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ARS Home » Southeast Area » Mississippi State, Mississippi » Crop Science Research Laboratory » Genetics and Sustainable Agriculture Research » Research » Publications at this Location » Publication #312549

Research Project: Integration of Site-Specific Crop Production Practices and Industrial and Animal Agricultural Byproducts to Improve Agricultural Competitiveness and Sustainability

Location: Genetics and Sustainable Agriculture Research

Title: Bayesian inference of the groundwater depth threshold in a vegetation dynamic model: a case study, lower reach, Tarim River

Author
item HAN, MING - Chinese Academy Of Sciences
item ZHAO, CHENGYI - Chinese Academy Of Sciences
item Feng, Gary
item SHI, FENGZHI - Chinese Academy Of Sciences

Submitted to: International Union for Quaternary Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/16/2015
Publication Date: 3/18/2015
Citation: Han, M., Zhao, C., Feng, G.G., Shi, F. 2015. Bayesian inference of the groundwater depth threshold in a vegetation dynamic model: a case study, lower reach, Tarim River. International Union for Quaternary Research. doi:10.1016/j.quaint.2015.02.035.

Interpretive Summary: Riparian and wetland ecosystems are known for their environmental value, as they provide various critical ecosystem service such as mitigating floods and droughts, supporting wildlife habitats, and most importantly, they prevent the desertification in arid regions. But the riparian and wetland are threatened because of human exploitation and climate change. The understanding of the riparian and wetland systems and their response to anthropogenic and natural disturbances are of foremost importance for management and restoration of these ecosystems. Integration of hydrology and ecology is needed, which is considered in the interdisciplinary research area of eco-hydrology. Eco-hydrological models provide a potentially useful tool in characterizing hydrological–ecological interactions. We applied Bayesian inference approach to estimate groundwater threshold for arid riparian vegetation in the lower reach of the Tarim River with a simplified vegetation dynamic model. The result showed that the simplified vegetation dynamic model had capacity to model the vegetation dynamic in the study area. The mean values of inferred groundwater threshold for section Yinsu, Kardayi, Alagan, and Yiganbujima were 4.99, 5.59, 5.74, and 5.85 m respectively, which were comparable with prior research, and showed significant spatial variability. The 90% confidence interval for parameter was large, the minimum 90% confidence interval was 1.27 m in section Yinsu. So this parameter inference approach was suggested to be carried out before complexity eco-hydrological modeling.

Technical Abstract: The responses of eco-hydrological systems to anthropogenic and natural disturbances have attracted much attention in recent years. The coupling and simulating feedback between hydrological and ecological components have been realized in several recently developed eco-hydrological models. However, little research has been carried out to study the estimation of threshold parameters in the eco-hydrological models. The aim of this paper is to infer the groundwater threshold parameter that connects the ecological and hydrological processes inside eco-hydrological models. A simplified vegetation dynamic model was set up, and used to simulate vegetation dynamic along the lower reach of Tarim River. Bayesian inference approach was applied with Markov Chain Monte Carlo sampling method to infer the probability distribution of groundwater threshold parameter. The result showed that the simplified vegetation dynamic model has capacity to model the vegetation dynamic in the study area. The mean values of inferred groundwater threshold parameters for section Yinsu, Kardayi, Alagan, and Yiganbujima are 4.99, 5.59, 5.74, and 5.85 m respectively, which are comparable with prior research, and showed significant spatial variability. The 90% confidence interval of groundwater threshold parameter is larger than 1.27 m. This threshold parameter inference approach was suggested to be carried out before complex eco-hydrological modeling is performed.