scholarly journals Large Scale Flood Risk Mapping in Data Scarce Environments: An Application for Romania

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1834 ◽  
Author(s):  
Raffaele Albano ◽  
Caterina Samela ◽  
Iulia Crăciun ◽  
Salvatore Manfreda ◽  
Jan Adamowski ◽  
...  
Keyword(s):  
Land Use ◽  
Flood Risk ◽  
Large Scale ◽  
Binary Classification ◽  
Depth Map ◽  
Return Time ◽  
Flood Damage ◽  
Landsat 8 ◽  
Machine Learning Classification ◽  
Cost Efficient

Large-scale flood risk assessment is essential in supporting national and global policies, emergency operations and land-use management. The present study proposes a cost-efficient method for the large-scale mapping of direct economic flood damage in data-scarce environments. The proposed framework consists of three main stages: (i) deriving a water depth map through a geomorphic method based on a supervised linear binary classification; (ii) generating an exposure land-use map developed from multi-spectral Landsat 8 satellite images using a machine-learning classification algorithm; and (iii) performing a flood damage assessment using a GIS tool, based on the vulnerability (depth–damage) curves method. The proposed integrated method was applied over the entire country of Romania (including minor order basins) for a 100-year return time at 30-m resolution. The results showed how the description of flood risk may especially benefit from the ability of the proposed cost-efficient model to carry out large-scale analyses in data-scarce environments. This approach may help in performing and updating risk assessments and management, taking into account the temporal and spatial changes in hazard, exposure, and vulnerability.

Large Scale Flood Damage Mapping: the case study of Romania Country

2020 ◽  
Author(s):  
Raffaele Albano ◽  
Aurelia Sole ◽  
Salvatore Manfreda ◽  
Caterina Samela ◽  
Iulia Craciun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
High Resolution ◽  
Flood Risk ◽  
Large Scale ◽  
Flood Damage ◽  
Emergency Operations ◽  
Hazard Exposure ◽  
Cost Efficient ◽  
Damage Mapping

<p>A large-scale flood risk analysis that properly evaluates and quantifies the three components of risk (hazard, exposure and vulnerability) is essential in order to support national and global policies, emergency operations and land use management. For example, governments can use risk information for the prioritisation of investments to implement measures for flood damage reduction, for emergency operations and for land-use policies, while reinsurance companies can improve the estimation of the flood risk-based insurance premiums.</p><p>Nevertheless, limits in time and data represent significant limitation this kind of applications: i) the significant amount of data and parameters required for the calibration and validation of traditional model; ii) the moderate/coarse resolution of data available at global scale and the sparse availability of high-resolution data that may affect the accuracy of analysis results; iii) the high cost and computational demand of hydraulic models. However, the growing availability of data from new technologies of Earth observation (EO) and environmental monitoring combined with the advances in newly developed algorithms (e.g. machine learning) have extended the range of possibilities for geoscientists, updating and re-inventing the way highly resource- and data-intensive processes, such as risk management and communication, are carried out.</p><p>The present study proposes a cost-efficient method for large-scale analysis and mapping of direct economic flood damage at medium resolution in data-scarce environments. The proposed methodological framework consists of three main stages. The first step concerns the derivation of a water depth map through a Digital Elevation Model (30m resolution)-based geomorphic method that uses supervised linear binary classification. The second step aims to realize an exposure map on the basis of a supervised land use classification through the use of a machine learning technique: the information extracted from Landsat-8 remotely sensed optical images were utilized in combination with the discontinuous (i.e. available for a few large cities in Europe) existing high-resolution Urban-Atlas land use maps in order to obtain a land-use map with a resolution of 30 m. Finally, the flood economic damage mapping was carried out using the results of the two previous steps in a GIS algorithm, developed by authors, based on the vulnerability (depth-damage) curves method. The proposed integrated framework has been tested in Romania for a 100-years return time event. The resulting map (at 30 m resolution) covers the entire Romanian territory including minor order rivers, which are often neglected in large-scale analyses.</p><p>The demonstrative application shows how the description of flood risk may particularly benefit from the integrated use of geomorphic methods, machine learning algorithms and EO freely available monitoring data. The ability of the proposed cost-efficient model to carry out high-resolution and large-scale analyses in data-scarce environments allows performing future risk assessments keeping abreast of temporal and spatial changes in terms of hazard, exposure and vulnerability.</p><p><em>Acknowledgement: This work was carried out during the tenure of an ERCIM ‘Alain Bensoussan’ Fellowship Programme.</em></p>

scholarly journals Enhancement of large-scale flood damage assessments using building-material-based vulnerability curves for an object-based approach

