CMIP5 GCM) data for historical (1995–2004) and future (2089–2098) periods using a regional climate model (RCM) at 15 km grid spacing and parametrized convection. We use these fields as initial and boundary conditions for convection-permitting kilometer-scale simulations. The 15 km grid spacing driving model is used as a reference to assess the added value of the kilometer-scale simulation. Additionally, the 3 km seasonal mean and projected 2 m temperature and the winter snow water equivalent are presented. The results show that the kilometer-scale simulation shows better performance of wet-hour intensity in all seasons, wet-hour frequency in the spring, fall, and winter, and extreme precipitation (99.9th percentile of all precipitation events, p99.9) in the winter and fall. The kilometer-scale simulation improves the projected precipitation distribution and modifies the signal of the precipitation frequency, intensity, and heavy precipitation change over some areas. A positive projected change in the wet-hour intensity is expected in all seasons (13.86% in spring, MAM, 17.48% in summer, JJA, 1.97% in fall, SON, and 17.43% in winter, DJF) and in the heavy precipitation in the spring (13.14%) and winter (31.19%) in the kilometer-scale experiment. The projected increase in mean winter precipitation is accompanied by a significant decrease in mean winter snowfall over lowlands (50−70%). The convection-permitting Regional Climate Model, version 4.7.1 (RegCM4.7.1) suggests an increase in winter snowfall over the highest parts of the country, but a significant increase in the 2 m temperatures there. The results of this study are encouraging and may be of interest to the community of climate scientists and users of climate data for making reliable estimates of the local impacts of future climate change. [ABSTRACT FROM AUTHOR]

General circulation models (GCMs) run at regional resolution or at a continental scale. Therefore, these results cannot be used directly for local temperatures and precipitation prediction. Downscaling techniques are required to calibrate GCMs. Statistical downscaling models (SDSM) are the most widely used for bias correction of GCMs. However, few studies have compared SDSM with multi-layer perceptron artificial neural networks and in most of these studies, results indicate that SDSM outperform other approaches. This paper investigates an alternative architecture of neural networks, namely the long-short-term memory (LSTM), to forecast two critical climate variables, namely temperature and precipitation, with an application to five climate gauging stations in the Lake Chad Basin. Lake Chad is a data scarce area which has been impacted by severe drought, where water resources have been influenced by climate change and recent agricultural expansion. SDSM was used as the benchmark in this paper for temperature and precipitation downscaling for monthly time–scales weather prediction, using grid resolution GCM output at a 5 degrees latitude × 5 degrees longitude global grid. Three performance indicators were used in this study, namely the root mean square error (RMSE), to measure the sensitivity of the model to outliers, the mean absolute percentage error (MAPE), to estimate the overall performance of the predictions, as well as the Nash Sutcliffe Efficiency (NSE), which is a standard measure used in the field of climate forecasting. Results on the validation set for SDSM and test set for LSTM indicated that LSTM produced better accuracy on average compared to SDSM. For precipitation forecasting, the average RMSE and MAPE for LSTM were 33.21 mm and 24.82% respectively, while the average RMSE and MAPE for SDSM were 53.32 mm and 34.62% respectively. In terms of three year ahead minimum temperature forecasts, LSTM presents an average RMSE of 4.96 degree celsius and an average MAPE of 27.16%, while SDSM presents an average RMSE of 8.58 degree celsius and an average MAPE of 12.83%. For maximum temperatures forecast, LSTM presents an average RMSE of 4.27 degree celsius and an average MAPE of 11.09 percent, while SDSM presents an average RMSE of 9.93 degree celsius and an average RMSE of 12.07%. Given the results, LSTM may be a suitable alternative approach to downscale global climate simulation models' output, to improve water management and long-term temperature and precipitations forecasting at local level. [ABSTRACT FROM AUTHOR]

In this paper, the patterns of the occurrences of fire incidents over sub-Saharan Africa are studied on the basis of satellite data. Patterns for the whole sub-Saharan Africa are contrasted with those for northern sub-Saharan Africa and southern-hemisphere Africa. This paper attempts to unravel linear trends and overriding oscillations using regression and spectral techniques. It compares fire patterns for aggregated vegetation with those for specific types, which are savannahs, grasslands, shrublands, croplands, and forests, to identify key trend drivers. The underlying cyclic trends are interpreted in light of climate change and model projections. Considering sub-Saharan Africa, northern sub-Saharan Africa, and southern-hemisphere Africa, we found declining linear trends of wildfires with overriding cyclic patterns that have a period of ∼5 years, seemingly largely driven by savannahs, grasslands, and croplands. [ABSTRACT FROM AUTHOR]

