Assessing the propagation of uncertainties in multi-objective optimization for agro-ecosystem adaptation to climate change

Holzkämper, Annelie; Klein, Tommy; Seppelt, Ralf; Fuhrer, Jürg

doi:10.1016/j.envsoft.2014.12.012

Cited by 47 publications

(31 citation statements)

References 44 publications

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“…Furthermore, Holzkämper et al . () pointed out that the uncertainties from climate and parameters in crop models were small compared with the influences of management and soil. Therefore, further investigations should focus on including the effects of uncertainties on simulation outputs as they relate to management and soil conditions and the uncertainty in crop model parameterizations (Zhou and Wang, ).…”

Section: Resultsmentioning

confidence: 99%

“…Therefore, multiple GCM-crop model ensembles should be given priority to address adequately the uncertainty when assessing the yield under climate change when the available information is limited. Furthermore, Holzkämper et al (2015b) pointed out that the uncertainties from climate and parameters in crop models were small compared with the influences of management and soil. Therefore, further investigations should focus on including the effects of uncertainties on simulation outputs as they relate to management and soil conditions and the uncertainty in crop model parameterizations (Zhou and Wang, 2015).…”

Section: Yield Uncertaintymentioning

confidence: 99%

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Higher contributions of uncertainty from global climate models than crop models in maize‐yield simulations under climate change

Zhang

Zhao

Feng

2018

Meteorological Applications

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Quantifying and separating different sources of uncertainty helps to improve the understanding of the projected effects of climate change and can inform decision‐making in adaptation planning. This paper (1) evaluated four process‐based crop models; (2) assessed the effects of climate change on maize yield using climate change outputs from seven global climate models (GCMs) under three representative concentration pathways (RCPs); and (3) disaggregated the contributions of multiple crop models, GCMs and RCPs to overall uncertainty. All four models captured more than 80% of the variation in days to silking, maturity and yield, indicating reasonably reproduced observations. Similarly, the root mean square errors were moderate for days to silking and maturity (fewer than 4 days) and yield (0.5–0.7 t/ha). Overall, the results indicate that the models could assess grain yield at the study sites reasonably well. The results of the multiple models ensemble indicate that the maize yield will decrease by 9–11% with a probability of 72–80% on average during the period 2010–2039 relative to the baseline (1976–2005). The uncertainty in the maize‐yield simulations might arise mostly from the GCM models, followed by the crop models and RCPs, the contribution of which could be neglected relative to the other factors. Therefore, the use of a multiple crop model and a GCM ensemble is advisable in order to account properly for uncertainties in crop assessments.

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Section: Resultsmentioning

confidence: 99%

Section: Yield Uncertaintymentioning

confidence: 99%

Higher contributions of uncertainty from global climate models than crop models in maize‐yield simulations under climate change

Zhang

Zhao

Feng

2018

Meteorological Applications

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“…PMF has been frequently applied throughout the world to quantify the contributions of individual sources to PM (Lee et al, 1999;Tian et al, 2013a;Huang et al, 2014;Alam et al, 2015), and its results have significant implications in policies related PM control and scientific investigations on PM and their impacts on public health (Zheng et al, 2005;Chen et al, 2012;Shen et al, 2012;Holzkamper et al, 2015;Lin et al, 2015;Panicker et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…Examples (which were from our following work) of such impacts are provided in Table S1 in the Supplementary Material, which shows that results vary significantly with the same ambient concentration dataset but different uncertainty inputs. Therefore, a proper understanding of how uncertainties affect model results is critical, not only for the appropriate use of PMF model, but also for subsequent policies implementations (Holzkamper et al, 2015;Panicker et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Effect of Uncertainty on Source Contributions from the Positive Matrix Factorization Model for a Source Apportionment Study

Shi

Chen

Tian

et al. 2016

Aerosol Air Qual. Res.

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Uncertainty estimation plays an important role in source apportionment models such as the positive matrix factorization (PMF) model. In this study, synthetic datasets were generated and analyzed using PMF with specified uncertainties at different levels to investigate the impact of uncertainty inputs on the results of PMF model, as well as the benefits and risks of emphasizing on certain species. The results showed that: (1) uncertainties for the PMF model should be estimated based on characteristics of the dataset being analyzed; (2) emphasizing on correct tracers will improved model performance; and (3) emphasizing on unsuitable tracers may lead to disruptive consequences that might not be captured by the Q metric. Tests were also performed on collected ambient PM 2.5 samples and similar conclusions were drawn: emphasizing on correct tracers was shown to improve the separation of important source categories from mixed sources. When emphasizing on incorrect tracers, a counterfeit factor of Fe industrial source was extracted, which are inconsistent with field observations. Results from this study provide insights on how uncertainties should be estimated for the PMF model.

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“…Estimating uncertainty is 14 of primary importance for all uses of crop models, for example for exploring crop 15 management options under current climate (Baigorria et al, 2007). It is also of major 16 importance for models in other fields, including climate modeling (Holzkämper et al, 2015; 17 Tebaldi and Knutti, 2007), environmental studies (Uusitalo et al, 2015) compare some criterion of model fit with that of a naïve predictor ( (Murphy 1988, Reichler 19 and Kim 2008). This is comparable to the approach above.…”

Section: Introductionmentioning

confidence: 99%

Estimating model prediction error: Should you treat predictions as fixed or random?

Wallach¹,

Thorburn

Asseng

et al. 2016

Environmental Modelling & Software

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Assessing the propagation of uncertainties in multi-objective optimization for agro-ecosystem adaptation to climate change

Cited by 47 publications

References 44 publications

Higher contributions of uncertainty from global climate models than crop models in maize‐yield simulations under climate change

Higher contributions of uncertainty from global climate models than crop models in maize‐yield simulations under climate change

Effect of Uncertainty on Source Contributions from the Positive Matrix Factorization Model for a Source Apportionment Study

Estimating model prediction error: Should you treat predictions as fixed or random?

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