Comparing uncertainty analysis techniques for a SWAT application to the Chaohe Basin in China
Distributed watershed models are increasingly being used to support decisions about alternative management strategies in the areas of land use change, climate change, water allocation, and pollution control. For this reason it is important that these models pass through a careful calibration and unc...
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Published in | Journal of hydrology (Amsterdam) Vol. 358; no. 1; pp. 1 - 23 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
Amsterdam
Elsevier B.V
30.08.2008
[Amsterdam; New York]: Elsevier Elsevier Science |
Subjects | |
Online Access | Get full text |
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Abstract | Distributed watershed models are increasingly being used to support decisions about alternative management strategies in the areas of land use change, climate change, water allocation, and pollution control. For this reason it is important that these models pass through a careful calibration and uncertainty analysis. To fulfil this demand, in recent years, scientists have come up with various uncertainty analysis techniques for watershed models. To determine the differences and similarities of these techniques we compared five uncertainty analysis procedures: Generalized Likelihood Uncertainty Estimation (GLUE), Parameter Solution (ParaSol), Sequential Uncertainty FItting algorithm (SUFI-2), and a Bayesian framework implemented using Markov chain Monte Carlo (MCMC) and Importance Sampling (IS) techniques. As these techniques are different in their philosophies and leave the user some freedom in formulating the generalized likelihood measure, objective function, or likelihood function, a literal comparison between these techniques is not possible. As there is a small spectrum of different applications in hydrology for the first three techniques, we made this choice according to their typical use in hydrology. For Bayesian inference, we used a recently developed likelihood function that does not obviously violate the statistical assumptions, namely a continuous-time autoregressive error model. We implemented all these techniques for the soil and water assessment tool (SWAT) and applied them to the Chaohe Basin in China. We compared the results with respect to the posterior parameter distributions, performances of their best estimates, prediction uncertainty, conceptual bases, computational efficiency, and difficulty of implementation. The comparison results for these categories are listed and the advantages and disadvantages are analyzed. From the point of view of the authors, if computationally feasible, Bayesian-based approaches are most recommendable because of their solid conceptual basis, but construction and test of the likelihood function requires critical attention. |
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AbstractList | Distributed watershed models are increasingly being used to support decisions about alternative management strategies in the areas of land use change, climate change, water allocation, and pollution control. For this reason it is important that these models pass through a careful calibration and uncertainty analysis. To fulfil this demand, in recent years, scientists have come up with various uncertainty analysis techniques for watershed models. To determine the differences and similarities of these techniques we compared five uncertainty analysis procedures: Generalized Likelihood Uncertainty Estimation (GLUE), Parameter Solution (ParaSol), Sequential Uncertainty FItting algorithm (SUFI-2), and a Bayesian framework implemented using Markov chain Monte Carlo (MCMC) and Importance Sampling (IS) techniques. As these techniques are different in their philosophies and leave the user some freedom in formulating the generalized likelihood measure, objective function, or likelihood function, a literal comparison between these techniques is not possible. As there is a small spectrum of different applications in hydrology for the first three techniques, we made this choice according to their typical use in hydrology. For Bayesian inference, we used a recently developed likelihood function that does not obviously violate the statistical assumptions, namely a continuous-time autoregressive error model. We implemented all these techniques for the soil and water assessment tool (SWAT) and applied them to the Chaohe Basin in China. We compared the results with respect to the posterior parameter distributions, performances of their best estimates, prediction uncertainty, conceptual bases, computational efficiency, and difficulty of implementation. The comparison results for these categories are listed and the advantages and disadvantages are analyzed. From the point of view of the authors, if computationally feasible, Bayesian-based approaches are most recommendable because of their solid conceptual basis, but construction and test of the likelihood function requires critical attention. |
Author | Xia, Jun Yang, Hong Yang, Jing Abbaspour, K.C. Reichert, Peter |
Author_xml | – sequence: 1 givenname: Jing surname: Yang fullname: Yang, Jing email: jing.yang@eawag.ch organization: Eawag: Swiss Federal Institute of Aquatic Science and Technology, Ueberlandstr. 133, P.O. Box 611, 8600 Duebendorf, Switzerland – sequence: 2 givenname: Peter surname: Reichert fullname: Reichert, Peter organization: Eawag: Swiss Federal Institute of Aquatic Science and Technology, Ueberlandstr. 133, P.O. Box 611, 8600 Duebendorf, Switzerland – sequence: 3 givenname: K.C. surname: Abbaspour fullname: Abbaspour, K.C. organization: Eawag: Swiss Federal Institute of Aquatic Science and Technology, Ueberlandstr. 133, P.O. Box 611, 8600 Duebendorf, Switzerland – sequence: 4 givenname: Jun surname: Xia fullname: Xia, Jun organization: Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographical Sciences and Natural Resources Research, CAS, Datun Road 11A, 100101 Beijing, China – sequence: 5 givenname: Hong surname: Yang fullname: Yang, Hong organization: Eawag: Swiss Federal Institute of Aquatic Science and Technology, Ueberlandstr. 133, P.O. Box 611, 8600 Duebendorf, Switzerland |
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SubjectTerms | Bayesian inference Bayesian theory Earth sciences Earth, ocean, space Engineering and environment geology. Geothermics Exact sciences and technology generalized likelihood uncertainty estimation GLUE hydrologic models Hydrology Hydrology. Hydrogeology importance sampling Markov processes mathematical models Monte Carlo method parameter solution ParaSol Pollution, environment geology sequential uncertainty fitting algorithm Soil and Water Assessment Tool model statistical analysis SUFI-2 Uncertainty analysis watershed hydrology Watershed modeling |
Title | Comparing uncertainty analysis techniques for a SWAT application to the Chaohe Basin in China |
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