Variability of effects of spatial climate data aggregation on regional yield simulation by crop models

Hoffmann, Holger; Zhao, Gang; Bussel, L.G.J. van; Enders, Andreas; Specka, Xenia; Sosa, Carmen; Yeluripati, Jagadeesh; Tao, Fulu; Constantin, Julie; Raynal, Hélène; Teixeira, Edmar; Grosz, Balázs; Doro, Luca; Zhao, Zhigan; Wang, E.; Nendel, Claas; Kersebaum, Kurt Christian; Haas, Edwin; Kiese, Ralf; Klatt, Steffen; Eckersten, Henrik; Vanuytrecht, Eline; Kuhnert, Matthias; Lewan, Elisabet; Rötter, Reimund P.; Roggero, Pier Paolo; Wallach, Daniel; Cammarano, Davide; Asseng, Senthold; Krauss, Gunther; Siebert, Stefan; Gaiser, Thomas; Ewert, Frank

doi:10.3354/cr01326

Cited by 43 publications

(37 citation statements)

References 67 publications

(41 reference statements)

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“…The groups of models with medium and strong aggregation effects are all models that determine NPP based on the radiation use efficiency, while the other models use temperature based approaches. Hoffmann et al (2015) investigated the aggregation effect on yields for the same set of models and the same region and suggested that the aggregation effect on radiation may not be much higher than on temperature, but the models might be sensitive to changes on radiation values. Despite trends of decreasing temperature and solar radiation at coarser resolution ( Fig.…”

Section: Model Specific Aggregation Effectmentioning

confidence: 98%

“…The models were not calibrated for the study area, but were adjusted based on 30-year yield averages of about 8 t ha −1 for winter wheat and 14 t ha −1 for silage maize. The weather data, presented and discussed by Zhao et al (2015) and Hoffmann et al (2015), show a 30 year average temperature of 9.7 • C, an average annual precipitation of 899 mm and mean annual global radiation of 3758 MJ m −2 a −1 with the standard deviations of 1.2 • C, 214.0 mm a −1 and 169.4 MJ m −2 a −1 , respectively. The coldest year was 2004, with an average temperature of 8.9 • C and the warmest year was 1983 (11.2 • C).…”

Section: Study Areamentioning

confidence: 99%

“…The settings for management are presented by Hoffmann et al (2015). The sowing date is fixed for all models, but the harvest date can vary for the phenological models.…”

Section: Modelling Applicationsmentioning

confidence: 99%

“…The chosen soil type is a sandy loam, which is typical for this region and the management includes ploughing, sowing, harvest and three (130, 52, 26 kg ha −1 ) and two (30, 208 kg ha −1 ) fertilizer applications during spring for wheat and maize, respectively (for details see Hoffmann et al, 2015). The models were not calibrated for the study area, but were adjusted based on 30-year yield averages of about 8 t ha −1 for winter wheat and 14 t ha −1 for silage maize.…”

Section: Study Areamentioning

confidence: 99%

“…de Wit et al (2005) and Hoffmann et al (2015) investigated the impact of climate data aggregation on crop yields. While de Wit et al (2005) varied precipitation and solar radiation only on the resolutions 10 km and 50 km, Hoffmann et al (2015) differentiated between five different resolutions between 1 and 100 km and also considered aggregation effects of temperature for 13 models. Both studies found only slight impacts of data aggregation on simulated yield over longer time periods at a regional scale.…”

Section: Introductionmentioning

confidence: 99%

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

Kuhnert

Yeluripati

Smith

et al. 2017

European Journal of Agronomy

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a b s t r a c tFor 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 km 2 ), 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

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Section: Model Specific Aggregation Effectmentioning

confidence: 98%

Section: Study Areamentioning

confidence: 99%

“…The settings for management are presented by Hoffmann et al (2015). The sowing date is fixed for all models, but the harvest date can vary for the phenological models.…”

Section: Modelling Applicationsmentioning

confidence: 99%

Section: Study Areamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Kuhnert

Yeluripati

Smith

et al. 2017

European Journal of Agronomy

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An advanced stochastic weather generator for simulating 2‐D high‐resolution climate variables

Peleg

Fatichi

Paschalis

et al. 2017

J Adv Model Earth Syst

116

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A new stochastic weather generator, Advanced WEather GENerator for a two‐dimensional grid (AWE‐GEN‐2d) is presented. The model combines physical and stochastic approaches to simulate key meteorological variables at high spatial and temporal resolution: 2 km × 2 km and 5 min for precipitation and cloud cover and 100 m × 100 m and 1 h for near‐surface air temperature, solar radiation, vapor pressure, atmospheric pressure, and near‐surface wind. The model requires spatially distributed data for the calibration process, which can nowadays be obtained by remote sensing devices (weather radar and satellites), reanalysis data sets and ground stations. AWE‐GEN‐2d is parsimonious in terms of computational demand and therefore is particularly suitable for studies where exploring internal climatic variability at multiple spatial and temporal scales is fundamental. Applications of the model include models of environmental systems, such as hydrological and geomorphological models, where high‐resolution spatial and temporal meteorological forcing is crucial. The weather generator was calibrated and validated for the Engelberg region, an area with complex topography in the Swiss Alps. Model test shows that the climate variables are generated by AWE‐GEN‐2d with a level of accuracy that is sufficient for many practical applications.

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The implication of spatial interpolated climate data on biophysical modelling in agricultural systems

Liu

Wang³

et al. 2019

Intl Journal of Climatology

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Spatial modelling of agricultural production has been more frequently analysed to assist with regional and long‐term planning. Typically, point‐scale crop models are used to construct these analyses with researchers using various sources of input data, including observed and interpolated gridded climate data. Understanding the implication of data choice on production estimates is crucial to appreciate the consequences of selecting methodological approaches for agricultural model outputs. In this study, we compared site observed climate data and gridded data sets interpolated from site observations that are commonly used the Australian climate data set. We assessed the consequences of the differences in climate variables (biases) to the modelled outputs. Our results showed that the major differences between gridded and observed climate data sets were for rainfall variables. Interpolated gridded data tended to have larger rainfall frequency and smaller rainfall intensity, leading to lower surface runoff and higher soil evaporation that caused less plant water uses and less nitrogen uptake, and ultimately resulted in biases in simulated crop biomass and yield. In addition, the results indicated that agricultural models implemented with gridded data could produce similar overall model outputs as those driven by observed climate data, but can result in more uncertainties in simulated spatial outputs. This is particularly evident in regions with high rainfall. Our results showed that applying agricultural models with observed data and then interpolating these results spatially might be the optimal approach to minimize the biases in production modelling outputs and reduce computing time and storage.

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Variability of effects of spatial climate data aggregation on regional yield simulation by crop models

Cited by 43 publications

References 67 publications

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

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

An advanced stochastic weather generator for simulating 2‐D high‐resolution climate variables

The implication of spatial interpolated climate data on biophysical modelling in agricultural systems

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