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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

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Operational Drought Monitoring and Forecasting at the USDA-NRCS

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Operational drought monitoring and forecasting at the usda nrcs

Operational Drought Monitoring

and Forecasting at the USDA-NRCS

Tom Pagano

[email protected]

503 414 3010


Operational drought monitoring and forecasting at the usda nrcs

Monitoring networks

Data products

Seasonal forecasts

Soil moisture

Challenges

Frontiers


Operational drought monitoring and forecasting at the usda nrcs

Monitoring networks


Operational drought monitoring and forecasting at the usda nrcs

1906

2005

Manual Snow Surveys

Metal tube inserted into snow and weighed to measure water content.

+300,000 snow course measurements as of June 2008


Operational drought monitoring and forecasting at the usda nrcs

Snotel (SNOw TELemetry) network

Automated, remote stations

Primary variables:

Snow water

Precipitation

Temperature

Also:

Snow depth

Soil moisture

SNOTEL and Snow course records often spliced together


Operational drought monitoring and forecasting at the usda nrcs

Snowcourse (solid) and SNOTEL (hashed) active station installation dates

Number of sites

Active year


Operational drought monitoring and forecasting at the usda nrcs

Soil climate analysis network (SCAN)

Soil moisture/energy balance emphasis

Short period of record (some from 1990s)

Data available but few products


Operational drought monitoring and forecasting at the usda nrcs

Manual snow-course

SNOTEL

SCAN


Operational drought monitoring and forecasting at the usda nrcs

Data products


Operational drought monitoring and forecasting at the usda nrcs

Time series charts


Operational drought monitoring and forecasting at the usda nrcs

CSV flat files

Google Earth


Operational drought monitoring and forecasting at the usda nrcs

Forecast products


Operational drought monitoring and forecasting at the usda nrcs

Location

Forecasts are coordinated with the National Weather Service (NWS).

Both agencies publish identical numbers.


Operational drought monitoring and forecasting at the usda nrcs

Historical

Average

Location

Time

Period

Forecasts are coordinated with the National Weather Service (NWS).

Both agencies publish identical numbers.


Operational drought monitoring and forecasting at the usda nrcs

Historical

Average

Location

Time

Period

“The” Forecast

Water Volume

Error Bounds

Forecasts are coordinated with the National Weather Service (NWS).

Both agencies publish identical numbers.


Operational drought monitoring and forecasting at the usda nrcs

Seasonal water supply volume forecasts

(available in a variety of formats) NRCS formats:


Operational drought monitoring and forecasting at the usda nrcs

Seasonal water supply volume forecasts

(available in a variety of formats) NRCS formats:


Operational drought monitoring and forecasting at the usda nrcs

Basic Forecasting Methods

Statistical regression

S Fork Rio Grande, Colo

Apr-Jul streamflow % avg

May 1 snowpack % avg


Operational drought monitoring and forecasting at the usda nrcs

Basic Forecasting Methods

Statistical regression

Simulation modeling

S Fork Rio Grande, Colo

Snow

Rainfall

Heat

Apr-Jul streamflow % avg

Snowpack

Runoff

Soil water

May 1 snowpack % avg


Operational drought monitoring and forecasting at the usda nrcs

Principal Components Regression (Garen 1992)

Prevents compensating variables. Filters “noise”.


Operational drought monitoring and forecasting at the usda nrcs

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


Operational drought monitoring and forecasting at the usda nrcs

Daily forecast updates

Existing seasonal forecasts issues once per month

Why not develop 365 forecast equations/year

and 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


Operational drought monitoring and forecasting at the usda nrcs

Period of record range (10,30,70,90 percentile)

1971-2000 avg

Period of record median


Operational drought monitoring and forecasting at the usda nrcs

Period of record range (10,30,70,90 percentile)

1971-2000 avg

Period of record median

Official coordinated outlooks


Operational drought monitoring and forecasting at the usda nrcs

Daily Update Forecasts

Period of record range (10,30,70,90 percentile)

