Capacity Bulding Marloes Mul - IHE Delft 3 May 2023

1. Component 2
Capacity building
IHE Delft Institute for Water Education

2. ▪ In country trainings and training plans
▪ OpenCourseWare
▪ MSc research
▪ Global tool
Capacity building updates

3. Identify
target
audience
WaPOR
introduction
training
Training
needs
assessment
Develop
training
plan
Implement
trainings
Identify
target
audience
Training
needs
assessment
Develop
training plan
Implement
trainings
General outcome:
WaPOR Introduction training important for identifying the country activities
Other topics considered (mentioned across countries):
WaPOR concepts
WaPOR validation
QGIS
Python
GEE
PyWaPOR
Country trainings updates
Approach:
WaPOR concepts and validation (also OCW)
Applied to specific type of system (related to identified topics)
Strong request for face to face training

4. WaPOR introduction
Iraq, Mali, Jordan (Palestine),
Sudan, Mozambique, Kenya,
Ethiopia, Algeria
Tunisia (tbd), Palestine +
training 2 (Aug 2023)
WaPOR concepts
and validation
Jordan (Palestine), Sudan, Mali..
Ethiopia (Oct 2023), Kenya (tbd)
QGIS
Mali, Sudan, Kenya
▪ Implemented
▪ Planned for 2023
Country trainings updates
Status of training implementation
Regional training WP (Iraq+Jordan+Palestina), incl 1
day validation
Regional training WP (Algeria, Tunisia)

5. WaPOR Introduction
▪ Mozambique: 20 participants: 3 women (15%), 17 men (85%) – Nov
2022
▪ Ethiopia: 30 participants: 5 women (16%), 25 men (84%) – Jan 2023
▪ Algeria: 30 participants: 15 women, 15 men – Jan 2023
▪ Sudan: 33 participants: 10 women (30%), 23 men (70%)- Sep 2022
▪ Kenya: 24 participants: 3 women (17%), 17 men (83%)- Dec 2022
▪ Iraq: 37 participants: 17 women (46%), 20 men (54%) – May 2022
▪ Jordan: 39 participants: 19 women, 20 men - July 2022
▪ Mali: 28 participants: 2 women, 26 men - June 2022
▪ Total: 243 participants 74 women (30%), 169 men (70%)
Participation

6. On the job training
Linked to specific country activities
Target audience: technical experts
Development of apps, dashboards etc by WaPOR team
- General concept
- Replicate the analyses
- Maintaining tools
- How to use the tools (end user training)
Training of trainers
- General concepts
- Standard analyses for selected case study
- Interpretation of analyses
- Identify tailored analyses, adapt scripts and implement analyses
Identify suitable candidates with technical
background
Regular online meeting (dedicated platform)
Possible face to face meeting (after sufficient
progress made)

7. Availability of training materials
WaPOR introduction
Materials (PPTs, exercises, standard schedule) available on google drive
Translations available
Arabic
French
Portuguese (new)
WaPOR Concepts and Validation
Materials used also for OpenCourseWare

8. General observations / for discussion
Selection of participants
Skills set of participants not always adequate, even for introductory training (varies between participants but
also from one country to another)
For the more advanced trainings, participants should have the required skill set (plus completion of first
training)
Level of active participation differs significantly
How to select the right candidates?
Value of face to face training vs online courses
When combined with needs assessment it had added value otherwise online course are more than sufficient
and ensure motivation (especially for the introductory training)
More advance training face to face but option to have it regional with good skilled participants
Coordination of local offices vs centralized planning by IHE
Implementation of trainings in designated high risk countries

9. Why did we develop OpenCourseWare in Phase 1?
• IHE Delft’s mission of open science (incl open data and
open education)
• WaPOR data is open data
• Implemented introduction trainings in 16 countries
between 2016-2019 (reaching around 300 people)
• At the end of the project, training materials could have
ended up on the shelves
• 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)

10. OpenCourseWare – our experiences
• Good reference materials openly available
• Great appreciation by the participants for the materials
(based on participants filling the end survey)
• Online engagement between participants limited
• Some frustration on restrictions and assignments
• Limited number of participants fully completing course
Materials used in phase 2 training (introduction)

11. WaPOR phase 2 - OCW plan
• WaPOR Concepts and Validation (June 2023)
• Water Accounting Plus using WaPOR (early 2024)
• WaPOR Gender and Social Inclusiveness (2025)
• TBD
PyWaPOR
Agricultural application (irrigation performance, bright/hotspots)
Groundwater assessment application
Spatial Aquacrop modelling/ forecasting with WaPOR (incl ML)
Drought monitoring
…

12. OCW– WaPOR Concepts and Validation
Learning Objectives
Upon the successful completion of the course, the participants will be able to
• Describe remote sensing and energy balance concepts and techniques
• Describe global ET products and approaches used for producing them
• Describe WaPOR methodology and input data components
• Explain spatial and temporal attributes of WaPOR database inputs
• Describe the structure, limitations, and challenges of ET models and WaPOR database
products
• Describe the quality assurance cycle and the role of data validation
• Describe validation techniques and steps for WaPOR database products
• Validate WaPOR database products (PCP, RET, AETI, NPP, biomass)
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”

13. OCW– WaPOR Concepts and Validation
Registration open 1 June, available starting 15 June
Cordoba station not covered (did not appear in literature review (Tran et al., 2023)/
uncertainty of the method)

14. Main Hackathon Theme: WaPOR for Monitoring and
Evaluation of Global Challenges
Sub-themes:
• Environmental challenges
• Tools to validate WaPOR Data
• WaPOR and SDG monitoring
• Social inclusion, gender and diversity
WaPOR Hackathon 2022 Overview of the two weeks: November 30, 2022 to
December 16, 2023
• Opening session - Nov 30th, 2022
• Closing of the first week - Dec 8th, 2022
• Final event - Dec 16th, 2022
• Meeting with experts and coaches during the 2
weeks
Phase 1:
268 participants
58 countries
30 team
13 pitches
submitted in
phase 1
Phase 2:
49 participants
14 countries
6 teams
Winning team: Team 16 from Sudan. A group of 6
academics from University of Khartoum, Water Research
Centre
Team Idea: Improvement of yield through accurate
application of fertilizer.

15. Distribution of
participants for the
WaPOR Hackathon
2022
(Red – Phase 1 Participants;
Green Phase 2 Participants)

16. General observations
/ for discussion
Completion rate low – how to improve
Is this needed? What is the objective of
the OCW?
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OCW Water Accounting and Water Productivity using WaPOR (module 1)
views users completion
Should we translate new OCWs as well (much lower participation)?
English – 2,118 participants
French – 183 participants
Arabic – 178 participants
Other option to have materials translated and subtitles added in youtube (but only one OCW)

17. ▪ Suzan Dehati: Equity assessment in
transboundary water resources management
using remote sensing data
Used WaPOR data for equity assessment in water resources
distribution in the Nile basin
MSc thesis
Precipitation Actual
Evapotranspiration
Available water
(WA+ output)
Paper under development

18. ▪ Haneen Muhammad: Deriving high-
resolution evapotranspiration
products using data fusion method
Created tood to downscale WaPOR ET data to
higher resolution for Zankalon and El Oweinat
MSc thesis
WaPOR Level 2 (100m) Sentinel natural color (10m) WaPOR downscaled (10m)
Paper under development

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

20. ▪ Andre Mawradhi Improved Crop Yield
Prediction using Coupled Remote Sensing
based Aquacrop-GIS and Machine Learning
Algorithms for Irrigated Sugarcane
Used WaPOR data to train spatial Aquacrop GIS for Metahara
Sugar estate, Ethiopia
MSc thesis
Multiple Linear regression model
Model result comparison

21. ▪ Zoubida Nemer Monitoring Crop Water Productivity in the
Mitidja Plain, Algeria using FAO WaPOR Data and QGIS
Used WaPOR data to compare groundwater abstractions and sea water intrusion
impact on productivity (ongoing)
Visiting researcher
Model result comparison
NOTE: didnt use the ET layer for this are because of artifacts

