Your search found 225 records
1 Central Bank of Sri Lanka. Statistics Department. 1985. Economic and social statistics of Sri Lanka. Vol. VIII. Colombo, Sri Lanka: Central Bank. ii, 108p.
(Location: IWMI-HQ Call no: 330.0212 G744 CEN Record No: H02640)
2 Clemmens, A. J. 1988. Method for analyzing field scale surface irrigation uniformity. Journal of Irrigation and Drainage Engineering, 114(1):74-88.
(Location: IWMI-HQ Call no: PER Record No: H03725)
3 Dale, A.; Arber, S.; Proctor, M. 1988. Doing secondary analysis. London, UK: Unwin Hyman. xi, 241p. (Contemporary social research no.17)
(Location: IWMI-HQ Call no: 300.72 G000 DAL Record No: H05972)
4 Barringer, T.; Battaglin, W.; Dunn, D.; Vowinkel, E. 1990. Problems and methods involved in relating land use to ground-water quality. Water Resources Bulletin, 26(1):1-9.
(Location: IWMI-HQ Call no: PER Record No: H06084)
5 Hosking, J. R. M.; Clarke, R. T. 1990. Rainfall-runoff relations derived from the probability theory of storage. Water Resources Research, 26(7):1455-1463.
(Location: IWMI-HQ Call no: PER Record No: H06601)
6 Lenton, R. L.; Rodriguez-Iturbe, I.; Schaake, J. C. Jr. 1973. A Bayesian approach to autocorrelation estimation in hydrologic autoregressive models. Cambridge, MA, USA: Massachusetts Institute of Technology. 121p. (Ralph M. Parsons Laboratory for Water Resources and Hydrodynamics report no.163)
(Location: IWMI-HQ Call no: 532.5 G000 LEN Record No: H06868)
7 Lenton, R. L.; Rodriguez-Iturbe, I. 1974. On the collection, the analysis and the synthesis of spatial rainfall data. Cambridge, MA, USA: Massachusetts Institute of Technology. 218p.
(Location: IWMI-HQ Call no: 551.5781 G000 LEN Record No: H06869)
8 Piccardi, C.; Soncini-Sessa, R. 1991. Stochastic dynamic programming for reservoir optimal control: Dense discretization and inflow correlation assumption made possible by parallel computing. Water Resources Research, 27(5):729-741.
(Location: IWMI-HQ Call no: PER Record No: H08510)
9 Wyant, T.; Shoemyen, J. 1980. Estimating irrigation water use through selective monitoring: A North Florida case study. Water Resources Bulletin, 16(6):1074-1079.
(Location: IWMI-HQ Call no: P 2043 Record No: H09087)
10 Azhar, A. H.; Murty, V. V. N.; Phien, H. N. 1992. Modeling irrigation schedules for lowland rice with stochastic rainfall. Journal of Irrigation and Drainage Engineering, 118(1):36-53.
Irrigation scheduling ; Rice ; Statistical analysis ; Rain ; Mathematical models ; Water balance / Thailand
(Location: IWMI-HQ Call no: PER Record No: H09911)
11 Parabrahman, P. B.; Bhanagay, S. A. 1987. Use of regional regression approach in rainfall-runoff regression analysis for hydrological studies of Mahanadi Basin. In Seminar on Application of Systems Analysis for Water Resources Development, 27-28 February 1987: Proceedings. New Delhi, India: Central Water Commission; Central Board of Irrigation and Power. pp.45-62.
(Location: IWMI-HQ Call no: 333.91 G635 SEM Record No: H010071)
12 Kottegoda, N. T. 1987. Applications of stochastic models. In Seminar on Application of Systems Analysis for Water Resources Development, 27-28 February 1987: Proceedings. New Delhi, India: Central Water Commission; Central Board of Irrigation and Power. pp.109-132.
(Location: IWMI-HQ Call no: 333.91 G635 SEM Record No: H010073)
13 Parabrahaman, P. B.; Illangovan, M. 1987. Generation of equally likely synthetic flow sequences for Hirakud reservoir by stochastic modelling. In Seminar on Application of Systems Analysis for Water Resources Development, 27-28 February 1987: Proceedings. New Delhi, India: Central Water Commission; Central Board of Irrigation and Power. pp.133-158.
(Location: IWMI-HQ Call no: 333.91 G635 SEM Record No: H010074)
14 Mukherjee, D.; Kottegoda, N. T. 1992. Stochastic model for soil moisture deficit in irrigated lands. Journal of Irrigation and Drainage Engineering, 118(4):527-542.
(Location: IWMI-HQ Call no: PER Record No: H010803)
15 Hunsaker, D. J.; Bucks, D. A. 1992. Statistical analysis of soil variability: Effects of variability on level-basin irrigation of wheat. Agricultural Water Management, 21(3):177-195.
(Location: IWMI-HQ Call no: PER Record No: H010821)
16 Bari, M. F. 1992. Cluster analysis as an aid in water resources planning and decision making. Journal of Irrigation Engineering and Rural Planning, 22:29-42.
(Location: IWMI-HQ Call no: PER Record No: H010827)
17 Nasoetion, A. H. 1973. An introduction to some tests of significance. New York, NY, USA: McGraw-Hill. 31p.
(Location: IWMI-HQ Call no: P 2562 Record No: H011832)
18 International Agricultural Development Service. 1981. Agricultural development indicators: Statistical comparison of 139 developing countries. New York, NY, USA: IADS. 17p.
(Location: IWMI-HQ Call no: 338.1 G000 INT Record No: H013034)
19 Oad, R.; Sampath, R. K. 1993. Performance measure for improving irrigation management. In Manor, S.; Chambouleyron, J. (Eds.). Performance measurement in farmer-managed irrigation systems. Proceedings of an International Workshop of the Farmer-Managed Irrigation Systems Network, Mendoza, Argentina, 12-15 November 1991. Colombo, Sri Lanka: International Irrigation Management Institute (IIMI). pp.63-72.
Irrigation management ; Performance indexes ; Performance evaluation ; Water delivery ; Statistical analysis / Pakistan
(Location: IWMI HQ Call no: IIMI 631.7.6.2 G000 MAN Record No: H013499)
(0.47 MB)
20 Mailhol, J. C.; Gonzalez, J. M. 1993. Furrow irrigation model for real-time applications on cracking soils. Journal of Irrigation and Drainage Engineering, 119(5):768-783.
Furrow irrigation ; Soil properties ; Infiltration ; Models ; Calibrations ; Field tests ; Statistical analysis ; Water use efficiency / France
(Location: IWMI-HQ Call no: PER Record No: H013359)
Clay soils frequently develop large cracks after irrigation. Infiltration equations therefore need to be modified to take cracking soil properties into account when modeling and optimizing furrow irrigation. In this article, we propose an infiltration equation for cracked soils, based on two parameters. Other parameters of the global model are assumed to be known (furrow length, shape, slope, and estimated roughness). One parameter is linked to the type of soil and is calibrated separately for the entire irrigation process. The second parameter takes soils' cracks into account and is calibrated in real time during the advance phase (before the water front overruns the middle of the plot). This global furrow irrigation model for cracked soils is derived from a conceptual approach and can be used either to predict in real time, water-use efficiency during irrigation (infiltrated volume, runoff water losses, uniformity) or to optimize irrigation parameters (head flow, irrigation duration). The model was tested and validated on three irrigation applications on 60 furrows in a corn field in Tarascon in southeastern France. This conceptual model can be easily adapted to the statistical approach, which is based on the variability of water advance between the furrows.
Powered by DB/Text WebPublisher, from
