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Klatt, S.; Haas, E.; Kiese, R. |
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Title |
Responses of soil N2O emissions and nitrate leaching on climate input data aggregation: a biogeochemistry model ensemble study |
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2014 |
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Models are increasingly used to estimate greenhouse gas emissions at site to regional and national scales and are outlined as the most advanced methodology for national emission reporting in the framework of UNFCCC. Process-based models incorporate the major processes of the carbon and nitrogen cycle and are thus thought to be widely applicable at various spatial and temporal scales. The definition of the spatial scale is determined by the objectives. GHG emission reporting requests spatially and temporally aggregated information whereas for the assessment of mitigation options on hot spots and hot moments of emissions a high spatial simulation resolution is required. In addition, other input data also determine the simulation scale. Low resolution simulations needs less effort in computation and data management, but important details could be lost during the process of data aggregation associated with high uncertainties of the simulation results. This study presents the aggregation effects of climate input data on the simulations of soil N2O emissions and nitrate leaching by comparing different biogeochemistry models. Using process-based models (DailyDayCent, LandscapeDNDC, Stics, Mode, Coup, Epic), we simulated a 30-year cropping system for two crops (winter wheat and maize monocultures) under water- and nutrient-limited conditions based on a 1 km resolution climate dataset. We aggregated the climate data to resolutions of 10, 25, 50, and 100 km and repeated the simulations on these spatial scales. We calculated the N2O emissions as well as the nitrate leaching on all scales. Results will be presented and discussed. |
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FACCE MACSUR Mid-term Scientific Conference |
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3(S) Sassari, Italy |
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FACCE MACSUR Mid-term Scientific Conference, 2014-04-01 to 2014-04-04, Sassari, Italy |
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MA @ admin @ |
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5123 |
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Zhao, G.; Hoffmann, H.; Van Bussel, L.; Enders, A.; Specka, X.; Sosa, C.; Yeluripati, J.; Tao, F.; Constantin, J.; Teixeira, E.; Grosz, B.; Doro, L.; Zhao, Z.; Nendel, C.; Raynal, H.; Eckersten, H.; Haas, E.; Kuhnert, M.; Lewan, E.; Bach, M.; Kersebaum, K.-C.; Roggero, P.P.; Rötter, R.; Wallach, D.; Krauss, G.; Siebert, S.; Gaiser, T.; Kiese, R.; Wang, E.; Ewert, F. |
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Title |
Weather data aggregation’s effects on simulation of cropping systems: a model, production system and crop comparison |
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Conference Article |
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2014 |
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Interactions of climate, soil and management practices in cropping systems can be simulated at different scales to provide information for decision making. Low resolution simulation need less effort, but important details could be lost through data aggregation effects (DAEs). This paper aims to provide a general method to assess the DAEs on weather data and the simulation of cropping systems, and further investigate how the DAEs vary with changing crop models, crops, variables and production systems. A 30-year continuous cropping system was simulated for winter wheat and silage maize and potential, water-limited and water-nitrogen-limited production situations. Climate data of 1 km resolution and aggregations to resolutions of 10 to 100 km was used as input for the simulations. The data aggregation narrowed the variation of weather data and DAEs increased with increasingly coarser spatial resolution, causing the loss of hot spots in simulated results. Spatial patterns were similar across different resolutions. Consistent with DAEs on weather data, the DAEs on simulated yield (0 to 1.2 t ha-1 for winter wheat and 0 to 1.7 t ha-1 for silage maize), evapotranspiration (3 to 45 mm yr-1 for winter wheat and 4 to 40 mm yr-1 for silage maize), and water use efficiency (0.02 to 0.25 kg m-3 for winter wheat and 0.04 to 0.4 kg m-3 for silage maize), increased with coarser spatial resolution. Thus, if spatial information is needed for local management decisions, higher resolution is needed to adequately capture the spatial heterogeneity or hot spots in the region. |
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FACCE MACSUR Mid-term Scientific Conference |
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3(S) Sassari, Italy |
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FACCE MACSUR Mid-term Scientific Conference, 2014-04-01 to 2014-04-04, Sassari, Italy |
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MA @ admin @ |
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5141 |
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Hoffmann, H.; Zhao, G.; Constantin, J.; Raynal, H.; Wallach, D.; Coucheney, E.; Sosa, C.; Lewan, E.; Eckersten, H.; Specka, X.; Kersebaum, K.-C.; Nendel, C.; Grosz, B.; Dechow, R.; Kuhnert, M.; Yeluripati, J.; Kiese, R.; Haas, E.; Klatt, S.; Teixeira, E.; Bindi, M.; Trombi, G.; Moriondo, M.; Doro, L.; Roggero, P.P.; Zhao, Z.; Wang, E.; Vanuytrecht, E.; Tao, F.; Rötter, R.; Cammarano, D.; Asseng, S.; Weihermüller, L.; Siebert, S.; Gaiser, T.; Ewert, F. |
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Title |
Effects of soil and climate input data aggregation on modelling regional crop yields |
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2015 |
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MACSUR Science Conference |
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MACSUR Science Conference, 2015-04-08 to 2015-04-10, Reading, United Kingdom |
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MA @ admin @ |
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5037 |
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Grosz, B.; Dechow, R.; Hoffmann, H.; Zhao, G.; Constantin, J.; Raynal, H.; Wallach, D.; Coucheney, E.; Lewan, E.; Eckersten, H.; Specka, X.; Kersebaum, K.-C.; Nendel, C.; Kuhnert, M.; Yeluripati, J.; Kiese, R.; Haas, E.; Klatt, S.; Teixeira, E.; Bindi, M.; Trombi, G.; Moriondo, M.; Doro, L.; Roggero, P.P.; Zhao, Z.; Wang, E.; Vanuytrecht, E.; Tao, F.; Rötter, R.; Cammarano, D.; Asseng, S.; Weihermüller, L.; Siebert, S.; Gaiser, T.; Ewert, F |
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Title |
The implication of input data aggregation on upscaling of soil organic carbon changes |
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2015 |
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Abbreviated Series Title |
MACSUR Science Conference |
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MACSUR Science Conference, 2015-04-08 to 2015-04-10, Reading, United Kingdom |
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MA @ admin @ |
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5038 |
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