Search Results (41)

Wildfires have become increasingly frequent and intense due to climate change, posing severe threats to ecosystems, infrastructure, and human lives. As a result, accurate wildfire spread prediction is critical for effective risk mitigation, resource allocation, and decision making in disaster management. In this study, we develop a deep learning model to predict wildfire spread using remote sensing data. We propose LinU-Mamba, a model with a U-Net-based vision Mamba architecture, with light spatial attention in skip connections, and an efficient linear attention mechanism in the encoder and decoder to better capture salient fire information in the dataset. The model is trained and evaluated on the two-dimensional remote sensing dataset Next Day Wildfire Spread (NDWS), which maps fire data across the United States with fire entries, topography, vegetation, weather, drought index, and population density variables. The results demonstrate that our approach achieves superior performance compared to existing deep learning methods applied to the same dataset, while showing an efficient training time. Furthermore, we highlight the impacts of pre-training and feature selection in remote sensing, as well as the impacts of linear attention use in our model. As far as we know, LinU-Mamba is the first model based on Mamba used for wildfire spread prediction, making it a strong foundation for future research. Full article

Drought is one of the most impactful natural disasters, and it significantly impacts three main sectors of a country: the environment, society, and the economy. Therefore, drought assessment and monitoring are essential for reducing vulnerability and risk. However, insufficient and sparse long-term in situ rainfall data limit drought assessment in developing countries. Recently developed satellite-based rainfall products, available at different temporal and spatial resolutions, offer a valuable alternative in data-poor regions like Sri Lanka, where rain gauge networks are sparse and maintenance issues are prevalent. This study evaluates the accuracy of satellite-based rainfall estimates compared to in situ observations for drought assessment within the Mi Oya River Basin, Sri Lanka. We assessed the performance of various satellite-based rainfall products, including IMERG, GSMaP, CHIRPS, PERSIANN, and PERSIANN-CDR, by comparing them with ground-based observations over 20 years, from 2003 to 2022. Our methodology involved checking detection accuracy using the False Alarm Ratio (FAR), Probability of Detection (POD), and Critical Success Index (CSI), and assessing accuracy through metrics such as Root Mean Square Error (RMSE), Pearson Correlation Coefficient (CC), Percentage Bias (PBias), and Nash–Sutcliffe Efficiency (NSE). The two best-performing satellite-based rainfall products were used for meteorological and hydrological drought assessment. In the accuracy detection metrics, the results indicate that while products like IMERG and GSMaP generally provide reliable rainfall estimates, others like PERSIANN and PERSIANN-CDR tend to overestimate rainfall. For instance, IMERG shows a CSI range of 0.04–0.25 for moderate and heavy rainfall and 0.10–0.30 for light rainfall. On a monthly scale, IMERG and CHIRPS showed the highest performance, with CC (NSE) values of 0.81–0.94 (0.53–0.83) and 0.79–0.86 (0.54–0.74), respectively. However, GSMaP showed the lowest bias, with a range of −17.1–13.2%. Recorded drought periods over 1981–2022 (1998–2022) were reasonably well captured by CHIRPS (IMERG) products in the Mi Oya River Basin. Our results highlighted uncertainties and discrepancies in the capability of different rainfall products to assess drought conditions. This research provides valuable insights for optimizing the use of satellite rainfall products in hydrological modeling and disaster preparedness in the Mi Oya River Basin. Full article

