This document summarizes several projects and studies utilizing WaPOR (Water Productivity through Open access of Remotely sensed imagery) data. It describes how WaPOR data was used to analyze crop water productivity and identify water saving opportunities in an irrigation scheme in Kenya. It also discusses how WaPOR data was used to train a spatial crop model to improve sugarcane yield predictions in Ethiopia. Finally, it provides information on accessing WaPOR data and tools, available training courses, and new developments like downscaling WaPOR evapotranspiration data to higher resolutions and a QGIS plugin for WaPOR calculations.

1. Sharing experiences
using WaPOR data
IHE Delft Institute for Water Education

2. Galana Kulalu Irrigation Scheme
Food Security project by Govt of Kenya
Indian
Ocean
• 24 Center pivots, 65 ha per CP
• In 2022, additional 17 CP’s are installed
increasing the cropped area from 2000
to 5000 ha
• WaPOR based analysis on crop WP
• Lower water productivity
• Water saving opportunity ?
Chukalla et al. 2022
https://github.com/wateraccounting/
WAPORWP

3. Galana Kulalu Irrigation Scheme
Food Security project by Govt of Kenya
Ø Installed and testing 3 soil moisture sensors in the
drip irrigation based sites close to Center pivots
Ø Testing now in the season of 2022/23
Ø The Scheme staff are trained by the JKUAT Khub
on installing, recording and interpreting data
Ø Frequency of drip irrigation reduced from daily
to once in 3 to 4 days. With no impact on
production
Ø Next step, will be to link it with WaPOR data and
test in CP’s

4. Using WaPOR data to train spatial Aquacrop GIS for
Metahara Sugar estate, Ethiopia
Improved Crop Yield Prediction using Coupled Remote Sensing based
Aquacrop-GIS and Machine Learning Algorithms for Irrigated Sugarcane
Aquacrop parameter Brel (crop fertility stress)
Aquacrop parameter Maximum canopy cover
Andre Mawradhi

5. Improved Crop Yield Prediction using Coupled Remote Sensing based
Aquacrop-GIS and Machine Learning Algorithms for Irrigated Sugarcane
Multiple Linear regression model
Model result comparison
Andre Mawradhi

6. ▪ SDG 6.4.1 Change in Water use effiency
𝑊𝑈𝐸 = 𝐴!" ∗ 𝑃# + 𝑀!" ∗ 𝑃$ + 𝑆!" ∗ 𝑃%
With:
Awe, Mwe, Swe = water use efficiency of agriculture, industry, and
services sector [USD/m3]
PA, Pm, Ps = proportion of total water withdrawn per sector [%]
Global tool – WaPOR4Awp: wapor4awp.org
𝐴!" =
#$%& ∗ '()'
$&
With:
GVAa = Gross Value Added by agriculture (river and marine fisheries and forestry
excluded) (USD)
Va = Volume of water used by the agricultural sector (including irrigation,
livestock, and aquaculture) (m3)
1-Cr = Proportion of agricultural GVA produced by irrigated agriculture obtained
from AQUASTAT
Instead of Va we use water consumed of the water withdrawal, also called Blue ET
(ETb) in the irrigated land estimated as follows:
𝑉!"# = ∑$%%&'(( 𝐸𝑇#,*
𝐸𝑇(,* = ∑+,-
-.
𝑚𝑎𝑥 𝐸𝑇 − 𝑃", 0
With ET being the actual evapotranspiration and interception from WaPOR.

7. Global tool – WaPOR4Awp: wapor4awp.org

8. ▪ What is WaPOR?
▪ What can you do with it?
▪ How do I access the data and how can I do analyses?
▪ How can I use the available tools?
Capacity building in focus countries (direct recipients of the project)
▪ Phase 1 >300 participants trained in 16 countries
▪ Phase 2: 243 participants trained in 8 countries
How to get to know more:

9. But what about the rest of the world?
• WaPOR data is open data soon available globally
• IHE Delft’s mission of open science (incl open data and
open education)
• In consultation with FAO we decided to turn the training
materials into an OpenCourseWare
Currently 2460 participants (more than 500 added in 2022)
are enrolled coming from over 92 countries (incl many
countries not covered by WaPOR phase 1 extent)
also available in French and Arabic
Join the WaPOR community any time from anywhere!!
OpenCourseWare for anyone else

10. New OCW– WaPOR Concepts and Validation
coming in June
The course is organised into three modules:
• Module 1: “Concepts of remote sensing ET (energy balance,
FAO-56 PM)”
• Module 2: “WaPOR database: structure, input data, and
methods”
• Module 3: “Validation of WaPOR database products”
Can’t wait? Recommendation to finish Module 1 of OCW “Water Productivity and Water Accounting using WaPOR”
https://wateraccounting.un-ihe.org/ocw-wapor-concepts-and-validation

11. A tool to downscale WaPOR ET data to
higher resolution for Zankalon and El
Oweinat , Egypt
Deriving high-resolution evapotranspiration products using data fusion method
WaPOR Level 2 (100m) Sentinel natural color (10m) WaPOR downscaled (10m)
Haneen Muhammad

12. QGIS – FAO Downloader and WAPlugin
Developed by MSc student from Vrije University of Brussels
Based on WaPOROCW scripts
WAPlugin idea from WaterPIP
hackathon team
Able to do simple calculation on WP
(no time series)

13. More information:
Project website: http://wateraccounting.un-ihe.org/
IHE Delft - Water Accounting
@wateraccounting
Email: wateraccounting_project@un-ihe.org