Irrigation Science

, Volume 31, Issue 3, pp 209–224 | Cite as

Simulation of peak-demand hydrographs in pressurized irrigation delivery systems using a deterministic–stochastic combined model. Part I: model development

  • Daniele Zaccaria
  • Nicola Lamaddalena
  • Christopher M. U. Neale
  • Gary P. Merkley
  • Nicola Palmisano
  • Giuseppe Passarella
Review
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Abstract

This study describes a model named HydroGEN that was conceived for simulating hydrographs of daily volumes and hourly flow rates during peak-demand periods in pressurized irrigation delivery networks with on-demand operation. The model is based on a methodology consisting of deterministic and stochastic components and is composed of a set of input parameters to reproduce the crop irrigation management practices followed by farmers and of computational procedures enabling to simulate the soil water balance and the irrigation events for all cropped fields supplied by each delivery hydrant in a distribution network. The input data include values of weather, crop, and soil parameters, as well as information on irrigation practices followed by local farmers. The resulting model outputs are generated flow hydrographs during the peak-demand period, which allow the subsequent analysis of performance achievable under different delivery scenarios. The model can be applied either for system design or re-design, as well as for analysis of operation and evaluation of performance achievements of on-demand pressurized irrigation delivery networks. Results from application of HydroGEN to a real pressurized irrigation system at different scales are presented in a companion paper (Part II: model applications).

Keywords

Soil Water Balance Irrigation Demand Crop Transpiration Basal Crop Coefficient Pressurize Irrigation System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Notes

Acknowledgments

The authors would like to express their gratitude to the Division of Land and Water Resources Management at the CIHEAM-Mediterranean Agronomic Institute of Bari (Italy), to the Water Users Organization “Consorzio per la bonifica della Capitanata” of Foggia (Italy), and to the Remote Sensing Services Laboratory, Department of Civil and Environmental Engineering—Irrigation Engineering Division, at Utah State University (USA), which jointly made this research work possible, and for the valuable assistance provided during all phases of data collection, processing and analysis. The research work presented in this study was conducted via a fellowship under the OECD Co-operative Research Programme 2010 on Biological Resource Management for Sustainable Agricultural Systems.

References

  1. Abdellaoui R (1986) Irrigation system design capacity for on demand operation. Ph.D. dissertation, Utah State University, LoganGoogle Scholar
  2. Adams JE, Arkin GF, Ritchie JT (1976) Influence of row spacing and straw mulch on first stage drying. Soil Sci Soc Am J 40:436–442CrossRefGoogle Scholar
  3. Allen RG, Pereira LS (2009) Estimating crop coefficient from fraction of ground cover and height. Irrig Sci. doi: 10.1007/S00271-009-0182-z
  4. Allen RG, Jensen ME, Wright JL, Burman RD (1989) Operational estimates of reference evapotranspiration. Agron J 81(4):650–662CrossRefGoogle Scholar
  5. Allen RG, Smith M, Perrier A, Pereira LS (1994a) An update for definition of reference evapotranspiration. ICID Bull 43(2):1–34Google Scholar
  6. Allen RG, Smith M, Perrier A, Pereira LS (1994b) An update for definition of reference evapotranspiration. ICID Bull 43(2):35–92Google Scholar
  7. Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration guidelines for computing crop water requirements. Irrigation and drainage paper, vol 56. Food and Agriculture Organization, RomeGoogle Scholar
  8. Allen RG, Pruitt WO, Wright JL, Howell TA, Ventura F, Snyder R, Itenfisu D, Steduto P, Berengena J, Baselga Yrisarry J, Smith M, Pereira LS, Raes D, Perrier A, Alves I, Walter I, Elliott R (2006) A recommendation on standardized surface resistance for hourly calculation of reference ETo by the FAO56 Penman–Monteith method. Agric Water Manage 81:1–22CrossRefGoogle Scholar
  9. Box GEP, Jenkins GM (1976) Time series analysis—forecasting and control, revised edn. In: Enders R (ed). OaklandGoogle Scholar
  10. Calejo MJ, Teixeira JL, Pereira LS, Lamaddalena N (2005) Modelling the irrigation demand hydrograph ina pressurized system. In: Boaventura Cunha J, Morais R (eds) Proceedings of the EFITA/WCCA 2005 joint conference on information technologies in agriculture, food and environment and computers in agriculture and natural resources (5th EFITA conference and 3rd WCCA, Vila Real). Universidade de Tras-os-Montes e Alto Doura, Vila Real, CD-ROM, pp 756–770Google Scholar
  11. Calejo MJ, Lamaddalena N, Teixeira JL, Pereira LS (2008) Performance analysis of pressurized irrigation systems operating on-demand using flow-driven simulation models. Agric Water Manage 95:154–162CrossRefGoogle Scholar
  12. Clément R (1966) Le calcul de débits dans le réseaux d’irrigation functionnant à la demand. La Houille Blanche 5:553–575CrossRefGoogle Scholar
  13. Clément R, Galand A (1979) Irrigation par aspersion et reseaux collectifs de distribution sous pression. Eyrolles Editeur, ParisGoogle Scholar
  14. D’Urso G (2001) Simulation and management of on-demand irrigation systems. Ph.D. dissertation, Wageningen University, The Netherlands, pp 174Google Scholar
  15. Doorenbos J, Pruitt WO (1977) Crop water requirements. Irrigation and drainage paper, vol 24. Food and Agriculture Organization, RomeGoogle Scholar
  16. Fereres E (ed) (1981) Drip irrigation management. Leaflet 21259, University of California, p 39Google Scholar
  17. Journel AG, Huijbregts CJ (1991) Mining geostatistics. Academy Press, San DiegoGoogle Scholar
  18. Khadra R, Lamaddalena N (2006) A simulation model to generate the demand hydrographs in large-scale irrigation systems. Biosyst Eng 93(3):335–346CrossRefGoogle Scholar
  19. Labye Y, Olson MA, Galand A, Tsiourtis N (1988) Design and optimization of irrigation distribution networks. Irrigation and drainage paper no. 44. Food and Agriculture Organization, RomeGoogle Scholar
  20. Lamaddalena N (1997) Integrated simulation modeling for design and performance analysis of on-demand pressurized irrigation systems. Ph.D. dissertation, Technical University of Lisbon, PortugalGoogle Scholar
  21. Lamaddalena N, Pereira LS (1998) Performance analysis of on-demand pressurized irrigation systems. In: Pereira LS, Gowing JW (eds) Water and the environment: innovation issues on irrigation and drainage (1st Inter-Regional Conference Environment-Water, Lisbon), E&FN Spon, London, pp 247–255Google Scholar
  22. Lamaddalena N, Sagardoy JA (2000) Performance analysis of on-demand pressurized irrigation systems. Irrigation and drainage paper, vol 59. Food and Agriculture Organization, RomeGoogle Scholar
  23. Lamaddalena N, Zaccaria D (2004) L’evoluzione delle reti irrigue collettive nel’Italia meridionale. In: Atti dell’Accademia dei Georgofili—Anno 2003—Settima Serie, vol L, Celebrazioni del 250° Anniversario, Firenze, Italy, 2004Google Scholar
  24. Lamaddalena N, Fratino U, Daccache A (2007) On-farm sprinkler irrigation performance as affected by the distribution system. Biosyst Eng 96(1):99–109CrossRefGoogle Scholar
  25. Lozano D, Mateos L (2008) Usefulness and limitations of decision support systems for improving irrigation scheme management. Agric Water Manage 95:409–418CrossRefGoogle Scholar