The 21st‑century Maritime Silk Road initiative highlights the importance of oceans as hubs for resources, ecology, and trade, yet a comprehensive understanding of marine natural disaster risks within this region remains limited. This study focused on 30 countries along the Maritime Silk Road and developed a multi-hazard natural disaster risk assessment framework tailored for large-scale regional evaluation. It goes beyond single-factor or single-disaster assessments to enhance disaster resilience and support effective disaster response strategies. The framework integrates 65 indicators across four dimensions: disaster-causing factors, disaster-conceiving environments, disaster-bearing bodies, and disaster reduction capacities. It employs five single-indicator evaluation models alongside a combination assessment method based on maximum deviations to evaluate national-scale natural disaster risks. Results reveal spatial consistency in risk evaluations and capture the exposure and sensitivity of 30 countries to different hazards. South Asia exhibits higher seismic risks, while Saudi Arabia consistently receives the lowest risk. Tropical countries like Vietnam and the Philippines face significant storm risks. Drought hazard risk is higher in the Middle East and East Africa, while it is lower in Brunei, Indonesia, and Malaysia. Flood risks are notably higher in Bangladesh, while Iran and Tanzania consistently receive lower risk ratings. Overall, South Asia exhibits higher multi-hazard risks, with medium-to-low risks along the Mediterranean and Southeast Asia. These findings provide technical support for disaster risk reduction by identifying high-risk areas, prioritising resource allocation, and strengthening disaster reduction strategies. Full article

Water resources management needs to be strengthened to address increasing flood and drought risks exacerbated by climate change and socio-economic development. This requires effective water governance mechanisms that can reduce vulnerability in disasters while managing complex stakeholder relationships. This paper analyzes the evolution of water governance in Japan over more than half a century, examining how the country transformed from a centralized, top-down approach to a more collaborative model of water management. Through an analysis of three significant water infrastructure projects, this study identifies key drivers of governance change and evaluates the effectiveness of various stakeholder engagement mechanisms. The findings reveal how catalytic events prompted institutional innovations in addressing social impacts, environmental concerns, and climate resilience. Challenges remain in balancing diverse interests, managing implementation timeframes, and incorporating climate change uncertainties into decision-making processes. This paper offers important lessons for developing countries working to strengthen their water governance frameworks, particularly regarding stakeholder engagement, social impact mitigation, and the development of flexible institutional arrangements that can adapt to emerging climate risks. This research contributes to governance theory by demonstrating how institutional evolution occurs through the interaction of formal mechanisms and informal processes in response to changing social, environmental, and climatic conditions. Full article

The frequent occurrence of disasters has brought significant challenges to increasingly complex urban systems. Resilient city planning and construction has emerged as a new paradigm for dealing with the growing risks. Infrastructure systems like transportation, lifelines, flood control, and drainage are essential to the operation of a city during disasters. It is necessary to measure how risks affect these systems’ resilience at different spatial scales. This paper develops an infrastructure risk and resilience evaluation index system in city and urban areas based on resilience characteristics. Then, a comprehensive infrastructure resilience evaluation is established based on the risk–resilience coupling mechanism. The overall characteristics of comprehensive infrastructure resilience are then identified. The resilience transmission level and the causes of resilience effects are analyzed based on the principle of resilience scale. Additionally, infrastructure resilience enhancement strategies under different risk scenarios are proposed. In the empirical study of Zhengzhou City, comprehensive infrastructure resilience shows significant clustering in the city area. It is high in the central city and low in the periphery. Specifically, it is relatively high in the southern and northwestern parts of the airport economy zone (AEZ) and low in the center. The leading driving factors in urban areas are risk factors like flood and drought, hazardous materials, infectious diseases, and epidemics, while resilience factors include transportation networks, sponge city construction, municipal pipe networks, and fire protection. This study proposes a “risk-resilience” coupling framework to evaluate and analyze multi-hazard risks and the multi-system resilience of urban infrastructure across multi-level spatial scales. It provides an empirical resilience evaluation framework and enhancement strategies, complementing existing individual dimensional risk or resilience studies. The findings could offer visualized spatial results to support the decision-making in Zhengzhou’s resilient city planning outline and infrastructure special planning and provide references for resilience assessment and planning in similar cities. Full article

In the context of global climate change environment, China’s power grid is faced with many extreme weather challenges, especially the southern China power grid region, which faces typhoons, torrential rain, high temperature, drought, frost and other disasters that greatly affect the safe and stable operation of the power system and the normal social order in this region. This study proposes a risk assessment model combining a genetic algorithm-optimized neural network (GA-BP) with GIS spatial analysis to evaluate transmission line faults under extreme weather in southern China. Experimental results demonstrate the model’s effectiveness in identifying high-risk regions, with significant correlations between extreme precipitation, prolonged drought, and circuit failures. Full article

