Abstract
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.
Highlights
Global climate change is widely believed to have an important impact on future agriculture, and food security under the changing climate is an important research topic (Lobell et al, 2011; Aaheim et al, 2012; Ma et al, 2012; Gosling, 2013; Rosenzweig et al, 2014)
The default vernalization resulted in peak leaf area index (LAI) occurring too early in the year, leading to significantly lower photosynthesis compared to the observations
The model modifications were tested for the respective crops at four TERENO and ICOS cropland sites in Germany and Belgium, Selhausen (DE-RuS), Merzenhausen (DE-RuM), Klingenberg (DE-Kli), and Lonzée (BE-Lon), for multiple years
Summary
Global climate change is widely believed to have an important impact on future agriculture, and food security under the changing climate is an important research topic (Lobell et al, 2011; Aaheim et al, 2012; Ma et al, 2012; Gosling, 2013; Rosenzweig et al, 2014). T. Boas et al.: Representing cropland sites in CLM5 many parts of the world (Urban et al, 2012; Challinor et al, 2014; Deryng et al, 2014; Rosenzweig et al, 2014; Tai et al, 2014; Levis et al, 2018), understanding the impact of climate change on crop production and improving its prediction at local to global scales is a research topic of great importance to society. The evaluation and improvement of integrated modeling approaches, including through incorporation of improved crop phenology, to simulate realistic land management and crop yield in response to climate conditions are the focus of many studies (Stehfest et al, 2007; Olesen et al, 2011; Van den Hoof et al, 2011; Rosenzweig et al, 2014)
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