IDEAS home Printed from https://ideas.repec.org/a/iwt/jounls/h049422.html
   My bibliography  Save this article

Revisiting telemetry in Pakistan’s Indus Basin Irrigation System

Author

Listed:
  • Bhatti, Muhammad Tousif
  • Anwar, Arif A.
  • Ali Shah, Muhammad Azeem

Abstract

The Indus Basin Irrigation System (IBIS) lacks a system for measuring canal inflows, storages, and outflows that is trusted by all parties, transparent, and accessible. An earlier attempt for telemetering flows in the IBIS did not deliver. There is now renewed interest in revisiting telemetry in Pakistan’s IBIS at both national and provincial scales. These investments are typically approached with an emphasis on hardware procurement contracts. This paper describes the experience from field installations of flow measurement instruments and communication technology to make the case that canal flows can be measured at high frequency and displayed remotely to the stakeholders with minimal loss of data and lag time between measurement and display. The authors advocate rolling out the telemetry system across IBIS as a data as a service (DaaS) contract rather than as a hardware procurement contract. This research addresses a key issue of how such a DaaS contract can assure data quality, which is often a concern with such contracts. The research findings inform future telemetry investment decisions in large-scale irrigation systems, particularly the IBIS.

Suggested Citation

  • Bhatti, Muhammad Tousif & Anwar, Arif A. & Ali Shah, Muhammad Azeem, 2019. "Revisiting telemetry in Pakistan’s Indus Basin Irrigation System," Papers published in Journals (Open Access), International Water Management Institute, pages 11(11):1-20.
    • Handle: RePEc:iwt:jounls:h049422
    DOI: 10.3390/w11112315
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-4441/11/11/2315/pdf
    Download Restriction: no
    File URL: https://libkey.io/10.3390/w11112315?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Stephan Kolassa & Roland Martin, 2011. "Percentage Errors Can Ruin Your Day (and Rolling the Dice Shows How)," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 23, pages 21-27, Fall.
    2. Akhmouch, Aziza & Correia, Francisco Nunes, 2016. "The 12 OECD principles on water governance – When science meets policy," Utilities Policy, Elsevier, vol. 43(PA), pages 14-20.
    3. Armstrong, J. Scott & Collopy, Fred, 1992. "Error measures for generalizing about forecasting methods: Empirical comparisons," International Journal of Forecasting, Elsevier, vol. 8(1), pages 69-80, June.
    4. Shah, M. Azeem Ali & Anwar, Arif A. & Bell, Andrew Reid & ul Haq, Zia, 2016. "Equity in a tertiary canal of the Indus Basin Irrigation System (IBIS)," Agricultural Water Management, Elsevier, vol. 178(C), pages 201-214.
    5. Flores, Benito E, 1986. "A pragmatic view of accuracy measurement in forecasting," Omega, Elsevier, vol. 14(2), pages 93-98.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Oghenekaro Nelson Odume & Blessing Nonye Onyima & Chika Felicitas Nnadozie & Gift Ochonogor Omovoh & Thandi Mmachaka & Blessing Odafe Omovoh & Jude Edafe Uku & Frank Chukwuzuoke Akamagwuna & Francis O, 2022. "Governance and Institutional Drivers of Ecological Degradation in Urban River Ecosystems: Insights from Case Studies in African Cities," Sustainability, MDPI, vol. 14(21), pages 1-14, October.
    2. Lindh, Thomas & Malmberg, Bo, 2007. "Demographically based global income forecasts up to the year 2050," International Journal of Forecasting, Elsevier, vol. 23(4), pages 553-567.
    3. Madden, Gary & Tan, Joachim, 2007. "Forecasting telecommunications data with linear models," Telecommunications Policy, Elsevier, vol. 31(1), pages 31-44, February.
    4. Armstrong, J. Scott & Green, Kesten C. & Graefe, Andreas, 2015. "Golden rule of forecasting: Be conservative," Journal of Business Research, Elsevier, vol. 68(8), pages 1717-1731.
