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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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Presentation Transcript

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



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


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


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

Number of sites

Active year


Soil climate analysis network (SCAN) installation dates

Soil moisture/energy balance emphasis

Short period of record (some from 1990s)

Data available but few products


Manual snow-course installation dates

SNOTEL

SCAN


Data products installation dates


Time series charts installation dates


CSV flat files installation dates

Google Earth


Forecast products installation dates


Location installation dates

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

Both agencies publish identical numbers.


Historical installation dates

Average

Location

Time

Period

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

Both agencies publish identical numbers.


Historical installation dates

Average

Location

Time

Period

“The” Forecast

Water Volume

Error Bounds

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

Both agencies publish identical numbers.


Seasonal water supply volume forecasts installation dates

(available in a variety of formats) NRCS formats:


Seasonal water supply volume forecasts installation dates

(available in a variety of formats) NRCS formats:


Basic Forecasting Methods installation dates

Statistical regression

S Fork Rio Grande, Colo

Apr-Jul streamflow % avg

May 1 snowpack % avg


Basic Forecasting Methods installation dates

Statistical regression

Simulation modeling

S Fork Rio Grande, Colo

Snow

Rainfall

Heat

Apr-Jul streamflow % avg

Snowpack

Runoff

Soil water

May 1 snowpack % avg


Principal Components Regression (Garen 1992) installation dates

Prevents compensating variables. Filters “noise”.


Principal Components Regression (Garen 1992) installation dates

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 installation dates

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


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

1971-2000 avg

Period of record median


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

1971-2000 avg

Period of record median

Official coordinated outlooks


Daily Update Forecasts installation dates

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

1971-2000 avg

Period of record median

Official coordinated outlooks


Official forecasts installation dates


Expected skill installation dates

Daily forecast 50% exceedence

Official forecasts


SWSI installation dates

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 installation dates

and runoff efficiency


Expansion of installation dates

soil moisture to SNOTEL network

(data starts ~2003)


Blue Mesa Basin, Colorado Soil Moisture 2001-2008 installation dates

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


Blue Mesa Basin, Colorado Soil Moisture 2001-2008 installation dates

(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? installation dates

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 installation datessequencing with snowmelt is also important

Rainfall

Snowmelt

Rainfall mixed with snowmelt

“normal”

Runoff

April July


Spring precipitation, especially the installation datessequencing 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.


Spring precipitation, especially the installation datessequencing 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.


Challenges and frontiers installation dates


Seasonality/lag of drought in snowmelt regions installation dates

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”)


Seasonality/lag of drought in snowmelt regions installation dates

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?


The future may have more and better: installation dates

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: installation dates

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 installation dates“Significance”

Snow 60-90

Fall precip 5-20

Winter precip 30-60

Spring precip 10-25

Baseflow 5-15

Soil Moisture 5-10

Temperature 10-25

Wind 5-20

Radiation 5-15

Relative humidity 5-10

Source:1972 Engineering Handbook


Daily forecast installation dates

Skill: (Correlation)2

Variance Explained

January 1


Daily forecast installation dates

Skill: (Correlation)2

Variance Explained

April 1


NWS formats: installation dates


NWS formats: installation dates


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