HGS RESEARCH HIGHLIGHT – Same soil, different climate: Crop model intercomparison on translocated lysimeters
Groh, J., Diamantopoulos, E., Duan, X., Ewert, F., Heinlein, F., Herbst, M., Holbak, M., Kamali, B., Kersebaum, K., Kuhnert, M., Nendel, C., Priesack, E., Steidl, J., Sommer, M., Pütz, T., Vanderborght, J., Vereecken, H., Wallor, E., Weber, T. K. D., … Gerke, H. H. (2022). Same soil, different climate: Crop model intercomparison on translocated lysimeters. In Vadose Zone Journal (Vol. 21, Issue 4). Wiley. https://doi.org/10.1002/vzj2.20202
“Simulated [actual bare soil evaporation] values agreed with observations fully for the [HydroGeoSphere] model and [multi-model] approach.”
Fig. 1. Map of selected TERENO (TERrestrial ENvironmental Observatories) SOILCan sites and associated hydrological watersheds in the northern part of Germany (adapted from Pütz et al., 2016) indicating the transfer routes of the soil monoliths from Dedelow (Dd) to Bad Lauchstädt (BL) and Selhausen (Se). The table in the left corner shows the differences (denoted as Δ) in climatic conditions for the hydrological years November 2014–October 2018 related to those at the Dedelow site as arrows (up = larger, down = smaller) for Δ annual global radiation (Rg), Δ mean annual rainfall (R), Δ mean annual grass reference evapotranspiration (ET0), Δ mean air temperature (Ta), and Δ mean daily wind speed (WS)
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In this research highlight, the authors explored the dynamics of crop modelling across diverse climatic conditions using translocated lysimeters. The study investigates how crop models perform when applied to the same soil under different climatic regimes, shedding light on the complex interplay between soil and crop dynamics amidst changing environmental conditions. Specifically, the researchers aimed to assess the predictive capabilities of crop models in simulating agronomic crop development and environmental fluxes across various climates.
11 different crop models were used in the study- utilizing data from lysimeters situated in different climatic zones. These lysimeters contained soil monoliths translocated from their original environment to regions with disparate climatic conditions, allowing for a comparison of crop and environmental fluxes under similar soil conditions but varying climates.
The findings highlight significant differences in model performance, particularly when predicting crop growth and environmental variables under conditions outside the range of the calibration data. While some models exhibited relatively accurate predictions at sites with climatic conditions similar to the calibration site, their performance was not as robust when extrapolated to sites with contrasting climates.
Fig. 4. Comparison of the model performance in terms of the normalized RMSE (nRMSE) for simulations of lysimeter data at the reference site Dedelow (i.e., model calibration) with those transferred to the locations Bad Lauchstädt (BL) and Selhausen (Se) for grain yield (GY), aboveground biomass (AgBio), leaf area index (LAI; no data at BL and Se), actual evapotranspiration (ETa), net drainage (NetQ) at 1.5-m soil depth, and average soil water content at 0-to-60-cm depth (SWC). The nRMSE for each site was averaged over the nRMSE values of the three profiles per model and site (MnRMSE). Models include AgroC (AC), Expert-N SPASS (SP), Expert-N SUCROS (SU), Expert-N CERES (CE), Expert-N GECROS (GE), HERMES (HE), MONICA (MO), THESEUS (TH), Theseus-HydroGeoSphere (HG), DailyDayCent (DC), Daisy (DY), and the multi-model mean (MM)
While HydroGeosphere does not include any crop growth module (and was therefor left out of the crop-growth intercomparison portion of the study), by leveraging HydroGeoSphere's advanced capabilities, the researchers gained insights into how changes in climate affect water fluxes and soil moisture levels, crucial factors influencing crop growth and ecosystem functioning.
HGS's ability to provide accurate estimates of hydrological parameters proved instrumental in understanding the intricate relationship between soil, water, and climate in agricultural systems. Its application allowed researchers to explore the nuances of hydrological processes, revealing how climate change stressors impact water availability and soil moisture dynamics, thereby affecting crop productivity and environmental sustainability.
This research highlight emphasizes the synergy between hydrology and crop modelling in understanding climate change impacts on agricultural systems. Leveraging HydroGeoSphere’s advanced hydrologic simulations to enrich crop modelling, the study delves into how crops respond to changing climatic conditions, paving the way for adaptive strategies in sustainable agriculture.
By spotlighting the significance of incorporating soil-related data into crop modelling, including water fluxes and system states, the research underscores the necessity for enhanced model calibration and data integration strategies. This holistic approach reveals the limitations of current crop models in predicting agronomic and environmental variables across diverse climates, driving the need for improved predictive capabilities in future agricultural assessments.
Plain Language Summary:
Crop model comparison studies typically focus on evaluating how well models predict crop growth using weather and basic soil data from the same location. However, accurately predicting crop performance becomes more challenging when considering the complex interactions between soil and crop dynamics under changing climates. In this study, researchers aimed to assess how well different crop models predict both crop growth and environmental factors related to water movement in the soil. They used data from weighing lysimeters, which measure both crop growth and water movement in the soil, to test these models. The lysimeters contained soil samples from one location but were translocated to different sites with varying climates. The researchers calibrated the models using data from one site and then used them to predict crop growth and soil water movement at the other sites. They found that while some models performed well at one site, they struggled to accurately predict conditions at the other sites, particularly when it came to predicting crop growth under extreme climatic conditions like heat stress.
Abstract:
Crop model intercomparison studies have mostly focused on the assessment of predictive capabilities for crop development using weather and basic soil data from the same location. Still challenging is the model performance when considering complex interrelations between soil and crop dynamics under a changing climate. The objective of this study was to test the agronomic crop and environmental flux-related performance of a set of crop models. The aim was to predict weighing lysimeter-based crop (i.e., agronomic) and water-related flux or state data (i.e., environmental) obtained for the same soil monoliths that were taken from their original environment and translocated to regions with different climatic conditions, after model calibration at the original site. Eleven models were deployed in the study. The lysimeter data (2014–2018) were from the Dedelow (Dd), Bad Lauchstädt (BL), and Selhausen (Se) sites of the TERENO (TERrestrial ENvironmental Observatories) SOILCan network. Soil monoliths from Dd were transferred to the drier and warmer BL site and the wetter and warmer Se site, which allowed a comparison of similar soil and crop under varying climatic conditions. The model parameters were calibrated using an identical set of crop- and soil-related data from Dd. Environmental fluxes and crop growth of Dd soil were predicted for conditions at BL and Se sites using the calibrated models. The comparison of predicted and measured data of Dd lysimeters at BL and Se revealed differences among models. At site BL, the crop models predicted agronomic and environmental components similarly well. Model performance values indicate that the environmental components at site Se were better predicted than agronomic ones. The multi-model mean was for most observations the better predictor compared with those of individual models. For Se site conditions, crop models failed to predict site-specific crop development indicating that climatic conditions (i.e., heat stress) were outside the range of variation in the data sets considered for model calibration. For improving predictive ability of crop models (i.e., productivity and fluxes), more attention should be paid to soil-related data (i.e., water fluxes and system states) when simulating soil–crop–climate interrelations in changing climatic conditions.