Potential of remote sensing and open street data for flood mapping in poorly gauged areas: a case study in Gonaives, Haiti

The Hispaniola Island, in the Caribbean tropical zone, is prone to extreme flood events. Floods are caused by tropical springs and hurricanes and may lead to human losses, economical damages, and spreading of waterborne diseases. Flood studies based upon hydrological and hydraulic modelling are hampered by almost complete lack of hydro-meteorological data. Thenceforth, and given the cost and complexity in the organization of field measurement campaigns, the need for exploitation of remote sensing data, and open source data bases. We present here a feasibility study to explore the potential of (i) high-resolution of digital elevation models (DEMs) from remote imagery and (ii) remotely sensed precipitation data, to feed hydrological flow routing and hydraulic flood modelling, applied to the case study of river La Quinte closed to Gonaives (585 km2), Haiti. We studied one recent flood episode, namely hurricane Ike in 2008, when flood maps from remote sensing were available for validation. The atmospheric input given by hourly rainfall was taken from downscaled Tropical Rainfall Measuring Mission (TRMM) daily estimates, and subsequently fed to a semi-distributed DEM-based hydrological model, providing an hourly flood hydrograph. Then, flood modelling using Hydrologic Engineering Center River Analysis System (HEC-RAS 1D, one-dimensional model for unsteady open channel flow) was carried out under different scenarios of available digital elevation models. The DEMs were generated using optical remote sensing satellite WorldView-1 and Shuttle Radar Topography Mission (SRTM), combined with information from an open source database (OpenStreetMap). Observed flood extent and land use have been extracted using Système Pour l’Observation de la Terre-4 (SPOT-4) imagery. The hydraulic model was tuned for floodplain friction against the observed flooded area. We compared different scenarios of flood simulation and the predictive power given by model tuning. Our study provides acceptable results in depicting flooded areas, especially considering the tremendous lack of ground data, and shows the potential of hydrological modelling approach fed by remote sensing information in Haiti, and in similarly data-scarce areas. Our approach may be useful to provide depiction of flooded areas for the purpose of (i) flood design for urban planning under a frequency-driven approach and (ii) forecasting of flooded areas for warning procedures, pending availability of weather forecast with proper lead time

An efficient decision support system for flood inundation management using intermittent remote-sensing data

Abstract: Timely acquisition of spatial flood distribution is an essential basis for flood-disaster monitoring and management. Remote-sensing data have been widely used in water-body surveys. However, due to the cloudy weather and complex geomorphic environment, the inability to receive remote-sensing images throughout the day has resulted in some data being missing and unable to provide dynamic and continuous flood inundation process data. To fully and effectively use remote-sensing data, we developed a new decision support system for integrated flood inundation management based on limited and intermittent remote-sensing data. Firstly, we established a new multi-scale water-extraction convolutional neural network named DEU-Net to extract water from remote-sensing images automatically. A specific datasets training method was created for typical region types to separate the water body from the confusing surface features more accurately. Secondly, we built a waterfront contour active tracking model to implicitly describe the flood movement interface. In this way, the flooding process was converted into the numerical solution of the partial differential equation of the boundary function. Space upwind difference format and the time Euler difference format were used to perform the numerical solution. Finally, we established seven indicators that considered regional characteristics and flood-inundation attributes to evaluate flood-disaster losses. The cloud model using the entropy weight method was introduced to account for uncertainties in various parameters. In the end, a decision support system realizing the flood losses risk visualization was developed by using the ArcGIS application programming interface (API). To verify the effectiveness of the model constructed in this paper, we conducted numerical experiments on the model’s performance through comparative experiments based on a laboratory scale and actual scale, respectively. The results were as follows: (1) The DEU-Net method had a better capability to accurately extract various water bodies, such as urban water bodies, open-air ponds, plateau lakes etc., than the other comparison methods. (2) The simulation results of the active tracking model had good temporal and spatial consistency with the image extraction results and actual statistical data compared with the synthetic observation data. (3) The application results showed that the system has high computational efficiency and noticeable visualization effects. The research results may provide a scientific basis for the emergency-response decision-making of flood disasters, especially in data-sparse regions

Evaluating Tidal Flood Risk on Salt Farming Land Empirical and Methodological Insights from a Case Study in Northern Java

