the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Upgrading 1D-2D flood models using satellite laser altimetry and multi-mission satellite surface water extent maps
Abstract. Digital elevation models (DEMs) are essential datasets, particularly for flood inundation mapping in one-dimensional (1D) to two-dimensional (2D) flood models. Given the current uncertainties stemming from changes in weather patterns affecting flooding, reducing inaccuracies in flood models is imperative. This study aims to enhance the performance of 1D-2D flood models using satellite Earth observation (EO) data in the lower Chao Phraya (CPY) basin. It introduces two workflows applied to upgrade the 1D-2D flood model: DEM analysis and flood map analysis.
The DEM analysis workflow evaluates 10 DEM products (LDD, JICA, merged LDD-JICA, ASTER GDEM V3, STRMv3, MERIT, GLO30, FABDEMv1-2, TanDEM-X, and TanDEM-EDEM) using satellite laser altimetry data from the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) according to standard criteria for DEM selection as input to the flood model. Findings indicate that the merged LDD-JICA and FABDEMv1-2 DEMs exhibit the highest level of accuracy, with root mean square error (RMSE) values of 1.93 and 1.95 m, respectively. The flood map analysis workflow involves comparing flood extent maps derived from multi-mission satellite datasets, and simulated flood maps. This study utilizes surface water extent (SWE) maps from the WorldWater project, obtained from the Sentinel-1 and Sentinel-2 imaging satellites, and flood maps from the Geo-Informatics and Space Technology Development Agency (GISTDA) in Thailand to verify flood maps produced by the 1D-2D flood model. The results reveal that the flood maps from the 1D-2D flood model tend to overestimate flood extent, with a critical success index (CSI) range of 0.072 – 0.230. Our study demonstrates the potential to enhance the skill of 1D-2D flood models using satellite EO data, thereby improving the reliability of flood inundation predictions.
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RC1: 'Comment on hess-2024-175', Anonymous Referee #1, 08 Sep 2024
- The author must constructively change the abstract in terms of adding error analysis values in terms of PBIAS to the result. The Author needs to write consistently.
- The author must mention where they got data and the frequency of data. Statistical analysis of data must be given in Tabular format (Like Table no 1, 10.1061/(ASCE)IR.1943-4774.0001689).
- Fig 2: it should be clearly described in terms of scientific manner.
- For better understanding, Please add more recent literature (2024) regarding the bidirectional flood models using satellite laser altimetry.
- Please modify the objective section for a clear understanding, i.e., the novelty part should be mentioned.
- There are so many techniques in the recent world for the flood model; why does the author use a specified Method for research purposes? Is there any specific reason for this?
- The author must add statistical components/parameters of collected data in the case study section.
- Eq 3-6; please add a recent citation for reference purposes. [Read this paper: 10.1016/j.gsd.2024.101178, 10.1016/j.clwat.2024.100003, 10.1016/j.hydres.2024.04.006, 10.1007/978-981-15-5397-4_75, 10.1038/s41598-024-63490-1, 10.2166/wcc.2021.221]
- A comparison statement (compare with other research articles) must be added in the result and discussion section to visualize the proposed research better.
- The author must add future scope in the last portion of the manuscript.
- The advantages and limitations of the proposed model must be added.
- For better analysis of the result, the author must add a Box plot, Taylor diagram, and ROC Curve
- The author must provide a flow chart and parameter table of proposed individual models.
- The author considered different input constraints; is there any scientific reason for the same
Citation: https://doi.org/10.5194/hess-2024-175-RC1 -
AC1: 'Reply on RC1', Theerapol Charoensuk, 24 Sep 2024
The comment was uploaded in the form of a supplement: https://hess.copernicus.org/preprints/hess-2024-175/hess-2024-175-AC1-supplement.pdf
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RC2: 'Reply on AC1', Anonymous Referee #1, 26 Sep 2024
Thank you for the revision; now the article may proceed to the next stage.
Citation: https://doi.org/10.5194/hess-2024-175-RC2 -
AC3: 'Reply on RC2', Theerapol Charoensuk, 28 Oct 2024
Thank yoi
Citation: https://doi.org/10.5194/hess-2024-175-AC3
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AC3: 'Reply on RC2', Theerapol Charoensuk, 28 Oct 2024
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RC2: 'Reply on AC1', Anonymous Referee #1, 26 Sep 2024
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RC3: 'Comment on hess-2024-175', Anonymous Referee #2, 14 Oct 2024
general comments
Interesting study on the use of different DEM inputs into a specific 1D-2D flood model. In the first part, the most accurate global and airborne DEMs are determined. Using these two DEMs as input, the resulting flood model maps are compared with 2 reference flood maps. Surprisingly, the global flood-optimized FABDEM derived from TanDEM-X achieves only slightly worse flood modelling quality statistics than the higher-resolution airborne DEM version LDD-JICA DEM.
