Impact analysis of climate data aggregation at different spatial scales on simulated net primary productivity for croplands

Matthias Kuhnert, Jagadeesh Yeluripati, Peter Smith, Holger Hoffmann, Marcel van Oijen, Julie Constantin, Elsa Coucheney, Rene Dechow, Henrik Eckersten, Thomas Gaiser, Balász Grosz, Edwin Haas, Kurt-Christian Kersebaum, Ralf Kiese, Steffen Klatt, Elisabet Lewan, Claas Nendel, Helene Raynal, Carmen Sosa, Xenia Specka & 7 others Edmar Teixeira, Enli Wang, Lutz Weihermüller, Gang Zhao, Zhigan Zhao, Stephen Ogle, Frank Ewert

Research output: Contribution to journalArticle

13 Citations (Scopus)

Abstract

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−1 for 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 aggregation. However, the scale of climate data is more relevant for impacts on annual averages of NPP or if the period is strongly affected or dominated by drought stress. There should be an awareness of the greater uncertainty for the NPP values in these situations if data are not available at high resolution. On the other hand, the results suggest that there is no need to simulate at high resolution for long term regional NPP averages based on the simplified assumptions (soil and management constant in time and space) used in this study.
Original languageEnglish
Pages (from-to)41-52
Number of pages12
JournalEuropean Journal of Agronomy
Volume88
Early online date29 Jun 2016
DOIs
Publication statusPublished - Aug 2017

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net primary production
primary productivity
climate
productivity
wheat
maize
corn
simulation
uncertainty
spatial data
crops
filling period
corn silage
space and time
crop
winter wheat
silage
analysis
cropland
soil

Keywords

  • climate
  • crop modelling
  • data aggregation
  • extreme events
  • net primary production
  • NPP
  • scaling

Cite this

Impact analysis of climate data aggregation at different spatial scales on simulated net primary productivity for croplands. / Kuhnert, Matthias; Yeluripati, Jagadeesh; Smith, Peter; Hoffmann, Holger; van Oijen, Marcel; Constantin, Julie; Coucheney, Elsa; Dechow, Rene; Eckersten, Henrik; Gaiser, Thomas; Grosz, Balász; Haas, Edwin; Kersebaum, Kurt-Christian; Kiese, Ralf ; Klatt, Steffen; Lewan, Elisabet; Nendel, Claas; Raynal, Helene; Sosa, Carmen ; Specka, Xenia; Teixeira, Edmar; Wang, Enli; Weihermüller, Lutz; Zhao, Gang; Zhao, Zhigan; Ogle, Stephen; Ewert, Frank.

In: European Journal of Agronomy, Vol. 88, 08.2017, p. 41-52.

Research output: Contribution to journalArticle

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 2017, 'Impact analysis of climate data aggregation at different spatial scales on simulated net primary productivity for croplands' European Journal of Agronomy, vol. 88, pp. 41-52. https://doi.org/10.1016/j.eja.2016.06.005
Kuhnert, Matthias ; Yeluripati, Jagadeesh ; Smith, Peter ; Hoffmann, Holger ; van Oijen, Marcel ; Constantin, Julie ; Coucheney, Elsa ; Dechow, Rene ; Eckersten, Henrik ; Gaiser, Thomas ; Grosz, Balász ; Haas, Edwin ; Kersebaum, Kurt-Christian ; Kiese, Ralf ; Klatt, Steffen ; Lewan, Elisabet ; Nendel, Claas ; Raynal, Helene ; Sosa, Carmen ; Specka, Xenia ; Teixeira, Edmar ; Wang, Enli ; Weihermüller, Lutz ; Zhao, Gang ; Zhao, Zhigan ; Ogle, Stephen ; Ewert, Frank. / Impact analysis of climate data aggregation at different spatial scales on simulated net primary productivity for croplands. In: European Journal of Agronomy. 2017 ; Vol. 88. pp. 41-52.
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abstract = "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−1 for 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 aggregation. However, the scale of climate data is more relevant for impacts on annual averages of NPP or if the period is strongly affected or dominated by drought stress. There should be an awareness of the greater uncertainty for the NPP values in these situations if data are not available at high resolution. On the other hand, the results suggest that there is no need to simulate at high resolution for long term regional NPP averages based on the simplified assumptions (soil and management constant in time and space) used in this study.",
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AU - Kuhnert, Matthias

AU - Yeluripati, Jagadeesh

AU - Smith, Peter

AU - Hoffmann, Holger

AU - van Oijen, Marcel

AU - Constantin, Julie

AU - Coucheney, Elsa

AU - Dechow, Rene

AU - Eckersten, Henrik

AU - Gaiser, Thomas

AU - Grosz, Balász

AU - Haas, Edwin

AU - Kersebaum, Kurt-Christian

AU - Kiese, Ralf

AU - Klatt, Steffen

AU - Lewan, Elisabet

AU - Nendel, Claas

AU - Raynal, Helene

AU - Sosa, Carmen

AU - Specka, Xenia

AU - Teixeira, Edmar

AU - Wang, Enli

AU - Weihermüller, Lutz

AU - Zhao, Gang

AU - Zhao, Zhigan

AU - Ogle, Stephen

AU - Ewert, Frank

N1 - This work was supported in part by the UK BBSRC through the “Modelling European Agriculture with Climate Change for Food Security” (MACSUR) project (BB/N004922/1).

PY - 2017/8

Y1 - 2017/8

N2 - 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−1 for 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 aggregation. However, the scale of climate data is more relevant for impacts on annual averages of NPP or if the period is strongly affected or dominated by drought stress. There should be an awareness of the greater uncertainty for the NPP values in these situations if data are not available at high resolution. On the other hand, the results suggest that there is no need to simulate at high resolution for long term regional NPP averages based on the simplified assumptions (soil and management constant in time and space) used in this study.

AB - 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−1 for 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 aggregation. However, the scale of climate data is more relevant for impacts on annual averages of NPP or if the period is strongly affected or dominated by drought stress. There should be an awareness of the greater uncertainty for the NPP values in these situations if data are not available at high resolution. On the other hand, the results suggest that there is no need to simulate at high resolution for long term regional NPP averages based on the simplified assumptions (soil and management constant in time and space) used in this study.

KW - climate

KW - crop modelling

KW - data aggregation

KW - extreme events

KW - net primary production

KW - NPP

KW - scaling

U2 - 10.1016/j.eja.2016.06.005

DO - 10.1016/j.eja.2016.06.005

M3 - Article

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EP - 52

JO - European Journal of Agronomy

JF - European Journal of Agronomy

SN - 1161-0301

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