DECISION-INFORMED FORECAST...
Transcript of DECISION-INFORMED FORECAST...
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DECISION-INFORMED FORECAST DESIGN
Esmee Mulder
Supervisors:
Hessel Winsemius
Susan Steele-Dunne
Corey Wright
Pieter van Gelder
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PROBLEM STATEMENT
• Climate change
• East African climate paradox (droughts long rains SST)
• Bimodal rains- Rains do not always come
• (Nov-Jan, Feb-May)
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PROBLEM STATEMENT
• Climate change
• East African climate paradox (droughts long rains SST)
• Bimodal rains- Rains do not always come
• (Nov-Jan, Feb-May)
• Available grazing land decreasing
• Land privatization
• Agricultural (and urban) expansion
• Conservation
• More severe droughts predicted (climate change)
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PROBLEM STATEMENT
• Climate change
• East African climate paradox (droughts long rains SST)
• Bimodal rains- Rains do not always come
• (Nov-Jan, Feb-May)
• Available grazing land decreasing
• Land privatization
• Agricultural (and urban) expansion
• Conservation
• More severe droughts predicted (climate change)
What if people in Sinya know a drought is coming?
Will that help them to better prepare and decrease losses?
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RESEARCH QUESTION
Can weather information contribute to the drought
management strategies of the Maasai communities in Sinya,
Tanzania?
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DROUGHT DEFINITIONS
• Meteorological: “a lack of precipitation over a region for a period of time”
• Agricultural: “a period with declining soil moisture and consequent crop failure without any reference to surface water resources”
• Hydrological: “related to a period with inadequate surface and subsurface water resources for established water uses of a given water resources management system”
• Socio-economic: “associated with failure of water resources systems to meet water demands and thus associating droughts with supply of and demand for an economic good (water). Socio-economic drought occurs when the demand for an economic good exceeds supply as a result of a weather-related shortfall in water supply.”
Source: (Mishra and Singh, 2010, p. 206)
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SUB-QUESTIONS
What is the current situation in Sinya?
• Experience, strategies, available info/knowledge
What weather outlook (system) is currently available?
• What info, skill, scale, lead-time, communication
What are recommendations for improvement?
• What (unavailable) weather info can contribute?
• Is that achievable with the current weather models?
• With what skill?
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FIELDWORK : WHAT DID I DO?
Focus groups
(3 sessions)
• 2x women, 2x elders, 2x morans
• Drought in Sinya & strategies
• Design outlook system
• Seasonal Outlook Game
Key interviews
• Leader Sinya
• Traditional leaders (2x) incl. ‘weather man’
• Ward executive
Family Portraits
• Drought management strategies
• Revisit in May/August
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DO PEOPLE IN SINYA PREDICT THE WEATHER?
Yes, they look at signs:
Stars
The way cows sleep
Whether cows excrete faeces at night
Scorpions
Frogs
Presence of elephants
Whether cows give birth
However, no drought predictions…
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IS WEATHER INFO RECEIVED IN SINYA?
Most people don’t receive any info
• Tv
• Radio
• Newspaper
• Relatives in cities
Some receive through:
One of the leaders claims to consult elders on weather predictions
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WHAT IS CURRENT WEATHER INFORMATION? (BY TMA)
• Rain/Sun/clouds , temperature
• Scale? One prediction for 400-500 km2
• Skill? Not good enough for people to have faith in the predictions
Technical
• Predictions are out once the period has started
• over a period of 10 days, a month, or the season (3 months)
• TMA communicates through Internet, radio, tv
• Reach Sinya? – Only those with tv/radio/newspaper, which is very few.
Communication
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WHAT ACTIONS ARE TAKEN TO MANAGE DROUGHTS BY PASTORALISTS IN SINYA?
MovingSelling
LivestockStoring
food/money
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WHICH INFO IS REQUIRED, WHEN, HOW COMMUNICATED, OF WHAT AREAS?
What:
• When rain
• Where rain
• How much (enough for grass?)
• How long it won’t rain
When
• Mobility = few days/ few weeks
• Selling = 1 week /few weeks
• Storing food = 1 week /few weeks
How
• Meeting
• Phone call system
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CAN WEATHER INFO
CONTRIBUTE TO DROUGHT MANAGEMENT
IN SINYA?
HOW?
Yes. But the quality of the forecasts has to increase.
How: Selling, storing money on bank/food
For migration only useful where it rained/grass regrew (Afriscout), not likely to move earlier. However…
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HOWEVER, IF…
Livestock still at home
Grasses decreased
March
Decide
• Leave
• Wait for rains
Info required:
• Will it rain (enough) the coming 2 weeks
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DROUGHT → ALAMEI
Alamei
Jenny: "the time of suffering, lack of food"
"dead time"
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ALAMEI MANAGEMENT
Grass
Regrowth
Grass
Consumption
Water recharge
& Consumption
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FACTORS INFLUENCING ALAMEI – SHORT TERM(GRASS/WATER)
• Rainfall
• Evaporation
• Wildlife
• Livestock migration into the area
• Grass growth limitation (e.g. vehicles)
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WHAT’S NEXT?
Can the threshold exceedance of
precipitation in these particular alamei
periods be forecasted with high enough skill
for Sinya?
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APPROACH
1. Determine Window-period
2. Determine Threshold
3. Analyse ERA5-reanalysis data to see if such a dry period occurred
4. Compare forecast with reanalysis to determine skill
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APPROACH
1. Window-period• Find when NDVI decreases to ‘very-little grass’ condition• Compare NDVI with this year [to get an idea what these values approximately
represent]• Compare NDVI to data from field on grass seasonality, occurrence of alamei sapo
and when there was a lot of grass [to see whether NDVI can be used as grass-indicator]
2. Threshold
3. Analyse ERA5-reanalysis data to see if such a dry period occurred
4. Compare forecast with reanalysis to determine skill
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APPROACH
1. Window-period
2. Threshold• Compare total precipitation – evaporation reanalysis with NDVI• Estimate threshold based on:o Nr. of rainfall days o Cumulative rainfall o Nr. of dry days
3. Analyse ERA5-reanalysis data to see if such a dry period occurred
4. Compare forecast with reanalysis to determine skill
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APPROACH
1. Window-period
2. Threshold
3. Analyse ERA5-reanalysis data to see if such a dry period occurred
• Only for identified window periods
4. Compare forecast with reanalysis to determine skill
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APPROACH
1. Window-period
2. Threshold
3. Analyse ERA5-reanalysis data to see if such a dry period occurred
4. Compare forecast with reanalysis to determine skill
• Determine what percentage of ensemble members has to pass the threshold for it to be a hit
• Hits/ misses/ false alarm/ correct negatives
• Create a ROC diagram
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LIMITATIONS
▪ No access to TMA data/models → ECMWF
▪ Only theoretical skill of model
• Limited weather stations around study area
▪ Global weather model
▪ Scale & Skill
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STUDY AREA - NDVI & TOTAL PRECIPITATION 2001 - 2018
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CONCLUSIONS
• Initial ideas challenged
• Combining knowledges versus top-down system
• Only one part of alamei prediction and management
• Local knowledge still vital
• Importance of clear communication
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EVIDENCE-BASED ADVOCACY
Study Area
NDVI & Total Precipitation
2001 - 2018
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THE ROLE OF SATELLITE TECHNOLOGY IN “DEVELOPMENT”
▪ Evidence-based advocacy
▪ Empowerment
▪ Info of marginalized (limited data) areas
▪ Open-source
▪ Challenges
▪ Unequal access to knowledge
▪ Adverse impacts
▪ Misinterpretation
▪ Misrepresentation
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THANK YOU
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ECMWF MODEL
• Resolution = 0.25°
• 51 ensemble members for forecast
• 10 ensemble members for reanalysis
• Use ocean analysis & global coupled ocean-atmospheric global circulation
model
• Observation inputs: Satellite data (90 products), weather stations, buoys, ships,
& aircraft observations