Using SWAT to simulate crop yields and salinity levels in the North Fork River Basin, USA

Aaron Ray Mittelstet, Daniel E. Storm, Art L. Stoecker

Abstract


Crop yields and salinity levels in the North Fork of the Red River (North Fork River) basin, located in southwestern Oklahoma and the Texas Panhandle, were analyzed based on the diverse climate in the region. Saline irrigation water is a major problem in the basin. The Elm Fork Creek flows through salt deposits, making the creek and its receiving stream, the North Fork River, too saline to use for irrigation. This greatly reduces the number of hectares that can be utilized for agricultural crops within the basin. A baseline SWAT model was setup, calibrated and validated to simulate streamflow and wheat and cotton yields. The SWAT model and a regression equation were used to analyze variable weather impacts on crop yields and salinity levels. Using the weather generator WXGEN and 58 years of observed weather data, ten 50-year weather datasets were generated. Output from the weather generator was input into the calibrated SWAT model to simulate wheat and dryland and irrigated cotton yields for the ten weather scenarios. Using an empirical relationship between ionic strength and streamflow, salinity levels were estimated. Though the crop yields varied greatly from year to year, the yields were not significantly different over the 50-year simulation period. The electrical conductivity (EC, expressed in decisiemens per meter or dS/m) at the US Geological Survey gage station just downstream of the salt deposits was significantly different with levels ranging from 40 to 65 dS/m. Though the water in the Elm Fork is much too saline to use for irrigation, the water in the North Fork River may be used as long as the flow rates in the river are greater than 0.60 m3/s. In order to optimize the available cropland, a salinity control must be installed upstream of the salt deposits on the Elm Fork Creek.
Keywords: salinity, SWAT, crop yield, wheat modeling, cotton modeling, Red River
DOI: 10.3965/j.ijabe.20150803.950 Online first on [2015-03-17]

Citation: Mittelstet A R, Storm D E, Stoecker A L. Using SWAT to simulate crop yields and salinity levels in the North Fork River Basin, USA. Int J Agric & Biol Eng, 2015; 8(3): 110－124.

Keywords


salinity, SWAT, crop yield, wheat modeling, cotton modeling, Red River

Full Text:

PDF

References


FAO (Food and Agriculture Organization of the United Nations). The use of saline waters for crop production. 1992. Available at: http://www.fao.org/docrep/T0667E/t0667 e06.htm. Accessed on [2012-06-03].

Sabo J L, Sinha T, Bowling L C, Schoups G H W, Wallender W W, Campana M E, et al. Reclaiming freshwater sustainability in the Cadillac Desert. Proceedings of the National Academy of Sciences, 2010; 107(50): 21263-21270.

Williams C. Is irrigated agriculture sustainable? The battle to counteract salinity. Southwest Hydrology, July/August 2005.

Laughlin D H, Lacewell R D. Agriculture benefits of salinity control on the Red River. Western Journal of Agricultural Economics, 1981; 6(2): 195-206.

Longenecker D E, Lyerly P J. Control of soluble salts in farming and gardening. Texas Agricultural Experiment Station, 1974; Available electronically from http://hdl.handle.net/1969.1/93158.

Boumans J H,van Hoorn J W, Kruseman G P, Tanwar B S. Water table control, resue and disposal of drainage water in Haryana. Agricultural Water Management, 1988; 14: 537-545.

Oster J D. Irrigation with poor quality water. Agricultural Water Management, 1994; 25: 271-297.

Rady A H M. Water, soil and crop management relating to the use of saline water. In: Proc. Expert Cons. On Water Soil and Crop Management relating to the Use of Saline Water, October 1989; AGL/MISC/16/90. FAO, Rome.

Chipanshi A C, Ripley E A, Lawford R G. Large-scale simulation of wheat yields in a semi-arid environment using a crop-growth model. Agricultural Systems, 1999; 59: 57-66.

Moulin A P, Beckie H J. Evaluation of the CERES and EPIC models for predicting spring wheat grain yield over time. Canadian Journal of Plant Science, 1993; 73: 713-719.

Cabelguenne M, Jones C A, Marty J R, Dyke P T, Williams J R. Calibration and validation of EPIC for crop rotations in southern France. Agricultural Systems, 1990; 33: 153-171.

Arnold J G, Srinivasan R, Muttiah R S, Williams J R. Large-area hydrologic modeling and assessment. Part 1: Model development. Journal of the American Water Resources Association, 1998; 34(1): 73-89.

