Location: Southeast Watershed Research2018 Annual Report
1. Quantify and assess the interactions among agroecosystems and landscape components and their impacts on water supply and water quality in agricultural watersheds of the southeastern U.S. 2. Quantify and assess the effects of agricultural conservation practices and managed land-use interfaces at field, landscape, and watershed scales in agricultural watersheds of the southeastern U.S. 3. As part of the LTAR network, and in concert with similar long-term, land-based research infrastructure in the Gulf Atlantic Coastal Plain (GACP), use the Little River Experimental Watershed (LREW) LTAR site to improve the observational capabilities and data accessibility of the LTAR network and support research to sustain or enhance agricultural production and environmental quality in agroecosystems characteristic of the Gulf Atlantic Coastal Plain (GACP) region. Research and data collection are planned and implemented based on the LTAR site application and in accordance with the responsibilities outlined in the LTAR Shared Research Strategy, a living document that serves as a roadmap for LTAR implementation. Participation in the LTAR network includes research and data management in support of the ARS GRACEnet and/or Livestock GRACEnet projects. 4. Utilize landscape and watershed scale assessment models to evaluate the long-term sustainability of agricultural watersheds.
The research integrates field, landscape, and watershed observations. As such, research sites are located at multiple scales each supporting watershed observations. The SEWRL operates watershed facilities Little River Experimental Watershed (LREW) that are the basis for our long-term hydrology and natural resources research. In addition to these watersheds, the SEWRL has established long-term research at plot (~0.2 Ha) and field (> 10 Ha) scales. The objectives in this plan contribute to the LTAR Common Experiment over-arching hypothesis that “aspirational treatments will increase overall carbon stocks and in particular, soil carbon…leading to increased ecosystem resiliency”. Individual sub-objectives are focused on providing an improved understanding of spatial and temporal drivers and ecosystem services responses associated with the three Common Experiment sub-hypotheses: 1) The magnitude, direction and rate of change will vary with topographic and soil characteristics of the landscape; 2) Sustainable ecosystem productivity, yield, and yield quality will be significantly improved by the development of specific and adaptive G x E x M x Social x Economic systems; and 3) Biologically-based inputs will drive the rate and magnitude of carbon stock increases (e.g., nutrient cycling, insect comminution, decomposition, etc.). The experiments presented are designed as an integrated systems approach to understanding processes at the plot-to-landscape scale using the LREW as the synthesis scale for testing and verification of the Long Term Agroecosystem Research Common Experiment hypothesis. Each objective and sub-objective is designed to address selected spatial and temporal scale processes, provide information for qualifying extrapolations between scales, and/or explore novel technical approaches for characterizing ecosystems services within the LREW. We will use remote sensing, geospatial modeling, statistical modeling and process modeling to evaluate linkages and identify information gaps across scales. Specific research will: 1) characterize the impacts that agricultural land management and land-cover have on water resources in southern coastal plain watersheds; 2) examine relationships between conservation practices (including winter cover), indicators of productivity (e.g. SOC, NPP), other drivers of land cover change, and water quality; 3) characterize composition of DOM with land-use; 4) quantify differences between watersheds with agricultural livestock impacts to watersheds with minimal agricultural livestock impact; 5) quantify stream flow and chemistry differences between urbanized and agricultural watersheds; 6) quantify the impact of agricultural irrigation ponds on watershed water balance; 7) quantify differences in provisioning and regulating ecosystem services between typical and aspirational agricultural production systems; 8) compare spatial and temporal variations between provisioning and regulating ecosystems services; and 9) use landscape and watershed scale assessment models to evaluate the long-term sustainability of agricultural watersheds.
