Search Results (157)

The main goal of this study is to present future changes in various precipitation indices at a kilometer-scale resolution for Bulgaria on an annual and seasonal basis. Numerical simulations were conducted using the Non-Hydrostatic Regional Climate Model version 4 (RegCM4-NH) following the Coordinated Regional Climate Downscaling Experiment Flagship Pilot Study protocol for three 10-year periods (1995–2004, 2041–2050, and 2090–2099), with horizontal grid resolutions of 15 km and 3 km, on the petascale supercomputer HPC Discoverer at Sofia Tech Park. Data from the Hadley Centre Global Environment Model version 2 (HadGEM2-ES), based on the Representative Concentration Pathway 8.5 (RCP8.5) scenario, were used as boundary conditions for the regional climate model (RCM) simulations, which were subsequently downscaled to the kilometer-scale (3 km) simulations using a one-way nesting approach. High-resolution model data were compared with high-resolution observational datasets as well as lower-resolution (15 km) data. Future changes in precipitation indices were analyzed on both annual and seasonal scales, including mean daily and hourly precipitation, the frequency and intensity of wet days (>1 mm/day) and wet hours (>0.1 mm/hour), extreme daily precipitation (99th percentile, p99), and extreme hourly precipitation (99.9th percentile, p99.9) for both future periods. Additionally, changes in near-surface (2 m) temperature and surface snow amount were also presented. There is no substantial difference in projected temperature change between the resolutions. A positive trend in annual mean precipitation is expected in the near future. Extreme precipitation (p99 and p99.9) is projected to increase in spring and winter, accompanied by a rise in daily and hourly precipitation intensity across both future periods. An increase in surface snow amount is observed in the central Danubian Plain, Thracian Lowland, and parts of the Rila and Pirin mountains for the near-future period. However, surface snow amount is expected to decrease by the end of the century. Full article

Tropical glaciers are highly sensitive to climate change, with their mass balance influenced by temperature and precipitation, which affects the accumulation area. In this study, we developed an open-source tool to map the accumulation area of glaciers in the Cordillera Blanca, Peru (1988–2023), using Landsat images, spectral indices, and the Otsu method. We analyzed trends and correlations between snow accumulation area, meteorological patterns from ERA5 data, and oscillation modes. The results were validated using field data and manual mapping. Greater discrepancies were observed in glaciers with debris cover or small clean glaciers (<1 km²). The Amazonian and Pacific sectors showed a significant trend in decreasing accumulation areas, with reductions of 8.99% and 10.24%, respectively, from 1988–1999 to 2010–2023. El Niño events showed higher correlations with snow accumulation, snowfall, and temperature during the wet season, indicating a stronger influence on the Pacific sector. The accumulation area was strongly anti-correlated with temperature and correlated with snowfall in both sectors at a 95% confidence level (α = 0.05). The highest correlations with meteorological parameters were observed during the dry season, suggesting that even minor changes in temperature or precipitation could significantly impact the accumulation area. Full article

The following study presents the results obtained from a comparative analysis of dry (powder and hard snow) and wet snow based on satellite data and in situ data methods for monitoring in the high mountain belt of Bulgaria. The aim of the study is to analyze the effectiveness of different spectral indices based on satellite data from Synthetic Aperture Radar (SAR), high-resolution (HR) imagery, and spectrometer data for assessing the state and dynamics of the snow cover. The methods studied and the results obtained were validated by instrument-based field observations, with instruments using thermal imaging cameras, spectrometer measurements, ground control points, and HR imagery. Satellite data offer an ever-widening view of trends in snow distribution over time. All these data combined provide a detailed picture of surface temperature and snow properties, which are crucial for understanding snowmelt processes and the energy balance in the high-altitude belt. The findings suggest that a multi-method approach, utilizing the combined advantages of SAR satellite data, offers the most comprehensive and accurate framework for satellite-based snow cover monitoring in the high mountain regions of Bulgaria, such as Rila Mountain. This integrative strategy not only improves the precision of snow cover estimates but can also support many water resource-related studies, such as snowmelt runoff studies, snow avalanche modeling, and better-informed decisions in the management and maintenance of winter tourism resorts. Full article

