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词条 Swat-CUP
释义

  1. Targets

  2. User Interface

  3. Users

  4. References

  5. External links

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SWAT-CUP (SWAT Calibration and Uncertainty Procedures) is a program designed to integrate various calibration/uncertainty analysis programs for SWAT (Soil & Water Assessment Tool) using the same interface. Currently the program can run SUFI2 (Abbaspour et al., 2007), GLUE (Beven and Binley, 1992), and ParaSol (van Griensven and Meixner, 2006). To create a project, the program guides the user through the input files necessary for running a calibration program. Each SWAT-CUP project contains one calibration method and allows user to run the procedure many times until convergence is reached. User can save calibration iterations in the iteration history for later use. Also we have made it possible to create charts of observed and simulated data and the predicted uncertainty about them.

Targets

  1. Integrate various calibration/uncertainty analysis procedures for SWAT in one user interface and visualize the results.
  2. Make the calibrating procedure easy to use for students and professional users of SWAT.
  3. Make the learning of the programs easier for beginners.
  4. Provide a faster way to do the time consuming calibration operations and standardize calibration steps.
  5. Add extra functionalities to calibration operations such as creating graph of calibrated results and data comparison.

User Interface

SWAT-CUP uses an advanced user-friendly interface, similar to the Microsoft Office 2007, with the same UI features. Everything has the same standard as Microsoft products, so all users can easily learn and use the program.

Users

As SWAT-CUP is related to SWAT software and has extra functionalities for calibration and uncertainty analysis, SWAT-CUP would be useful for all SWAT users.

SWAT-CUP2 was made possible with contributions from:

  • Mahdi Vejdani and Sohail Haghighat of Neprash Company who wrote the SWAT-CUP interface
  • Raghvan Srinivasan of Texas A&M University who provided financial support
  • Jing Yang of Eawag who initially linked the optimization procedure to SWAT and created the MCMC algorithm as part of his PhD thesis with supervision of Peter Reichert and Karim C.Abbaspour.

