Uncertainty assessment for watershed water quality modeling: A Probabilistic Collocation Method based approach

Zheng, Yi; Wang, Weiming; Feng, Han‐Zhong; Ping, Jing

doi:10.1016/j.advwatres.2011.04.016

Cited by 32 publications

(27 citation statements)

References 36 publications

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“…Some MCMC algorithms developed for hydrological modeling include the Adaptive Metropolis algorithm (AM) (Haario et al 2001), the Shuffled Complex Evolution Metropolis algorithm (SCEM-UA) (Vrugt et al 2003), the Delayed Rejection Adaptive Metropolis algorithm (DRAM) (Haario et al 2006), the Differential Evolution Monte Carlo algorithm (DEMC) (ter Braak 2006), and the Differential Evolution Adaptive Metropolis (DREAM) algorithm and its follow-ups (Laloy and Vrugt 2012;Vrugt et al 2009b). Note that the computational cost of MCMC is relatively high, and response surface-based methods like Probabilistic Collocation Method (e.g., Zheng et al 2011;Keller et al 2014;Wu et al 2014) demonstrate a much higher efficiency. Nevertheless, such techniques cannot perform data assimilation, unless they are coupled with other techniques such as MCMC (Laloy et al 2013) and Ensemble Kalman Filter (Rochoux et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Markov Chain Monte Carlo (MCMC) uncertainty analysis for watershed water quality modeling and management

Zheng

Feng

2015

Stoch Environ Res Risk Assess

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Watershed-scale water quality (WWQ) models are now widely used to support management decisionmaking. However, significant uncertainty in the model outputs remains a largely unaddressed issue. In recent years, Markov Chain Monte Carlo (MCMC), a category of formal Bayesian approaches for uncertainty analysis (UA), has become popular in the field of hydrological modeling, but its applications to WWQ modeling have been rare. This study systematically evaluated the applicability of MCMC in assessing the uncertainty of WWQ modeling, using Differential Evolution Adaptive Metropolis (DREAM (ZS) ) and SWAT as the representative MCMC algorithm and WWQ model, respectively. The nitrate pollution in Newport Bay watershed was the case study for numerical experiments. It has been concluded that the efficiency and effectiveness of a MCMC algorithm would depend on some critical designs of the UA, including: (i) how many and which model parameters to be considered as random in the MCMC analysis; (ii) where to fix the non-random model parameters; and (iii) which criteria to stop the Markov Chain. The study results also indicate that the MCMC UA has to be management-oriented, that is, management objectives should be factored into the designs of the UA, rather than be considered after the UA.

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Section: Introductionmentioning

confidence: 99%

Markov Chain Monte Carlo (MCMC) uncertainty analysis for watershed water quality modeling and management

Zheng

Feng

2015

Stoch Environ Res Risk Assess

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“…As in the spline-smoothing-based approaches, stochastic response surface methods seek to construct a response surface using results from a set of fullmodel runs for different parameter combinations; however, it does so in the stochastic space by treating uncertain model parameters as random variables. Of particular interest here is a stochastic collocation method called the probabilistic collocation method (PCM) (Li and Zhang 2007;Zheng et al 2011;Sun et al 2013). A major advantage of the PCM is that it does not require modification of an existing model (i.e., non-intrusive).…”

Section: Introductionmentioning

confidence: 99%

“…A disadvantage of the PCM is that it assumes the probability distributions of random variables are accessible, which may not always be the case. Recently, Zheng et al (2011) applied PCM to approximate the Watershed Analysis Risk Management Framework (WARMF) model that is documented in Chen et al (2005).…”

Section: Introductionmentioning

confidence: 99%

Development of multi-metamodels to support surface water quality management and decision making

Sun

Miranda

2014

Environ Earth Sci

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Watershed management and planning is a complex decision-making process, which not only involves deliberation using one or more watershed models, but also requires collaboration among multiple stakeholder groups with different ideologies, interests, and demographics. Web-based decision support tools have great potentials to enhance the transparency and participation of such decision making processes. Although physically based surface water quality models are well suited for offline water quality analyses, they are often too computationally demanding to be deployed in a web-based environment. In this work, three metamodels are developed to support decision-making activities related to surface water quality management at Arroyo Colorado Watershed, a coastal watershed located in Texas, US. All three metamodels are trained using an existing Soil and Water Assessment Tool (SWAT) model developed for the watershed. The main objectives of the metamodels are to support web-based decision support, including near-term nutrient load forecasting, online sensitivity study, and long-term load reduction planning. All metamodels either replicate or extend the capabilities of the original SWAT model and, thus, provide proxies for regulators and stakeholders to examine and discuss model results interactively. The novel, multi-metamodel methodology taken here is not only useful for supporting multigroup decision making and public education, but also provides a more effective way to leverage existing investment on watershed models.

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“…60,61 Recently it has been applied to determine watershed modeling uncertainty using variance decomposition. 62 By using a Polynomial Chaos Expansion (PCE) to approximate WARMF model output, PCM can capture the changes in output by using different orders of a single variable as well as their cross terms (cf. the Supporting Information).…”

Section: ■ Methodsmentioning

confidence: 99%

Attenuation Coefficients for Water Quality Trading

Keller

Chen

Fox

et al. 2014

Environ. Sci. Technol.

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Water quality trading has been proposed as a cost-effective approach for reducing nutrient loads through credit generation from agricultural or point source reductions sold to buyers facing costly options. We present a systematic approach to determine attenuation coefficients and their uncertainty. Using a process-based model, we determine attenuation with safety margins at many watersheds for total nitrogen (TN) and total phosphorus (TP) loads as they transport from point of load reduction to the credit buyer. TN and TP in-stream attenuation generally increases with decreasing mean river flow; smaller rivers in the modeled region of the Ohio River Basin had TN attenuation factors per km, including safety margins, of 0.19−1.6%, medium rivers of 0.14−1.2%, large rivers of 0.13−1.1%, and very large rivers of 0.04−0.42%. Attenuation in ditches transporting nutrients from farms to receiving rivers is 0.4%/km for TN, while for TP attenuation in ditches can be up to 2%/km. A 95 percentile safety margin of 30−40% for TN and 6−10% for TP, applied to the attenuation per km factors, was determined from the in-stream sensitivity of load reductions to watershed model parameters. For perspective, over 50 km a 1% per km factor would result in 50% attenuation = 2:1 trading ratio.

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Uncertainty assessment for watershed water quality modeling: A Probabilistic Collocation Method based approach

Cited by 32 publications

References 36 publications

Markov Chain Monte Carlo (MCMC) uncertainty analysis for watershed water quality modeling and management

Markov Chain Monte Carlo (MCMC) uncertainty analysis for watershed water quality modeling and management

Development of multi-metamodels to support surface water quality management and decision making

Attenuation Coefficients for Water Quality Trading

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