Leveraging mesh modularization to lower the computational cost of localized updates to regional 2D hydrodynamic model outputs

References

• Alonso, R., Santillana, M., & Dawson, C. (2008). On the diffusive wave approximation of the shallow water equations. European Journal of Applied Mathematics, 19(5), 575–606. https://doi.org/10.1017/S0956792508007675
   Google Scholar
• Bates, P. (2022). LISFLOOD-FP [WWW Document]. LISFLOOD-FP. http://www.bristol.ac.uk/geography/research/hydrology/models/lisflood/.
   Google Scholar
• Bermúdez, M., Cea, L., & Puertas, J. (2019). A rapid flood inundation model for hazard mapping based on least squares support vector machine regression. Journal of Flood Risk Management, 12(S1), e12522. https://doi.org/10.1111/jfr3.12522
   Google Scholar
• BMT Commercial Australia. (2022). TUFLOW Homepage. https://www.tuflow.com/.
   Google Scholar
• Brunner, G. W. (2021). HEC-RAS, river analysis system hydraulic reference manual. https://www.hec.usace.army.mil/software/hec-ras/documentation/HEC-RAS_6.0_Reference_Manual.pdf.
   Google Scholar
• Duruk Mutlubas, N., Geyer, A., & Quirchmayr, R. (2020). Well-posedness of a highly nonlinear shallow water equation on the circle. Nonlinear Analysis, 197, 111849. https://doi.org/10.1016/j.na.2020.111849
   Web of Science ®Google Scholar
• esri. (2013). Arc hydro: GIS for water resources 2. https://www.esri.com/content/dam/esrisites/sitecore-archive/Files/Pdfs/library/fliers/pdfs/archydro.pdf.
   Google Scholar
• Garcia, M., Juan, A., & Bedient, P. (2020). Integrating reservoir operations and flood modeling with HEC-RAS 2D. Water, 12(8), 2259. https://doi.org/10.3390/w12082259
   Web of Science ®Google Scholar
• Ghader, S., & Nordström, J. (2014). Revisiting well-posed boundary conditions for the shallow water equations. Dynamics of Atmospheres and Oceans, 66, 1–9. https://doi.org/10.1016/j.dynatmoce.2014.01.002
   Web of Science ®Google Scholar
• HCFCD, H.C.F.C.D. (2022). Spring floods 2016 [WWW Document]. https://www.hcfcd.org/About/Harris-Countys-Flooding-History/Spring-Floods-2016.
   Google Scholar
• Herzmann, D. (2022). Iowa Environmental Mesonet [WWW Document]. https://mesonet.agron.iastate.edu/.
   Google Scholar
• HGAC, H.-G.A.C. (2018). LiDAR Imagery [WWW Document]. https://www.h-gac.com/imagery/lidar.
   Google Scholar
• Hosseiny, H. (2021). A deep learning model for predicting river flood depth and extent. Environmental Modelling & Software, 145, 105186. https://doi.org/10.1016/j.envsoft.2021.105186
   Web of Science ®Google Scholar
• Innovyze. (2022). InfoWorks ICM product page. https://www.innovyze.com/en-us/products/infoworks-icm.
   Google Scholar
• Kabir, S., Patidar, S., Xia, X., Liang, Q., Neal, J., & Pender, G. (2020). A deep convolutional neural network model for rapid prediction of fluvial flood inundation. Journal of Hydrology, 590, 125481. https://doi.org/10.1016/j.jhydrol.2020.125481
   Web of Science ®Google Scholar
• Kalyanapu, A. J., Burian, S. J., & McPherson, T. N. (2010). Effect of land use-based surface roughness on hydrologic model output. Journal of Spatial Hydrology, 9.
   Google Scholar
• Lehman, W. (2022). GitHub – hydrologic engineering center [WWW Document]. GitHub. https://github.com/HydrologicEngineeringCenter.
   Google Scholar
• L’Heureux, A., Grolinger, K., Elyamany, H. F., & Capretz, M. A. M. (2017). Machine learning With Big data: Challenges and approaches. IEEE Access, 5, 7776–7797. https://doi.org/10.1109/ACCESS.2017.2696365
   Web of Science ®Google Scholar
• Liu, Y., & Pender, G. (2015). A flood inundation modelling using v-support vector machine regression model. Engineering Applications of Artificial Intelligence, 46, 223–231. https://doi.org/10.1016/j.engappai.2015.09.014
   Web of Science ®Google Scholar
• Mobley, W., Sebastian, A., Blessing, R., Highfield, W. E., Stearns, L., & Brody, S. D. (2021). Quantification of continuous flood hazard using random forest classification and flood insurance claims at large spatial scales: A pilot study in southeast Texas. Natural Hazards and Earth System Sciences, 21(2), 807–822. https://doi.org/10.5194/nhess-21-807-2021
   Web of Science ®Google Scholar
• Mosavi, A., Ozturk, P., & Chau, K. (2018). Flood prediction using machine learning models: Literature review. Water, 10(11), 1536. https://doi.org/10.3390/w10111536
   Web of Science ®Google Scholar
• MRLC, M.-R.L.C.C. (2020). Data | multi-resolution land characteristics (MRLC) consortium [WWW Document]. https://www.mrlc.gov/data?f%5B0%5D=category%3Aland%20cover&f%5B1%5D=region%3Aconus.
