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Kipling, R. P., Topp, C. F. E., Bannink, A., Bartley, D. J., Blanco-Penedo, I., Cortignani, R., et al. (2019). To what extent is climate change adaptation a novel challenge for agricultural modellers. Env. Model. Softw., 120, Unsp 104492.
Abstract: Modelling is key to adapting agriculture to climate change (CC), facilitating evaluation of the impacts and efficacy of adaptation measures, and the design of optimal strategies. Although there are many challenges to modelling agricultural CC adaptation, it is unclear whether these are novel or, whether adaptation merely adds new motivations to old challenges. Here, qualitative analysis of modellers’ views revealed three categories of challenge: Content, Use, and Capacity. Triangulation of findings with reviews of agricultural modelling and Climate Change Risk Assessment was then used to highlight challenges specific to modelling adaptation. These were refined through literature review, focussing attention on how the progressive nature of CC affects the role and impact of modelling. Specific challenges identified were: Scope of adaptations modelled, Information on future adaptation, Collaboration to tackle novel challenges, Optimisation under progressive change with thresholds, and Responsibility given the sensitivity of future outcomes to initial choices under progressive change.
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Grosz, B., Dechow, R., Gebbert, S., Hoffmann, H., Zhao, G., Constantin, J., et al. (2017). The implication of input data aggregation on up-scaling soil organic carbon changes. Env. Model. Softw., 96, 361–377.
Abstract: In up-scaling studies, model input data aggregation is a common method to cope with deficient data availability and limit the computational effort. We analyzed model errors due to soil data aggregation for modeled SOC trends. For a region in North West Germany, gridded soil data of spatial resolutions between 1 km and 100 km has been derived by majority selection. This data was used to simulate changes in SOC for a period of 30 years by 7 biogeochemical models. Soil data aggregation strongly affected modeled SOC trends. Prediction errors of simulated SOC changes decreased with increasing spatial resolution of model output. Output data aggregation only marginally reduced differences of model outputs between models indicating that errors caused by deficient model structure are likely to persist even if requirements on the spatial resolution of model outputs are low. (C)2017 Elsevier Ltd. All rights reserved.
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Van Oijen, M., Cameron, D., Levy, P. E., & Preston, R. (2017). Correcting errors from spatial upscaling of nonlinear greenhouse gas flux models. Environmental Modelling & Software, 94, 157–165.
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Houska, T., Kraft, P., Liebermann, R., Klatt, S., Kraus, D., Haas, E., et al. (2017). Rejecting hydro-biogeochemical model structures by multi-criteria evaluation. Env. Model. Softw., 93, 1–12.
Abstract: Highlights • New method to investigate biogeochemical model structure performance. • Process based hydrological modelling can improve biogeochemical model predictions. • Modelling efficiency dramatically drops with multiple objectives. Abstract This work presents a novel way for assessing and comparing different hydro-biogeochemical model structures and their performances. We used the LandscapeDNDC modelling framework to set up four models of different complexity, considering two soil-biogeochemical and two hydrological modules. The performance of each model combination was assessed using long-term (8 years) data and applying different thresholds, considering multiple criteria and objective functions. Our results show that each model combination had its strength for particular criteria. However, only 0.01% of all model runs passed the complete rejectionist framework. In contrast, our comparatively applied assessments of single thresholds, as frequently used in other studies, lead to a much higher acceptance rate of 40–70%. Therefore, our study indicates that models can be right for the wrong reasons, i.e., matching GHG emissions while at the same time failing to simulate other criteria such as soil moisture or plant biomass dynamics.
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Wallach, D., Thorburn, P., Asseng, S., Challinor, A. J., Ewert, F., Jones, J. W., et al. (2016). Estimating model prediction error: Should you treat predictions as fixed or random. Env. Model. Softw., 84, 529–539.
Abstract: Crop models are important tools for impact assessment of climate change, as well as for exploring management options under current climate. It is essential to evaluate the uncertainty associated with predictions of these models. We compare two criteria of prediction error; MSEPfixed, which evaluates mean squared error of prediction for a model with fixed structure, parameters and inputs, and MSEPuncertain(X), which evaluates mean squared error averaged over the distributions of model structure, inputs and parameters. Comparison of model outputs with data can be used to estimate the former. The latter has a squared bias term, which can be estimated using hindcasts, and a model variance term, which can be estimated from a simulation experiment. The separate contributions to MSEPuncertain(X) can be estimated using a random effects ANOVA. It is argued that MSEPuncertain(X) is the more informative uncertainty criterion, because it is specific to each prediction situation.
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