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Funded Research

Integrating Field Experiments, Remote Sensing, and Process-based Modeling Toward Improved Understanding and Quantification of Watershed Scale Carbon

Qi, Junyu: University of Maryland (Project Lead)
Daughtry, Craig: USDA (Co-Investigator)
Deng, Jia: University of New Hampshire (Co-Investigator)
Gao, Feng: USDA-ARS HRSL (Co-Investigator)
Huang, Chengquan (Cheng): University of Maryland (Co-Investigator)
McCarty, Greg (Gregory): USDA - ARS (Co-Investigator)
Sadeghi, Ali: USDA (Co-Investigator)
Zhang, Xuesong: USDA Agricultural Research Service (Co-Investigator)
Arnold, Jeffrey: USDA (Collaborator)
Lang, Megan: USDA FS (Collaborator)
Lee, Sangchul: University of Maryland (Collaborator)
Sharifi, Amirreza: University of Maryland (Collaborator)
Yang, Qichun: Pacific NW National Lab (Collaborator)
Huang, Chang: University of Maryland (Participant)

Project Funding: 2017 - 2022

NRA: 2016 NASA: Carbon Cycle Science   

Funded by NASA

Abstract:
Recent estimates indicate that agricultural technologies and practices hold the potential to mitigate 0.3-4.6 Pg CO2eq yr-1 emissions at the global scale, therefore have been recognized as an important measure to limit global temperature to 2 Celsius degree above pre-industrial levels. However, most of these estimates are based on measurements and/or modeling of the terrestrial carbon (C) balance without explicit accounting of consequences for C cycling in downstream aquatic ecosystems. The lack of clear understanding and reliable quantification of the linkages between interconnected terrestrial and aquatic ecosystems represents a significant uncertainty in assessment of C flows and stocks as influenced by human activities and under various climate change scenarios. Therefore, our goal is to develop and test an integrated data-model approach to advance the representation of C cycling at the watershed scale that encompasses interconnected upland, wetland and riverine ecosystems. To achieve this goal, we will use the Choptank River Watershed (CRW) as the testbed and pursue tasks in the following three aspects: [1] enhance the widely used Soil and Water Assessment Tool (SWAT) model with new capability of representing C cycling Across upland, wetland and riverine Landscapes at the Watershed scale (referred to as SWAT-CALW), by incorporating wetland-DNDC, WetQual, and revised Dissolved Organic Carbon (DOC) algorithms into SWAT; [2] leverage a wide array of field measurements of terrestrial and aquatic ecosystems as supported by USDA’s Conservation Effects Assessment Project (CEAP) and Long-term Agricultural Research (LTAR) and numerous NASA remote sensing products, as well as expand wetland and stream hydrology & biogeochemistry field experiments in the CRW to parameterize, drive and constrain SWAT-CALW; and [3] apply SWAT-CALW to understand compelling scientific questions, such as What is the magnitude and spatial distribution of C transported between terrestrial and aquatic ecosystems? How do the linkages between landscapes influence C cycling at the watershed scale? To what extent is terrestrial based C accounting different from coupled terrestrial-aquatic C balance? How do human activities and climate change (e.g. conservation and temperature increase) influence coupled C cycling at the watershed scale? The outcome of this research will directly contribute to the NASAs objective to quantify, understand, and predict changes in Earths ecosystems and biogeochemical cycles. The proposed research also addresses the overarching scientific questions outlined in A U.S. Carbon Cycle Science Plan: how do natural processes and human actions affect C cycle on land and how are ecosystems and natural sources impacted by changes in climate and C management decisions? Our research activities are closely aligned with USDA-NIFAs strategic sub-goal 1.2: Advance the development & of science for agricultural, forest, and range systems & to mitigate climate impacts, as well as USDA Strategic Plan for 2010-2015s Goal 2, Objective 2.2, particularly the strategy to develop models, national observing and monitoring systems, decision support tools and new technology and adaptation strategies for communities, agriculture producer, and natural resource managers. The open-source SWAT-CALW model developed here will be shared with the public through the SWAT website. The methods developed here on how to integrate NASA remote sensing data into SWAT-CALW will also be shared, facilitating sustained use of NASA remote sensing data to support societal benefits. Particularly, we will seek opportunities to disseminate the scientific knowledge and analytic tools resulting from the proposed research via long-term research/community programs (such as NACP, CEAP and LTAR, and US FWS’s National Wetland Inventory).

Publications:

Qi, J., Zhang, X., McCarty, G. W., Sadeghi, A. M., Cosh, M. H., Zeng, X., Gao, F., Daughtry, C. S., Huang, C., Lang, M. W., Arnold, J. G. 2018. Assessing the performance of a physically-based soil moisture module integrated within the Soil and Water Assessment Tool. Environmental Modelling & Software. 109, 329-341. DOI: 10.1016/j.envsoft.2018.08.024

Yang, Q., Almendinger, J. E., Zhang, X., Huang, M., Chen, X., Leng, G., Zhou, Y., Zhao, K., Asrar, G. R., Srinivasan, R., Li, X. 2018. Enhancing SWAT simulation of forest ecosystems for water resource assessment: A case study in the St. Croix River basin. Ecological Engineering. 120, 422-431. DOI: 10.1016/j.ecoleng.2018.06.020

Yang, Q., Zhang, X., Almendinger, J. E., Huang, M., Leng, G., Zhou, Y., Zhao, K., Asrar, G. R., Li, X., Qiu, J. 2018. Improving the SWAT forest module for enhancing water resource projections: A case study in the St. Croix River Basin. Hydrological Processes. DOI: 10.1002/hyp.13370

Yang, Q., Zhang, X., Xu, X., Asrar, G. R. 2017. An Analysis of Terrestrial and Aquatic Environmental Controls of Riverine Dissolved Organic Carbon in the Conterminous United States. Water. 9(6), 383. DOI: 10.3390/w9060383

Zhang, X. 2018. Simulating eroded soil organic carbon with the SWAT-C model. Environmental Modelling & Software. 102, 39-48. DOI: 10.1016/j.envsoft.2018.01.005


More details may be found in the following project profile(s):