CALIFORNIA WATER RESOURCES RESEARCH AND APPLICATIONS CENTER Norman L. Miller, Principal Investigator Regional Climate Center, Earth Sciences Division Lawrence

CALIFORNIA WATER RESOURCES CALIFORNIA WATER RESOURCES RESEARCH AND APPLICATIONS CENTERRESEARCH AND APPLICATIONS CENTER

Norman L. Miller, Principal Investigator

Regional Climate Center, Earth Sciences Division

Lawrence Berkeley National Laboratory

University of California

NASA/RESAC Annual Meeting

Stennis Space Flight Center

May 11, 2000

RESEARCH AND APPLICATION TEAM

Dr. Norman Miller - Hydrometeorologist, LBNL, Regional Climate Center Group Leader

Dr. Jinwon Kim - Meteorologist, LBNL Staff Scientist, Regional Climate Center (RCC)

Dr. Phaedon Kyriakidis - Geostatistician, LBNL Postdoctoral Scholar, RCC

Dr. Nigel Quinn - Water Resources Engineer, LBNL Staff Scientist, and USBR

Prof. William Dietrich - Geomorphologist, UC-Berkeley, Geology Dept. Chairman, and RCC

Dr. Mauro Casadei - Geomorphologist, UC-Berkeley, Postdoctoral Scholar, Geology Dept.

Prof. George Brimhall - Geologist, UC-Berkeley/Space Science Center, 2 Grad. Students

Prof. James Frew - Computational Geographer, UC-Santa Barbara, Sch. Env. Sci. & Man.

Prof. John Dracup* - Civil Engineer, UC-Berkeley Civil Engineering Dept. and RCC

Prof. Xu Liang* - Macroscale Hydrologic Modeler, UC-Berkeley Civil Eng. Dept. and RCC

Senior Advisory Committee:

Dr. Sally Benson -Geohydrologist, LBNL Earth Sciences Division Director

Prof. Inez Fung - Atmospheric Scientist, UC-Berkeley, Atmos. Sciences Center Director

Prof. Jeff Dozier - Computational Geographer, UC-Santa Barbara, Sch. Env. Sci. & Man.

* new member of the Regional Climate Center (RCC)

COLLABORATING PARTNERSHIPS

NOAA California-Nevada River Forecast Center NOAA National Weather Service-Sacramento

NOAA NCEP Climate Prediction Center SIO Experimental Climate Prediction Center

California Department of Water Resources San Joaquin River Management Program

California Department of Conservation UCB Earth Resources Center

California Department of Forestry and Fire Protection UCSB Alexandria Digital Library

U.S. Geological Service U.S. Forest Service

U.S. Bureau of Reclamation NOAA International Research Institute

Korean Meteorological Administration Changwon National University, South Korea

Queensland Department of Natural Resources University of Queensland, Australia

Chinese Ministry of Water Resources Arkwright Insurance Company

Application Approach and Significant Results

• Dynamic climate and stream flow modeling and analysis

• Statistical climate modeling of precipitation and stream flow uncertainty

• Landslide and sediment transport measurements and modeling

• Water quality monitoring and modeling

• Identification of mine contaminants

• Satellite data applications - current and future

• Impact assessment reports and publications, workshops, and lessons learned

PreprocessorsPreprocessors Process ModelsProcess Models PostprocessorsPostprocessors

Atmospheric Inputs:

GCM, Reanalysis,Synoptic Models

Surface Inputs:

Land Analysis Sys.Reanalysis, ClimateWatershed Info.,River networks

Remotely SensedData

Satellite,Radar,AWS,Gauges

Mesoscale AtmosphericSimulation (MAS) Model

Wind, Temp, Humidity, Prcptn,Radiation

Soil-Plant-Snow (SPS) Model

Soil/SFC energy/water budgetSnow budget

Hydrologic Model

Runoff, Riverflow

Crop Response, Agro-economicsForest ecosystems

Sediment Transport, Deep GNDWater, Water Quality

Predictions andClimate ImpactsAssessments

Weather (QPF),

Soil Water Content,

River Flow,

Watershed-Scale Hydrology,

Climate Trend andits Variability,

Water Resources,

Crop Responses,

Ecological Impacts,

EnvironmentalImpacts, ETC.

