A web application for hydrogeomorphic flood hazard mapping

Costa, Ricardo Tavares da; Manfreda, Salvatore; Luzzi, Valerio; Samela, Caterina; Mazzoli, Paolo; Castellarin, Attilio; Bagli, Stefano

doi:10.1016/j.envsoft.2019.04.010

Cited by 42 publications

(47 citation statements)

References 71 publications

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“…This has been achieved by establishing functional relationships in the form of linear and nonlinear regression models between specific isolines of a flood descriptor (TH) and geomorphic and climatic-hydrologic characteristics of elementary catchments. This advancement extends a previous approach employed in Tavares da Costa, Manfreda, et al (2019), by relaxing its complete dependence on benchmark flood maps and by providing a physical basis for the transferability of the TH between catchments, also giving a physical basis to the extrapolation and downscaling goals described in Tavares da Costa, Manfreda, et al (2019). The results obtained in this work are promising for such a novel approach and give an optimistic view about future directions that could improve or extend it.…”

Section: Discussion and Future Researchmentioning

confidence: 58%

Predictive Modeling of Envelope Flood Extents Using Geomorphic and Climatic‐Hydrologic Catchment Characteristics

Costa

Zanardo

Bagli

et al. 2020

Water Resources Research

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A topographic index (flood descriptor) that combines the scaling of bankfull depth with morphology was shown to describe the tendency of an area to be flooded. However, this approach depends on the quality and availability of flood maps and assumes that outcomes can be directly extrapolated and downscaled. This work attempts to relax these problems and answer two questions: (1) Can functional relationships be established between a flood descriptor and geomorphic and climatic-hydrologic catchment characteristics? (2) If so, can they be used for low-complexity predictive modeling of envelope flood extents? Linear stepwise and random forest regressions are developed based on classification outcomes of a flood descriptor, using high-resolution flood modeling results as training benchmarks, and on catchment characteristics. Elementary catchments of four river basins in Europe (Thames, Weser, Rhine, and Danube) serve as training data set, while those of the Rhône river basin in Europe serve as testing data set. Two return periods are considered, the 10-and 10,000-year. Prediction of envelope flood extents and flood-prone areas show that both models achieve high hit rates with respect to testing benchmarks. Average values were found to be above 60% and 80% for the 10-and the 10,000-year return periods, respectively. In spite of a moderate to high false discovery rate, the critical success index value was also found to be moderate to high. It is shown that by relating classification outcomes to catchment characteristics, the prediction of envelope flood extents may be achieved for a given region, including ungauged basins. Plain Language Summary Topographic features can be extracted from a digital terrain to identify floodplains. In turn, the classification of these features can be used to represent flood extents. The classification, however, depends on existing flood maps to be used as the reference for its calibration in a small portion of the study area, before flood extents can be extrapolated. This work seeks to improve this dependence on the existence of a reference and the assumption that extrapolation can be performed without considering the physical differences between areas. To do so, a machine learning model was developed using the classification outcomes and characteristics of four river basins in Europe (Thames, Weser, Rhine, and Danube) and was tested in the Rhône river basin in Europe, showing promising results. This development should help stakeholders to allocate time and money better, by giving them nearly instantaneous views of areas that can potentially be affected by floods, with an associated probability of occurrence, a physical basis for extrapolation, and without needing a reference flood map for calibration for each case.

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Section: Discussion and Future Researchmentioning

confidence: 58%

Predictive Modeling of Envelope Flood Extents Using Geomorphic and Climatic‐Hydrologic Catchment Characteristics

Costa

Zanardo

Bagli

et al. 2020

Water Resources Research

Self Cite

View full text Add to dashboard Cite

show abstract

“…This has been achieved by establishing functional relationships in the form of linear and non-linear regression models between specific isolines of a flood descriptor (TH) and geomorphic and climatic-hydrologic characteristics of elementary catchments. This advancement extends a previous approach employed in Tavares da Costa et al (2019a), by relaxing its complete dependence on benchmark flood maps and by providing a physical basis for the transferability of the TH between catchments, also giving a physical basis to the extrapolation and downscaling goals described in Tavares da Costa et al (2019a). The results obtained in this work are promising for such a novel approach and give an optimistic view about future directions that could improve or extend it.…”

