Seasonal Precipitation and Snowpack Forecasting Tool for ... · at 4 snow stations in Owens Valley April 1, Images: NASA Earth 2015 Observatory April 1, 2011 Mammoth PassRock Creek

Seasonal Precipitation and Snowpack Forecasting Tool

for California Reservoirs

Presentation for Floodplain Management Association

September 7, 2018

Mariza Costa-Cabral (NHC)John S. Rath (TetraTech)

Patty Dillon (NHC)

[email protected]

water resource specialists


Forecasting Model

April 1snowpack

Feb. & Marchtotal precip

Feb. 1forecast


Forecasting Model

Feb. 1forecast

April 1snowpack

Feb. & Marchtotal precip

2-month leadStorage forecast

and decisions

Acknowledgements:This work was funded by

1. NASA Ames Research Center(scientific basis)

2. Los Angeles Department of Water and Power(model development, and application

to Owens Valley snow stations)


Presentation Outline

1. Scientific basis of our model

2. Results of model application to Owens Valley

3. Potential for application across California

4. Conclusions


Part 1

Scientific Basis

Relationship between California winter precipitation

and the strength of the North Pacific High in January


Costa-Cabral et al (2016) in J.Climate, doi:10.1175/JCLI-D-15-0525


Summer Winter

Figure from book “McKnight’s Physical Geography” by D. Hess (12th edition)

The North Pacific High


The North Pacific High

The two branches of the Hadley-Walker circulation in the Pacific region.The North Pacific High brings storm tracks from the topical Pacific towards North America.

Figure from Schwing et al. (2002) in Prog. Oceanogr.


January sea level pressure patterns in wet and dry years

January mean sea level pressure in a typical wet year

January mean sea level pressure in a typical dry year

mb mb


Sea level pressure anomaliescomposite maps

Sea level pressure anomalies composite of the 11 wettest years

Sea level pressure anomalies composite of the 11 driest years

mb


Correlation between climate indicesand December-March precipitation

Linear correlation

North Pacific High Southern Oscillation Index

Precipitation in the US Southwest is strongly correlated with the North Pacific High, but not with the Southern

Oscillation Index or other ENSO indices.(Data for 1948-2014.)


Cointegrated relationship

NPHMoffett Field Precipitation

Figure from Costa-Cabral et al (2016) in J. Climate, doi:10.1175/JCLI-D-15-0525

NPH data source: NCEP/NCAR Reanalysis


Cointegrated relationship

Figure from Costa-Cabral et al (2016) in J. Climate, doi:10.1175/JCLI-D-15-0525

NPHCalifornia Precipitation

Data source for both variables: NCEP/NCAR Reanalysis


5-month lead prediction of Palmer Drought Severity Index

Figure from Costa-Cabral et al (2016)

in J. Climate, doi:10.1175/JCLI-D-15-0525

May-September(NPH leads by 5

months)

Figure: Linear correlation

between NPH anomaly and the

PDSIfor 1949-2012.

Part 2

Snowpack forecasting modelfor the Eastern Sierra


Rath and Costa-Cabral (2018) in Frontiers in Earth Science


Sierra Nevada, California

Image: NASA Earth Observatory, April 2, 2016


Sierra Nevada, California

Image: NASA Earth Observatory, April 2, 2016

Mammoth PassRock Creek #2SawmillCottonwood


Mono-Owens watershedand L.A. Aqueduct


Goal: On February 1, Forecast April 1 snowpack

at 4 snow stations in Owens Valley

April 1, 2015Images: NASA Earth

Observatory

April 1, 2011

Mammoth PassRock Creek #2Sawmi

ll

Cottonwood

Mammoth PassRock Creek #2Sawmi

ll

Cottonwood


Forecasting April 1 snowpack based on February 1 snowpack alone

MAMMOTH PASS ROCK CREEK #2

SAWMILL

R2 = 0.63 R2 = 0.59

R2 = 0.67COTTONWOOD #1

R2 = 0.56

Hindcasts for

1948-2016


Our model uses the cointegrated relationship with the NPH

MAMMOTH PASS

Figure from Rath and Costa-Cabral (2018) in Frontiers in Earth Sciences,

doi:10.3389/feart.2008.00054


Adding NPH (VECM Model)…MAMMOTH PASS ROCK CREEK #2

SAWMILL

R2 = 0.76 R2 = 0.74

R2 = 0.79COTTONWOOD #1

R2 = 0.72

Hindcasts for

1951-2016(gray)and

Forecasts for 2017-

2018(red)