2019 ◽  
Author(s):  
Johanna Englhardt ◽  
Hans de Moel ◽  
Charles K. Huyck ◽  
Marleen C. de Ruiter ◽  
Jeroen C. J. H. Aerts ◽  
...  
Keyword(s):  
Land Use ◽  
Built Environment ◽  
Rural Areas ◽  
Flood Risk ◽  
Building Material ◽  
Large Scale ◽  
Flood Damage ◽  
Risk Assessments ◽  
Object Based ◽  
Coarse Information

Abstract. In this study, we developed an enhanced approach for large-scale flood damage and risk assessments that uses characteristics of buildings and the built environment as object-based information to represent exposure and vulnerability to flooding. Most current large-scale assessments use an aggregated land-use category to represent the exposure, treating all exposed elements the same. For large areas where previously only coarse information existed such as in Africa, more detailed exposure data is becoming available. For our approach, a direct relation between the construction type and building material of the exposed elements is used to develop vulnerability curves. We further present a method to differentiate flood risk in urban and rural areas based on characteristics of the built environment. We applied the model to Ethiopia, and found that rural flood risk accounts for about 22 % of simulated damages; rural damages are generally neglected in the typical land-use-based damage models particularly at this scale. Our approach is particularly interesting for studies in areas where there is a large variation in construction types in the building stock, such as developing countries. It also enables comparison across different natural hazard types that also use material-based vulnerability, paving the way to the enhancement of multi-risk assessments.

scholarly journals Towards risk-based flood management in highly productive paddy rice cultivation – concept development and application to the Mekong Delta

2018 ◽  
Vol 18 (11) ◽  
pp. 2859-2876 ◽  
Author(s):  
Nguyen Van Khanh Triet ◽  
Nguyen Viet Dung ◽  
Bruno Merz ◽  
Heiko Apel
Keyword(s):  
Land Use ◽  
Flood Risk ◽  
Large Scale ◽  
Flood Hazard ◽  
Mekong Delta ◽  
Flood Management ◽  
Paddy Rice ◽  
Rice Cultivation ◽  
Spatially Explicit ◽  
Risk Maps

Abstract. Flooding is an imminent natural hazard threatening most river deltas, e.g. the Mekong Delta. An appropriate flood management is thus required for a sustainable development of the often densely populated regions. Recently, the traditional event-based hazard control shifted towards a risk management approach in many regions, driven by intensive research leading to new legal regulation on flood management. However, a large-scale flood risk assessment does not exist for the Mekong Delta. Particularly, flood risk to paddy rice cultivation, the most important economic activity in the delta, has not been performed yet. Therefore, the present study was developed to provide the very first insight into delta-scale flood damages and risks to rice cultivation. The flood hazard was quantified by probabilistic flood hazard maps of the whole delta using a bivariate extreme value statistics, synthetic flood hydrographs, and a large-scale hydraulic model. The flood risk to paddy rice was then quantified considering cropping calendars, rice phenology, and harvest times based on a time series of enhanced vegetation index (EVI) derived from MODIS satellite data, and a published rice flood damage function. The proposed concept provided flood risk maps to paddy rice for the Mekong Delta in terms of expected annual damage. The presented concept can be used as a blueprint for regions facing similar problems due to its generic approach. Furthermore, the changes in flood risk to paddy rice caused by changes in land use currently under discussion in the Mekong Delta were estimated. Two land-use scenarios either intensifying or reducing rice cropping were considered, and the changes in risk were presented in spatially explicit flood risk maps. The basic risk maps could serve as guidance for the authorities to develop spatially explicit flood management and mitigation plans for the delta. The land-use change risk maps could further be used for adaptive risk management plans and as a basis for a cost–benefit of the discussed land-use change scenarios. Additionally, the damage and risks maps may support the recently initiated agricultural insurance programme in Vietnam.

