Workshop on Operationalizing the Regional Collaborative Platform to Address ‘Water Consumption, Water Productivity and Drought Management’ in Agriculture, 27 - 29 October 2015, Cairo,Egypt

1. Christopher Neale
Director of Research
Daugherty Water for Food Institute
at the University of Nebraska

2. • Ground data for validating each component of water
productivity: crop yield and transpiration (evapotranspiration)
• Data needed preferably at field scales to integrate and match
remotely sensed information at pixel level
• Multi-year records under different climatic conditions (normal,
drought, wet)
• Irrigated and dryland crops

3. • Data from automated weather stations that estimate reference
evapotranspiration using Penman-Monteith equation, their
location and surrounding vegetation
• Historical records from these weather stations
• Evapotranspiration data for different regional crops from
lysimeters if available and the systems are well managed
• Energy Balance Fluxes from Eddy Covariance or Bowen Ratio
flux towers, their location and surfaces they represent
• Water flow measurements in irrigation systems (canal inflows,
lateral canal flows, drainage and operational spills,
groundwater levels etc.) to establish a water balance

4. Typically provide hourly averages of weather parameters and ET0

5. 4 sets of soil heat flux plates
distributed in rows and furrows
Corn Soybean

6. From: NEBRASKA WATER AND ENERGY FLUX MEASUREMENT, MODELING, AND RESEARCH NETWORK (NEBFLUX) by Suat irmak

7. -150
-50
50
150
250
350
-150 -50 50 150 250 350
Observed H (W m-2
)
OLEMpredictedH(Wm-2)dd
174 soy
182 soy
189 soy
174 corn
182 corn
189 corn
-150
-50
50
150
250
350
-150 -50 50 150 250 350
Observed H (W m-2
)
TSMpredictedH(Wm-2)dd
174 soy
182 soy
189 soy
174 corn
182 corn
189 corn
Results: Energy Balance Models Using Remote Sensing
(closure forced with the residual method)
Compared with Eddy Covariance flux tower measurements
Sensible Heat Flux (H)

8. 200
300
400
500
600
700
800
200 300 400 500 600 700 800
Observed LE (W m-2
)
OLEMpredictedLE(W-2)dd
174 soy
182 soy
189 soy
174 corn
182 corn
189 corn
200
300
400
500
600
700
800
200 300 400 500 600 700 800
Observed LE (W m-2
)TSMpredictedLE(Wm-2)dd
174 soy
182 soy
189 soy
174 corn
182 corn
189 corn
Latent Heat Fluxes

9. 50
150
250
50 150 250
Observed daily LE (W m-2
)
OLEMpredicteddailyLE(Wm-2)dd
174 soy
182 soy
189 soy
174 corn
182 corn
189 corn
50
150
250
50 150 250
Observed daily LE (W m-2
)TSMpredicteddailyLE(Wm-2)dd
174 soy
182 soy
189 soy
174 corn
182 corn
189 corn
Daily Evapotranspiration Integrated
Using the Evaporative Fraction

10. • Total grain yield or production for individual fields
• Spatial yield from GPS yield monitors on harvesting equipment
(would be fantastic!)
• Representative biomass, leaf area index for individual fields
and different crops
• Aggregated yield statistics by county and crop type
• Crop classification layers at field scales by season

11. 2013
2014

12. Measurements of concurrent biomass and leaf area index and other
canopy biophysical parameters along with ET measurements
Experimental analysis in corn (C4) and soybeans (C3) in eastern Nebraska

13. www.yieldgap.org

14. • Quality of seeds
• Lack of inputs (fertilizers) or micro-financing to purchase inputs
• Inappropriate agricultural practices
• Low value of crops or lack of accessibility to markets
• Water deficit in rainfed areas, over or under irrigation
• Poor infrastructure (roads, maintenance of irrigation systems)
• Poor water management of irrigation systems
• Low soil fertility, depleted organic matter

15. 15
Provided by USDA NASS, based on Landsat Thematic mapper and other satellite image data

16. 16
Northeastern Nebraska Corn/Soybean Rotation
2013 2014
Many satellite-based evapotranspiration and yield models require the knowledge
of the crop type at the surface

17. Source: USDA Natural Resource
Conservation Service
(http://websoilsurvey.sc.egov.usda.gov/Ap
p/WebSoilSurvey.aspx)
Map of water holding capacity in the 1st m. profile RGB color composition, L8 Date 07/19/2913
Variables include: Soil type, texture, depth,
layers, water holding capacity, infiltration
rates, organic matter content etc.

18. ICBA-MOA, Qatar Training Course May 15-18, 2011, Doha, Qatar International Center for Biosaline Agriculture, Dubai, UAE
Soil moisture measurements in Tunisia

19. Soil Water Content 0-60 cm
80
100
120
140
29-Jun 30-Jun 1-Jul 2-Jul 3-Jul 4-Jul 5-Jul
SWC(mm)
Depletion of soil water content during daytime from ET
Source: Makram Belhaj Fraj and Ian McCann (ICBA)

20. Example of Use of Water Balance Data of an Irrigated
Area for verifying remote sensing based ET models
Palo Verde Irrigation District, CA
20Saleh Taghvaeian* and Christopher M. U. Neale. 2011. Water balance of irrigated areas: a remote sensing approach.
Hydrological. Process. (2011) Published online in Wiley Online Library, (wileyonlinelibrary.com) DOI: 10.1002/hyp.8371;

21. 21
Water balance of irrigation schemes
I + P = ET + DP + RO + ΔS
I: Applied irrigation water;
P: Precipitation;
ET: Evapotranspiration;
DP: Deep percolation;
RO: Surface runoff; and,
ΔS: Change in soil water storage.

