Free Access
Ann. Limnol. - Int. J. Lim.
Volume 51, Number 1, 2015
Page(s) 23 - 35
Published online 15 January 2015
  • Abbaspour K.C., 2007. User Manual for SWAT-CUP, SWAT Calibration and Uncertainty Analysis Programs, Swiss Federal Institute of Aquatic Science and Technology, Eawag, Duebendorf, Switzerland, 103 p. [Google Scholar]
  • Arnold J.G., Moriasi D.N., Gassman P.W., Abbaspour K.C., White M.J., Srinivasan R. and Jha M.K., 2012. SWAT: model use, calibration, and validation. Trans. ASABE, 55, 1491–1508. [CrossRef] [Google Scholar]
  • Babel M.S., Shrestha B. and Perret S., 2011. Hydrological impact of biofuel production: a case study of the Khlong Phlo Watershed in Thailand. Agric. Water Manage., 101, 8–26. [CrossRef] [Google Scholar]
  • Bagnold R.A., 1977. Bed load transport by natural rivers. Water Resour. Res., 13, 303–312. [CrossRef] [Google Scholar]
  • Benkobi L., Trlica M.J. and Smith J.L., 1994. Evaluation of a refined surface cover subfactor for use in RUSLE. J. Range Manage., 47, 74–78. [CrossRef] [Google Scholar]
  • Blume T., 2008. Hydrological processes in volcanic ash soils – measuring, modeling and understanding runoff generation in an undisturbed catchment. PhD thesis, Faculty of Mathematics and Natural Sciences, University of Potsdam, Germany, 153 p. [Google Scholar]
  • Boardman J., Shepheard M.L., Walker E. and Foster I.D.L., 2009. Soil erosion and risk-assessment for on- and off-farm impacts: a test case using the Midhurst area, West Sussex, UK. J. Environ. Manage., 90, 2578–2588. [CrossRef] [PubMed] [Google Scholar]
  • Boithias L., Sauvage S., Taghavi L., Merlina G., Probst J.L. and Sánchez-Pérez J.M., 2011. Occurrence of metolachlor and trifluralin losses in the Save River agricultural catchment during floods. J. Hazard. Mater., 196, 210–219. [CrossRef] [PubMed] [Google Scholar]
  • Boithias L., Srinivasan R., Sauvage S., Macary F. and Sánchez-Pérez J.M., 2013. Daily nitrate losses: implication on long-term river quality in an intensive agricultural catchment (south-western France). J. Environ. Qual., 43, 46–54. [CrossRef] [Google Scholar]
  • Cerro I., Sánchez-Pérez J.M., Ruiz-Romera E. and Antigüedad I., 2013. Variability of particulate (SS, POC) and dissolved (DOC, NO3) matter during storm events in the Alegria agricultural watershed. Hydrol. Process., 28, 2855–2867. [CrossRef] [Google Scholar]
  • Cerro I., Antigüedad I., Srinivasan R., Sauvage S., Volk M. and Sánchez-Pérez J.M., 2014. Simulating land management options to reduce nitrate pollution in an agricultural watershed dominated by an alluvial aquifer. J. Environ. Qual., 43, 67–74. [CrossRef] [PubMed] [Google Scholar]
  • Chu T.W., Shirmohammadi A., Montas H. and Sadeghi A., 2004. Evaluation of the SWAT model's sediment and nutrient components in the Piedmont physiographic region of Maryland. Trans. ASAE, 47, 1523–1538. [CrossRef] [Google Scholar]
  • Cultivated Soil Classification committee, Japan, 1995. Classification of Cultivated Soil in Japan, Third Approximation, Miscellaneous Publication, National Institute for Agro-Environmental Sciences, 17, Tsukuba, 79 p. [Google Scholar]
  • Easton Z.M., Fuka D.R., White E.D., Collick A.S., Biruk Ashagre B., McCartney M., Awulachew S.B., Ahmed A.A. and Steenhuis T.S., 2010. A multi basin SWAT model analysis of runoff and sedimentation in the Blue Nile, Ethiopia, Hydrol. Earth Syst. Sci., 14, 1827–1841. [Google Scholar]
  • Ekholm P. and Lehtoranta J., 2012. Does control of soil erosion inhibit aquatic eutrophication? J. Environ. Manage., 93, 140–146. [CrossRef] [PubMed] [Google Scholar]
  • Gao P., 2008. Understanding watershed suspended sediment transport. Prog. Phys. Geog., 32, 243–263. [CrossRef] [Google Scholar]
  • Haag I., Kern U. and Westrich B., 2001. Erosion investigation and sediment quality measurements for a comprehensive risk assessment of contaminated aquatic sediments. Sci. Total Environ., 266, 249–257. [Google Scholar]