THE REGIONAL CLIMATE SYSTEM MODEL

Western US Domains at 36km and 12km Resolutions

Downscaled Information for Water Resource ManagementGlobal-Scale Data[O(100km)]Regional-Scale Data[O(10km)]PRCPKRCSMStreamflow Input to reservoirSnowmelt into streamflowSubsurface flowinto streamflowFlow into deep groundSnowfallFreezing levelRainfallPRCPLarge-ScaleReservoir OperationPlanning (2-3 days in advance).

Debris flow initiation and runout

Rainfall meterologival downscaling at catchment scale

Site characterization,Remote sensing,DEM analysis

Hillslope hydrological response modeling

Short-Term and Season Prediction Results

• During the California wet season (November 1999 to April 2000) we have posted daily forecast products on our web http://esd.lbl.gov/RCC

• The National Weather Service is a serious user, the CA Dept. of Water Resources and others are being entrained as serious users.

• We completed a November 1999 to April 2000 seasonal forecast inn collaboration with NOAA and UCLA.

• Twelve California River Basins are coupled to the RCSM now.

• Analysis of this year’s seasonal simulation is in progress.

OBSERVED 30-YEAR PRECIPITATION CLIMATOLOGY

SIMULATED 8-YEAR PRECIPITATION CLIMATOLOGY

0

5

10

Coop

MAS36

AZ: 88-95

Jan88 Dec 89 Dec 91 Dec 93 Dec95

0

5

10

15

20

Coop

MAS36

OR: 88-95


5

10

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20

Observation

Simulation

CA: 88-95


0

5

10

Coop

MAS36

NM: 88-95


Fig. 2 Observed and MAS-simulated monthly precipitation in California, Oregon, Arizona, and New Mexico for 1988-1995.

Mean-Monthly Observed and Simulated PrecipitationCalifornia, Oregon, Arizona, Mexico (1988-1995)

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Hindcast Evaluation at North Fork Dam, American River

RCSM -> PRMS

Qobs

Qsim

Psim

Pobs

Time (mean-month)

r

p

= 0.86r

q

= 0.73

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100

150

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250

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350

400 0

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Hindcast Evaluation at Markleeville, Carson River

RCSM -> PRMS

Qobs

Q(Psim)sim

Pobs

Psim

Time (mean-month)

r

q

= 0.67 r

p

= 0.82

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350

400 0

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100

Hindcast Evaluation at Happy Isles, Merced River

RCSM -> PRMS

Qobs

Q(Psim)sim

Pobs

Psim

Time (mean-month)

r

q

= 0.61 r

p

= 0.86

0

100

200

300

400

500

600

700

800 0

10

20

30

40

50

60

Hindcast Evaluation at Hopland, Russian River

RCSM -> TOPMODEL

Qobs

Q(Psim)sim

Psim

Pobs

Time (mean-month)

r

q

= 0.87 r

p

= 0.89

Multi-Year Hindcast at Basin Scale

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30

0 5 10 15 20 25 30


Psim

Observed Precipitation (mm/month)

rp = 0.86

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Psim


rp = 0.86

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Hindcast Evaluation at Markleville, Carson River

Psim


rp = 0.82

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Psim


rp = 0.89

Precipitation Evaluation

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Q(Pobs)simQ(Psim)sim

Simulated Streamflow (CMS)

Observed Streamflow (CMS)

r qs,s

= 0.73

rqo,s

= 0.83

0

50

100

150

0 50 100 150





rqo

= 0.92

rqs

= 0.61

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1000

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rqso

= 0.86

rqss

= 0.87

0

50

100

150

200

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Hindcast Evaluation at Markleville, Carson River


Simulated Precipitation (mm/month)


rq = 0.97

rq = 0.67

Streamflow Evaluation

Mean-Monthly Percent Streamflow Occurrence

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20 30 40 50 60 70 80 90 100 110 120 130 140 150

American

Simulated StreamflowObserved Streamflow

Occ

urr

ence

CMS

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0.05

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Carson


Occ

urr

ence

CMS

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20 30 40 50 60 70 80 90 100 110 120 130 140 150

Russian


Occ

urr

ence

CMS

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Merced

Simulated StreamflowObserbed Streamflow

Occ

urr

ence

CMS

Multi-Year Hindcast Results

• Eight-year hindcast captures major precipitation features well.

