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Dietrich, J. P., Popp, A., & Lotze-Campen, H. (2013). Reducing the loss of information and gaining accuracy with clustering methods in a global land-use model. Ecol. Model., 263, 233–243.
Abstract: Global land-use models have to deal with processes on several spatial scales, ranging from the global scale down to the farm level. The increasing complexity of modern land-use models combined with the problem of limited computational resources represents a challenge to modelers. One solution of this problem is to perform spatial aggregation based on a regular grid or administrative units such as countries. Unfortunately this type of aggregation flattens many regional differences and produces a homogenized map of the world. In this paper we present an alternative aggregation approach using clustering methods. Clustering reduces the loss of information due to aggregation by choosing an appropriate aggregation pattern. We investigate different clustering methods, examining their quality in terms of information conservation. Our results indicate that clustering is always a good choice and preferable compared to grid-based aggregation. Although all the clustering methods we tested delivered a higher degree of information conservation than grid-based aggregation, the choice of clustering method is not arbitrary. Comparing outputs of a model fed with original data and a model fed with aggregated data, bottom-up clustering delivered the best results for the whole range of numbers of clusters tested. (C) 2013 Elsevier B.V. All rights reserved.
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Klosterhalfen, A., Herbst, M., Weihermueller, L., Graf, A., Schmidt, M., Stadler, A., et al. (2017). Multi-site calibration and validation of a net ecosystem carbon exchange model for croplands. Ecol. Model., 363, 137–156.
Abstract: Croplands play an important role in the carbon budget of many regions. However, the estimation of their carbon balance remains difficult due to diversity and complexity of the processes involved. We report the coupling of a one-dimensional soil water, heat, and CO2 flux model (SOILCO2), a pool concept of soil carbon turnover (RothC), and a crop growth module (SUCROS) to predict the net ecosystem exchange (NEE) of carbon. The coupled model, further referred to as AgroC, was extended with routines for managed grassland as well as for root exudation and root decay. In a first step, the coupled model was applied to two winter wheat sites and one upland grassland site in Germany. The model was calibrated based on soil water content, soil temperature, biometric, and soil respiration measurements for each site, and validated in terms of hourly NEE measured with the eddy covariance technique. The overall model performance of AgroC was sufficient with a model efficiency above 0.78 and a correlation coefficient above 0.91 for NEE. In a second step, AgroC was optimized with eddy covariance NEE measurements to examine the effect of different objective functions, constraints, and data-transformations on estimated NEE. It was found that NEE showed a distinct sensitivity to the choice of objective function and the inclusion of soil respiration data in the optimization process. In particular, both positive and negative day- and nighttime fluxes were found to be sensitive to the selected optimization strategy. Additional consideration of soil respiration measurements improved the simulation of small positive fluxes remarkably. Even though the model performance of the selected optimization strategies did not diverge substantially, the resulting cumulative NEE over simulation time period differed substantially. Therefore, it is concluded that data transformations, definitions of objective functions, and data sources have to be considered cautiously when a terrestrial ecosystem model is used to determine NEE by means of eddy covariance measurements. (C) 2017 Elsevier B.V. All rights reserved.
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Sakschewski, B., von Bloh, W., Huber, V., Müller, C., & Bondeau, A. (2014). Feeding 10 billion people under climate change: How large is the production gap of current agricultural systems. Ecol. Model., 288, 103–111.
Abstract: The human population is projected to reach more than 10 billion in the year 2100. Together with changing consumption pattern, population growth will lead to increasing food demand. The question arises whether or not the Earth is capable of fulfilling this demand. In this study, we approach this question by estimating the carrying capacity of current agricultural systems (K-C), which does not measure the maximum number of people the Earth is likely to feed in the future, but rather allows for an indirect assessment of the increases in agricultural productivity required to meet demands. We project agricultural food production under progressing climate change using the state-of-the-art dynamic global vegetation model LPJmL, and input data of 3 climate models. For 1990 to 2100 the worldwide annual caloric yield of the most important 11 crop types is simulated. Model runs with and without elevated atmospheric CO2 concentrations are performed in order to investigate CO2 fertilization effects. Country-specific per-capita caloric demands fixed at current levels and changing demands based on future GDP projections are considered to assess the role of future dietary shifts. Our results indicate that current population projections may considerably exceed the maximum number of people that can be fed globally if climate change is not accompanied by significant changes in land use, agricultural efficiencies and/or consumption pathways. We estimate the gap between projected population size and K-C to reach 2 to 6.8 billion people by 2100. We also present possible caloric self-supply changes between 2000 and 2100 for all countries included in this study. The results show that predominantly developing countries in tropical and subtropical regions will experience vast decreases of self-supply. Therefore, this study is important for planning future large-scale agricultural management, as well as the critical assessment of population projections, which should take food-mediated climate change feedbacks into account
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Hidy, D., Barcza, Z., Haszpra, L., Churkina, G., Pintér, K., & Nagy, Z. (2012). Development of the Biome-BGC model for simulation of managed herbaceous ecosystems. Ecol. Model., 226, 99–119.
