Prediction uncertainty in an ecological model of the oosterschelde estuary

Klepper, O.; Schölten, H.; Kamer, J. P. G. De Van

doi:10.1002/for.3980100111

Cited by 65 publications

(36 citation statements)

References 15 publications

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“…However, a single optimal parameter set may not exist and the uncertainties associated with the optimal parameter sets could be large (e.g., Gupta et al, 1998;Klepper et al, 1991;Van Straten and Keesman, 1991;Beven and Binley, 1992;Yapo et al, 1996). Moreover, a model with the optimal parameter set may provide the best fit over the calibration period, but multiple parameter sets may result in comparable misfits and are likely to be the "true" values with certain probability, and therefore are considered to be acceptable or equally probable parameter sets (Van Straten and Keesman, 1991;Klepper et al, 1991). Stochastic approaches can address these limitations by describing the input parameter and output uncertainties in a statistical manner.…”

Section: Introductionmentioning

confidence: 99%

Inverse modeling of hydrologic parameters using surface flux and runoff observations in the Community Land Model

Sun

Hou

Huang

et al. 2013

Hydrol. Earth Syst. Sci.

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Abstract. This study demonstrates the possibility of inverting hydrologic parameters using surface flux and runoff observations in version 4 of the Community Land Model (CLM4). Previous studies showed that surface flux and runoff calculations are sensitive to major hydrologic parameters in CLM4 over different watersheds, and illustrated the necessity and possibility of parameter calibration. Both deterministic least-square fitting and stochastic Markov-chain Monte Carlo (MCMC)-Bayesian inversion approaches are evaluated by applying them to CLM4 at selected sites with different climate and soil conditions. The unknowns to be estimated include surface and subsurface runoff generation parameters and vadose zone soil water parameters. We find that using model parameters calibrated by the samplingbased stochastic inversion approaches provides significant improvements in the model simulations compared to using default CLM4 parameter values, and that as more information comes in, the predictive intervals (ranges of posterior distributions) of the calibrated parameters become narrower. In general, parameters that are identified to be significant through sensitivity analyses and statistical tests are better calibrated than those with weak or nonlinear impacts on flux or runoff observations. Temporal resolution of observations has larger impacts on the results of inverse modeling using heat flux data than runoff data. Soil and vegetation cover have important impacts on parameter sensitivities, leading to different patterns of posterior distributions of parameters at different sites. Overall, the MCMC-Bayesian inversion approach effectively and reliably improves the simulation of CLM under different climates and environmental conditions. Bayesian model averaging of the posterior estimates with different reference acceptance probabilities can smooth the posterior distribution and provide more reliable parameter estimates, but at the expense of wider uncertainty bounds.

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Section: Introductionmentioning

confidence: 99%

Inverse modeling of hydrologic parameters using surface flux and runoff observations in the Community Land Model

Sun

Hou

Huang

et al. 2013

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…limitation is the woeful inadequacy of current strategies in the face of the emerging generation of multi-input-output hydrologic models [e.g., Beven and Kirkby, 1979;Kuczera, 1982Kuczera, , 1983a Keesman, 1990; van Straten and Keesman, 1991], and the prediction uncertainty method (PU) [see, e.g., Klepper et al, 1991]. All of these approaches are directly or indirectly related to the generalized sensitivity analysis (GSA) method of G. M. Hornberger and colleagues at the University of Virginia [see Spear and Hornberger, 1980].…”

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confidence: 99%

Toward improved calibration of hydrologic models: Multiple and noncommensurable measures of information

Gupta¹,

Sorooshian²,

Yapo³

1998

Water Resources Research

1,291

1,045

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Abstract. Several contributions to the hydrological literature have brought into question the continued usefulness of the classical paradigm for hydrologic model calibration. With the growing popularity of sophisticated "physically based" watershed models (e.g., landsurface hydrology and hydrochemical models) the complexity of the calibration problem has been multiplied many fold. We disagree with the seemingly widespread conviction that the model calibration problem will simply disappear with the availability of more and better field measurements. This paper suggests that the emergence of a new and more powerful model calibration paradigm must include recognition of the inherent multiobjective nature of the problem and must explicitly recognize the role of model error. The results of our preliminary studies are presented. Through an illustrative case study we show that the multiobjective approach is not only practical and relatively simple to implement but can also provide useful information about the limitations of a model.

