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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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Fürst, C., Helming, K., Lorz, C., Müller, F., & Verburg, P. H. (2013). Integrated land use and regional resource management--a cross-disciplinary dialogue on future perspectives for a sustainable development of regional resources. J. Environ. Manage., 127 Suppl, S1–S5.
Abstract: Our paper introduces objectives and ideas of the special issue “Integrated land use and regional resource management – A cross-disciplinary dialogue on future perspectives for a sustainable development of regional resources” and provides an overview on the contributions of the single papers in the special issue to this topic. Furthermore, we discuss and present major challenges and demands on integrated land use and regional resource management and we come up with an analytical framework how to correspond these demands.
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König, H. J., Uthes, S., Schuler, J., Zhen, L., Purushothaman, S., Suarma, U., et al. (2013). Regional impact assessment of land use scenarios in developing countries using the FoPIA approach: findings from five case studies. J. Environ. Manage., 127 Suppl, S56–S64.
Abstract: The impact of land use changes on sustainable development is of increasing interest in many regions of the world. This study aimed to test the transferability of the Framework for Participatory Impact Assessment (FoPIA), which was originally developed in the European context, to developing countries, in which lack of data often prevents the use of data-driven impact assessment methods. The core aspect of FoPIA is the stakeholder-based assessment of alternative land use scenarios. Scenario impacts on regional sustainability are assessed by using a set of nine regional land use functions (LUFs), which equally cover the economic, social and environmental dimensions of sustainability. The cases analysed in this study include (1) the alternative spatial planning policies around the Merapi volcano and surrounding areas of Yogyakarta City, Indonesia; (2) the large-scale afforestation of agricultural areas to reduce soil erosion in Guyuan, China; (3) the expansion of soil and water conservation measures in the Oum Zessar watershed, Tunisia; (4) the agricultural intensification and the potential for organic agriculture in Bijapur, India; and (5) the land degradation and land conflicts resulting from land division and privatisation in Narok, Kenya. All five regions are characterised by population growth, partially combined with considerable economic development, environmental degradation problems and social conflicts. Implications of the regional scenario impacts as well as methodological aspects are discussed. Overall, FoPIA proved to be a useful tool for diagnosing regional human-environment interactions and for supporting the communication and social learning process among different stakeholder groups.
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Mitter, H., Schoenhart, M., Larcher, M., & Schmid, E. (2018). The Stimuli-Actions-Effects-Responses (SAER)-framework for exploring perceived relationships between private and public climate change adaptation in agriculture. J. Environ. Manage., 209, 286–300.
Abstract: Empirical findings on actors’ roles and responsibilities in the climate change adaptation process are rare even though cooperation between private and public actors is perceived important to foster adaptation in agriculture. We therefore developed the framework SAER (Stimuli-Actions-Effects-Responses) to investigate perceived relationships between private and public climate change adaptation in agriculture at regional scale. In particular, we explore agricultural experts’ perceptions on (i) climatic and non climatic factors stimulating private adaptation, (ii) farm adaption actions, (iii) potential on-farm and off-farm effects from adaptation, and (iv) the relationships between private and public adaptation. The SAER-framework is built on a comprehensive literature review and empirical findings from semi structured interviews with agricultural experts from two case study regions in Austria. We find that private adaptation is perceived as incremental, systemic or transformational. It is typically stimulated by a mix of bio-physical and socio-economic on-farm and off-farm factors. Stimulating factors related to climate change are perceived of highest relevance for systemic and transformational adaptation whereas already implemented adaptation is mostly perceived to be incremental. Perceived effects of private adaptation are related to the environment, weather and climate, quality and quantity of agricultural products as well as human, social and economic resources. Our results also show that public adaptation can influence factors stimulating private adaptation as well as adaptation effects through the design and development of the legal, policy and organizational environment as well as the provision of educational, informational, financial, and technical infrastructure. Hence, facilitating existing and new collaborations between private and public actors may enable farmers to adapt effectively to climate change. (C) 2018 Elsevier Ltd. All rights reserved.
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