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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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Conference Article |
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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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Call Number |
MA @ admin @ |
Serial |
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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Year |
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 @ |
Serial |
5141 |
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Author |
Kuhnert, M.; Yeluripati, J.; Smith, P.; Hoffmann, H.; van Oijen, M.; Constantin, J.; Coucheney, E.; Dechow, R.; Eckersten, H.; Gaiser, T.; Grosz, B.; Haas, E.; Kersebaum, K.-C.; Kiese, R.; Klatt, S.; Lewan, E.; Nendel, C.; Raynal, H.; Sosa, C.; Specka, X.; Teixeira, E.; Wang, E.; Weihermüller, L.; Zhao, G.; Zhao, Z.; Ogle, S.; Ewert, F. |
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Title |
Impact analysis of climate data aggregation at different spatial scales on simulated net primary productivity for croplands |
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Journal Article |
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Year |
2016 |
Publication |
European Journal of Agronomy |
Abbreviated Journal |
European Journal of Agronomy |
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Volume |
88 |
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41-52 |
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Keywords |
Net primary production; NPP; Scaling; Extreme events; Crop modelling; Climate Data; aggregation |
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For spatial crop and agro-systems modelling, there is often a discrepancy between the scale of measured driving data and the target resolution. Spatial data aggregation is often necessary, which can introduce additional uncertainty into the simulation results. Previous studies have shown that climate data aggregation has little effect on simulation of phenological stages, but effects on net primary production (NPP) might still be expected through changing the length of the growing season and the period of grain filling. This study investigates the impact of spatial climate data aggregation on NPP simulation results, applying eleven different models for the same study region (∼34,000 km2), situated in Western Germany. To isolate effects of climate, soil data and management were assumed to be constant over the entire study area and over the entire study period of 29 years. Two crops, winter wheat and silage maize, were tested as monocultures. Compared to the impact of climate data aggregation on yield, the effect on NPP is in a similar range, but is slightly lower, with only small impacts on averages over the entire simulation period and study region. Maximum differences between the five scales in the range of 1–100 km grid cells show changes of 0.4–7.8% and 0.0–4.8% for wheat and maize, respectively, whereas the simulated potential NPP averages of the models show a wide range (1.9–4.2 g C m−2 d−1 and 2.7–6.1 g C m−2 d−1for wheat and maize, respectively). The impact of the spatial aggregation was also tested for shorter time periods, to see if impacts over shorter periods attenuate over longer periods. The results show larger impacts for single years (up to 9.4% for wheat and up to 13.6% for maize). An analysis of extreme weather conditions shows an aggregation effect in vulnerability up to 12.8% and 15.5% between the different resolutions for wheat and maize, respectively. Simulations of NPP averages over larger areas (e.g. regional scale) and longer time periods (several years) are relatively insensitive to climate data. |
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2016-09-13 |
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English |
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Newsletter July |
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1161-0301 |
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Notes |
CropM, ft_macsur |
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Call Number |
MA @ admin @ |
Serial |
4775 |
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Author |
Zhao, G.; Hoffmann, H.; van Bussel, L.G.J.; Enders, A.; Specka, X.; Sosa, C.; Yeluripati, J.; Tao, F.L.; Constantin, J.; Raynal, H.; Teixeira, E.; Grosz, B.; Doro, L.; Zhao, Z.G.; Nendel, C.; Kiese, R.; Eckersten, H.; Haas, E.; Vanuytrecht, E.; Wang, E.; Kuhnert, M.; Trombi, G.; Moriondo, M.; Bindi, M.; Lewan, E.; Bach, M.; Kersebaum, K.C.; Rotter, R.; Roggero, P.P.; Wallach, D.; Cammarano, D.; Asseng, S.; Krauss, G.; Siebert, S.; Gaiser, T.; Ewert, F. |
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Title |
Effect of weather data aggregation on regional crop simulation for different crops, production conditions, and response variables |
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Journal Article |
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Year |
2015 |
Publication |
Climate Research |
Abbreviated Journal |
Clim. Res. |
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65 |
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141-157 |
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Keywords |
crop model; model comparison; spatial resolution; data aggregation; spatial heterogeneity; scaling; climate-change scenarios; sub-saharan africa; winter-wheat; spatial-resolution; yield response; input data; systems simulation; large-scale; soil data; part i |
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We assessed the weather data aggregation effect (DAE) on the simulation of cropping systems for different crops, response variables, and production conditions. Using 13 process-based crop models and the ensemble mean, we simulated 30 yr continuous cropping systems for 2 crops (winter wheat and silage maize) under 3 production conditions for the state of North Rhine-Westphalia, Germany. The DAE was evaluated for 5 weather data resolutions (i.e. 1, 10, 25, 50, and 100 km) for 3 response variables including yield, growing season evapotranspiration, and water use efficiency. Five metrics, viz. the spatial bias (Delta), average absolute deviation (AAD), relative AAD, root mean squared error (RMSE), and relative RMSE, were used to evaluate the DAE on both the input weather data and simulated results. For weather data, we found that data aggregation narrowed the spatial variability but widened the., especially across mountainous areas. The DAE on loss of spatial heterogeneity and hotspots was stronger than on the average changes over the region. The DAE increased when coarsening the spatial resolution of the input weather data. The DAE varied considerably across different models, but changed only slightly for different production conditions and crops. We conclude that if spatially detailed information is essential for local management decision, higher resolution is desirable to adequately capture the spatial variability for heterogeneous regions. The required resolution depends on the choice of the model as well as the environmental condition of the study area. |
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0936-577x |
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CropM, ft_macsur |
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Call Number |
MA @ admin @ |
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4754 |
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Author |
Molina-Herrera, S.; Haas, E.; Klatt, S.; Kraus, D.; Augustin, J.; Magliulo, V.; Tallec, T.; Ceschia, E.; Ammann, C.; Loubet, B.; Skiba, U.; Jones, S.; Brümmer, C.; Butterbach-Bahl, K.; Kiese, R. |
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A modeling study on mitigation of N2O emissions and NO3 leaching at different agricultural sites across Europe using LandscapeDNDC |
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Journal Article |
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Year |
2016 |
Publication |
Science of the Total Environment |
Abbreviated Journal |
Science of the Total Environment |
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Volume |
553 |
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128-140 |
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Keywords |
Agricultural management; LandscapeDNDC; Mitigation; N₂O emission; NO₃ leaching; Optimization |
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The identification of site-specific agricultural management practices in order to maximize yield while minimizing environmental nitrogen losses remains in the center of environmental pollution research. Here, we used the biogeochemical model LandscapeDNDC to explore different agricultural practices with regard to their potential to reduce soil N2O emissions and NO3 leaching while maintaining yields. In a first step, the model was tested against observations of N2O emissions, NO3 leaching, soil micrometeorology as well as crop growth for eight European cropland and grassland sites. Across sites, LandscapeDNDC predicts very well mean N2O emissions (r(2)=0.99) and simulates the magnitude and general temporal dynamics of soil inorganic nitrogen pools. For the assessment of site-specific mitigation potentials of environmental nitrogen losses a Monte Carlo optimization technique considering different agricultural management options (i.e., timing of planting, harvest and fertilization, amount of applied fertilizer as well as residue management) was used. The identified optimized field management practices reduce N2O emissions and NO3 leaching from croplands on average by 21% and 31%, respectively. Likewise, average reductions of 55% for N2O emissions and 16% for NO3 leaching are estimated for grasslands. For mitigating environmental loss – while maintaining yield levels – it was most important to reduce fertilizer application rates by in average 10%. Our analyses indicate that yield scaled N2O emissions and NO3 leaching indicate possible improvements of nitrogen use efficiencies in European farming systems. Moreover, the applied optimization approach can be used also in a prognostic way to predict optimal timings and fertilization options (rates and splitting) upon accurate weather forecasts combined with the knowledge of modeled soil nutrient availability and plant nitrogen demand. |
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0048-9697 |
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CropM, ft_macsur |
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MA @ admin @ |
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4727 |
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