Flow data and water quality collection and analysis efforts on the Little River Experimental Watershed (LREW), Gibbs Farm, Tifton Urban Watersheds, and Upper Suwannee River in south Georgia continue (Obj. 1-4). Geographical Information System (GIS) databases of soils, hydrography, and land-cover across the LREW are being updated. Historical land-cover data are being assembled in a geodatabase. Intensive sampling activities continue across private landowner fields in the LREW (Obj. 3). Water samples are being collected at all sites in the LREW to relate dissolved organic matter (DOM) quality to land-use (Obj. 1). Bi-weekly water samples from the LREW, Dairy Farm, Gibbs Farm and Tifton Urban Watersheds, are being analyzed for DOM optical characteristics (started in November 2016). The resulting optical data is being processed using parallel factor (PARAFAC) analysis. After major rainfall events, water samples from select sites were extracted and the extracts will be analyzed for DOM molecular-level composition using high resolution mass spectrometry (FT- ICR MS). Existing sites at the Dairy Farm, LREW Watershed O3, and LREW Watershed O, along with new sites at the Wilson Farm will be used to evaluate livestock impacts at the watershed scale (Obj. 1). New boundary maps for Watersheds O3 and New River were developed based on newly released 1m resolution digital elevation models for Tift County. Automated flow monitoring and sample collection at existing sites at the Dairy Farm, LREW O3, and LREW O continues. A cooperative agreement was initiated with the cooperator/owner of the Wilson Farm to allow access to the Farm to initiate installation of hydrologic equipment. A meteorological station was installed at the Farm and data are being collected. For both the Dairy Farm and Wilson Farm, deep core samples were collected. Dairy Farm cores have been processed for analysis of microbial biomass carbon (C) and nitrogen (N), processing of the Wilson Farm cores is underway. Re-design of field scale plots at the University of Georgia Gibbs and Ponder Farms was begun in the spring of 2018 for purposes of this project (Obj. 2, 3). As part of this project and our participation in the ARS Long Term Agroecosystem Research (LTAR) network, we have implemented studies to compliment ongoing research conducted at all participating LTAR locations (Obj. 3). Data collection continues at the Southeast Watershed Research Laboratory (SEWRL) LTAR meteorological and phenology stations. These data are available through the National Agricultural Library (https://ltar.nal.usda.gov/) (Obj. 3). Eddy covariance data are being collected at two sites for quantifying the exchange rates of trace gases over natural ecosystems (Obj. 3). Very high resolution multispectral imagery from an unmanned aerial vehicle (UAV) borne sensor are being gathered throughout the growing season on two private landowner farms for model development to scale yield measurements from field to landscape (Obj. 3, 4). We are utilizing landscape and watershed scale assessment models to evaluate the long-term sustainability of agricultural watersheds (Obj. 4). Toward this goal, a framework for Soil and Water Assessment Tool (SWAT) simulation of the LREW has been established. The model has been used to quantify the impacts of conservation practices and winter covers in the LREW. A geodatabase has been designed and geophysical characterization of the watershed is being incorporated into the database.
1. A spatially explicit watershed model. Precise and cost-effective watershed management requires spatial and temporal details of hydrologic characteristics, such as runoff generation areas and flow pathways that can be provided by distributed hydrologic models. However, there is a gap between the complexity of hydrologic processes at field or hillslope scales and the simplicity of operational models at the watershed scale. ARS scientists in Tifton, Georgia, collaborated with ARS and university scientists in Temple, Texas, and developed a spatially explicit watershed model to reduce this gap. The model uses empirical equations and limited parameters to represent the most critical hydrologic processes, soil moisture and surface runoff. The model was tested on three watersheds in the Coastal Plain region of the U.S. Results indicate good agreement between observed and simulated soil moisture and streamflow.
2. Economic competitiveness of napiergrass as a renewable biofuel feedstock. Although the southeastern U.S. has been identified as the most promising region for production of renewable biofuels feedstocks, developing a reliable supply chain that can provide 50% of the nation’s feedstock production requires both agronomic and economic information that producers and lenders can use to evaluate the bottom-line. A team of ARS scientists from Dawson and Tifton, Georgia, compared the production cost of napier grass as a cellulosic feedstock crop to that of a traditional peanut, cotton, corn row crop rotation in the coastal plain region of Georgia. The team demonstrated that the price required for napier grass to compete ranged from $99 to $121 Mg-1 with total costs mainly dependent on irrigation, and that napier grass would outcompete the traditional cropping system only under non-irrigated production regimes.