Gassman P W, Reyes M R, Green C H, Arnold J G. The Soil and Water Assessment Tool: historical development, applications, and future research directions. Transactions of the ASABE, 2007; 50(4): 1211-1250.

Vaghefi S A, Mousavi S J, Abbaspour K C, Srinivasan R, Yang H. Analysis of the impact of climate change on water resources components, drought and wheat yields in semiarid regions: Karkheh River Basin in Iran. Hydrological Process, 2014; 228: 2018-2032.

Faramarzi M, Yang H, Schulin R, Abbaspour K C. Modeling wheat yield and crop water productivity in Iran: Implications of agricultural water management for wheat production. Agricultural Water Management, 2010; 97: 1861-1875.

Sun C, Ren R. Assessing crop yield and water productivity and optimizing irrigation scheduling of winter wheat and summer maize in the Haihe plain using SWAT model. Hydrological Process, 2014; 28: 2478-2498.

Nair S S, King K W, Witter J D, Sohngen B L, Fausey N R. Importance of crop yield in calibrating watershed water quality simulation tools. Journal of the American Water Resources Association, 2010; 47(6): 1825-1297.

Guerra L C, Hoogenboom G, Boken V K, Hook J E, Thomas D L, Harrison K A. Evaluation of the EPIC model for simulating crop yield and irrigation demand. Transactions of the ASABE, 2004; 47(6): 2091-2100.

Ko J, Piccinni G, Steglich E. Using EPIC model to manage irrigated cotton and maize. Agricultural Water Management, 2009; 96: 1323-1331.

Ko J, Maas S J, Lascano R J, Wanjura D. Modification of the GRAMI model for cotton. Agronomy Journal, 2005; 97: 1374-1379.

Sarkar S, Miller S A, Frederick J R, Chamberlain J F. Modeling nitrogen loss from switchgrass agricultural systems. Biomass and Bioenergy, 2011; 35: 4381-4389.

Panagopoulos Y, Makropoulos C, Mimikou M. Decision support for agricultural water management. Global Nest Journal, 2012; 14(3): 255-263.

Gikas G D, Yiannakopoulou T, Tsihrintzis V A. Modeling of non-point source pollution in a Mediterranean drainage basin. Environ Mod and Assess, 2006; 11: 219-233.

Ghimire M, Bhavsar H, Choi J S, Vitale J, Stoecker A. Integration of GIS and hydrological models in a feasibility study of irrigation under salinity. Agricultural & Applied Economics Association’s 2012 AAEA Annual Meeting, Seattle, Washington, USA, August 12-14, 2012.

Gikas G D, Yiannakopoulou T, Tsihrintzis V A. Hydrodynamic and nutrient modeling in a Mediterranean coastal lagoon. Journal of Environmental Science and Health Part A, 2009; 44: 1400-1423.

iman T, Lennaerts T, Southalack P. Assessment of hydrological changes in the lower Mekong Basin from basin-wide development scnearios. Hydrological Process, 2013; 27: 2115-2125.

Somura H, Arnold J, Hoffman D, Takeda I, Mori Y, Luzio M D. Impact of climate change on the Hii River basin and salinity in Lake Shinji: A case study using the SWAT model and a regression curve. Hydrological Process, 2009; 23: 1887-1900.

USEPA. 2009. Clean Water Act Section 319(h) grant funds history. U.S. Environmental Protection Agency. Available at: http://www.epa.gov/owow/nps/319hhistory.html. Accessed July 1, 2011.

USGS a. 2012. USGS water data for the nation. U.S. Department of the Interior. U.S. Geological Survey. Available at: http://waterdata.usgs.gov/nwis. Accessed on [2010-05-20].

Ayers R S, Westcot D W. Water quality for agriculture. FAO Irrigation and Drainage Paper 29, Rev. 1, 1985.

USDA. 2007. United States Census of Agriculture. United States Department of Agriculture; Washington, D.C. United States Census Bureau; Washington, D.C.

Neitsch S L, Arnold J G, Kiniry J R, Williams J R. 2005. Soil and Water Assessment Tool – Theoretical Documentation, Version 2005.Grassland, Soil and Water Research Laboratory, Agricultural Research Service, Blackland Research Center, Texas Agricultural Experiment Station, Texas.