Section: Discussion and Future Researchmentioning

confidence: 59%

“…Scaling is achieved by resorting to the minimum and maximum values of the GFI (Tavares da Costa et al, 2019a). The rescaled GFI can effectively be used as a classifier of flood-prone areas (Manfreda et al, 2015;Samela et al, 2016Samela et al, , 2017Tavares da Costa et al, 2019a) and of the extent of the envelope of major floods that is confined to the floodplain, between the active river channel at bankfull and the surrounding marked topography. The GFI computation is summarized in Figure 2.…”

Section: The Geomorphic Flood Indexmentioning

confidence: 99%

Predictive modelling of envelope flood extents using geomorphic and climatic-hydrologic catchment characteristics

Costa¹,

Zanardo²,

Bagli³

et al. 2020

Preprint

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A topographic index, or flood descriptor, that combines the scaling of bankfull depth with morphology was shown to describe well the tendency of an area to be flooded. However, this approach depends on the quality and availability of flood maps and assumes that outcomes can be directly extrapolated and downscaled. This work attempts to relax these problems and answer two questions: 1) Can functional relationships be established between a flood descriptor and geomorphic and climatic-hydrologic catchment characteristics? 2) If so, can they be used for low-complexity predictive modelling of envelope flood extents? Linear stepwise and random forest regressions are developed based on classification outcomes of a flood descriptor, using high-resolution flood modelling results as training benchmarks, and on catchment characteristics. Elementary catchments of four river basins in Europe (Thames, Weser, Rhine and Danube) serve as training dataset, while those of the Rhône river basin in Europe serve as testing dataset. Two return periods are considered, the 10- and 10,000-year. Prediction of envelope flood extents and flood-prone areas show that both models achieve high hit rates with respect to testing benchmarks. Average values were found to be above 60% and 80% for the 10- and the 10,000- year return periods, respectively. In spite of a moderate to high false discovery rate, the critical success index value was also found to be moderate to high. It is shown that by relating classification outcomes to catchment characteristics the prediction of envelope flood extents may be achieved for a given region, including ungauged basins.

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“…With the development of new technologies for processing terrain analysis, this even brought the application into even wider applications of topography analysis [15]. Digital terrain analysis is now being widely used in watershed analysis [12], hydrogeomorphic and geographic analysis [16][17][18], floodplain delineation [19], and flood inundation analysis [20].…”

Section: Introductionmentioning

confidence: 99%

Application of Nonhydraulic Delineation Method of Flood Hazard Areas Using LiDAR-Based Data

et al. 2020

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Fluvial dynamics are an important aspect of land-use planning as well as ecosystem conservation. Lack of floodplain and flood inundation maps can cause severe implication on land-use planning and development as well as in disaster management. However, flood hazard delineation traditionally involves hydrologic models and uses hydraulic data or historical flooding frequency. This entails intensive data gathering, which leads to extensive amount of cost, time, and complex models, while typically only covers a small portion of the landscape. Therefore, alternative approaches had to be explored. This study explores an alternative approach in delineating flood hazard areas through a straightforward interpolation process while using high-resolution LiDAR-based datasets. The objectives of this study are: (1) to delineate flood hazard areas through a straightforward, nonhydraulic, and interpolation procedure using high-resolution (LiDAR-based) datasets and (2) to determine whether using high-resolution data, coupled with a straightforward interpolation procedure, will yield reliable potential flood hazard maps. Results showed that a straightforward interpolation method using LiDAR-based data produces a reliable potential flood zone map. The resulting map can be used as supplementary information for rapid analysis of the topography which could have implications in area development planning and ecological management and best practices.

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A web application for hydrogeomorphic flood hazard mapping

Cited by 42 publications

References 71 publications

Predictive Modeling of Envelope Flood Extents Using Geomorphic and Climatic‐Hydrologic Catchment Characteristics

Predictive Modeling of Envelope Flood Extents Using Geomorphic and Climatic‐Hydrologic Catchment Characteristics

Predictive modelling of envelope flood extents using geomorphic and climatic-hydrologic catchment characteristics

Application of Nonhydraulic Delineation Method of Flood Hazard Areas Using LiDAR-Based Data

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