Optimal selection of GCM predictors combined with a new procedure for statistical downscaling of precipitation

World Environmental and Water Resources Congress 2010: Challenges of Change - Proceedings of the World Environmental and Water Resources Congress 2010

Volumn , Issue , 2010, Pages 4598-4607

  Najafi, Mohammad Reza ^a   Moradkhani, Hamid ^a   Wherry, Susan ^a  

^a PORTLAND STATE UNIVERSITY   (United States)

Author keywords

Climate change; Precipitation; Statistics

Indexed keywords

ADAPTIVE NETWORK BASED FUZZY INFERENCE SYSTEM; ANFIS MODEL; CLIMATE CHANGE IMPACT; CROSS VALIDATION; DOWN-SCALING; GENERAL CIRCULATION MODEL; GRID CELL SIZE; GRID CELLS; INDEPENDENT SIGNALS; MULTI-LINEAR REGRESSION; OPTIMAL SELECTION; OREGON , USA; PRECIPITATION; RIVER BASINS; ROBUST NETWORK; STATISTICAL APPROACH; STATISTICAL DOWNSCALING; SVM MODEL; SVM(SUPPORT VECTOR MACHINE); TEST DATA; TEST SETS; TIME-SCALES; TRAINING DATA SETS; WILLAMETTE;

CLIMATE MODELS; FUZZY INFERENCE; HEMODYNAMICS; INDEPENDENT COMPONENT ANALYSIS; UNCERTAINTY ANALYSIS; WATER RESOURCES;

CLIMATE CHANGE;

EID: 77955012012     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1061/41114(371)467     Document Type: Conference Paper

References (12)

1. Anandhi, A., Srinivas, V. V., Nanjundiah, R. S., Nagesh Kumar, D. (2008), Downscaling precipitation to river basin in India for IPCC SRES scenarios using support vector machine, International Journal of Climatology, 28(3), 401-420.
2. Fowler, H. J., Blenkinsop, S., Tebaldi, C. (2007), Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling, International Journal of Climatology, 27(12), 1547-1578. (Pubitemid 47604181)
3. Ghosh, S., and P. P. Mujumdar (2008), Statistical downscaling of GCM simulations to streamflow using relevance vector machine, Advances in Water Resources, 31(1), 132-146.
4. Haykin, S. (1999), Neural Networks, A Comprehensive Foudation, New Jersey: Prentice Hall.
5. Hewitson, B. C., and R. G. Crane (1996), Climate downscaling: techniques and application, Climate Research, 7, 85-95.
6. Jang, J. S. R. (1993), ANFIS: Adaptive-network-based fuzzy inference system, IEEE transactions on systems, man, and cybernetics, 23(3), 665-685.
7. Moradkhani, H., M., Meier, (200x), Long-Lead Water Supply Forecast using Large-scale Climate Predictors and Independent Component Analysis, Journal of Hydrologic Engineering, in review
8. Sugeno, M., and G. T. Kang (1988), Structure identification of fuzzy model, Fuzzy sets and systems, 28(1), 15-33.
9. Tripathi, S., Srinivas, V. V., Nanjundiah, R. S. (2006), Downscaling of precipitation for climate change scenarios: A support vector machine approach, Journal of Hydrology, 330(3-4), 621-640.
10. Vapnik, V. N. (1999), An overview of statistical learning theory, IEEE transactions on neural networks, 10(5), 988-999.
11. Wilby, R. L., Charles, S. P., Zorita, E., Timbal, B., Whetton, P., Mearns, L. O. (2004), Guidelines for use of climate scenarios developed from statistical downscaling methods, Available at: ipccddc. cru. uea. ac. uk/guidelines/dgm-no2-v1-09-2004. pdf.
12. Wood, A. W., Leung, L. R., Sridhar, V., Lettenmaier, D. P. (2004), Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs, Climatic Change, 62(1), 189-216.