The Coupled Routing and Excess STorage model (CREST, jointly developed by the University of Oklahoma and NASA SERVIR) is a distributed hydrological model developed to simulate the spatial and temporal variation of land surface, and subsurface water fluxes and storages by cell-to-cell simulation. CREST’s distinguishing characteristics include: (1) distributed rainfall–runoff generation and cell-to-cell routing; (2) coupled runoff generation and routing via three feedback mechanisms; and (3) representation of sub-grid cell variability of soil moisture storage capacity and sub-grid cell routing (via linear reservoirs). The coupling between the runoff generation and routing mechanisms allows detailed and realistic treatment of hydrological variables such as soil moisture. Furthermore, the representation of soil moisture variability and routing processes at the sub-grid scale enables the CREST model to be readily scalable to multi-scale modelling research.

What’s New

Version 2.1

  • The major upgrade is about the routing scheme. The cell-to-cell routing scheme used in previous versions of CREST is a quasi-distributed linear reservoir routing (QDLRR) method which we found incompatible with a distributed hydrological model in practice. In CREST v2.1, a fully distributed LRR method (FDLRR) is proposed and used to replace the QDLRR module of CREST. The left and right figures below shows the conception of the QDLRR and the FDLRR. Suppose that in one time step, water moves from A to D, B to E and C to F and take C as the observation cell. Using QDLRR, only the water from C to F contributes to the final runoff (discharge) of C while water from A to D and B to F is denied of contribution as if the water jumps over cell C. On the contrary, Using FDLRR in CREST v2.1, all these three terms contributes to the runoff of cell C because they either sets off from or passed via cell C.
    QDLRR used in previous versions FDLRR used in CREST v2.1
    QLDRR scheme used in previous versions FDLRR scheme used in CREST v2.1


  •  CREST v2.1 does not underestimate the discharge at arbitrary spatial and temporal resolutions. The simulated versus observed hydrograph of at daily scale of Kankakee River in Illinois, USA and Gan River in Jiangxi, China are shown below.
    QDLRR used in previous versions FDLRR used in CREST v2.1
    hydrographs in Kankakee River hydrographs in Gan River
  • Calibration of CREST v2.1 is significantly easier than previous versions and the final NSCE is generally higher.
  • Instead of generating a discontinuous and bumpy discharge value along the river network, the FDLRR in CREST v2.1 produces a  continuous and basically monotonic discharge from upstream to downstream as shown below.
    QDLRR used in CREST v2.0 FDLRR used in CREST v2.1
    discharge along the river network by previous versions discharge along the river network by CREST v2.1
  • For more details of CREST v2.1, please refer our recent submission under review.

Executable File Download and User Manual

Version 2.1

Language Windows Download Linux Download User Manual Example Basin
MATLAB CREST, Dependency CREST, Dependency Download Download


  1. 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?[J]. Journal of Hydrology, 499(0): 91-99. doi: 10.1016/j.jhydrol.2013.06.042.[PDF] [WebLink]
  2. Wang. J., Y. Hong, L. Li, J.J. Gourley, K. Yilmaz, S. I. Khan, F.S. Policelli, R.F. Adler, S. Habib, D. Irwn, S.A. Limaye, T. Korme, and L. Okello, 2011: The Coupled Routing and Excess STorage (CREST) distributed hydrological model. Hydrol. Sciences Journal, 56, 84-98. [PDF] [WebLink]

Additional References

  1. 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. [WebLink]
  2. Khan, S. I., P. Adhikari, Y. Hong, H. Vergara, T. Grout, R. F. Adler, F. Policelli, D. Irwin, T. Korme, and L. Okello, 2011: Observed and simulated hydroclimatology using distributed hydrologic model from in-situ and multi-satellite remote sensing datasets in Lake Victoria region in East Africa, Hydrol. Earth Syst. Sci. Discuss., 7, 4785-4816, doi:10.5194/hessd-7-4785. [PDF] [WebLink]
  3. 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. [PDF] [WebLink]
  4. 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. [PDF] [WebLink]