Higher R2


Results for Mammoth Pass (1951-2018)

No bias (slope of the regression

line is 1)




line is 1)

76% of years forecast in the correct category




line is 1)

76% of years forecast in the correct category

No forecasts in the

opposite category


Results for LADWP Stations(1951-2018)

MAMMOTH PASS ROCK CREEK #2

SAWMILL COTTONWOOD #1


Probabilistic Forecastand Categorical Model

Example year 2010


Conclusions of LADWP Study

• VECM model is a successful forecasting aid for Owens Valley

• Provides LADWP system operators information for water management decisions

• Economically important benefits given the exceptional year-to-year variability of precipitation

Part 3

Potential application to other California watersheds



MSC – Mount Shasta CitySHA – Shasta DamMNR – MineralQRD – QuincyBCM – Brush CreekSRR – Sierraville RSBYM – Blue CanyonPCF – Pacific House

Evidence of Potential forNorthern Sierra

Cumulative std.

anomaly

Cointegration of NPH and 8-station prcp index


Evidence of Potential for Central Sierra

CVT – Calaveras Big TreesHTH – Hetch HetchyYSV – Yosemite HdqrtNFR – North Fork RSHNT – Huntington Lake


Cumulative std.

anomaly


Evidence of Potential for Southern Sierra

BAL – Balch PHGNF – Giant Forest

ASM – Ash Mtn.SGV – SpringvillePSC – Pascoes

ISB – Isabella Dam


Cumulative std.

anomaly


Potential Applications

Reservoir Management

• Management of flood control storage through late winter

• Estimation of water availability for public supply and groundwater recharge

• Estimation of reservoir storage for hydropower generation

Main California reservoirssource: Wikipedia



Environmental• Plan for environmental

discharge releases (based on water year type)

• Manage instream flow and water temperature requirements

• Assess available flows for sensitive species, e.g. salmonid spawning and outmigration

Photo: Phil Ready



• Fire danger outlook• Seasonal drought

potential• Water rights

curtailment

Source: NOAA Climate Prediction C t


Summary

• NHC’s VECM model exploits relationship between NPH and California precipitation to provide seasonal forecasting skill for late winter/early spring.– Preliminary evidence suggests that this type of model would apply

in watersheds across California.• Potential applications to support planning and decision

making related to water supply, hydropower, fisheries, fire management, etc.

• Future possibilities to extend forecast lead times and link VECM with climate and/or hydrologic models.

37

Thank You!

[email protected]

pdillon@[email protected]: Owens

Valley (Mariza)

Backup slides follow

38


Linear correlation between precipitation and ENSO climatic indices

SOI Niño 3.4 MEI


North Pacific High and Southern Oscillation


MSC – Mount Shasta CitySHA – Shasta DamMNR – MineralQRD – QuincyBCM – Brush CreekSRR – Sierraville RSBYM – Blue CanoyonPCF – Pacific House

Strong correlation between NPH and 8-Station Index:

Evidence of Potential forNorthern Sierra

NPH

prcp


Evidence of Potential for Central Sierra


CVT – Calaveras Big TreesHTH – Hetch HetchyYSV – Yosemite HdqrtNFR – North Fork RSHNT – Huntington Lake

NPH

prcp


Evidence of Potential for Southern Sierra


BAL – Balch PHGNF – Giant Forest

ASM – Ash Mtn.SGV – SpringvillePSC – Pascoes

ISB – Isabella Dam

NPH

prcp


May – September(NPH leads by 5 months)

Figure: Linear correlation of the NPH anomaly with the Palmer

Drought Severity Index

(PDSI)

Drought Index Correlation


Linear Correlation

Annual time series Cumulative time series


VECM Model Equations

𝑦 𝑎 𝐴 𝑦 𝐴 𝑦 ⋯ 𝐴 𝑦 𝜀 Equation 1

∆𝑦 𝑦 𝑦 Equation 2

Documents

Seasonal Precipitation and Snowpack Forecasting Tool for ... · at 4 snow stations in Owens Valley April 1, Images: NASA Earth 2015 Observatory April 1, 2011 Mammoth PassRock Creek