  26. Maidment DR, Hutchinson PD (1983) Modeling water demands of irrigation projects. J Irrig Drain Div ASCE 109(4):405–418CrossRefGoogle Scholar
  27. Merriam JL, Davis GG (1986) Demand irrigation schedule pilot project: Sri Lanka. J Irrig Drain Eng ASCE 112(3):185–202CrossRefGoogle Scholar
  28. Moreno MA, Plannells P, Ortega JF, Tarjuelo J (2007) New methodology to evaluate flow rates in on-demand irrigation networks. J Irrig Drain Eng ASCE 133(4):298CrossRefGoogle Scholar
  29. O’Sullivan D, Unvin DJ (2003) Geographic information analysis. Wiley, HobokenGoogle Scholar
  30. Pereira LS, Calejo MJ, Lamaddalena N, Douieb A, Bounoua R (2003) Design and performance analysis of low pressure irrigation distribution systems. Irrig Drain Syst 17(4):305–324CrossRefGoogle Scholar
  31. Prajamwong S, Merkley GP, Allen RG (1997) Decision support model for irrigation water management. J Irrig Drain Eng 123(2):106–113CrossRefGoogle Scholar
  32. Price W (1999) Guidelines for rehabilitation and modernization of irrigation projects. International Commission on Irrigation and Drainage (ICID), New DelhiGoogle Scholar
  33. Pulido-Calvo I, Lopez R, Roldan J (1998) Caracterizacion horaria y estacional de la demanda en una red de distribucion de agua para riego. In: Proc., XVI Congreso Nacional de Riegos. Palma de MallorcaGoogle Scholar
  34. Raes D (1982) A summary simulation model of the water budget of a cropped soil. Dissertationes de Agricultura n. 122. K.U. Leuven University, LeuvenGoogle Scholar
  35. Raes D, Steduto P, Hsiao TC, Fereres E (2009) AquaCrop—the FAO crop model to simulate yield response to water: II. Main algorithms and software description. Agron J 101(3):438–447CrossRefGoogle Scholar
  36. Reca J, Martinez J, Zapata A, Lopez JG, Callejon JL (1999) Estimacion de cudales cinrculantes en reded de distribucion de agua a presion, ramificadas y a la demanda para sistemas de cultivos intensivos. In: Proc., Actas del VII Congreso Nacional de Riegos, AERYD, MurciaGoogle Scholar
  37. Ritchie JT (1972) Model for predicting evaporation from a row crop with incomplete cover. Water Resour Res 8(5):1204–1213CrossRefGoogle Scholar
  38. Ritchie J, Crum J (1989) Converting soil survey characterization data into IBSNAT crop model input. In: Bouma J, Bret AK (eds) Land qualities in space and time. Pudoc, Wageningen, pp 155–169Google Scholar
  39. Rodriguez Diaz JA, Camacho Poyato E, Lopez Luque R (2007) Model to forecast maximum flows in on-demand irrigation distribution networks. J Irrig Drain Eng ASCE 133(3):222CrossRefGoogle Scholar
  40. Rossman LA (2000) EPANET users manual. US Environmental Protection Agency, Drinking Water Research Division, Risk Reduction Engineering Laboratory, CincinnatiGoogle Scholar
  41. Rossman LA, Boulos PF, Altman TA (1993) Discrete volume element method for network water quality models. J Water Resour Plan Manag ASCE 119(5):505–517CrossRefGoogle Scholar
  42. Saxton KE, Johnson HP, Shaw RH (1974) Modeling evapotranspiration and soil moisture. Trans ASAE l7(4):673–677Google Scholar
  43. Snyder R, Eching S (2005) Urban landscape evapotranspiration. Landscape water use. California water plan update 2005, vol 4, pp 691–693Google Scholar
  44. Villalobos FJ, Fereres E (1990) Evaporation measurement beneath corn, cotton and sunflower canopies. Agron J 82:1153–1159CrossRefGoogle Scholar
  45. Walker WR, Prajamwong S, Allen RG, Merkley GP (1995) USU command area decision support model—CADSM. In: Pereira LS, van den Broek BJ, Kabat P, Allen RG (eds) Crop-water-simulation models in practice. Wageningen Pers, Wageningen, pp 231–271Google Scholar
  46. Yamashita S, Walker WR (1994) Command area water demands: I. Validation and calibration of UCA model. J Irrig Drain Eng 120:1025–1042CrossRefGoogle Scholar
  47. Zaccaria D, Neale MU, Lamaddalena N (2006) A methodology for conducting diagnostic analyses and operational simulation in large-scale pressurized irrigation systems. In: Proceedings of SPIE conference on remote sensing for agriculture, ecosystems, and hydrology, Stockholm, Sweden, 11–14 Sept 2006. Proceedings of SPIE 6359, 635910. doi: 10.1117/12.690406

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Daniele Zaccaria
    • 1
  • Nicola Lamaddalena
    • 1
  • Christopher M. U. Neale
    • 2
  • Gary P. Merkley
    • 2
  • Nicola Palmisano
    • 3
  • Giuseppe Passarella
    • 3
  1. 1.Division of Land and Water Resources ManagementMediterranean Agronomic Institute of Bari (CIHEAM-IAMB)Valenzano, BariItaly
  2. 2.Irrigation Engineering Division, Department of Civil and Environmental EngineeringUtah State UniversityLoganUSA
  3. 3.Istituto di Ricerca Sulle Acque (IRSA)Consiglio Nazionale delle Ricerche (CNR)BariItaly

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