Same soil, different climate: Crop model intercomparison on translocated lysimeters

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.

Assessing Climate Change Impacts on Crop Yields and Exploring Adaptation Strategies in Northeast China

“…In our analysis, we focus solely on the relative changes in future mean yields and have not conducted historical simulations for model validation. While previous studies have demonstrated the reliability of PCSE (WOFOST) in crop yield simulations (Asseng et al., 2019; Gaso et al., 2021; Groh et al., 2022; Müller et al., 2021), its validation in NEC has not been performed to date. Furthermore, the potential impact of new pest and disease distributions resulting from climate change is qualitatively understood but has not been incorporated into any models, leading to an overly optimistic assessment (Tubiello et al., 2007).…”

“…While the mean annual air temperature depicted in Figure 3f does not exceed this threshold, under SSP585, 66.40% of the days, particularly during the growing season (April-September), recorded temperatures above 22°C. The multi-model ensemble mean/median consistently outperforms individual models under high temperature conditions (Asseng et al, 2019;Groh et al, 2022). Therefore, the development of intercomparison projects for crop modeling should be considered as an effective approach to mitigate uncertainties during periods of high temperature in future studies.…”

Interpretability of negative latent heat fluxes from eddy covariance measurements in dry conditions

“…SWC is measured within each lysimeter at a depth of 0.1 m below the surface with time domain reflectometry probes (CS610, Campbell Scientific, North Logan, UT, USA) at a resolution of 0.1 % SWC, according to the manufacturer. More details on the technical specifications of lysimeter facilities within SOILCan are given in Pütz et al (2016), on excavation methods in Pütz and Groh (2023), and on the Selhausen facility in Groh et al (2022).…”

Fifteen Years of Integrated Terrestrial Environmental Observatories (TERENO) in Germany: Functions, Services, and Lessons Learned

“…Peters et al, 2017). Lysimeter data was used to determine the impact of changing climate and land use management on terrestrial hydrology and nutrient cycles for grasslands (Fu et al, 2017), and arable land (Groh et al, 2022). The temporally highly resolved measurements of hydraulic state variables and water fluxes have allowed to (a) advance the understanding of soil hydrology and inform new models (Hannes et al, 2016;Herbrich & Gerke, 2017) The TERENO design also extends the experimental catchment concept to stream reaches for improved understanding of aquatic ecosystem functions.…”

“…TERENO‐derived algorithms assure data quality and compute the water fluxes across the upper and lower boundaries of the lysimeters (e.g., Hannes et al., 2015; A. Peters et al., 2017). Lysimeter data was used to determine the impact of changing climate and land use management on terrestrial hydrology and nutrient cycles for grasslands (Fu et al., 2017), and arable land (Groh et al., 2022). The temporally highly resolved measurements of hydraulic state variables and water fluxes have allowed to (a) advance the understanding of soil hydrology and inform new models (Hannes et al., 2016; Herbrich & Gerke, 2017), (b) evaluate energy balance closure of eddy‐covariance stations (Mauder et al., 2018), (c) test crop yield models (Kamali et al., 2022), (d) predict impacts of climate change water use efficiency and plant growths (Jarvis et al., 2022), and (e) validate large scale model simulations of the German Drought Monitor (Boeing et al., 2022) and remotely sensed products (Trigo et al., 2018).…”