The paper is generally well structured and balanced, but the context and objective of the two parts are not consistently clear.
The claimed objective of your study to present two new workflows for updating 1D-2D flood models is not plausible. It seems like your starting point for this study was "what can we do with EO data", but what you describe is how to test the input DEMs and validate your results. I can't see any real improvement in the 1D-2D flood model itself:An extensive DEM evaluation does not make sense for every new 1D-2D flood model, as the number of input DEMs is limited and you have shown extensive evaluation here. Similarily, the validation of flood modeling results with existing flood maps is not an integral update of a model.
In my opinion, there is a simple way out: move the “workflows” to the discussion/ conclusion and stick to terms like “evaluation of DEMs for” … and “validation of flood model results” … .
However, please clarify this throughout the paper, even in the title!!!
specific comments
- Please improve the abstract (and title) with regard to the readability and research focus of your study. Main point: The paper gives a kind of performance test. So, your description given in 4.2. (“ .. to evaluate the performance of simulated flood maps … using various DEM products”) comprises the content more appropriate than an “upgrade by two workflows”.
- In that sense, unclear in the abstract: are you evaluating the 1D-2D model results with the surface water extent maps or has this any relation to the DEM analysis part? Scientifically using SWE maps are for validation.
- The same applies to the title. Please use a more precise title (laser altimetry was solely used for performance assessment, same applies for the water extent maps (validation, not for an software/model udgrade itself, …) Something like Influence of DEM quality /Performance assessment using global DEM / …
- Abstract/Intro: The first part “DEM analysis” evaluates 10 DEMs compared to ICESat-2. Please explain your motivation. -> advantage of EO DEMs. The DEM choise is rather heterogenious -> Please categorize the used 10 DEMs e.g. from satellite to airborne DEMs.
- In General: There might exist some specific/logical requirements for DEMs to test or mentioning in advance if they are suited for flood modeling (e. g. like in Gesch, Front. Earth Sci., 2018, Best Practices for Elevation-Based Assessments of Sea-Level Rise and Coastal Flooding Exposure, https://doi.org/10.3389/feart.2018.00230). Against this background, please justify why you start analyzing for so many DEMs the quality from scratch!
- Methodology: Chapter 4, Your goal is to find out the best DEM : better in terms of the most accurate DEM compared to ground control points
- 4.1.1 Not described: how do you use/prepare ICESat-2 ATL03 data? If you don’t explain it, omit it.
- Comment: Apart of the point comparison I like the additional value of grid and track-wise comparison.
- Results: Sec. 5.2.1 To what table do you refer with the statement given in l.444 (global DEM products was +1.62 m)? Same for statistics in line 449 (no common global DEM statistic in table given).
- please omit the wording “over- and underestimation” when describing small biases of 1-2 m without regarding the RMSE. (i.e. having an RMSE of 2m: an ME of 1 m is within the noise level! No real over- or underestimation) Please scan the whole document! Better use neutral terms like small positive/negative bias.
- Please re-work the text in Section 5.3. to make it more readable and comprehensive.
- Section 6.2.: Message is unclear, as the different maps for validation seems to have its deficits.
- Please just list the used data sets / days in Appendix Tables A1 or omit table completely, Table A2 can be omitted completely. It is a service with regular, almost daily acquisitions.
- The visualization of the geoid models Fig A2 should be omitted.
Minor comments:
Abstract:” Given the current uncertainties stemming from changes in weather patterns affecting flooding, reducing inaccuracies in flood models is imperative”: Please be more precise. Are the uncertainties improving?
Include in abstract: Which DEMs are finally used for your flood map analysis and why (motivation to chose one global and one local DEM)
l23-l25: Abstract: Think about your message! EO data for validation of DEM and Flood model maps were used. Conclusion/Result?
Line 480 … which can be attributed to the fact that vegetation and buildings are eliminated in this DEM …
514: we implemented: please re-word to e.g. “performed test with two DEMs,…”
Figure caption: difference of Fig. caption A7 and A9 not clear; RMSE?
Citation: https://doi.org/10.5194/hess-2024-175-RC3 -
AC2: 'Reply on RC3', Theerapol Charoensuk, 21 Oct 2024
The comment was uploaded in the form of a supplement: https://hess.copernicus.org/preprints/hess-2024-175/hess-2024-175-AC2-supplement.pdf
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