Stochastic Forecast of Water Losses
Publication: Journal of Irrigation and Drainage Engineering
Volume 114, Issue 3
Abstract
For the purpose of irrigation and water resources planning, it is important to know the water loss for a drainage basin. Unfortunately individual measurements of elements of the total water loss are not realistic, at least for the large watersheds studied in this research. Based on the water budget approach, annual water loss series are formulated in this paper. A modeling technique that includes the homogeneity test of data and the best model selection is developed to fit the water loss series by a stochastic process. The results of this study reveal the existence of data nonhomogeneities in the annual water loss series from the Ohio River Basin, which requires adjustments before the mode! fitting by a stochastic process. The selected best model by the criterion of the parsimony of parameter was successfully used to forecast the regional water losses based on the proposed procedure.
Get full access to this article
View all available purchase options and get full access to this article.
References
1.
Akaike, H. (1974). “A new look at the statistical model identification.” IEEE Trans. on Automatic Control, AS‐19, 6, 716–723.
2.
Box, G. E. P., and Jenkins, G. B. (1976). Time Series Analysis: Forecasting and Control. Holden‐Day, San Francisco, CA.
3.
Chang, Tiao J. (1981). “Daily precipitation and streamflow modeling by discrete autoregressive moving average processes,” Ph.D. Dissertation, Purdue University.
4.
Chang, Tiao J., Kavvas, M. L., and Delleur, J. W. (1982). “Stochastic daily precipitation modeling and daily streamflow transfer processes.” Technical Report No. 146, Water Res. Research Center, Purdue University.
5.
Chang, Tiao J. (1985). “Microcomputer applications in stochastic hydrology,” Proc. Hydr. Div., ASCE, 371–375.
6.
Chang, Tiao J., Delleur, J. W., and Kavvas, M. L. (1987). “Application of discrete autoregressive moving average models for estimation of daily runoff,” J. of Hydr., 9(1), 119–135.
7.
Clapp, R. B., Hornberger, G. M., and Cosby, B. J. (1983). “Estimating spatial variability in soil moisture with a simplified dynamic model,” Water Res. Res., 12(5), 953–970.
8.
Davidson, M. R. (1985). “Asymptotic behavior of infiltration in soils containing cracks and holes,” Water Res. Res., 21(9), 1345–1353.
9.
Delleur, J. W., Tao, P. C., and Kavvas, M. L. (1976). “An estimation of the practicality and complexity of some rainfall and runoff time series,” Water Res. Res., 12(5), 953–970.
10.
Jensen, M. E., and Wright, J. L. (1978). “The role of evaporation models in irrigation scheduling,” Trans., ASAE, 21(1), 82–87.
11.
Munro, D. S. (1979). “Daytime energy exchange and evaporation from a wooded swamp,” Water Res. Res., 15(5), 1259–1265.
12.
Salas, J. D. et al. (1985). Applied modeling of hydrologic time series. Water Resources Publications, Littleton, CO.
13.
Vecchi, A. V., et al. (1983). “Aggregation and estimation of low‐order periodic ARIMA models,” Water Res. Res., 19(5), 1297–1306.
Information & Authors
Information
Published In
Copyright
Copyright © 1988 ASCE.
History
Published online: Aug 1, 1988
Published in print: Aug 1988
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.