Developing rainfall intensity-duration-frequency curves for alabama under future climate scenarios using artificial neural networks

Journal of Hydrologic Engineering

Volumn 19, Issue 11, 2014, Pages

  Mirhosseini, Golbahar ^a   Srivastava, Puneet ^b   Fang, Xing ^b  

^a AUBURN UNIVERSITY   (United States)

^b AUBURN UNIVERSITY   (United States)

Author keywords

Artificial neural network (ANN); Climate change; General circulation models (GCMs); Intensity duration frequency (IDF) curve; Temporal downscaling

Indexed keywords

BACKPROPAGATION; CLIMATE CHANGE; CLIMATE MODELS; EARTH ATMOSPHERE; GREENHOUSE GASES; RAIN; STOCHASTIC MODELS; STOCHASTIC SYSTEMS; WATER MANAGEMENT;

DOWN-SCALING; DOWNSCALING METHODS; FUTURE CLIMATE SCENARIOS; GENERAL CIRCULATION MODEL; INTENSITY-DURATION-FREQUENCY CURVES; MANAGEMENT INFRASTRUCTURE; RAINFALL INTENSITY DURATION FREQUENCY; REGIONAL CLIMATE MODELS;

NEURAL NETWORKS;

ARTIFICIAL NEURAL NETWORK; CLIMATE CHANGE; CLIMATE MODELING; CLIMATE PREDICTION; DOWNSCALING; GENERAL CIRCULATION MODEL; PRECIPITATION INTENSITY;

ALABAMA; UNITED STATES;

EID: 84911885101     PISSN: 10840699     EISSN: 19435584     Source Type: Journal    
DOI: 10.1061/(ASCE)HE.1943-5584.0000962     Document Type: Article

References (42)

