operational drought monitoring and forecasting at the usda-nrcs
DESCRIPTION
Operational Drought Monitoring and Forecasting at the USDA-NRCS. Tom Pagano [email protected] 503 414 3010. Monitoring networks Data products Seasonal forecasts Soil moisture Challenges Frontiers. Monitoring networks. 1906. 2005. Manual Snow Surveys - PowerPoint PPT PresentationTRANSCRIPT
Operational Drought Monitoring and Forecasting at the USDA-NRCS
Tom Pagano [email protected]
503 414 3010
Monitoring networksData products
Seasonal forecastsSoil moistureChallengesFrontiers
Monitoring networks
Manual Snow SurveysMetal tube inserted into snow and weighed to measure water content.+300,000 snow course measurements as of June 2008
1906
2005
Snotel (SNOw TELemetry) network
Automated, remote stationsPrimary variables:
Snow waterPrecipitationTemperature
Also: Snow depthSoil moisture
SNOTEL and Snow course records often spliced together
Snowcourse (solid) and SNOTEL (hashed) active station installation dates
Active year
Nu
mb
er o
f si
tes
Soil climate analysis network (SCAN)Soil moisture/energy balance emphasis
Short period of record (some from 1990s)Data available but few products
Manual snow-course
SCAN
SNOTEL
Data products
Time series charts
CSV flat files Google Earth
Forecast products
Location
Forecasts are coordinated with the National Weather Service (NWS).Both agencies publish identical numbers.
Location
Time Period
Historical Average
Forecasts are coordinated with the National Weather Service (NWS).Both agencies publish identical numbers.
Location
Time Period
“The” ForecastWater Volume
Historical Average
Error Bounds
Forecasts are coordinated with the National Weather Service (NWS).Both agencies publish identical numbers.
Seasonal water supply volume forecasts(available in a variety of formats) NRCS formats:
Seasonal water supply volume forecasts(available in a variety of formats) NRCS formats:
Basic Forecasting MethodsStatistical regression
May 1 snowpack % avg
Ap
r-Ju
l st
ream
flo
w %
avg
S Fork Rio Grande, Colo
Statistical regression
May 1 snowpack % avg
Ap
r-Ju
l st
ream
flo
w %
avg
S Fork Rio Grande, Colo
Snowpack
Soil water
Snow
Rainfall
Runoff
Heat
Simulation modeling
Basic Forecasting Methods
Principal Components Regression (Garen 1992)Prevents compensating variables. Filters “noise”.
Principal Components Regression (Garen 1992)Prevents compensating variables. Filters “noise”.
Z-Score Regression (Pagano 2004)Prevents compensating variables.
Aggregates like predictors, emphasizing best ones.Does not require serial completeness.
Relative contribution of predictors
Daily forecast updates
Existing seasonal forecasts issues once per month
Why not develop 365 forecast equations/yearand automate the guidance?
We currently do Apr-Jul Streamflow = a * April 1 Snowpack + b
Why not something like Apr-Jul Streamflow = a * April 8 Snowpack + b
1971-2000 avg
Period of record median
Period of record range (10,30,70,90 percentile)
1971-2000 avg
Period of record median
Period of record range (10,30,70,90 percentile)
Official coordinated outlooks
1971-2000 avg
Period of record median
Period of record range (10,30,70,90 percentile)
Official coordinated outlooks
Daily Update Forecasts
Official forecasts
Daily forecast 50% exceedence
Official forecasts
Expected skill
SWSI
Methodology varies by state
Available 8 Western states
Rescaled percentile of[reservoir + streamflow]
Calibrated on observed,forced with streamflow forecasts
(real-time variance too low)
No consistent calibration period
Soil moisture and runoff efficiency
Expansion of soil moisture to
SNOTEL network(data starts ~2003)
Blue Mesa Basin, Colorado Soil Moisture 2001-2008(According to the Univ Washington Model- top 2 layers)
Blue Mesa Basin, Colorado Soil Moisture 2001-2008(According to the Univ Washington Model- top 2 layers)
(According to Park Cone Snotel- ~0-30” depth)
Snotel does poorly in frozen soils, so that has been censored
Model resembles snotel, but also remember we’re comparing basin average with point measurement
What influence humans?Does it matter?
Blue Mesa
For each site, all measurements Jan-Jun, Jul-Dec are averaged by year. Station half-year data then converted into standardized anomaly (o-avg(o))/std(o) vs
period of record for the half year. Multiple stations are then averaged.
Spring precipitation, especially the sequencing with snowmelt is also important
Runoff
Snowmelt Rainfall
Rainfall mixed with snowmelt“normal”
April July
Spring precipitation, especially the sequencing with snowmelt is also important
Runoff
Snowmelt Rainfall
Rainfall mixed with snowmelt“normal”
Rainfall boosting snowmeltLarger volumes
Snowmelt and rainfall separateNot enough “momentum” to produce big volumes
All these interactions are tough to “cartoonize”;Simulation models can handle this… but still it’s tough to predict beyond 1-2 weeks.
April July
Spring precipitation, especially the sequencing with snowmelt is also important
April July
Runoff
Snowmelt Rainfall
Rainfall mixed with snowmelt“normal”
Rainfall boosting snowmeltLarger volumes
Snowmelt and rainfall separateNot enough “momentum” to produce big volumes
Even then, however,high heat and no rain
can lead to “pouring sunshine”
All these complex interactions are tough to “cartoonize”;Simulation models can handle this… but still it’s tough to predict beyond 1-2 weeks.
Challenges and frontiers
Seasonality/lag of drought in snowmelt regionsPrecipitation and impacts can be separated by months.
Highly managed systemsHow to separate drought from poor planning or overbuilding?Also: Humans react to forecasts e.g. evacuating reservoirs
Regional/local vulnerabilityWhose drought?
Stickiness of droughtWhen is the drought over? Never… (also risk of “Drought fatigue”)
Seasonality/lag of drought in snowmelt regionsPrecipitation and impacts can be separated by months.
Highly managed systemsHow to separate drought from poor planning or overbuilding?Also: Humans react to forecasts e.g. evacuating reservoirs
Regional/local vulnerabilityWhose drought?
Stickiness of droughtWhen is the drought over? Never… (also risk of “Drought fatigue”)
Incomplete understanding of natural system (esp soil moist, sublim)Can we even close the water balance?
Institutional and infrastructure barriersLimited agency resources, increasing restrictions
Non-stationarityCould climate change be the new normal?
The future may have more and better:
Products from and understanding of soil moisture data
Automation and “smart” objectification of forecast process
Quantification and use of anecdotal evidence
Forecast transparency (i.e. access to raw guidance)
The future may have more and better:
Products from and understanding of soil moisture data
Automation and “smart” objectification of forecast process
Quantification and use of anecdotal evidence
Forecast transparency (i.e. access to raw guidance)
Communication of uncertainty, especially graphically
Understanding of local user vulnerabilities
Consolidation of data from multiple networks:universal, uniform access and multi-agency products
Understanding of the “long view”: how relevant is data from 10, 50, 100, 500 years ago?
Variable “Significance”Snow 60-90
Fall precip 5-20Winter precip 30-60Spring precip 10-25
Baseflow 5-15Soil Moisture 5-10
Temperature 10-25Wind 5-20Radiation 5-15Relative humidity 5-10
Source:1972 Engineering Handbook
Daily forecastSkill: (Correlation)2
Variance ExplainedJanuary 1
Daily forecastSkill: (Correlation)2
Variance ExplainedApril 1
NWS formats:
NWS formats: