1. IWMI-Water Productivity WaPOR project
Component 4
Capacity development of stakeholders to increase water
productivity sustainably
Objective 3
Lebanon: Develop, design, pilot, and evaluate potential
solutions to increase water productivity sustainably (Use of ICT
tools in agriculture)
Marie Therese Abi Saab

2. To upgrade LARI-LEB for supporting, through instructions and warnings, farmers of
the Bekaa valley of Lebanon for irrigation management
Figure 1. The geographical location of the Bekaa
valley, Lebanon
Objective of the study
LARI-LEB
Developing a new operational module in LARI-LEB for information on
irrigation management
1. Selection of ET
product
2. Design of LARI-LEB
irrigation module
Study area: Bekaa valley
Crops: wheat, potato, table grapes

3. Spatial mapping of ETo
LARI-Weather Network
Earth Engine Evapotranspiration Flux
1. Selection of ET product
Why validating in terms of ETo???
- Complexity of validating ET data
- ETo and ET common intermediate data (such as weather data)
- Both ETo and ET are used for irrigation scheduling purposes. In fact, crop coefficient Kc can be extracted from ETo and ET data from
the same image date, then after, daily ETo data can be multiplied by Kc layer in order to extrapolate and get ET for other dates.
April-July 2019

4. ETo mapping
LARI-Weather NetworkProducts
ETo maps (daily basis, weekly,
monthly, etc.)
Output ETo maps (8 days-
interval)
ETo maps (daily basis,
decadal, etc.)
Validation
Comparing ETo maps derived from local weather stations and those derived from EEFLUX/WAPOR
Mapping Mean Bias Error (MBE) (% area of Bekaa valley with MBE between -1 and 1 mm/day; comparing min, max and mean MBE for each date)
Daily weather data from 19 weather
stations
www.fieldclimate.com/
Data extraction
https://eeflux-
level1.appspot.com/.
https://wapor.apps.fao.org/
home/WAPOR_2/1.
GIS software
Spatial interpolation
Data processing
GIS software
Stitch mosaic raster, clip
for Bekaa
GIS software
Resample raster, clip for
Bekaa
1. Selection of ET product

5. Table 6. Some criteria to be considered for the selection of the appropriate database to be
used in LARI-LEB upgrade
Criteria ETo-LARI-Stations ETo-EEFLUX ETo-WAPOR
Validity in terms of MBE in respect to LARI-Stations _
Range of mean MBE (mm/day) for each considered date -1.2 to 0.85 -0.95 to 1.5
% of Bekaa area with MBE between -1 and 1 mm/day _ 45 to 94% 50 to 98%
Output product_Time basis
ETo Daily, weekly, dekadal, etc. 8-days interval Daily
ETc Daily, weekly, dekadal, etc. 8-days interval Dekadal, monthly, yearly
Input product_Image resolution
ETo _ continental scale (CFSV2 weather) continental scale (16 km)
ETc _ 30 m
National scale (100 m); basin
scale (30 m) available only for
the Upper Litani
Staff_ Time requirement Time consuming Fast Fast

6. 2. Design of LARI-LEB irrigation module
Rain ET
NIR (mm/day)
GIS
Table 9. Example of Irrigation guiding table for sprinkler irrigated wheat (English version)
Range of
NIR
(mm/day)
Considered
NIR
(mm/day)
Wette
d area
(%)
Average Net
Irrigation Amount
(mm/day)
Efficiency of
irrigation system
(%)
Average Gross
Irrigation Amount
(mm/day)
Irrigation
Amount
(m3/dunum/
day)
0-1 1 100 1 70 1.43 1.43
1-2 2 100 2 70 2.86 2.86
2-3 3 100 3 70 4.29 4.29
3-4 4 100 4 70 5.71 5.71
4-5 5 100 5 70 7.14 7.14
5-6 6 100 6 70 8.57 8.57
6-7 7 100 7 70 10.00 10.00
7-8 8 100 8 70 11.43 11.43
8-9 9 100 9 70 12.86 12.86
9-10 10 100 10 70 14.29 14.29
What is behind the APP?
Developing tables providing GIR for different combinations of crops and irrigation methods
GIR (m3/day/dunum)

7. Crop
Irrigation method
Map of NIR (mm/day)1
Field location
Selection of2
Field size
Water emitter flow (L/hr)
Distance between water
emitters (m)
Distance between rows of
emitters (m)
Information on3
One value of GIR
(m3/day/dunum)
Irrigation duration
(hr/day/farmer field size)
Frequency of irrigation
Information on4
2. Design of LARI-LEB irrigation module How to operate?

