The CREST-iMAP model framework integrates hydrologic model (CREST) and hydrodynamic model (anuga) to target heavy rainfall-induced flash flood.
Taking advantage of two models, CREST-iMAP is capable of simulating hydrologic streamflows, flood extend, flood depth, pushing the territory of traditional 1D streamflow simulation to 2D (extent) and 3D (depth).
Li, Z., Chen, M., Gao, S., Luo, X., Gourley, J. J., Kirstetter, P., Yang, T., Kolar, R., McGovern, A., Wen, Y., Rao, B., Yami, T., & Hong, Y. (2021). CREST-iMAP v1.0: A fully coupled hydrologic-hydraulic modeling framework dedicated to flood inundation mapping and prediction. Environmental Modelling & Software, 141, 105051. https://doi.org/10.1016/j.envsoft.2021.105051
Li, Z., Chen, M., Gao, S., Wen, Y., Gourley, J. J., Yang, T., Kolar, R., & Hong, Y. (2022). Can re-infiltration process be ignored for flood inundation mapping and prediction during extreme storms? A case study in Texas Gulf Coast region. Environmental Modelling & Software, 155, 105450. https://doi.org/10.1016/j.envsoft.2022.105450
Chen, M., and Coauthors, 2021: A Comprehensive Flood Inundation Mapping for Hurricane Harvey Using an Integrated Hydrological and Hydraulic Model. J. Hydrometeor., 22, 1713–1726, https://doi.org/10.1175/JHM-D-20-0218.1.
Chen, M., Li, Z., & Gao, S. Multisensor Remote Sensing and the Multidimensional Modeling of Extreme Flood Events. 87-104. https://doi.org/10.1002/9781119159131.ch5
Chen, M., Li, Z., Gao, S., Xue, M., Gourley, J. J., Kolar, R. L., & Hong, Y. (2022). A flood predictability study for Hurricane Harvey with the CREST-iMAP model using high-resolution quantitative precipitation forecasts and U-Net deep learning precipitation nowcasts. Journal of Hydrology, 612, 128168. https://doi.org/10.1016/j.jhydrol.2022.128168
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