| NSF Org: |
OAC Office of Advanced Cyberinfrastructure (OAC) |
| Recipient: |
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| Initial Amendment Date: | July 29, 2018 |
| Latest Amendment Date: | July 29, 2018 |
| Award Number: | 1761772 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Martin Halbert
OAC Office of Advanced Cyberinfrastructure (OAC) CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | August 1, 2018 |
| End Date: | July 31, 2022 (Estimated) |
| Total Intended Award Amount: | $100,000.00 |
| Total Awarded Amount to Date: | $100,000.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
1350 BEARDSHEAR HALL AMES IA US 50011-2103 (515)294-5225 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
518 Farm House Ln Ames IA US 50011-1054 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | BD Spokes -Big Data Regional I |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
This project will develop a cyberinfrastructure framework to facilitate research on the efficient management of agricultural practices and their impact on water resources in the Upper Mississippi River Basin (UMRB). Large-scale data acquisition, integration, analysis, and visualization using data-enabled information technologies will accelerate the dissemination of knowledge, experience, and shared resources (e.g., technology, equipment, and people) among communities and partners. The key element of the project is a new cyber platform, the Upper Mississippi Information System (UMIS), which will provide water quality data within a rich spatio-temporal hydrologic context. The UMIS directly addresses three of the Grand Challenges for Engineering identified by the National Academy of Engineering: i) provide access to clean drinking water; ii) manage the nitrogen cycle; and iii) engineer the tools of scientific discovery. The UMIS will immediately begin facilitating data access, integration, and scientific discovery for water quality challenges in the UMRB.
UMIS will offer internet-based open access to water quality information in its meteorological, hydrological, and geographical context, providing almost endless potential benefits for stakeholders. For example, the experimental design of the UMIS will enable researchers to study spatial scaling, efficiency of various land use and agricultural practices to improve water quality, and the impact of climate change on land management and water quality. Decision-makers, producers, and extension staff will be able to assess the relative efficacy of local (e.g., best management practices) versus system-level (e.g., state programs) solutions designed to reduce pollution, optimize the use of resources, and evaluate tradeoffs among competing objectives. For all stakeholders, the UMIS will support partnerships and collaborations, increase dissemination of information about a critical natural resource to empower stakeholders at all levels, and set new standards in the communication of scientific data.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
The research reported in the two journal article studies led by Dr. Manyu Chen provide important insights regarding the effects of different climate data sources, potential evapotranspiration (PET) methods and/or the effects of different delineation schemes. The Soil and Water Assessment Tool (SWAT) ecohydrological model (https://swat.tamu.edu/) was used in both analyses, within the Hydrologic and Water Quality System (HAWQS) on-line platform (https://hawqs.tamu.edu/#/).
In the first study (Chen et al., 2020), two PET methods, Hargraves (HG) and Penman-Monteith (PM), were compared three climate data sets: (1) National Climatic Data Center (NCDC), (2) Livneh (https://psl.noaa.gov/data/gridded/data.livneh.html), and (3) Parameter-elevation Regressions on Independent Slopes Model (PRISM) climate data set (https://www.prism.oregonstate.edu/). The HG and PM PET methods, and NCDC and PRISM climate data, are provided within HAWQS. The Livneh climate data were obtained from a source external to HAWQS. Baseline calibration was performed at three gauge sites along the main stem of the Mississippi River and spatial validation was performed for one main stem gauge site and nine tributary gauge sites. The results showed that the HG PET method and PRISM climate data were the preferred options for performing future UMRB SWAT analyses.
The second study (Chen et al., 2021) provided insights regarding the delineation of the UMRB into either 8-digit or 12-digit watersheds, based on the hydrologic unit classification (HUC) schemed developed by the U.S. Geological survey and cooperating federal agencies (https://pubs.usgs.gov/tm/11/a3/). To our knowledge, the UMRB study represents the largest area ever evaluated for such SWAT delineation analyses in the literature to date. The simulations were performed with five different delineation schemes in combination with two climate data sets. The delineation schemes consisted of subbasins based on either 8-digit or 12-digit watersheds, with varying levels of hydrologic response units (HRUs) configured within the subbasins. Two versions of PRISM climate data were used in the study, with the second one representing a more refined grid for 12-digit delineation schemes. The more refined delineation schemes and climate data did not result in improved SWAT hydrologic results for this study, which did not follow expectations. The results point to the need for additional similar research to be performed for the UMRB and other large river systems.
Two additional studies led by Dr. Yuelu Xu provide expanded insights for the entire Mississippi-Atchafalaya River Basin (MARB). Applications of SWAT within HAWQS was a key component of the overall integrated modeling system used in these studies.
The first study, published in Resource and Energy Economics, investigated the impacts of energy and nitrogen fertilizer prices on nitrogen export to the Gulf of Mexico. The results revealed that energy prices had a modest effect on nitrogen export. However, ±30% changes in nitrogen fertilizer prices resulted in a greater impact on nitrogen export (e.g., 3.5% and 1.5% increase in nitrogen use and cropland losses for a 30% decrease in nitrogen fertilizer price). The study also found that the opportunity cost of reducing N runoff from cropland landscapes by 45% to the Gulf was predicted to be $6 billion annually, which corresponds to an average cost of $29.3 per kg of N runoff reduction.
The second study, submitted to Ecological Economics, reports the results of using the integrated modeling system to investigate the impact of U.S. and China agricultural commodity trade interactions on nitrogen export from the MARB to the Gulf of Mexico. One key finding of the study was that a 25% Chinese tariff on U.S. soybean exports would result in an increase of annual nitrogen loads to the Gulf by 2,000 metric tons, due to an increase in planted acreage of crops managed with more intensive nitrogen fertilizer inputs (especially corn) across the MARB. Another example outcome of the study was that a 5% Chinese tariff on U.S. corn imports would result in an reduced MARB corn production by 8.8% and a 10% reduction in nitrogen loading to the Gulf of Mexico.
A real-time application of a Soil and Water Assessment Tool (SWAT) model was also developed for the Upper Mississippi River Basin (UMRB). This SWAT model incorporates soil, topographic, land use, climate, and agricultural cropping system and management data that is representative of the UMRB. The real-time system is designed to be updated on a daily basis with climate data provided by the Iowa State University Agronomy Department. The SWAT model, input data and climate data were provided to the University of Iowa IIHR research team for installation on their website. Post-processing techniques of SWAT output were also developed to support on-line presentation of hydrologic, and nitrate and phosphorus loadings, on a daily or monthly basis.
Last Modified: 12/01/2022
Modified by: Philip W Gassman
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