Abstract
A probabilistic crop forecast based on ensembles of crop model output (CMO) estimates offers a myriad of possible realizations and probabilistic forecasts of green water components (precipitation and evapotranspiration), crop yields and green water footprints (GWFs) on monthly or seasonal scales. The present paper presents part of the results of an ongoing study related to the application of ensemble forecasting concepts for agricultural production. The methodology used to produce the ensemble CMO using the ensemble seasonal weather forecasts as the crop model input meteorological data without the perturbation of initial soil or crop conditions is presented and tested for accuracy, as are its results. The selected case study is for winter wheat growth in Austria and Serbia during the 2006-2014 period modelled with the SIRIUS crop model. The historical seasonal forecasts for a 6-month period (1 March-31 August) were collected for the period 2006-2014 and were assimilated from the European Centre for Medium-range Weather Forecast and the Meteorological Archival and Retrieval System. The seasonal ensemble forecasting results obtained for winter wheat phenology dynamics, yield and GWF showed a narrow range of estimates. These results indicate that the use of seasonal weather forecasting in agriculture and its applications for probabilistic crop forecasting can optimize field operations (e.g., soil cultivation, plant protection, fertilizing, irrigation) and takes advantage of the predictions of crop development and yield a few weeks or months in advance.
Highlights
Both plants and the atmosphere are non-linear dynamic systems
The deviation from the observations was more pronounced in GE than in NS for both the control run (CR) and ensemble averages (EA) datasets. (b) The Tmax forecast based on EA in both locations is underestimated every year. (c) The accuracy of the P forecast significantly varied between seasons at both locations, while the results obtained using EA were closer to the observed results
The presented simulation results and verification statistics, those related to the GW components, yield and green water footprints (GWFs), allow the reader to (a) assess the capacity of the ensemble forecast to offer a sufficiently narrow range of the possible realizations of the selected variables; (b) identify the differences between the ensemble and deterministic weather forecast (CR) applications; (c) assess the uncertainties in the ensemble estimates, i.e. the probabilistic forecast application for the selected crop model output (CMO) and GWF; and (d) understand the ability of seasonal weather forecast (SWF) to reproduce the real inter-annual variability in the CMOs and GWF on long-term scale
Summary
Both plants and the atmosphere are non-linear dynamic systems. An important feature of such systems is that even small perturbations of the initial conditions can cause the system to evolve along significantly different paths (Lorenz 1963). In the case of plants and the atmosphere, the exact values of the initial conditions are unknown. Even Charney’s first numerical weather prediction (NWP) (Charney et al 1950) was a deterministic one, and the impact of uncertainties in the initial conditions on the NWP outputs soon became an important topic of short-range and, long-range (monthly and seasonal) weather forecasting. An ensemble seasonal weather forecast (SWF) is assimilated either from an ensemble of atmospheric models run with the same initial and boundary conditions or from an ensemble of multiple runs of one atmospheric model with perturbed initial conditions
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