Microdochium species are harmful pathogens of winter cereals, causing snow mould and stem base diseases such as root rot. With changing climatic conditions, including prolonged wet autumns and mild winters, addressing pathogens that thrive at low positive temperatures has become increasingly important. Integrated strategies, including optimized sowing times, resistant cultivars, and the use of seed treatment fungicides have been suggested as effective approaches to mitigate Microdochium-induced damage. Field trials were conducted between 2021 and 2024 using five winter wheat cultivars treated with different seed treatment fungicides and sown at either optimal or delayed sowing times. Laboratory analyses identified Microdochium spp. as the dominant pathogens on the stem base across all trial years. Disease severity assessments indicated that seed treatment fungicides were generally effective against root rot, with products containing fludioxonil and SDHI group fungicides delivering the best performance. While disease pressure varied between optimal and late sowing experiments, late-sown winter wheat exhibited slightly reduced damage in most years. Additionally, some of the tested winter wheat cultivars demonstrated better performance against Microdochium spp. damage compared to others, highlighting the importance of selecting resistant cultivars. This study provides valuable insights into the control of Microdochium spp. under changing climatic conditions, particularly during the early growth stages of winter wheat. Full article

Flood events pose significant risks to infrastructure and populations worldwide, particularly in Punjab, Pakistan, where critical infrastructure must remain operational during adverse conditions. This study aims to predict flood-prone areas in Punjab and assess the vulnerability of critical infrastructures within these zones. We developed a robust Flood Susceptibility Model (FSM) utilizing the Maximum Likelihood Classification (MLC) model and Analytical Hierarchy Process (AHP) incorporating 11 flood-influencing factors, including “Topographic Wetness Index (TWI), elevation, slope, precipitation (rain, snow, hail, sleet), rainfall, distance to rivers and roads, soil type, drainage density, Land Use/Land Cover (LULC), and the Normalized Difference Vegetation Index (NDVI)”. The model, trained on a dataset of 850 training points, 70% for training and 30% for validation, achieved a high accuracy (AUC = 90%), highlighting the effectiveness of the chosen approach. The Flood Susceptibility Map (FSM) classified high- and very high-risk zones collectively covering approximately 61.77% of the study area, underscoring significant flood vulnerability across Punjab. The Sentinel-1A data with Vertical-Horizontal (VH) polarization was employed to delineate flood extents in the heavily impacted cities of Dera Ghazi Khan and Rajanpur. This study underscores the value of integrating Multi-Criteria Decision Analysis (MCDA), remote sensing, and Geographic Information Systems (GIS) for generating detailed flood susceptibility maps that are potentially applicable to other global flood-prone regions. Full article

Climate change has increased the risk of snow drought, which is associated with a deficit in snowfall and snowpack. The objectives of this research are to improve drought identification in a warming climate by developing a new snow-based hydroclimatic aggregate drought index (SHADI) and to assess the impacts of snowpack and snowmelt in drought analyses. To derive the SHADI, an R-mode principal component analysis is performed on precipitation, snowpack, surface runoff, and soil water storage. Then, a joint probability distribution function of drought frequencies and drought classes, conditional expectation, and k-means clustering are used to categorize droughts. The SHADI was applied to the Red River of the North Basin (RRB), a typical cold climate region, to characterize droughts in a mostly dry period from 2003 to 2007. The SHADI was compared with the hydroclimatic aggregate drought index (HADI) and U.S. drought monitor (USDM) data. Cluster analysis was also utilized as a benchmark to compare the results of the HADI and SHADI. The SHADI showed better alignment with cluster analysis results than the HADI, closely matching the identified dry/wet conditions in the RRB. The major differences between the SHADI and HADI were observed in cold seasons and in transition periods (dry to wet or wet to dry). The derived variable threshold levels for different categories of drought based on the SHADI were close to, but different from, those of the HADI. The SHADI can be used for short-term lead prediction of droughts in cold climate regions and, in particular, can provide an early warning for drought in the warming climate. Full article

