operational drought monitoring and forecasting at the usda-nrcs

Operational Drought Monitoring and Forecasting at the USDA-NRCS

Tom Pagano Tom.Pagano@por.usda.gov

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: