Monte Carlo-based calibration and uncertainty analysis of a coupled plant growth and hydrological model

Abstract

Abstract.Computer simulations are widely used to support decision making and planning in the agriculture sector. On the one hand, many plant growth models use simplified hydrological processes and structures -for example, by the use of a small number of soil layers or by the application of simple water flow approaches. On the other hand, in many hydrological models plant growth processes are poorly represented. Hence, fully coupled models with a high degree of process representation would allow for a more detailed analysis of the dynamic behaviour of the soil-plant interface.We coupled two of such high-process-oriented independent models and calibrated both models simultaneously. The catchment modelling framework (CMF) simulated soil hydrology based on the Richards equation and the van Genuchten-Mualem model of the soil hydraulic properties. CMF was coupled with the plant growth modelling framework (PMF), which predicts plant growth on the basis of radiation use efficiency, degree days, water shortage and dynamic root biomass allocation.The Monte Carlo-based generalized likelihood uncertainty estimation (GLUE) method was applied to parameterize the coupled model and to investigate the related uncertainty of model predictions. Overall, 19 model parameters (4 for CMF and 15 for PMF) were analysed through 2 × 10 6 model runs randomly drawn from a uniform distribution.The model was applied to three sites with different management in Müncheberg (Germany) for the simulation of winter wheat (Triticum aestivum L.) in a cross-validation experiment. Field observations for model evaluation included soil water content and the dry matter of roots, storages, stems and leaves. The shape parameter of the retention curve n was highly constrained, whereas other parameters of the retention curve showed a large equifinality. We attribute this slightly poorer model performance to missing leaf senescence, which is currently not implemented in PMF. The most constrained parameters for the plant growth model were the radiation-use efficiency and the base temperature. Cross validation helped to identify deficits in the model structure, pointing out the need for including agricultural management options in the coupled model.

Closing the N-Budget: How Simulated Groundwater-Borne Nitrate Supply Affects Plant Growth and Greenhouse Gas Emissions on Temperate Grassland

“…However, this is rarely done, particularly not with intrinsically dependent target values such as GHG emissions, soil moisture and biomass. Houska et al [70] achieved good results applying such an approach to a coupled plant growth-hydrological model to simulate SWC and different kinds of plant dry matter. Sitch et al [71] achieved good results by simulating several ecosystem variables, including soil moisture and net ecosystem exchange, on local to global scales.…”

Incremental model breakdown to assess the multi-hypotheses problem

“…To finally compare the models that were able to produce behavioral runs, we used the median to evaluate the typical behavior of a run of a given model and the maximal value to determine the best possible performance. The sampling of the parameter space for calibration was done by Latin hypercube sampling (McKay et al, 1979) implemented via SPOTPY (Houska et al, 2015). All models were run 300 000 times each, using a High-Performance Computing Cluster.…”

Rainfall–Runoff Processes and Modelling in Regions Characterized by Deficiency in Soil Water Storage

“…The parameter b, RWMM, and state variable γ constitute the vertical distribution for the averaged relative water storage capacity over the basin (Equation (1)). The Markov chain Monte Carlo (MCMC) method [37], the Latin hypercube one factor at a time (LH-OAT) method [38], and the generalized likelihood uncertainty estimation (GLUE) [39] approach can be used to conduct sensitivity and uncertainty analysis on parameters. In this work, GLUE is employed to conduct brief sensitivity and uncertainty analysis on parameters b and RWMM to reveal their characteristics and the effect on hydrograph.…”