Improving the representation of cropland sites in the Community Land Model (CLM) version 5.0

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.

Midwest US Croplands Determine Model Divergence in North American Carbon Fluxes

“…Overall, our results indicate an urgent need to bring about a more realistic representation of the seasonal cycles of cropland carbon uptake to prognostic models for better understanding of space‐time patterns in North American carbon fluxes. Recent advances have shown promise in reducing phenology biases through improved mechanistic representation of leaf area index dynamics (Wu et al., 2016), the implementation of active management processes (Lombardozzi et al., 2020), crop‐specific parameterization (Boas et al., 2021; Peng et al., 2018), and/or intensive calibration with flux tower data (Bilionis et al., 2015; Chen et al., 2018). With concerted model development, calibration, and evaluation, the next‐generation of TBMs will undoubtedly be better positioned to deliver more robust estimation of North American carbon fluxes.…”

CLM-FruitTree: A new sub-model for deciduous fruit trees in the Community Land Model (CLM5)

“…6). Low inter-annual variability of yield has also been observed in previous crop simulations with CLM5 for winter wheat (Boas et al, 2021) suggesting that additional drivers of yield variability such as specific management practices are not represented with sufficient detail in CLM5.…”

Integrating partitioned evapotranspiration data into hydrologic models: Vegetation parameterization and uncertainty quantification of simulated plant water use

“…The stomatal resistance from the Ball‐Berry approach is combined with plant‐specific parameters and atmospheric variables to calculate transpiration. Due to the number of parameters and the difficulty in measuring many of them, the parameters that control transpiration are assumed constant across all vegetation types within the LSMs, even though evidence exists that this is not accurate (Boas et al, 2021; Kattage et al, 2009; Lombardozzi et al, 2020). Due to this limitation, many studies have focused on generic croplands or row crops such as corn and soybean to predict ET in agricultural settings (Lombardozzi et al, 2020; Rajib et al, 2018; Srivastava et al, 2020).…”

“…Sensitivity analyses of the CLM showed that the maximum rate of carboxylation and the Ball‐Berry stomatal resistance slope parameter are the most sensitive parameters that affect simulated ET (Göhler et al, 2013; Yamazaki et al, 2013). More recent efforts have added a crop tool to new versions of CLM (versions 4.5 and 5.0) and parameterized certain row crops using total ET estimated from flux towers, focusing on winter vegetation (Boas et al, 2021; Lu et al, 2017). To our knowledge, vegetation parameterization within CLM has only been done using the total ET , rather than T , which could bias the vegetation‐specific parameterization.…”