ESRI (Environmental Systems Resource Institute). 2009. ArcMap 9.2. ESRI, Redlands, California.

USDA. 2008. National Agricultural Statistics Service Database. Washington, D.C.: USDA National Agricultural Statistics Service. Available at: www.nass.usda.gov. Accessed on [2010-05-20].

NLCD. 2001. National Land Cover Dataset: 2001. The USGS Land Cover Institute. Available at: http://landcover.usgs.gov/ natllandcover.php.

Homer C, Huang C, Yang L, Wylie B, Coan M. Development of a 2001 national landcover database for the United States. Photogrammetric Engineering & Remote Sensing, 2004; 70(7): 829-840.

USGS b. 2012. USGS Global Visualization Viewer. U.S. Department of the Interior. U.S. Geological Survey. Available at: http://glovis.usgs.gov/. Accessed on [2010-05-20].

Chandler G, Markham B L, Helder D L. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sensing of Environment, 2009; 113: 893-903.

USDA. 2009. National Agricultural Imagery Program Mosaic: 2008. Datagateway Database. USDA, Washington, DC. Available at: http://datagateway.nrcs.usda.gov/. Accessed on [2010-05-20].

USDA. 1995. State Soil Geographic (STATSGO) database. Misc. Pub. 1492. Lincoln, Neb.: USDA-NRCS National Soil Survey Center. Available at: http://soils.usda.gov/survey/ geography/statsgo/. Accessed on [2010-05-20].

NOAA. 2012. National Weather Service. Cooperative Observer Program. Office of Climate, Water, and Weather Services. Available at: http://www.nws.noaa.gov/om/coop/. Accessed on [2010-05-20].

USEPA. 2012. Envirofacts. U.S. Environmental Protection Agency. Available at: http://epa.gov/enviro/html/pcs/ pcs_query_java.html. Accessed on [2010-05-20].

WCDS. 2012. Tulsa District U.S. Army Corp of Engineers. Real-time Gage Data for Reservoirs and Stream Gages. Available at: http://www.swt-wc.usace.army.mil/. Accessed on [2010-05-20].

Whitis G N. Watershed Fish Production Ponds Guide to Site Selection and Construction, Southern Regional Aquiculture Center, Publication No. 102, September, 2002.

Richardson C W. Stochastic simulation of daily precipitation, temperature and solar radiation. Water Resource Research, 1981; 17:182-190.

Wallis T W, Griffiths J F. An assessment of the weather generator (WXGEN) used in the erosion/productivity impact calculator (EPIC). Agricultural and Forest Meteorology, 1995; 73: 115-133.

Muttiah R S, Miyamoto S, Borah M, Walker C H. 2005. Instream salinity modeling of middle Rio Grande and Wichita Basins. In "Water for Texas". Norwine, Giardino and Krishnamurthy (Eds). Texas A&M Univ. Press. pp. 249-256, Chapter 24.

Krause P, Boyle D P, Base F. Comparison of different efficiency criteria for hydrological model assessment. Advances in Geosciences, 2005; 5: 89-97.

Moriasi D N, Arnold J G, Van Liew M W, Bingner R L, Harmel R D, Veith T L. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 2007; 50(3): 885-900.

Legates D R, McCabe G J. Evaluating the use of “goodness of fit” measures in hydrologic and hydroclimatic model validation. Water Resource Research, 1999; 35(1): 233-241.

Nash J E, Sutcliffe J V. River flow forecasting through conceptual models: Part I. A discussion of principles. Journal of Hydrology, 1970; 10(3): 282-290.

Miyamoto S, Moore J, Stichler C. 1984. Overview of saline water irrigation in Far West Texas. In: Replogle J A and Renard K G (Editors), Water Today and Tomorrow. Proc. Specialty Conf. Irrigation and Drainage Division of ASCE, Flagstaff, Arizona, 24-26 July 1984. ASCE, New York, pp. 222-230.

Dutt G R, Pennington D A, Turner F. 1984. Irrigation as a solution to salinity problems in river basins. In: French R H (Editor), Salinity in Watercourses and Reservoirs. Ann Arbor Science, Michigan, pp. 465-472.

NOAA Technical Report NESDIS 142-4. Regional climate trends and scenarios for the U.S. national climate assessment. Part 4. Climate of the U.S. Great Plains. Washington D.C., January 2013.




Copyright (c)



2023-2026 Copyright IJABE Editing and Publishing Office