The goal of the third LIFE project (2006-2011) [[21]] was the reintroduction of the Iberian lynx to some select areas of Andalucía (southern Spain), the region holding two of the more important Iberian lynx populations at that time. In their recent paper, Van Hassel and Bovenkerk [[1]] raised the question of how we should aid wild animals in coping with climate change and they used the Iberian lynx ( I Lynx pardinus i ) as a case study. References 1 van Hassel F., Bovenkerk B. How should we help wild animals cope with climate change? Comment on van Hassel, F.; Bovenkerk, B. How Should We Help Wild Animals Cope with Climate Change?. [Extracted from the article]

Achieving a good level of resilience to extreme events caused by severe weather conditions is a major target for operators in modern power systems due to the increasing frequency and intensity of extreme weather phenomena. Moreover, regulatory authorities are pushing transmission and distribution operators to prepare resilience plans suitably supported by Cost–Benefit Analyses (CBAs). In this context, this paper proposes a CBA framework based on Optimization via Simulation (OvS) for the selection of the optimal portfolio of resilience enhancement measures. Starting from a comprehensive set of candidate grid hardening and operational measures, the optimal mix is identified by applying a novel two-step procedure based on an efficient application of the generalized pattern search heuristic technique. Risk indicators for the CBA are quantified, accounting for probabilistic models of climate changes. Moreover, the potential cascading outages due to multiple component failures provoked by extreme events are simulated on selected scenarios. The examples carried out on an IEEE test system show the effectiveness of the approach in identifying the best portfolio of resilience enhancement measures depending on climate change projections and costs of the measures, while the application to the model of a large portion of the Italian EHV transmission system demonstrates the practicability of the approach in real-world studies to support operators in different power system management phases, from planning to operation. [ABSTRACT FROM AUTHOR]

Determining the height of plume clouds is crucial for various applications, including global climate models. Smokestack plume rise refers to the altitude at which the plume cloud travels downwind until its momentum dissipates and the temperatures of the plume cloud and its surroundings become equal. While most air-quality models employ different parameterizations to forecast plume rise, they have not been effective in accurately estimating it. This paper introduces a novel framework that utilizes Deep Convolutional Neural Networks (DCNNs) to monitor smokestack plume clouds and make real-time, long-term measurements of plume rise. The framework comprises three stages. In the first stage, the plume cloud is identified using an enhanced Mask R-CNN, known as the Deep Plume Rise Network (DPRNet). Next, image processing analysis and least squares theory are applied to determine the plume cloud's boundaries and fit an asymptotic model to its centerlines. The z-coordinate of the critical point of this model represents the plume rise. Finally, a geometric transformation phase converts image measurements into real-world values. This study's findings indicate that the DPRNet outperforms conventional smoke border detection and recognition networks. In quantitative terms, the proposed approach yielded a 22% enhancement in the F1 score, compared to its closest competitor, DeepLabv3. [ABSTRACT FROM AUTHOR]

The identification of parameters that can quantitatively describe the different characteristics of urban morphology is fundamental to studying urban ventilation and microclimate at the local level and developing parameterizations of the dynamic effect of an urban area in mesoscale models. This paper proposes a methodology to calculate four morphological parameters, namely mean height, aspect ratio, sky view factor, and plan area ratio, of five cities located in southern (Bari and Lecce), central (Naples and Rome), and northern (Milan) Italy. The calculation is performed using the Geographical Information System (GIS), starting from morphological and land use data collected and analyzed in shapefiles. The proposed methodology, which can be replicated in other cities, also presents in detail the procedure followed to properly build input data to calculate the sky view factor using the UMEP GIS tool. The results show a gradual increase in the plan area index, λp, and mean building height, H ¯ , moving from the south to the north of Italy. Maximum values of λp and H ¯ are obtained in the regions of Milan, Rome, and Naples, where the highest spatially-averaged values are also found, i.e., λp = 0.22, H ¯ = 10.9 m in Milan; λp = 0.19, H ¯ = 12.7 m in Rome; λp = 0.20, H ¯ = 12 m in Naples. Furthermore, for all the cities investigated, areas characterized by the Corine Land Cover class as "continuous urban fabric" are those with medium sky view factor SVF values (around 0.6–0.7) and λp values (around 0.3) typical of intermediate/compact cities. The methodology employed here for calculating morphological parameters using GIS proves to be replicable in different urban contexts. This opens to a better classification of cities in local climate zones (LCZ), as shown for the Lecce region, useful for urban heat island (UHI) studies and to the development of parameterizations of the urban effects in global and regional climate models. [ABSTRACT FROM AUTHOR]