scholarly journals Feature Importance Analysis for Local Climate Zone Classification Using a Residual Convolutional Neural Network with Multi-Source Datasets

2018 ◽  
Vol 10 (10) ◽  
pp. 1572 ◽  
Author(s):  
Chunping Qiu ◽  
Michael Schmitt ◽  
Lichao Mou ◽  
Pedram Ghamisi ◽  
Xiao Zhu
Keyword(s):  
Neural Network ◽  
Land Use ◽  
Convolutional Neural Network ◽  
Large Scale ◽  
Landsat 8 ◽  
Climate Zone ◽  
Local Climate ◽  
Feature Importance ◽  
Local Climate Zone ◽  
Sentinel 2

Global Local Climate Zone (LCZ) maps, indicating urban structures and land use, are crucial for Urban Heat Island (UHI) studies and also as starting points to better understand the spatio-temporal dynamics of cities worldwide. However, reliable LCZ maps are not available on a global scale, hindering scientific progress across a range of disciplines that study the functionality of sustainable cities. As a first step towards large-scale LCZ mapping, this paper tries to provide guidance about data/feature choice. To this end, we evaluate the spectral reflectance and spectral indices of the globally available Sentinel-2 and Landsat-8 imagery, as well as the Global Urban Footprint (GUF) dataset, the OpenStreetMap layers buildings and land use and the Visible Infrared Imager Radiometer Suite (VIIRS)-based Nighttime Light (NTL) data, regarding their relevance for discriminating different Local Climate Zones (LCZs). Using a Residual convolutional neural Network (ResNet), a systematic analysis of feature importance is performed with a manually-labeled dataset containing nine cities located in Europe. Based on the investigation of the data and feature choice, we propose a framework to fully exploit the available datasets. The results show that GUF, OSM and NTL can contribute to the classification accuracy of some LCZs with relatively few samples, and it is suggested that Landsat-8 and Sentinel-2 spectral reflectances should be jointly used, for example in a majority voting manner, as proven by the improvement from the proposed framework, for large-scale LCZ mapping.

scholarly journals Large-scale application of the flood damage model RAilway Infrastructure Loss (RAIL)

2016 ◽  
Vol 16 (11) ◽  
pp. 2357-2371 ◽  
Author(s):  
Patric Kellermann ◽  
Christine Schönberger ◽  
Annegret H. Thieken
Keyword(s):  
Risk Management ◽  
Risk Aversion ◽  
Flood Risk ◽  
Structural Damage ◽  
Large Scale ◽  
Damage Model ◽  
Flood Damage ◽  
Railway Infrastructure ◽  
Increased Risk ◽  
The Impact

Abstract. Experience has shown that river floods can significantly hamper the reliability of railway networks and cause extensive structural damage and disruption. As a result, the national railway operator in Austria had to cope with financial losses of more than EUR 100 million due to flooding in recent years. Comprehensive information on potential flood risk hot spots as well as on expected flood damage in Austria is therefore needed for strategic flood risk management. In view of this, the flood damage model RAIL (RAilway Infrastructure Loss) was applied to estimate (1) the expected structural flood damage and (2) the resulting repair costs of railway infrastructure due to a 30-, 100- and 300-year flood in the Austrian Mur River catchment. The results were then used to calculate the expected annual damage of the railway subnetwork and subsequently analysed in terms of their sensitivity to key model assumptions. Additionally, the impact of risk aversion on the estimates was investigated, and the overall results were briefly discussed against the background of climate change and possibly resulting changes in flood risk. The findings indicate that the RAIL model is capable of supporting decision-making in risk management by providing comprehensive risk information on the catchment level. It is furthermore demonstrated that an increased risk aversion of the railway operator has a marked influence on flood damage estimates for the study area and, hence, should be considered with regard to the development of risk management strategies.

scholarly journals Agricultural land use and cover change in the Cerrado/Amazon ecotone: A case study of the upper Teles Pires River basin