1. Bhunya, P., Jain, S., Ojha, C., and Agarwal, A. (2007). "Simple parameter estimation technique for three-parameter generalized extreme value distribution." J. Hydrol. Eng., 10.1061/(ASCE)1084-0699(2007)12:6(682), 682-689.
2. Bowden, G. J., Dandy, G. C., and Maier, H. R. (2005). "Input determination for neural network models in water resources applications. Part 1-Background and methodology." J. Hydrol., 301(1), 75-92.
3. Burian, S. J., et al. (2000). "Rainfall disaggregation using artificial neural networks." J. Hydrol. Eng., 10.1061/(ASCE)1084-0699(2000)5:3(299), 299-307.
4. Burian, S. J., Durrans, S. R., Nix, S. J., and Pitt, R. E. (2001). "Training artificial neural networks to perform rainfall disaggregation." J. Hydrol. Eng., 10.1061/(ASCE)1084-0699(2001)6:1(43), 43-51.
5. Dawson, C. W., and Wilby, R. L. (2001). "Hydrologic modeling using artificial neural networks." Prog. Phys. Geogr., 25(1), 80-108.
6. Dibikie, Y. B., and Coulibaly, P. (2006). "Temporal neural networks for downscaling climate variability and extremes." Neural Networks, 19(2), 135-144.
7. Durrans, S. R., and Brown, P. A. (2001). "Estimation and internet-based dissemination of extreme rainfall information." Transportation Research Record 1743, Transportation Research Board, National Research Council, Washington, DC, 41-48.
8. Frederick, R. H., Myers, V. A., and Auciello, E. P. (1977). "Five- to 60-min precipitation frequency for the eastern and central United States." NOAA Technical Memorandum NWS HYDRO-35, National Weather Service, U.S. Dept. of Commerce, Washington, D.C.
9. Halff, A. H., Halff, H. M., and Azmoodeh, M. (1993). "Predicting runoff from rainfall using neural networks." J. Hydrol. Eng., 760-765.
10. Hershfield, D. M. (1961). "Rainfall frequency atlas of the United States for durations from 30 min to 24 h and return periods from 1 to 100 years." Technical Paper No. 40, Weather Bureau, U.S. Dept. of Commerce, Washington, D.C.
11. Hosking, J. R. M., Wallis, J. R., andWood, E. F. (1985). "Estimation of the generalized extreme value distribution by method of probability weighted moments." Technometrics, 27(3), 251-261.
12. Isik, S., Kalin, L., Schoonover, J. E., Srivastava, P., and Lockaby, B. G. (2013). "Modeling effects of changing land use/cover on daily streamflow: An artificial neural network and curve number based hybrid approach." J. Hydrol., 485, 103-112, 10.1016/j.jhydrol.2012.08.032.
13. Islam, S., Entekhabi, D., and Bras, R. L. (1990). "Parameter estimation and sensitivity analysis for the modified Bartlett-Lewis rectangular pulses model of rainfall." J. Geophys. Res., 95(D3), 2093-2100.
14. Kalin, L., Isik, S., Schoonover, J. E., and Lockaby, B. G. (2010). "Predicting water quality in unmonitored watersheds using artificial neural networks." J. Environ. Qual., 39(4), 1429-1440.
15. Knutti, R., and Sedlácek, J. (2013). "Robustness and uncertainties in the new CMIP5 climate model projections." Nat. Clim. Change, 3(4), 369-373.
16. Li, H., Sheffield, J., and Wood, E. F. (2010). "Bias correction of monthly precipitation and temperature fields from Intergovernmental Panel on Climate Change AR4 models using equidistant quantile matching." J. Geophys. Res. Atmos., 115(D10).
17. Liu, S., Fu, C., Shiu, C., Chen, J., and Wu, F. (2009). "Temperature dependence of global precipitation extremes." Geophys. Res. Lett., 36(17).
18. Maier, H. R., and Dandy, G. C. (2000). "Neural networks for the prediction and forecasting of water resources variables: A review of modeling issues and applications." Environ. Model. Software, 15(1), 101-123.
19. Massey, F. J. (1951). "The Kolmogorov-Smirnov test for goodness of fit." Am. Stat. Assoc., 46(253), 68-78.
20. May, R. J., Dandy, G. C., Maier, H. R., and Nixon, J. B. (2008). "Application of partial mutual information variable selection to ANN forecasting of water quality in water distribution systems." Environ. Model. Software, 23(10), 1289-1299.
21. McCuen, R. (1998). Hydrologic analysis and design, Prentice-Hall, Englewood Cliffs, N.J.
22. Minns, A.W., and Hall, M. J. (1996). "Artificial neural networks as rainfallrunoff models." Hydrol. Sci. J., 41(3), 399-417.
23. Mirhosseini, G., Srivastava, P., and Stefanova, L. (2013). "The impact of climate change on rainfall intensity-duration-frequency (IDF) curves in Alabama." Reg. Environ. Change, 13(1), 25-33.
24. Nash, J. E., and Sutcliffe, J. V. (1970). "River flow forecasting through conceptual models. Part 1: A discussion of principles." J. Hydrol., 10(3), 282-290.
25. NCDC Online Climate Data Directory. NOAA National Climatic Data Center (NCDC). .
26. Ormsbee, L. E. (1989). "Rainfall disaggregation model for continuous hydrologic modeling." J. Hydrol. Eng., 10.1061/(ASCE)0733-9429(1989)115:4(507), 507-525.
27. Pearson, R. G., Dawson, T. P., Berry, P. M., and Harrison, P. A. (2002). "SPECIES: A spatial evaluation of climate impact on the envelope of species." Ecol. Model., 154(3), 289-300.
28. Prodanovic, P., and Simonovic, S. P. (2007). "Development of rainfall intensity duration frequency curves for the City of London under the changing climate." Water Resources Research Rep. No. 058, Univ. of Western Ontario, London, ON, Canada.
29. Qi, M., and Zhang, G. P. (2001). "An investigation of model selection criteria for neural network time series forecasting." Eur. J. Oper. Res., 132(3), 666-680.
30. Ren, L., and Zhao, M. Z. (2002). "An optimal neural network and concrete strength modeling." Adv. Eng. Softw., 33(3), 117-130.
31. Richard, J., Wilfran, M. O., and Simon, T. (2007). The North American regional climate change assessment program dataset, National Center for Atmospheric Research Earth System Grid Data Portal, Boulder, CO, (May 4, 2014).
32. Rodriguez-Iturbe, I., Cox, D. R., and Isham, V. (1987). "Some models for rainfall based on stochastic point processes." Proc. R. Soc. A, Lond., 410(1839), 269-288.
33. Sebastien, B., Daniel, C., and René, L. (2007). The North American regional climate change assessment program dataset, National Center for Atmospheric Research Earth System Grid Data Portal, Boulder, CO, (May 4, 2014).
34. Singh, V. P., Jain, S. K., and Tyagi, A. K. (2007). Risk and reliability analysis: A handbook for civil and environmental engineers, ASCE, Reston, V.A.
35. Smith, J., and Eli, R. N. (1995). "Neural-network models of rainfall-runoff process." J. Water Resour. Plann. Manage., 10.1061/(ASCE)0733- 9496(1995)121:6(499), 499-508.
36. Socolofsky, S., Adams, E., and Entekhabi, D. (2001). "Disaggregation of daily rainfall for continuous watershed modeling." J. Hydrol. Eng., 10.1061/(ASCE)1084-0699(2001)6:4(300), 300-309.
37. Von Storch, H. (1999). "Representation of conditional random distributions as a problem of "spatial" interpolation." GeoENV II-Geostatistics for environmental applications, Springer, Netherlands, 13-23.
38. Von Storch, H., Hewitson, B., and Mearns, L. (2000). "Review of empirical downscaling techniques. Regional climate development under global warming." General Technical Rep. No. 4, Conf. Proc. Regional Climate Spring Meeting Jevnaker, Torbjørnrud, Norway, 29-46.
39. Weichert, A., and Burger, G. (1998). "Linear versus nonlinear techniques in downscaling." Clim. Res., 10(2), 83-93.
40. Weigend, A. S., and Gershenfeld, N. A. (1993). "Results of the time series prediction competition at the Santa Fe Institute." Neural Networks, IEEE Int. Conf., IEEE, 1786-1793.
41. Wolcott, S. B., Mroz, M., and Basile, J. (2009). "Application of northeast regional climate center research results for the purpose of evaluating and updating intensity-duration-frequency (IDF) curves. Case study: Rochester, New York." Proc., World Environmental and Water Resources Congress 2009, ASCE, Reston, V.A.
42. Zhao, Z., Zhang, Y., and Liao, H. (2008). "Design of ensemble neural network using the Akaike information criterion." Eng. Appl. Artif. Intell., 21(8), 1182-1188.