Search Results (187)

Floods are among the most devastating hydrometeorological natural disasters worldwide, causing massive infrastructure and economic loss in low-lying, flood-prone developing countries like Bangladesh. Effective disaster mitigation relies on organized and detailed flood damage information to facilitate emergency evacuation, coordinate relief distribution, and formulate an effective disaster management policy. Nevertheless, the nation confronts considerable obstacles due to insufficient historical flood damage data and the underdevelopment of near-real-time (NRT) flood monitoring systems. This study addresses this issue by developing a replicable methodology for flood damage assessment and NRT monitoring systems. Using the Google Earth Engine (GEE) platform, we analyzed flood events from 2019 to 2023, integrating geospatial layers such as roads, cropland, etc. Analysis of flood events over the five-year period revealed substantial impacts, with 21.60% of the total area experiencing inundation. This flooding affected 6.92% of cropland and 4.16% of the population. Furthermore, 18.10% of the road network, spanning over 21,000 km within the study area, was also affected. This system has the potential to enhance emergency response capabilities during flood events and inform more effective disaster mitigation policies. Full article

Extreme climate events are becoming more intense, and how coastal mangroves respond to the alternating intense cyclones and severe droughts is less understood, which challenges the sustainability of the ecosystem services they provide to coastal communities. To address this, we analyzed spatiotemporal dynamics of coastal mangroves in a Caribbean island in response to major hurricanes in 2017, which followed a severe multi-year drought in 2014–2015, using multiple indices derived from multispectral optical images. We further explored the roles of hurricane forces, local hydro-geomorphic environment, and rainfall dynamics in the damage and the following recovery. In addition to the hurricane forces, such as gusty wind and rainfall, the local hydro-geomorphic environment largely determines the spatial variations of damage. Lower-lying, flatter, and wetter mangrove areas sustained more damage, possibly due to prolonged inundation susceptibility and tall canopy configurations. Recovery is mainly limited by the severity of damage. However, sufficient rainfall gradually becomes important to facilitate the recovery. While the pre-hurricane severe drought (2014–2015) largely degraded the mangroves at dry sites, the drought after the hurricanes exacerbated the hurricane damage and retarded the recovery. We also found that the spectral distance and the mangrove vegetation index revealed slower and more spatiotemporally heterogenous mangrove recovery than indices of greenness, implying they are better measures for monitoring mangroves’ response to disturbance. Six years after the disturbance, the greenness of mangroves near the hurricane landfall reached 84% of the pre-hurricane values. However, the mangrove vegetation index showed that healthy mangrove coverage was only 10%, in comparison to 76% before the disturbance. The sluggish recovery at this site with the severest damage may be associated with the loss of pre-established seedlings and the difficulty to have new ones established, thus human efforts are in need to restore the system. Full article

In the Asia–Pacific, where rice is an essential crop for food security and economic activity, tropical cyclones and consecutive floods can cause substantial damage to rice fields. Humanitarian organizations have developed impact-based forecasting models to be able to trigger early actions before floods arrive. In this study we show how Sentinel-1 SAR data and Otsu thresholding can be used to estimate flooding and damage caused to rice fields, using the case study of tropical storm Talas (2017). The current most accurate global Digital Elevation Model FABDEM was used to derive flood depths. Subsequently, rice yield loss curves and rice field maps were used to estimate economic damage. Our analysis results in a total of 475 km² of inundated rice fields in seven Northern Vietnam provinces. Flood depths were mostly shallow, with 2 km² having a flood depth of more than 0.5 m. Using these flood extent and depth values with rice damage curves results in lower damage values than the ones based on ground reporting, indicating a likely underestimation of flood depth. However, this study demonstrates that Sentinel-1-derived flood maps with the high-resolution DEM can deliver rapid damage estimates, also for those areas where there is no ground-based reporting of rice damage, showing its potential to be used in impact-based forecasting model training. Full article

