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

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

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)

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 

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

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

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

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

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)

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)

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

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

WP3Task 3.6 – results on modelling and optimization in Denmark AquaCrop

WP3Task 3.6 – results on modelling and optimization in Denmark AquaCrop

WP3Task 3.6 – results on modelling and optimization in Denmark Daisy

Figaro potato data in Denmark simulated with Daisy New experiment with drip irrigated and N fertigated potato in 2013. 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: I0N0 : No irrigation No nitrogen I0N3 : No irrigation, 140 kg N/ha I1N0 : Full irrigation, No nitrogen I1N3 : 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??

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

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

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

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

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

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

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

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

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

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

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

Results for cotton in Greece

Results for cotton in Greece

Results for cotton in Greece

Results for cotton in Greece

Results for cotton in Greece

Results for cotton in Greece

Results for cotton in Greece

Results for cotton in Greece

Results for cotton in Greece

Results for cotton in Greece

Results for cotton in Greece

Results for maize in Kansas

Results for maize in Kansas

Results for maize in Kansas