8. 2. Design of LARI-LEB irrigation module
Example: sprinkler irrigated
wheat

13. Conclusion
The present work constitutes a first attempt to develop an irrigation module in LARI-LEB. However, further studies are needed in order
to continue with the following activities:
 Processing WAPOR (or other remote sensing products) ETo and ET data in a way to extract Kc and provide updated NIR map very
frequently (each three days for example).
 Add a module in LARI-LEB related to crop health in terms of primary production (g of biomass / m2) from WAPOR. This would
help farmers to understand if the crop is growing in a good way.
 Work on the possibility of adding forecasted NIR requirements for the next few days
 Include more crops in the APP
 Extrapolate the work to cover not only the Bekaa valley but also the whole Lebanon
 Work on field testing the APP (probably on a selected crop and community of farmers)

14. Thank you

15. Figure 2. Distribution of weather stations in pilot area
Station name X Y Altitude (m)
Inside the Bekaa valley
Ammik 35.784302 33.714857 876
Bar Elias 35.940008 33.786783 882
Doures 36.153133 34.00312 1066
El Kaa 36.522961 34.397645 583
Hosh El Oumara 35.91063 33.815888 884
Jabouleh 36.337611 34.234847 847
Kaa2 36.51197 34.386833 583
Kferden 36.046434 34.009472 1049
Kherbit Kanafar 35.719775 33.638183 1005
Machghara 35.667451 33.540318 936
Mansoura 36.412194 34.4306 682
Mchaytiyeh 36.085245 34.15498 1470
Ras Baalbeck 36.427436 34.296443 851
Tal Amara 35.986899 33.856866 915
Talia 36.09336 33.928649 1031
Terbol 35.991025 33.808023 874
Outside the Bekaa valley
Aamatour 35.602861 33.626686 702
Rachaya El Fakhar 35.662762 33.358787 797
Mymis 35.701846 33.441369 820
Table 1. The list of weather stations used in this study and their corresponding geographical coordinates

16. Figure 3. ETo maps elaborated on weekly basis (weekly average of ETo (mm/day)

17. Figure 4. ETo maps elaborated on daily basis for selected days from April till July 2019 (Daily ETo (mm/day)

18. • Version of METRIC (Mapping
Evapotranspiration at high
Resolution with Internalized
Calibration) that operates on the
Google Earth Engine system.
• EEFlux processes individual
Landsat scenes from any period
from 1984 through present and for
nearly every land area on the Globe.
Figure 5. Example of Landsat image covering Lebanon and corresponding ETo image, as extracted from EEFLUX

19. Figure 6. ETo maps produced from EEFLUX

20. Figure 7. FAO-WAPOR portal

21. Figure 8. ETo maps produced from WAPOR

22. Comparing EEFLUX or WAPOR to LARI-Stations maps of ETo
Image Date % of Bekaa area with MBE values between -1 and 1 mm/day
16-Apr 85
24-Apr 94
02-May 58
10-May 77
18-May 64
26-May 92
03-Jun 71
11-Jun 72
19-Jun 45
27-Jun 65
05-Jul 58
13-Jul 57
21-Jul 56
29-Jul 57
Table 2. Percentage of Bekaa area with MBE values between -1 and 1 mm/day, for
each EEFLUX-image date
Figure 6. Example of Mean Bias Error (MBE) map that show the deviation of
values in respect to those obtained from interpolating the local weather data
45 to 94%

