Modeling effects of changing land use/cover on daily streamflow: An Artificial Neural Network and curve number based hybrid approach

Journal of Hydrology

Volumn 485, Issue , 2013, Pages 103-112

  Isik, Sabahattin ^a   Kalin, Latif ^a   Schoonover, Jon E ^b   Srivastava, Puneet ^c   Graeme Lockaby, B ^a  

^a AUBURN UNIVERSITY   (United States)

^b SOUTHERN ILLINOIS UNIVERSITY   (United States)

^c AUBURN UNIVERSITY   (United States)

Author keywords

ANN; Baseflow; Land use land cover; SCS CN; Streamflow; Ungauged watershed

Indexed keywords

ANN; BASEFLOWS; LAND COVER; SCS-CN; UNGAUGED WATERSHED;

FORECASTING; FORESTRY; NEURAL NETWORKS; SOIL CONSERVATION; STREAM FLOW; WATERSHEDS;

LANDFORMS;

ARTIFICIAL NEURAL NETWORK; HYDROLOGICAL RESPONSE; LAND COVER; LAND USE CHANGE; MODEL VALIDATION; NUMERICAL MODEL; PASTURE; STREAMFLOW; URBAN AREA; WATERSHED;

CONSERVATION; EDUCATION; HYDROLOGY; LAND USE; NEURAL NETWORKS; PRECIPITATION; STREAM FLOW; WATER SHEDS;

GEORGIA; UNITED STATES;

EID: 84875520036     PISSN: 00221694     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhydrol.2012.08.032     Document Type: Article

References (66)

