Comparative Analysis of Performance and Mechanisms of Flood Inundation Map Generation using Height Above Nearest Drainage Article Swipe
Zhouyayan Li
,
Felipe Quintero Duque
,
Trevor Grout
,
B. Bates
,
İbrahim Demir
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.31223/x59k9g
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.31223/x59k9g
For flood inundation extent prediction, it is important to have a faster, more accurate, and input-parsimonious model during response and recovery efforts. Height Above Nearest Drainage (HAND) is a simplified conceptual model whose efficacy and utility have been demonstrated in previous studies. This study aims to provide a comprehensive assessment of prediction performance of the rating-curve-based HAND generated with the framework adopted at NOAA's National Water Center and the non-rating-curve-based HAND inundation maps created with a web-based flood inundation mapping system. The study presents an in-depth analysis on the performance of HAND with varying model configurations, conditions where the HAND fails to provide accurate predictions, and underlying mechanism and guideline to overcome these challenges. The study also includes analysis of the model performance with bathymetry-based measurements. The results show that in areas where the water depth indicated by the synthetic rating curve are relatively consistent with those in catchments, the non-rating-curve-based HAND can generate comparable inundation extent predictions with fewer inputs. Otherwise, the non-rating-curve-based HAND may result in significant underestimations due to a combination of factors. The underestimation can be reduced by using a multi-depth technique to calculate water depth. Furthermore, the results show that the optimal HAND threshold is a percentage ranging between 8% and 12% of the basin drainage area, rather than a specific number as reported in previous studies. In comparison to the single-depth approach, the results show that proposed multi-point water depth calculation approaches are more robust against the causes of underestimation. However, there are no notable differences in prediction performance between proposed multi-point approaches. Finally, bathymetry measurements cause underestimation by increasing HAND values for non-drainage pixels. As a result, they should be handled with caution, as underestimation is riskier than overestimation when it comes to flood preparedness.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.31223/x59k9g
- OA Status
- gold
- Cited By
- 11
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4226217122
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Raw OpenAlex JSON
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https://openalex.org/W4226217122Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.31223/x59k9gDigital Object Identifier
- Title
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Comparative Analysis of Performance and Mechanisms of Flood Inundation Map Generation using Height Above Nearest DrainageWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-04-08Full publication date if available
- Authors
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Zhouyayan Li, Felipe Quintero Duque, Trevor Grout, B. Bates, İbrahim DemirList of authors in order
- Landing page
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https://doi.org/10.31223/x59k9gPublisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.31223/x59k9gDirect OA link when available
- Concepts
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Rating curve, Flood myth, Drainage, Environmental science, Drainage basin, Hydrology (agriculture), Statistics, Computer science, Geology, Mathematics, Cartography, Geotechnical engineering, Geography, Geomorphology, Sediment, Biology, Archaeology, EcologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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11Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 2, 2023: 6, 2022: 1Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.approach, | 227 |
| abstract_inverted_index.calculate | 187 |
| abstract_inverted_index.framework | 60 |
| abstract_inverted_index.generated | 57 |
| abstract_inverted_index.guideline | 109 |
| abstract_inverted_index.important | 7 |
| abstract_inverted_index.indicated | 136 |
| abstract_inverted_index.mechanism | 107 |
| abstract_inverted_index.synthetic | 139 |
| abstract_inverted_index.technique | 185 |
| abstract_inverted_index.threshold | 198 |
| abstract_inverted_index.web-based | 76 |
| abstract_inverted_index.Otherwise, | 161 |
| abstract_inverted_index.approaches | 237 |
| abstract_inverted_index.assessment | 49 |
| abstract_inverted_index.bathymetry | 260 |
| abstract_inverted_index.comparable | 154 |
| abstract_inverted_index.comparison | 223 |
| abstract_inverted_index.conceptual | 30 |
| abstract_inverted_index.conditions | 96 |
| abstract_inverted_index.consistent | 144 |
| abstract_inverted_index.increasing | 265 |
| abstract_inverted_index.inundation | 2, 71, 78, 155 |
| abstract_inverted_index.percentage | 201 |
| abstract_inverted_index.prediction | 51, 253 |
| abstract_inverted_index.relatively | 143 |
| abstract_inverted_index.simplified | 29 |
| abstract_inverted_index.underlying | 106 |
| abstract_inverted_index.approaches. | 258 |
| abstract_inverted_index.calculation | 236 |
| abstract_inverted_index.catchments, | 148 |
| abstract_inverted_index.challenges. | 113 |
| abstract_inverted_index.combination | 173 |
| abstract_inverted_index.differences | 251 |
| abstract_inverted_index.multi-depth | 184 |
| abstract_inverted_index.multi-point | 233, 257 |
| abstract_inverted_index.performance | 52, 89, 122, 254 |
| abstract_inverted_index.prediction, | 4 |
| abstract_inverted_index.predictions | 157 |
| abstract_inverted_index.significant | 168 |
| abstract_inverted_index.Furthermore, | 190 |
| abstract_inverted_index.demonstrated | 38 |
| abstract_inverted_index.measurements | 261 |
| abstract_inverted_index.non-drainage | 269 |
| abstract_inverted_index.predictions, | 104 |
| abstract_inverted_index.single-depth | 226 |
| abstract_inverted_index.comprehensive | 48 |
| abstract_inverted_index.measurements. | 125 |
| abstract_inverted_index.preparedness. | 291 |
| abstract_inverted_index.overestimation | 285 |
| abstract_inverted_index.configurations, | 95 |
| abstract_inverted_index.underestimation | 177, 263, 281 |
| abstract_inverted_index.bathymetry-based | 124 |
| abstract_inverted_index.underestimation. | 245 |
| abstract_inverted_index.underestimations | 169 |
| abstract_inverted_index.input-parsimonious | 15 |
| abstract_inverted_index.rating-curve-based | 55 |
| abstract_inverted_index.non-rating-curve-based | 69, 150, 163 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/6 |
| sustainable_development_goals[0].score | 0.5400000214576721 |
| sustainable_development_goals[0].display_name | Clean water and sanitation |
| citation_normalized_percentile.value | 0.78356489 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |