Abstract:
Global climate change will alter the water, nitrogen and
carbon cycles of agroecosystems. To predict future agricultural
production under climate change, numerical soil-crop models
are used. These soil-crop models can represent the complex
and coupled processes of agroecosystems in a deterministic
manner for a given environment. The projections made by soilcrop
models suffer from two kinds of uncertainty: (1) epistemic
uncertainty and (2) parameter uncertainty. Additionally, it is
assumed that the parameterization is applicable to other
environments. Therefore, this study has two major aims. The
first aim is to quantify the above-mentioned uncertainties
simultaneously by combining two methods: multi-model
ensemble modeling and Bayesian statistics. The multi-model
ensemble allows to quantify epistemic uncertainty by
comparing individual model outputs. This has been
demonstrated in many studies. Bayesian methods are common
to assess credible parameter intervals for highly nonlinear
process models. The second aim of this study is to provide a
framework for assessing the robustness of the parametrization
of soil-crop models. Therefore, a preliminary numerical study
was conducted to test different calibration schemes and to
investigate parameters sensitivities in dependence of the
environment. The soil-crop modelling software ExpertN 3.0
will be used to set up a multi-model ensemble with eight soilcrop
models. The model output will be analyzed by comparison
with data from two sites, five soil types and two crops gathered
by the DFG Research Unit 1695 since 2010. To achieve the
second aim a global sensitivity analyses was conducted to rank
the input factors for each soil-crop model. The result of the
global sensitivity analyses will clarify the impact of model input
on model output in regard to environment, model
combinations, and extent. Additionally, different calibration
schemes will be tested to identify the method yielding the most
robust parametrization. We used a Latin Hypercube sampling
scheme. In total, the whole study requires 1,000,000 CPU
hours. We expect that the results will enable us to develop a
generally applicable and feasible strategy of how soil-crop
models have to be set up to produce reliable predictions of
agroecosystem behavior under climate change.