    5. Kumar, V. & Sunder, Sarang & Sharma, Amalesh, 2015. "Leveraging Distribution to Maximize Firm Performance in Emerging Markets," Journal of Retailing, Elsevier, vol. 91(4), pages 627-643.
    6. Hu, Xincheng & Banks, Jonathan & Wu, Linping & Liu, Wei Victor, 2020. "Numerical modeling of a coaxial borehole heat exchanger to exploit geothermal energy from abandoned petroleum wells in Hinton, Alberta," Renewable Energy, Elsevier, vol. 148(C), pages 1110-1123.
    7. Garcia-Ferrer, Antonio & Bujosa-Brun, Marcos, 2000. "Forecasting OECD industrial turning points using unobserved components models with business survey data," International Journal of Forecasting, Elsevier, vol. 16(2), pages 207-227.
    8. Ariana Chang & Tian‐Shyug Lee & Hsiu‐Mei Lee, 2024. "Applying sustainable development goals in financial forecasting using machine learning techniques," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 31(3), pages 2277-2289, May.
    9. Massimo Guidolin & Manuela Pedio, 2019. "Forecasting and Trading Monetary Policy Effects on the Riskless Yield Curve with Regime Switching Nelson†Siegel Models," Working Papers 639, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    10. Kourentzes, Nikolaos & Athanasopoulos, George, 2021. "Elucidate structure in intermittent demand series," European Journal of Operational Research, Elsevier, vol. 288(1), pages 141-152.
    11. Jaewon Kwak & Huiseong Noh & Soojun Kim & Vijay P. Singh & Seung Jin Hong & Duckgil Kim & Keonhaeng Lee & Narae Kang & Hung Soo Kim, 2014. "Future Climate Data from RCP 4.5 and Occurrence of Malaria in Korea," IJERPH, MDPI, vol. 11(10), pages 1-19, October.
    12. Armstrong, J. Scott & Collopy, Fred & Yokum, J. Thomas, 2005. "Decomposition by causal forces: a procedure for forecasting complex time series," International Journal of Forecasting, Elsevier, vol. 21(1), pages 25-36.
    13. Paroissien, Emmanuel, 2020. "Forecasting bulk prices of Bordeaux wines using leading indicators," International Journal of Forecasting, Elsevier, vol. 36(2), pages 292-309.
    14. Philippe St-Aubin & Bruno Agard, 2022. "Precision and Reliability of Forecasts Performance Metrics," Forecasting, MDPI, vol. 4(4), pages 1-22, October.
    15. Barrow, Devon K., 2016. "Forecasting intraday call arrivals using the seasonal moving average method," Journal of Business Research, Elsevier, vol. 69(12), pages 6088-6096.
    16. Drechsel, Katja & Scheufele, Rolf, 2010. "Should We Trust in Leading Indicators? Evidence from the Recent Recession," IWH Discussion Papers 10/2010, Halle Institute for Economic Research (IWH).
    17. repec:cup:judgdm:v:14:y:2019:i:4:p:395-411 is not listed on IDEAS
    18. Serrano-Cinca, Carlos & Gutiérrez-Nieto, Begoña & Bernate-Valbuena, Martha, 2019. "The use of accounting anomalies indicators to predict business failure," European Management Journal, Elsevier, vol. 37(3), pages 353-375.
    19. Fildes, Robert & Goodwin, Paul & Lawrence, Michael & Nikolopoulos, Konstantinos, 2009. "Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning," International Journal of Forecasting, Elsevier, vol. 25(1), pages 3-23.
    20. Kumar, V. & Leone, Robert P. & Gaskins, John N., 1995. "Aggregate and disaggregate sector forecasting using consumer confidence measures," International Journal of Forecasting, Elsevier, vol. 11(3), pages 361-377, September.
    21. Rocha Souza, Leonardo & Jorge Soares, Lacir, 2007. "Electricity rationing and public response," Energy Economics, Elsevier, vol. 29(2), pages 296-311, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:iwt:jounls:h049422. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chandima Gunadasa (email available below). General contact details of provider: https://edirc.repec.org/data/iwmiclk.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.