method for assessing the goodness of computer simulation of soil processes

Whitmore, A. P.

doi:10.1111/j.1365-2389.1991.tb00410.x

Cited by 151 publications

(53 citation statements)

References 6 publications

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“…Where measurements are replicated, the coincidence between simulated and measured values is best expressed as the lack-of-fit statistic, and the significance of the coincidence determined using an F-test (Whitmore 1991). However, the NSIS data set is not replicated, so instead the degree of coincidence was determined by calculating the total error as the root mean squared error (RMSE) and the bias in the error as the relative error (Loague & Green 1991, Smith et al 1996b, 1997.…”

Section: Calculating Uncertaintymentioning

confidence: 99%

Estimating changes in Scottish soil carbon stocks using ECOSSE. I. Model description and uncertainties

Smith¹,

Gottschalk²,

Bellarby³

et al. 2010

Clim. Res.

111

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To predict the response of C-rich soils to external change, models are needed that accurately reflect the conditions of these soils. Estimation of Carbon in Organic Soils -Sequestration and Emissions (ECOSSE) is a model that allows simulations of soil C and N turnover in both mineral and organic soils using only the limited meteorological, land-use and soil data that is available at the national scale. Because it is able to function at field as well as national scales if appropriate input data are used, field-scale evaluations can be used to determine uncertainty in national simulations. Here we present an evaluation of the uncertainty expected in national-scale simulations of Scotland, using data from the National Soil Inventory of Scotland. This data set provides measurements of C change for the range of soils, climates and land-use types found across Scotland. The simulated values show a high degree of association with the measurements in both total C and change in C content of the soil. Over all sites where land-use change occurred, the average deviation between the simulated and measured values of percentage change in soil C was less than the experimental error (11% simulation error, 53% measurement error). This suggests that the uncertainty in the national-scale simulations will be ~11%. Only a small bias in the simulations was observed compared to the measured values, suggesting that a small underestimate of the change in soil C should be expected at the national scale (-4%).

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Section: Calculating Uncertaintymentioning

confidence: 99%

Estimating changes in Scottish soil carbon stocks using ECOSSE. I. Model description and uncertainties

Smith¹,

Gottschalk²,

Bellarby³

et al. 2010

Clim. Res.

111

View full text Add to dashboard Cite

show abstract

“…Reversing the question from whether the model falls within the observed variability of the data to whether the data falls within the observed variability of the model also distinguishes our approach from those of Whitmore (1991) and others based on replicate data sets. This is largely a matter of convenience from our perspective since, as mentioned above, replicate data sets from identical situations (e.g.…”

Section: Ecological Model Validation and Goodness-of-fitmentioning

confidence: 96%

Monte Carlo assessments of goodness-of-fit for ecological simulation models

Waller

Smith

Childs

et al. 2003

Ecological Modelling

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One often develops stochastic ecologic simulation models based on local interactions between individuals or groups and bases systemic conclusions on trends summarized over multiple data sets generated from the model. In many cases, such models generate data sets ("realizations") each violating the usual assumptions associated with traditional statistical tests of goodness-of-fit, most notably that of independent observations. Monte Carlo hypothesis tests applied to multiple realizations from such models provide appropriate goodness-of-fit tests regardless of within-model peculiarities. The Monte Carlo tests address the question "Do the observed data appear consistent with the model?" in contrast to the usual question "Does the model appear consistent with the observed data?". In addition, such tests can make use of the same data sets used to draw systemic inference (i.e. the tests require no additional simulation runs). We illustrate the concept using Pearson's chi-square statistic with correlated data. We also consider the behavior of a similar statistic and of "modeling efficiency" in assessing the fit of a simulation model for the spatial spread of raccoon rabies in Connecticut.