Drought is one of the most severe natural disasters globally, with its frequency and intensity escalating due to climate change, posing significant threats to agricultural production. This is particularly critical in major food-producing regions, where drought profoundly impacts crop yields. Such impacts can trigger food crises in affected regions and disrupt global food trade patterns, thereby posing substantial risks to global food security. Based on historical data, this study examines the yield response characteristics of key crops—maize, rice, soybean, spring wheat, and winter wheat—under drought conditions during their growth cycles, highlighting variations in drought sensitivity among major food-producing countries. The findings reveal that maize and soybean yield in China, the United States, and Brazil are among the most sensitive and severely affected by drought. Furthermore, using precipitation simulation data from CMIP6 climate models, the study evaluates drought trends and associated crop yield risks under different future emission scenarios. Results indicate that under high-emission scenarios, crops face heightened drought risks during their growth cycles, with China and the United States particularly vulnerable to yield reductions. Additionally, employing copula functions, the study analyzes the probability of simultaneous drought occurrences across multiple countries, shedding light on the evolving trends of multicountry drought events in major food-producing regions. These findings provide a scientific basis for assessing global food security risks and offer policy recommendations to address uncertainties in food supply under climate change. Full article

Assessing community-level resilience and implementing strategies to enhance it are essential for maintaining fundamental community functions, coping with and mitigating risks, effectively reducing hazards, and promoting sustainable regional development. Accordingly, this study aimed to measure hazard exposure and livelihood resilience in the counties of Khuzestan Province. Hazard exposure to earthquakes, flooding, and drought was evaluated using decision-making techniques within a geographic information system (GIS). Additionally, a multi-criteria decision-making approach incorporating eight indicators was employed to calculate the integrated livelihood resilience indicator for the counties. The results indicated that the northern and northeastern counties exhibit the highest potential for flooding and earthquake hazards, whereas the southern and southwestern counties are most vulnerable to flooding and drought. Moreover, Dezful, Shadegan, and Ahvaz counties demonstrated the highest levels of livelihood resilience, while Lali, Haftkel, and Andika counties exhibited the lowest levels. Assessing hazard exposure and livelihood resilience represents critical steps in risk reduction management programs and initiatives. Evaluating community-level livelihood resilience in multi-hazard areas is a vital component in advancing the global objectives of the Sendai Framework for Disaster Risk Reduction and the Sustainable Development Goals. Full article

The present study aims to assess the responses of growth, development, and yield of summer maize to the effects of drought–flood abrupt alternation through comparative tests under single flood, single-drought, and drought–flood abrupt alternation treatments with varying degrees from the elongation to the tasseling stage during the 2021 and 2022 growing seasons. In addition, a water production function model for summer maize was preliminarily established based on the results obtained under the drought–flood abrupt alternation scenarios. The results indicated that drought–flood abrupt alternation with early moderate drought had a certain restricting effect on summer maize, while early moderate drought followed by waterlogging had a compensation effect on the cultivated summer maize. Furthermore, both mild and severe drought followed by waterlogging exert a significant combined constraint on the normal growth and development of summer maize, leading to a sharp decline in maize yield, necessitating a shorter timeframe for mitigating and reducing the effects of waterlogging. Additionally, the water production function model established through a multiple linear regression equation exhibits a high degree of fit and demonstrates a strong linear relationship. This study provides crucial insights for agricultural practices and water resource management strategies, particularly in the evaluation of the integrated impacts of drought and waterlogging on crop yields and the formulation of effective disaster risk reduction and mitigation measures in response to these impacts. Full article

