8 research outputs found
Inverse determination of heterotrophic soil respiration response to temperature and water content under field conditions
Heterotrophic soil respiration is an important flux within the global carbon cycle. Exact knowledge of the response functions for soil temperature and soil water content is crucial for a reliable prediction of soil carbon turnover. The classical statistical approach for the in situ determination of the temperature response (Q(10) or activation energy) of field soil respiration has been criticised for neglecting confounding factors, such as spatial and temporal changes in soil water content and soil organic matter. The aim of this paper is to evaluate an alternative method to estimate the temperature and soil water content response of heterotrophic soil respiration. The new method relies on inverse parameter estimation using a 1-dimensional CO2 transport and carbon turnover model. Inversion results showed that different formulations of the temperature response function resulted in estimated response factors that hardly deviated over the entire range of soil water content and for temperature below 25A degrees C. For higher temperatures, the temperature response was highly uncertain due to the infrequent occurrence of soil temperatures above 25A degrees C. The temperature sensitivity obtained using inverse modelling was within the range of temperature sensitivities estimated from statistical processing of the data. It was concluded that inverse parameter estimation is a promising tool for the determination of the temperature and soil water content response of soil respiration. Future synthetic model studies should investigate to what extent the inverse modelling approach can disentangle confounding factors that typically affect statistical estimates of the sensitivity of soil respiration to temperature and soil water content
Same soil, different climate: Crop model intercomparison on translocated lysimeters.
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
The chaos in calibrating crop models: Lessons learned from a multi-model calibration exercise.
Calibration, the estimation of model parameters based on fitting the model to experimental data, is among the first steps in many applications of process-based models and has an important impact on simulated values. We propose a novel method of developing guidelines for calibration of process-based models, based on development of recommendations for calibration of the phenology component of crop models. The approach was based on a multi-model study, where all teams were provided with the same data and asked to return simulations for the same conditions. All teams were asked to document in detail their calibration approach, including choices with respect to criteria for best parameters, choice of parameters to estimate and software. Based on an analysis of the advantages and disadvantages of the various choices, we propose calibration recommendations that cover a comprehensive list of decisions and that are based on actual practices