2018 ◽  
Vol 48 (2) ◽  
pp. 168-177 ◽  
Author(s):  
Ana Paula Sousa Rodrigues ZAIATZ ◽  
Cornélio Alberto ZOLIN ◽  
Laurimar Goncalves VENDRUSCULO ◽  
Tarcio Rocha LOPES ◽  
Janaina PAULINO
Keyword(s):  
Land Use ◽  
River Basin ◽  
Large Scale ◽  
Agricultural Land ◽  
Temporal Dynamics ◽  
Direct Conversion ◽  
Landsat 8 ◽  
Error Matrix ◽  
Mato Grosso ◽  
Land Use And Cover

ABSTRACT The upper Teles Pires River basin is a key hydrological resource for the state of Mato Grosso, but has suffered rapid land use and cover change. The basin includes areas of Cerrado biome, as well as transitional areas between the Amazon and Cerrado vegetation types, with intensive large-scale agriculture widely-spread throughout the region. The objective of this study was to explore the spatial and temporal dynamics of land use and cover change from 1986 to 2014 in the upper Teles Pires basin using remote sensing and GIS techniques. TM (Thematic Mapper) and TIRS (Thermal Infrared Sensor) sensor images aboard the Landsat 5 and Landsat 8, respectively, were employed for supervised classification using the “Classification Workflow” in ENVI 5.0. To evaluate classification accuracy, an error matrix was generated, and the Kappa, overall accuracy, errors of omission and commission, user accuracy and producer accuracy indexes calculated. The classes showing greatest variation across the study period were “Agriculture” and “Rainforest”. Results indicated that deforested areas are often replaced by pasture and then by agriculture, while direct conversion of forest to agriculture occured less frequently. The indices with satisfactory accuracy levels included the Kappa and Global indices, which showed accuracy levels above 80% for all study years. In addition, the producer and user accuracy indices ranged from 59-100% and 68-100%, while the errors of omission and commission ranged from 0-32% and 0-40.6%, respectively.

Analysis of potential flood damage on crops at global scale

2020 ◽  
Author(s):  
Antonio Annis ◽  
Davide Danilo Chiarelli ◽  
Fernando Nardi ◽  
Maria Cristina Rulli
Keyword(s):  
Food Security ◽  
Flood Risk ◽  
Large Scale ◽  
Flood Hazard ◽  
Strong Support ◽  
Flood Damage ◽  
Natural Ecosystems ◽  
Remotely Sensed Data ◽  
Regulated Rivers ◽  
Cropland Area

<p>Most of the food production connected to crops is located in fluvial corridors because of their suitable morphology and fertile soils. The knowledge and large scale quantification of the agricultural resources at flood risk has a crucial importance for improving urban and regional planning. Recent advances in satellite derived products related to land use, digital terrain and hydrologic variables can give a strong support on extensive analyses on cropland areas in floodplains and their interactions with natural ecosystems and human activities. In this work, we present a global assessment of cropland at flood risk in terms of extension, productivity and the related calories adopting the Global Cropland Area Database (GCAD), the Global Floodplain Dataset (GFPLAIN250m), the Global flood hazard maps (GFHM) in conjunction with continental remotely-sensed data representing free flowing (versus artificially regulated) rivers and urban density maps. Spatially distributed and aggregated results of the research allow to identify the most critical areas in terms of food security and floods, thus allowing to support intervention strategies for food security management at large scale and for different socio-economic contexts.</p>

scholarly journals Enhancement of large-scale flood risk assessments using building-material-based vulnerability curves for an object-based approach in urban and rural areas

2019 ◽  
Vol 19 (8) ◽  
pp. 1703-1722 ◽  
Author(s):  
Johanna Englhardt ◽  
Hans de Moel ◽  
Charles K. Huyck ◽  
Marleen C. de Ruiter ◽  
Jeroen C. J. H. Aerts ◽  
...  
Keyword(s):  
Land Use ◽  
Built Environment ◽  
Rural Areas ◽  
Flood Risk ◽  
Building Material ◽  
Large Scale ◽  
Risk Assessments ◽  
Object Based ◽  
Coarse Information ◽  
Urban And Rural Areas