Accurately evaluating drought and its effects on the natural environment is difficult in regions with limited climate monitoring stations, particularly in the hyper-arid region of the Sultanate of Oman. Rising global temperatures and increasing incidences of insufficient precipitation have turned drought into a major natural disaster worldwide. In Oman, drought constitutes a major threat to food security. In this study, drought indices (DIs), such as temperature condition index (TCI), vegetation condition index (VCI), and vegetation health index (VHI), which integrate data on drought streamflow, were applied using moderate resolution imaging spectroradiometer (MODIS) data and the Google Earth Engine (GEE) platform to monitor agricultural drought and assess the drought risks using the drought hazard index (DHI) during the period of 2001–2023. This approach allowed us to explore the spatial and temporal complexities of drought patterns in the Najd region. As a result, the detailed analysis of the TCI values exhibited temporal variations over the study period, with notable minimum values observed in specific years (2001, 2005, 2009, 2010, 2014, 2015, 2016, 2017, 2019, 2020, and 2021), and there was a discernible trend of increasing temperatures from 2014 to 2023 compared to earlier years. According to the VCI index, several years, including 2001, 2003, 2006, 2008, 2009, 2013, 2015, 2016, 2017, 2018, 2020, 2021, 2022, and 2023, were characterized by mild drought conditions. Except for 2005 and 2007, all studied years were classified as moderate drought years based on the VHI index. The Pearson correlation coefficient analysis (PCA) was utilized to observe the correlation between DIs, and a high positive correlation between VHI and VCI (0.829, p < 0.01) was found. Based on DHI index spatial analysis, the northern regions of the study area faced the most severe drought hazards, with severity gradually diminishing towards the south and east, and approximately 44% of the total area fell under moderate drought risk, while the remaining 56% was classified as facing very severe drought risk. This study emphasizes the importance of continued monitoring, proactive measures, and effective adaptation strategies to address the heightened risk of drought and its impacts on local ecosystems and communities. Full article

Drought, as a widespread natural calamity, leads to the most severe agricultural losses among all such disasters. Alterations in the yield of major global agricultural products are pivotal factors influencing food prices, food security, and land use decisions. China’s rapidly expanding demand for sustenance will persist over the forthcoming decades, emphasizing the critical need for an accurate assessment of drought’s impact on food production. Consequently, we conducted a comprehensive evaluation of the drought risk in China and its repercussions on agricultural output. Additionally, we delved into the underlying factors driving changes in yield for three primary grain crops (wheat, corn, and rice), which hold particular relevance for shaping effective strategies to mitigate future drought challenges. The findings divulge that both the number of drought months (DM) and the drought magnitude index (DMI) have displayed an upward trajectory over 60 years with a correlation coefficient of 0.96. The overall severity of meteorological drought has escalated across China, and it is particularly evident in regions such as the southwest and central parts of the Huang-Huai-Hai region, the northwestern middle region, and the Xinjiang region. Conversely, there has been some relief from drought conditions in southern China and the Yangtze River Delta. Shifts in the total grain output (TGO) during this period were compared: it underwent three stages, namely “fluctuating growth” (1961–1999), then a “sharp decline” (2000–2003), followed by “stable growth” (2004–2018). Similarly, changes in the grain planting area (GPA) experienced two stages, “continuous reduction” (1961–2003) succeeded by “stable growth” (2004–2018), while maintaining an upward trend for grain yield per unit area (GY) throughout. Furthermore, it was revealed that the drought grade serves as a significant constraint on continuous expansion within China’s grain output—where the drought damage rate’s influence on the TGO outweighs that from the GY. Our research outcomes play an instrumental role in deepening our comprehension regarding how drought impacts agricultural production within China while furnishing the scientific groundwork to devise efficacious policies addressing these challenges. Full article

