Search Results (6)

Forests in Croatia are characterized by higher levels of biodiversity in species composition. Three significant events occurred in Croatian forests over the past ten years, all of which have a common denominator—sanitary felling. The challenge in the sustainable development of forests started with the ice storm of 2014 that amounted to damage and raised costs in forest stands to EUR 231,180,921. The second challenge was in 2017 when the bark beetle outbreak occurred in the Gorski Kotar region. In December 2017, a windstorm in the same area caused damage to approximately 500,000 m³ of wood stock. The third climate extreme was in the summer of 2023 when three storms with strong winds and heavy rain damaged even-aged forests of common beech and pedunculated oak. The damage was substantial: 3,954,181 m³ of timber was mostly broken and destroyed across 21,888.61 ha of area, and the most damage was in the pedunculate oak forests of Slavonia, i.e., Quercus robur subsp. Slavonica, at 1,939,175 m³. For the main meteorological stations in lowland Croatia, data on precipitation amounts (mm) and wind speeds (m/s) were collected for the period 1981–2023, and the results of our analysis for the last decade are presented. Meteorological drought was analyzed using the rain anomaly index RAI. Data regarding open space fires in the Mediterranean karst area of Croatia were collected from the Croatian Firefighting Association, and the calculation of the burned area index (BAI) was determined. Throughout the entire area of Gorski Kotar County, a sample of permanent plots was set and used to assess the extent of forest damage from the ice storm in 2014 and for the establishment of permanent monitoring of the recovery of trees and forests damaged by the ice storm. The monitoring of bark beetles in the Gorski Kotar region started in 1995 and is still in progress. The aftermath of bark beetle outbreaks in two uneven-aged silver fir stands was studied after a bark beetle outbreak and a sanitary felling of 4655.34 m³. In the area of lowland Croatia, a statistically significant and positive correlation was found between sanitary fellings, maximum wind speeds, and rain anomaly indices in even-aged forests. In conclusion, sustainable development will be at risk due to difficult recovery, rising costs, and overall climate change in the years to come. Full article

The second-largest wildfire in the history of South Korea occurred in 2022 due to strong winds and dry climates. Quantitative evaluation of soil erosion is necessary to prevent subsequent sediment disasters in the wildfire areas. The erosion rates in two watersheds affected by the wildfires were assessed using the revised universal soil loss equation (RUSLE), a globally popular model, and the soil erosion model for mountain areas (SEMMA) developed in South Korea. The GIS-based models required the integration of maps of the erosivity factor, erodibility factor, length and slope factors, and cover and practice factors. The rainfall erosivity factor considering the 50-year and 80-year probability of rainfall increased from coastal to mountainous areas. For the LS factors, the traditional version (TV) was initially used, and the flow accumulation version (FAV) was additionally considered. The cover factor of the RUSLE and the vegetation index of the SEMMA were calculated using the normalized difference vegetation index (NDVI) extracted from Sentinel-2 images acquired before and after the wildfire. After one year following the wildfire, the NDVI increased compared to during the year of the wildfire. Although the RUSLE considered a low value of the P factor (0.28) for post-fire watersheds, it overestimated the erosion rate by from 3 to 15 times compared to the SEMMA. The erosion risk with the SEMMA simulation decreased with the elapsed time via the vegetation recovery and stabilization of topsoil. While the FAV of RUSLE oversimulated by 1.65~2.31 times compared to the TV, the FAV of SEMMA only increased by 1.03~1.19 times compared to the TV. The heavy rainfall of the 50-year probability due to Typhoon Khanun in 2023 generated rill and gully erosions, landslides, and sediment damage in the post-fire watershed on forest roads for transmission tower construction or logging. Both the RUSLE and SEMMA for the TV and FAV predicted high erosion risks for disturbed hillslopes; however, their accuracy varied in terms of the intensity and extent. According to a comparative analysis of the simulation results of the two models and the actual erosion situations caused by heavy rain, the FAV of SEMMA was found to simulate spatial heterogeneity and a reasonable erosion rate. Full article