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“…Simulation results were compared with observations using Pearson's correlation (Draper and Smith 1966), concordance correlation (Lin 2000) and the lack of fit test (Whitmore 1991). The Pearson's correlation coefficient (r) does not assess goodness of fit, but evaluates the association between simulated and measured values (precision).…”

Section: Model Evaluation and Comparisonmentioning

confidence: 99%

“…The significances of difference in r and CCC between two models were compared according to Zar (1999). The lack of fit tests systemic errors, which allows the experimental errors to be distinguished from the failure of the model (Whitmore 1991). The statistical significance of lack of fit was obtained by comparing the F value for the lack of fit [F (LOFIT)] with the F critical values at the 0.05 probability level (F 0.05 ) (Smith et al 1997).…”

Section: Model Evaluation and Comparisonmentioning

confidence: 99%

Improving SHAW long-term soil moisture prediction for continuous wheat rotations, Alberta, Canada

Wang¹,

Flerchinger²,

Lemke³

et al. 2010

Can. J. Soil. Sci.

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Sprout, C. 2010. Improving SHAW long-term soil moisture prediction for continuous wheat rotations, Alberta, Canada. Can. J. Soil Sci. 90: 37Á53. The Decision Support System for Agrotechnology Transfer-Cropping System Model (DSSAT-CSM) is a widely used modeling package that often simulates wheat yield and biomass well. However, some previous studies reported that its simulation on soil moisture was not always satisfactory. On the other hand, the Simultaneous Heat and Water (SHAW) model, a more sophisticated, hourly time step soil microclimate model, needs inputs of plant canopy development over time, which are difficult to measure in the field especially for a long-term period (longer than a year). The SHAW model also needs information on surface residue, but treats them as constants. In reality, however, surface residue changes continuously under the effect of tillage, rotation and environment. We therefore proposed to use DSSAT-CSM to simulate dynamics of plant growth and soil surface residue for input into SHAW, so as to predict soil water dynamics. This approach was tested using three conventionally tilled wheat rotations (continuous wheat, wheat-fallow and wheat-wheat-fallow) of a long-term cropping systems study located on a Thin Black Chernozemic clay loam near Three Hills, Alberta, Canada. Results showed that DSSAT-CSM often overestimated the drying of the surface layers in wheat rotations, but consistently overestimated soil moisture in the deep soil. This is likely due to the underestimation of root water extraction despite model predictions that the root system reached 80 cm. Among the eight growth/residue parameters simulated by DSSAT-CSM, root depth, leaf area index and residue thickness are the most influential characteristics on the simulation of soil moisture by SHAW. The SHAW model using DSSAT-CSM-simulated information significantly improved prediction of soil moisture at different depths and total soil water at 0Á120 cm in all rotations with different phases compared with that simulated by DSSAT-CSM.Key words: Soil moisture, modeling, Decision Support System for Agrotechnology Transfer-Cropping System Model, Simultaneous Heat and Water ModelWang, H., Flerchinger, G. N., Lemke, R., Brandt, K., Goddard, T. et Sprout, C. 2010. Ame´lioration du mode`le SHAW pour la pre´vision a`long terme de la teneur en eau du sol avec la monoculture du ble´en Alberta (Canada). Can. J. Soil Sci. 90: 37Á53. Le Decision Support System for Agrotechnology Transfer-Cropping System Model (DSSAT-CSM) est un mode`le largement utilise´qui simule souvent bien le rendement et la biomasse du ble´. Quelques e´tudes ante´rieures rapportaient ne´anmoins que ce mode`le ne simule pas toujours de manie`re satisfaisante la teneur en eau du sol. De son coˆte´, le mode`le SHAW (Simultaneous Heat and Water), qui simule de manie`re plus complexe le microclimat du sol d'heure en heure, a besoin de donne´es sur le de´veloppement de la ve´ge´tation dans le temps, donne´es difficiles a`obtenir sur le terrain, surtout sur de longues pe´riode...

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method for assessing the goodness of computer simulation of soil processes

Cited by 151 publications

References 6 publications

Estimating changes in Scottish soil carbon stocks using ECOSSE. I. Model description and uncertainties

Estimating changes in Scottish soil carbon stocks using ECOSSE. I. Model description and uncertainties

Monte Carlo assessments of goodness-of-fit for ecological simulation models

Improving SHAW long-term soil moisture prediction for continuous wheat rotations, Alberta, Canada

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