   Google Scholar
• Navon, I. M., & Cai, Y. (1993). Domain decomposition and parallel processing of a finite element model of the shallow water equations. [WWW Document]. Computer Methods in Applied Mechanics and Engineering, 106(1–2), 179–212.
   Web of Science ®Google Scholar
• Nevo, S., Morin, E., Gerzi Rosenthal, A., Metzger, A., Barshai, C., Weitzner, D., Voloshin, D., Kratzert, F., Elidan, G., Dror, G., Begelman, G., Nearing, G., Shalev, G., Noga, H., Shavitt, I., Yuklea, L., Royz, M., Giladi, N., Peled Levi, N., … Matias, Y. (2022). Flood forecasting with machine learning models in an operational framework. Hydrology and Earth System Sciences, 26(15), 4013–4032. https://doi.org/10.5194/hess-26-4013-2022
   Web of Science ®Google Scholar
• NOAA, N.O. and A.A. (2018). Atlas14_Volume11. https://www.weather.gov/media/owp/oh/hdsc/docs/Atlas14_Volume11.pdf.
   Google Scholar
• Panakkal, P., Juan, A., Garcia, M., Padgett, J.E., & Bedient, P. 2019. Towards enhanced response: Integration of a flood alert system with road infrastructure performance models. Confrence paper from SEI part of ASCE. https://doi.org/10.1061/9780784482223.029
   Google Scholar
• Sanders, W., Li, D., Li, W., & Fang, Z. N. (2022). Data-Driven flood alert system (FAS) using extreme gradient boosting (XGBoost) to forecast flood stages. Water, 14(5), 747. https://doi.org/10.3390/w14050747
   Web of Science ®Google Scholar
• Shustikova, I., Domeneghetti, A., Neal, J. C., Bates, P., & Castellarin, A. (October 26, 2019). Comparing 2D capabilities of HEC-RAS and LISFLOOD-FP on complex topography. Hydrological Sciences Journal, 64(14), 1769–1782. https://doi.org/10.1080/02626667.2019.1671982
   Web of Science ®Google Scholar
• Tezduyar, T., & Osawa, Y. (2001). The multi-domain method for computation of the aerodynamics of a parachute crossing the far wake of an aircraft. Computer Methods in Applied Mechanics and Engineering, Minisymposium on Methods for Flow Simulation and Modeling, 191, 705–716. https://doi.org/10.1016/S0045-7825(01)00310-3
   Web of Science ®Google Scholar
• USDA. (2022). Tools | NRCS soils [WWW Document]. https://www.nrcs.usda.gov/wps/portal/nrcs/main/soils/survey/tools/.
   Google Scholar
• USDA, U.S.D. of A. (2019). Part 630 hydrology national engineering handbook. https://directives.sc.egov.usda.gov/OpenNonWebContent.aspx?content=43924.wba
   Google Scholar
• Wang, Z., Lai, C., Chen, X., Yang, B., Zhao, S., & Bai, X. (2015). Flood hazard risk assessment model based on random forest. Journal of Hydrology, 527, 1130–1141. https://doi.org/10.1016/j.jhydrol.2015.06.008
   Web of Science ®Google Scholar
• World Bank. (2022). TELEMAC-2D [WWW Document]. http://www.appsolutelydigital.com/ModelPrimer/chapter4_section7.html.
   Google Scholar
• Wu, Z., Zhou, Y., Wang, H., & Jiang, Z. (2020). Depth prediction of urban flood under different rainfall return periods based on deep learning and data warehouse. Science of The Total Environment, 716, 137077. https://doi.org/10.1016/j.scitotenv.2020.137077
   PubMed Web of Science ®Google Scholar
• Yang, S.-Y., Jhong, B.-C., Jhong, Y.-D., Tsai, T.-T., & Chen, C.-S. (2022). Long short-term memory integrating moving average method for flood inundation depth forecasting based on observed data in urban area. Natural Hazards, 116, 2339–2361. https://doi.org/10.1007/s11069-022-05766-1
   Google Scholar
• Zou, Y., Wang, J., Lei, P., & Li, Y. (2023). A novel multi-step ahead forecasting model for flood based on time residual LSTM. Journal of Hydrology, 620, 129521. https://doi.org/10.1016/j.jhydrol.2023.129521
   Web of Science ®Google Scholar