1. Figaro WP3 results Second reporting period
Input from WP3 partners, presented by WP3 partners
Second yearly meeting Jan. 2015, FAO, Rome, Italy

2. WP3 Objectives
Develop interfaces, customize and deliver a range of models, optimization and simulation tools for soil, plant, weather and network hydraulics tailored to receive and assimilate the data stream provided by the new sensors and platform (WP2, WP4, WP6), and finally report their outputs to the DSS engine.
Develop a modular DSS engine that can be coupled to the platform (WP2, WP6) which will receive its input from the simulation and optimization models and provide the platform with data for further analysis

3. WP3 tasks, milestones and deliverables
T3.1 (M3-M12) definitions of modeling, data and work flows - Finn
T3.2 (M3-M15) plugins - Adelio
T3.3 (M3-M24) Meteorological data, models and forecasting – Slavco, Tom
T3.4 (M3-M24) Hydraulic modeling and delivery optimization - Fernando
T3.5 (M3-M24) energy and costs simulation and optimization - Adelio
T3.6 (M3-M24) dynamic crop growth simulation optimization – Finn
T3.6 AquaCrop updates – Dirk and model optimization Rafi
T3.7 (M3-M32) Online quality control of data and predictions - Finn
T3.8 (M12-M36) DSS engine development - Finn
MS3.1 Interfaces for models written and ready for use with sensors and sources of data (M12)
D3.1 Design of data and models plugins (M15)

4. WP3 tasks, milestones and deliverables
T3.1 (M3-M12) definitions of modeling, data and work flows - Finn
T3.2 (M3-M15) plugins - Adelio
T3.3 (M3-M24) Meteorological data, models and forecasting – Slavco, Tom
T3.4 (M3-M24) Hydraulic modeling and delivery optimization - Fernando
T3.5 (M3-M24) energy and costs simulation and optimization - Adelio
T3.6 (M3-M24) dynamic crop growth simulation optimization – Finn
T3.6 AquaCrop updates – Dirk and model optimization Rafi
T3.7 (M3-M32) Online quality control of data and predictions - Finn
T3.8 (M12-M36) DSS engine development - Finn
MS3.1 Interfaces for models written and ready for use with sensors and sources of data (M12)
D3.1 Design of data and models plugins (M15)
Done 
Done 
Done 

5. WP3 results in the second reporting period
T3.3 (M3-M24) Meteorological data, models and forecasting – Slavco, Tom and ?
Give some few slides
T3.4 (M3-M24) Hydraulic modeling and delivery optimization – Fernando
T3.5 (M3-M24) Energy and costs simulation and optimization – Adelio and ?
T3.6 (M3-M24) dynamic crop growth simulation optimization, AquaCrop and other models – Finn, Adriano, George, Pedro, Rafi

6. WP3 Task 3.3 – results on meteorological data, models and forecasting
Give some few slides

7. WP3 Task 3.4 – results on hydraulic modelling and delivery optimization
Give some few slides

8. WP3 Task 3.5 – results on energy and costs simulation and optimization
Give some few slides

9. T3.5 Energy and costs simulation and optimization
Energy optimization is the result of the adoption of a number of procedures such as:
Pumps efficiency control
Reducing leaks and promoting the use of smart grids in order to deliver only the necessary amount of water (should be the main focus of FIGARO)
Adoption of proper costs policies
Redefinition of the water paths and hydraulic controls in the distribution network in order to reduce the necessary pumping effort (mostly effective in grid networks)

10. T3.5 Energy and costs simulation and optimization
Hidromod has been working mostly in water leakage control, pumping efficiency and adoption of proper network controls derived from data mining and modelling
In FIGARO these procedures are to be tested in Valencia. The goal is to include the Valencia EPANET model, the data collected by the SCADA system and the optimization algorithms being developed within the project in FIGARO Platform
This task is however delayed. Hidromod has been working in generic procedures for pumping optimization and leakage control but the work for integration of EPANET have just started now.
Even so it will be necessary to figure how these results will be integrated in the Platform (a discussion to be held in the framework of FIGARO Platform v1)

