Inundation Area Research Articles

Assessment of urban inundation and prediction of combined flood disaster in the middle reaches of Yellow river basin under extreme precipitation

Watershed Cities often face the dual flood risks of upstream floods and urban waterlogging. This compound disaster is caused by the interaction of multiple factors (topography, storms, upstream water and urban drainage, etc.), and is particularly affected by extreme rainfall events and the large and small drainage carrying capacity. To explore the combined effects of extreme rainfall and drainage systems, this study applied the Chicago rainfall hyetograph method and constructed design under different return periods. A comprehensive model combining the verified SWMM and LISFLOOD hydraulic models was utilised to build one-dimensional and two-dimensional coupled watershed urban flood models, simulating inundation depth and duration. By considering urban rainfall, runoff, topography, and drainage system characteristics, the flood processes under different extreme rainfall scenarios were simulated, and the node overflow and inundation depth were obtained. A flood risk assessment of watershed cities was conducted by combining methods such as the HR threshold method, evaluation level, and threshold division. In this study, Huyi District of Xi’an City, China, was used as the research area. The main research findings show: (1) the precipitation design values for different return periods were calculated based on the proposed method, with amount for a 100-year event being 87.196 mm, suitable for composite flood simulation; (2) applying the SWMM-LISFLOOD coupled model constructed for midstream urban areas ensured the accurate simulation of composite flood processes. Under different extreme rainfall return period scenarios, the percentage of flood inundation depth exceeding 1 m caused by multiple disaster factors increased from 0.73 % to 5.19 %; (3) extreme rainfall leads to different levels of internal flooding risk. When the return period of extreme rainfall is 5 years, the area inundated by composite floods in the research area is 67.21 km2. In comparison, when the return period of extreme rainfall is 50 years, the area inundated by composite floods in the research area significantly increases to a total inundation area of 286.85 km2. This study is expected to provide scientific references for flood prevention planning, composite disaster risk management, and urban resilience building in watershed urban areas while significantly impacting modern economic growth, social stability, and the safety of watershed cities.

Probabilistic assessment of postfire debris-flow inundation in response to forecast rainfall

Abstract. Communities downstream of burned steep lands face increases in debris-flow hazards due to fire effects on soil and vegetation. Rapid postfire hazard assessments have traditionally focused on quantifying spatial variations in debris-flow likelihood and volume in response to design rainstorms. However, a methodology that provides estimates of debris-flow inundation downstream of burned areas based on forecast rainfall would provide decision-makers with information that directly addresses the potential for downstream impacts. We introduce a framework that integrates a 24 h lead-time ensemble precipitation forecast with debris-flow likelihood, volume, and runout models to produce probabilistic maps of debris-flow inundation. We applied this framework to simulate debris-flow inundation associated with the 9 January 2018 debris-flow event in Montecito, California, USA. When the observed debris-flow volumes were used to drive the probabilistic forecast model, analysis of the simulated inundation probabilities demonstrates that the model is both reliable and sharp. In the fully predictive model, however, in which debris-flow likelihood and volume were computed from the atmospheric model ensemble's predictions of peak 15 min rainfall intensity, I15, the model generally under-forecasted the inundation area. The observed peak I15 lies in the upper tail of the atmospheric model ensemble spread; thus a large fraction of ensemble members forecast lower I15 than observed. Using these I15 values as input to the inundation model resulted in lower-than-observed flow volumes which translated into under-forecasting of the inundation area. Even so, approximately 94 % of the observed inundated area was forecast to have an inundation probability greater than 1 %, demonstrating that the observed extent of inundation was generally captured within the range of outcomes predicted by the model. Sensitivity analyses indicate that debris-flow volume and two parameters associated with debris-flow mobility exert significant influence on inundation predictions, but reducing uncertainty in postfire debris-flow volume predictions will have the largest impact on reducing inundation outcome uncertainty. This study represents a first step toward a near-real-time hazard assessment product that includes probabilistic estimates of debris-flow inundation and provides guidance for future improvements to this and similar model frameworks by identifying key sources of uncertainty.

Associating reservoir operations with 2D inundation risk and climate uncertainty

ABSTRACT Bridging the research gap between reservoir operations and inundation risks under the future climate, this study integrates a hydrologic reservoir management model with a 2D hydrodynamic model, comparing the conventional regulations and the optimized reservoir operations based on the particle swarm optimization (PSO) algorithm. Results reveal that optimized operations using the PSO algorithm consistently outperform conventional strategies by better-managing peak discharges and controlling downstream inundation. The study further differentiates between PSO-optimized plans: PSO1, which focuses on minimizing inundation areas, and PSO2, which prioritizes peak reduction at the flood control point. Interestingly, PSO2 proves superior for single-point peak reduction, typically the primary objective in current practices, whereas PSO1, despite lesser peak reduction, achieves a smaller inundation area, enhancing basin-scale flood resilience. This discrepancy reveals the need to consider downstream inundation risks as critical evaluation metrics in reservoir optimization, a factor often overlooked in existing studies. The research underscores the importance of updating operational frameworks to incorporate 2D inundation risks and adapt to increased flood risks under changing climate conditions. Despite optimization, future climate scenarios predict increased flood exposure, indicating that the current safety discharge rates and flow regulations at control points are outdated and require revision.

Forecasting ecological water demand of an arid oasis under a drying climate scenario based on deep learning methods

Ecological water diversion projects (EWDP) are an effective management tool for restoring oasis ecosystems in arid regions. Given the potential for drier climatic conditions in arid regions in the future, it is essential to develop water diversion strategies that can adapt to climate change and reduce the risk of oasis ecosystem degradation. Here, this study used a Bayesian optimization-based long- and short-term memory (BO-LSTM) model to determine the optimal amount of water diversion needed to maintain healthy growth of oasis vegetation under future climate change scenarios in the Qingtu Oasis, which is a typical downstream oasis of inland rivers restored by EWDP in China. The results showed that the BO-LSTM model effectively captured the response of oasis vegetation to changes in water inundation areas and drought stress with low computational cost and high accuracy. The study revealed that regional vegetation became more vulnerable than previously thought when extreme drought and a drying trend were taken into account. It was found that if the amount of water entering the oasis ranges from 10 to 15 million m3, there will be a decline in the growth of oasis vegetation as indicated by the normalized difference vegetation index (NDVI). Even if current levels of water diversion (20 million m3) are maintained, oasis vegetation may still face growth decline due to meteorological drought. The optimal amount of water diversion was determined to be 25 million m3, resulting in a 21.7% increase in NDVI regardless of drought events. This study represents an innovative approach as it couples EWDP, climate change, and oasis vegetation dynamics based on deep learning models, which unveils divergent responses of oasis vegetation to climate change and EWDP and verifies a non-linear relationship between water diversion amounts and ecological benefits generated.

Analisis Penggunaan Tanaman Vetiver terhadap Stabilitas Lereng pada Bendungan Way Sekampung

Problems that occur in the western part of As Way Sekampung have the potential for landslides to occur. Efforts used to handle slope strengthening by planting vetiver plants. The research method used to analyze slopes uses the limit equilibrium method on slip surface sections using the Morgenstren-Price method with the help of Geostudio 2023.1.0 software. SLOPE/W to get the safety factor under normal conditions and under seismic load conditions. By using a seismic load with a return period of 500 years (PE 10% in 50 years) with a Peak Ground Acceleration (PGA) value for the Way Sekampung Dam inundation area of 0,265 g. In vetiver plants, the root depth is simulated according to the top soil depth of 0,0 – 0,1 m. Soil planted with vetiver plants experienced an increase in the value of cohesion (c) by 74,42% and a decrease in the value of the angle of internal friction (φ˚) by 49,40%. The conclusion obtained was that slope stability in normal existing conditions was 4,525 and in normal vetiver plant strengthening conditions the safety factor increased to 4,529. Then, in the existing seismic conditions, the safety factor is 2,624 and in strengthening vetiver plants with seismic loads, the safety factor value increases to 2,626. It can be concluded that vetiver plants are able to strengthen slope conditions.

