Parameter estimation and uncertainty analysis for a watershed model

Gallagher, Marcus; Doherty, John

doi:10.1016/j.envsoft.2006.06.007

Cited by 185 publications

(139 citation statements)

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“…MCMC simulation cycle for deriving posterior parameter distributions of VIC parameters using the Metropolis-Hastings algorithm. Kuczera and Parent [1998], Bates and Campbell [2001], Gallagher and Doherty [2007], among others.…”

Section: Mcmc Methodsmentioning

confidence: 99%

“…This approach assumes the existence of measurement noise and acknowledges that different parameter estimates would have been obtained for different realizations of measurement noise [Gallagher and Doherty, 2007]. In contrast, the Bayesian approach assumes model parameters are random variables with probability distributions.…”

Section: Bayesian Approachmentioning

confidence: 99%

“…Formulation of model error structure and parameter likelihood are primary to parameter/predictive uncertainty analysis. It may thus be important to achieve homoscedasticity and serial uncorrelation through appropriate transformations to the data [Gallagher and Doherty, 2007]. Kuczera [1983] found that if correlation of residuals is not accounted for in calculating a parameter or predictive confidence interval, it may lead to underestimation of the width of the interval.…”

Section: Raje and Krishnan: Bayesian Uncertainty Modeling In Macroscamentioning

confidence: 99%

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Bayesian parameter uncertainty modeling in a macroscale hydrologic model and its impact on Indian river basin hydrology under climate change

Raje

Krishnan

2012

Water Resources Research

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[1] Macroscale hydrologic models (MHMs) were developed to study changes in land surface hydrology due to changing climate over large domains, such as continents or large river basins. However, there are many sources of uncertainty introduced in MHM hydrological simulation, such as model structure error, ineffective model parameters, and low-accuracy model input or validation data. It is hence important to model the uncertainty arising in projection results from an MHM. The objective of this study is to present a Bayesian statistical inference framework for parameter uncertainty modeling of a macroscale hydrologic model. The Bayesian approach implemented using Markov Chain Monte Carlo (MCMC) methods is used in this study to model uncertainty arising from calibration parameters of the Variable Infiltration Capacity (VIC) MHM. The study examines large-scale hydrologic impacts for Indian river basins and changes in discharges for three major river basins with distinct climatic and geographic characteristics, under climate change. Observed/reanalysis meteorological variables such as precipitation, temperature and wind speed are used to drive the VIC macroscale hydrologic model. An objective function describing the fit between observed and simulated discharges at four stations is used to compute the likelihood of the parameters. An MCMC approach using the Metropolis-Hastings algorithm is used to update probability distributions of the parameters. For future hydrologic simulations, bias-corrected GCM projections of climatic variables are used. The posterior distributions of VIC parameters are used for projection of 5th and 95th percentile discharge statistics at four stations, namely, Farakka, Jamtara, Garudeshwar, and Vijayawada for an ensemble of three GCMs and three scenarios, for two time slices. Spatial differences in uncertainty projections of runoff and evapotranspiration for years 2056-2065 for the a1b scenario at the 5th and 95th percentile levels are also projected. Results from the study show increased mean monthly discharges for Farakka and Vijayawada stations, and increased low, mid and high duration flows at Farakka, Jamtara and Vijayawada for the future. However, it is seen that uncertainty introduced due to choice of GCM, is larger than that due to parameter uncertainty for the VIC MHM. The largest effects of runoff predictive uncertainty due to uncertainty in VIC parameters are seen in the Himalayan foothills belt, and the high-precipitation Northeast region of the country. It is demonstrated through the study that it is relevant and feasible to provide Bayesian uncertainty estimates for macroscale models in projection of large-scale and regional hydrologic impacts.Citation: Raje, D., and R. Krishnan (2012), Bayesian parameter uncertainty modeling in a macroscale hydrologic model and its impact on Indian river basin hydrology under climate change, Water Resour. Res., 48, W08522,

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Section: Mcmc Methodsmentioning

confidence: 99%

Section: Bayesian Approachmentioning

confidence: 99%

Section: Raje and Krishnan: Bayesian Uncertainty Modeling In Macroscamentioning

confidence: 99%

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Bayesian parameter uncertainty modeling in a macroscale hydrologic model and its impact on Indian river basin hydrology under climate change