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“…Monte Carlo Set Membership procedure [Keesman, 1990;van Strafen and Keesman, 1991], the Prediction Uncertainty method [Klepper et al, 1991], and the Multi Objective Generalized Sensitivity Analysis [Bastidas et al, 1997] are all based on GSA. None of above studies addressed the uncertainty of distributed precipitation inputs to a hydrologic model.…”

Section: Figurementioning

confidence: 99%

Propagation of Radar-Rainfall Uncertainty in Runoff Predictions

Ogden¹,

Sharif²

2001

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Public Reporting burden for this collection of mfcrtnanon a «tunned u average I hour per response, including Ihenme lor reviewing instrucnoro. searching cuaing dan sources, gathering and maintaining Ihe data needed and completing and reviewing Ihe collection of inlbrmarioa Send cornment regarding this burden estimates or any orher aspect oflhiscollecnonof informal»«, including suggestions for reducing this burden, lo Washington Headquarters Services. Directorate for information Operanons and Reports. 1215 SUPPLEMENTARY NOTESThe views, opinions and/or findings contained in this Army position, policy or decision, unless so designated by ABSTRACT (Maximum 200 words)This final report dociments the results obtained through the final phase of the research study. It consists of two papers that have been submitted for publication based on the research. THe objective of this project is to quantitatively evaluate the worth of radar-rainfall estimates for physicallybased hydrologic modeling. These two papers contain valuable results. To sunnarize, our study has shown that the physical relationship between radar reflectivity and rainfall rate begins to disappear at a range of approximately 60 km from the radar site for radars with approximately 1 degree beam width. At farther ranges, radar returns are significantly affected by the horizontal and vertical gradients of cloud water. This result was verified through careful testing using a simulation methodology wherein convective rainfall is simulated using the Advanced Regional Prediction Model (ARPS), a radar simulator, and the two-dimensional hydrologic model CASC2D. Techniques to correct runoff predictions for likely errors using a Bayesian approach are suggested. The primary advantage of radar observations of precipitation compared with traditional rain gauge measurements is their high spatial and temporal resolution and large areal coverage. Unfortunately, radar data require vigorous quality control before being converted into precipitation products that can be used as input to hydrologic models. In this study, a physically-based atmospheric model of convective rainfall is coupled with an active microwave radiative transfer model to simulate radar observation of thunderstorms. Radar observations of these storms are generated and used to evaluate the propagation of radar-rainfall errors through distributed hydrologic simulations. This physically-based methodology allows one to directly examine the impact of radar-rainfall estimation errors on land-surface hydrologic predictions and to avoid the limitations imposed by the use of rain gauge data. Results indicate that the geometry of the radar beam and coordinate transformations, due to radar-watershed-storm orientation, have an effect on radar-rainfall estimation and runoff prediction errors. In addition to uncertainty in the radar reflectivity vs. rainfall intensity relationship, there are significant range-dependent and orientation-related radar-rainfall estimation errors that should be Hatim Osman Sharif-University ...

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Prediction uncertainty in an ecological model of the oosterschelde estuary

Cited by 65 publications

References 15 publications

Inverse modeling of hydrologic parameters using surface flux and runoff observations in the Community Land Model

Inverse modeling of hydrologic parameters using surface flux and runoff observations in the Community Land Model

Toward improved calibration of hydrologic models: Multiple and noncommensurable measures of information

Propagation of Radar-Rainfall Uncertainty in Runoff Predictions

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