• Simulated basin-average precipitation correlates with observation better than 80% in four CA basins.

• Hydrologic models show good verification in the four CA basins.

– American rqo = .83; Carson rqo = .97; Merced rqo = .92; Russian rqo = .86

• Four CA basin coupled streamflow simulation generally overpredicted.

– American rqs = .73; Carson rqs = .67; Merced rqs = .61; Russian rqs = .87

• Need to better understand upscaling of point (rain gauge) precipitation and downscaling of gridded precipitation.

• Data availability and quality control need improvement (e.g. dam release).

Water Vapor Climate Change Difference: 2xCO2-Control

CALIFORNIA CLIMATE SENSITIVITY STUDYHadCM2 -> RCSM

HISTORICAL CONTROL CLIMATE 2xCO2 - CONTROL CLIMATE

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Russian River

Q2CO2

Qcontrol

Pcontrol

P2co2

Month

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100

200

300

400

500 0

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Russian River

Q2CO2

Qcontrol

Pcontrol

P2co2

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American River

Q2co2

Qctl

Pcontrol

P2co2

Month

CALIFORNIA CLIMATE SENSITIVITY STUDYBasin Scale Precipitation and Streamflow Response

Climate Change Sensitivity Study Results• American River stream flow may double in volume from January to May due to

temperature increase effect on snowmelt.

• American River may decrease by half during May to July due to decreased snowpack in storage during the winter.

• American River may triple in volume August to November due to the very high water vapor in the HadCM2 2xCO2 scenario.

• Russian River may increase in volume from December to February, and decrease in volume from February to May.

• Peak stream flow shifts from May-June to February-March for the American, and from March to February for the Russian.

• Present 2xCO2 climate projections need further improvements, we can discuss likelihood but not certainties.

Stochastic Downscaling Approach

• Establish parametric time series models of environmental variables from observed records at monitoring station locations.

• Distribute model parameters in space, accounting for correlation with satellite products, e.g. digital elevation models or land-cover types.

• Generate stochastic simulations of alternative parameter realizations (maps), which exactly reproduce the observed model parameters at station locations, and measures of scale-dependent (cross)correlation with satellite products.

• Output is a set of time series models at any location, used for generating alternative simulated records of environmental variables, e.g. precipitation or temperature.

• The set of alternative realizations is used for propagating uncertainty into impact assessment studies, e.g. stream-flow modeling.

Monthly Characteristics of Simulated Precipitation

Box plot indicates range of variability, dot=observed, whiskers=50&95%Prob.Interval

Number of Dry Days/Month Number of Dry Days/Month

Number of Wet Days/Month Number of Wet Days/Month

Uncertainty Bounds for Stream flow due to Stochastic Precipitation Input

Stochastic Downscaling Results

• A stochastic method for simulating precipitation has been completed.

• Statistical analysis has produced new spatial information on the mean proportion of wet days per month.

• Uncertainty propagation of climate model forecasts to hydrological forecasts is completed.

• Stochastically generated 95% probability of stream flow volume has been generated.

• A spatial-temporal set of predictors for fine-scale impacts (e.g. landslides) has been generated and will be further refined with our mesoscale model output.

Predicting Shallow Landslides and Debris FlowsApproach

• Shallow landslides and debris flows are an increasing hazard due in part to development in hilly unstable terrain.

• Timber harvesting and poor road construction is a major cause of river sedimentation and declining salmon runs in the west.

• We have developed a dynamic model and performed several tests using rainfall time series in California watersheds.

• Estimations of the consequent path, potential size, and final depositional area of debris flows applied to the SHALSTAB model (steady state model).

• The steady state model is being used by USGS, Bureau of Land Management, CA Dept. of Mines, CA Dept. of Forestry and Fire Protection.

Landslide and Debris Flow ModelingResults

• Statistical-dynamical downscaled precipitation is being generated.

• Spatio-temporal distribution maps of the relative potential for debris flow initiation due to precipitation are being generated.

• Landslide Factor of Safety (FS) threshold values for the SF Bay area is being generated.