The flood risk of urban buildings has been continuously increasing, owing to the increasing frequency and severity of floods. There is an urgent need to implement precise mitigation strategies to address the unique characteristics of urban residential structures. In this study, an indicator system consisting of 17 indicators in four dimensions (extent of hazard, degree of exposure, vulnerability, and response ability) was developed for the flood risk of residential buildings. The assessment was conducted in Katsushika Ward, Tokyo, and the ANALYTIC HIERARCHY PROCESS(AHP)—Criteria Importance Through Intercriteria Correlation (CRITIC) method was integrated with Geographic Information System(GIS) technology. The spatial distribution of residential flood risk exhibits marked heterogeneity, with ‘extremely high’ and ‘high’ risk areas concentrated in northwestern and southwestern riverine zones. These regions exhibit dense populations, substantial assets, deep immersion depths, prolonged inundation durations, high proportions of wooden houses, and narrow roads impeding rescue operations. The mitigation priorities are the following: Enhance flood-resistant building heights and quality in riverside areas, strengthen vacant house management, widen rescue access routes, promote mid-/high-rise buildings, and optimize subsidies for tenants and single-person households to minimize losses. Full article

Flood risk to rice production has previously been examined in terms of river basins or administrative units, incorporating data about the flood year, inundated area, precipitation, elevation, and impacts. However, there is limited knowledge about this topic, as most flood impact studies have focused on loss and damage to people and the economy. It remains important to identify how flood risk to rice production can be better identified within a long-term, community-based, analytical framework. In addition, flood risk studies in Sri Lanka tend to focus on single-year flood events within an administrative boundary, making it difficult to fully comprehend risks to rice production. This paper aims to fill these gaps by investigating long-term flood risk levels on rice production. With this aim, we collected and analyzed information about rice production, geospatial data, and 15-year precipitation records. Temporal-spatial maps were generated using Google Earth Engine JavaScript coding, Google Earth Pro, and OpenStreetMap. In addition, focus group discussions with farmers and key informant interviews were conducted to verify the accuracy of online information. The collected data were analyzed using descriptive statistics, GIS, and linear regression analysis methods. Regarding rice production impacts, we found that floods in the years 2006–2007, 2010–2011, and 2014–2015 had significant impacts on rice production with 20.5%, 75.8%, and 16.6% reductions, respectively. Flood risk maps identified low-, medium-, and high-risk areas based on 15-year flood events, elevation, proximity to water bodies, and 15-year flood-induced damage to rice fields. High risk areas were further studied through field discussions and interviews, showing the connection between past floods and poor water governance practices in terms of dam management. Our linear regression analysis found a marginal negative correlation between total seasonal rainfall and rice production. Full article

In the case of extreme disasters such as local rainstorm and excessive flood, the safety risk analysis and prevention and control of cascade reservoirs face new challenges. Therefore, this article conducted a risk analysis based on typical watersheds and proposed a method for calculating the risk rate of overtopping in cascade reservoir groups, dynamically simulated the evolution process of overtopping floods in cascade reservoirs under different scenarios, delineated the scope of flood inundation, and evaluated the risk of overtopping of cascade reservoirs under different scenarios. Research has shown that dam failure floods in cascade reservoirs have both cumulative and cumulative effects, with scenario 3 being the most unfavorable. In scenario 3, the peak flow rates at the dam sites of each reservoir reached 24,500, 19,200, and 20,100 m³/s. According to the comprehensive risk assessment criteria, scenarios 1 and 2 are classified as moderate risks, while scenario 3 is classified as mild risk. Research has found that although the probability of dam overflow is extremely low, the high vulnerability calculated for each scenario indicates that a breach will cause significant social losses. This study can provide reference for the risk assessment of overtopping in cascade reservoirs and flood control and disaster reduction. Full article