22. 22
Over 260 piezometers
1 mile by 1 mile grid
Monthly measurements

23. 23
Remote Sensing of Energy Balance
Rn = H + G + LE
Rn: Net Radiation
H: Sensible Heat Flux
G: Soil Heat Flux
LE: Latent Heat Flux (Evapotranspiration)
Surface Energy Balance Algorithm for Land (SEBAL)
Developed by Dr. Wim Bastiaanssen, Wageningen, The Netherlands

24. 24
EToF

25. 25
Total volume of water consumption by PVID crops for 20 dates of Landsat overpass
based on SEBAL estimates of evapotranspiration

26. 26
Precipitation

27. 27
0
400
800
1200
1600
2000
2400
J-08 M-08 M-08 J-08 S-08 N-08 J-09
DailyAverageFlowRate(cfs)
Main Canal
Outfall Drain
Operational Spills
Surface Water Inflows and Outflows

28. 28
0
3
6
9
12
15
18
J-08
F-08
M-08
A-08
M-08
J-08
J-08
A-08
S-08
O-08
N-08
D-08
J-09
F-09
Depth(mm) Precip.
Inflow
0
3
6
9
12
15
18
J-08
F-08
M-08
A-08
M-08
J-08
J-08
A-08
S-08
O-08
N-08
D-08
J-09
F-09
Depth(mm)
ETa
Outflow
Depth (mm) Percentage
Precipitation 71 3
Surface inflow 2479 97
Σ Inputs 2550 100
Canal Spills 284 11
Drainage 998 39
Evapotranspiration 1286 50
Σ Outputs 2568 100
Σ Inputs – Σ Outputs -18 -0.7
Closing the Water Budget

29. 29
Depleted fraction (DF)
- DFg = ETa / (Pg + Vd)
- DFn = ETa / (Pg + Va)
0.0
0.2
0.4
0.6
0.8
1.0
DF
DFg
DFn Nilo Coelho:
DFn = 0.60
PVID:
DFn = 0.55
Estimation of System Performance Indicators

30. • Allows for checking remote sensing based ET models over
larger scales
• Diversions into main canal and later canals are useful even if
no drainage or groundwater levels are measured

31.  Analysis of the relationship between Yield (grain) and Actual Irrigation over Simulated
Irrigation Requirements.
FIELD WATER BALANCE APPROACH USING RS:
PRELIMINARY RESULTS IN NEBRASKA
Under-Irrigation Over-Irrigation

32. Courtesy of Dr. Wim Bastiaanssen

33. Water Productivity Score – Continental Wheat
Courtesy of Dr. Wim Bastiaanssen

34. Find the local champion in Doukalla Irrigation Scheme, Morocco
Farmer Ahmed is with 1.33 kg/m3 the most productive
Courtesy of Dr. Wim Bastiaanssen

35. Standardization by crop zones
CV=0.41
CV=0.30
CV=0.27
CV=0.21
CV=0.17
CV=0.13
CV=0.08
Courtesy of Dr. Wim Bastiaanssen

36. • Identify high and low end users in different agricultural regions
• Work with country government agencies, regional and local
water management and agricultural agencies
• On the ground visits to interview farmers and identify sources
of problems, farmers with good practices
• Identify technical solutions and policy changes that will
improve local agriculture production and water management
practices
• Implement practices through training, demonstrations, change
of governance structure etc.

37. Christopher Neale
Director of Research
Daugherty Water for Food Institute
at the University of Nebraska

38. • Based on the VIIRS (Visible Infrared Imaging Radiometer Suite)
Satellite Instrument – Launched in 2013, expected lifetime is 15 years
• Uses thermal infrared and shortwave bands of VIIRS
• Daily global coverage with improved spatial resolution (375 m) over
MODIS (250 m, 1000 m)
• ALEXI (Atmospheric Land Exchange Inverse model) remote sensing
based surface energy balance model
• To be run at the University of Nebraska-Lincoln supercomputer center
for the lifetime of the VIIRS instrument (approximately 15 years)
• Partners:
Dr. Martha Anderson, USDA-ARS Hydrology Laboratory, Beltsville
Dr. Christopher Hain, NOAA/University of Maryland
ICBA, NDMC at UNL, CALMIT at UNL, UNESCO-IHE
Funding: USAID, WFI, FAO

39. • Water balance of River basins and watersheds => water
accounting
• Drought early warning systems – ESI evaporative stress index
Composite Drought Index
• Upper boundary condition for downscaling of ET to higher
spatial resolutions to allow for estimates of crop water use and
water productivity

40. • Crop water productivity (Kg/m3, g/m2)
• Will require downscaling of daily ET product using DisALEXI
model, or SEBAL 3.0 using Landsat Thematic Mapper Satellite
Imagery and other higher resolution systems
• Initially produced for selected irrigated areas of participating
countries
• Partners:
Dr. Martha Anderson, USDA-ARS Hydrology Laboratory, Beltsville
Dr. Christopher Hain, NOAA/University of Maryland
Dr. Wim Bastiaansen, UNESCO – IHE
Funding: WFI, FAO, USAID

41. • Joint venture, multiple countries
• Ground verification data needs to be, collected, analyzed
and interpreted jointly so the learning is mutual
• Training can be provided on QC and analysis of data,
interpretation
• Joint publications
• Solutions need to be pursued with appropriate government,
regional and local agricultural and water management
agencies
Collaborative approach is the best way to obtain impact on
the ground and meaningful change
Products needs to be verified in the field, to allow for
feedback, model modifications and improvements

42. www.gwpforum.org
THANK YOU