1971-2000 avg

Period of record median

Official coordinated outlooks


Operational drought monitoring and forecasting at the usda nrcs

Official forecasts


Operational drought monitoring and forecasting at the usda nrcs

Expected skill

Daily forecast 50% exceedence

Official forecasts


Operational drought monitoring and forecasting at the usda nrcs

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


Operational drought monitoring and forecasting at the usda nrcs

Soil moisture

and runoff efficiency


Operational drought monitoring and forecasting at the usda nrcs

Expansion of

soil moisture to SNOTEL network

(data starts ~2003)


Operational drought monitoring and forecasting at the usda nrcs

Blue Mesa Basin, Colorado Soil Moisture 2001-2008

(According to the Univ Washington Model- top 2 layers)


Operational drought monitoring and forecasting at the usda nrcs

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


Operational drought monitoring and forecasting at the usda nrcs

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.


Operational drought monitoring and forecasting at the usda nrcs

Spring precipitation, especially the sequencing with snowmelt is also important

Rainfall

Snowmelt

Rainfall mixed with snowmelt

“normal”

Runoff

April July


Operational drought monitoring and forecasting at the usda nrcs

Spring precipitation, especially the sequencing with snowmelt is also important

Rainfall

Snowmelt

Rainfall mixed with snowmelt

“normal”

Runoff

Rainfall boosting snowmelt

Larger volumes

Snowmelt and rainfall separate

Not enough “momentum”

to produce big volumes

April July

All these interactions are tough to “cartoonize”;

Simulation models can handle this… but still it’s tough to predict beyond 1-2 weeks.


Operational drought monitoring and forecasting at the usda nrcs

Spring precipitation, especially the sequencing with snowmelt is also important

Rainfall

Snowmelt

Rainfall mixed with snowmelt

“normal”

Runoff

Rainfall boosting snowmelt

Larger volumes

Snowmelt and rainfall separate

Not enough “momentum”

to produce big volumes

Even then, however,

high heat and no rain

can lead to “pouring sunshine”

April July

All these complex interactions are tough to “cartoonize”;

Simulation models can handle this… but still it’s tough to predict beyond 1-2 weeks.


Operational drought monitoring and forecasting at the usda nrcs

Challenges and frontiers


Operational drought monitoring and forecasting at the usda nrcs

Seasonality/lag of drought in snowmelt regions

Precipitation and impacts can be separated by months.

Highly managed systems

How to separate drought from poor planning or overbuilding?

Also: Humans react to forecasts e.g. evacuating reservoirs

Regional/local vulnerability

Whose drought?

Stickiness of drought

When is the drought over? Never… (also risk of “Drought fatigue”)


Operational drought monitoring and forecasting at the usda nrcs

Seasonality/lag of drought in snowmelt regions

Precipitation and impacts can be separated by months.

Highly managed systems

How to separate drought from poor planning or overbuilding?

Also: Humans react to forecasts e.g. evacuating reservoirs

Regional/local vulnerability

Whose drought?

Stickiness of drought

When 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 barriers

Limited agency resources, increasing restrictions

Non-stationarity

Could climate change be the new normal?


Operational drought monitoring and forecasting at the usda nrcs

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)


Operational drought monitoring and forecasting at the usda nrcs

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?


Operational drought monitoring and forecasting at the usda nrcs

Variable“Significance”

Snow60-90

Fall precip 5-20

Winter precip30-60

Spring precip10-25

Baseflow 5-15

Soil Moisture 5-10

Temperature10-25

Wind 5-20

Radiation 5-15

Relative humidity 5-10

Source:1972 Engineering Handbook


Operational drought monitoring and forecasting at the usda nrcs

Daily forecast

Skill: (Correlation)2

Variance Explained

January 1


Operational drought monitoring and forecasting at the usda nrcs

Daily forecast

Skill: (Correlation)2

Variance Explained

April 1


Operational drought monitoring and forecasting at the usda nrcs

NWS formats:


Operational drought monitoring and forecasting at the usda nrcs

NWS formats:


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