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Distributed Model Calibration

Ordered Physics-Based Adjustment

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Distributed Hydrologic Modeling Using GIS

Part of the book series: Water Science and Technology Library ((WSTL,volume 74))

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Abstract

We show that given certain constraints, such as the spatial pattern of parameters, unique solutions do exist, thus making it possible to calibrate a distributed model. As we have shown in previous chapters, the drainage length, slope, and other parameters extracted from DEMs and geospatial data are resolution dependent. Thus, it is more likely than not, such parameters would require some adjustment. This chapter presents a method for calibrating a distributed model consistent with the conservation equations that underlie PBD models.

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References

  • Blum, J., F.-X. Le Dimet, and I.M. Navon. 2009. Data assimilation for geophysical fluids. Handbook of numerical analysis 14: 385–441.

    Google Scholar 

  • Boyle, D.P., H.V. Gupta, S. Sorooshian, V. Koren, Z. Zhang, and M. Smith. 2001. Toward improved streamflow forecasts: Value of semidistributed modeling. Water Resources Research 37(11): 2749–2759.

    Article  Google Scholar 

  • Cheng, C.T., C.P. Ou, and K.W. Chau. 2002. Combining a fuzzy optimal model with a genetic algorithm to solve multi-objective rainfall–runoff model calibration. Journal of Hydrology 268(1): 72–86.

    Article  Google Scholar 

  • Cosgrove, B. 2015. December. Hydrologic modeling at the national water center: Operational implementation of the WRF-Hydro model to support national weather service hydrology. In 2015 AGU fall meeting. AGU.

    Google Scholar 

  • Duan, Q., V.K. Gupta, and S. Sorooshian. 1993. A shuffled complex evolution approach for effective and efficient optimization. Journal of Optimization Theory Application 76(3): 501–521.

    Article  Google Scholar 

  • Duan, Q., S. Sorooshian, and V.K. Gupta. 1992. Effective and efficient global optimization for conceptual rainfall-runoff models. Water Resources Research 24(8): 1015–1031.

    Article  Google Scholar 

  • Duan, Q., S. Sorooshian, and V.K. Gupta. 1994. Optimal use of the SCE-UA global optimization method for calibrating watershed models. Journal of Hydrology 158: 265–284.

    Article  Google Scholar 

  • Duan, Q., S. Sorooshian, H.V. Gupta, A.N. Rousseau, R. Turcotte, 2003. Calibration of watershed models, water science and application series, 6. American Geophysical Union, ISBN 0-87590-355-X.

    Google Scholar 

  • Entekhabi, D., H. Nakamura, and E.G. Njoku. 1994. Solving the inverse problem for soil moisture and temperature profiles by sequential assimilation of multi-frequency remotely sensed observations. IEEE Transactions on Geoscience and Remote Sensing 32(2): 438–448.

    Article  Google Scholar 

  • Francés, F., Vélez, J.I., and Vélez, J.J., 2007. Split-parameter structure for the automatic calibration of distributed hydrological models. Journal of Hydrology 332(1): 226–240.

    Google Scholar 

  • Graham, W.D., and D.B. McLaughlin. 1991. A stochastic model of solute transport in groundwater: Application to the Borden, Ontario, tracer test. Water Resources Research 27(6): 1345–1359.

    Article  Google Scholar 

  • Gupta, H.V., K.J. Beven, and T. Wagener. 2005. Model calibration and uncertainty estimation. Encyclopedia of Hydrological Sciences.

    Google Scholar 

  • Hooke, R., and T.A. Jeeves. 1961. Direct search solutions of numerical and statistical problems. Journal of Association for Computing Machinery 8(2): 212–229.

    Article  Google Scholar 

  • Hosseinpour, A., Dolcine, L., and Fuamba, M., 2014. Natural flow reconstruction using kalman filter and water balance–based methods I: Theory. Journal of Hydrologic Engineering 19(12): 04014029.

    Google Scholar 

  • Kalman, R.E., and R.S. Bucy. 1961. New results in linear filtering and prediction theory. Journal of Basic Engineering 83(1): 95–108.

    Article  Google Scholar 

  • Koren, V.I., M. Smith, D. Wang, and Z. Zhang. 2000. Use of soil property data in the derivation of conceptual rainfall runoff model parameters, paper presented at the 15th Conference on Hydrology. American Meteorological Society Long Beach, California.