• Dynamic and physically-based hillslope hydrology model has been developed to capture subsurface flow distributions, including fractured bedrock transmissivity.

• Channel particle size distribution mapping begun, initial stochastic model formulated. Statistical-dynamical sediment transport model will be developed.

WATER QUALITY MODELING AND MONITORING PROGRESS

• REAL-TIME SALINITY FORECASTING IN TSAN JOAQUIN RIVER

• Development of a decision support system to communicate flow and salinity conditions in the San Joaquin River and to determine source of oxygen depletion in Stockton Ship Channel.

• Formation of an interagency team (LBNL, USBR, DWR, CRWQCB) to continue development of river water quality monitoring network.

• Cooperative work with Panoche-Silver Creek Resource Conservation District to develop early warning flood forecasting system has started.

• Develop enhanced precipitation monitoring system in the upper watershed to enhance water quality forecasting and management in the San Joaquin River.

• Cooperation with Central Valley Regional Water Quality Control Board to develop dynamic TMDL’s sensitive to future climate change.

Weekly Forecasts of Flow and Water Quality Conditions are on our Website

Mine Site Runoff Identification

Approach • Identify minerals that cause acidification to aquatic ecosystems.

• Develop portable UV, Visible, and IR spectrometers that identify hazardous runoff and also non-hazardous runoff.

• Develop software to automatically reduce IR spectra for digitally mapping mine dumps.

• Verification of spectrometers in the field.

• Laboratory analysis of field samples.

Mine Site Runoff IdentificationResults

• Developed the portable UV/VIS/IR spectrometer’s capability to identify weathered minerals that impact water quality, field testing at the Cerro Gordo abandoned mine site, Inyo Mountains, CA.

• Acquired JPL flight time of AVIRIS over Cerro Gordo mine and processed the resulting images.

• Irene Sanchez Montero completed a Master Science, Geological and Environmental Studies, UC-Berkeley, 1999. Characterization of abandoned mine waste piles at the Penn Mine, Calavaras County, CA using UV/VIS/IR spectrometers.

• Developing the automated mineral identification routine for reflectance spectra in the UV/VIS/IR; spectra libraries acquired, utilizing AVIRIS at Penn Mine Site.

• Lab development of the real-time computer program to automatically reduce the IR spectra for reconnaissance digital mapping of abandoned mine dumps.

REMOTELY SENSED DATA APPLICATIONS

• AVHRR and SAR: Snow Cover Area (SCA) is beginning to be evaluated with model snow depth. Daily SCA maps will be produced in collaboration with the Arizona RESAC, USCOE, and UCSB/ESSW. Snow water equivalent (SWE) will also be generated.

• AVHRR: monthly Leaf Area Index and Green Leaf Fraction is used in Land-Surface model.

• Digital Terrain Elevation Data is being used to compute hydrologic model parameters.

• High resolution altimetry data is being used, and fine-scale 1-4m DEM via IKONOS will be used for landslide model testing.

• Planning to utilize data buyback and data from future missions

– Advanced Microwave Scanning Radiometer (2000 launch) SWE

– Shuttle Radar Topographic Mission fine-scale topographic data

– MODIS snow cover area

– Vegetation Canopy Lidar (2000 launch) 2 year vegetation mapping

– Apply satellite products to statistical downscaling applications

Impact Assessments, Reports, Workshops, Outreach

• The IPCC 2000 Scientific Assessment - N. Miller and J. Kim ( Contributing Authors to the IPCC Third Assessment Report, Chapter 10. Regional Climate Simulation – Evaluation and Projections)

• U.S. National Assessment 2000 - N. Miller (Contributing Author on Mega-West Report, Coastal Report). Jan. 2000; U.S. National Assessment 2000 Water Sector - N. Miller, J. Kim, R. Hartman (Authors to manuscript JAWRA Special Issue on Climate Change and Water Resources). Dec. 1999

• Confronting Climate Change in California: Ecological Impacts on the Golden State, (C. Field, F. Davis, C.

Gaines, P. Matson, J, Melack, and N. Miller), sponsored by the Ecological Society of America and the Union of Concerned Scientists. Nov. 1999.

