EGU23-9070, updated on 26 Feb 2023
EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Wet grassland biomass yield prediction considering species composition dynamic

Valeh Khaledi1,2, Gunnar Lischeid1,3, Bahareh Kamali1,4, Ottfried Dietrich1, and Claas Nendel1,2
Valeh Khaledi et al.
  • 1Leibniz Centre for Agricultural Landscape Research (ZALF), Germany (
  • 2Institute of Biochemistry and Biology, University of Potsdam, Germany
  • 3Institute of Environmental Sciences and Geography, University of Potsdam, Germany
  • 4Institute for Crop Science and Resource Conservation (INRES), University of Bonn, Germany


Every grassland has considerable annual vegetation composition dynamics, especially in sites with shallow water levels (Toogood & Joyce, 2009). These wet grasslands, where the vegetation is regularly consuming capillary water, are very sensitive to water availability and respond rapidly by changing their species composition. As different species produce different biomass, the biomass yield is constantly altering alongside species composition change (White et al., 2000). These dynamics limit the use of mechanistic models for the prediction of biomass yields, especially in response to the water supply. Grassland models have been developed to simulate vegetation growth since the late 1980s (Coffin & Lauenroth, 1990; Thornley & Verberne, 1989). However, none of the existing models can deal with capillary water ascending from shallow groundwater considering the vegetation composition change. In this study, we demonstrate that mechanistic plant growth models for grassland productivity would benefit from the consideration of vegetation composition change in wet grasslands.

Material and method

The data from an extensively agriculturally used wet grassland lysimeter station in Germany, Spreewald (SPW, 51◦52´ N, 14◦02´ E, 50.5 m above sea level) (Dietrich & Kaiser, 2017) was used in this study. In this study, we followed an analytical approach and a modeling approach to reveal the importance of vegetation composition change impact on biomass yield prediction. First, we did a Pearson correlation analysis between vegetation composition indices and biomass. In the modeling approach, the mechanistic process-based simulation model MONICA (MOdel for NItrogen and Carbon dynamics in Agroecosystems) was employed to simulate water fluxes in the soil. In the model, an empirical approach was used for ascending water in the capillary fringe above the groundwater table, using daily rise rates from the German Soil Survey Manual ("Bodenkundliche Kartieranleitung. ," 2005)


The correlation analysis showed a significant association between the vegetation index and biomass yield, with a time lag of one year between the groundwater level and the respective response in the vegetation index. The results from the modeling approach showed that the model did not reproduce the year-to-year variation in biomass well. However, when we removed the effect of the groundwater level on the vegetation composition from the biomass data, the simulation model agreed much better with the remaining pattern. As a result, we conclude that long-term biomass patterns can only be reproduced with mechanistic simulation models when vegetation composition dynamics are considered, e.g. by using it alongside a species competition model.

Keywords: Wet grassland, vegetation composition, capillary rise, process-based model


Dietrich, O., & Kaiser, T. (2017). Impact of groundwater regimes on water balance components of a site with a shallow water table [RESEARCH A R T I C L E]. Ecohydrology.

Toogood, S., & Joyce, C. (2009). Effects of raised water levels on wet grassland plant communities. Applied Vegetation Science, 12, 283-294.

White, R., Murray, S., & Rohweder, M. (2000). PILOT ANALYSIS OF GLOBAL ECOSYSTEMS (Grassland Ecosystems).

How to cite: Khaledi, V., Lischeid, G., Kamali, B., Dietrich, O., and Nendel, C.: Wet grassland biomass yield prediction considering species composition dynamic, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-9070,, 2023.

Corresponding supplementary materials formerly uploaded have been withdrawn.