Raje

Krishnan

2012

Water Resources Research

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“…Details are available at http://www.epa.gov/ceampubl/swater/hspf/index.htm. By supporting conventional and toxic organic pollutants from both point sources and diffuse sources, HSPF is one of few comprehensive watershed hydrology and water quality models that allow the integrated simulation of land and soil contaminant runoff processes with instream hydraulic, water temperature, sediment transport, nutrients, and sediment-chemical interactions (Gallagher and Doherty, 2007;Ribarova et al, 2008).…”

Section: Hydrological Simulation Program -Fortran Was Developed By Usmentioning

confidence: 99%

A Review of Modelling Tools for Implementation of the EU Water Framework Directive in Handling Diffuse Water Pollution

Yang

Wang

2009

Water Resour Manage

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A numerical catchment-scale model capable of simulating diffuse water pollution is necessary in sustainable environmental management for better implementation of the EU Water Framework Directive. This paper provides critical reviews of most popular and free models for diffuse water modelling, with detailed sources and application potential. Based upon these reviews, further work of selecting and testing the HSPF model was carried out, with a case study in the Upper Bann Catchment, NorthernIreland. The calibrated and validated HSPF model can well represent the characteristics of surface water quantity and quality. Climate change scenario evaluation in five years showed that when the annual mean temperature increase 3°C the mean yearly total runoff volume will decrease by 11.1% and the mean daily river flow 11.4%. If 20% crop and pasture land is converted into forest land in the study area, the mean river concentration of nitrate, nitrite, NH 4 and PO 4 in five years will decrease by 19.4%, 33.3%, 31.3% and 31.3% respectively. When applying filter strip method in 80% crop and pasture land in the area, the reduction of the mean concentration of nitrate, nitrite, NH 4 and PO 4 in five years will be 15.3%, 33.3%, 31.3%, and 5.6% respectively. This Software availabilitySoftware name/source: all reviewed software are mentioned in the text.

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“…The PEST program for example, one of the most commonly used algorithms set for optimization (Doherty, 2003), adjusts model parameters until the fit between model outputs and observations is optimized in the weighted least squares sense. For example, PEST has been recently used with success in order to optimize parameter estimation in hydrological studies (Gallagher and Doherty, 2007;Tischler et al, 2007). PEST is a nonlinear estimator using the Gauss-Marquardt-Levenberg method, needing fewer runs than most of the other estimation methods.…”

Section: Introductionmentioning

confidence: 99%

Parameterization of a process-based tree-growth model: Comparison of optimization, MCMC and Particle Filtering algorithms

Gaucherel

Campillo

Misson

et al. 2008

Environmental Modelling & Software

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a b s t r a c tFinely tuned process-based tree-growth models are of considerable help in understanding the variations of biomass increments measured in the dendrochronological series. Using site and species parameters, as well as daily climate variables, the MAIDEN model computes the water balance at ecosystem level and the daily increment of carbon storage in the stem through photosynthesis processes to reproduce the structure of the tree-ring series. In this paper, we use three techniques to calibrate this model with Pinus halepensis data sampled in the Mediterranean part of France: a standard optimization (PEST), Monte Carlo Markov Chains (MCMC) and Particle Filtering (PF). Contrary to PEST, which tries to find an optimum fit (giving the lowest error between observations and simulations), the principle of MCMC and PF is to walk, from a priori distributions, in the parameter space according to particular statistical rules to compute each parameter distribution. The PEST and MCMC calibrations of our dendrochronological series lead to rather similar adjustments between simulations and observations. PF and MCMC calibrations give different parameter distributions, showing how complementary are these methods, with a better fit for MCMC. Yet, independent validations over 11 independent meteorological years show a higher efficiency of the recent PF method over the others.

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Parameter estimation and uncertainty analysis for a watershed model

Cited by 185 publications

References 31 publications

Bayesian parameter uncertainty modeling in a macroscale hydrologic model and its impact on Indian river basin hydrology under climate change

Bayesian parameter uncertainty modeling in a macroscale hydrologic model and its impact on Indian river basin hydrology under climate change

A Review of Modelling Tools for Implementation of the EU Water Framework Directive in Handling Diffuse Water Pollution

Parameterization of a process-based tree-growth model: Comparison of optimization, MCMC and Particle Filtering algorithms

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