• California Climate, Impacts, and Information Workshop - Lawrence Berkeley National Laboratory, Oct. 4, 1999, Report is on our web under OUTREACH. (N. Miller host)

• California Climate Change Panel - D. Cayan, M. Dettinger, W. Gutowski, R. Howitt, J. Lund, N. Miller, T. Wigley

• Meetings with the CA Energy and Agriculture Commissioners, several public forums.

New Funding Leveraged Partially from the Berkeley RESAC

• NASA/SENH- CASSANDRA: A storm based model for forecasting the initiation and runout of debris flows. W. Dietrich (UC-Berkeley), A. Howard (UV-Charlottesville), N. Miller (LBNL), J. Kim (LBNL), M. Casadei (UC-Berkeley)

• NASA/IDS - Applications of Remotely-Sensed Data for Seasonal and Long-Term Hydroclimate Predictions. J. Kim (LBNL), N. Miller (LBNL), R. Bales (UA), L. Mearns (NCAR).

• EPA/STAR - Vulnerability assessment of San Joaquin Basin water supply, ecological resources, and rural economy due to climate variability and extreme weather events. J. Dracup (UCLA), N. Miller (LBNL), N. Quinn (LBNL), R. Howitt (UC-Davis), L. Grober (Central Valley Regional Water Quality Control Board)

• CALFED - Real-Time forecasting of contaminant loading from the Panoche/Silver Creek watershed to the San Joaquin River. N. Quinn (LBNL), N. Miller (LBNL), M. Martin (Westside Resources conservation District), N. Drake (Coordinated Resource Management Program), F. Charles (McGulley, Frick, and Gillman, Inc.), C. Eacock (USBR)

PROGRESS PROJECTION: YEAR ONE

√ Task 1. Establish western U.S. domain at 36 km resolution, begin western U.S. baseline simulation using NCEP reanalysis as hindcast, and prepare an expanded database.

√ Task 2. Couple CNRFC basins and parameters to the existing land-surface module of the RCSM.

√ Task 3. Begin 2xCO2 climate sensitivity data study using the Hadley Centre’s IPCC scenario.

√ Task 4. Begin landslide and sediment transport model development.√ Task 5. Begin environmental site inventory of abandoned mines in the Sierra

Foothills.√ Task 6. Advance water quality monitoring and build Decision Support System√ Task 7. Begin to link RCSM output data to NASA, USBR, DWR, NWS, NCEP,

ESSW user interfaces.√ Task 8. Significant Results Conference: October 4, 1999

PROGRESS PROJECTION: YEAR TWO

• Task 1. Continue with tasks identified in Year 1.

• Task 2. Complete web-based user information system, expand and improve based on user feedback.

√ Task 3. Evaluate western U.S. baseline simulation using NCEP reanalysis as hindcast.

• Task 4. Evaluate downscaled 2xCO2 climate sensitivity study, prepare a second climate change study coordinated with the State.

• Task 5. The landslide model will be tested as a hindcast using observed precipitation.

• Task 6. Abandoned mine site inventory in the Mojave desert will begin.

• Task 7. Significant Results Conference, expand user and stakeholders group.

• Task 8. NASA RESAC Progress Report.

PROGRESS PROJECTION: YEAR THREE

• Task 1. Continue with tasks from Year 1 and Year 2.

• Task 2. Significant Results and Stakeholder Workshop, assess effectiveness.

• Task 3. Reports on advances to users and stakeholders .

• Task 4. NASA RESAC Report.

• Task 5. Generate long-term sustainable resources for continuing activities.

LESSONS LEARNED

• Completion of tasks, peer reviewed publications, and scientific presentations are only part of the definition of success.

• Outreach to both scientific users, the general public, and Congress, as well as delivery of NASA value-added products (e.g. climate simulations) may be the definition of a successful Regional Earth Science Applications Center.

• Participation in the USGCRP and regional climate change research programs are very important to the RESAC program.

• Computational capability and data storage is a growing limitation, additional hardware will always be needed.

• Long-Term funding is a necessity for gaining public support as a center.

Documents

CALIFORNIA WATER RESOURCES RESEARCH AND APPLICATIONS CENTER Norman L. Miller, Principal Investigator Regional Climate Center, Earth Sciences Division Lawrence