Traditional inundation mapping often relies on deterministic methods that offer only binary outcomes (inundated or not) based on satellite imagery analysis. While widely used, these methods do not convey the level of confidence in inundation classifications to account for ambiguity or uncertainty, limiting their utility in operational decision-making and rapid response contexts. To address these limitations, we propose a rapid probabilistic inundation mapping method that integrates local thresholds derived from Sentinel-1 SAR images and land cover data to estimate surface water probabilities. Tested on different flood events across five continents, this approach proved both efficient and effective, particularly when deployed via the Google Earth Engine (GEE) platform. The performance metrics—Brier Scores (0.05–0.07), Logarithmic Loss (0.1–0.2), Expected Calibration Error (0.03–0.04), and Reliability Diagrams—demonstrated reliable accuracy. VV (vertical transmit and vertical receive) polarization, given appropriate samples, yielded strong results. Additionally, the influence of different land cover types on the performance was also observed. Unlike conventional deterministic methods, this probabilistic framework allows for the estimation of inundation likelihood while accounting for variations in SAR signal characteristics across different land cover types. Moreover, it enables users to refine local thresholds or integrate on-the-ground knowledge, providing enhanced adaptability over traditional methods. Full article

Land use and land cover change (LULCC) is a key driver of carbon storage changes, especially in complex coastal ecosystems such as the Yellow River Delta (YRD), which is jointly influenced by climate change and resource development. The compounded effects of sea-level rise (SLR) and land subsidence (LS) are particularly prominent. This study is the first to integrate the dual impacts of SLR and LS into a unified framework, using three climate scenarios (SSP1–26, SSP2–45, SSP5–85) provided in the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6), along with LS monitoring data, to comprehensively assess future inundation risks. Building on this, and taking into account land use and ecological protection policies in the YRD, three strategic scenarios—Ecological Protection Scenario (EPS), Natural Development Scenario (NDS), and Economic Growth Scenario (EGS)—are established. The PLUS and InVEST models are used to jointly simulate LULCC and carbon storage changes across these scenarios. Unlike previous studies focusing on single driving factors, this research innovatively develops a dynamic simulation system for LULCC and carbon storage driven by the SLR-LS compound effects, providing scientific guidance for land space development and coastal zone planning in vulnerable coastal areas, while enhancing carbon sink potential. The results of the study show the following: (1) Over the past 30 years, the land use pattern of the YRD has generally extended toward the sea, with land use transitions mainly from grasslands (the largest reduction: 1096.20 km²), wetlands, reservoirs and ponds, and paddy fields to drylands, culture areas, construction lands, salt pans, and tidal flats. (2) Carbon storage in the YRD exhibits significant spatial heterogeneity. Low-carbon storage areas are primarily concentrated in the coastal regions, while high-carbon storage areas are mainly found in grasslands, paddy fields, and woodlands. LULCC, especially the conversion of high carbon storage ecosystems to low carbon storage uses, has resulted in an overall net regional carbon loss of 2.22 × 10⁶ t since 1990. (3) The risk of seawater inundation in the YRD is closely related to LS, particularly under low sea-level scenarios, with LS playing a dominant role in exacerbating this risk. Under the EGS, the region is projected to face severe seawater inundation and carbon storage losses by 2030 and 2060. Full article

Floods resulting from dam failures are highly destructive, characterized by intense impact forces, widespread inundation, and rapid flow velocities, all of which pose significant threats to public safety and social stability in downstream regions. To improve evacuation efficiency during such emergencies, it is essential to study flood evacuation route planning. This study aimed to minimize evacuation time and reduce risks to personnel by considering the dynamic evolution of dam-break floods. Using aerial photography from an unmanned aerial vehicle, the downstream road network of a reservoir was mapped. A coupled flood–road network coupling model was then developed by integrating flood propagation data with road network information. This model optimized evacuation route planning by combining the dynamic evolution of flood hazards with real-time road network data. Based on this model, a flood evacuation route planning method was proposed using Dijkstra’s algorithm. This methodology was validated through a case study of the Shanmei Reservoir in Fujian, China. The results demonstrated that the maximum flood level reached 18.65 m near Xiatou Village, and the highest flow velocity was 22.18 m/s near the Shanmei Reservoir. Furthermore, evacuation plans were developed for eight affected locations downstream of the Shanmei Reservoir, with a total of 13 evacuation routes. These strategies and routes resulted in a significant reduction in evacuation time and minimized the risks to evacuees. The life-loss risk was minimized in the evacuation process, and all evacuees were able to reach safe locations. These findings confirmed that the proposed method, which integrated flood dynamics with road network information, ensured the safety and effectiveness of evacuation routes. This approach met the critical needs of emergency management by providing timely and secure evacuation paths in the event of dam failure. Full article