Volk, M., Bosch, D.D., Narasimhan, B., Nangia, V. 2016. SWAT: Agricultural water and nonpoint source pollution management at a watershed scale. Agricultural Water Management. 175:1-3. https://doi.10.1016/j.agwat.2016.06.013.
Shellito, P., Small, E., Colliander, A., Bindlish, R., Cosh, M.H., Berg, A., Bosch, D.D., Caldwell, T., Goodrich, D.C., Lopez-Baeza, E., McNairn, H., Prueger, J.H., Starks, P.J. 2016. SMAP soil moisture drying more rapid than observed in situ following rainfall events. Geophysical Research Letters. 43(15):8068-8075.
Colliander, A., Jackson, T.J., Bindlish, R., Chan, S., Das, N., Kim, S., Cosh, M.H., Dunbar, R., Dang, L., Pashaian, L., Asanuma, J., Aida, K., Berg, A., Rowlandson, T., Bosch, D.D., Caldwell, T., Caylor, K., Goodrich, D.C., Jassar, H., Lopez-Baeza, E., Martinez-Fernandez, J., Gonzalez-Zamora, Livingston, M.S., McNairn, H., Pacheco, A., Moghaddam, M., Montzka, C., Notarnicola, C., Niedrist, G., Pellarin, T., Prueger, J.H., Pulliainen, J., Rautiainen, K., Ramo, J., Seyfried, M.S., Starks, P.J., Su, Z., Zeng, Y., Velde, R., Thibeault, M., Dorigo, W., Vreugdenhil, M., Walker, J., Wu, X., Monerris, A., O'Neill, P., Entekhabi, D., Njoku, E., Yueh, S. 2017. Validation of SMAP surface soil moisture products with core validation sites. Remote Sensing of Environment. 192:238-262.
Cho, J., Her, Y., Bosch, D.D. 2016. Sensitivity of simulated conservation practice effectiveness to representation of field and in-stream processes in the Little River Watershed. Environmental Modeling and Assessment. 22(2):159-173.
Olson, D.M., Prescott, K.K., Zeilinger, A.R., Hou, S., Coffin, A.W., Smith, C.M., Ruberson, J.R., Andow, D.A. 2018. Landscape effects on reproduction of Euschistus servus (Hemiptera: Pentatomidae), a mobile, polyphagous, multivoltine arthropod herbivore. Environmental Entomology. 47(3):660-668. https://doi.org/10.1093/ee/nvy045.
Xavier, S., Olson, D.M., Coffin, A.W., Strickland, T.C., Schmidt, J. 2017. Perennial grass and native wildflowers: a synergistic approach to habitat management. Insects. 8(4):104-117. https://doi.org/10.3390/insects8040104.
Volk, M., Bosch, D.D., Nangia, V., Narasimhan, B. 2017. SWAT: Agricultural water and nonpoint source pollution management at a watershed scale - Part II. Agricultural Water Management. https://doi.org/10.1016/j.agwat.2016.09/029.
Colliander, A., Jackson, T.J., Chan, S., O'Neill, P., Bindlish, R., Cosh, M.H., Caldwell, T., Walker, J., Berg, A., McNairn, H., Thibeault, M., Martinez-Fernandez, J., Jensen, K., Asanuma, J., Seyfried, M.S., Bosch, D.D., Starks, P., Holifield Collins, C.D., Prueger, J.H., Su, Z., Lopez-Beeza, E., Yeuh, S. 2018. An assessment of the differences between spatial resolution and grid size for the SMAP enhanced soil moisture product over homogeneous sites. Remote Sensing of Environment. 207:65-70.
Van Liew, M.N., Wortmann, C.S., Moriasi, D.N., King, K.W., Flanagan, D.C., Veith, T.L., McCarty, G.W., Bosch, D.D., Tomer, M.D. 2017. Evaluating the APEX model for simulating streamflow and water quality on ten agricultural watersheds in the U.S. Transactions of the ASABE. 60(1):123-146. https://doi.org/10.13031/trans.11903.