Coastal regions have been threatened by coastal hazards, including tidal flooding, in recent years. Many studies have focused on evaluating flood risks in urban areas because complete data is available. However, tidal flooding in data-poor rural coastal areas has hardly been discussed. In addition, there is still no consensus among academics concerning the development of methods to evaluate flood risk in rural coastal areas with different local set-tings, including agriculture, aquaculture, or even salt farming. Filling the re-search gaps on developing flood risk evaluation on data-sparse regions is cru-cial to support disaster risk reduction policies integrated with local economic resources. This dissertation presents an original approach in integrating a hy-drodynamic model, geospatial data, and a geographic information system (GIS) in the rural coastal area of Cirebon, West Java, Indonesia, with a salt farming setting. The study focuses on answering the central topic: Developing an initial model to evaluate tidal flood risk in a data-scarce region using geo-spatial data. Because limited data is available, the model developed must be able to be implemented in rural coastal areas that are subject to regular tidal flooding. The current thesis evaluates the tidal flood hazard through depth and duration factors. The physical vulnerability from natural science and engineering perspectives have been manifested through the so-called damage function. This study also presents detailed economic loss figures for each par-cel representing the risk level in different stages of production. Moreover, a comparison method has been implemented by multicriteria analysis (MCA) using an Analytical Hierarchical Process (AHP) to validate the flood risk model. This MCA-AHP process involves experts justifying all selected variables in the hazard, vulnerability, and risk analysis. The results reveal that tidal floods have affected rural coastal regions, especially in the salt farming area of Cirebon. The established hydrodynamic model has successfully identified the magnitude and distribution of tidal flooding for two events of 2016 and 2018 on salt ponds. This finding extends the utilization of the hydrodynamic model to simulate specific tidal flood events for rural coastal settings with restricted datasets. A synthetic approach using local information from farmers has contributed to the measurement of the expected monetary loss for these two former tidal flood events. This method is employed to construct a damage function that portrays a simple form of physical vulnerability of salt farming based on flood depth and dura-tion factors. The two tidal flood events studied represented the pre-production and harvesting periods had minimal economic impacts. Lastly, the multicriteria approach has also portrayed the risk condition of salt farming by using hazard and vulnerability parameters. With limited data available, this approach has successfully identified the tidal flood risk in salt ponds into the maps. The comparison of this parametric approach with the hydrodynamical approach has shown a strong statistical correlation that marks the relation between risk level and expected loss. However, there are some uncertainties from data input in the numerical hydrodynamic model and the subjectivity of the experts in the analytical process. The findings in this thesis can be converted to the advancement of cru-cial policy implications. The thesis has the potential to improve disaster risk reduction, specifically in salt-farming areas. The integration of flood risk maps based on hydrodynamic and multicriteria inputs can support the im-plementation of a targeted disaster risk reduction policy under data-poor conditions. Finally, the risk evaluation analysis can assist government policies targeting the increase of productivity of salt farming, such as the national Indonesian target of salt self-sufficiency, flood risk reduction via structural measures, mangrove conservation and integrated coastal planning policies

Inundation resilience analysis of metro-network from a complex system perspective using the grid hydrodynamic model and FBWM approach : a case study of Wuhan

The upward trend of metro flooding disasters inevitably brings new challenges to urban underground flood management. It is essential to evaluate the resilience of metro systems so that efficient flood disaster plans for preparation, emergency response, and timely mitigation may be developed. Traditional response solutions merged multiple sources of data and knowledge to support decision-making. An obvious drawback is that original data sources for evaluations are often stationary, inaccurate, and subjective, owing to the complexity and uncertainty of the metro station’s actual physical environment. Meanwhile, the flood propagation path inside the whole metro station network was prone to be neglected. This paper presents a comprehensive approach to analyzing the resilience of metro networks to solve these problems. Firstly, we designed a simplified weighted and directed metro network module containing six characteristics by a topological approach while considering the slope direction between sites. Subsequently, to estimate the devastating effects and details of the flood hazard on the metro system, a 100-year rainfall–flood scenario simulation was conducted using high-precision DEM and a grid hydrodynamic model to identify the initially above-ground inundated stations (nodes). We developed a dynamic node breakdown algorithm to calculate the inundation sequence of the nodes in the weighted and directed network of the metro. Finally, we analyzed the resilience of the metro network in terms of toughness strength and organization recovery capacity, respectively. The fuzzy best–worst method (FBWM) was developed to obtain the weight of each assessment metric and determine the toughness strength of each node and the entire network. The results were as follows. (1) A simplified three-dimensional metro network based on a complex system perspective was established through a topological approach to explore the resilience of urban subways. (2) A grid hydrodynamic model was developed to accurately and efficiently identify the initially flooded nodes, and a dynamic breakdown algorithm realistically performed the flooding process of the subway network. (3) The node toughness strength was obtained automatically by a nonlinear FBWM method under the constraint of the minimum error to sustain the resilience assessment of the metro network. The research has considerable implications for managing underground flooding and enhancing the resilience of the metro network