Flood triggered by heavy rains and typhoons leads to extensive damage to land and structures putting rural communities in crucial condition. Most of the studies on risk assessment focus on environmental factors, and building attributes have not been given attention. The five most expensive typhoon events in the Philippines were recorded in 2008–2013, causing USD 138 million in damage costs. This indicates the lack of tool/s that would aid in the creation of appropriate mitigation measure/s and/or program/s in the country to reduce damage caused by typhoons and flooding. Hence, this study highlights a structure vulnerability assessment approach employing the combination of analytical hierarchy process, physical structure attributes, and existing flood hazard maps by the local government unit. The available flood hazard maps were layered into base maps, and building attributes were digitized using a geographic information system. The result is an essential local scale risk map indicating the building risk index correlated to the structural information of each exposed structure. It was recorded that of 3094 structures in the community, 370 or 10.25% were found to be at moderate risk, 3094 (76.79%) were found to be high risk, and 503 (12.94%) were very high risk. The local government unit can utilize the resulting maps and information to determine flood risk priority areas to plan flood mitigation management strategies and educate people to improve the structural integrity of their houses. A risk map gives people an idea of what to improve in their houses to reduce their vulnerability to natural disasters. Moreover, the result of the study provides direction for future studies in the country to reduce loss and enhance structure resiliency against flooding. Full article

Crops and ecosystems constantly change, and risks are derived from heavy rains, hurricanes, droughts, human activities, climate change, etc. This has caused additional damages with economic and social impacts. Natural phenomena have caused the loss of crop areas, which endangers food security, destruction of the habitat of species of flora and fauna, and flooding of populations, among others. To help in the solution, it is necessary to develop strategies that maximize agricultural production as well as reduce land wear, environmental impact, and contamination of water resources. The generation of crop and land-use maps is advantageous for identifying suitable crop areas and collecting precise information about the produce. In this work, a strategy is proposed to identify and map sorghum and corn crops as well as land use and land cover. Our approach uses Sentinel-2 satellite images, spectral indices for the phenological detection of vegetation and water bodies, and automatic learning methods: support vector machine, random forest, and classification and regression trees. The study area is a tropical agricultural area with water bodies located in southeastern Mexico. The study was carried out from 2017 to 2019, and considering the climate and growing seasons of the site, two seasons were created for each year. Land use was identified as: water bodies, land in recovery, urban areas, sandy areas, and tropical rainforest. The results in overall accuracy were: 0.99% for the support vector machine, 0.95% for the random forest, and 0.92% for classification and regression trees. The kappa index was: 0.99% for the support vector machine, 0.97% for the random forest, and 0.94% for classification and regression trees. The support vector machine obtained the lowest percentage of false positives and margin of error. It also acquired better results in the classification of soil types and identification of crops. Full article

For risk assessment, two methods, quantitative risk assessment and qualitative risk assessment, are used. In this study, we identified the regional risk level for a disaster-prevention plan for an overall area at the national level using qualitative risk assessment. To overcome the limitations of previous studies, a heavy rain damage risk index (HDRI) was proposed by clarifying the framework and using the indicator selection principle. Using historical damage data, we also carried out hierarchical cluster analysis to identify the major damage types that were not considered in previous risk-assessment studies. The result of the risk-level analysis revealed that risk levels are relatively high in some cities in South Korea where heavy rain damage occurs frequently or is severe. Five causes of damage were derived from this study—A: landslides, B: river inundation, C: poor drainage in arable areas, D: rapid water velocity, and E: inundation in urban lowlands. Finally, a prevention project was proposed considering regional risk level and damage type in this study. Our results can be used when macroscopically planning mid- to long-term disaster prevention projects. Full article

The quantitative analysis of the disaster effect on water supply systems can provide useful information for water supply system management. In this study, a total disaster index (TDI) was developed using open-source public data in 419 water treatment plants in Korea with 23 input variables. The TDI quantifies the possible effects or damage caused by three major disasters (typhoons, heavy rain, and earthquakes) on water supply systems. The four components (regional factor, risk factor, urgency factor, and response and recovery factor) were calculated using input variables to determine the disaster index (DI) of each disaster. The weight of the input variables was determined using principal component analysis (PCA), and the weights of the DI of three natural disasters and four components used to calculate the TDI were determined by the analytical hierarchy process (AHP). Specifically, two ensemble machine learning models, random forest (RF) and XGBoost (XGB), were used to develop models to predict the TDI. Both models predicted the TDI with the coefficient of determination and root-mean-square error-observations standard deviation ratio of 0.8435 and 0.3957 for the RF model and 0.8629 and 0.3703 for the XGB model, respectively. The relative importance analysis suggests that the number of input variables can be minimized, which improves the models’ practical applicability. Full article