Crest

The Coupled Routing and Excess STorage (CREST) model, jointly developed by the University of Oklahoma and NASA SERVIR, is a distributed hydrologic modeling system designed to simulate the spatial and temporal dynamics of surface and subsurface water fluxes and storages through grid-based, cell-to-cell computation. Conceived as a remote-sensing–native model, CREST operates naturally at the spatial and temporal scales of satellite and radar observations. Its core innovations include fully distributed rainfall–runoff generation coupled with routing, dynamic feedbacks linking runoff production and flow processes, and explicit representation of sub-grid soil-moisture variability using linear reservoir formulations. This structure enables physically consistent simulation of key hydrologic states such as soil moisture, runoff, and streamflow while maintaining computational efficiency.

CREST has evolved into a model family (including CREST-VEC, EF5, and CREST-iMAP) that supports ensemble forecasting, hydrologic–hydraulic coupling, flood inundation mapping, and operational hazard prediction. By resolving hydrologic processes across grid and sub-grid scales, the CREST framework is readily scalable from local watersheds and urban environments to regional, continental, and global domains. The CREST model family underpins flood forecasting and water-resources applications worldwide, particularly in data-sparse and rapidly changing environments, providing a flexible, observation-driven foundation for flood resilience and hazard risk assessment. All CREST-family models are openly available to the community through GitHub (see links section below), supporting transparent, reproducible, and extensible hydrologic research and applications.

Version 3.0 (C++)

1. Add a conceptual groundwater module (similar to National Water Model)
2. Multi-model ensemble framework

Version 2.1 (C++)

1. For the cell-to-cell routing scheme in CREST we proposed a fully distributed LRR method (FDLRR) to replace the existing quasi- distributed LRR (QDLRR) method.
2. As a result, CREST v2.1 does not underestimate the discharge at arbitrary spatial and temporal resolutions.
3. Calibration of CREST v2.1 is significantly easier than previous versions and the final NSCE is generally higher.
4. Instead of generating discontinuous and bumpy discharge values along the river network, the FDLRR in CREST v2.1 produces a continuous and basically monotonic discharge from upstream to downstream.

Version 2.0 (Fortran)

1. A modular design framework to accommodate research, development and system enhancements (see Fig. 2(a) in Xue et al. (2013))
2. Inclusion of the optimization scheme SCEUA to enable automatic calibration of the CREST model parameters (see Fig. 2(a) in Xue et al. (2013))
3. All the parameters in CREST v1.6c were classified into three types: Initial Conditions, Physical Parameters ( to be derived by a-priori parameter method and/or be calibrated) and conceptual parameters (to be calibrated), some of the parameters were omitted (more details in user manual)
4. Model implementation with options of either spatially uniform, semi-distributed, or distributed parameter values
5. A multi-site cascading calibration framework was used to calibrate the model using multi-site gauge data from upstream to downstream (Users should manually prepare the data)
6. Enhancement of the computation capability using matrix manipulation to make the model more efficient
7. Project file was used to replace the original control file, and users can pass the project file to the CREST model instead of putting the crest model executable file and the control file in the same path. More than that, the statements in the project file can be in any order and more flexible
8. Model can output all the variables in any time (spatial data) and any locations results (Time series)
9. Some bugs were fixed

Version 1.6c (Fortran)

1. Coupled Routing and Excess STorage (CREST) model was developed jointly by the University of Oklahoma and NASA SERVIR
2. Distributed rainfall–runoff generation and cell-to-cell routing
3. Coupled runoff generation and routing via three feedback mechanisms
4. Representation of sub-grid cell variability of soil moisture storage capacity and sub-grid cell routing (via linear reservoirs)
5. The coupling between the runoff generation and routing mechanisms allows detailed and realistic treatment of hydrological variables such as soil moisture

Pre-CREST Era - Early Remote Sensing-Native Hydrological Modeling Efforts, 2006-2009:

1. Hong, Y, K.L. Hsu, H. Moradkhani and S. Sorooshian, 2006: Uncertainty quantification of satellite precipitation estimation and Monte Carlo assessment of the error propagation into hydrologic response, Water Resources Research., 42(8). Established the multi-dimension (temporal-spatial-intensity) multiscale end-to-end framework for quantifying satellite precipitation uncertainty and its nonlinear propagation into hydrologic response, providing a benchmark for research and operational users.
2. Hong, Y., R. Adler, and G. Huffman, 2006: Evaluation of the potential of NASA multi-satellite precipitation in global landslide hazard assessment, GRL 33(22). The scientific foundation for global satellite real-time rainfall-based landslide monitoring systems
3. Hong, Y., R.F. Adler, A. Negri, and G.J. Huffman, 2007: Flood and landslide applications of near real-time satellite rainfall estimation, Natural Hazards, 43(2), 510-521. The first TRMM satellite-era near real-time global flood and landslide application system
4. Hong, Y., R. Adler, F. Hossain, S. Curtis, and G. Huffman, 2007: A first approach to global runoff simulation using satellite rainfall estimation, Water Res. Research, 43(8). The first satellite-driven remote sensing-native global runoff modeling system
5. Kirschbaum, D., R. Adler, Y. Hong, and A Lerner-Lam, 2009: Evaluation of a preliminary satellite-based landslide hazard algorithm using global landslide inventories, Nat. Hazards Earth Syst. Sci., 9, 673–686.

CREST 1.0 - CREST 2.0, 2011-2017:

1. Wang, J., Hong, Y., Li, L., Gourley, J.J., Khan, S.I., Yilmaz, K.K., Adler, R.F., Policelli, F.S., Habib, S., Irwn, D., Limaye, A.S., Korme, T., Okello, L., 2011. The coupled routing and excess storage (CREST) distributed hydrological model. Hydrological Sciences Journal 56, 84–98. 10.1080/02626667.2010.543087 Introduced the remote-sensing–native, coupled routing-storage distributed model CREST V1.0 that became the foundation of the CREST model family (CREST/EF5/CRES-iMAP/CREST-VEC/iCRESLIDE/iCRESTRIGRS) used worldwide.
2. Khan, S. I., Y. Hong, J. Wang, K.K. Yilmaz, J.J. Gourley, R.F. Adler, G.R. Brakenridge, F. Policelli, S. Habib, and D. Irwin, 2011: Satellite Remote Sensing and Hydrologic Modeling for Flood Inundation Mapping in Lake Victoria Basin: Implications for Hydrologic Prediction in Ungauged Basins, IEEE Transactions on Geosciences and Remote Sensing, 49(1), 85-95, Jan. 2011, doi: 10.1109/TGRS.2010.2057513.
3. Khan, S. I., P. Adhikari, Y. Hong, H. Vergara, R. F. Adler, F. Policelli, D. Irwin, T. Korme, and L. Okello, 2011: Hydroclimatology of Lake Victoria region using hydrologic model and satellite remote sensing data, Hydrol. Earth Syst. Sci., 15, 107–117, doi: 10.5194/hess-15-107-2011
4. Khan, S. I., Y. Hong, H. J. Vergara, et al.,2012. Microwave Satellite Data for Hydrologic Modeling in Ungauged Basins, Geoscience and Remote Sensing Letters, IEEE, 9(4), 663-667, doi: 10.1109/LGRS.2011.2177807
5. Xue X., Y. Hong, A. S. Limaye, et al. (2013), Statistical and hydrological evaluation of TRMM-based Multi-satellite Precipitation Analysis over the Wangchu Basin of Bhutan: Are the latest satellite precipitation products 3B42V7 ready for use in ungauged basins? Journal of Hydrology, 499(0): 91-99. doi: 10.1016/j.jhydrol.2013.06.042. (Introduction of CREST v2.0)
6. Zhang, Y., Y. Hong, et al., 2014: Hydrometeorological Analysis and Remote Sensing of Extremes: Was the July 2012 Beijing Flood Event Detectable and Predictable by Global Satellite Observing and Global Weather Modeling Systems?, Journal of Hydrometeorology, doi: 10.1175/JHM-D-14-0048.1
7. Shen, X., Hong, Y., Zhang, K., Hao, Z., and Wang, D. (2014) “CREST v2.1 Refined by a Distributed Linear Reservoir Routing Scheme.” Proc., AGU Fall Meeting, H33G-0918.
8. Vergara, H., Kirstetter, P.-E., Gourley, J.J., Flamig, Z.L., Hong, Y., Arthur, A., Kolar, R., 2016. Estimating a-priori kinematic wave model parameters based on regionalization for flash flood forecasting in the Conterminous United States. Journal of Hydrology 541, 421–433. 10.1016/j.jhydrol.2016.06.011
9. Xue, X., Zhang, K., Hong, Y., Gourley, J.J., Kellogg, W., McPherson, R.A., Wan, Z., Austin, B.N., 2016. New multisite cascading calibration approach for hydrological models: case study in the Red River Basin using the VIC model. Journal of Hydrologic Engineering 21. 10.1061/(ASCE)HE.1943-5584.0001282.
10. Shen, X., Hong, Y., Zhang, K., Hao, Z., 2017. Refining a Distributed Linear Reservoir Routing Method to Improve Performance of the CREST Model. J. Hydrol. Eng. 22, 04016061. 10.1061/(ASCE)HE.1943-5584.0001442 (Description of CREST v2.1)

