Hydrologic Modeling in the Far North: n eeds, challenges, and progress

Hydrologic Modeling in the Far North: needs, challenges, and progress

Hydrologic Modeling in the Far North: needs, challenges, and progress

Jessica CherryInternational Arctic Research Center (IARC) and Institute of Northern Engineering,

University of Alaska Fairbanks (UAF) & Northern Science Services

Bob Bolton, IARC and Scenarios Network for Alaska Planning (SNAP) at UAF

Katrina Bennett, IARC

Stephanie McAfee, SNAP

Support Acknowledged from the JAMSTEC-IARC Cooperative Agreement, the U.S. Department of Energy, the Alaska Climate Science

Center, and the National Science Foundation

Needs for Hydrologic Projection in AlaskaNeeds for Hydrologic Projection in Alaska

Communities: many Alaskans live in environmentally sensitive communities, with tenuous water supplies, near rivers that are subject to flooding

J. Cherry

Needs for Hydrologic Projection in AlaskaNeeds for Hydrologic Projection in Alaska

Municipal, Rural, and Resource Land Managers: many agencies are increasingly accountable for anticipating future changes to their management areas. May or may not have the staff expertise to interpret and make use of projections.

Needs for Hydrologic Projection in AlaskaNeeds for Hydrologic Projection in Alaska

Technical experts: such as engineers, scientists (including NOAA’s WFO/RFC) are potentially most aware of the model shortcomings, but are obligated to provide best available information for their end-users.

J. Cherry

Challenges for Hydrologic Projection in AlaskaChallenges for Hydrologic Projection in Alaska

• Climate Change is most amplified in the Arctic

• Climate Models do a poor job of representing many cryospheric processes such as permafrost dynamics, glacial change, river ice, changes in subsurface water storage, etc

• The historical observations in this region are particularly sparse and short

• Our technology for measuring solid precipitation works poorly and changes are difficult to detect

Challenges for Hydrologic Projection in AlaskaChallenges for Hydrologic Projection in Alaska

Roles of Empirical and Process-based Models:

•The latter may be particularly important for getting the mechanisms correct, i.e. getting projections right for the right reasons•‘Tipping points’ are significant in the Arctic•The former may provide better short-term projections, as measured by prediction of historical events, because historical record may inadequately sample key mechanisms•Doing both gives credibility to projections, esp. when true uncertainties are difficult to quantify

Progress in Hydrologic Projection in AlaskaProgress in Hydrologic Projection in Alaska

Three approaches highlighted here:

•Downscaled climate products and derived hydrologic fields

•Improvement of cryospheric components of process-based, empirical, and complex models

•Reducing uncertainty in observational record

Progress in Hydrologic Projection in Alaska: downscaled climate products and derived hydrologic fieldsProgress in Hydrologic Projection in Alaska: downscaled climate products and derived hydrologic fields

Scenarios Network for Alaska and Arctic Planning:

•Statistically downscaled projections of temperature and precipitation for 5 IPCC models that perform best in Alaska available (AR4 done, AR5 in the works), 2km, 800 m, scenarios: B1, A1B, A2, Method: Delta using PRISM •Gridded, downscaled historical data available, 2 km, 800 m •Additional model parameters are being downscaled (wind)•Parameters that are not directly (or poorly) represented in the climate models are being derived (PET, snowfall, vegetation, permafrost distributions, growing season, etc.)•Selected periods of dynamical downscaling available from WRF runs

MODEL Δ(2080-2099 minus1980-1999)

OBS PET(Hamon)

ENSEMBLE

Figure: S. McAfee

MODEL Δ(2080-2099 minus1980-1999)

OBS P-PET(Hamon)

ENSEMBLE

Figure: S. McAfee

Progress in Hydrologic Projection in Alaska:improvement of cryospheric components

Progress in Hydrologic Projection in Alaska:improvement of cryospheric components

Slide: B. Bolton

Slide: B. Bolton

Springsnow melt period

Growing season

Autumn

Timing, Magnitude and Pathways

Slide: B. Bolton & J. Cable

Slide: B. Bolton & J. Cable

NWS River Forecast Center Hydrologic Modeling Framework: Flood Early Warning System/ Community Hydrologic Prediction System

Progress in Hydrologic Projection in Alaska:Reducing uncertainty in observational record

Progress in Hydrologic Projection in Alaska:Reducing uncertainty in observational record

Slide: K. Bennett

MODIS Snow Cover ExtentApril 1st, 2011

Slide: K. Bennett

MODIS Snow Cover ExtentMay 23rd, 2011

Slide: K. Bennett

SNOW-17/ SAC-SMA - RFC

• SNOW-17, snow air temperature index model• SACramento Soil Moisture Accounting model,

medium complexity, conceptual water balance model, run in lumped mode

Anderson, 2006

Burnash et al. 1973Burnash, 1976Slide: K. Bennett

UAF-managed Total Precipitation ‘Hot Plate’ Sensor Network: alternative for environments where gauges perform poorly

= potential future hot plate installations

Bar

Atq Olik

TlkUmt

Fox

ATQASUK

BARROW Used for in situ studies and validation of models

J. Cherry

WRF model comparison

= other IARC snow research sites w/o TPS

Poker FlatKougarok

~5 m/s

~10 m/s

Images: J. Cherry, M. Itomlenskis (ARSC summer intern)

X & Y Velocity fields

MIT wind tunnel testing of ‘Hot Plate’, IR measurement, algorithm development, CFD modeling

J. Cherry

Digital Surface Models from Structure from Motion Algorithms

RECAP:Needs, Challenges, Progress in Hydrologic Projection in Alaska

RECAP:Needs, Challenges, Progress in Hydrologic Projection in Alaska

Progress highlighted here:•Downscaled climate products and derived hydrologic fields•Improvement of cryospheric components of process-based, empirical, and complex models •Reducing uncertainty in observational record

Challenges:•Climate Change is most amplified in the Arctic•Climate Models do a poor job of representing many cryospheric processes such as permafrost dynamics, glacial change, river ice, changes in subsurface water storage, etc•The historical observations in this region are particularly sparse and short•Our technology for measuring solid precipitation works poorly and changes are difficult to detect

Needs highlighted here:•Communities•Municipal, Rural, and Resource Land Managers•Technological Experts

Questions?

JCHERRY@IARC.UAF.EDU

SPRING (April-May)

SUMMER (June-July)

AUTUMN (Aug-Sept)

Priestley-Taylor ( __ ) Hamon (…)

Figure: S. McAfee