Detection of hidden model errors by combining single and multi-criteria calibration

Houska, Tobias; Kraft, Philipp; Jehn, Florian Ulrich; Bestian, Konrad; Kraus, David

doi:10.1016/j.scitotenv.2021.146218

Cited by 4 publications

(3 citation statements)

References 46 publications

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“…The existence of compensating effect during parameter calibration in crop models (Sima et al., 2020) leading to nonuniqueness, as defined already for hydrological models in the early 1990s (Beven, 1993) is still a challenging aspect in the calibration approach. The problem that models are often right for the wrong reason (Kirchner, 2006), when only a single criterion is considered in model calibration (Houska et al., 2021), is added here as a challenge to calibration.…”

Section: Discussionmentioning

confidence: 99%

“…The problem that models are often right for the wrong reason (Kirchner, 2006), when only a single criterion is considered in model calibration (Houska et al, 2021), is added here as a challenge to calibration. When the focus is on increasing food production (Hamidov et al, 2018), soil processes are often compromised although soil functions have important feedbacks on ecosystem productivity, nutrient cycling, energy, and water (Deckmyn et al, 2020;Zwetsloot et al, 2020).…”

Section: Modeling Agroecosystemsmentioning

confidence: 99%

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Same soil, different climate: Crop model intercomparison on translocated lysimeters

Groh

Diamantopoulos

Duan

et al. 2022

Vadose Zone Journal

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Crop model intercomparison studies have mostly focused on the assessment of predictive capabilities for crop development using weather and basic soil data from the same location. Still challenging is the model performance when considering complex interrelations between soil and crop dynamics under a changing climate. The objective of this study was to test the agronomic crop and environmental flux‐related performance of a set of crop models. The aim was to predict weighing lysimeter‐based crop (i.e., agronomic) and water‐related flux or state data (i.e., environmental) obtained for the same soil monoliths that were taken from their original environment and translocated to regions with different climatic conditions, after model calibration at the original site. Eleven models were deployed in the study. The lysimeter data (2014–2018) were from the Dedelow (Dd), Bad Lauchstädt (BL), and Selhausen (Se) sites of the TERENO (TERrestrial ENvironmental Observatories) SOILCan network. Soil monoliths from Dd were transferred to the drier and warmer BL site and the wetter and warmer Se site, which allowed a comparison of similar soil and crop under varying climatic conditions. The model parameters were calibrated using an identical set of crop‐ and soil‐related data from Dd. Environmental fluxes and crop growth of Dd soil were predicted for conditions at BL and Se sites using the calibrated models. The comparison of predicted and measured data of Dd lysimeters at BL and Se revealed differences among models. At site BL, the crop models predicted agronomic and environmental components similarly well. Model performance values indicate that the environmental components at site Se were better predicted than agronomic ones. The multi‐model mean was for most observations the better predictor compared with those of individual models. For Se site conditions, crop models failed to predict site‐specific crop development indicating that climatic conditions (i.e., heat stress) were outside the range of variation in the data sets considered for model calibration. For improving predictive ability of crop models (i.e., productivity and fluxes), more attention should be paid to soil‐related data (i.e., water fluxes and system states) when simulating soil–crop–climate interrelations in changing climatic conditions.

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Section: Discussionmentioning

confidence: 99%

Section: Modeling Agroecosystemsmentioning

confidence: 99%

Same soil, different climate: Crop model intercomparison on translocated lysimeters

Groh

Diamantopoulos

Duan

et al. 2022

Vadose Zone Journal

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show abstract

“…Posterior distribution and uncertainty analysis quantify the variations found in the circulating auxiliary water system owing to the source of water. The risk of false conclusions is kept small through Bayesian analysis of the water quality parameter (Houska et al, 2021; Li et al, 2021; Rammay et al, 2019; Taheriyoun & Moradinejad, 2015). The characteristics of the data source are more significant than the number of data points.…”

Section: Introductionmentioning

confidence: 99%

Multivariate regression studies for investigating and setting the action levels for the system water quality parameters of a nuclear plant

Suganya

Swaminathan

Anoop

2022

Water & Environment J

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A broad spectrum of water quality parameters is regularly monitored and assessed in auxiliary water systems. The integrity and availability status of a system are ensured by observing the trend of water quality indices which further warrants investigating the regulatory requirements of both safety and non‐safety‐related systems of a nuclear power plant. To evaluate the water chemistry state in a raw water system, we conducted a statistical study investigating water quality indices over 3 years. Obligatory statistical tools were utilized to comprehend and correlate the effects of the source water on the system. Most water quality parameters were found to be within an acceptable range, and correlations were found between the parameters, namely, conductivity, sodium, and chloride. Multivariate optimization and regression studies using the Box–Behnken design were conducted to minimize the system conductivity levels. The experimental data were fitted into a quadratic equation with an r2 value of 0.84. Model validation was conducted, and the fit between the experimental and model results was within 10%. This assessment identifies preventive action levels to avoid impairment of system integrity at an early stage.

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Common agronomic adaptation strategies to climate change may increase soil greenhouse gas emission in Northern Europe

Grados,

Kraus,

Haas

et al. 2024

Agricultural and Forest Meteorology

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Detection of hidden model errors by combining single and multi-criteria calibration

Cited by 4 publications

References 46 publications

Same soil, different climate: Crop model intercomparison on translocated lysimeters

Same soil, different climate: Crop model intercomparison on translocated lysimeters

Multivariate regression studies for investigating and setting the action levels for the system water quality parameters of a nuclear plant

Common agronomic adaptation strategies to climate change may increase soil greenhouse gas emission in Northern Europe

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