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| Home > Publications database > Advancing the representation of agricultural systems in Land Surface Models: systematic model evaluations and technical model developments |
| Book/Dissertation / PhD Thesis | FZJ-2024-06103 |
2024
Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag
Jülich
ISBN: 978-3-95806-777-6
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Please use a persistent id in citations: urn:nbn:de:0001-20241120141009871-7593620-2 doi:10.34734/FZJ-2024-06103
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).
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