    Google Scholar 

  • Le Dimet, F.X., H.E. Ngodock, and M. Navon. 1996. Sensitivity analysis in variational data assimilation. In Siam meeting in automatic differentiation. New Mexico, USA: Santa Fe.

    Google Scholar 

  • Le Dimet, François-Xavier, et al. 2009. Data assimilation in hydrology: variational approach. In Data assimilation for atmospheric, oceanic and hydrologic applications, 367–405. Berlin Heidelberg: Springer.

    Google Scholar 

  • Liu, D.C., and J. Nocedal. 1989. On the Limited BFGS method for large scale optimization. Journal of Mathematical Programming 45: 503–528.

    Article  Google Scholar 

  • Looper, J.P., B.E. Vieux, and M.A. Moreno. 2012. Assessing the impacts of precipitation bias on distributed hydrologic model calibration and prediction accuracy. Journal of Hydrology 418–419: 110–122.

    Article  Google Scholar 

  • McLaughlin, D. 1995. Recent developments in hydrology data assimilation. Review of Geophysics 977–984.

    Google Scholar 

  • Mendoza, P.A., M.P. Clark, M. Barlage, B. Rajagopalan, L. Samaniego, G. Abramowitz, and H. Gupta. 2015. Are we unnecessarily constraining the agility of complex process-based models? Water Resources Research 51: 716–728. doi:10.1002/2014WR015820.

    Article  Google Scholar 

  • Nash, J.E., and J. Sutcliffe. 1970. River flow forecasting through conceptual models, Part IA discussion of principles. Journal of Hydrology 10: 282–290.

    Article  Google Scholar 

  • Nelder, J.A., and R. Mead. 1965. A simplex method for function minimization. Computer Journal 7: 308–313.

    Article  Google Scholar 

  • Niedda, M., 2004. Upscaling hydraulic conductivity by means of entropy of terrain curvature representation. Water Resources Research 40(4).

    Google Scholar 

  • Oudin, L., V. Andréassian, C. Perrin, and F. Anctil. 2004. Locating the sources of low‐pass behavior within rainfall‐runoff models. Water Resources Research 40(11).

    Google Scholar 

  • Oudin, L., C. Perrin, T. Mathevet, V. Andreassian, and C. Michel. 2006. Impact of biased and randomly corrupted inputs on the efficiency and the parameters of watershed models. Journal of Hydrology 320(1): 62–83.

    Google Scholar 

  • Pokhrel, P. (2007). Estimation of spatially distributed model parameters using a regularization approach.

    Google Scholar 

  • Pokhrel, P.H., V. Gupta, and T. Wagener. 2008. A spatial regularization approach to parameter estimation for a distributed watershed model. Water Resources Research 44: W12419. doi:10.1029/2007WR006615.

    Article  Google Scholar 

  • Pokhrel, P.H., and V. Gupta. 2010. On the use of spatial regularization strategies to improve calibration of distributed watershed models. Water Resources Research 46: W01505. doi:10.1029/2009WR008066.

    Article  Google Scholar 

  • Sene, Kevin. 2012. Flash floods: Forecasting and warning. Springer Science & Business Media.

    Google Scholar 

  • Smith, M.B., D.J. Seo, V.I. Koren, S. Reed, Z. Zhang, Q.Y. Duan, S. Cong, F. Moreda, and R. Anderson. 2004. The distributed model intercomparison project (DMIP): Motivation and experiment design. Journal of Hydrology 298(1–4): 4–26.

    Article  Google Scholar 

  • Smith, M.B., V. Koren, Z. Zhang, Y. Zhang, S.M. Reed, Z. Cui, F. Moreda, B.A. Cosgrove, N. Mizukami, E.A. Anderson, and DMIP 2 Participants. 2012. Results of the DMIP 2 Oklahoma experiments. Journal of Hydrology 418–419: 17-48.

    Google Scholar 

  • Sorooshian, S., and J.A. Dracup. 1980. Stochastic parameter estimation procedures for hydrologic rainfall-runoff models: correlated and heteroscedastic error cases. Water Resources Research 29(4): 1185–1194.

    Article  Google Scholar 

  • Vieux, B.E., and N.S. Farajalla. 1994. Capturing the essential spatial variability in distributed hydrological modeling: Hydraulic roughness. Journal of Hydrologic Processes 8: 221–236.

    Article  Google Scholar 

  • Vieux, B.E., F. Le Dimet, D. Armand. 1998a. Optimal control and adjoint methods applied to distributed hydrologic model calibration. Proceedings of International Association for Computational Mechanics, IV World Congress on Computational Mechanics, June 29–July 2, Buenos Aires, Argentina. Computational Mechanics: New Trends and Applications, eds. S.R. Idelsohn, E. Onate, E.N. Dvorkin, CIMNE, Barcelona, Spain.

    Google Scholar 

  • Vieux, B.E., F. Le Dimet, D. Armand. 1998b. Inverse problem formulation for spatially distributed river basin model calibration using the adjoint method. EGS, Annales Geophysicae, Part II, Hydrology, Oceans and Atmosphere, Supplement II to Vol. 16, p. C501.

    Google Scholar 

  • Vieux, B.E., and F.G. Moreda. 2003. Ordered Physics-Based Parameter Adjustment of a Distributed Model. In: ed. Q. Duan, S. Sorooshian, H.V. Gupta, A.N. Rousseau, R. Turcotte, Calibration of watershed models, Water Science and Application Series, 6, 267–281, American Geophysical Union, ISBN 0-87590-355-X.

    Google Scholar 

  • Vieux, B.E., and F.G. Moreda. 2003. Ordered physics-based parameter adjustment of a distributed model. In: Ed. Q. Duan, H.V. Gupta, S. Sorooshian, A.N. Rousseau, R Turcotte, Calibration of watershed models, Water science and application, vol. 6, 267–281. American Geophysical Union.

    Google Scholar 

  • Vieux, B.E., Z. Cui, and A. Gaur. 2004. Evaluation of a physics-based distributed hydrologic model for flood forecasting. Journal of Hydrology 298(1): 155–177.

    Article  Google Scholar 

  • Vieux, B.E., Jin-Hyeog Park, and Boosik Kang. 2009. Distributed hydrologic prediction: Sensitivity to accuracy of initial soil moisture conditions and radar rainfall input. Journal of Hydrology 14(7): 671–689.

    Google Scholar 

  • White, L.W., B.E. Vieux, and D. Armand. 2002. Surface flow model: Inverse problems and predictions. Journal Advances in Water Resources 25(3): 317–324.

    Article  Google Scholar 

  • White, L., B. Vieux, D. Armand, and F.-X. Le Dimet. 2003. Estimation of optimal parameters for a surface hydrology model. Journal Advances in Water Resources 26(3): 337–348.

    Article  Google Scholar 

  • Wagener, T., H.S. Wheater, and H.V. Gupta. 2004. Rainfall-runoff modelling in gauged and ungauged catchments, 306. London: Imperial College Press.

    Book  Google Scholar 

  • Yapo, P.O., H.V. Gupta, and S. Sorooshian. 1996. Automatic calibration of conceptual rainfall-runoff models: Sensitivity to calibration data. Journal of Hydrology 181(1): 23–48.

    Article  Google Scholar 

  • Yapo, P.O., H.V. Gupta, and S. Sorooshian. 1998. Multi-objective global optimization for hydrologic models. Journal of Hydrology 204: 83–97.

    Article  Google Scholar 

  • Yucel, I., A. Onen, K.K. Yilmaz, and D.J. Gochis. 2015. Calibration and evaluation of a flood forecasting system: Utility of numerical weather prediction model, data assimilation and satellite-based rainfall. Journal of Hydrology 523: 49–66.

    Article  Google Scholar 

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Authors and Affiliations

  1. Professor Emeritus, School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, OK, USA

    Baxter E. Vieux

  2. Principal, Vieux & Associates, Inc., Norman, OK, USA

    Baxter E. Vieux

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  1. Baxter E. Vieux
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Correspondence to Baxter E. Vieux .

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Vieux, B.E. (2016). Distributed Model Calibration. In: Distributed Hydrologic Modeling Using GIS. Water Science and Technology Library, vol 74. Springer, Dordrecht. https://doi.org/10.1007/978-94-024-0930-7_10

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