Performance plays a critical role in the practical use of global streamflow reanalysis. This paper presents the combined use of random forest and the Shapley additive explanation to examine the mechanism by which catchment attributes influence the accuracy of streamflow estimates in reanalysis products. In particular, the reanalysis generated by the Global Flood Awareness System streamflow is validated by streamflow observations provided by the Catchment Attributes and MEteorology for Large-sample Studies dataset. Results highlight that with regard to the Kling–Gupta efficiency, the reanalysis surpasses mean flow benchmarks in 93% of catchments across the continental United States. In addition, twelve catchment attributes are identified as major controlling factors with spatial patterns categorized into five clusters. Topographic characteristics and climatic indices are also observed to exhibit pronounced influences. Streamflow reanalysis performs better in catchments with low precipitation seasonality and steep slopes or in wet catchments with a low frequency of precipitation events. The partial dependence plot slopes of most key attributes are consistent across the four seasons but the slopes’ magnitudes vary. Seasonal snow exhibits positive effects during snow melting from March to August and negative effects associated with snowpack accumulation from September to February. Catchments with very low precipitation seasonality (values less than −1) show strong seasonal variation in streamflow estimations, with negative effects from June to November and positive effects from December to May. Overall, this paper provides useful information for applications of global streamflow reanalysis and lays the groundwork for further research into understanding the seasonal effects of catchment attributes. Full article

This research studies the characteristics of snow-covered area (SCA) from two vastly different sensors: optical (Moderate-Resolution Imaging Spectroradiometer, or MODIS, equipped on board the Terra satellite) and radar (Synthetic Aperture Radar (SAR) on-board Sentinel-1 satellites). The focus are the five mountain ranges of the Iberian Peninsula (Cantabrian System, Central System, Iberian Range, Pyrenees, and Sierra Nevada). The MODIS product was selected to identify SCA dynamics in these ranges using the Probability of Snow Cover Presence Index (PSCPI). In addition, we evaluate the potential advantage of the use of SAR remote sensing to complete optical SCA under cloudy conditions. For this purpose, we utilize the Copernicus High-Resolution Snow and Ice SAR Wet Snow (HRS&I SWS) product. The Pyrenees and the Sierra Nevada showed longer-lasting SCA duration and a higher PSCPI throughout the average year. Moreover, we demonstrate that the latitude gradient has a significant influence on the snowline elevation in the Iberian mountains (R² ≥ 0.84). In the Iberian mountains, a general negative SCA trend is observed due to the recent climate change impacts, with a particularly pronounced decline in the winter months (December and January). Finally, in the Pyrenees, we found that wet snow detection has high potential for the spatial gap-filling of MODIS SCA in spring, contributing above 27% to the total SCA. Notably, the additional SCA provided in winter is also significant. Based on the results obtained in the Pyrenees, we can conclude that implementing techniques that combine SAR and optical satellite sensors for SCA detection may provide valuable additional SCA data for the other Iberian mountains, in which the radar product is not available. Full article

The snowmelt process plays a crucial role in hydrological forecasting, climate change, disaster management, and other related fields. Accurate detection of wet snow distribution and its changes is essential for understanding and modeling the snow melting process. To address the limitations of conventional fixed-threshold methods, which suffer from poor adaptability and significant interference from scattering noise, we propose a weakly supervised deep learning change detection algorithm with Sentinel-1 multi-temporal data. This algorithm incorporates the Multi-Region Convolution Module (MRC) to enhance the central region while effectively suppressing edge noise. Furthermore, it integrates the ResNet residual network to capture deeper image features, facilitating wet snow identification through feature fusion. Various combinations of differential images, polarization data, elevation, and slope information during and after snowmelt were input into the model and tested. The results suggest that the combination of differential images, VV polarization data, and slope information has greater advantages in wet snow extraction. Comparisons between our method, the fixed-threshold method, OTSU algorithm, and FCM algorithm against the results of Landsat images indicates that the overall accuracy of our method improves significantly when the proportion of wet snow cover is large, and the average overall accuracy of wet snow extraction is 85.2%. This study provides clues for the accurate identification of wet snow during the mid-snowmelt phase. Full article

