Accounting for both parameter and model structure uncertainty in crop model predictions of phenology: A case study on rice

Abstract. There is an increasing demand for assessment of water provisioning ecosystem services. While simple models with low data and expertise requirements are attractive, their use as decision-aid tools should be supported by uncertainty characterization. We assessed the performance of the InVEST annual water yield model, a popular tool for ecosystem service assessment based on the Budyko framework. Our study involved the comparison of ten subcatchments in the Cape Fear watershed, NC, ranging in size and land use configuration. We analyzed the model sensitivity to the eco-hydrological parameters and the effect of extrapolating a lumped theory to a fully distributed model. Comparison of the model predictions with observations and with a lumped water balance model confirmed that the model is able to represent differences in land uses. Our results also emphasize the effect of climate input errors, especially annual precipitation, and errors in the eco-hydrological parameter Z, which are both comparable to the model structure uncertainties. In practice, our case study supports the use of the model for predicting land use change effect on water provisioning, although its use for identifying areas of high water yield will be influenced by precipitation errors. While the results are inherently local, analysis of the model structure suggests that many insights from this study will hold globally. Further work toward characterization of uncertainties in such simple models will help identify the regions and decision contexts where the model predictions may be used with confidence.

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Simultaneously determining global sensitivities of model parameters and model structure

Hydrology and Earth System Sciences ◽

10.5194/hess-24-5835-2020 ◽

2020 ◽

Vol 24 (12) ◽

pp. 5835-5858

Author(s):

Juliane Mai ◽

James R. Craig ◽

Bryan A. Tolson

Keyword(s):

Sensitivity Analysis ◽

Hydrological Modeling ◽

Structural Model ◽

Hydrologic Model ◽

Model Parameters ◽

Model Structure ◽

Modeling Framework ◽

Model Structure Uncertainty ◽

Model Variability

Abstract. Model structure uncertainty is known to be one of the three main sources of hydrologic model uncertainty along with input and parameter uncertainty. Some recent hydrological modeling frameworks address model structure uncertainty by supporting multiple options for representing hydrological processes. It is, however, still unclear how best to analyze structural sensitivity using these frameworks. In this work, we apply the extended Sobol' sensitivity analysis (xSSA) method that operates on grouped parameters rather than individual parameters. The method can estimate not only traditional model parameter sensitivities but is also able to provide measures of the sensitivities of process options (e.g., linear vs. non-linear storage) and sensitivities of model processes (e.g., infiltration vs. baseflow) with respect to a model output. Key to the xSSA method's applicability to process option and process sensitivity is the novel introduction of process option weights in the Raven hydrological modeling framework. The method is applied to both artificial benchmark models and a watershed model built with the Raven framework. The results show that (1) the xSSA method provides sensitivity estimates consistent with those derived analytically for individual as well as grouped parameters linked to model structure. (2) The xSSA method with process weighting is computationally less expensive than the alternative aggregate sensitivity analysis approach performed for the exhaustive set of structural model configurations, with savings of 81.9 % for the benchmark model and 98.6 % for the watershed case study. (3) The xSSA method applied to the hydrologic case study analyzing simulated streamflow showed that model parameters adjusting forcing functions were responsible for 42.1 % of the overall model variability, while surface processes cause 38.5 % of the overall model variability in a mountainous catchment; such information may readily inform model calibration and uncertainty analysis. (4) The analysis of time-dependent process sensitivities regarding simulated streamflow is a helpful tool for understanding model internal dynamics over the course of the year.

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Combining input uncertainty and residual error in crop model predictions: A case study on vineyards

European Journal of Agronomy ◽

10.1016/j.eja.2013.09.008 ◽

2014 ◽

Vol 52 ◽

pp. 191-197 ◽

Cited By ~ 15

Author(s):

Sébastien Roux ◽

François Brun ◽

Daniel Wallach

Keyword(s):

Crop Model ◽

Residual Error ◽

Input Uncertainty ◽

Model Predictions

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Simultaneously Determining Global Sensitivities of Model Parameters and Model Structure

10.5194/hess-2020-215 ◽

2020 ◽

Author(s):

Juliane Mai ◽

James R. Craig ◽

Bryan A. Tolson

Keyword(s):

Sensitivity Analysis ◽

Hydrological Modeling ◽

Structural Model ◽

Hydrologic Model ◽

Model Parameters ◽

Model Structure ◽

Modeling Framework ◽

Model Structure Uncertainty ◽

Model Variability

Abstract. Model structure uncertainty is known to be one of the three main sources of hydrologic model uncertainty along with input and parameter uncertainty. Some recent hydrological modeling frameworks address model structure uncertainty by supporting multiple options for representing hydrological processes. It is, however, still unclear how best to analyze structural sensitivity using these frameworks. In this work, we apply an Extended Sobol' Sensitivity Analysis (xSSA) method that operates on grouped parameters rather than individual parameters. The method can estimate not only traditional model parameter sensitivities but is also able to provide measures of the sensitivities of process options (e.g., linear vs. non-linear storage) and sensitivities of model processes (e.g., infiltration vs. baseflow) with respect to a model output. Key to the xSSA method's applicability to process option and process sensitivity is the novel introduction of process option weights in the Raven hydrological modeling framework. The method is applied to both artificial benchmark models and a watershed model built with the Raven framework. The results show that: (1) The xSSA method provides sensitivity estimates consistent with those derived analytically for individual as well as grouped parameters linked to model structure. (2) The xSSA method with process weighting is computationally less expensive than the alternative aggregate sensitivity analysis approach performed for the exhaustive set of structural model configurations, with savings of 81.9 % for the benchmark model and 98.6 % for the watershed case study. (3) The xSSA method applied to the hydrologic case study analyzing simulated streamflow showed that model parameters adjusting forcing functions were responsible for 42.1 % of the overall model variability while surface processes cause 38.5 % of the overall model variability in a mountainous catchment; such information may readily inform model calibration. (4) The analysis of time dependent process sensitivities regarding simulated streamflow is a helpful tool to understand model internal dynamics over the course of the year.

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Structural Analysis of an Offshore Jacket Model Under Seismic Excitation

Volume 1: Offshore Technology ◽

10.1115/omae2008-57263 ◽

2008 ◽

Cited By ~ 1

Author(s):

Helder J. D. Correia ◽

Anto´nio C. Mendes ◽

Carlos A. F. S. Oliveira

Keyword(s):

Point Of View ◽

Shaking Table ◽

Seismic Excitation ◽

Model Structure ◽

Structural Members ◽

Jacket Structures ◽

Reaction Forces ◽

Nodal Displacements ◽

Good Agreement

In the present work the action of earthquakes upon offshore jacket structures is analysed by means of ADINA software. Our case-study refers to an existing model structure, previously constructed at the Laboratory of Fluid Mechanics of UBI, which has been analysed from the hydrodynamic point of view — Mendes et al. [1, 2]. The seismic excitation will be imposed at the base of this model structure, with frequencies and amplitudes corresponding to actual earthquake conditions transposed to the model scale of 1:45. The FEM software is utilised to calculate the natural frequencies of the model and to obtain stresses at selected members, as well as their nodal displacements. Our purpose is to quantify maximum stresses occurring in critical structural members and to verify the survivability criterion. The predictions of the numerical model, in terms of the reaction forces at the base and acceleration at the top of the structure, are then correlated with the experimental measurements performed when the model structure is excited in an especially designed shaking table (Correia [3]), revealing a good agreement between both results.

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