InundatEd-v1.0: a height above nearest drainage (HAND)-based flood risk modeling system using a discrete global grid system

Chaudhuri, Chiranjib; Gray, Annie; Robertson, Colin

doi:10.5194/gmd-14-3295-2021

Cited by 17 publications

(2 citation statements)

References 79 publications

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“…The HAND method is frequently used to predict flood inundation extents (Hu and Demir, 2021;Li et al, 2022) to support flood mitigation and impact analysis (Alabbad et al, 2021;Yildirim and Demir, 2022). HAND has been applied as a standalone flood mapping approach (Chaudhuri et al, 2021;Li and Demir, 2022a;Li et al, 2023), a supplementary dataset to refine the flood extent extracted from remotely sensed images (Zeng et al, 2020;He et al, 2021;Li and Demir, 2022b), and an independent data layer in flood extent extraction with machine learning approaches (Aristizabal et al, 2020;Bosch et al, 2020;Esfandiari et al, 2020;Liu et al, 2020). The HAND layer comes from the 10 m HAND dataset for the continental United States (CONUS) created by Liu et al, (2016) and is organized by HUC6 basins.…”

Section: Methodsmentioning

confidence: 99%

MA-SARNet: A one-shot nowcasting framework for SAR image prediction with physical driving forces

Li¹,

Xiang²,

Demiray³

et al. 2023

Preprint

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Remote sensing imagery is one of the most widely used data sources for large-scale earth observations with consistent spatial and temporal quality. However, the current usage scenarios of remote sensing images are largely limited to retrospective tasks as they can only capture existing phenomena. This study proposes MA-SARNet, a one-shot forecasting framework built with a modified MA-Net structure and ResNet50 as the backbone, to predict the backscatter values of Synthetic-Aperture Radar (SAR) images using the previous observations, meteorological, and geomorphic data layers as input. The model was trained, validated, and tested with SAR images collected during the catastrophic 2019 Midwest U.S. Floods that affected several states on the Missouri and Mississippi River tributaries. Compared to the benchmark performance, model predictions show an increase of 31.9% and 17.8% for the mean and median AAI (Assemble Accuracy Index) and an increase of 37.9% and 15.1% for the mean and median NSE (Nash-Sutcliffe Efficiency) on the test set. Results showed that the flood extent derived from backscatter predictions is more robust against misclassifications caused by pixel-level noise compared to the flood map derived using the real backscatters and those from two additional flood map repositories. Results from spatial and temporal robustness tests demonstrate that the model has sufficient generalization potential for real-time, physically informed, deep learning-based earth surface prediction tasks to facilitate fast response to and mitigation for future floods.

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Section: Methodsmentioning

confidence: 99%

MA-SARNet: A one-shot nowcasting framework for SAR image prediction with physical driving forces

Li¹,

Xiang²,

Demiray³

et al. 2023

Preprint

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show abstract

“…This model is based on the height above nearest drainage (HAND)‐synthetic rating curve (SRC) approach, developed and adopted by others for rapid identification of flood risk hazards over large scales (Zheng et al., 2018). In recent years, the HAND‐SRC approach has been widely applied in the US, Canada, Brazil and India (Bhatt & Rao, 2018; Chaudhuri et al., 2021; Johnson et al., 2019; Nobre et al., 2011; Speckhann et al., 2018). Building on the HAND‐SRC approach, the probHAND model adds an uncertainty analysis to the identification of flood depths for specified flood frequencies at the reach scale (10–100 km).…”

Section: Methodsmentioning

confidence: 99%

Inequities in the distribution of flood risk under floodplain restoration and climate change scenarios

et al. 2022

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The combined impacts of climate change and ecological degradation are expected to worsen inequality within society. These dynamics are exemplified by increases in flood risk globally. In general, low‐income and socially vulnerable populations disproportionately bear the cost of flood damages. Climate change is expected to increase the number of people exposed to fluvial flood risk and cause greater property damages. Floodplain restoration has the potential to mitigate these impacts, but the distribution of future risks among different types of property owners under these altered conditions is often unknown. Here, we develop a simple probabilistic approach for estimating flood risk to property owners under floodplain restoration and climate change scenarios for a range of flood recurrence intervals. We apply this approach in the Vermont, USA portion of the Lake Champlain Basin. Over a 100‐year time horizon, we estimate that the value of property damages caused by flood inundation is approximately $2.13 billion under the baseline scenario. Climate change is expected to increase damages to $5.29 billion, a 148% increase; however, floodplain restoration has the potential to reduce these impacts by approximately 20%. For all scenarios, a larger proportion of lower‐value properties, specifically mobile homes, face greater flood risk compared to higher‐value properties. Climate change is expected to cost higher‐value properties and commercial properties more than other types of properties, but these same groups are also expected to benefit most from floodplain restoration. In general, these results raise concern that those least able to prepare for and recover from flood damages are also the people who face the greatest threats. In response, public policy interventions must consider not only where flood risk is most severe, but also the vulnerability of people exposed to such risk. Read the free Plain Language Summary for this article on the Journal blog.

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GIS derived synthetic rating curves and HAND model to support on-the-fly flood mapping

2021

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A timely and cost-effective method of creating inundation maps could assist first responders in allocating resources and personnel in the event of a flood or in preparation of a future disaster. The Height Above Nearest Drainage (HAND) model could be implemented into an on-the-fly flood mapping application for a Canada-wide service. The HAND model requires water level (m) data inputs while many sources of hydrological data in Canada only provide discharge (m3/sec) data. Synthetic rating curves (SRCs), created using river geometry/characteristics and the Manning’s formula, could be utilized to provide an approximate water level given a discharge input. A challenge with creating SRCs includes representing how multiple different land covers will slow impact flow due to texture and bulky features (i.e., smooth asphalt versus rocky river channel); this relates to the roughness coefficient (n). In our study, two methods of representing multiple n values were experimented with (a weighted method and a minimum-median method) and were compared to using a fixed n method. A custom ArcGIS tool, Canadian Estimator of Ratings Curves using HAND and Discharge (CERC-HAND-D), was developed to create SRCs using all three methods. Control data were sourced from gauge stations across Canada in the form of rating curves. Results indicate that in areas with medium to medium–high river gradients (S > 0.002 m/m) or with river reaches under 5 km, the CERC-HAND-D tool creates more accurate SRCs (NRMSE = 3.7–8.8%, Percent Bias = −7.8%—9.4%), with the minimum-median method being the preferred n method.

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InundatEd-v1.0: a height above nearest drainage (HAND)-based flood risk modeling system using a discrete global grid system

Cited by 17 publications

References 79 publications

MA-SARNet: A one-shot nowcasting framework for SAR image prediction with physical driving forces

MA-SARNet: A one-shot nowcasting framework for SAR image prediction with physical driving forces

Inequities in the distribution of flood risk under floodplain restoration and climate change scenarios

GIS derived synthetic rating curves and HAND model to support on-the-fly flood mapping

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