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@INPROCEEDINGS{Boas:917349,
author = {Boas, Theresa and Bogena, Heye and Ryu, Dongryeol and
Vereecken, Harry and Western, Andrew and Hendricks-Franssen,
Harrie-Jan},
title = {{U}sing {SEAS}5 {S}easonal {W}eather {F}orecasts for
{R}egional {C}rop {Y}ield {P}rediction in a {L}and {S}urface
{M}odelling {A}pproach},
reportid = {FZJ-2023-00577},
year = {2022},
abstract = {Seasonal weather forecasts can provide important
information for water resources and agricultural planning.
However, their coarse spatial and temporal resolution limit
the usage for modelling applications such as crop and land
surface models and have hindered their widespread use in
such models. In this study, we applied sub-seasonal and
seasonal weather forecasts from the latest ECMWF SEAS5
forecasting system in a land surface modelling approach
using the Community Land Model version 5.0 (CLM5).
Simulations were conducted for multiple years forced with
sub-seasonal and seasonal weather forecasts over two
different domains, one over the German state of North
Rhine-Westphalia characterized by heterogeneous land cover
and diverse agricultural land use, the other over the
Australian state of Victoria that is dominated by large
agricultural fields of mostly rainfed winter grain crops.
Our results show that the simulations forced with seasonal
and sub-seasonal forecasts were able to reproduce recorded
inter-annual trends of crop yield, but the inter-annual
variability of crop yields was significantly lower compared
to the records. The forecast-forced simulations were able to
reproduce the generally higher inter-annual variability in
crop yield throughout the Australian domain (approx. 50 $\%$
inter-annual variability in recorded yields and 20 $\%$ in
simulated yields) compared to the German domain (approx. 15
$\%$ inter-annual variability in recorded yields and 5 $\%$
in simulated yields). Also, sub-seasonal and seasonal
simulations reflected the early harvest in the drought year
of 2018 throughout the German domain, thus capturing one of
the main contributing factors to the low annual crop yield.
While general soil moisture trends, such as the European
drought in 2018, were reproduced in the results from the
sub-seasonal and seasonal experiments, we found systematic
biases compared to satellite products that could also be
observed in the reference simulations forced with reanalysis
weather data. The observed biases in the representation of
soil moisture, as well as the relatively low inter-annual
variability of simulated crop yield, indicate that the
representation of these variables in CLM5 still needs to be
improved to increase the model sensitivity to drought stress
and other crop stressors (e.g., pests, hail, wind).},
month = {Dec},
date = {2022-12-12},
organization = {American Geophysical Union Fall
Meeting, Chicago, IL (United States),
12 Dec 2022 - 16 Dec 2022},
subtyp = {Other},
cin = {IBG-3},
cid = {I:(DE-Juel1)IBG-3-20101118},
pnm = {2173 - Agro-biogeosystems: controls, feedbacks and impact
(POF4-217) / 2B2 - TERENO (CTA - CCA) (POF4-2B2)},
pid = {G:(DE-HGF)POF4-2173 / G:(DE-HGF)POF4-2B2},
typ = {PUB:(DE-HGF)6},
url = {https://juser.fz-juelich.de/record/917349},
}