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@PHDTHESIS{Boas:1032264,
author = {Boas, Theresa},
title = {{A}dvancing the representation of agricultural systems in
{L}and {S}urface {M}odels: systematic model evaluations and
technical model developments},
volume = {640},
school = {RWTH Aachen University},
type = {Dissertation},
address = {Jülich},
publisher = {Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag},
reportid = {FZJ-2024-06103},
isbn = {978-3-95806-777-6},
series = {Reihe Energie $\&$ Umwelt / Energy $\&$ Environment},
pages = {xxi, 145},
year = {2024},
note = {Dissertation, RWTH Aachen University, 2024},
abstract = {Global climate change, with its projected increase in
weather extremes and drought risk, presents global and
egional agriculture with vulnerability and new challenges.
It is crucial to gain a comprehensive understanding and
accurate quantification of the intricate dynamics of
agricultural land cover and its role within the terrestrial
system, especially in the context of climate change. Land
surface models play a central role for the research on
climate change effects on the Earth's surface and hold
particular value in xamining the influence of weather
patterns on agricultural land at larger spatial scales. The
incorporation of a comprehensive crop module in land surface
models offers the possibility to study the effect of
agricultural land use and land management changes on the
terrestrial water, energy and biogeochemical cycles. It may
help to improve the simulation of biogeophysical and
biogeochemical processes on regional and global scales and
thus to study climate change impacts on terrestrial
ecosystem as well as the significance of human land cover
changes for climate change. Land surface models simulate the
complex interactions at the terrestrial land surface in
response to atmospheric states, based on land cover and soil
type information. In combination with data from different
sources, like seasonal weather forecasts, land surface
models can potentially provide useful information for water
resources or agricultural planning. In this thesis, a
systematic evaluation of the state-of-the-art land surface
model, the Community Land Model version 5.0 (CLM5), was
conducted from point to regional scales in combination with
data from a multitude of sources, e.g. from remote sensing,
numerical predictions and field observations. A special
focus was placed on the representation of arable land and
its feedback to weather related factors in the context of
climate change. In the first part of this thesis, the
performance of the crop module of CLM5 was evaluated at
point scale with site specific field data focussing on the
simulation of seasonal and inter-annual variations in crop
growth, planting and harvesting cycles, and crop yields as
well as water, energy and carbon fluxes. In order to better
represent agricultural sites, the model was modified by (1)
implementing the winter wheat subroutines after Lu et al.
(2017) in CLM5; (2) implementing plant specific parameters
for sugar beet, potatoes and winter wheat, thereby adding
the two crop functional types (CFT) for sugar beet and
potatoes to the list of actively managed crops in CLM5; (3)
introducing a cover cropping subroutine that allows multiple
crop types on the same column within one year. The latter
modification allows the simulation of cropping during winter
months before usual cash crop planting begins in spring,
which is an agricultural management technique with a long
history that is regaining popularity to reduce erosion,
improve soil health and carbon storage, and is commonly used
in the regions evaluated in this study. In comparison with
field data, the crop specific parameterizations, as well as
the winter wheat subroutines, led to a significant
simulation improvement in terms of energy fluxes (RMSE
reduction for latent and sensible heat by up to 57 $\%$ and
59 $\%,$ respectively), leaf area index (LAI), net ecosystem
exchange and crop yield (up to 87 $\%$ improvement in winter
wheat yield prediction) compared with default model results.
The cover cropping subroutine yielded a substantial
improvement in representing field conditions after harvest
of the main cash crop (winter season) in terms of LAI
magnitudes and seasonal cycle of LAI, and latent heat flux
(reduction of winter time RMSE for latent heat flux by 42
$\%).$ Our modifications significantly improved model
simulations and should therefore be applied in future
studies with CLM5 to improve regional yield predictions and
to better understandlarge-scale impacts of agricultural
management on carbon, water and energy fluxes. These model
improvements were then ported to the regional scale and
tested in combination with sub-seasonal and seasonal weather
forecasts in the second part of this thesis. Long-range
weather forecasts provide predictions of atmospheric, ocean
and land surface conditions that can potentially be used in
land surface and hydrological models to predict the water
and energy status of the land surface or in crop growth
models to predict yield for water resources or agricultural
planning. However, the coarse spatial and temporal
resolutions of available forecast products have hindered
their widespread use in such modelling applications, which
usually require high-resolution input data. In this study,
we applied sub-seasonal (up to 4 months) and seasonal (7
months) weather forecasts from the latest European Centre
for Medium-Range Weather Forecasts (ECMWF) seasonal
forecasting system (SEAS5) in a land surface modelling
approach using the Community Land Model version 5.0 (CLM5).
Simulations were conducted for 2017-2020 forced with
sub-seasonal and seasonal weather forecasts over two
different domains with contrasting climate and cropping
conditions: the german state of North Rhine-Westphalia
(DE-NRW) and the Australian state of Victoria (AUS-VIC). We
found that, after pre-processing of the forecast products
(i.e. temporal downscaling of precipitation and incoming
short-wave radiation), the simulations forced with seasonal
and subseasonal forecasts were able to provide a model
output that was very close to the reference simulation
results forced by reanalysis data (the mean annual crop
yield showed maximum differences of 0.28 and 0.36 t/ha for
AUSVICand DE-NRW, respectively). Differences between
seasonal and sub-seasonal experiments were insignificant.
The forecast experiments were able to satisfactorily capture
recorded inter-annual variations of crop yield. In addition,
they also reproduced the generally higher inter-annual
differences in crop yield across the AUS-VIC domain
(approximately 50 $\%$ inter-annual differences in recorded
yields and up to 17 $\%$ inter-annual differences in
simulated yields) compared to the DE-NRW domain
(approximately 15 $\%$ inter-annual differences in recorded
yields and up to 5 $\%$ in simulated yields). The high- and
low-yield seasons (2020 and 2018) among the 4 simulated
years were clearly reproduced in the forecast simulation
results. Furthermore, sub-seasonal and seasonal simulations
reflected the early harvest in the drought year of 2018 in
the DE-NRW domain. However, simulated inter-annual yield
variability was lower in all simulations compared to the
official statistics. While general soil moisture trends,
such as the European drought in 2018, were captured by the
seasonal experiments, we found systematic overestimations
and underestimations in both the forecast and reference
simulations compared to the Soil Moisture Active Passive
Level-3 soil moisture product (SMAP L3) and the Soil
Moisture Climate Change Initiative Combined dataset from the
European Space Agency (ESA-CCI). These observed biases of
soil moisture and the low inter-annual differences in
simulated crop yield indicate the need to improve the
representation of these variables in CLM5 to increase the
model sensitivity to drought stress and other crop
stressors. While extensive research is dedicated to
investigating the impacts of changing climate conditions on
global food security, the specific implications for regional
inter-annual yield variability remain largely uncertain. In
the final part of this thesis, the model’s ability to
represent the inter-annual variability of crop yield in
comparison to recorded yield variability was evaluated in
multi-decadal simulations (1999-2019) that were forced with
the WFDE5 reanalysis. Additionally, synthetic experiments
were performed for both regional domains, AUS-VIC and
DE-NRW, and forced with a reduced precipitation rate $(50\%$
of the reanalysis precipitation), allowing for a more
detailed analysis of crop water stress regimes and
correlations between seasonal rainfall and crop yields.
Overall, the simulation results were able to reproduce the
total annual crop yields of certain crops, with RMSE values
between 0.52 t/ha to 1.76 t/ha in AUS-VIC and 0.61 t/ha and
1.58 t/ha in DE-NRW, while also capturing the differences in
total yield magnitudes between the domains. However, the
simulations showed limitations in correctly capturing
inter-annual differences of crop yield compared to official
yield records, in particular for winter crops, which
resulted in relatively low correlations (maximum correlation
coefficients of 0.39 in AUS-VIC and 0.42 in DE-NRW).
Specifically, the mean absolute anomaly of simulated winter
wheat yields was up to 4.6 times lower compared to
state-wide records from 1999 to 2019. Our results suggest
the following limitations of CLM5 in predicting inter-annual
variability in crop yields: (1) limitations in simulating
yield responses from plant hydraulic stress; (2) errors in
simulating soil moisture contents compared to
satellite-derived data; and (3) errors in the representation
of cropland in general, e.g. crop parameterizations,
differentiations of crop varieties, and human influences
(such as management decisions, fertilizer types, and
application techniques).},
cin = {IBG-3},
cid = {I:(DE-Juel1)IBG-3-20101118},
pnm = {2173 - Agro-biogeosystems: controls, feedbacks and impact
(POF4-217)},
pid = {G:(DE-HGF)POF4-2173},
typ = {PUB:(DE-HGF)3 / PUB:(DE-HGF)11},
urn = {urn:nbn:de:0001-20241120141009871-7593620-2},
doi = {10.34734/FZJ-2024-06103},
url = {https://juser.fz-juelich.de/record/1032264},
}
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