  • Hayakawa A., Woli K.P., Shimizu M., Nomaru K., Kuramochi K. and Hatano R., 2009. The nitrogen budget and relationships with riverine nitrogen exports of a dairy cattle farming catchment in eastern Hokkaido, Japan. Soil Sci. Plant Nutr., 55, 800–819. [CrossRef] [Google Scholar]
  • Heathwaite A.L., Dils R.M., Liu S., Carvalho L., Brazier R.E., Pope L., Hughes M., Philips G. and May L., 2005. A tiered risk-based approach for predicting diffuse and point source phosphorus losses in agricultural areas. Sci. Total Environ., 344, 225–239. [CrossRef] [PubMed] [Google Scholar]
  • IUSS, ISRIC, FAO, 2006. World Reference Base for Soil Resources. World Soil Resources Reports 103. International Union of Soil Sciences, ISRIC World Soil Information, FAO, Rome, 128 p. [Google Scholar]
  • Jayakrishnan R., Srinivasan R., Santhi C. and Arnold J.G., 2005. Advances in the application of the SWAT model for water resources management. Hydrol. Process., 19, 749–762. [CrossRef] [Google Scholar]
  • Jiang R., Li Y., Wang Q., Kuramochi K., Hayakawa A., Woli K.P. and Hatano R., 2011. Modeling the water balance processes for understanding the components of river discharge in a non-conservative watershed. Trans. ASABE, 54, 2171–2180. [CrossRef] [Google Scholar]
  • Jiang R., Wang C.Y., Hatano R., Hayakawa A., Woli K.P. and Kuramochi K., 2014. Simulation of stream nitrate-nitrogen export using the Soil and Water Assessment Tool model in a dairy farming watershed with an external water source. J. Soil Water Conserv., 69, 75–85. [CrossRef] [Google Scholar]
  • Jolley R.L., Lockaby B.G. and Cavalcanti G.G., 2010. Changes in riparian forest composition along a sedimentation rate gradient. Plant Ecol., 210, 317–330. [CrossRef] [Google Scholar]
  • Kerr J.G., Burford M.A., Olley J.M., Bunn S.E. and Udy J., 2011. Examining the link between terrestrial and aquatic phosphorus speciation in a subtropical catchment: the role of selective erosion and transport of fine sediments during storm events. Water Res., 45, 3331–3340. [CrossRef] [PubMed] [Google Scholar]
  • Kim J.G., Park Y.S., Yoo D., Kim N., Engel B.A., Kim S., Kim K.S. and Lim K.J., 2009. Development of a SWAT patch for better estimation of sediment yield in steep sloping watersheds. J. Am. Water Resour. Assoc., 45, 963–972. [CrossRef] [Google Scholar]
  • Kinnell P.I.A., 2005. Why the universal soil loss equation and the revised version of it do not predict event erosion well. Hydrol. Process., 19, 851–854. [CrossRef] [Google Scholar]
  • Kronvang B., Laubel A. and Grant R., 1997. Suspended sediment and particulate phosphorus transport and delivery pathways in an arable catchment, Gelbaek stream, Denmark. Hydrol. Process., 11, 627–642. [CrossRef] [Google Scholar]
  • Lal R., 1998. Soil erosion impact on agronomic productivity and environment quality. Crit. Rev. Plant Sci., 17, 319–464. [CrossRef] [Google Scholar]
  • Le Moine N., Andréassian V., Perrin C. and Michel C., 2007. How can rainfall-runoff models handle intercatchment groundwater flows? Theoretical study based on 1040 French catchments. Water Resour. Res., 43, W06428. [CrossRef] [Google Scholar]
  • Le Moine N., Andréassian V. and Mathevet T., 2008. Confronting surface and groundwater balances on the La Rochefoucauld-Touvre karstic system (Charente, France). Water Resour. Res., 44, W03403. [CrossRef] [Google Scholar]
  • Morgan R.P.C., Quinton J.N., Smith R.E., Govers G., Poesen J.W.A., Auerswald K., Chisci G., Torri D. and Styczen M.E., 1998. The European soil erosion model (EUROSEM): a dynamic approach for predicting sediment transport from fields and small catchments. Earth Surf. Proc. Land., 23, 527–544. [CrossRef] [Google Scholar]
  • Mukundan R., Radcliffe D.E. and Risse L.M., 2010. Spatial resolution of soil data and channel erosion effects on SWAT model predictions of flow and sediment. J. Soil Water Conserv., 65, 92–104. [CrossRef] [Google Scholar]
  • Nash J.E. and Sutcliffe J.V., 1970. River flow forecasting through conceptual models. Part 1: a discussion of principles. J. Hydrol., 10, 282–290. [Google Scholar]
  • Nearing M.A., Foster G.R., Lane L.J. and Finkner S.C., 1989. A process-based soil erosion model for USD-Water Erosion Prediction Project Technology. Trans. ASAE, 32, 1587–1593. [CrossRef] [Google Scholar]