1. Anmala J., Zhang B., Gavindaraju R.S. Comparison of ANNs and empirical approaches for predicting watershed runoff. J. Water Resour. Plann. Manage. 2000, 126(3):156-166.
2. Arnold J.G., Allen P.M. Validation of automated methods for estimating baseflow and groundwater recharge from stream flow records. J. Am. Water Resour. Assoc. 1999, 35:411-424.
3. Arnold C.L., Gibbons G.C. Impervious surface coverage: the emergence of a key environmental indicator. J. Am. Plann. Assoc. 1996, 62(2):243-259.
4. ASCE Task Committee, 2000. Artificial neural network in hydrology I: preliminary concepts. J. Hydrol. Eng. 5(2), 115-123.
5. Baker D.B., Richards R.T., Loftus T., Kramer J.W. A new flashiness index: characteristics and applications to Midwestern rivers and streams. J. Am. Water Resour. Assoc. 2004, 40(2):503-522.
6. Besaw L.E., Rizzo D.M., Bierman P.R., Hackett W.R. Advances in ungauged streamflow prediction using artificial neural networks. J. Hydrol. 2010, 386:27-37.
7. Bormann H., Diekkrüger B., Hauschild M. Impacts of landscape management on the hydrological behaviour of small agricultural catchments. Phys. Chem. Earth 1999, 24(4):291-296.
8. Bowden G.J., Dandy G.C., Maier H.R. Input determination for neural network models in water resources applications. Part 1-background and methodology. J. Hydrol. 2005, 301:75-92.
9. Bras, R.L., 1990. Hyrology: An Introduction to Hydrologic Sciences. Addison-Wesley Publishing Company, New York.
10. Breuer L., Huisman J.A., Willems P., Bormann H., Bronstert A., Croke B.F.W., Frede H.G., Gräff T., Hubrechts L., Jakeman A.J., Kite G., Lanini J., Leavesley G., Lettenmaier D.P., Lindström G., Seibert J., Sivapalan M., Viney N.R. Assessing the impact of land use change on hydrology by ensemble modeling (LUCHEM) I: model intercomparison of current land use. Adv. Water Resour. 2009, 32:129-146.
11. Bronstert A., Niehoff D., Burger G. Effects of climate and land-use change on storm runoff generation: present knowledge and modeling capabilities. Hydrol. Process. 2002, 16:509-529.
12. Chen C.S., Chou F.N.F., Chen B.P.T. Spatial information-based back-propagation neural network modeling for outflow estimation of ungauged catchment. Water Resour. Manage. 2010, 24(14):4175-4197.
13. Chow V.T., Maidment D.R., Mays L.W. Applied Hydrology 1988, McGraw-Hill, New York.
14. Corzo G.A., Solomatine D.P. Baseflow separation techniques for modular artificial neural networks modelling in flow forecasting. Hydrol. Sci. J. 2007, 52(3):491-507.
15. Costa M.H., Botta A., Cardille J.A. Effects of large-scale changes in land cover on the discharge of the Tocantins River, Southeastern Amazonia. J. Hydrol. 2003, 283:206-217.
16. Crim, J.F., 2007. Water Quality Changes Across an Urban-Rural Land Use Gradient in Streams of the West Georgia Piedmont. M. Sc. Thesis. Auburn Univ., Auburn, Alabama.
17. Croke B.F.W., Meritt W.S., Jakeman A.J. A dynamic model for predicting hydrologic response to land cover changes in gauged and ungauged catchments. J. Hydrol. 2004, 291:115-131.
18. Dawson C.W., Wilby R.L. Hydrologic modeling using artificial neural networks. Prog. Phys. Geog. 2001, 25(1):80-108.
19. Govindaraju, R.S., Ramachandra Rao, A., 2000. Artificial Neural Networks in Hydrology. Kluwer Academic, Dordrecht.
20. Graf W.L. Network characteristics in suburbanizing streams. Water Resour. Res. 1977, 13(2):459-463.
21. Ha H., Stenstrom M.K. Identification of land use with water quality data in stormwater using a neural network. Water Res. 2003, 37:4222-4230.
22. Hamon W.R. Computation of direct runoff amounts from storm rainfall. Int. Assoc. Sci. Hydrol. Pub. 1963, 63:52-62.
23. Harmel R.D., Cooper R.J., Slade R.M., Haney R.L., Arnold J.G. Cumulative uncertainty in measured streamflow and water quality data for small watersheds. Trans. ASABE 2006, 49(3):689-701.
24. Haykin S. Neural Networks: A Comprehensive Foundation 1999, Prentice Hall, New Jersey.
25. Hirsch, R.M., Walker, J.F., Day, J.C., Kallio, R., 1990. The influence of man on hydrological systems. In: Wolman, M.G., Riggs, H.C. (Eds.), Surface Water Hydrology. The Geological Society of North America, Boulder, Colorado.
26. Hsu K.-L., Gupta H.V., Sorooshian S. Artificial neural network modeling of the rainfall-runoff process. Water Resour. Res. 1995, 31(10):2517-2530.
27. Kalin L., Hantush M.M. An auxiliary method to reduce potential adverse impacts of projected land developments: subwatershed prioritization. Environ. Manag. 2009, 43(2):311-325.
28. Kalin L., Isik S., Schoonover J.E., Lockaby B.G. Predicting water quality in unmonitored watersheds using artificial neural networks. J. Environ. Qual. 2010, 39(4):1429-1440.
29. Klemes V. Operational testing of hydrological simulation models. Hydrol. Sci. J. 1986, 31(1):13-24.
30. Lin Y.P., Hong N.M., Wu P.J., Wu C.F., Verburg P.H. Impact of land use change scenarios on hydrology and land use patterns in the Wu-Tu watershed in Northern Taiwan. Landsc. Urban Plann. 2007, 82:111-126.
31. Lockaby B.G., Zhang D., McDaniels J., Tian H., Pan S. Interdisciplinary research at the urban-rural interface. The WestGA project. Urban Ecosyst. 2005, 8:7-21.
32. Lu J., Sun G., McNulty S.G., Amatya D.M. A comparison of six potential evapotranspiration methods for regional use in the southeastern United States. J. Am. Water Resour. Assoc. (JAWRA) 2005, 41(3):621-633.
33. Maier H.R., Dandy G.C. Neural networks for the prediction and forecasting of water resources variables: a review of modeling issues and applications. Environ. Model. Softw. 2000, 15:101-123.
34. MATLAB version 7.10.0, 2010. Computer Software. The MathWorks Inc., Natick, Massachusetts.
