Most of the Eurasian steppe grasslands, including the Balagaer River watershed located in north China, have an arid/semiarid climatic, and thus a vulnerable ecohydrologic, condition. The grass growth in such a region is critical to combat negative eco-environmental issues such as land desertification and the subsequent degradation of pasture productivity. How to predict responses of grass growth to climatic variations and human activities (e.g., grazing) is important for the utilization and protection of steppe grasslands. However, the information of such predictions is yet incomplete in existing literature. Taking the Balagaer River watershed as a test bed, this study parameterized a WOFOST (WOrld FOod STudies) simulation model to predict the potential plant growth as influenced by climate and human. The model is calibrated by manually adjusting various eco-physiological parameters, whose initial values were estimated using existing literature, field experiments, and remote sensing techniques. The soil-water parameters (e.g., porosity and saturated hydraulic conductivity) were determined by analyzing least-disturbed soil samples, while the physiological parameters (e.g., assimilation rate) of the dominant vegetation species of Stipa Grandis and Leymus Chinensis were determined by laboratory analyses of grass samples as well as from literature. The grazing frequency and intensity by sheep, horses, and cows were modeled as possible management scenarios. The model was driven by historical climate data recorded in a past half century at a weather station within the watershed. This study firstly expanded the WOFOST’s application to tracing dynamics of steppe grasses, while its results would likely be used to understand the threshold conditions for possibly irreversible degradation of steppe grasslands. In this presentation, we will highlight our successes, challenges, and solutions in parameterizing such a WOFOST model, and show the simulation results.
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Simulating Soil Moisture and Grass Growth in a Eurasian Steppe Grassland by SWAP
The accelerating degradation of native grasslands is becoming a threat to the world’s biome supply and has raised serious environmental concerns such as desertification and dust storm. Given that the steppe grasslands, such as those located in the Inner Mongolia Plateau of north China, have a dry climatic condition, the grass growth closely relies on available soil water, which in turn depends on precipitation prior to the growing season (in particular from May to July). However, our understanding of steppe hydrology and water consumption by grasses is incomplete. In this study, the agro-hydrologic Soil Water Plant Atmosphere (SWAP) model was used to mimic the long-term variations in soil water and vegetation growth in a typical steppe grassland of north China to further understand how alterations of hydrologic processes are related to grassland degradation. A field experiment was conducted to collect the data needed to set up the model. The SWAP model was calibrated using continuous observations of soil moisture and soil temperature at various depths for a simulation period of 2014 to 2017. The results indicated that the SWAP model can be used to simulate the responses of soil moisture and vegetation growth to climates. Moreover, this study examines the water balance and chronological variations of precipitation, evapotranspiration, soil water, and runoff. This study will add new knowledge of steppe hydrologic processes into existing literature.
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- Award ID(s):
- 1654957
- NSF-PAR ID:
- 10085163
- Date Published:
- Journal Name:
- AGU Fall Meeting 2018
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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