Flood frequencies, along with the associated loss of life and property, have risen significantly due to climate change and increasing human activities. While prior research has primarily focused on high-intensity rainfall events and reservoir management in flood management, the influence of sediment-starved water—termed “hungry water”—released from dams in controlling flood dynamics has not gained much attention. The present study is aimed at exploring the potential role of sediment-starved water, or the “hungry water effect” on the valley degradation, bed material changes and flood inundation in the Pamba River during the Kerala Flood, 2018, through a detailed characterization of bed materials and their deposition in the channel bed. The release of sediment-starved water from the Kakki reservoir during the episodic precipitation event (15 to 17 August 2018) resulted in significant bed degradation and scouring of the valley slopes, leading to the deposition of large boulders and rock masses and the inundating of approximately 196 km² of floodplains. This study highlights the need for integrated sediment management strategies in reservoir operations by providing essential insights into sediment transport dynamics during extreme weather events. Understanding these processes is crucial for formulating effective flood mitigation strategies and improving the resilience of riverine ecosystems, particularly as the interaction between intense rainfall and sediment-depleted releases significantly exacerbated the flood’s severity. Full article

A tsunami can cause significant economic losses for tourism areas like Batukaras Village, which is located on the southern coast of Java Island. This paper seeks to elaborate on the calculation of economic losses in tourism areas due to damage to buildings, loss of land production, and loss of income, based on high-resolution geospatial data. The data are derived from UAV photogrammetry surveys and high-resolution tsunami run-up models. The tsunami worst-case scenario run-off model provides an inundation area of 43 ha with 185 buildings and 24.4 ha of productive land. The estimated losses from the tsunami disaster amounted to IDR 208.79 billion, consisting of 49.63 billion from building damage, 6.73 billion from productive land, and 152.43 billion from the tourism sector. These results show that the tsunami disaster will severely affect tourism areas, because the tourism sector makes up 73% of the total economic losses. Reductions in the amount of economic loss, in addition to spatial planning near the coastline to reduce the number of impacted buildings and productive land, can be achieved by accelerating the recovery period so that economic activities after the tsunami disaster can be carried out immediately, including in the tourism sector. Full article

This paper presents an assessment of potential losses and damage costs to rare coastal plant species of Lake Baikal (UNESCO World Heritage Site) as a result of inundation at high water levels. The lake’s ecosystem is characterized by an exceptional diversity of rare and endemic animal and plant species. The construction of a hydroelectric power plant caused an increase in the water level of Lake Baikal, resulting in the inundation of low-lying coastal areas, the destruction of the coastline, alterations to the hydrological regime, etc. However, there are practically no works devoted to water-level modeling and the assessment of its impact on riparian vegetation, including rare species. We conducted fieldwork to determine the abundance of four vulnerable species and identified inundation zones at different high water levels on the basis of digital elevation models based on aerial photography data. The analysis revealed that at the maximum level of inundation, the number of plant species affected would total 5164, amounting to a financial loss of biodiversity estimated at 3098.4 thousand rubles. To mitigate the projected losses, it is imperative to implement measures that restrict water-level fluctuations above the 457.00 m threshold. The absence of flora as an object of state environmental monitoring, which is not specified in the regulatory legal document, must be rectified in a timely manner. Full article

Floods have significant impacts on economic development and cause the loss of both lives and property, posing a serious threat to social stability. Effectively identifying the evolution patterns of floods could enhance the role of flood monitoring in disaster prevention and mitigation. Firstly, in this study, we utilized low-cost multi-source multi-temporal remote sensing to construct an HIS-NSCT fusion model based on SAR and optical remote sensing in order to obtain the best fusion image. Secondly, we constructed a regional growth model to accurately identify floods. Finally, we extracted and analyzed the extent, depth, and area of the farmland submerged by the flood. The results indicated that the HIS-NSCT fusion model maintained the spatial characteristics and spectral information of the remote sensing images well, as determined through subjective and objective multi-index evaluations. Moreover, the regional growth model could preserve the detailed features of water body edges, eliminate misclassifications caused by terrain shadows, and enable the effective extraction of water bodies. Based on multi-temporal remote sensing fusion images of Poyang Lake, and incorporating precipitation, elevation, cultivated land, and other data, the accurate identification of the flood inundation range, inundation depth, and inundated cultivated land area can be achieved. This study provides data and technical support for regional flood identification, flood control, and disaster relief decision-making, among other aspects. Full article