35. Meierdiercks K.L., Smith J.A., Baeck M.L., Miller A.J. Analyses of urban drainage network structure and its impact on hydrologic response. J. Am. Water Resour. Assoc. (JAWRA) 2010, 46(5):932-943. 10.1111/j.1752-1688.2010.00465.x.
36. Nagy C., Lockaby B.G., Helms B., Kalin L., Stoeckel D. Water resources and land use and cover in a humid region: the Southeastern United States. J. Environ. Qual. 2011, 40:867-878.
37. Nash J.E., Sutcliffe J.V. River flow forecasting through conceptual models part I-a discussion of principles. J. Hydrol. 1970, 10(3):282-290.
38. Nathan R.J., McMahon T.A. Evaluation of automated techniques for baseflow and recession analysis. Water Resour. Res. 1990, 26(7):1465-1473.
39. Pijanowski B.C., Brown D.G., Shellito B.A., Manik G.A. Using neural networks and GIS to forecast land use changes: a land transformation model. Comput. Environ. Urban Syst. 2002, 26:553-575.
40. Qi M., Zhang G.P. An investigation of model selection criteria for neural network time series forecasting. Eur. J. Oper. Res. 2001, 132:666-680.
41. Rajukar M.P., Kothyari U.C., Chaube U.C. Modeling of the daily rainfall-runoff relationship with artificial neural network. J. Hydrol. 2004, 285:96-113.
42. Ren L., Zhao M.Z. An optimal neural network and concrete strength modeling. Adv. Eng. Softw. 2002, 33:117-130.
43. Salas J.D., Markus M., Tokar A.S. Streamflow forecasting based on artificial neural networks. Artificial Neural Networks in Hydrology 2000, 23-51. Kluwer Academic Publishers, The Netherlands. R.S. Govindaraju, A.R. Rao (Eds.).
44. Schoonover, J.S., 2005. Hydrology, Water Quality, and Channel Morphology Across an Urban-Rural Land Use Gradient in the Georgia piedmont, USA. Ph.D. Diss. Auburn Univ., Auburn, Alabama.
45. Schoonover J.S., Lockaby B.G. Land cover impacts on stream nutrients and fecal coliform in the lower piedmont of west Georgia. J. Hydrol. 2006, 331:371-382.
46. Schoonover J.S., Lockaby B.G., Helms B. Effects of watershed land use on perennial streams of west Georgia, USA: 1. Influence of urban development on stream hydrology. J. Environ. Qual. 2006, 35:2123-2131.
47. Schoonover J.S., Lockaby B.G., Shaw J.N. Channel morphology and sediment origin in streams draining the Georgia piedmont. J. Hydrol. 2007, 342:110-123.
48. Schulze R.E. Modelling hydrological responses to land use and climatic change: a Southern African perspective. Roy. Swed. Acad. Sci. 2000, 29(1):12-22.
49. SCS, 1986. Urban Hydrology for Small Watersheds. Tech. Release No. 55. Soil Conservation Service, U.S.D.A., Washington D.C.
50. Siriwardena L., Finlayson B.L., McMahon T.A. The impact of land use change on catchment hydrology in large catchments: the Comet River, Central Queensland, Australia. J. Hydrol. 2006, 326:199-214.
51. Smith J.A., Baeck M.L., Morrison J.E., Sturdevant-Rees P., Turner-Gillespie D.F., Bates P.D. The regional hydrology of extreme floods in an urbanizing drainage basin. J. Hydrometeorol. 2002, 3(3):267-282.
52. Soil Survey Staff, 2011. Natural Resources Conservation Service, United States Department of Agriculture. Soil Survey Geographic (SSURGO) Database for Georgia. (accessed 03.10.11). http://soildatamart.nrcs.usda.gov.
53. Sola J., Sevill J. Importance of input data normalization for the application of neural networks to complex industrial problems. IEEE Trans. Nucl. Sci. 1997, 44(3):1464-1467.
54. Srivastava P., McNair J.N., Johnson T.E. Comparison of process-based and artificial neural network approaches for streamflow modeling in an agricultural watershed. J. Am. Water Resour. Assoc. 2006, 42(3):545-563.
55. Stonestrom, D.A., Scanlon, B.R., Zhang, L., 2009. Introduction to Special Section on Impacts of Land Use Change on Water Resources. Water Resour. Res., 45, W00A00. doi:10.1029/2009WR007937.
56. Trimble, S.W., 2008. Man-Induced Soil Erosion on the Southern Piedmont: 1700-1970, second ed. Soil and Water Conservation Society, Ankeny, IA.
57. United States Forest Service (USFS), 2005. Ecological Subregions of the United States. Department of Agriculture. (accessed 05.16.11). http://www.fs.fed.us/land/pubs/ecoregions/ch20.html.
58. van Dijk A.I.J.M., Pena-Arancibia J.L., Bruijnzeel L.A. Top-down analysis of collated streamflow from heterogeneous catchments leads to underestimation of land cover influence. Hydrol. Earth Sci. 2011, 8:4121-4150.
59. Wear, D.N., Greis, J.G., 2002. Southern Forest Resource Assessment - Technical Report. Gen. Tech. Rep. SRS-53. U.S. Department of Agriculture, Forest Service, Southern Research Station, Asheville, NC, 635p.
60. White E.D., Feyereisen G.W., Veith T.L., Bosch D.D. Improving daily water yield estimates in the little river watershed: SWAT adjustments. ASABE 2009, 52(1):69-79.
61. Woodward, D.E., Hawkins, R.H., Jiang, R., Hjelmfelt, A.T., Van Mullem, J.A., Quan, Q.D., 2003. Runoff curve number method: Examination of the initial abstraction ratio. In: Bizier, P., DeBarry, P. (Eds.), World Water and Environmental Resources Congress 2003 and Related Symposia. ASCE, Reston, Va.
62. Zealand C.M., Burn D.H., Simonovic S.P. Short term streamflow forecasting using artificial neural networks. J. Hydrol. 1999, 214(1-4):32-48.
63. Zhang Y.-K., Schilling K.E. Increasing streamflow and baseflow in Mississippi River since the 1940s: effect of land use change. J. Hydrol. 2006, 324:412-422.
64. Zhao Z., Zhang Y., Liao H. Design of ensemble neural network using the Akaike information criterion. Eng. Appl. Artif. Intell. 2008, 21:1182-1188.
65. Zheng, H., Zhang, L., Zhu, R., Liu, C., Sato, Y., Fukushima, Y., 2009. Responses of streamflow to climate and land surface change in the headwaters of the Yellow River Basin. Water Resour. Res., 45, W00A19. doi:10.1029/2007WR006665.
66. Zurada J.M. Introduction to Artificial Neural Systems 1992, PWS Publishing Company, Boston, Massachusetts.