Conditional interval reduction method: A possible new direction for the optimization of process based models

Hollós, Roland; Fodor, Nándor; Merganičová, Katarína; Hidy, Dóra; Árendás, Tamás; Grünwald, Thomas; Barcza, Zoltán

doi:10.1016/j.envsoft.2022.105556

Cited by 3 publications

(13 citation statements)

References 59 publications

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“…The newer version (v6.2) simulates crop development and yield similar to mechanistic crop models. Biome‐BGCMuSo now runs simulations of varied management conditions for fifteen crops, including drought, heat and nitrogen stresses (Hidy et al, 2021; Hollós et al, 2022). However, most of the published studies assessed grasslands and carbon dynamics (Dobor et al, 2022; Hidy et al, 2022; Hollós et al, 2022; Huang et al, 2022), which does not qualify for a table of studies assessing food security.…”

Section: Crop Modelsmentioning

confidence: 99%

Crop models and their use in assessing crop production and food security: A review

Gavasso‐Rita,

Papalexiou,

et al. 2023

Food and Energy Security

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Agriculture is directly related to food security as it determines the global food supply. Research in agriculture to predict crop productivity and losses helps avoid high food demand with little supply and price spikes. Here, we review ten crop models and one intercomparison project used for simulating crop growth and productivity under various impacts from soil–crop–atmosphere interactions. The review outlines food security and production assessments using numerical models for maize, wheat, and rice production. A summary of reviewed studies shows the following: (1) model ensembles provide smaller modeling errors compared to single models, (2) single models show better results when coupled with other types of models, (3) the ten reviewed crop models had improvements over the years and can accurately predict crop growth and yield for most of the locations, management conditions, and genotypes tested, (4) APSIM and DSSAT are fast and reliable in assessing broader output variables, (5) AquaCrop is indicated to investigate water footprint, quality and use efficiency in rainfed and irrigated systems, (6) all models assess nitrogen dynamics and use efficiency efficiently, excluding AquaCrop and WOFOST, (7) JULES specifies in evaluating food security vulnerability, (8) ORYZA is the main crop model used to evaluate paddy rice production, (9) grain filling is usually assessed with APSIM, DAISY, and DSSAT, and (10) the ten crop models can be used as tools to evaluate food production, availability, and security.

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Section: Crop Modelsmentioning

confidence: 99%

Crop models and their use in assessing crop production and food security: A review

Gavasso‐Rita,

Papalexiou,

et al. 2023

Food and Energy Security

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“…Complex PBMs typically contain many parameters specifying physiology, biochemistry, phenology, and allocation patterns of different vegetation types or species (Cameron et al, 2013;van Oijen, 2017). Parameter values are estimated based on different field or laboratory measurements, trial-and-error parameter adjustments or probabilistic methods (Forrester et al, 2021;Hollós et al, 2022). Thereby, each measurable parameter has its intrinsic variability that emerges from environmental conditions, sampling and measurement errors.…”

Section: Introductionmentioning

confidence: 99%

“…a situation when various combinations of parameter values produce the same results (Beven, 2006), can challenge calibration efforts. To this end, different calibration approaches, such as trial-and-error parameter adjustments or probabilistic methods (Forrester et al, 2021;Hollós et al, 2022) including Bayesian methods (van Oijen, 2017;Fer et al, 2018) or the Generalized Likelihood Uncertainty Estimation (GLUE) (Beven and Binley, 2014) have been proposed. The calibration can focus on one or several variables simultaneously.…”

Section: Introductionmentioning

confidence: 99%

“…In this study, we addressed the process-based model Biome-BGCMuSo (BBGCMuSo, Hidy et al, 2016Hidy et al, , 2022Hidy et al, , 2012, which is the most rapidly developing member of the Biome-BGC model family (Thornton, 1998). It simulates storages and fluxes of these elements within and between the pools.…”