Climate change has recently increased the frequency of landslides in alpine areas. Susceptibility mapping is crucial for anticipating and assessing landslide risk. However, traditional methods focus on static environmental variables to emphasize the spatial distribution of landslides, ignoring temporal dynamics in landslide development in the context of climate change. In this work, we focused on static and dynamic environment factors and utilized the certainty factor-logistic regression (CF-LR) model to assess and predict landslide susceptibility in Taxkorgan County, located in the Karakorum. The assessment and prediction were based on a catalog of climate change-related landslides over the past 20 years, the causative factors, and predicted climatic variables for the Shared Socioeconomic Pathways (SSP1-2.6) scenario. The results indicated that elevation, slope, groundwater, slope length gradient (LS) factor, Topographic Wetness Index (TWI), valley depth, and maximum precipitation were the key causes of slides below the snow line. The key factors causing debris flow above the snow line were elevation, slope, topographic relief, aspect, LS factor, distance to the river, and maximum temperature. The accuracy of slide and debris flow susceptibility was 0.92 and 0.89, respectively. The area of slides with medium, high, and very high susceptibility is 25.5% of the Taxkorgan. In addition, 82.6% of the slides happened in this region, and 49.5% of the entire area is covered by debris flows with medium, high, and very high susceptibility. Moreover, this area accounts for 91.8% of all debris flows. Until 2060, the region’s climate is anticipated to become warmer and wetter. Slides below the snow line will gradually decrease and shift eastward, and debris flows above the snow line will expand. Our findings will contribute to the management of landslide risks at the regional scale. Full article

At the end of the Pleniglacial and the first half of the Late Glacial period, approximately between 18 and 14 ka BP, rivers of the central and southern parts of the East European Plain had channels up to 10 times as large as the present day channels of the same rivers. These ancient channels, called large meandering palaeochannels, are widespread in river floodplains and low terraces. The hydrological regime of these large rivers is of great interest in terms of the palaeoclimatology of the late Marine Isotope Stage 2 (MIS 2). In this study, we aimed at quantitative estimation of maximum flood discharges of rivers in the Dnepr, Don and Volga basins in the late MIS 2. To approach this, we used massive measurements of the morphometric characteristics of large palaeochannels on topographic maps and remote sensing data—palaeochannel width, meander wavelength and their relationships with river flow parameters. The runoff depth of the maximum flood, which corresponds to the maximum depth of daily snow thaw during the snowmelt period, was obtained for unit basins with an area of <1000 km². The mean value for the southern megaslope of the East European Plain was 44.2 mm/day (6 times the modern value), with 46 mm/day for the Volga River (5.5 times), 45 mm/day (6.3 times) for the Don River and 39 mm/day (8 times the modern value) for the Dnepr River basins. In general, the Dnepr basin was drier than the Don and Volga basins, which corresponds well to the modern distribution of humidity. At the same time, the westernmost part of the Dnepr River basin was relatively wet in the past, and the decrease in humidity from the past to the modern situation was greater there than in the eastern and central regions. The obtained results contradict the prevailing ideas, based mainly on climatic modeling and palynological data, that the climate of Europe was cold and dry during MIS 2. The reason is that palaeoclimatic reconstructions were made predominantly for the LGM epoch (23–20 ka BP). On the East European Plain, the interval 18–14 ka BP is rather poorly studied. Our results of paleoclimatological and palaeohydrological reconstructions showed that the Late Pleniglacial and the first half of the Late Glacial period was characterized by a dramatic increase in precipitation and river discharge relative to the present day. Full article

