Terrain Analysis Enhancements to the Height Above Nearest Drainage Flood Inundation Mapping Method

Garousi‐Nejad, Irene; Tarboton, David G.; Aboutalebi, Mahyar; Torres‐Rua, Alfonso F.

doi:10.1029/2019wr024837

Cited by 53 publications

(54 citation statements)

References 51 publications

(87 reference statements)

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“…This ensures consistency between the modeled stream network and calculated flow directions. Hydrological conditioning of DEMs enabled us to remove obstacles (e.g., roads and culverts) along the streamlines and pits; Garousi‐Nejad et al (2019) describe the procedure in detail. Following DEM preprocessing, we calculated flow direction (deterministic 8), catchment area, stream order, total flow path, and longest flow path.…”

Section: Methodsmentioning

confidence: 99%

Carbon Dioxide Partial Pressure and Emission Throughout the Scandinavian Stream Network

Martinsen

Kragh

Sand‐Jensen

2020

Global Biogeochemical Cycles

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Stream networks transport and emit substantial volumes of carbon dioxide (CO 2) into the atmosphere. We gathered open monitoring data from streams in three Scandinavian countries and estimated CO 2 partial pressure (pCO 2) at 2,298 sites. Most of the sites (87%) were supersaturated when averaged across the year with an overall mean pCO 2 of 1,464 μatm (range: 17-15,646). Using remote sensing data, we modeled a realistic stream network including streams above~2.5 m wide and calculated catchment averages of multiple variables associated with geomorphometry, stream network proximity, and land cover. We compared the ability of eight machine learning models to predict pCO 2 and found that the Random Forest model achieved the highest accuracy, with a root-mean-square error of 0.22 (log 10 (pCO 2)) and R 2 of 0.66. Mean catchment elevation, slope, and permanent water cover were the most important predictor variables. We used the predictive model to create a high-resolution (25-m resolution) map with predicted stream pCO 2 throughout the 268.807 km stream network in Denmark, Sweden, and Finland. Predicted pCO 2 averaged 1,134 μatm (range: 154-8,174). We used surface runoff, air temperature, and stream channel slope to estimate gas transfer velocity and CO 2 flux throughout the network. Mean stream CO 2 fluxes ranged from 1.0 and 1.2 in Sweden and Finland, respectively, 3 to 3.2 g C m −2 day −1 in Denmark. Better-performing models improve our ability to predict pCO 2 in stream networks and reduce the uncertainty of upscaling estimates of carbon emissions from inland waters to countries and continents.

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Section: Methodsmentioning

confidence: 99%

Carbon Dioxide Partial Pressure and Emission Throughout the Scandinavian Stream Network

Martinsen

Kragh

Sand‐Jensen

2020

Global Biogeochemical Cycles

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“…Model output variability is most sensitive to uncertainty in hydraulic geometry. This suggests that improvements to the accuracy of the underlying topography, from which reach-average hydraulic geometry measurements are extracted, should be a priority [ 47 ]. Availability of high-quality topographic data (i.e., supporting <10 m resolution DEM’s) for the United States is growing, increasing the opportunity for probHAND models to use high resolution DEMs.…”

Section: Discussionmentioning

confidence: 99%

“…By expanding the probability of inundation to include areas with a low, but real, chance of inundation (i.e., 5 th percentile), the full width of potential floodplain areas increases by 30–32% (depending on the magnitude of the flood event), adding 57 and 87 m of floodplain width to the 2- and 500-year floods, respectively. Along approximately 15% of the targeted streams, the presence of a reservoir or improper hydro-flattening of the DEM caused errors in the floodplain mapping process [ 47 ]. We removed these areas from the presentation of results.…”