Proposal of a flood damage road detection method based on deep learning and elevation data

Identifying an inundation area after a flood event is essential for planning emergency rescue operations. In this study, we propose a method to automatically determine inundated road segments by floods using image recognition technology, a deep learning model, and elevation data. First, we develop a training model using aerial photographs captured during a flood event. Then, the model is applied to aerial photographs captured during another flood event. The model visualizes the inundation status of roads on a 100-m mesh-by-mesh basis using aerial photographs and integrating the information on whether the mesh includes targeted road segments. Our results showed that the F-score was higher, 89%–91%, when we targeted only road segments with 15 m or less. Moreover, visualizing in GIS facilitated the classification of inundated roads, even within the same 100-m mesh, which is a relevant finding that complements deep learning object detection.

Cropland inundation mapping in a mountain dominated region based on multi-resolution remotely sensed imagery and active learning for semantic segmentation

Accurate and timely mapping of cropland inundated area is critical to loss assessment and recovery plan development. However, existing methods for cropland inundation mapping using Synthetic Aperture Radar (SAR) imagery are primarily designed for plain regions and may not be suitable for mountainous regions. The complex rugged terrain at large scale present challenges such as significant noise from radar shadows, smooth surfaces, floodplains, and permanent water bodies. To remove these noise for effectively mapping of cropland inundation, this study proposes a robust two-stage framework integrating the multi-resolution remotely sensed imageries and semantic segmentation method. First, active learning for cropland extraction is performed with Google Very-High-Resolution (VHR) imagery and Segformer model. Second, the cropland inundation area is obtained using marker-controlled watershed segmentation (MCW) on Sentinel-1 SAR imagery with area-of-interest (AOI) filtering. Results show that the extracted cropland mask are superior to six public cropland data products including ESA, ESRI, FCS30, FROM, GL30, and SLOPE. In addition, with AOI filtering, the F-score of the potential flood identification using MCW was increased from 0.685 to 0.828. At last, the monthly cropland inundation caused by intense rainfall varied approximately from 6.6 km2 to 436.1 km2 during monsoon season of 2017–2022 in Guangdong. Our approach offers a fast and feasible solution for mapping cropland inundation in large and complex mountainous regions, without requiring extensive parameter tuning or additional data.

DAM DEVELOPMENT AND FOOD SECURITY OF DIRECTLY AFFECTED HOUSEHOLDS: A CASE STUDY IN JATIGEDE DAM, SUMEDANG, WEST JAVA

The Jatigede Dam construction project has caused many residents in the inundation area of the Jatigede reservoir to move to new areas. The affected households by the Jatigede Dam in new places must adapt to obtain sources of income for food and other necessities. This research aims to elucidate the agroecosystem production, off-farm jobs, and food adequacy and food security of the affected people of Jatigede Dam in Jatimekar Hamlet, Jemah Village. The research method used was a combination of qualitative and quantitative. The results revealed that the affected households of the Jatigede Dam Development Project who live in Jatimekar Hamlet, Jemaah Village, have less agricultural land ownership such as rice fields, mixed gardens and home gardens than before the Jatigede Dam existed. Consequently, some of the affected households of Jatigede Dam, they are also involved in various off-farm jobs. Based on food security, it shows that the affected people of Jatigede Dam in Jatimekar Hamlet, Jemah Village are in a vulnerable condition, due to low family income, and farming also faces various problems, such as water scarcity in the dry season and agricultural inputs, such as inorganic fertilizers, pesticides, and plant seeds have high price.

Non-market value losses to coastal ecosystem services and wetlands from sea-level rise and storm surge, 2050 to 2100: The Kimberley Region, Western Australia

Accelerating climate change from the thermal expansion of oceans and losses from terrestrial glaciers and ice sheets causes sea level rise (SLR), damaging coastal ecosystems and generating non-market value losses. Using an innovative Artificial Intelligence (AI) projection method and 18 existing geographical SLR projections, we project future sea level rise and storm surge (SLR/S) for the 1600 km coastline of the Kimberley Region, Western Australia, a region with a larger surface area than Great Britain, Ireland, and Portugal combined and with a permanent population of 40,000 people of which more than 16,000 identify as Indigenous. Using our projected coastal area inundation from future SLR, we estimated the non-market value losses of the Kimberley's coastal ecosystem and wetlands with projected SLR/S to 2050 and 2100 for Representative Concentration Pathway (RCPs) 4.5 and 8.5. Average annual non-market losses from SLR/S in 2050 and 2100 in the Kimberley are large relative to the size of the regional economy and are estimated to range from A$2.7 to A$4.3 billion (2050) and A$8.1 to A$15.8 billion (2100), depending on the RCP and temperature pathway. Our findings highlight the need for adequately resourced community-based approaches to climate adaptation.

A Sponge Village Flood Response Method Based on GIS and RS Analysis Formation—A Case Study of Jiangou Village

This study was conducted in response to the Beijing–Tianjin–Hebei mega heavy rainfall event at the end of July 2023, and the severely affected and representative Jiangou village in Beijing was selected as the study area. A variety of methods were used to synthesize and analyze the situation and propose an adaptive response to heavy rainfall and flooding in the village. Based on multi-source remote sensing (RS) data, a comprehensive topographic and hydrological characterization was carried out, and the precipitation before and after the disaster was analyzed; the flood inundation area was extracted using the improved normalized water body index (MNDWI) and OTSU thresholding methods, and the changes of water bodies during the flooding period were quantitatively analyzed; and an improved convolutional-neural-network-based building identification and extraction model was constructed to extract the research distribution of buildings in the area. The sponge city construction (SPCC) method was improved to obtain a method that can mitigate flood risk and adapt to villages by constructing small artificial lakes and local topographic buffers to improve the water storage and drainage capacity of villages. The study shows that these methods are innovative in flood hazard analysis and mitigation but still need further improvement in data accuracy, simulation depth, and system evaluation.

Quantification of the flood mitigation ecosystem service by coupling hydrological and hydrodynamic models

Flood mitigation service provides crucial information for reducing flood disasters and assessing ecosystem capacities by quantifying how much damage is reduced and how many benefiting areas are protected during flood events. However, there remains a gap in the full-process quantification, which results in less precise simulation outcomes. In this study, we introduce a novel methodology to accurately quantify the flood mitigation service of ecosystems by coupling hydrological and hydrodynamic models. We utilized the Hydrological Simulation Program-Fortran (HSPF) model to simulate peak flow and flood volume and then used these data as inputs for the Environmental Fluid Dynamics Code (EFDC) hydrodynamic model to simulate the spatial extent and depth of flood inundation. The contribution and capacity of the ecosystem are reflected through the reduction in peak flow, flood volume, and inundation areas. We used the Nandu Basin flood event in October 2010 as a case study to illustrate our approach, comparing our assessment results with those simulated by the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model and the Height Above Nearest Drainage (HAND) model. The results demonstrate that coupling the HSPF model (R2 = 0.93) with the EFDC model (overlap ratio = 83.71 %) allows for precise quantification of flood mitigation service. The process-based hydrological and hydrodynamic models show a high correlation with the simpler and faster InVEST and HAND model simulations, with the full-process models reducing relative errors by 7.66 % and 5.25 % respectively. This study offers a promising approach for accurately and comprehensively assessing flood mitigation ecosystem service and provides a basis for model selection.