23. Figure 6. Example of Mean Bias Error (MBE) map that show the deviation of
values in respect to those obtained from interpolating the local weather data
Table 3. Percentage of Bekaa area with MBE values between -1 and 1 mm/day, for each
WAPOR-image date
Date % of Bekaa area with MBE values between -1 and 1 mm/day
08-Apr 98
16-Apr 97
24-Apr 98
02-May 65
10-May 89
18-May 72
26-May 96
03-Jun 75
11-Jun 81
19-Jun 67
27-Jun 70
05-Jul 58
13-Jul 67
21-Jul 50
29-Jul 57
50 to 98%
Comparing EEFLUX or WAPOR to LARI-Stations maps of ETo

24. Table 4. EEFLUX-ETo as compared to ETo derived from local data and corresponding Mean Bias Error (MBE), for selected days
EEFLUX-ETo (mm/day) Stations-ETo (mm/day) MBE (mm/day)
Date Min value
Max
value
Mean
value Min value
Max
value
Mean
value
8-Apr-19 3 4 3.5 2.9 4.2 3.55 -0.05
16-Apr-19 1 3 2 2 4.4 3.2 -1.2
24-Apr-19 4 5 4.5 3.2 5 4.1 0.4
2-May-19 3 6 4.5 3.1 6 4.55 -0.05
10-May-19 4 5 4.5 3.6 5.3 4.45 0.05
18-May-19 6 7 6.5 4.7 9.1 6.9 -0.4
26-May-19 5 6 5.5 4.6 7.3 5.95 -0.45
3-Jun-19 6 7 6.5 5.3 8.1 6.7 -0.2
11-Jun-19 6 7 6.5 5.2 10 7.6 -1.1
19-Jun-19 6 7 6.5 4.8 9 6.9 -0.4
27-Jun-19 6 8 7 4.3 8 6.15 0.85
5-Jul-19 6 7 6.5 3.6 8 5.8 0.7
13-Jul-19 6 7 6.5 5 10 7.5 -1
21-Jul-19 6 7 6.5 4.8 7.8 6.3 0.2
29-Jul-19 6 7 6.5 4.9 8.8 6.85 -0.35
-1.2 to 0.85 (mm/day)
Comparing EEFLUX or WAPOR to LARI-Stations maps of ETo

25. Table 5. WAPOR-ETo as compared to ETo derived from local data and corresponding Mean Bias Error (MBE), for selected days
WAPOR-ETo (mm/day) Stations-ETo (mm/day) MBE (mm/day)
Date Min value Max value Mean value Min value Max value Mean value
8-Apr 3.5 4 3.75 2.9 4.2 3.55 0.2
16-Apr 1.7 2.8 2.25 2 4.4 3.2 -0.95
24-Apr 3.7 4.4 4.05 3.2 5 4.1 -0.05
2-May 3.8 5.8 4.8 3.1 6 4.55 0.25
10-May 3.9 4.6 4.25 3.6 5.3 4.45 -0.2
18-May 5.4 6.4 5.9 4.7 9.1 6.9 -1
26-May 5.8 6.6 6.2 4.6 7.3 5.95 0.25
3-Jun 6.7 8 7.35 5.3 8.1 6.7 0.65
11-Jun 6.3 7.2 6.75 5.2 10 7.6 -0.85
19-Jun 5.4 6.5 5.95 4.8 9 6.9 -0.95
27-Jun 7.1 8 7.55 4.3 8 6.15 1.4
5-Jul 6.9 7.7 7.3 3.6 8 5.8 1.5
13-Jul 6.4 7.6 7 5 10 7.5 -0.5
21-Jul 6.9 8.2 7.55 4.8 7.8 6.3 1.25
29-Jul 6.3 7.4 6.85 4.9 8.8 6.85 0
-0.95 to 1.5 (mm/day)
Comparing EEFLUX or WAPOR to LARI-Stations maps of ETo

26. Figure 7. ET mapping as generated from WAPOR

27. Figure 8. Rain mapping as generated from WAPOR

28. Figure 9. NIR (mm/day) mapping as generated from WAPOR