  • Neitsch S.L., Arnold J.G., Kiniry J.R., Williams J.R. and King K.W., 2005. Soil and Water Assessment Tool. Theoretical Documentation: Version 2005. TWRI TR–191. Texas Water Resources Institute, College Station, Texas, 476 p. [Google Scholar]
  • Oeurng C., Sauvage S. and Sánchez-Pérez J.M., 2011. Assessment of hydrology, sediment and particulate organic carbon yield in a large agricultural catchment using the SWAT model. J. Hydrol., 401, 145–153. [CrossRef] [Google Scholar]
  • Phomcha P., Wirojanagud P., Vangpaisal T. and Thaveevouthti T., 2011. Predicting sediment discharge in an agricultural watershed: a case study of the Lam Sonthi watershed, Thailand. Science Asia, 37, 43–50. [CrossRef] [Google Scholar]
  • Qiu L.J., Zheng F.L. and Yin R.S., 2012. SWAT-based runoff and sediment simulation in a small watershed, the loessial hilly-gullied region of China: capabilities and challenges. Int. J. Sediment Res., 27, 226–234. [CrossRef] [Google Scholar]
  • Saghafian B., Sima S., Sadeghi C. and Jeirani F., 2012. Application of unit response approach for spatial prioritization of runoff and sediment sources. Agric. Water Manage., 109, 36–45. [CrossRef] [Google Scholar]
  • Salerno F. and Tartari G., 2009. A coupled approach of surface hydrological modelling and wavelet analysis for understanding the baseflow components of river discharge in karst environments. J. Hydrol., 376, 295–306. [CrossRef] [Google Scholar]
  • Santhi C., Arnold J.G., Williams J.R., Dugas W.A., Srinivasan R. and Hauck L.M., 2001. Validation of the SWAT model on a large river basin with point and nonpoint sources. J. Am. Water Resour. Assoc., 37, 1169–1188. [Google Scholar]
  • SCS (Soil Conservation Service), 1972. Section 4: Hydrology in National Engineering Handbook, U.S. Department of Agriculture-Soil Conservation Service, Washington, D.C., 55 p. [Google Scholar]
  • Talebizadeh M., Morid S., Ayyoubzadeh S.A. and Ghasemzadeh M., 2010. Uncertainty analysis in sediment load modeling using ANN and SWAT model. Water Resour. Manage., 24, 1747–1761. [CrossRef] [Google Scholar]
  • Thomas R.B., 1988. Monitoring baseline suspended sediment in forested basin: the effect of sampling on suspended sediment rating curves. Hydrol. Sci. J., 33, 499–514. [CrossRef] [Google Scholar]
  • Van Remortel R.D., Hamilton M.E. and Hickey R.J., 2001. Estimating the LS factor for RUSLE through iterative slope length processing of digital elevation data within Arclnfo grid. Cartography, 30, 27–35. [CrossRef] [Google Scholar]
  • Van Rompaey A.J.J., Verstraeten G., Van Oost K., Govers G. and Poesen J., 2001. Modelling mean annual sediment yield using a distributed approach. Earth Surf. Proc. Land., 2, 1221–1236. [CrossRef] [Google Scholar]
  • Wang G.S., Wente G. and Anderson A., 2002. Improvement in mapping vegetation cover factor for the universal soil loss equation by geostatistical methods with Landsat Thematic Mapper images. Int. J. Remote Sens., 23, 3649–3667. [CrossRef] [Google Scholar]
  • Williams J.R., 1975. Sediment routing for agricultural watersheds. J. Am. Water Resour. Assoc., 11, 965–974. [CrossRef] [Google Scholar]
  • Williams J.R., 1995. The EPIC model. In: Singh V.P. (ed.), Computer Models of Watershed Hydrology, Water Resources Publications, Highlands Ranch, CO, 909–1000. [Google Scholar]
  • Woli K.P., Nagumo T., Kuramochi K. and Hatano R., 2004. Evaluating river water quality through land use analysis and N budget approaches in livestock farming areas. Sci. Total Environ., 329, 61–74. [CrossRef] [PubMed] [Google Scholar]
  • Wu Y. and Chen J., 2012. Modeling of soil erosion and sediment transport in the East River Basin in southern China. Sci. Total Environ., 441, 159–168. [CrossRef] [PubMed] [Google Scholar]
  • Yang X., 2014. Deriving RUSLE cover factor from time-series fractional vegetation cover for hillslope erosion modelling in New South Wales. Soil Res., 52, 253–261. [CrossRef] [Google Scholar]

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