Retention Basin for Flood Mitigation (Case Study: Magetan Regency)

Flooding is a natural event that often occurs in Indonesia. Magetan Regency has several areas drained by various rivers that have the potential to cause flooding problems in the event of high rainfall. Hydrological analysis is needed to determine the various rivers that are potentially unable to accommodate water capacity due to high rainfall, causing flooding problems. Thus, research aims to assess flood mitigation control based on a 25-year return period flood discharge (Q25) using the HSS Snyder method and HEC-HMS software. The flood inundation area due to river runoff is analysed using HEC-RAS 6.4.1 software using a 25-year return period discharge (Q25). This research uses retention basin as one of the efforts in flood mitigation, reducing the amount of flood inundation that occurs by using embankment planning. Retention basin is a water structure that functions to collect rainwater and water runoff to reduce flooding. Retention basin is one of the important steps in the flood inundation that occurred in Magetan Regency using HEC-RAS 6.4.1 software analysis was 3,75 km2. The largest flood inundation occurred in Kartoharjo District with an area 1,31 km2. The design of the Kartoharjo Retention Basin in Magetan Regency can reduce the flood inundation area by 22% to 2,94 km2.

Roles and responsibilities of stakeholders involved in local-level flood risk management: a study in the Deduru Oya basin in Sri Lanka

PurposeDisasters are occurring worldwide, and Sri Lanka has also become a victim of multiple disasters. Though there is a disaster management mechanism from the national level to the local level in Sri Lanka, several gaps are identified in pre- and postdisaster management activities. Among them, the responsibilities of local-level stakeholders in flood management activities are not performing at a satisfactory level. Therefore, the flood damage from the previous events was too great. Accordingly, the purpose of this study is to assess the roles, responsibilities and performance of stakeholders involved in local-level flood risk management activities.Design/methodology/approachThe Deduru Oya basin in Sri Lanka was selected as the study area. The study hypothesized that “ineffective roles, responsibilities and performance of local level stakeholders have weakened the flood risk management in the Deduru Oya basin.” The stratified random sampling method selected the sample (n = 425) from the flood-vulnerable population. Primary data were collected through a questionnaire survey, key informant interviews and field observations. Secondary data were collected from reports, work plans, journal papers and published maps. Descriptive data analysis methods and thematic data analysis methods were used in the study.FindingsAbout 69% of the respondents state that they are not satisfied with the involvement of government agencies in local-level flood risk management activities, while 10% of the respondents state that they are satisfied. The thematic analysis reveals several gaps, such as the lack of reservations in the Deduru Oya, improper land use practices, no inundation areas are identified and mapped out and the effects of illegal sand mining in the Deduru Oya. In addition to that, the lack of legal power vested in local governments in disaster management and the weaknesses of current legislation on flood risk management are the other major courses of local-level ineffectiveness.Research limitations/implicationsThe current research was limited to studying the involvement of local-level stakeholders in flood risk management, but the involvement of subnational and national-level stakeholders should be studied in future research.Originality/valueThis paper examines the involvement of local-level stakeholders in flood risk management. The results of the study confirm that the roles and responsibilities of local-level stakeholders are not well-defined and mandated, and therefore, the performance of stakeholders involved is minimal. Therefore, the results highlight the need for a well-established community-based flood risk management mechanism.

Application of Numerical Modeling and GIS for Simulating Inundation Under Dam Failure Scenarios

Abstract Dam failure and unsafe dam reservoirs cause extremely serious flooding and affecting the environment and ecosystem in the downstream area. The study applied numerical modelling (MIKE 11, MIKE 21, MIKE FLOOD) combined with GIS technology to simulate flooding due to the collapse of Dong Be reservoir, Thanh Hoa, Vietnam, with flood scenarios of frequency P=1.5% and P=0.5%. The study calibrated and validated the MIKE FLOOD model for two historic flood events in 2010 and 2017. Assessment results show that they were relatively consistent with flood trace survey data. Inundation results in two scenarios show that the inundation area with the P=0.5% scenario tends to increase compared to the P=1.5% scenario. The evaluation of simulation results for flooding under two scenarios, when overlaid with administrative, population, and construction layers, will help assess the impact of dam failure and establish its consequences.

