Articles | Volume 23, issue 7
https://doi.org/10.5194/nhess-23-2663-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-23-2663-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
An integrated modeling approach to evaluate the impacts of nature-based solutions of flood mitigation across a small watershed in the southeast United States
Betina I. Guido
CORRESPONDING AUTHOR
Department of Hydroinformatics and Socio-Technical Innovation, IHE
Delft Institute for Water Education, Delft, 2611 AX, the Netherlands
Ioana Popescu
Department of Hydroinformatics and Socio-Technical Innovation, IHE
Delft Institute for Water Education, Delft, 2611 AX, the Netherlands
Vidya Samadi
Department of Agricultural Sciences, Clemson University, Clemson, SC 29634, United States of America
Biswa Bhattacharya
Department of Hydroinformatics and Socio-Technical Innovation, IHE
Delft Institute for Water Education, Delft, 2611 AX, the Netherlands
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Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-261, https://doi.org/10.5194/hess-2024-261, 2024
Preprint under review for HESS
Short summary
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Recent progress in neural network accelerated improvements in the performance of catchment modeling systems. Yet flood modeling remains a very difficult task. Focusing on two headwater streams, this paper developed N-HiTS and N-BEATS models and benchmarked them with LSTM to predict flooding events. Analysis suggested that both N-HiTS and N-BEATS outperformed LSTM for short-term (1-hour) flood predictions.
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This research paper focused on creating a new paradigm for flood evacuation decisions – so-called human-AI Convergence (HAC) system. A Natural Language Processing (NLP) method was used to mine and filter human data from X posts that were deemed relevant to flooding. The human data along with a river hydraulic model and AI algorithms were integrated into an evacuation re-routing algorithm to forecast flood depth and define evacuation decisions.
Faisal Sardar, Muhammad Haris Ali, Ioana Popescu, Andreja Jonoski, Schalk Jan van Andel, and Claudia Bertini
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-276, https://doi.org/10.5194/hess-2023-276, 2023
Manuscript not accepted for further review
Short summary
Short summary
This article analyzes surface and groundwater interactions in a small transboundary lowland catchment. The study also investigates the influence of rainfall representation in model on surface subsurface hydrological simulations. Emphasizing the significance of these interactions, the research highlighted the role of subsurface baseflow in contributing to river discharge. Despite minimal impact on streamflow, spatial variability in rainfall can cause localized fluctuations in groundwater levels.
Cited articles
Abbott, M. B., Bathurst, J. C., Cunge, J. A., O'Connell, P. E., and Rasmussen, J.: An introduction to the European Hydrological System – Systeme Hydrologiquee Europeen, “SHE”: History and philosophy of a physically-based, distributed modeling system, J. Hydrol., 87, 45–59, https://doi.org/10.1016/0022-1694(86)90114-9, 1986.
Anselmo, V., Galeati, G., Palmieri, S., Rossi, U., and Todini, E.: Flood
risk assessment using an integrated hydrological hydraulic modeling
approach: a case study, J. Hydrol., 175, 533–554, https://doi.org/10.1016/S0022-1694(96)80023-0, 1996.
Bhatt, C. M., Rao, G. S., Diwakar, P. G., and Dadhwal, V. K.: Development
of flood inundation extent libraries over a range of potential flood levels:
a practical framework for quick flood response, Geomat. Nat. Haz. Risk, 8, 384–401, https://doi.org/10.1080/19475705.2016.1220025, 2017.
Bhattacharya, B., Mazzoleni, M., and Ugay, R.: Flood inundation mapping of
the sparsely gauged large-scale Brahmaputra basin using remote sensing
products, Remote Sens., 11, 501, https://doi.org/10.3390/rs11050501, 2019.
Bhusal, A., Parajuli, U., Regmi, S., and Kalra, A.: Application of Machine
Learning and Process-Based Models for Rainfall-Runoff Simulation in DuPage
River Basin, Illinois, Hydrology, 9, 117, https://doi.org/10.3390/hydrology9070117, 2022.
Brink, E., Aalders, T., Ádám, D., Feller, R., Henselek, Y.,
Hoffmann, A., Ibe, K., Matthey-Doret, A., Meyer, M., Negrut, N. L., Rau, A.
L., Riewerts, B., von Schuckmann, L., Törnros, S., von Wehrden, H.,
Abson, D. J., and Wamsler, C.: Cascades of green: A review of
ecosystem-based adaptation in urban areas, Global Environ. Chang., 36,
111–123, https://doi.org/10.1016/j.gloenvcha.2015.11.003, 2016.
Bronstert, A., Niehoff, D., and Brger, G.: Effects of climate and land-use
change on storm runoff generation: Present knowledge and modeling
capabilities, Hydrol. Process., 16, 509–529, https://doi.org/10.1002/hyp.326, 2002.
Chen, M., Li, Z., Gao, S., Luo, X., Wing, O. E. J., Shen, X., Gourley, J.
J., Kolar, R. L., and Hong, Y.: A comprehensive flood inundation mapping
for Hurricane Harvey using an integrated hydrological and hydraulic model,
J. Hydrometeorol., 22, 1713–1726, https://doi.org/10.1175/JHM-D-20-0218.1, 2021.
Cohen-Shacham, E., Walters, G., Janzen, C., and Maginnis, S.: Nature-based solutions to address global societal challenges, IUCN International Union for
Conservation of Nature, https://doi.org/10.2305/iucn.ch.2016.13.en, 2016.
Dartmouth Flood Observatory: Hurricane Matthew Flooding, Dartmouth Flood Observatory, https://floodobservatory.colorado.edu/Events/2016USA4402/2016USA4402.html
(last access: 15 November 2022), 2016.
Dartmouth Flood Observatory: Tropical Storm Florence, Dartmouth Flood Observatory, https://floodobservatory.colorado.edu/Events/4676/2018USA4676.html (last access: November 2022), 2018.
DNR: South Carolina State Climatology Office, DNR, https://www.dnr.sc.gov/climate/sco/ (last access: November 2022), 2021.
Doll, B., Kurki-Fox, J., and Associate, R.: Evaluating the Capacity of
Natural Infrastructure for Flood Abatement at the Watershed Scale,
Goldsboro, NC, North Carolina Sea Grant, https://ncseagrant.ncsu.edu/ (last access: November 2022), 2020.
Dutta, D., Herath, S., and Musiake, K.: An application of a flood risk
analysis system for impact analysis of a flood control plan in a river
basin, Hydrol. Process., 20, 1365–1384, https://doi.org/10.1002/hyp.6092, 2006.
Duane, S., Kennedy, A. D., Pendleton, B. J., and Roweth, D.: Hybrid Monte Carlo, Phys. Lett. B, 195, 216–222, https://doi.org/10.1016/0370-2693(87)91197-X, 1987.
Dutta, D., Teng, J., Vaze, J., Lerat, J., Hughes, J., and Marvanek, S.:
Storage-based approaches to build floodplain inundation modeling capability
in river system models for water resources planning and accounting, J. Hydrol., 504, 12–28, https://doi.org/10.1016/j.jhydrol.2013.09.033, 2013.
Ecologic Institute: Rivers and estuaries – Coastal Management Webguide – RISC KIT, https://coastal-management.eu/coastal-element/rivers-estuaries.html (last access: November 2022), 2019.
Edwards, A.: Pee Dee town struggles to recover from two floods in three years, Carolina News and Reporter, https://carolinanewsandreporter.cic.sc.edu/nichols-special-project/ (last access: November 2022), 2020.
EESI: Nature as Resilient Infrastructure – An Overview of Nature-Based
Solutions, Environmental and Energy Study Institute,
https://www.eesi.org/papers/view/fact-sheet-nature-as-resilient-infrastructure-an-overview-of-nature-based-solutions
(last access: November 2022), 2019.
El Gharamti, M., McCreight, J. L., Noh, S. J., Hoar, T. J., RafieeiNasab, A., and Johnson, B. K.: Ensemble streamflow data assimilation using WRF-Hydro and DART: novel localization and inflation techniques applied to Hurricane Florence flooding, Hydrol. Earth Syst. Sci., 25, 5315–5336, https://doi.org/10.5194/hess-25-5315-2021, 2021.
Emergency Management NC: Lumber River basin flood analysis and mitigation
strategies study, Emergency Management NC, https://www.rebuild.nc.gov/media/77/open (last access: November 2022), 2018.
European Commission: Nature-based solutions, NetworkNature, https://networknature.eu/, last access: November 2022.
European Parliament: Directive 2007/60/EC of the European Parliament and of
the Council on the assessment and management of flood risks, EUR-Lex, https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex:32007L0060 (last access: November 2022), 2017.
Fish, U. S. and Dahl, T. E.: South Carolina's Wetlands: Status and Trends,
1982–1989, FWS (Fish and Wildlife Service), United States, https://www.fws.gov/wetlands/documents/South-Carolinas-Wetlands-Status-and-Trends-1982-1989.pdf (last access: 13 July 2023), 1999.
