A quantitative probabilistic interpretation of SARCOF forecasts for agricultural production...
-
Upload
christian-howe -
Category
Documents
-
view
215 -
download
0
Transcript of A quantitative probabilistic interpretation of SARCOF forecasts for agricultural production...
A quantitative probabilistic
interpretation of SARCOF forecasts for agricultural
production
USGS/FEWSNET, UCSB, SADC RRSU, SADC DMCPresented by
Tamuka Magadzire
Outline Objective Background FACT – probability and rainfall
amounts Crop Water Requirements Example study for FMA 2003 Future work
Objective
COF forecast p(crop success)
To generate a crop-specific interpretation of the COF forecasts
Background Analysis based on two concepts
Analysis of the relationship between probability and rainfall amounts
Enabled by the FEWSNET AgroClimatology Toolkit (FACT)
Crop water requirements, and their relationship with yield
Derived from Water Requirements Satisfaction Index (WRSI) analysis
Probability Distribution Function
Bar chart gives indication of frequency of events
Can be used to construct derive probabilities
Increasing quantity
Probability Distribution Function
Area under the curve equals probability of an event falling within a range of values
Normal Distribution-2 0 2
0.0
0.1
0.2
0.3
0.4
We can model the rainfall distribution using a pdf with appropriate parameters. This pdf can be used to completely describe the rainfall distribution, and the probability of rainfall of different amounts.
Probability Distribution Function
Has been shown to perform well for rainfall
Never less than zero Can be very flexible in form Described by only 2
parameters – shape and scale
We use Gamma Distribution rather than normal distribution because:
Probability-Accumulation Relationship
Fitting a probability distribution to rainfall events at a location allows for the querying of the likelihood of a particular event
Similarly, the amount of rainfall corresponding to a particular likelihood can be derived
Finding Probability
Use x-axis to locate the rainfall accumulation of interest
Trace up until it meets the curve
Trace left to find the probability of being less than the amount
Finding Rainfall
Use the y-axis to find the likelihood of interest
Trace right to the curve
Trace down to find the accumulation associated with the likelihood
Obstacle How can a
relationship between historical data and forecast probabilities be made?
Is it possible to make a meaningful connection between forecasts and accumulations?
Drawing From Terciles Hypothetical forecast
for the region calls for 45/35/20
Draw user-defined number of samples randomly from theoretical terciles in proportion to forecast
New distribution parameters calculated
33% 33% 33%
DRY MID WET 20 35 45
Old v New Distribution
Old shape: 2.59 Old scale: 13.64 New shape: 3.12 New scale: 13.04 New distribution
reflects lower probability of dryness and increased wetness
5 30 55 80 105 130 155 180
0.000
0.005
0.010
0.015
0.020
Old Theoretical DistNew Theoretical Dist
Old and New Theoretical Distributions
A Practical Example
Water Requirements
Gro
wth
-ch
ang
e in
si
ze;
Wat
er R
equ
irem
ent
Growth and Development of a Maize crop
2
15-25 25-40 15-25 35-40 10-15
Est Veg Tass Silk Yld Rip
Days
15-25
40-65
55-85
100-145
90-130
1
2
34
Development –change in phenological stage
Most critical
stages of maize
Water Requirements
Crop Water Balance
WHC
PPTi ETi
Surplus
SWi = SWi-1 + PPTi - ETi
Drainage
Runoff
ET = WR (Water Requirement)
ET < WR
SWi
Water requirements satisfaction index
Based on water balance model. WRSI correlates well to yield in water-limited areas.
Examples
Climatological probability of exceedence of 80% of Water Requirements for (a) 120-day maize and (b) 90-day sorghum in JFM
A study for FMA 2003
A study for FMA 2003
A study for FMA 2003
A study for FMA 2003
Future work
Incorporating cropping areas into forecast Producing a single crop forecast for most
major cereal crops Interpreting the SARCOF-7 forecast, and
future COF forecasts in similar manner