Preprints
https://doi.org/10.5194/hess-2021-409
https://doi.org/10.5194/hess-2021-409

  10 Sep 2021

10 Sep 2021

Review status: this preprint is currently under review for the journal HESS.

A Framework for Irrigation Performance Assessment Using WaPOR data: The case of a Sugarcane Estate in Mozambique

Abebe Demissie Chukalla1, Marloes L. Mul1, Pieter van der Zaag1,2, Gerardo van Halsema3, Evaristo Mubaya4, Esperança Muchanga5, Nadja den Besten6,2, and Poolad Karimi1 Abebe Demissie Chukalla et al.
  • 1The Department of Land and Water Management, IHE Delft Institute for Water Education, 2611 AX, Delft, The Netherlands
  • 2Water Management Department, Delft University of Technology, 2600 AA, Delft, The Netherlands
  • 3Water Resources Management Group, Wageningen University & Research, 6700 AA, Wageningen, the Netherlands
  • 4Xinavane Estate, Xinavane, Mozambique
  • 5Afri-Food Inclusive Value Chain Development Programme (PROCAVA) - FDA, Maputo, Mozambique
  • 6VanderSat B.V., Agri, Food and Commodity Unit, Wilhelminastraat 43a, 2011VK Haarlem, the Netherlands

Abstract. The growing competition for the finite land and water resources and the need to feed an ever-growing population requires new techniques to monitor the performance of irrigation schemes and improve land and water productivity. Datasets from FAO’s portal to monitor Water Productivity through Open access Remotely sensed derived data (WaPOR) is increasingly applied as a cost-effective means to support irrigation performance assessment and identifying possible pathways for improvement. This study presents a framework that applies WaPOR data to assess irrigation performance indicators including uniformity, equity, adequacy and land and water productivity differentiated by irrigation method (furrow, sprinkler and centre pivot) at the Xinavane sugarcane estate, Mozambique. The WaPOR data on water, land and climate is near-real-time and spatially distributed, with the finest spatial resolution in the area of 100 m. The WaPOR data were first validated agronomically by examining the biomass response to water, then the data was used to systematically analyse seasonal indicators for the period 2015 to 2018 on ~8,000 ha. The WaPOR based yield estimates were found to be comparable to the estate-measured yields with ±20 % difference, root mean square error of 19 ± 2.5 ton/ha and mean absolute error of 15 ± 1.6 ton/ha. A climate normalization factor that enables the spatial and temporal comparison of performance indicators are applied. The assessment highlights that in Xinavane no single irrigation method performs the best across all performance indicators. Centre pivot compared to sprinkler and furrow irrigation shows higher adequacy, equity, and land productivity, but lower water productivity. The three irrigation methods have excellent uniformity (~94 %) in the four seasons and acceptable adequacy for most periods of the season except in 2016, when a drought was observed. While this study is done for sugarcane in one irrigation scheme, the approach can be broadened to compare other crops across fields or irrigation schemes across Africa with diverse management units in the different agro-climatic zone within FaO WaPOR coverage. We conclude that the framework is useful for assessing irrigation performance using the WaPOR dataset.

Abebe Demissie Chukalla et al.

Status: open (until 16 Dec 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-409', Anonymous Referee #1, 14 Oct 2021 reply

Abebe Demissie Chukalla et al.

Abebe Demissie Chukalla et al.

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Short summary
New techniques to monitor performance of irrigation schemes is vital to improve land and water productivity. We developed a framework and applied the remotely-sensed FAO WaPOR dataset to assess uniformity, equity, adequacy, and land and water productivity at Xinavane sugarcane estate, segmented by three irrigation method. The developed performance assessment framework and the Python script in Jupyter Notebooks can serve broadened such analysis in different agro-climatic regions.