Assessing the impact of sea level rise on the Indus delta in Pakistan: A comprehensive analysis of flooded areas and future vulnerabilities

This research investigates the impact of sea level rise (SLR) on the Indus Delta, a vital ecosystem increasingly vulnerable to climate change repercussions. The objective of this study is to comprehensively assess the flooded areas under various shared socioeconomic pathway (SSP) scenarios based on the Intergovernmental Panel on Climate Change's (IPCC) 6th Assessment Report. The study employs a GIS-based bathtub model, utilizing historical (1995–2014) and IPCC-projected (2020–2150) tide gauge data from Karachi, Kandla, and Okha stations to identify potential inundated areas threatened by coastal flooding. Additionally, it analyzes LANDSAT-derived multispectral images to identify coastal erosion hotspots and changes in the landscape. A supervised random forest classifier is used to classify major landforms and understand alterations in land cover. Furthermore, neural network-based cellular automata simulations are applied to predict future land cover for 2050, 2100, and 2150 at risk of inundation. The results indicate that under different SSP scenarios, the estimated inundated land area varies from 307.36 km2 (5 % confidence on SSP1-1.9) to 7150.8 km2 (95 % confidence on SSP5-8.5). By 2150, the region will lose over 550 km2 of agricultural land and 535 km2 of mangroves (mean SLR projection). This work emphasizes identifying sensitive land cover for SLR-induced coastal flooding. It might fuel future policy and modeling endeavors to reduce SLR uncertainty and build effective coastal inundation mitigation methods.

Comparison of 1D, coupled 1D–2D, and 2D shallow water numerical modeling for dam-break flow analysis of Way-Ela dam, Indonesia

This study analyzes the flood inundation area using shallow water numerical modeling with HEC-RAS 6.3 software by comparing 1D, coupled 1D–2D, and 2D approaches. As a case study, the 2013 Way-Ela dam-break event in Indonesia is selected. Way-Ela Dam naturally formed by landslides in 2012, collapsed due to a piping mechanism after a heavy rainfall event. To estimate the breach outflow hydrograph, an empirical parametric model based on regression formula is used. Compared with the observed data, the numerical results show the 2D model produces the most accurate results among others, with reasonable computational time, while the 1D model, despite being computationally very efficient, misinterprets the flood extent map. The coupled 1D–2D model produces results similar to that of the 2D model; however, this coupled approach, which is expected to be more computationally efficient than the 2D one, interestingly yields a significantly longer calculation time. Some possible reasons are thus discussed. Additionally, comparisons for the water level and velocity are also presented in several locations to point out the difference between each model. Our finding informs the selection of an appropriate hydrodynamic model for dam-break simulations, balancing the result accuracy and computational cost.

Effect of urbanization on surface runoff and performance of green roofs and permeable pavement for mitigating urban floods

Floods are increasingly becoming a significant concern due to climate change, global warming, and excessive urbanization. The Intergovernmental Panel on Climate Change (IPCC) has projected that global warming will continue to contribute to more frequent and severe floods and hydrological extremes. In response to these challenges, nature-based solutions (NBSs) have gained recognition as effective approaches to mitigate the adverse impacts of floods by focusing on ecosystem conservation, restoration, and sustainable utilization of natural resources. This study examines a flood that occurred in the Erkilet District of Kayseri, Türkiye on September 22, 2022, as a result of intense rainfall. It involves a thorough on-site investigation to assess the hydraulic, hydrologic, and geotechnical attributes of the study area. The findings from the field study indicate that the primary cause of the flood is attributed to excessive urbanization. To further analyze the impact of urbanization, a hydraulic model is developed considering both the physical and topographical conditions of the study area for both the year 2006 and 2022. The simulation results reveal that the extent of inundation area and water depth has increased significantly due to the excessive urbanization that occurred within a 16-year period. Additionally, the effectiveness of green roofs and permeable pavements as NBSs to mitigate urban flooding is explored. The implementation of green roofs and permeable pavements shows promising results, reducing the adverse effects of urban floods by 3% to 8%, depending on their specific locations and configurations. However, the results suggest that NBSs alone cannot fully prevent floods so they should complement gray infrastructure. The novelty of the study lies in its ability to demonstrate the impact of urbanization and the effectiveness of nature-based solutions in mitigating flood extent based.