Soil moisture can be an important indicator of climate change in humid and semi-arid areas. This indicator can more efficiently propose different public policies related to climate change than just using precipitation and temperature data. Given the above, the objective of this study is to evaluate changes in soil moisture in the state of Pernambuco during the period 1961–2021, using the System of Hydrological Response Units for Pernambuco. In this study, two river basins in the state of Pernambuco that represent the different climatic conditions of the state were chosen. The results show that in the coastal region there is a tendency towards more saturated soils, and in the semi-arid region there is a tendency towards drier soils. With these results, it is possible to conclude that public policy decisions for the economy, environment, and society must consider this vital water balance variable. Leveraging soil moisture and precipitation data makes it possible to differentiate between flood risks and landslide vulnerabilities, particularly in regions characterized by higher levels of rainfall. Monitoring soil water content in humid and semi-arid areas can significantly enhance early warning systems, thereby preventing loss of life and minimizing the socio-economic impacts of such natural events. As such, this study provides a holistic understanding of the relationship between climatic patterns, soil moisture dynamics, and the occurrence of droughts and floods, ultimately contributing to more effective disaster preparedness and response measures in Pernambuco and similar regions. Full article

Urban development and climate change have strengthened the possibility of floods and droughts in cities. In this study, we evaluated the influences of these disasters and related social damage in nine major basins during the past 50 years. Unusually, the following conclusions were drawn from the analysis of relevant indicators before and after urbanization: (1) agricultural loss area (flood), grain loss, and direct economic loss showed an upward trend, while other indicators showed the opposite. (2) Floods most often occur in the Yangtze River Basin (58, 26.2%), followed by the Liaohe River Basin (49, 22.2%), which is closely related to the topography and economic progress of the area. (3) The modified Mann–Kendall (MK) analysis results are consistent with the indicators trend. Finally, the regularity of the climate change and urbanization process is revealed by the migration of the standard deviation ellipse and the mean center of the four indicators. China needs to integrate urban water/drought policy development with sustainable urbanization policy development to cope with the changing natural and social environment and to minimize urban ecological risks. Full article

Drought is one natural disaster with the greatest impact worldwide. Southern Africa (SA) is susceptible and vulnerable to drought due to its type of climate. In the last four decades, droughts have occurred more frequently, with increasing intensity and impacts on ecosystems, agriculture, and health. The work consists of a systematic literature review on the drought regime’s characteristics in the SA under current and future climatic conditions, conducted on the Web of Science and Scopus platforms, using the PRISMA2020 methodology, with usual and appropriate inclusion and exclusion criteria to minimize/eliminate the risk of bias, which lead to 53 documents published after the year 1987. The number of publications on the drought regime in SA is still very small. The country with the most drought situations studied is South Africa, and the countries with fewer studies are Angola and Namibia. The analysis revealed that the main driver of drought in SA is the ocean–atmosphere interactions, including the El Niño Southern Oscillation. The documents used drought indices, evaluating drought descriptors for some regions, but it was not possible to identify one publication that reports the complete study of the drought regime, including the spatial and temporal distribution of all drought descriptors in SA. Full article

Drought is a damaging and costly natural disaster that will become more serious in the context of global climate change in the future. Constructing a reliable drought risk assessment model and presenting its spatial pattern could be significant for agricultural production. However, agricultural drought risk mapping scientifically still needs more effort. Considering the whole process of drought occurrence, this study developed a comprehensive agricultural drought risk assessment model that involved all risk components (exposure, hazard, vulnerability and mitigation capacity) and their associated criteria using geospatial techniques and fuzzy logic. The comprehensive model was applied in Songnen Plain to justify its applicability. ROC and AUC techniques were applied to evaluate its efficiency, and the prediction rate was 88.6%. The similar spatial distribution of water resources further verified the model’s reliability. The southwestern Songnen Plain is a very-high-risk (14.44%) region, determined by a high vulnerability, very high hazardousness and very low mitigation capacity, and is the region that should be paid the most attention to; the central part is a cross-risk region of high risk (24.68%) and moderate risk (27.28%) with a serious disturbance of human agricultural activities; the northeastern part is a dry grain production base with a relatively optimal agricultural production condition of very low risk (22.12%) and low risk (11.48%). Different drought mitigation strategies should be adopted in different regions due to different drought causes. The findings suggest that the proposed model is highly effective in mapping comprehensive drought risk for formulating strong drought mitigation strategies and could be used in other drought-prone areas. Full article