Evaluating the capability of UAV-Derived DEMs for flood hazard analysis

ABSTRACTSiberia is a huge and relatively sparsely populated territory in the centre of Eurasia with a harsh climate and diverse flow formation conditions. Nevertheless, it is characterized by a high flood hazard, as most of the settlements and industrial centres are located along river banks. The flood hazard can vary across space and time against a background of climatic and anthropogenic changes, as well as socio-economic factors. We carried out a geographical analysis of floods as a tool to identify the present status of flood hazard in Siberia. The analysis included: analysis of flood hazard during a period of climatic change (1985–2016); analysis of ice jams and ice dams as a specific natural factor causing floods; and identification of areas with different flood hazards using multi-scale mapping. Rainfall floods, mixed floods (due to rainfall and snowmelt) and floods resulting from ice dams are the most dangerous in Siberia. They have the highest rate of recurrence, strength of impact, and number of fatalities and displaced people. The locations of areas with different flood hazards were identified by multi-scale mapping. The highest flood hazard in Siberia is in the most populated and economically developed southern regions along the Ob, Lena, Yenisei rivers and in the Lake Baikal basin. In areas with a high flood hazard, complex measures are needed, taking into account regional peculiarities.

The increasing frequency and intensity of flooding, driven by climate change, are having a detrimental impact on Phnom Penh’s urban ecosystem. Disaster managers and decision-makers increasingly recognize the value of tools that can directly translate flood hazards—specifically, the extent of inundation during flood events—into estimates of expected socio-economic impacts, including affected populations and economic losses. While previous studies have often focused on broad-scale flood impacts at local or regional levels, this paper presents a straightforward approach for estimating impacts at a finer, more localized scale within the affected area, using flood hazard data and damage functions. This study aims to assess flood inundation areas and the direct economic losses resulting from flood events in the southern and southwestern parts of Phnom Penh, the capital city of Cambodia. The methodology involves applying Google Earth Engine (GEE) with Sentinel-1 synthetic aperture radar (SAR) data to assess flooding, including the generation of flood extent and depth maps. These maps are then used as input for an impact assessment module, which translates the estimated flood extents and depths into quantitative assessments of socio-economic impacts. The results indicate that the inundation areas in the southern and southwestern urban areas of Phnom Penh reached 1,638 ha, with flood depths ranging from 0.5 to 6 m, during the October 2020 flood event. The total estimated economic damage was approximately USD 20 million. Agricultural areas were the most affected, followed by residential and commercial areas. This study provides a valuable tool for rapid assessment of urban flood impacts, offering crucial data on flood extent, depth, and economic consequences for urban policymakers and planners.

Hydrodynamic flow modeling is carried out using a coupled 1D and 2D hydrodynamic flow model in northern India where an industrial plant is proposed. Two flooding scenarios, one considering the flooding source at regional/catchment level and another considering all flooding sources at local level have been simulated. For simulating flooding scenario due to flooding of the upstream catchment, the probable maximum flood (PMF) in the main river is routed and its flooding impact at the plant site is studied, while at the local level flooding, in addition to PMF in the main river, the probable maximum precipitation at the plant site and breaches in the canals near the plant site have been considered. The flood extent, depth, level, duration and maximum flow velocity have been computed. Three parameters namely the flood depth, cross product of flood depth and velocity and flood duration have been used for assessing the flood hazard, and a flood hazard classification scheme has been proposed. Flood hazard assessment for flooding due to upstream catchment and study on local scale facilitates determination of plinth level for the plant site and helps in identifying the flood protection measures.

The use of 1D, 2D, and 1D/2D modelling techniques to identify flood prone areas is a critical component of any flood hazard management project (e.g., APDF – action plan for dam failure) in the proximity of big dams and reservoirs. In this work, we manage to computed flood hazard models using 2D HEC-RAS module based on Digital Elevation Models (DEM’s) derived from Light Detection and Ranging (LiDAR) data and pre- and post-processed using Geographic Information Systems (GIS)-based software (e.g., ArcGIS, HEC-RAS). Therefore, to produce urban flood hazard (FH) maps downstream of the Strâmtori-Firiza reservoir (S-Fr) in NW Romania, a multi-scenario approach based on LiDAR-derived DEM integration, 2D hydraulic modeling, and remote sensing (RS) data validation is provided. In this context, to assess the flood control capacity (FCC) of the S-Fr hydro-technical system, three flood tests based on S-Fr flow rate with 5% (167 m3/s), 1% (270 m3/s) and 0.1% (447 m3/s) return periods were performed. The flood impact within the urban area of Baia Mare located downstream of S-Fr was achieved for each flood scenarios using four spatial data derived from the RAS Mapper module: flood extent (FE), flood depth (FD), flood velocity (FV) and flood hazard (FH). The results indicate that a large area of Baia Mare city can be affected by a potential flood caused by a dam failure and also contribute to the APDF update of S-Fr dam.

Computational simulators of complex physical processes, such as inundations, require a robust characterization of the uncertainties involved to be useful for flood hazard and risk analysis. While flood extent data, as obtained from synthetic aperture radar (SAR) imagery, has become widely available, no methodologies have been implemented that can robustly assimilate this information source into fully probabilistic estimations of the model parameters, model structural deficiencies, and model predictions. This paper proposes a fully Bayesian framework to calibrate a 2D physics-based inundation model using a single observation of flood extent, explicitly including uncertainty in the floodplain and channel roughness parameters, simulator structural deficiencies, and observation errors. The proposed approach is compared to the current state-of-practice Generalized Likelihood Uncertainty Estimation (GLUE) framework for calibration and with a simpler Bayesian model. We found that discrepancies between the computational simulator output and the flood extent observation are spatially correlated, and calibration models that do not account for this, such as GLUE, might consistently mispredict flooding over large regions. The added structural deficiency term succeeds in capturing and correcting for this spatial behavior, improving the rate of correctly predicted pixels. We also found that binary data does not have information relative to the magnitude of the observed process (e.g. flood depths), raising issues in the identifiability of the roughness parameters, and the additive terms of structural deficiency and observation errors. The proposed methodology, while computationally challenging, is proven to perform better than existing techniques. It also has the potential to consistently combine observed flood extent data with other data such as sensor information and crowd-sourced data, something which is not currently possible using GLUE calibration framework.

This research aimed to evaluate flood hazards and risk areas in the Gidabo Watershed using remote sensing (RS), Geographic Information Systems (GIS), and analytical hierarchy process (AHP). Six main factors were considered to identify flooding hazard zones: drainage density (DD), soil, elevation, rainfall, slope, and land use land cover (LULC). Population density, flood hazard zone, and LULC were considered for mapping the flood risk zone in the Gidabo watershed. A weighted overlay analysis tool has been utilized to integrate the thematic layers to identify both flood hazard and flood risk zones. The findings indicated that about 41.6% (337 km2) of the watershed falls within the high and very high flooding hazard zones. Conversely, 31.11% (252 km2) of the watershed is categorized under very-low and low flooding hazards. Moreover, the study identified five flood risk zones in the area viz; very high, high, moderate, low, and very low. The result of the flood risk map revealed that 199.5 km2 (24.5%) of the watershed has a higher and very higher risk of flooding. These zones were validated using the receiver operating characteristic (ROC) curve, showing a correlation coefficient of 0.943. These results emphasize the need to implement prediction of floods, early warning systems, and effective management practices on a regular and sustainable basis.

Flood disasters are one of the most destructive hazards faced across the globe, and thus flood hazard analysis is an essential factor for flood management. Due to the randomness and fuzziness of flood hazard, a series of possibilities is required for a certain flood level rather than just one exact probability. As a method for fuzzy hazard analysis derived from the information diffusion theory, the Interior-Outer-Set Model (IOSM) has the potential to reflect the randomness and fuzziness of flood hazard, however, the controlling intervals might have no sample points particularly when there are extraordinarily large flood peak flows or the flood samples concentrate. Based on this, the current study proposes a new framework for flood hazard analysis. First, flood samples are extracted from daily observed peak flow data. Second, the traditional IOSM is improved using design peak flows from flood frequency analysis (FFA) as the controlling points. From this, probability and floods hazard values are estimated via the FFA based IOSM (FFA-IOSM).The proposed framework is applied using data from the Dongjiang River, South China. Results demonstrate that the estimated flood probability was able to more effectively reflect the randomness and fuzziness of flood hazard compared to the traditional IOSM. This study provides a basis for reasonable flood engineering practices and supports the government with effective guidance on flood risk management, particularly under the increasing frequencies of the extreme precipitation events.

The paper is devoted to routing an unmanned aerial vehicle (UAV) during the Zaporizhzhia nuclear power plant monitoring without returning to the depot due to the deployment of an automatic battery replacement aerial system in specified places. The monitoring includes the overflight of the 11 monitoring stations to collect radiation monitoring data from them in case of damage of wired networks between the monitoring stations and the crisis centre. The shortest possible UAV route that visits every monitoring station (route point) exactly once and returns to the UAV depot is found by solving the Travelling Salesman Problem. An algorithm for determining the places for deployment of the automatic battery replacement aerial system is presented. A flight schedule both for the UAV and the automatic battery replacement aerial system is developed. The DJI Mavic 2 Enterprise Dual quadcopter is considered as a UAV for monitoring missions. The SL-231 Scout helicopter capable to operate in an unmanned mode is considered as a platform for the automatic battery replacement system.

