Search Results (37)

Wetlands have been noted to provide us with a wide range of ecosystem services—climate, water quality, flood regulation, and habitat benefits among others. The evidence that supports these service benefits is fairly well documented in the literature. However, it is often scattered across studies, metrics, and valuation methods, making it difficult for planners and landscape architects to use in physical projects and plans. The empirical result of this difficulty is that wetlands are often overlooked and under-utilized as part of broader ecosystem and land based planning solution sets. This paper addresses this usability deficiency using an ecosystem accounting framework that translates wetland science into spatially specific design and planning metrics. The framework follows the System of Environmental-Economic Accounting-Ecosystem Accounting (SEEA EA), an international statistical framework adopted by the UN to measure the environment’s contribution to the economy and human well-being. In our study, wetland extents in the state of Illinois are mapped on a statewide 30 m × 30 m grid. Individual wetland system conditions are estimated from floristic quality using a generalized additive model trained on 244 wetland sites that are part of the long-term Critical Trends Assessment Program (CTAP) at the Illinois Department of Natural Resources (IDNR). A floristic condition scalar, $w\left(x\right)$, provides a screening-level measure of ecological condition. It is applied only to a nonmonetary habitat-condition account and is not used to scale the monetary service accounts. Climate regulation, water purification, and flood regulation are quantified through service-specific physical models and reported as carbon-price and replacement-cost proxies. Under the central scenarios, these three monetary proxy accounts produce a combined subtotal of USD 1408.6 million per year. The annualized surface-storage replacement-cost scenario accounts for USD 1048.5 million, load-gated nitrogen-removal replacement cost for USD 338.0 million, and the climate-regulation carbon-price proxy for USD 22.1 million. These estimates are planning proxies rather than observed market benefits or realized avoided damages. The separate habitat-condition account totals 198,280 condition-weighted hectares, with a statewide mean $w\left(x\right)$ of 0.502, and is not added to the monetary subtotal. Climate performance varies across wetland types: methane emissions cause some emergent wetland categories to function as net greenhouse-gas sources under the central assumptions, while other categories remain net sinks. A protection-gap analysis shows that Tier 4 wetlands contain 70.1% of the classified vegetated-wetland area and 67.0% of the condition-weighted habitat area within the classified domain. Broadly, the framework demonstrates how ecosystem accounting can translate fragmented wetland evidence into consistent spatial metrics for planning. The resulting layers support statewide screening, comparison among wetland categories, conservation and restoration screening, and protection-gap analysis while preserving the distinction between monetary service proxies and nonmonetary ecological condition. Full article

Flooding is one of the most damaging natural hazards worldwide, particularly in data-scarce watersheds where long-term hydrometeorological records are limited. This study focuses on the R’Dom watershed in the Meknes region, Morocco, and aims to improve flood susceptibility and relative flood risk assessment by integrating remote sensing, Geographic Information Systems (GIS), and multi-criteria decision-making (MCDM) approaches. The Analytic Hierarchy Process (AHP) and Fuzzy Analytic Hierarchy Process (Fuzzy AHP/FAHP) were applied to evaluate flood hazard, vulnerability, and overall risk using seventeen conditioning factors, including topographic, hydrological, geological, land-cover, socio-economic, and infrastructure-related variables. The Flood Hazard Index (FHI), Flood Vulnerability Index (FVI), and Flood Risk Index (FRI) were calculated to produce flood susceptibility, vulnerability, and relative flood risk maps. Model validation was performed using a point-based flood inventory dataset composed of 900 locations, including 450 flood and 450 non-flood points, compiled from historical flood information, field observations, local information, official reports, and satellite-based interpretation. The dataset was divided into 70% for training and 30% for testing, and model performance was assessed using receiver operating characteristic–area under the curve (ROC-AUC) analysis. The AHP and Fuzzy AHP models showed good to excellent predictive performance, with testing AUC values ranging from 0.767 to 0.935. The AHP-based models achieved the highest testing performance, while Fuzzy AHP remained useful for representing uncertainty in expert judgment and gradual spatial transitions. The final flood risk map indicates that approximately 18.78% of the study area, corresponding to 240.62 km², is classified as having a high to very high flood risk, mainly around the Meknes conurbation and locally near the El Hajeb region. These results provide a practical decision-support tool for identifying priority areas for flood mitigation, land-use planning, and watershed management. However, the proposed GIS–MCDA approach produces relative flood susceptibility and risk classes and does not replace hydrological or hydraulic modeling for estimating flood depth, discharge, inundation extent, or return-period-based flood hazard. Full article