References

  • Abbaspour, K.C., J. Yang, I. Maximov,., R. Siber, K. Bogner, J. Mieleitner, J. Zobrist, R.Srinivasan. 2007. Modelling hydrology and water quality in the pre-alpine/alpine Thur watershed using SWAT. Journal of Hydrology, 333:413-430.
  • Abbaspour, K.C., 2005. Calibration of hydrologic models: when is a model calibrated In Zerger, A. and Argent, R.M. (eds) MODSIM 2005 International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, December 2005, pp. 2449–12455. {{ISBN|0-9758400-2-9}}. http://www.mssanz.org.au/modsim05/papers/abbaspour.pdf
  • Abbaspour, K.C., Johnson, A., van Genuchten, M.Th, 2004. Estimating uncertain flow and transport parameters using a sequential uncertainty fitting procedure. Vadose Zone Journal 3(4), 1340-1352.
  • Abbaspour, K. C., R. Schulin, M. Th. Van Genuchten, 2001. Estimation of unsaturated soil hydraulic parameters using ant colony optimization. Advances in Water Resources,24: 827-841.
  • Abbaspour, K. C., M. Sonnleitner, and R. Schulin. 1999. Uncertainty in Estimation of Soil Hydraulic Parameters by Inverse Modeling: Example Lysimeter Experiments. Soil Sci. Soc. of Am. J., 63: 501-509.
  • Abbaspour, K. C., M. Th. van Genuchten, R. Schulin, and E. Schläppi. 1997. A sequential uncertainty domain inverse procedure for estimating subsurface flow and transport parameters. Water Resour. Res., v. 33, no. 8., pp. 1879–1892.
  • Arnold, J.G., Srinivasan R., Muttiah R.S., Williams J.R., 1998. Large area hydrologic modeling and assessment - Part 1: Model development. Journal of the American Water Resources Association 34(1), 73-89.
  • Bard, 1974. Non Linear Parameter Estimation. Academic Press, New York N.Y.
  • [George E. P. Box|Box, G.E.P.]; G.C.Tiao (1973) Bayesian Inference in Statistical Analysis, Addison-Wesley- Longman, Reading, Mass
  • Beven, K. and Freer, J., 2001. Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology. Journal of Hydrology, 249(1-4): 11-29.
  • Beven, K. and Binley, A., 1992. The Future of Distributed Models - Model Calibration and Uncertainty Prediction. Hydrological Processes, 6(3): 279-298.
  • Duan, Q., Global Optimization for Watershed Model Calibration, in Calibration of Watershed Models, edited by Q. Duan, H. V. Gupta, S. Sorooshian, A. N. Rousseau, and R. Turcotte, pp. 89–104, AGU, Washington, DC, 2003.
  • Duan, Q., V. K. Gupta, and S. Sorooshian, Effective and efficient global optimization for conceptual rainfall-runoff models, Water. Resourc. Res., 28:1015-1031, 1992.
  • Duan, Q., S. Sorooshian, H. V. Gupta, A. N. Rousseau, and R. Turcotte, Advances in Calibration of Watershed Models, AGU, Washington, DC, 2003.
  • Eckhardt K and J.G. Arnold. Automatic calibration of a distributed catchment model., J.Hydrol., 251: 103-109. 2001.
  • Faramarzi, M., K.C. Abbaspour, H. Yang, R. Schulin. 2008. Application of SWAT to quantify internal renewable water resources in Iran. Hydrological Sciences. {{doi|10.1002/hyp.7160}}.
  • Gelman, S., Carlin, J.B., Stren, H.S., Rubin, D.B., 1995. Bayesian Data Analysis, Chapman and Hall, New York, USA.
  • Gupta, H. V., S. Sorooshian, and P. O. Yapo, Toward improved calibration of hydrologic models: multiple and noncommensurable measures of information, Water. Resourc.Res., 34:751-763, 1998.
  • Holland, J.H. Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor, MI, 183 p, 975, 1975.
  • Hornberger, G.M. and Spear, R.C., 1981. An Approach to the Preliminary-Analysis of Environmental Systems. Journal of Environmental Management, 12(1): 7-18.
  • Kuczera, G., Parent, E., 1998. Monte Carlo assessment of parameter uncertainty in conceptual catchment models: the Metropolis algorithm. Journal of Hydrology,211(1-4): 69-85.
  • Legates, D. R. and G. J. McCabe, Evaluating the use of "goodness-of-fit" measures in hydrologic and hydroclimatic model validation, Water. Resourc. Res., 35:233-241,1999.
  • Madsen, H., Parameter estimation in distributed hydrological catchment modelling using automatic calibration with multiple objectives. Advances in water resources, 26, 205-216, 2003.
  • Marshall, L., D. Nott, and A. Sharma 2004. A comparative study of Markov chain Monte Carlo methods for conceptual rainfall-runoff modeling. Water Resources Research, 40, W02501, {{doi|10.1029/2003WR002378}}.
  • McKay, M.D., Beckman, R. J., Conover, W.J., 1979. A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics. 21, 239-245.
  • Nash, J. E., J. V. Sutcliffe, 1970. River Flow Forecasting through Conceptual Models 1. A Discussion of Principles. Journal of Hydrology 10(3), 282-290.
  • Nelder, J.A., R. A. Mead, simplex method for function minimization, Computer Journal, 7,308-313, 1965.
  • Press, W.H., Flannery, B.P., Teukolsky, S.A., Vetterling, W.T., 1992. Numerical Recipe, The Art of Scientific Computation. 2nd ed. Cambridge University Press, Cambridge, Great Britain.
  • Romanowicz, R. J., Beven K., and Tawn J. 1994. Evaluation of Predictive Uncertainty in Nonlinear Hydrological Models Using a Bayesian Approach. In: Statistics for the Environment 2, Water Related Issues, eds V. Barnett and K. F. Turkman, 297-315, Wiley, Chichester.
  • Schuol, J., K.C. Abbaspour, R. Srinivasan, and H.Yang. 2008a. Modelling Blue and Green Water Availability in Africa at monthly intervals and subbasin level. Water Resources Research. VOL. 44, W07406, {{doi|10.1029/2007WR006609}}.
  • Schuol, J., Abbaspour, KC., Sarinivasan, R., Yang, H. 2008b. Estimation of freshwater availability in the West African Sub-continent using the SWAT hydrologic model. Journal of Hydroloy. 352(1-2):30-49.
  • Van Griensven A. and W. Bauwens. 2003. Multi-objective auto-calibration for semidistributed water quality models, Water. Resourc. Res. 39 (12): Art. No. 16 December 1348.
  • Van Griensven, A., Meixner, T., 2006. Methods to quantify and identify the sources of uncertainty for river basin water quality models. Water Science and Technology, 53(1): 51-59.
  • Vrugt, J. A., H. V. Gupta, W. Bouten, and S. Sorooshian. 2003. A shuffled Complex Evolution Metropolis Algorithm for Estimating Posterior Distribution of Watershed Model Parameters, in Calibration of Watershed Models, ed. Q. Duan, S. Sorooshian,
  • H. V. Gupta, A. N. Rousseau, and R. Turcotte, AGU Washington DC, {{doi|10.1029/006WS07}}.
  • Yang, J., Reichert, P., Abbaspour, K.C., Yang, H., 2007. Hydrological Modelling of the Chaohe Basin in China: Statistical Model Formulation and Bayesian Inference. Journal of Hydrology, 340: 167-182.
  • Yang, J., Abbaspour K. C., Reichert P., and Yang H. 2008. Comparing uncertainty analysis techniques for a SWAT application to Chaohe Basin in China. In review. Journal of Hydrology. 358(1-2):1-23.
  • Yapo, P. O., Gupta, H.V., Sorooshian, S., 1998. Multi-

External links

  •  
  • EAWAG.CH
  • NeprashGostar.com
  • Neprash.com
  • Texas University
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