22. WaterPIP knowledge hub, Kenya
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 ?

23. WaterPIP knowledge hub, Kenya
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.
Ø Next step, will be to link it with WaPOR data and
test in CP’s

24. ▪ Sherine ElWattar Entangled logics of water and land
productivity – conversation among Egyptian farmers and
WaPOR’s remote sensing data
Evaluated the relevance of water productivity as defined by WaPOR for Egyptan
farmers in the Nile Delta
MSc thesis
Water use – farmers did not perceive water as a constraining
factor and did not act towards water saving
Crop choice - had various reasons for crop choise (not only for
economic reasons)
Land – long term sustainability of land health key factor in
decision making

25. ▪ Walter Hettler Assessment of groundwater recharge using
remote sensing information – Utilising FAO WaPOR to
estimate groundwater recharge in the Hashemite Kingdom of
Jordan
Compared two models for groundwater recharge estimates (PixSWAB and
DailySWAB)
MSc thesis
WaPOR data used:
PixSWAB (AETI, PCP, LCC)
DSWAB (LCC, RET, PCP)
Ongoing :
Further evaluation of the results (Aysha Esha)
DSWAB PixSWAB

26. ▪ Ahmed Rafique Understanding and estimating water stress in Pakistan
Used remote sensing data to evaluate SDG 6.4.2 in Pakistan. Development and testing
of an approach
MSc thesis
TFWW is Total Fresh Water Withdrawal from surface and grondwater
TRWR is Total Renewable Freshwater Resources is the sum of Internal Renewable
Water Resources (IRWR) and External Renewable Water Resources (ERWR).
EFR is Environmental Flow Requirement
ERWR
ERWR
IRWR
TFWW
Derived TFWW from groundwater using ET (water consumed) data
(surface water withdrawals were obtained from the field)

27. ▪ Ahmed Rafique Understanding and estimating water stress in Pakistan
Used remote sensing data to evaluate SDG 6.4.2 in Pakistan. Development and testing
of an approach
MSc thesis
TFWW is Total Fresh Water Withdrawal from surface and grondwater
TRWR is Total Renewable Freshwater Resources is the sum of Internal Renewable
Water Resources (IRWR) and External Renewable Water Resources (ERWR).
EFR is Environmental Flow Requirement
Derived TFWW from groundwater using ET (water consumed) data
(surface water withdrawals were obtained from the field)
Possibility to do similar replacement of water consumed in place of water withdrawal
ERWR
ERWR
IRWR
ETb

28. ▪ Celine Safi Monitoring SDG
6.4.1 indicator at national
and sub-national scale using
open access remote
sensing-derived data - Case
study in Lebanon
Used WaPOR data to develop framework
to monitor Water Use Efficiency in
agriculture
Paper under review
MSc thesis

29. ▪ 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,,. = ∑/0)
)1
𝑚𝑎𝑥 𝐸𝑇 − 𝑃", 0
With ET being the actual evapotranspiration and interception from WaPOR.

30. Global tool – WaPOR4Awp: wapor4awp.org

31. Global tool – WaPOR4Awp: wapor4awp.org

32. Global tool – WaPOR4Awp: wapor4awp.org
Next steps
Development of a Jupyter Notebook/ Colab
- Based on available python script
- For one area (eg country)
- Step by step guide through the code
- Add options to upload own data (eg irrigated area map)
Updating analyses using WaPOR Global (300m)
Training on dashboard and Jupyter Notebook
- Webinar
- Videos and instruction manual
Paper on Awp data product
Example WaPORWP Jupyter notebook

33. 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)

34. Outreach
Presentation by PhD researcher Zoubida Nemer (USTHB) of preliminary results on Monitoring Crop Water Productivity in the
Mitidja Plain at the international QGIS User Conference in ‘s-Hertogenbosch, the Netherlands

35. FOSS4G conference session June 2023