Abstract. The incorporation of a comprehensive crop module in land surface models offers the possibility to study the effect of agricultural land use and land management changes on the terrestrial water, energy, and biogeochemical cycles. It may help to improve the simulation of biogeophysical and biogeochemical processes on regional and global scales in the framework of climate and land use change. In this study, the performance of the crop module of the Community Land Model version 5 (CLM5) was evaluated at point scale with site-specific field data focusing on the simulation of seasonal and inter-annual variations in crop growth, planting and harvesting cycles, and crop yields, as well as water, energy, and carbon fluxes. In order to better represent agricultural sites, the model was modified by (1) implementing the winter wheat subroutines following Lu et al. (2017) in CLM5; (2) implementing plant-specific parameters for sugar beet, potatoes, and winter wheat, thereby adding the two crop functional types (CFTs) for sugar beet and potatoes to the list of actively managed crops in CLM5; and (3) introducing a cover-cropping subroutine that allows multiple crop types on the same column within 1 year. The latter modification allows the simulation of cropping during winter months before usual cash crop planting begins in spring, which is an agricultural management technique with a long history that is regaining popularity as it reduces erosion and improves soil health and carbon storage and is commonly used in the regions evaluated in this study. We compared simulation results with field data and found that both the new crop-specific parameterization and the winter wheat subroutines led to a significant simulation improvement in terms of energy fluxes (root-mean-square error, RMSE, reduction for latent and sensible heat by up to 57 % and 59 %, respectively), leaf area index (LAI), net ecosystem exchange, and crop yield (up to 87 % improvement in winter wheat yield prediction) compared with default model results. The cover-cropping subroutine yielded a substantial improvement in representation of field conditions after harvest of the main cash crop (winter season) in terms of LAI magnitudes, seasonal cycle of LAI, and latent heat flux (reduction of wintertime RMSE for latent heat flux by 42 %). Our modifications significantly improved model simulations and should therefore be applied in future studies with CLM5 to improve regional yield predictions and to better understand large-scale impacts of agricultural management on carbon, water, and energy fluxes.

<p>The incorporation of a comprehensive crop module in land surface models offers the possibility to study the effect of agricultural land use and land management changes on the terrestrial water, energy and biogeochemical cycles. It may help to improve the simulation of biogeophysical and biogeochemical processes on regional and global scales in the framework of climate and land use change. In this study, the performance of the crop module of the Community Land Model version 5 (CLM5) was evaluated at point scale with site specific field data focussing on the simulation of seasonal and inter-annual variations in crop growth, planting and harvesting cycles, and crop yields as well as water, energy and carbon fluxes. In order to better represent agricultural sites, the model was modified by (1) implementing the winter wheat subroutines after Lu et al. (2017) in CLM5; (2) implementing plant specific parameters for sugar beet, potatoes and winter wheat, thereby adding the two crop functional types (CFT) for sugar beet and potatoes to the list of actively managed crops in CLM5; (3) introducing a cover cropping subroutine that allows multiple crop types on the same column within one year. The latter modification allows the simulation of cropping during winter months before usual cash crop planting begins in spring, which is an agricultural management technique with a long history that is regaining popularity to reduce erosion and improve soil health and carbon storage and is commonly used in the regions evaluated in this study. We compared simulation results with field data and found that both the new crop specific parameterization, as well as the winter wheat subroutines, led to a significant simulation improvement in terms of energy fluxes (RMSE reduction for latent and sensible heat by up to 57 % and 59 %, respectively), leaf area index (LAI), net ecosystem exchange and crop yield (up to 87 % improvement in winter wheat yield prediction) compared with default model results. The cover cropping subroutine yielded a substantial improvement in representation of field conditions after harvest of the main cash crop (winter season) in terms of LAI magnitudes and seasonal cycle of LAI, and latent heat flux (reduction of winter time RMSE for latent heat flux by 42 %). Our modifications significantly improved model simulations and should therefore be applied in future studies with CLM5 to improve regional yield predictions and to better understand large-scale impacts of agricultural management on carbon, water and energy fluxes.</p>

Abstract. The incorporation of a comprehensive crop module in land surface models offers the possibility to study the effect of agricultural land use and land management changes on the terrestrial water, energy and biogeochemical cycles. It may help to improve the simulation of biogeophysical and biogeochemical processes on regional and global scales in the framework of climate and land use change. In this study, the performance of the crop module of the Community Land Model version 5 (CLM5) was evaluated at point scale with site specific field data focussing on the simulation of seasonal and inter-annual variations in crop growth, planting and harvesting cycles, and crop yields as well as water, energy and carbon fluxes. In order to better represent agricultural sites, the model was modified by (1) implementing the winter wheat subroutines after Lu et al. (2017) in CLM5; (2) implementing plant specific parameters for sugar beet, potatoes and winter wheat, thereby adding these crop functional types (CFT) to the list of actively managed crops in CLM5; (3) introducing a cover cropping subroutine that allows multiple crop types on the same column within one year. The latter modification allows the simulation of cropping during winter months before usual cash crop planting begins in spring, which is a common agricultural management technique in humid and sub-humid regions. We compared simulation results with field data and found that both the parameterization of the CFTs, as well as the winter wheat subroutines, led to a significant simulation improvement in terms of energy fluxes, leaf area index (LAI), net ecosystem exchange (RMSE reduction for latent and sensible heat by up to 57 % and 59 % respectively) and crop yield (up to 87 % improvement in winter wheat yield prediction) compared with default model results. The cover cropping subroutine yielded a substantial improvement in representation of field conditions after harvest of the main cash crop (winter season) in terms of LAI curve and latent heat flux (reduction of winter time RMSE for latent heat flux by 42 %). We anticipate that our model modifications offer opportunities to improve yield predictions, to study the effects of large-scale cover cropping on energy fluxes, soil carbon and nitrogen pools, and soil water storage in future studies with CLM5.

<p>Global climate change with a predicted increase in weather extremes entails vulnerability and new challenges to regional agriculture. While the general impacts of climate change on global food security are a much studied topic, the implications for regional inter-annual yield variability remain unclear. In this study, we analysed the effects of weather trends on regional crop productivity within two agriculturally managed regions in different climate zones, simulated with the latest version of the Community Land Model (version 5.0) over two decades (1999-2019). We evaluated the models’ potential to represent the inter-annual variability of crop yield in comparison to recorded yield variability and different weather indicators, e.g., drought index and growing season length and evaluated which variables (i.e., temperature, precipitation, initial soil moisture content) dominantly drive changes in CLM5-predicted yield variability. The simulation results were able to reproduce the sign of crop yield anomalies, and thus provide a basis on which to study the effects of different weather patterns on inter-annual yield variability. However, the simulations showed limitations in correctly capturing inter-annual differences of crop yield in terms of total magnitudes (up to 10 times lower than in official records). Our results indicate that these limitation arise mainly from uncertainties in the representation of the subsurface soil moisture regime and a corresponding lack of sensitivity towards drought stress. Insights from this work were used to summarize implications for future analysis of CLM5-BGC simulation results over agriculturally managed land and allowed us to discuss and investigate possible technical model improvements.</p>

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Supplement to chapter 2.2: State-wide agricultural statisticsTable S1: Cropping area and production of major cash crops in Victoria, Australia, from 2014/15 to 2020/21, and six year average (Source: ABARES, 2020). YearArea (''000 ha) Production (kt) Yield (t/ha)

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Interactive comment on "Improving the representation of cropland sites in the Community Land Model (CLM) version 5.0" by Theresa Boas et al. Theresa Boas et al.