This study presents a comprehensive flood hazard assessment of a potential breach of the Attabad landslide dam in Gilgit-Baltistan, Pakistan, using the HEC-RAS 2D hydraulic model. Four breach scenarios – overtopping and piping failure modes – were simulated to analyze downstream flood dynamics. Peak outflows ranged from 33,129 to 235,611 m³/s, attenuating over a 121 km stretch of the Hunza River. Sensitivity analysis with the Morris method (240 simulations) identified breach width and formation time as the most critical parameters, while Manning’s coefficients significantly influenced downstream flow. Froehlich’s breach parameterization method was adopted for critical failure time predictions. Flood inundation mapping was carried out to visualize the spatial extent of potential flooding, highlighting areas at highest risk. Flood depth, velocity, and arrival time were quantified for key infrastructure, including Sarat and Gilgit bridges. Findings underscore scenario-based modeling and sensitivity analysis as essential for enhancing dam safety and emergency preparedness.

Flood is one of the major natural calamity often strikes the state of West Bengal, India, during monsoon. With rapid ongoing human activities in the river basin regions, the effect of flood on human society becomes matter of panic day by day. The analysis of flood hazards and assessing potential vulnerable areas along with level of vulnerability is very much essential for proper disaster risk assessment and management of the region. Remote sensing and GIS platforms can be considered very handy for analysing of the flood mapping, risk monitoring, hazard analysis, and disaster management planning. In this study, flood-inundated area was obtained in GIS platform by taking contour pattern of the area developed from DEM imagery, river gauge height at normal and peak flow condition as input layers. This model can further be used for flood prediction and identification of the vulnerable area. The risk assessment and hazard identification have been carried out by considering the affected population along with the inundated area as input and getting the number of villages and people affected for different stage heights of the water level. It is further used for planning of flood shelter location considering the flood-affected area and topography of the region along with other infrastructural parameters. The described model was used in a case study in two blocks, namely Ausgram II and Nanoor within the Ajay river basin in the state of West Bengal, India, which are regular flood prone regions, and a proposed risk management solution was suggested. For flood analysis, flood spread area and flood height are considered. From the result of flood prediction in Ausgram II, 13% of the block area and 17% of the block population and, in Nanoor, 21% of the block area and 28% of the population are affected.

Rapidly-industrializing, developing countries face increasing environmental disaster potential. Rising disaster losses are an indicator of non-sustainable development. Analysis of flood hazard in peninsular Malaysia exemplifies these problems. Malaysia has a rapidly-growing economy and evidence suggests that the flood hazard is worsening largely because of rapid urbanization and development. This is revealed by analysing trends in five 'flood hazard factors': variables which explain flood disaster potential. Flood risk, exposure and damage potential are all increasing. High levels of poverty in flood plains suggest that peoples' vulnerability to floods is not being reduced. A large proportion of interviewed flood-prone households have low incomes, especially in eastern peninsula locations, and reported flood damages are high. Indigenous household flood adjustments, including 'stilt houses', limit flood vulnerability especially in the rural east. Unfortunately, modernization threatens traditional life-styles and erodes indigenous hazard-adaptations. State response is heavily engineering-oriented and is failing to address development-induced flood hazard potential. Policies are required which marry hazard reduction to poverty reduction and sustainable development.

Growing military and civilian reliance on multirotor unmanned aerial vehicles (UAVs) accentuates the need for resilient, field-deployable power supplies capable of continuous operation whenever conventional grids are inaccessible or hazardous. The paper addresses this challenge by proposing the comprehensive methodology for choosing photovoltaic (PV) converters and complementary storage technologies that can sustain round-the-clock wireless charging of UAV batteries under Northern-Ukrainian insolation conditions. The problem is formulated as joint optimisation of the daily number of battery charging cycles, overall-energy demand and PV-array configuration while accounting for climatic variability and flight-mission logistics.The research objective is to substantiate the autonomous solar power plant sized to support three DJI Mavic 3 class UAVs performing uninterrupted aerial reconnaissance. Methods include energy-balance modelling, scenario scheduling of flight/charge cycles, comparative efficiency analysis of mono-, poly- and thin-film PV modules, and evaluation of two battery chemistries—LiFePO₄ and AGM/GEL—using manufacturer data and recent literature.The key results show that, for Chernihiv’s mean global tilted irradiation of 3.61 kWh m⁻² day⁻¹, a 3.5 kWh daily yield suffices to cover 35 full 100 W charges. LiFePO₄ storage (round-trip η ≈ 95 %) cuts required array area by 5–15 % versus AGM/GEL (η ≈ 85 %), enabling a lighter, cheaper and more mobile installation. Among thirteen commercially available panels analysed, high-efficiency monocrystalline modules (η > 20 %) teamed with LiFePO₄ batteries minimise total mass (≈ 55 kg) and capital cost (< 400 USD) while meeting mission autonomy. The proposed selection algorithm may be adapted to other regions and fleet sizes, and the findings facilitate rapid deployment of reliable solar-powered UAV charging outposts for de-fence, rescue and environmental monitoring tasks.

Unmanned aerial vehicle (UAV) imagery is considered the best approach to acquire high-resolution data over a large area in a short time, such as in the case of coastal wetlands comprising small-sized patches of perennial plants. However, to the best of our knowledge, no previous studies have used UAV imagery to quantitatively estimate blue carbon in coastal wetlands. Therefore, this study aimed to estimate the area-wide carbon stocks accumulated in halophyte habitats through the case studies of the southern coastal wetlands in South Korea using UAV imagery. Accordingly, object-based image analysis was used to identify the spatial distribution of major halophytes based on the spectral reflectance derived from multispectral UAV images. Elemental analysis (EA) was performed to estimate the content of organic carbon accumulated in individual halophytes, and the halophyte communities identified in the case study area were confirmed to have accumulated 20.80 MgC of blue carbon. These results demonstrate that applying EA to UAV images can be an innovative method to solve the spatial uncertainty encountered in the monitoring of blue carbon in existing field-based surveys and traditional remote sensing.

Palembang and the surrounding areas there are many slums, especially alongside the river areas, the land is located in the wetlands area in Palembang. The growth of slums Village 5 Ulu Seberang Ulu I district, due to its strategic position in the region where the services and trade, thus becoming the choice of those newcomers to settle into the community and economic life along the river make migrants from villages to bring the crop to trade. From trading there are needs for a place to stay, and they set up lodges in a way ride with landlords and natives Palembang along the river. Eventually from makeshift cottage house, and began to grow houses on stilts. Because it's near the center of trade and services, attracting residents urbanization outside of Palembang to choose to live in this village to find work with do not have the education and special skills, so many emerging slums without complete infrastructure housing is especially sanitation.In Palembang, floodsseems to have a tendency to increase every year. Increasing trend of flooding in Palembang not only the breadth of course, but the loss also increases as well. Materials used in the study was DEM, topographic maps, land use maps, maps tides, river flow data, the coefficient manning, cross section of the river and drainage system data. Value DEM manipulation, spatial patterns of river that flooded as a result of tidal depicted in the map indicates that the area is mostly in the form of alluvial land. Based onthe results ofa GIS analysis of the research region obtained five areas of flood hazard that area11.43% very high hazard, high hazard 8.71%, 5.99% medium hazard, low hazard 3.59%, 70.28% very low hazard, Where almost all areas of research into the danger area is very high, high, low and very low. Looking at the results that have been obtained through a process of spatial data processing almost the whole area along the river included in the criteria of high hazard is due to the use of land in the form of slums, the soil type is alluvial soil, and most drainage network density contrast is less well. Almost the entire District of Seberang Ulu I have a region surrounding the flat category.

The reconstruction of past flash floods in ungauged basins leads to a high level of uncertainty, which increases if other processes are involved such as the transport of large wood material. An important flash flood occurred in 1997 in Venero Claro (Central Spain), causing significant economic losses. The wood material clogged bridge sections, raising the water level upstream. The aim of this study was to reconstruct this event, analysing the influence of woody debris transport on the flood hazard pattern. Because the reach in question was affected by backwater effects due to bridge clogging, using only high water mark or palaeostage indicators may overestimate discharges, and so other methods are required to estimate peak flows. Therefore, the peak discharge was estimated (123 ± 18 m3 s–1) using indirect methods, but one‐dimensional hydraulic simulation was also used to validate these indirect estimates through an iterative process (127 ± 33 m3 s–1) and reconstruct the bridge obstruction to obtain the blockage ratio during the 1997 event (~48%) and the bridge clogging curves. Rainfall–Runoff modelling with stochastic simulation of different rainfall field configurations also helped to confirm that a peak discharge greater than 150 m3 s–1 is very unlikely to occur and that the estimated discharge range is consistent with the estimated rainfall amount (233 ± 27 mm).It was observed that the backwater effect due to the obstruction (water level ~7 m) made the 1997 flood (~35‐year return period) equivalent to the 50‐year flood. This allowed the equivalent return period to be defined as the recurrence interval of an event of specified magnitude, which, where large woody debris is present, is equivalent in water depth and extent of flooded area to a more extreme event of greater magnitude. These results highlight the need to include obstruction phenomena in flood hazard analysis. Copyright © 2012 John Wiley & Sons, Ltd.