Grimaldi, S., Schumann, G. J. P., Shokri, A., Walker, J. P., and Pauwels,
V. R. N.: Challenges, Opportunities, and Pitfalls for Global Coupled
Hydrologic-Hydraulic Modeling of Floods, Water Resour. Res., 55,
5277–5300, https://doi.org/10.1029/2018WR024289, 2019.
Howie, L.: Community Involvement in Flood Mitigation, A Survey-Based
Approach in Marion County, SC, Coastal Carolina University Electronic Theses
and Dissertations, 122, United States, https://digitalcommons.coastal.edu/etd/122 (last access: 13 July 2023), 2020.
IPCC: Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Pörtner, H.-O., Roberts, D. C., Tignor, M., Poloczanska, E. S., Mintenbeck, K., Alegría, A., Craig, M., Langsdorf, S., Löschke, S., Möller, V., Okem, A., and Rama, B., Cambridge University Press. Cambridge University Press, Cambridge, UK and New York, NY, USA, 3056 pp., https://doi.org/10.1017/9781009325844, 2022.
IUCN: Nature-based Solutions, International Union for Conservation of Nature,
https://www.iucn.org/our-work/nature-based-solutions, last access: November 2022.
Kalantari, Z., Ferreira, C. S. S., Keesstra, S., and Destouni, G.: Nature-based solutions for flood-drought risk mitigation in vulnerable urbanizing parts of East Africa, Current Opinion in Environmental Science & Health, 5, 73–78, https://doi.org/10.1016/j.coesh.2018.06.003, 2018.
Knebl, M. R., Yang, Z.-L., Hutchison K., and Maidment, D. R.: Regional scale
flood modeling using NEXRAD rainfall, GIS, and HEC-HMS/RAS: a case study for
the San Antonio River Basin Summer 2002 storm event, J. Environ. Manage., 75, 325–336, https://doi.org/10.1016/j.jenvman.2004.11.024, 2005.
Knutson, T.: Global Warming and Hurricanes, NOAA Geophysical Fluid Dynamics Laboratory, https://www.gfdl.noaa.gov/global-warming-and-hurricanes/, last access: November 2022.
Kossin, J. P.: A global slowdown of tropical-cyclone translation speed,
Nature, 558, 104–107, https://doi.org/10.1038/s41586-018-0158-3, 2018.
Kumar, P., Debele, S. E., Sahani, J., Rawat, N., Marti-Cardona, B., Alfieri, S. M., Basu, B., Basu, A. S., Bowyer, P., Charizopoulos, N., Gallotti, G., Jaakko, J., Leo, L. S., Loupis, M., Menenti, M., Mickovski, S. B., Mun, S. J., Gonzalez-Ollauri, A., Pfeiffer, J., Pilla, F., Pröll, J., Rutzinger, M., Santo, M. A., Sannigrahi, S., Spyrou, C., Tuomenvirta, H., and Zieher, T.:
Nature-based solutions efficiency evaluation against natural hazards:
Modeling methods, advantages, and limitations, Sci. Total
Environ., 784, 147058, https://doi.org/10.1016/j.scitotenv.2021.147058, 2021.
Lama, G. F. C., Giovannini, M. R. M., Errico, A., Mirzaei, S., Padulano, R.,
Chirico, G. B., and Preti, F.: Hydraulic efficiency of green-blue flood
control scenarios for vegetated rivers: 1D and 2D unsteady simulations,
Water, 13, 2620, https://doi.org/10.3390/w13192620, 2021.
Ly, S., Charles, C., and Degré, A.: Geostatistical interpolation of daily rainfall at catchment scale: the use of several variogram models in the Ourthe and Ambleve catchments, Belgium, Hydrol. Earth Syst. Sci., 15, 2259–2274, https://doi.org/10.5194/hess-15-2259-2011, 2011.
Mishra, S. K. and Singh, V. P.: SCS-CN Method, in: Soil Conservation Service Curve Number (SCS-CN) Methodology, Water Science and Technology Library, vol. 42, Springer, Dordrecht, https://doi.org/10.1007/978-94-017-0147-1_2, 2003.
MRLC: Multi-Reolution Land Characteistics Consortium viewer, Multi-Resolution Land Characteristics (MRLC) Consortium [data set], https://www.mrlc.gov/viewer/, last access: 13 July 2023.