Bieger, K., Arnold, J.G., Rathjens, H., White, M.J., Bosch, D.D., Allen, P.M., Volk, M., Srinivasan, R. 2017. Introduction to SWAT+, a completely restructured version of the soil and water assessment tool. Journal of the American Water Resources Association. 53(1):115-130. https://doi.org/10.1111/1752-1688.12482.
Pisani, O., Gao, M., Maie, N., Miyoshi, T., Childers, D., Jaffe, R. 2017. Compositional aspects of herbaceous litter decomposition in the freshwater marshes of the Florida Everglades. Plant and Soil. 423(1-2):87-98. https://doi.org.10.1007/s11104-017-3495-3.
Bosch, D.D., Arnold, J.G., Allen, P., Lim, K., Shik, Y. 2017. Temporal variations in baseflow for the Little River Experimental Watershed in South Georgia, USA. Journal of Hydrology. 10:110-121. https://doi.org/10.1016/j.ejrh.2017.02.002.
Li, S., Gitau, M., Bosch, D.D., Engel, B., Zhang, L., Du, Y. 2017. Development of a soil moisture-based distributed hydrologic model for determining hydrologically based critical source areas. Hydrological Processes. Pp 1-15. https://doi.org.10.1002/hyp.11276.
Pfannerstill, M., Bieger, K., Guse, B., Bosch, D.D., Fohrer, N., Arnold, J.G. 2017. How to constrain multi-objective calibrations of the SWAT model using water balance components. Journal of the American Water Resources Association. https://doi.org/10.1111/jawra.1752-1688.12524.
Chaubey, I., Bosch, D.D., Monoz-Carpena, R., Harmel, R.D., Douglas-Mankin, K., Nejadhashemi, P., Srivastava, P., Shirmohammadi, A. 2016. Climate change: a call for adaptation and mitigation strategies. Transactions of the ASABE. 59(6):1709-1713. https://doi.org/10.13031/trans.59.12138.
Chan, S., Bindlish, R., O'Neill, P., Jackson, T.J., Njoku, E., Dunbar, R., Chaubell, J., Peipmeier, J., Yueh, S., Entekhabi, D., Colliander, A., Chen, F., Cosh, M.H., Caldwell, T., Walker, J., Berg, A., McNairn, H., Thibeault, M., Martinez-Fernandez, J., Udall, F., Seyfried, M.S., Bosch, D.D., Starks, P.J., Holifield Collins, C.D., Prueger, J.H., Crow, W.T. 2018. Development and assessment of the SMAP enhanced passive soil moisture product. Remote Sensing of Environment. 204:931-941. https://doi.org/10.1016/j.rse.2017.08.025.
Bindlish, R., Cosh, M.H., Jackson, T.J., Koike, T., Fuiji, X., De Jeu,, R., Chan, S., Asanuma, J., Berg, A., Bosch, D.D., Caldwell, T., Holifield Collins, C.D., McNairn, H., Martinez-Fernandez, J., Prueger, J.H., Rowlandson, T., Seyfried, M.S., Starks, P.J., Su, Z., Thibeault, M., van der Velde, R., Walker, J., Coopersmith, E. 2018. GCOM-W AMSR2 soil moisture product validation using core validation sites. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 11(1):209-219. https://doi.org/10.1109/JSTARS.2017.2754293.
Chan, S., Bindlish, R., O'Neill, P., Njoku, E., Jackson, T.J., Colliander, A., Chen, F., Burgin, M., Dunbar, R., Peipmeier, J., Yueh, S., Entekhabi, D., Cosh, M.H., Caldwell, T., Walker, J., Wu, X., Berg, A., Rowlandson, T., Pacheco, A., McNairn, H., Thibeault, M., Martinez-Fernandez, J., Gonzalez-Zamora, A., Seyfried, M.S., Bosch, D.D., Starks, P.J., Goodrich, D.C., Prueger, J.H., Palecki, M., Small, E., Zreda, M., Calvet, J., Crow, W.T., Kerr, Y. 2016. Assessment of the SMAP level 2 passive soil moisture product. IEEE Transactions on Geoscience and Remote Sensing. 54(8):1-14. doi:10.1109/TGRS.2016.2561938.