SCO Application Case Studies

Slide1: 

Selected Applications of Seasonal Climate Forecasts in Agriculture and Water Resources - The Australian Experience Yahya Abawi Department of Primary Industries and Fisheries Bureau of Meteorology/AusAID project Vanuatu 5 July 2004

Slide3: 

Assessing the value of SCF in irrigated agriculture (The Australian Experience) Assessing the value of SCF in water allocation decisions (The Australian Experience) Application of climate forecasts in Water and Crop Management (The Indonesian Experience) Application of Climate Forecasts in Harvest Risk Management Constraints in using climate forecasts Application of Climate Forecasts in Agricultural Decisions

Slide4: 

Evaporation Drainage Transpiration Runoff stream flow Catchment Hydrology (IQQM) Groundwater Demand (OZCOT) Climate Variability (ENSO) Approach

Slide5: 

The Concept of Model Calibration

The Concept of Model Calibration: 

The Concept of Model Calibration

The Concept of Model Calibration: 

The Concept of Model Calibration

Slide8: 

Use of Climate Forecasts in Irrigation Decisions

Slide9: 

Evaporation Drainage Transpiration Runoff stream flow Catchment Hydrology (IQQM) Groundwater Demand (OZCOT) Climate Variability (ENSO) Approach

IQQM: 

IQQM Integrated Quantity Quality Model is a planning model used to assess the impact of river management changes on streamflow, water quality and water users Hydrological model – daily time step represent the major hydrologic and water management processes which occur in a river basin simulates streamflow, reservoir operation, irrigation demands and many other processes in a river basin

Slide11: 

IQQM Node Types Stream gauging station Tributary inflow Fixed demand (town water supply) Transfer node Effluent / Loss Effluent return Irrigation Confluence Floodplain Wetland

Slide12: 

Basin boundary inflows Reservoir Example inflows inflows inflows

Slide13: 

Gauging Station dam Basin boundary dam Example

Slide14: 

Gauging Station dam dam inflows Basin boundary dam Example

Slide15: 

Gauging Station dam dam inflows Basin boundary dam inflows Example

Slide16: 

Gauging Station dam dam inflows local inflows Basin boundary dam inflows Example

Slide17: 

6000 ML/a Gauging Station dam dam inflows local inflows Basin boundary dam inflows Example

Slide18: 

6000 ML/a 4000 ML/a 500 ML ofs 700 ML ofs 100 ha 150 ML ofs Gauging Station dam dam inflows local inflows Basin boundary dam inflows Example

Slide19: 

      Observed vs Simulated Flow at Keling (E=0.68 SR=0.99)

Typical Representation of a River System in IQQM: 

Typical Representation of a River System in IQQM

Slide22: 

Simulated Water Allocation at a Typical Irrigation Node

Slide23: 

Sustainable Area and SOI SOI Consistently Positive SOI Consistently Negative All years

Slide24: 

Ice Cream and Pie Man at Rugby Grand Final

Slide25: 

Ice Cream and Pie Man at Rugby Grand Final

Slide26: 

Ice Cream and Pie Man at Rugby Grand Final What is the value of perfect information?

Slide28: 

Economic Value of SCF in Irrigated Cotton

Slide29: 

Mean-Variance Analysis for different crop area decisions SOI Max Area 7 Ml/ha 6 Ml/ha 5 Ml/ha

Slide30: 

Use of Seasonal Climate Forecasts For Water Allocation

Slide31: 

Inflow into Pindari Dam (Mar-Sep)

Slide32: 

Inflow into Pindari Dam (Oct-Feb)

Median inflow (Giga Liters) by ENSO events: 

Median inflow (Giga Liters) by ENSO events The net volume represents a crop area of 14000 ha or 25% of the total irrigated area of 57000 ha within the Border Rivers Catchment

Slide34: 

Water Availability as affected by ENSO

Slide35: 

Use of Seasonal Climate Forecasts in water and crop management The Indonesian Experience

Slide41: 

Study Case: Batujai Irrigation Area

Slide43: 

SOI Crop Type Pot. Yield Est. Yield Soil Types Crop Optimizer Water Demand Optimal Cropping Strategy SPI Water Balance IQQM Simulation Water Supply Climate Variability

Slide44: 

Output of Crop Optimization for Batujai Irrigation Area

Slide46: 

Risk Assessment at Harvest Yahya Abawi Department of Primary Industries and Fisheries Industry Task Force June 2000

Slide47: 

Average Rainfall Merriden 1914 - 1988 Average Rainfall Wanbi 1958 - 1988 Average Rainfall Maryborough 1880 - 1988 Average Rainfall Narrabri 1880 - 1988 Average Rainfall Dalby 1871 - 1988 Distribution of rainfall in the main wheat growing regions of Australia

Slide48: 

On average 10% of Australian Wheat is downgraded due to weather damage, costing the industry about $30 million Annualy

Slide49: 

Relationship Between Summer Rainfall And Weather Damage Wheat at Receival Data for Goondiwindi 1963-1988

Receivals of Weather Damaged Wheat (Queensland 1995-1999): 

Receivals of Weather Damaged Wheat (Queensland 1995-1999)

Slide51: 

Weather Model Daily rainfall, Minimum & maximum temperature, Relative humidity Outputs Annual costs, Annual production, Grain losses, Crop return, Harvest duration, Production by grade System Parameters Machine performance data, Machinery costs, Crop loss characteristics, Depreciation, Interest rate, Operating costs, Labour, Fuel costs Inputs Crop data, Yield, Variety price, Grade structure, Maturity date, Area, Harvest moisture content Machinery data Harvesting capacity, Drying capacity, Wet storage capacity. Harvest Management Model

The effect of grain moisture content on costs of harvesting: 

The effect of grain moisture content on costs of harvesting Quality losses Shedding losses Drying costs Harvest costs

The effect of harvest moisture content on return for various cropping areas: 

The effect of harvest moisture content on return for various cropping areas 800 ha 600 ha 250 ha 100 ha

Receivals of Weather Damaged Wheat (Queensland 1995-1999): 

Receivals of Weather Damaged Wheat (Queensland 1995-1999)

Constraints to Use of Climate Forecasts: 

Constraints to Use of Climate Forecasts Technical barriers to forecast use include; Accessibility – official forecasts vs other agencies Credibility – lack of evaluation impede their use Understandability – official forecasts are misinterpreted partly because of their probabilistic nature Relevance – Seasonal rainfall vs flood forecasting Timing – Different decisions require different lead time

Constraints to Use of Climate Forecasts: 

Constraints to Use of Climate Forecasts Technical barriers to forecast use include; Accessibility – official forecasts vs other agencies Credibility – lack of evaluation impede their use Understandability – official forecasts are misinterpreted partly because of their probabilistic nature Relevance – Seasonal rainfall vs flood forecasting Timing – Different decisions require different lead time