Section: Application To the Lake Champlain Basin Vermontmentioning

confidence: 99%

Improving flood hazard datasets using a low-complexity, probabilistic floodplain mapping approach

et al. 2021

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As runoff patterns shift with a changing climate, it is critical to effectively communicate current and future flood risks, yet existing flood hazard maps are insufficient. Modifying, extending, or updating flood inundation extents is difficult, especially over large scales, because traditional floodplain mapping approaches are data and resource intensive. Low-complexity floodplain mapping techniques are promising alternatives, but their simplistic representation of process falls short of capturing inundation patterns in all situations or settings. To address these needs and deficiencies, we formalize and extend the functionality of the Height Above Nearest Drainage (i.e., HAND) floodplain mapping approach into the probHAND model by incorporating an uncertainty analysis. With publicly available datasets, the probHAND model can produce probabilistic floodplain maps for large areas relatively rapidly. We describe the modeling approach and then provide an example application in the Lake Champlain Basin, Vermont, USA. Uncertainties translate to on-the-ground changes to inundated areas, or floodplain widths, in the study area by an average of 40%. We found that the spatial extent of probable inundation captured the distribution of observed and modeled flood extents well, suggesting that low-complexity models may be sufficient for representing inundation extents in support of flood risk and conservation mapping applications, especially when uncertainties in parameter inputs and process simplifications are accounted for. To improve the accuracy of flood hazard datasets, we recommend investing limited resources in accurate topographic datasets and improved flood frequency analyses. Such investments will have the greatest impact on decreasing model output variability, therefore increasing the certainty of flood inundation extents.

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“…These methods are applied either directly from the DTM for the AutoRoute method (Follum et al, 2017(Follum et al, , 2020, or from a Height Above Nearest Drainage raster (Nobre et al, 2011) derived from the DTM: f2HAND (Speckhann et al, 2017); Geoflood ; MHYST (Rebolho et al, 2018); Hydrogeomorphic FHM (Tavares da Costa et al, 2019). All these methods determine a local discharge/height relationship from i) the cross-section and longitudinal profile geometries extracted from the DTM (locally for Autoroute, averaged at river reach scale for HAND-based approaches), and ii) a local hydraulic formula: Manning-Strickler (Zheng Xing et al, 2018;Johnson et al, 2019;Garousi-Nejad et al, 2019) or Debord (Rebolho et al, 2018). These approaches are very efficient in terms of computation times, and can therefore be suitable for real time inundation forecasting at continental scales (Liu Yan Y. et al, 2018), or for probabilistic or multi-scenario modelling (Teng et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Performance of automated flood inundation mapping methods in a context of flash floods: a comparison of three methods based either on the Height Above Nearest Drainage (HAND) concept, or on 1D/2D shallow water equations

Hocini

Payrastre

Bourgin

et al. 2020

Preprint

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Abstract. Flash floods observed in headwater catchments often cause catastrophic material and human damage worldwide. Considering the large number of small watercourses possibly affected, the use of automated methods for flood inundation mapping at a regional scale can be of great help for the identification of threatened areas and the prediction of potential impacts of these floods. An application of three mapping methods of increasing level of complexity (HAND/MS, caRtino 1D, and Floodos 2D) is presented herein. These methods are used to estimate the flooded areas of three major flash floods observed during the last ten years in South-Eastern France: the 15th of June 2010 flood on the Argens river and its tributaries (585 km of river reaches), the 3rd of October 2015 flood on small coastal rivers of the French Riviera (131 km of river reaches) and the 15th of October 2018 floods on the Aude river and its tributaries (561 km of river reaches). The common features of the three mapping approaches are their high level of automation, their application based on a high-resolution (5 m) DTM, and their reasonable computation times. Hydraulic simulations are run in steady-state regime, based on peak discharges estimated using a rainfall-runoff model preliminary adjusted for each event. The simulation results are compared with the reported flood extent maps and the high water level marks. A clear grading of the tested methods is revealed, illustrating some limits of the HAND/MS approach and an overall better performance of hydraulic models solving the shallow water equations. With these methods, the inundated areas are overall well retrieved, and the errors on water levels remain mostly below 80 cm for the 2D Floodos approach. The most important remaining errors are related to limits of the digital elevation model such as the lack of bathymetric information, uncertainties on embankment elevation and to possible bridge blockages not accounted for in the models.

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Terrain Analysis Enhancements to the Height Above Nearest Drainage Flood Inundation Mapping Method

Cited by 53 publications

References 51 publications

Carbon Dioxide Partial Pressure and Emission Throughout the Scandinavian Stream Network

Carbon Dioxide Partial Pressure and Emission Throughout the Scandinavian Stream Network

Improving flood hazard datasets using a low-complexity, probabilistic floodplain mapping approach

Performance of automated flood inundation mapping methods in a context of flash floods: a comparison of three methods based either on the Height Above Nearest Drainage (HAND) concept, or on 1D/2D shallow water equations

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