ICRESLIDE and iCRESTRGRS: Coupled systems for flood-landslide prediction, 2015-2016:

1. Hong, Y., K. Zhang, and J.J. Gourley (2015). iCRESLIDE: Integration of Coupled Routing and Excess Storage and Slope-Infiltration-Distributed Equilibrium for the Cascading Hydrologic-Geotechnical Modeling (NH51D-1918). 2015 AGU Fall Meeting, San Francisco, California, Dec. 14-18, 2015.
2. Zhang, K., Xue, X., Hong, Y., Gourley, J. J., Lu, N., Wan, Z., Hong, Z., and Wooten, R.: iCRESTRIGRS: A coupled modeling system for cascading flood-landslide disaster forecasting, Hydrol. Earth Syst. Sci. Discuss., doi: 10.5194/hess-2016-143, 2016.

CREST2.0/EF5 National Flash Flood Early Warning System, 2017-2020:

1. Gourley, J.J., Flamig, Z.L., Vergara, H., Kirstetter, P.-E., Clark, R.A., Argyle, E., Arthur, A., Martinaitis, S., Terti, G., Erlingis, J.M., Hong, Y., Howard, K.W., 2017. The FLASH Project: Improving the Tools for Flash Flood Monitoring and Prediction across the United States. Bulletin of the American Meteorological Society 98, 361–372. doi: 10.1175/BAMS-D-15-00247.1Introduced the CREST/EF5 Flash Flood System for CONUS-wide application in NOAA.
2. Clark, R.A., Flamig, Z.L., Vergara, H., Hong, Y., Gourley, J.J., Mandl, D.J., Frye, S., Handy, M., Patterson, M., 2017. Hydrological Modeling and Capacity Building in the Republic of Namibia. Bulletin of the American Meteorological Society 98, 1697–1715. doi: 10.1175/BAMS-D-15-00130.1
3. Flamig, Z. L., Vergara, H., and Gourley, J. J., 2020. The Ensemble Framework For Flash Flood Forecasting (EF5) v1.2: description and case study, Geosci. Model Dev., 13, 4943–4958, doi: 10.5194/gmd-13-4943-2020

CREST-iMAP: Coupled Hydrology-Hydraulic Inundation Mapping for urbans and coastal regions: 2020-

1. Chen, M., Li, Z., Gao, S., Luo, X., Wing, O.E.J., Shen, X., Gourley, J.J., Kolar, R.L., Hong, Y., 2021. A comprehensive flood inundation mapping for Hurricane Harvey using an integrated hydrological and hydraulic model. Journal of Hydrometeorology. doi: 10.1175/JHM-D-20-0218.1
2. 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. doi: 10.1016/j.envsoft.2021.105051
3. 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. doi: 10.1016/j.jhydrol.2022.128168
4. 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. doi: 10.1016/j.envsoft.2022.105450

CREST-VEC: Continental to global scale real-time prediction, 2022:

1. Li, Z., Gao, S., Chen, M., Gourley, J., Mizukami, N., Hong, Y., 2022. CREST-VEC: a framework towards more accurate and realistic flood simulation across scales. Geosci. Model Dev. 15, 6181–6196. doi: 10.5194/gmd-15-6181-2022
2. Li, Z., Gao, S., Chen, M., Gourley, J.J., Hong, Y., 2022. Spatiotemporal Characteristics of US Floods: Current Status and Forecast Under a Future Warmer Climate. Earth’s Future 10, e2022EF002700. doi: 10.1029/2022EF002700
3. Li, Z., Gao, S., Chen, M., Gourley, J.J., Liu, C., Prein, A.F., Hong, Y., 2022. The conterminous United States are projected to become more prone to flash floods in a high-end emissions scenario. Commun Earth Environ 3, 86. 10.1038/s43247-022-00409-6

CREST 3.0: CONUS-wide parameter calibrations: 2023:

1. Mengye Chen, Zhi Li, Humberto J Vergara, Jonathan J Gourley, Ming Xue, Yang Hong, Xiao-Ming Hu, Hector Mayol Novoa, Elinor R Martin, Renee A McPherson, Shang Gao, Andres Vitaliano Perez, Isaac Yanqui Morales, 2023. CONUS-wide model calibration and validation for CRESTv3.0 – An improved Coupled Routing and Excess STorage distributed hydrological model. Journal of Hydrology, 626: 130333. 10.1016/j.jhydrol.2023.130333