Assessing vulnerability and enhancing resilience of port systems in southeast Texas facing sea-level rise

Climate change and the associated sea level rise (SLR) are presenting newfound challenges to the port systems and coastal transportation infrastructure of southeast Texas. This paper introduces a Geographic Information Systems (GIS) based model designed to simulate inundation scenarios under various sea-level projections, aiming to assess the vulnerabilities of both port facilities and road networks. The study area encompasses a specific region within Jefferson County, southeast Texas, encompassing three major ports: Port Arthur, Beaumont, and Orange. Utilizing a high-resolution (1-m) Digital Elevation Model (DEM) derived from the 2017 LiDAR dataset, this model is integrated with NASA’s sea-level rise projections to compute the extent and volume of inundation across low, medium, and high SLR scenarios. Drawing from monthly mean sea level data spanning from 1958 to 2020, the lowest SLR projections, derived from the relative sea-level trend measured at the Sabine Pass, TX gauge station, indicate a yearly increase of 6.16 mm, with a 95% confidence interval of +/- 0.74 mm. Projections for 2050 and 2,100 show the lowest SLR at 0.17 m and 0.48 m, respectively. In contrast, the medium to high RSLR projections under the IPCC SSP3-7.0 scenario for 2050 and 2,100 stand at 0.54 m and 1.34 m, respectively. The findings reveal that, under medium to high SLR scenarios, the extent of inundated areas in the study region is expected to expand by 12.4% in 2050 and 19.9% in 2,100, compared to the lowest SLR projection. Additionally, the length of submerged roadways is predicted to increase by 6.9% in 2050 and 13.3% in 2,100, in comparison to the lowest SLR projection. It is worth noting that some margin of error may be introduced due to factors such as the width of the port area and access roads, the high-resolution DEM, and the alignment of computed inundated areas with the existing topography. Overall, the manuscript highlights the urgency of proactive planning and underscores the importance of safeguarding critical infrastructure in the context of climate change and SLR.

Applications of satellite data for rapid inundation assessment - A case study in Thua Thien Hue province

Floods are one of the most significant devastating natural hazards around the globe. Recent development in remote sensing technology supports faster and low-cost analysis of flood hazards. This study used the Sentinel-1 SAR data for flood mapping and damage assessment. We selected the October 2022 flood event in Thua Thien Hue province, Vietnam. The Change Detection and Thresholding (CDAT) method was adopted to detect the inundation areas. The results showed that The flood event affected to several districts with Quang Dien being the worst hit, followed by Phu Loc and Huong Tra. Phu Vang had the smallest flooded area. The total inundated area was up to 33,384.08ha. The derived flood map was overlaid onto the current land cover map to conduct an initial evaluation of the potential flood damage corresponding to each land use category. The damage to the land cover was estimated that most of the affected areas were in the cropland and accounted for 77.07% of the total inundated area. The results can assist decision-makers in monitoring and assessing flood damage in Central Vietnam

Global WaterPack - The development of global surface water over the past 20 years at daily temporal resolution

Open surface water across the globe is essential for many life forms and is an important source for human settlements, agriculture, and industry. The presence and variation in time and space is influenced by different natural conditions (e.g. climate, topography, geology) and human use (e.g. irrigation, flood protection). The information on the spatial and temporal distribution of open surface water is fundamental for many disciplines and is also required as an essential parameter for hydrological and climatological modelling. Here, we present a dataset derived from satellite earth observation, which is based on more than 6.3 million single MODIS products with a volume of approx. 300 TB. The resulting dataset reflects the situation of open surface water on a global scale for each day over the time period from 2003 to 2022 at a spatial resolution of 250 m. The dataset enables the analysis of the development of lake and reservoir surface areas, freezing cycles, and inundation areas.