Flood damage assessment remains challenging, as conventional flood risk management mainly relies on hydraulic hazard maps that do not explicitly reproduce observed damage patterns. Recent advances in remote sensing and machine learning (ML) enable the integration of environmental and socio-economic data with historical impact information to improve flood damage modeling. This study proposes an explainable machine learning framework for flood damage susceptibility mapping, using observed institutional damage records from the 2011 and 2013 flood events combined with 17 geospatial flood risk factors (FRFs) representing hazard, exposure, and vulnerability. This approach enables the capture of non-linear relationships between flood damage and FRFs. For comparison purposes, the same framework was also applied using hydraulically modeled flood extents corresponding to return periods of 30, 200, and 500 years. The framework was tested along the Basilicata Ionian coast in southern Italy, a Mediterranean region characterized by complex geomorphology, intense rainfall events, and recurrent flood impacts. An eXtreme Gradient Boosting (XGBoost) model was trained using 17 FRFs related to hazard, exposure, and vulnerability at a spatial resolution of 20 m. The model achieved high performance with an accuracy of 0.988, an F1-score for the minority class of 0.860, and an ROC-AUC (test) of 0.996. High to very high flood damage probability was predicted in approximately 4.1% of the study area, mainly in low-lying floodplains near river corridors and infrastructure. SHAP-based explainability analysis revealed that damage susceptibility was predominantly driven by hazard and exposure factors: Drainage density (17.10%), Railway distance (16.33%), and Elevation (15.42%), extreme precipitation (Max rainfall, 10.66%) and Street distance (7.51%), with socio-economic vulnerability contributing less than 4%. The observed damage target exhibited clear threshold-like patterns (e.g., sharp risk increases below ~25/35 m elevation or within ~150/200 m of road infrastructure), contrasting with the smoother, continuous gradients produced by hydraulic scenarios. This analysis identified the most influential predictors and their response ranges. The proposed framework complements hydraulic hazard mapping by explicitly modeling observed flood damage, supporting flood risk assessment in flood-prone coastal regions. Full article

Machine Learning (ML) modeling for disaster management is a growing field, but existing works focus more on mapping the extent of floods or broad categories of damage and they lack methods for explainability to help users understand model outputs. In this study, we propose Flood Assessment using Dempster–Shafer Fusion (FADS-Fusion), a tool for addressing post-flood damage assessment using Dempster–Shafer fusion to combine outputs from multiple deep learning models. FADS-Fusion is generalized to use any pretrained models, once outputs are post-processed for consistency, making it applicable for other disaster management or change detection applications. The novelty of our work comes from the application of Dempster–Shafer for multi-model fusion and uncertainty quantification on a flood dataset for segmenting both buildings and roads. We trained and evaluated models using the SpaceNet 8 challenge dataset and demonstrated that the fusion of the SpaceNet 8 Baseline (SN8) and Siamese Nested UNet (SNUNet) models has a modest overall improvement +1.93% to mAP, while a +12.3% increase for Precision and a −15.0% decrease in Recall are statistically significant compared to the baseline. FADS-Fusion also quantifies uncertainty by using the conflict of evidence, with a discount factor, with Dempster–Shafer fusion as both a quantitative and qualitative explainability method. While uncertainty correlates with a drop in performance, this relationship depends on values for class-weighted uncertainty and location. Mapping uncertainty back onto the original image allows for a visual inspection on fusion quality and indicates areas where a human will need to reassess. Our work demonstrates that FADS-Fusion improves post-flood segmentation performance and adds the benefit of uncertainty quantification for explainability, an aspect important for reliability and user decision-making but understudied in ML for disaster management in the literature. Full article

