On constraining the predictions of a distributed model: The incorporation of fuzzy estimates of saturated areas into the calibration process

Franks, Stewart W.; Gineste, Philippe; Beven, Keith; Mérot, Philippe

doi:10.1029/97wr03041

Cited by 221 publications

(186 citation statements)

References 22 publications

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“…Their GLUE procedure has been applied to many cases (e.g. [10,38,39,124]) including one where information on saturated area was incorporated with more common runoff estimation to constrain predictive uncertainty [37]. The information on saturated area was obtained from a combined analysis of SAR data and terrain modelling, but in this case, just the total area saturated (rather than information on the spatial pattern) was used.…”

Section: Calibration and Testing--a Role For Pattern Comparison Methodsmentioning

confidence: 99%

“…The pioneering work of Dunne [29,30] mapped saturated areas in the field and in very recent times, their utility has been ''re-discovered'', with a number of mapping projects occurring (e.g. [37,61]). These ideas have been extended by Peschke et al [82] who mapped the type of runoff generation mechanism that occurs for a given catchment state, based on many years of field investigation in the 4.6 km 2 Wernersbach catchment in Germany.…”

Section: Binary Patternsmentioning

confidence: 99%

See 1 more Smart Citation

Advances in the use of observed spatial patterns of catchment hydrological response

Grayson

Blöschl

Western

et al. 2002

Advances in Water Resources

215

181

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Section: Calibration and Testing--a Role For Pattern Comparison Methodsmentioning

confidence: 99%

Section: Binary Patternsmentioning

confidence: 99%

Advances in the use of observed spatial patterns of catchment hydrological response

Grayson

Blöschl

Western

et al. 2002

Advances in Water Resources

215

181

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“…The generalized likelihood uncertainty estimate (GLUE) (Beven and Binley, 1992;Franks et al, 1998;Shulz et al, 1999) method was used for the crop model optimization to determine the best set of parameters from such a number of samples. The GLUE method is a Bayesian method that assumes that in large models with many parameters, there is no exact inverse solution.…”

Section: Parameter Estimationmentioning

confidence: 99%

Process-based simple model for simulating sugarcane growth and production

Marin

Jones

2014

Sci. agric. (Piracicaba, Braz.)

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Dynamic simulation models can increase research effi ciency and improve risk management of agriculture. Crop models are still little used for sugarcane (Saccharum spp.) because the lack of understanding of their capabilities and limitations, lack of experience in calibrating them, diffi culties in evaluating and using models, and a general lack of model credibility. This paper describes the biophysics and shows a statistical evaluation of a simple sugarcane processbased model coupled with a routine for model calibration. Classical crop model approaches were used as a framework for this model, and fi tted algorithms for simulating sucrose accumulation and leaf development driven by a source-sink approach were proposed. The model was evaluated using data from fi ve growing seasons at four locations in Brazil, where crops received adequate nutrients and good weed control. Thirteen of the 27 parameters were optimized using a Generalized Likelihood Uncertainty Estimation algorithm using the leave-one-out cross-validation technique. Model predictions were evaluated using measured data of leaf area index, stalk and aerial dry mass, and sucrose content, using bias, root mean squared error, modeling effi ciency, correlation coeffi cient and agreement index. The model well simulated the sugarcane crop in Southern Brazil, using the parameterization reported here. Predictions were best for stalk dry mass, followed by leaf area index and then sucrose content in stalk fresh mass. IntroductionDynamic simulation models can increase research effi ciency by allowing the analyst to search for strategies and analyze system performance, improve risk management, and interpret fi eld experiments that deal with crop responses to soil, management, genetic or environmental factors (Keating et al., 1999).Sugarcane (Saccharum spp.) is of major social and economic importance in Brazil. Worldwide, there have been several models developed specifi cally for sugarcane crop simulation (Pereira and Machado, 1986;Jones et al., 1989;Langellier and Martine, 2007;Keating et al., 1999;Thorburn et al., 2005;Inman-Bamber, 1991;Singels et al., 2008).Some of the physiological development and growth parameters that appear in the functions vary among sugarcane cultivars, meaning that they have to be estimated from data in order to predict growth and yield. Some of the parameters cannot be measured directly in typical experiments; instead, they have to be estimated based on data that are measured in experiments. Recent literature contains relatively little work on parameter estimation for crop models (Ahuja and Ma, 2011; Makowski et al., 2006). Makowski et al. (2006) point out the importance of raising the quality of calibration in crop models with automatic procedures for parameter adjustment. This would help ensure that the data are always used appropriately and in the same way for parameter estimation (Wallach et al., 2001), but such procedures are not available for direct use with existing sugarcane models.A new sugarcane model was developed ...