Section: Introductionmentioning

confidence: 99%

“…The model has been extensively used in the research of forest ecosystems concerning their productivity (Kimball et al, 1997;Sever et al, 2017), carbon (Churkina et al, 2003;Ostrogović Sever et al, 2021;Yan et al, 2016), water (Pietsch et al, 2003), and nitrogen dynamics (Pietsch et al 2003;Merganičová, Pietsch, a Hasenauer 2005), including effects of climate change (White et al, 1999;Jager et al, 2000;Churkina and Running, 2000;Hlásny et al, 2011). The recent developments of BBGCMuSo included a multilayer soil profile; complex water cycling between soil, vegetation, and atmosphere; intra-annual phenology; and complex management operations (Hidy et al, 2022(Hidy et al, , 2016(Hidy et al, , 2012. However, robust testing of ecological plausibility and model performance in forest ecosystems as well as the regionally calibrated species-specific parameter sets are still lacking.…”

Section: Introductionmentioning

confidence: 99%

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Biogeochemical model Biome-BGCMuSo v6.2 provides plausible and accurate simulations of carbon cycle in Central European beech forests

Merganičová,

Merganič,

Dobor

et al. 2024

Preprint

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Abstract. Process-based ecosystem models are increasingly important for predicting forest dynamics under future environmental conditions, which may encompass non-analogous climate coupled with unprecedented disturbance regimes. However, challenges persist due to the extensive number of model parameters, scarce calibration data, and the trade-offs between the local precision and the model wide-ranging applicability. In this paper, we describe a protocol that allows a modeller to collect transferable ecosystem properties based on ecosystem characteristic criteria and to compile the parameters that need to be described in the field. We applied the procedure to develop a new parameterization for the European beech (Fagus sylvatica L.) for the Biome-BGCMuSo model, the most advanced member of the Biome-BGC family. For model calibration and testing, we utilized multiyear forest carbon data from 87 plots distributed across five European countries. The initial values of 48 new ecophysiological parameters were defined based on the literature review. The final values of 6 calibrated parameters were optimised for single sites as well as for multiple sites using the Generalised Likelihood Uncertainty Estimation and model output conditioning that ensured plausible simulations based on user-defined ranges of carbon stock output variables (carbon stock in aboveground wood biomass, soil, and litter) and finding the intersections of site-specific plausible parameter hyperspaces. To support the model use, we tested the model performance in simulating tree aboveground wood, soil, and litter carbon across a large geographical gradient of Central Europe and evaluated the trade-offs between parameters tailored to single plots and parameters estimated using multiple sites. Our findings indicated that parameter sets derived from single sites provided an improved local accuracy of simulations of aboveground wood, soil, and litter carbons stocks by 35 %, 55 %, and 11 % in comparison to the a priori parameter set. However, their broader applicability was very limited. A multi-site optimised parameter set, on the other hand, performed satisfactorily across the entire geographical domain studied here, including sites not involved in the parameter estimation, but the errors were on average by 26 %, 35 % and 9 % greater for the aboveground wood, soil, and litter carbons stocks than those obtained with the site-specific parameter sets. Importantly, model simulations demonstrated plausible responses across large-scale environmental gradients, featuring a clear production optimum of beech that aligns with empirical studies. These findings suggest that the model is capable of accurately simulating the dynamics of the European beech across its range and can be used for more comprehensive experimentations.

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Inversion and forward estimation with process-based models: An investigation into cost functions, uncertainty-based weights and model-data fusion

Rabonza

Nguyen

Biass

et al. 2023

Environmental Modelling & Software

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Conditional interval reduction method: A possible new direction for the optimization of process based models

Cited by 3 publications

References 59 publications

Crop models and their use in assessing crop production and food security: A review

Crop models and their use in assessing crop production and food security: A review

Biogeochemical model Biome-BGCMuSo v6.2 provides plausible and accurate simulations of carbon cycle in Central European beech forests

Inversion and forward estimation with process-based models: An investigation into cost functions, uncertainty-based weights and model-data fusion

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