Floods are considered one of the most devastating natural hazards, frequently resulting in substantial loss of lives and widespread damage to infrastructure. In the period of 4–7 September 2023, the region of Thessaly experienced unprecedented rainfall rates due to Storm Daniel, which caused significant flooding and many damages and fatalities. The southeastern areas of Trikala were among the many areas of Thessaly that suffered the effects of these rainfalls. In this research, a flood susceptibility assessment (FSA) of the broader area surrounding the settlement of Megala Kalyvia is carried out through the analytical hierarchy process (AHP) as a multicriteria analysis method, using Geographic Information Systems (GIS). The purpose of this study is to evaluate the prolonged flood susceptibility indicated within the area due to the past floods of 2018, 2020, and 2023. To determine the flood-prone areas, seven factors were used to determine the influence of flood susceptibility, namely distance from rivers and channels, drainage density, distance from confluences of rivers or channels, distance from intersections between channels and roads, land use–land cover, slope, and elevation. The flood susceptibility was classified as very high and high across most parts of the study area. Finally, a comparison was made between the modeled flood susceptibility and the maximum extent of past flood events, focusing on that of 2023. The results confirmed the effectiveness of the flood susceptibility assessment map and highlighted the need to adapt to the changing climate patterns observed in September 2023. Full article

Flooding is among the world’s most destructive natural disasters. From 27 July to 1 August 2023, Zhuozhou City and surrounding areas in Hebei Province experienced extreme rainfall, severely impacting local food security. To swiftly map the spatial and temporal distribution of the floodwaters and assess the damage to major crops, this study proposes a water body identification method with a dual polarization band combination for synthetic-aperture radar (SAR) data to solve the differences in water body feature recognition in SAR due to different polarization modes. Based on the SAR water body extent, the flood inundation extent was mapped with GF-6 optical data. In addition, Landsat-8 data were used to generate information on significant crops in the study area, while Sentinel-2 data and the Google Earth Engine (GEE) platform were used to classify the extent of crop damage. The results indicate that the flood inundated 700.51 km², 14.10% of the study area. Approximately 40,700 hectares (ha) or 8.46% of the main crops were affected, including 33,700 ha of maize, 4300 ha of vegetables, and 2800 ha of beans. Moderate crop damage was the most widespread, affecting 37.62% of the crops, while very extreme damage was the least, affecting 5.10%. Zhuozhou City experienced the most significant impact, with 13,700 ha of crop damage, accounting for 33.70% of the total. This study provides a computational framework for rapid flood monitoring using multi-source remote sensing data, which also serves as a reference for post-disaster recovery, agricultural production, and crop risk assessment. Full article

Flood disasters are among the most severe natural calamities worldwide and typically occur in densely populated areas with abundant lakes and high rainfall. These disasters cause significant damage to the environment and human settlements. Therefore, accurately monitoring and understanding the occurrence and evolution of floods, as well as studying the influencing factors, is of great importance. This study employs CYGNSS satellite data from a constellation of small satellites equipped with reflective radar, which observe the Earth’s surface with high spatial and temporal resolution. Such systems effectively monitor the distribution of water bodies and hydrological processes on land surfaces. By collecting and analyzing CYGNSS data, we can map the distribution of water bodies during flood events to assess the extent and severity of the flooding. Additionally, this study examines various factors influencing flooding, including rainfall, land use, and topography. By compiling relevant meteorological, geographical, and hydrological data, we aim to develop a model that elucidates the impacts of these factors on the initiation and progression of floods. Ultimately, this research offers a comprehensive analysis based on CYGNSS data for monitoring floods and their influencing factors. The goal is to yield significant insights and explore the potential of using CYGNSS data in flood monitoring efforts. In the context of global climate change and the increasing frequency of flood disasters, these findings are expected to provide a crucial scientific basis for improving flood prevention and management strategies, thereby helping to mitigate losses and enhance our warning and disaster response capabilities. Full article

Floods are significant global disasters, but their impact in developing countries is greater due to the lower shock tolerance, many subsistence farmers, land fragmentation, poor adaptation strategies, and low technical capacity, which worsen food security and livelihoods. Therefore, accurate and timely monitoring of flooded crop areas is crucial for both disaster impact assessments and adaptation strategies. However, most existing methods for monitoring flooded crops using remote sensing focus solely on estimating the flood damage, neglecting the need for adaptation decisions. To address these issues, we have developed an approach to mapping flooded rice fields using Earth observation and machine learning. This approach integrates high-resolution multispectral satellite images with Sentinel-1 data. We have demonstrated the reliability and applicability of this approach by using a manually labelled dataset related to a devastating flood event in north-western Nigeria. Additionally, we have developed a land suitability model to evaluate potential areas for paddy rice cultivation. Our crop extent and land use/land cover classifications achieved an overall accuracy of between 93% and 95%, while our flood mapping achieved an overall accuracy of 99%. Our findings indicate that the flood event caused damage to almost 60% of the paddy rice fields. Based on the land suitability assessment, our results indicate that more land is suitable for cultivation during natural floods than is currently being used. We propose several recommendations as adaptation measures for stakeholders to improve livelihoods and mitigate flood disasters. This study highlights the importance of integrating multispectral and synthetic aperture radar (SAR) data for flood crop mapping using machine learning. Decision-makers will benefit from the flood crop mapping framework developed in this study in a number of spatial planning applications. Full article

Ground hazards are a significant problem in the global economy, costing millions of dollars in damage each year. Railroad tracks are vulnerable to ground hazards like flooding since they traverse multiple terrains with complex environmental factors and diverse human developments. Traditionally, flood-hazard assessments are generated using models like the Hydrological Engineering Center–River Analysis System (HEC-RAS). However, these maps are typically created for design flood events (10, 50, 100, 500 years) and are not available for any specific storm event, as they are not designed for individual flood predictions. Remotely sensed methods, on the other hand, offer precise flood extents only during the flooding, which means the actual flood extents cannot be determined beforehand. Railroad agencies need daily flood extent maps before rainfall events to manage and plan for the parts of the railroad network that will be impacted during each rainfall event. A new approach would involve using traditional flood-modeling layers and remotely sensed flood model outputs such as flood maps created using the Google Earth Engine. These new approaches will use machine-learning tools in flood prediction and extent mapping. This new approach will allow for determining the extent of flood for each rainfall event on a daily basis using rainfall forecast; therefore, flooding extents will be modeled before the actual flood, allowing railroad managers to plan for flood events pre-emptively. Two approaches were used: support vector machines and deep neural networks. Both methods were fine-tuned using grid-search cross-validation; the deep neural network model was chosen as the best model since it was computationally less expensive in training the model and had fewer type II errors or false negatives, which were the priorities for the flood modeling and would be suitable for developing the automated system for the entire railway corridor. The best deep neural network was then deployed and used to assess the extent of flooding for two floods in 2020 and 2022. The results indicate that the model accurately approximates the actual flooding extent and can predict flooding on a daily temporal basis using rainfall forecasts. Full article

Mapping spatial data is essential for the monitoring of flooded areas, prognosis of hazards and prevention of flood risks. The Ganges River Delta, Bangladesh, is the world’s largest river delta and is prone to floods that impact social–natural systems through losses of lives and damage to infrastructure and landscapes. Millions of people living in this region are vulnerable to repetitive floods due to exposure, high susceptibility and low resilience. Cumulative effects of the monsoon climate, repetitive rainfall, tropical cyclones and the hydrogeologic setting of the Ganges River Delta increase probability of floods. While engineering methods of flood mitigation include practical solutions (technical construction of dams, bridges and hydraulic drains), regulation of traffic and land planning support systems, geoinformation methods rely on the modelling of remote sensing (RS) data to evaluate the dynamics of flood hazards. Geoinformation is indispensable for mapping catchments of flooded areas and visualization of affected regions in real-time flood monitoring, in addition to implementing and developing emergency plans and vulnerability assessment through warning systems supported by RS data. In this regard, this study used RS data to monitor the southern segment of the Ganges River Delta. Multispectral Landsat 8-9 OLI/TIRS satellite images were evaluated in flood (March) and post-flood (November) periods for analysis of flood extent and landscape changes. Deep Learning (DL) algorithms of GRASS GIS and modules of qualitative and quantitative analysis were used as advanced methods of satellite image processing. The results constitute a series of maps based on the classified images for the monitoring of floods in the Ganges River Delta. Full article

Floods are a natural phenomenon that cause damage to structures and property as well as negatively affect human life. Assessing the extent, speed, power, and depth of flooding has always been a challenge for water resource planners. This research developed a hydraulic simulation model for the Cubanicay and Bélico urban rivers embedded in the city of Santa Clara, Cuba. The methodology was based on a one-dimensional model of the Hydrological Engineering River Analysis System (HEC-RAS) and GIS-based methods. The HEC-RAS model (Beta) and three modeling flood tests for scenarios of 1% (100 years), 2% (50 years), and 10% (10 years) of probability for hydrometeorological events were analyzed. Bank lines, flow path lines, and cross-section cut lines were extracted from Digital Elevation Models. Manning’s roughness coefficients were considered for the channel morphology and soil typology. The flood Beta model results were accurate with a difference of ±0.10 m considering the water footprint found in the field. The results showed that the areas near the control section 2 + 87 presented a high risk of flooding. The flood limit map for urban areas could be an important tool for researchers, planners, and local governments for risk assessment and to develop evacuation plans and flood mitigation strategies in order to reduce human and economic losses during a flood. Full article

Precipitation changes caused by climate change are a phenomenon that may have far more serious consequences in urban areas than previously thought. Preventing the flood extent in rural and urban areas is crucial for the protection of human well-being and private and public land, and for the prevention of human casualties. Modeling potential floods and studying historic ones are key parameters to prevent damages from this natural disaster. European Union Directive 2007/60/EK obligated Member States to compile flood risk maps, showing the potential flood’s extent and depth. Using Cyprus river flood risk maps of the region of Larnaca, Corine land cover data, and open-access cadaster data, this study attempts to assess the economic damage. Cadaster data of buildings such as floor number and basement existence are incorporated in the damage functions used for flood recurrence intervals of 20, 100, and 500 years return periods. The study found that in the 500 year return flood period, 562 buildings would be potentially affected. The flood risk areas in the region of Larnaca consist of mainly urban (48%) and cultivation zones (50%), both economically important. The results suggest that economic damage assessment based on the applied data, combined with flood risk maps, can provide valuable information to disaster responders. Full article

Dams are expensive technical constructions that ensure food production, sustain farmers’ income, and cover a large percentage of urban water supply demands. However, the threat of a dam break flood, which can be extremely dangerous for the local society, should be taken into account, and proactive mitigation measures should be planned. Towards this direction, dam break modelling and flood hazard assessment are essential for developing flood crisis management and evacuation plans. In this study, a hypothetical case of failure of the Papadia dam in the Florina Regional Unit in northern Greece is examined. Two scenarios of failure were considered: overtopping and piping. A two-dimensional numerical model for the two failure scenarios was used to simulate the dam break process and flood wave routing using HEC-RAS software. A sensitivity analysis of the mesh size and breach parameters was performed to better understand their impact on the critical outputs of the simulation model. Flood hazard maps were produced in GIS environment based on water depth and velocity criteria. Furthermore, two classification approaches were adopted to assess the flood hazard using the product of water depth and velocity. The results showed that the extent of the inundated area could affect most of the study area and could cause severe damage to agricultural activities. Full article

Floods are frequent natural hazards, triggering significant negative consequences for the economy every year. Their impact is expected to increase in the near future due to socio-economic development and climate change. In order to minimize the probability and magnitude of expected economic losses and compensation costs, it is essential that flood risk managers are properly informed about potential damage related to hazard features and exposure. In this paper, a flood damage estimation method was proposed for the assessment of flood risk in the Drammen River basin by using a hydraulic model, GIS, and a flood loss estimation model. Hazard variables such as flood depth, flood extent, and flood velocity were computed for the current and future climatic scenarios using the hydraulic model for flood damage assessment. To visualize the flood extent, velocity, depth, and their impact, the results of modelling are illustrated in the form of flood inundation maps produced in GIS. A flood loss estimate included buildings and other infrastructure that are major exposures in flood-prone areas. The flood damage model is formulated based on stage–damage relationships between different flood depths and land-use categories. It calculates the economic loss related to different land-use features based on the simulated flood parameter obtained from the hydraulic model from 100- to 1000-year return periods. For the case study, the results show that the highest proportion of the total damage in each repetition interval (approximately 90–92%) is expected to occur in buildings. In addition, results showed that the effects of climate change will raise the total damage from floods by 20.26%. Full article

This communication study aims to provide evidence on how Sentinel sensors and Copernicus Programme’s contributing missions can support heritage endangered by natural hazards. The recent catastrophic floods of 2022 in Pakistan, mainly in the Indus Valley, threatened one of the most iconic sites within the country; thus, the UNESCO World Heritage Site of Mohenjo-Daro was selected here as a case study. Even if the main area of the site was not directly affected by the floods but rather by the heavy rains, its landscape suffered profound damage. This study aims to report a combined remote-sensing methodology for a rapid assessment of the flooded areas around the site. By using Sentinel-1 radar data in combination with mid-resolution Sentinel-2 data and Planet Lab images, it was possible to accurately map the affected areas near the site. This approach can be used to better understand the extent of the affected areas and build a better recovery strategy. A near-real-time satellite-based investigation and observations combining various sensors and resolutions (Sentinel 1 and 2 images, as well as Planet Lab images) can provide valuable insights for local heritage managers. Full article