Abstract. In this study, we developed an enhanced approach for large-scale flood damage and risk assessments that uses characteristics of buildings and the built environment as object-based information to represent exposure and vulnerability to flooding. Most current large-scale assessments use an aggregated land-use category to represent the exposure, treating all exposed elements the same. For large areas where previously only coarse information existed such as in Africa, more detailed exposure data are becoming available. For our approach, a direct relation between the construction type and building material of the exposed elements is used to develop vulnerability curves. We further present a method to differentiate flood risk in urban and rural areas based on characteristics of the built environment. We applied the model to Ethiopia and found that rural flood risk accounts for about 22 % of simulated damage; rural damage is generally neglected in the typical land-use-based damage models, particularly at this scale. Our approach is particularly interesting for studies in areas where there is a large variation in construction types in the building stock, such as developing countries.

Application of TPI for Analysis of Landforms and LULC of Adama Wereda, Ethiopia

2019 ◽  
Vol 2 (2) ◽  
pp. 100-109
Author(s):  
Daniel Alemayehu ◽  
Meseret Tadesse ◽  
Mohammed Abdul Athick
Keyword(s):  
Land Use ◽  
Precision Agriculture ◽  
Large Scale ◽  
Water Bodies ◽  
Landsat 8 ◽  
Barren Land ◽  
Gis Applications ◽  
Topographic Slope ◽  
Landform Classification ◽  
Elevation Model

Topographic Position Index (TPI) algorithm is useful for landform classification using Digital Elevation Model (DEM) to identify upper, middle and lower parts of the landscape. Topographic slope positions and landform classifications can be automated and measured by employing the Jenness algorithm based on the Set of Rules in the TPI. Adama Wereda in Ethiopia has been selected to study the different landforms classified using DEM (30m resolution) and Landsat 8 OLI data. Spatial statistics and GIS applications were also used to distinguish the geomorphologic properties of DEM. Adama Wereda encompasses 2.15% of the valley, 33.79% of flat lands, 15.85 % of lower slope, 33.07% of average slope, 12.85% of upper slope and 2.24% of ridges. Nine land use and land cover (LULC) classes, specifically rocky terrain, built up area, forests, water bodies, roads, agriculture, barren land, hill/mountain and fallow land have been analyzed. Landforms of Adama Wereda are ranging from large-scale features such as plains and mountain ranges to minor features such as hills and valleys. The built-up area and agriculture are covering most of the landform classes. U shaped valley was observed in water bodies. The various classifications of TPI and landform can be used in precision agriculture, land-use alteration studies, etc.

Land-use change and floods: what do we need most, research or management?

2002 ◽  
Vol 45 (8) ◽  
pp. 183-190 ◽  
Author(s):  
Arne Tollan
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Flood Risk ◽  
Human Impacts ◽  
Management Strategies ◽  
Flood Management ◽  
Flood Damage ◽  
Flood Frequency ◽  
Small Scale ◽  
Infiltration Capacity

Land-cover change (urbanisation, deforestation, and cultivation) results in increased flood frequency and severity. Mechanisms include reduced infiltration capacity, lower soil porosity, loss of vegetation, and forest clearing, meaning lower evapotranspiration. Major research challenges lie in quantification of effects in terms of flood characteristics under various conditions, ascertaining the combined effects of gradual changes over long time periods, and developing model tools suitable for land-use management. Large floods during the 1990s gave a new focus on these problems. Reference is made to the Norwegian HYDRA research programme on human impacts on floods and flood damage. The paper concludes that land-use change effects on floods are most pronounced at small scale and for frequent flood magnitudes. Model simulations of effects of land-use change can now be used to reduce flood risk. Modern flood management strategies have abandoned the position that dams and dikes are the only answers to mitigating flood disasters. Today, the strategic approach is more often: do not keep the water away from the people, keep people away from the water. Flood management strategies should include flood warnings, efficient communication, risk awareness, civil protection and flood preparedness routines, effective land-use policies, flood risk mapping, … as well as structural measures.

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