11. T3.5 Energy and costs simulation and optimization
Pump efficiency monitoring
Leakage detection
AQUASAFE OPC-Connector
OPC server
AQUASAFE
Demand forecast
AQUASAFE Data flow management
Leak flow Chart
Leak Alarm Table
Create Leak
Report

12. WP3 Task 3.6 – results on modelling and optimization Finn, Adriano, George, Pedro, Rafi

13. WP3Task 3.6 – results on modelling and optimization in Denmark
AquaCrop

14. WP3Task 3.6 – results on modelling and optimization in Denmark
AquaCrop

15. WP3Task 3.6 – results on modelling and optimization in Denmark
Daisy

16. Figaro potato data in Denmark simulated with Daisy
New experiment with drip irrigated and N fertigated potato in Fully randomized experiment (four replicates) in a coarse sand at Jyndevad
Simulated with Daisy based on one set of C/N and hydraulic parameters
Treatments:
I0N : No irrigation No nitrogen
I0N : No irrigation, 140 kg N/ha
I1N : Full irrigation, No nitrogen
I1N : Full irrigation, 140 kg N/ha
IdaisyNdaisy : Full irrigation, 100 kg N/ha
Irrigation and N fertigation
guided by Daisy
What caused the observed variation??
Spatial variation in hydraulic and C/N parameters??

17. WP3Task 3.6 – results on modelling and optimization in Italy AquaCrop
Give some few slides

18. WP3Task 3.6 – results on modelling and optimization in Portugal
AquaCrop
Mohid land/SVAT
Give some few slides

19. Soil moisture calibration
1D Scale
Soil moisture calibration
Sprinkler irrigation system
Irrigation applied: 365mm
Monitoring 2 plots in 4 locations
Crop: Maize
Models used: AquaCrop and 1D MOHID
Mohid vs Obs
AQ vs. Obs. r
0.82
0.95 R2
0.67
0.90
RMSE
7.03
11.63
NRMSE
0.04
0.06 ME
0.61
0.69 W
0.89
0.88

20. Plant growth calibration
1D Scale
Plant growth calibration
Mohid AQ r
0.99 R2
0.98
RMSE
4.61
4.6
NRMSE
6.19
6.1 ME W
1.00
Mohid AQ r
0.99
0.97 R2
0.98
0.94
RMSE
2.51
3.2
NRMSE
0.19
20.6 ME
0.96 W

21. Impact of spatial variability of irrigation on soil moisture
Sub plot Scale
Impact of spatial variability of irrigation on soil moisture
m3/m3
The model used for the 3D simulation is MOHID
The variation was caused by uneven water distribution
Christiansen uniformity coef. of 71%
The irrigation varied between 116 – 697mm

22. Impact of plant growth variability on biomass
Field Scale
Impact of plant growth variability on biomass

23. WP3Task 3.6 – results on modelling and optimization in Greece AquaCrop
Give some few slides

24. WP3Task 3.6 – results on modelling and optimization by Rafi, Israel

25. Optimization - Technion
Full optimization of daily irrigation events
Hybrid optimization (can be implemented in real-time)

26. Hybrid optimization Optimize irrigation event
Optimize trigger/replenishment levels
Related to crop-specific stress response thresholds

28. Results for cotton in Greece
expected
DAY 25
DAY 61
implemented
planned
actual
DAY 101
DAY 141

29. Results for cotton in Greece

30. Results for cotton in Greece

31. Results for cotton in Greece

32. Results for cotton in Greece

33. Results for cotton in Greece

34. Results for cotton in Greece

35. Results for cotton in Greece

36. Results for cotton in Greece

37. Results for cotton in Greece

38. Results for cotton in Greece

39. Results for cotton in Greece

40. Results for maize in Kansas

41. Results for maize in Kansas

42. Results for maize in Kansas