Mubeen, A., Ruangpan, L., Vojinovic, Z., Sanchez Torrez, A., and
Plavšić, J.: Planning and Suitability Assessment of Large-scale
Nature-based Solutions for Flood-risk Reduction, Water Resour. Manag.,
35, 3063–3081, https://doi.org/10.1007/s11269-021-02848-w, 2021.
Muller, C.: How to Save Nichols, SC: A Small Town Lost in the Floods,
Woolpert presentation, https://www.seswa.org/assets/Services/Annual-Conference/2020/3%20-%20Muller.pdf
(last access: November 2022), 2020.
NASA: Matthew (Atlantic Ocean), NASA,
https://www.nasa.gov/feature/goddard/2016/matthew-atlantic-ocean (last
access: March 2023), 2016.
Nash, J. E. and Sutcliffe, J. V.: River Flow Forecasting through Conceptual Model. Part 1 – A Discussion of Principles, J. Hydrol., 10, 282–290, https://doi.org/10.1016/0022-1694(70)90255-6, 1970.
National Hurricane Center: Hurricane Florence, National Hurricane Center,
https://www.nhc.noaa.gov/archive/2018/al06/al062018.discus.055.shtml
(last access: March 2023), 2018.
NOAA: Inland flooding – A hidden danger of tropical cyclones, National Oceanic and Atmospheric Administration,
https://www.noaa.gov/stories/inland-flooding-hidden-danger-of-tropical-cyclones
(last access: November 2022), 2018.
NOAA: Hurricanes, National Oceanic and Atmospheric Administration,
https://www.noaa.gov/education/resource-collections/weather-atmosphere/hurricanes#:~:text=Hurricanes%2C%20known%20generically%20as%20tropical,energy%20from%20warm%20ocean%20waters
(last access: November 2022), 2020.
NRCS Kansas: Manning's n Values for Various Land Covers to Use for Dam
Breach Analyses, NRCS Kansas,
https://rashms.com/wp-content/uploads/2021/01/Mannings-n-values-NLCD-NRCS.pdf
(last access: November 2022), 2016.
Phillips R. C., Samadi S. Z., and Meadows M. E.: How extreme was the October 2015 flood in the Carolinas? An assessment of flood frequency analysis and distribution tails, J. Hydrol., 562, 648–663,
https://doi.org/10.1016/j.jhydrol.2018.05.035, 2018.
Ruangpan, L., Vojinovic, Z., Di Sabatino, S., Leo, L. S., Capobianco, V., Oen, A. M. P., McClain, M. E., and Lopez-Gunn, E.: Nature-based solutions for hydro-meteorological risk reduction: a state-of-the-art review of the research area, Nat. Hazards Earth Syst. Sci., 20, 243–270, https://doi.org/10.5194/nhess-20-243-2020, 2020.
Sahani, J., Kumar, P., Debele, S., Spyrou, C., Loupis, M., Aragão, L., Porcù, F., Shah, M. A. R., and Di Sabatino, S.: Hydro-meteorological risk assessment methods and management by nature-based solutions, Sci. Total Environ., 696, 133936, https://doi.org/10.1016/j.scitotenv.2019.133936, 2019.
Samadi, S., Pourreza-Bilondi, M., Wilson, C. A. M. E., and Hitchcock, D. B.: Bayesian model averaging with fixed and flexible priors: Theory, concepts, and calibration experiments for rainfall-runoff modeling, J. Adv. Model. Earth Syst., 12, e2019MS001924, https://doi.org/10.1029/2019MS001924, 2020.
SCDNR: Boating Guide to the Little Pee Dee Scenic River Water Trail in Dillon County, South Carolina Department of Natural Resources, South Carolina State Library, http://hdl.handle.net/10827/25678 (last access: November 2022), 2009.
SCDNR: Flood Mitigation Program, South Carolina Department of Natural Resources, https://www.dnr.sc.gov/water/flood/ (last access: November 2022), 2020.
SCS: National Engineering Handbook, Section 4, Soil Conservation Service,
U.S. Department of Agriculture, Washington, D.C., https://directives.sc.egov.usda.gov/OpenNonWebContent.aspx?content=18393.wba (last access: 13 July 2023), 1972.
Smith, A. B.: U.S. Billion-dollar Weather and Climate Disasters, 1980–present (NCEI Accession 0209268), NOAA National Centers for Environmental Information [data set], https://doi.org/10.25921/stkw-7w73, 2020.
Soulis, K. X.: Soil Conservation Service Curve Number (SCS-CN) Method: Current Applications, Remaining Challenges, and Future Perspectives, Water, 13, 192, https://doi.org/10.3390/w13020192, 2021.