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“…GLUE has therefore found extensive application in the assessment of predictive uncertainty of many hydrologic variables like streamflow, flood inundation, ground water flow, land surface fluxes, etc. (Schulz and Beven, 2003;Christaens and Feyen, 2002;Beven and Freer, 2001;Schulz et al, 2001;Romanowicz and Beven, 1998;Franks et al, 1998;Franks and Beven, 1997;Freer et al, 1996; among many others). Recently, the GLUE technique has also proved to be a powerful tool in understanding the implications of remotely sensed rainfall error adjustment on flood prediction uncertainty (Hossain et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

“…Thus, GLUE application for Bayesian estimation of uncertainty in land surface-atmosphere flux predictions has so far been limited to relatively simpler conceptualizations of soil-vegetation-atmosphere transfer (SVAT) schemes (e.g. Schulz and Beven, 2003;Schulz et al, 2001;Franks et al, 1998;Franks and Beven, 1997). A more realistic Bayesian assessment of uncertainty requires the application of GLUE to physically complex operational LSMs such as Common Land Model (CLM; Dai et al, 2003), NOAH-LSM (Pan and Mahrt, 1987), BATS (Dickinson et al, 1986) or SiB (Sellers et al, 1986).…”

Section: Introductionmentioning

confidence: 99%

A non-linear and stochastic response surface method for Bayesian estimation of uncertainty in soil moisture simulation from a land surface model

Hossain

Anagnostou

Lee

2004

Nonlin. Processes Geophys.

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Abstract. This study presents a simple and efficient scheme for Bayesian estimation of uncertainty in soil moisture simulation by a Land Surface Model (LSM). The scheme is assessed within a Monte Carlo (MC) simulation framework based on the Generalized Likelihood Uncertainty Estimation (GLUE) methodology. A primary limitation of using the GLUE method is the prohibitive computational burden imposed by uniform random sampling of the model's parameter distributions. Sampling is improved in the proposed scheme by stochastic modeling of the parameters' response surface that recognizes the non-linear deterministic behavior between soil moisture and land surface parameters. Uncertainty in soil moisture simulation (model output) is approximated through a Hermite polynomial chaos expansion of normal random variables that represent the model's parameter (model input) uncertainty. The unknown coefficients of the polynomial are calculated using limited number of model simulation runs. The calibrated polynomial is then used as a fast-running proxy to the slower-running LSM to predict the degree of representativeness of a randomly sampled model parameter set. An evaluation of the scheme's efficiency in sampling is made through comparison with the fully random MC sampling (the norm for GLUE) and the nearest-neighborhood sampling technique. The scheme was able to reduce computational burden of random MC sampling for GLUE in the ranges of 10%-70%. The scheme was also found to be about 10% more efficient than the nearestneighborhood sampling method in predicting a sampled parameter set's degree of representativeness. The GLUE based on the proposed sampling scheme did not alter the essential features of the uncertainty structure in soil moisture simulation. The scheme can potentially make GLUE uncertainty estimation for any LSM more efficient as it does not impose any additional structural or distributional assumptions.

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On constraining the predictions of a distributed model: The incorporation of fuzzy estimates of saturated areas into the calibration process

Cited by 221 publications

References 22 publications

Advances in the use of observed spatial patterns of catchment hydrological response

Advances in the use of observed spatial patterns of catchment hydrological response

Process-based simple model for simulating sugarcane growth and production

A non-linear and stochastic response surface method for Bayesian estimation of uncertainty in soil moisture simulation from a land surface model

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