In recent years, there has been a notable increase in the frequency and intensity of floods and heavy rains, which has resulted in the frequent inundation of rice-growing areas. Flooding during the heading–flowering stages of early rice can result in significant yield losses. To elucidate the response of rice to sediment content, flooding, and drainage processes and their underlying mechanisms, a pot experiment was conducted to investigate the effects of sediment contents (S₁: 0, S₂: 0.10 kg m⁻³, and S₃: 0.25 kg m⁻³), flooding time (F₁: 3 days and F₂: 6 days), and drainage time (D₁: 3 days and D₂: 6 days) during the heading–flowering stage on the oxidation resistance and grain yield of early rice in the Poyang Lake Region. At the same time, an experimental control group (CK) was set up with no sediment, no flooding, or no drainage treatment. The results showed that the flag leaf area of S₁F₁D₂ treatment was diminished by flooding. The relative chlorophyll content (SPAD) reached its lowest value prior to drainage. The treatment of S₂F₂D₁ showed the greatest decrease in SPAD value of 41.57%, which was only 53.88% of that of the control treatment. The activity of superoxide dismutase (SOD), peroxidase (POD), and the content of malondialdehyde (MDA) were observed to increase during the flooding period in comparison to the control treatment. The maximum values for these parameters were recorded at 5.68, 3.09, and 1.9 times higher than those of the control treatment, respectively. However, a decrease was observed after drainage. Furthermore, the occurrence of flooding during the early rice heading–flowering stage resulted in a notable reduction in the grain number per spike and the fruiting rate, consequently leading to a considerable decline in grain yields, with a decrease ranging from 31.81% to 69.96%. The findings indicate that flooding during the heading–flowering stage resulted in a reduction in early rice grain yield yet enhanced the antioxidant capacity of the leaves. Regression analyses indicated that a prediction model for the actual yield after flooding stress at the heading–flowering stage of early rice could be constructed using SFW as the independent variable. The findings of this study provide a theoretical basis for the formulation of a scientific and reasonable drainage scheme with the objective of reducing yield loss following rice flooding in the southern rice-growing region of China. Full article

Based on statistical analyses applied to official data from the Digital Atlas of Disasters in Brazil over the last 25 years, we evidenced a consistent intensification in the annual occurrence of natural disasters in the state of Pará, located in the eastern Brazilian Amazon. The quantitative comparison between the averages of the most intense period of disasters (2017 to 2023) and the earlier years (1999 to 2016) revealed a remarkable percentage increase of 473%. Approximately 81% of the state’s municipalities were affected, as indicated by disaster mapping. A clear seasonal pattern was observed, with Hydrological disasters (Inundations, Flash floods, and Heavy rainfall) peaking between February and May, while Climatological disasters (Droughts and Forest fires) were most frequent from August to October. The catastrophic impacts on people and the economy were documented, showing a significant rise in the number of homeless individuals and those directly affected, alongside considerable material damage and economic losses for both the public and private sectors. Furthermore, we conducted a comprehensive composite analysis on the tropical ocean–atmosphere dynamic structure that elucidated the various triggering mechanisms of disasters arising from Inundations, Droughts, and Forest fires (on seasonal scale), and Flash floods and Heavy rainfall (on sub-monthly scale) in Pará. The detailed characterization of disasters on a municipal scale is relevant in terms of the scientific contribution applied to the strategic decision-making, planning, and implementation of public policies aimed at early risk management (rather than post-disaster response), which is critical for safeguarding human well-being and strengthening the resilience of Amazonian communities vulnerable to climate change. Full article