Implementation of dynamic crop growth processes into a land surface model: evaluation of energy, water and carbon fluxes under corn and soybean rotation

Abstract

Abstract. Worldwide expansion of agriculture is impacting the earth's climate by altering carbon, water, and energy fluxes, but the climate in turn is impacting crop production. To study this two-way interaction and its impact on seasonal dynamics of carbon, water, and energy fluxes, we implemented dynamic crop growth processes into a land surface model, the Integrated Science Assessment Model (ISAM). In particular, we implemented crop-specific phenology schemes and dynamic carbon allocation schemes. These schemes account for light, water, and nutrient stresses while allocating the assimilated carbon to leaf, root, stem, and grain pools. The dynamic vegetation structure simulation better captured the seasonal variability in leaf area index (LAI), canopy height, and root depth. We further implemented dynamic root distribution processes in soil layers, which better simulated the root response of soil water uptake and transpiration. Observational data for LAI, above-and belowground biomass, and carbon, water, and energy fluxes were compiled from two AmeriFlux sites, Mead, NE, and Bondville, IL, USA, to calibrate and evaluate the model performance. For the purposes of calibration and evaluation, we use a corn-soybean (C4-C3) rotation system over the period 2001-2004. The calibrated model was able to capture the diurnal and seasonal patterns of carbon assimilation and water and energy fluxes for the corn-soybean rotation system at these two sites. Specifically, the calculated gross primary production (GPP), net radiation fluxes at the top of the canopy, and latent heat fluxes compared well with observations. The largest bias in model results was in sensible heat flux (SH) for corn and soybean at both sites. The dynamic crop growth simulation better captured the seasonal variability in carbon and energy fluxes relative to the static simulation implemented in the original version of ISAM. Especially, with dynamic carbon allocation and root distribution processes, the model's simulated GPP and latent heat flux (LH) were in much better agreement with observational data than for the static root distribution simulation. Modeled latent heat based on dynamic growth processes increased by 12-27% during the growing season at both sites, leading to an improvement in modeled GPP by 13-61 % compared to the estimates based on the original version of the ISAM.

Biotic responses buffer warming‐induced soil organic carbon loss in Arctic tundra

“…In the permafrost region where the mean annual growing season temperature is much lower than 20°C, those models are likely to exhibit an increased photosynthetic rate under warming conditions. Phenological responses to warming, such as changes in leaf onset dates, are parameterized with an accumulated temperature, e.g., growing degree days (GDDs), for most models, including CLM4.5, G'DAY (Botta, Viovy, Ciais, Friedlingstein, & Monfray, 2000), ISAM (Song, Jain, & Mcisaac, 2013), LPJ (Sitch et al, 2003), LPJ-GUESS (Smith et al, 2014), O-CN (Krinner et al, 2005), ORCHIDEE (Krinner et al, 2005), SDGVM (Woodward & Lomas, 2004), and TECO (Weng & Luo, 2008). Warming usually has leaf The insert plot shows the difference between the modeled SOC with and without parameter adjustments However, the estimated changes in SOC pool baseline turnover rates in this study may not be reproducible by many of the land models that follow a similar structure of first-order kinetics of C transfer among multiple pools, as in the CENTURY model (Parton, Schimel, Cole, & Ojima, 1987;Parton, Stewart, & Cole, 1988).…”

Evaluation of JULES-crop performance against site observations of irrigated maize from Mead, Nebraska

“…It was not possible to fit p root accurately to the expression from de Vries et al (1989) for approximately a DVI of 1.0 to 1.4 given the constraints above. In addition, in reality, water stress can also increase the fraction of NPP going to the roots (see discussion in, e.g., de Vries et al, 1989 andSong et al, 2013), but this effect is not taken into account in JULEScrop. However, we do not see a notable difference between the irrigated sites US-Ne1 and US-Ne2 (blue and green lines respectively) and rainfed site US-Ne3 (red lines) in Fig.…”

“…Similarly, the incorporation of a phenology scheme into the SImple Biosphere (SIB) model improved the prediction of both leaf area index and carbon fluxes for maize, soybean and wheat crops at a number of sites in North America (Lokupitiya et al, 2009). Song et al (2013) implemented crop-specific phenology and carbon allocation schemes into the Integrated Science Assessment Model (ISAM) landsurface model and calibrated against observational data from a corn-soybean rotation at Mead and Bondville (US) sites. This model was able to reproduce the diurnal and seasonal variability of carbon, water and energy fluxes.…”

Climate-driven uncertainties in modeling terrestrial energy and water fluxes: a site-level to global-scale analysis

“…Utilizing the observed site-level meteorology and eddy covariance data, we used the 'trial and error' approach to tune several PFT-specific parameters in ISAM, to concurrently optimize the modeled GPP, LE, and H. An analogous model calibration approach has also been used in other studies using the ISAM framework (El-Masri et al, 2013;Song et al, 2013). During model calibration of each PFT, our goal was to optimize the overall model performance across sites within the PFT.…”