Snow cover and runoff play an important role in the Arctic environment, which is increasingly affected by climate change. Over the past 30 years, winter temperatures in northern Sweden have risen by 2 °C, accompanied by an increase in precipitation. This has led to a higher incidence of thaw–freeze and rain-on-snow events. Snow properties, such as the snow depth and longevity, and the timing of snowmelt in spring significantly impact the alpine tundra vegetation. The emergent vegetation at the edge of the snow patches during spring and summer constitutes an essential nutrient supply for reindeer. We have used Sentinel-1 synthetic aperture radar (SAR) to determine the onset of the surface melt and the end of the snow cover in the core reindeer grazing area of the Laevás Sámi reindeer-herding community in northern Sweden. Using SAR data from March to August during the period 2017 to 2021, the start of the surface melt is identified by detecting the season’s backscatter minimum. The end of the snow cover is determined using a threshold approach. A comparison between the results of the analysis of the end of the snow cover from Sentinel-1 and in situ measurements, for the years 2017 to 2020, derived from an automatic weather station located in Laevásvággi reveals a 2- to 10-day difference in the snow-free ground conditions, which indicates that the method can be used to investigate when the ground is free of snow. VH data are preferred to VV data due to the former’s lower sensitivity to temporary wetting events. The outcomes from the season backscatter minimum demonstrate a distinct 25-day difference in the start of the runoff between the 5 investigated years. The backscatter minimum and threshold-based method used here serves as a valuable complement to global snowmelt monitoring. Full article

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. Full article

Drought monitoring and early detection have improved greatly in recent decades through the development and refinement of numerous indices and indicators. However, a lack of guidance, based on user experience, exists as to which drought-monitoring tools are most appropriate in a given location. This review paper summarizes the results of targeted user engagement and the published literature to improve the understanding of drought across North America and to enhance the utility of drought-monitoring tools. Workshops and surveys were used to assess and make general conclusions about the perceived performance of drought indicators, indices and impact information used for monitoring drought in the five main Köppen climate types (Tropical, Temperate, Continental, Polar Tundra, Dry) found across Canada, Mexico, and the United States. In Tropical, humid Temperate, and southerly Continental climates, droughts are perceived to be more short-term (less than 6 months) in duration rather than long-term (more than 6 months). In Polar Tundra climates, Dry climates, Temperate climates with dry warm seasons, and northerly Continental climates, droughts are perceived to be more long-term than short-term. In general, agricultural and hydrological droughts were considered to be the most important drought types. Drought impacts related to agriculture, water supply, ecosystem, and human health were rated to be of greatest importance. Users identified the most effective indices and indicators for monitoring drought across North America to be the U.S. Drought Monitor (USDM) and Standardized Precipitation Index (SPI) (or another measure of precipitation anomaly), followed by the Normalized Difference Vegetation Index (NDVI) (or another satellite-observed vegetation index), temperature anomalies, crop status, soil moisture, streamflow, reservoir storage, water use (demand), and reported drought impacts. Users also noted the importance of indices that measure evapotranspiration, evaporative demand, and snow water content. Drought indices and indicators were generally thought to perform equally well across seasons in Tropical and colder Continental climates, but their performance was perceived to vary seasonally in Dry, Temperate, Polar Tundra, and warmer Continental climates, with improved performance during warm and wet times of the year. The drought indices and indicators, in general, were not perceived to perform equally well across geographies. This review paper provides guidance on when (time of year) and where (climate zone) the more popular drought indices and indicators should be used. The paper concludes by noting the importance of understanding how drought, its impacts, and its indicators are changing over time as the climate warms and by recommending ways to strengthen the use of indices and indicators in drought decision making. Full article

On average, one hundred people die each year under avalanche snow. Despite extensive global research on gas exchange in buried avalanche victims, it remains unclear how the diffusion of respiratory gases affects survival under avalanche snow. This study aims to determine how oxygen and carbon dioxide diffuse through snow, as well as through wet and dry perlite, which may serve as a surrogate for avalanche snow. A custom-made apparatus to study the diffusion of respiratory gases consisted of a plastic cylinder (1200 mm long, ID 300 mm) with 13 gas sampling needles evenly spaced along the axis of the cylinder filled with the tested material. Following 60 min of free diffusion, gas samples were analyzed using a vital signs monitor with a module for respiratory gas analysis (E-CAiOVX, Datex-Ohmeda, GE Healthcare, Chicago, IL, USA). A combination of 16% oxygen, 5% carbon dioxide, and 79% nitrogen was used. The rates of diffusion for both respiratory gases were comparable in snow and both forms of perlite. Oxygen propagated faster than carbon dioxide. Due to similar diffusion characteristics to snow, perlite possesses the potential to stand in as an effective substitute for soft snow for the study of respiratory dynamics, for conducting breathing experiments, and for testing avalanche safety equipment. Full article