Stewart, S. R. and Berg, R.: Hurricane Florence, National Hurricane Center,
Tropical Cyclone Report, https://www.nhc.noaa.gov/data/tcr/AL062018_Florence.pdf (last access: 13 July 2023), 2019.
Stone, M. H. and Cohen, S.: The influence of an extended Atlantic hurricane season on inland flooding potential in the southeastern United States, Nat. Hazards Earth Syst. Sci., 17, 439–447, https://doi.org/10.5194/nhess-17-439-2017, 2017.
Tang, Y., Leon, A. S., and Kavvas, M. L.: Impact of Size and Location of
Wetlands on Watershed-Scale Flood Control, Water Resour. Manag.,
34, 1693–1707, https://doi.org/10.1007/s11269-020-02518-3, 2020.
Tedesco, M., McAlpine, S., and Porter, J. R.: Exposure of real estate properties to the 2018 Hurricane Florence flooding, Nat. Hazards Earth Syst. Sci., 20, 907–920, https://doi.org/10.5194/nhess-20-907-2020, 2020.
Teng, J., Jakeman, A. J., Vaze, J., Croke, B. F. W., Dutta, D., and Kim,
S.: Flood inundation modeling: A review of methods, recent advances, and
uncertainty analysis, Environ. Model. Softw., 90, 201–216,
https://doi.org/10.1016/j.envsoft.2017.01.006, 2017.
Thomas, H. and Nisbet, T. R.: An assessment of the impact of floodplain
woodland on flood flows, Water Environ. J., 21, 114–126,
https://doi.org/10.1111/j.1747-6593.2006.00056.x, 2007.
US Army Corps of Engineers: HEC-RAS River Analysis System Hydraulic Reference Manual, Version 5.0, US Army Corps of Engineers, https://www.hec.usace.army.mil/software/hec-ras/documentation/HEC-RAS%205.0%20Reference%20Manual.pdf (last access: 13 July 2023), 2016.
US Army Corps of Engineers: Hydrologic Modeling System HEC-HMS Technical Reference Manual CPD-74B, US Army Corps of Engineers, https://www.hec.usace.army.mil/software/hec-hms/documentation/HEC-HMS_Technical%20Reference%20Manual_(CPD-74B).pdf (last access: 13 July 2023), 2021.
USDA – NRCS: Web Soil Survey, U.S. Department of Agriculture (USDA) [data set], https://websoilsurvey.nrcs.usda.gov/app/WebSoilSurvey.aspx, last access: 13 July 2023.
USGS: National Water Information System, United States Geological Survey, https://waterdata.usgs.gov/nwis (last access: 13 July 2023), 2023.
Williams, T., Song, B., Hitchcock, B., and O'Halloran, T.: Hurricane
Florence Flooding in Georgetown County: A Qualitative Explanation of the
Interactions of Estuary and Tidal River, Journal of South Carolina Water
Resources, 6, 35–49, https://doi.org/10.34068/JSCWR.06.04, 2019.
Williams, T., Song, B., Hitchcock, B., and O'Halloran, T.: Floodplain
Geomorphology and Response to Hurricanes: Lower Pee Dee Basin, South
Carolina. Journal of South Carolina Water Resources, 7, 81–90,
https://doi.org/10.34068/JSCWR.07.06, 2020.
Wing, O. E. J., Sampson, C. C., Bates, P. D., Quinn, N., Smith, A. M., and
Neal, J. C.: A flood inundation forecast of Hurricane Harvey using a
continental-scale 2D hydrodynamic model, J. Hydrol., 4, 100039,
https://doi.org/10.1016/j.hydroa.2019.100039, 2019.
Zhou, X., Ma, W., Echizenya, W., and Yamazaki, D.: The uncertainty of flood frequency analyses in hydrodynamic model simulations, Nat. Hazards Earth Syst. Sci., 21, 1071–1085, https://doi.org/10.5194/nhess-21-1071-2021, 2021.
Short summary
We used an integrated model to evaluate the impacts of nature-based solutions (NBSs) on flood mitigation across the Little Pee Dee and Lumber River watershed, the Carolinas, US. This area is strongly affected by climatic disasters, which are expected to increase due to climate change and urbanization, so exploring an NBS approach is crucial for adapting to future alterations. Our research found that NBSs can have visible effects on the reduction in hurricane-driven flooding.
We used an integrated model to evaluate the impacts of nature-based solutions (NBSs) on flood...
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