The paper presents the estimated changes in the soil water content, the total runoff, the sediment yield and the actual evapotranspiration for the small Bystra catchment in the east of Poland. The findings are based on the results of three simulations covering the years of 2041–2050. The simulations were based on a calibrated and validated SWAT model (2010–2017). The first variant covers just the climate change and the existing structure of soil cultivation for the three regional climate models supported by the EC-EARTH global climate model in the emission scenarios RCP4.5 and RCP8.5. Variants two and three are based on the first variant in terms of the changing climate. The second variant, however, involves placing a pond in each farm in the catchment, while the third variant involves designing huge reservoirs as a result of land consolidation. Variants two and three occur in five adaptation scenarios each. The first adaptation scenario (V2.1 and V3.1) involves only increasing the number of ponds on the farm or increasing the number of reservoirs for non-irrigated arable land crops, i.e., WWHT (winter cereals), BARL (spring cereals), CANP (rapeseed) and CRDY (other crops). The second adaptation scenario (V2.2 and V3.2) involves growing vegetables without irrigation (instead of cereals). The third adaptation scenario (V2.3 and V3.3) involves growing vegetables with irrigation (instead of cereals). The fourth adaptation scenario (V2.4 and V3.4) involves partial cultivation of vegetables and cereals. The fifth adaptation scenario (V2.5 and V3.5) involves partial cultivation of orchards and cereals. The adaptation scenarios of the irrigation of vegetables from deep water-bearing layers (second variant) or reservoirs (third variant) contribute to the increase in water content in the soil, especially in summer, in comparison with the adaptation scenarios for vegetable cultivation without irrigation. What is more, the actual evapotranspiration was higher in the adaptation scenarios involving irrigation than in scenarios without irrigation. It is known that the changes in water content in soil and the intensification of water erosion are gravely affected by modifications in crops and soil cultivation. A change from cereal cultivation to irrigated vegetable cultivation or orchards increased the water content in the soil in most climatic projections. However, the increase in the number of ponds in the second variant had little impact on the soil water content, actual evapotranspiration and overall runoff, while the erosion loss decreased. With the lower precipitation levels in the years 2041–2050 relative to 2010–2017, as presented in the emissive scenario RCP 4.5, the soil water content decreases by up to 14% for most variants. Total runoff for most variants will also be lower by 4–35%. The percentage change in sediment yield will fluctuate between −86% and 116%. On the other hand, the actual evapotranspiration for most variants will be higher. With higher precipitation levels in the years 2041–2050 relative to 2010–2017, as presented in the emissive scenario RCP 8.5, the soil water content changes slightly from −7% to +3%. Total runoff for most variants will also be higher by as much as 43%. Sediment yield for most scenarios may increase by 226%. The actual evapotranspiration for most variants will also be higher. Irrigation variants tend to increase soil available water while increasing evapotranspiration and total outflow in the catchment as compared to non-irrigated LULC. The largest increase in the soil water content is observed in most irrigation variants for RCP 4.5 (annual average 316–319 mm) (V2.3-V2.5, V3.2, and V3.3) and RCP 8.5 (annual average 326–327 mm) (V2.3-V2.5 and V3.3) as compared to V1 (BaU) (315 mm–RCP 4.5 and 324 mm–RCP 8.5) for the years 2041–2050. On the other hand, the lowest increase in soil water content is observed in the V3.5 variant, with an annual average of 292 mm for RCP 4.5 and an annual average of 311 mm for RCP 8.5. Thus, for future climate change scenarios, irrigation with water reservoirs (ponds and storage reservoirs) should be considered. The study proves the rationale behind building ponds in small catchments in order to increase water resources in a landscape and also to counteract adverse effects of climate changes, i.e., sediment outflow and surface water erosion. [ABSTRACT FROM AUTHOR]

The emergence of climate change (CC) is affecting and changing the development of the natural environment, biological species, and human society. In order to better understand the influence of climate change and provide convincing evidence, the need to quantify the impact of climate change is urgent. In this paper, a climate change model is constructed by using a radial basis function (RBF) neural network. To verify the relevance between climate change and extreme weather (EW), the EW model was built using a support vector machine. In the case study of Canada, its level of climate change was calculated as being 0.2241 ("normal"), and it was found that the factors of CO2 emission, average temperature, and sea surface temperature are significant to Canada's climate change. In 2025, the climate level of Canada will become "a little bad" based on the prediction results. Then, the Pearson correlation value is calculated as being 0.571, which confirmed the moderate positive correlation between climate change and extreme weather. This paper provides a strong reference for comprehensively understanding the influences brought about by climate change. [ABSTRACT FROM AUTHOR]

Estimation of satellite-based remotely sensed evapotranspiration (ET) as consumptive use has been an integral part of agricultural water management. However, less attention has been given to future predictions of ET at watershed-scales especially since with a changing climate, there are additional challenges to planning and management of water resources. In this paper, we used nine years of total seasonal ET derived using a satellite-based remote sensing model, Mapping Evapotranspiration at Internalized Calibration (METRIC), to develop a Random Forest machine learning model to predict watershed-scale ET into the future. This statistical model used topographic and climate variables in agricultural areas of Lower Yakima, Washington and had a prediction accuracy of 88% for the region. This model was then used to predict ET into the future with changed climatic conditions under RCP4.5 and RCP8.5 emission scenarios expected by 2050s. The model result shows increases in seasonal ET across some areas of the watershed while decreases in other areas. On average, growing seasonal ET across the watershed was estimated to increase by +5.69% under the low emission scenario (RCP4.5) and +6.95% under the high emission scenario (RCP8.5). [ABSTRACT FROM AUTHOR]

Our climate is projected to change gradually over time. Mycotoxin occurrence in cereal grains is both directly and indirectly related to local weather and to climate changes. Direct routes are via the effects of precipitation, relative humidity, and temperatures on both fungal infection of the grain and mycotoxin formation. Indirect routes are via the effects of the wind dispersal of spores, insect attacks, and shifts in cereal grain phenology. This review aimed to investigate available modeling studies for climate change impacts on mycotoxins in cereal grains, and to identify how they can be used to safeguard food safety with future climate change. Using a systematic review approach, in total, 53 relevant papers from the period of 2005–2020 were retrieved. Only six of them focused on quantitative modeling of climate change impacts on mycotoxins, all in pre-harvest cereal grains. Although regional differences exist, the model results generally show an increase in mycotoxins in a changing climate. The models do not give an indication on how to adapt to climate change impacts. If available models were linked with land use and crop models, scenario analyses could be used for analyzing adaptation strategies to avoid high mycotoxin presence in cereal grains and to safeguard the safety of our feed and food. [ABSTRACT FROM AUTHOR]

Environmental conditions are the basis of plant reproduction and are the critical factors controlling seed dormancy and germination. Global climate change is currently affecting environmental conditions and changing the reproduction of plants from seeds. Disturbances in germination will cause disturbances in the diversity of plant communities. Models developed for climate change scenarios show that some species will face a significant decrease in suitable habitat area. Dormancy is an adaptive mechanism that affects the probability of survival of a species. The ability of seeds of many plant species to survive until dormancy recedes and meet the requirements for germination is an adaptive strategy that can act as a buffer against the negative effects of environmental heterogeneity. The influence of temperature and humidity on seed dormancy status underlines the need to understand how changing environmental conditions will affect seed germination patterns. Knowledge of these processes is important for understanding plant evolution and adaptation to changes in the habitat. The network of genes controlling seed dormancy under the influence of environmental conditions is not fully characterized. Integrating research techniques from different disciplines of biology could aid understanding of the mechanisms of the processes controlling seed germination. Transcriptomics, proteomics, epigenetics, and other fields provide researchers with new opportunities to understand the many processes of plant life. This paper focuses on presenting the adaptation mechanism of seed dormancy and germination to the various environments, with emphasis on their prospective roles in adaptation to the changing climate. [ABSTRACT FROM AUTHOR]

This study examines the decadal change in the relationship between two major Indian Ocean (IO) sea surface temperature patterns, namely the Indian Ocean dipole (IOD) and northern IO and the East Asia summer monsoon (EASM) in the early 2000s. In 1991–1999, the former epoch, the interannual variability of EASM was associated with the IOD-like pattern in the original paper and its relationship weakened in 2000–2016. There are two possible causes for this decadal change; stronger land-sea thermal contrast as a local forcing in latter epoch, which may result in the weakening of the relationship between the IO and the EASM. In addition, the influence of El Niño-southern Oscillation (ENSO) on the western North Pacific subtropical high (WNPSH) could be changed depending on the frequency of ENSO. In the 2000s, the intensity of the low frequency (LF)-type ENSO (42–86 months period) events was weaker compared to the former epoch but that of quasi-biennial (QB)-type ENSO (16–36 months period) remained persistent. This could explain that the QB-type ENSO is remote forcing that modulates the change in the relationship between the tropical IO patterns and EASM in the 2000s. [ABSTRACT FROM AUTHOR]