Abstract: Apart from measurements, numerical models are the most convenient instruments to analyze the carbon and water balance of terrestrial ecosystems and their interactions with changing environmental conditions. The process-based Biome-BGC model is widely used to simulate the storage and flux of water, carbon, and nitrogen within the vegetation, litter, and soil of unmanaged terrestrial ecosystems. Considering herbaceous vegetation related simulations with Biome-BGC, soil moisture and growing season control on ecosystem functioning is inaccurate due to the simple soil hydrology and plant phenology representation within the model. Consequently, Biome-BGC has limited applicability in herbaceous ecosystems because (1) they are usually managed; (2) they are sensitive to soil processes, most of all hydrology; and (3) their carbon balance is closely connected with the growing season length. Our aim was to improve the applicability of Biome-BGC for managed herbaceous ecosystems by implementing several new modules, including management. A new index (heatsum growing season index) was defined to accurately estimate the first and the final days of the growing season. Instead of a simple bucket soil sub-model, a multilayer soil sub-model was implemented, which can handle the processes of runoff, diffusion and percolation. A new module was implemented to simulate the ecophysiological effect of drought stress on plant mortality. Mowing and grazing modules were integrated in order to quantify the functioning of managed ecosystems. After modifications, the Biome-BGC model was calibrated and validated using eddy covariance-based measurement data collected in Hungarian managed grassland ecosystems. Model calibration was performed based on the Bayes theorem. As a result of these developments and calibration, the performance of the model was substantially improved. Comparison with measurement-based estimate showed that the start and the end of the growing season are now predicted with an average accuracy of 5 and 4 days instead of 46 and 85 days as in the original model. Regarding the different sites and modeled fluxes (gross primary production, total ecosystem respiration, evapotranspiration), relative errors were between 18-60% using the original model and 10-18% using the developed model; squares of the correlation coefficients were between 0.02-0.49 using the original model and 0.50-0.81 using the developed model. (c) 2011 Elsevier B.V. All rights reserved.
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Hjelkrem, A. - G. R., Höglind, M., van Oijen, M., Schellberg, J., Gaiser, T., & Ewert, F. (2017). Sensitivity analysis and Bayesian calibration for testing robustness of the BASGRA model in different environments. Ecol. Model., 359, 80–91.
Abstract: Highlights • The parameters to be fixed were consistent across sites. • Model calibration must be performed separately for each specific case. • Possible to reduce model parameters from 66 to 45. • Strong model reductions must be avoided. • The error term for the training data were characterised by timing (phase shift). Abstract Proper parameterisation and quantification of model uncertainty are two essential tasks in improvement and assessment of model performance. Bayesian calibration is a method that combines both tasks by quantifying probability distributions for model parameters and outputs. However, the method is rarely applied to complex models because of its high computational demand when used with high-dimensional parameter spaces. We therefore combined Bayesian calibration with sensitivity analysis, using the screening method by Morris (1991), in order to reduce model complexity by fixing parameters to which model output was only weakly sensitive to a nominal value. Further, the robustness of the model with respect to reduction in the number of free parameters were examined according to model discrepancy and output uncertainty. The process-based grassland model BASGRA was examined in the present study on two sites in Norway and in Germany, for two grass species (Phleum pratense and Arrhenatherum elatius). According to this study, a reduction of free model parameters from 66 to 45 was possible. The sensitivity analysis showed that the parameters to be fixed were consistent across sites (which differed in climate and soil conditions), while model calibration had to be performed separately for each combination of site and species. The output uncertainty decreased slightly, but still covered the field observations of aboveground biomass. Considering the training data, the mean square error for both the 66 and the 45 parameter model was dominated by errors in timing (phase shift), whereas no general pattern was found in errors when using the validation data. Stronger model reduction should be avoided, as the error term increased and output uncertainty was underestimated.
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