Lake Tahoe Watershed Model. Lessons Learned through the Model Development Process

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1 Lake Tahoe Watershed Model Lessons Learned through the Model Development Process

2 Presentation Outline Discussion of Project Objectives Model Configuration/Special Considerations Data and Research Integration Methodology Hydrology Results (Ward Creek example) Assessment of Model Results Summary of Lessons Learned

3 Overall Project Objectives 1. To determine basin-wide estimates for watershed loading of sediment and nutrients to Lake Tahoe based on land use type 2. To provide input to the Lake Clarity TMDL Model 3. To create a platform for load allocation 4. To project load reductions from BMPs and other management scenarios.

4 Climate Data Total Upland Flow & Load Subwatershed Boundaries and Stream Network Stream Processes Landuse Distribution Land Processes

5 Model Development Sequence Snow Calibration: Hydrology: Water Quality: Lake Inputs: Test the model at various locations and elevations: volume/timing of snow accumulation and melting. Identify influential land use features and factors. Route flow to streams. Check base flow, runoff, seasonal. Disaggregate runoff from baseflow-associated loads. Distribute runoff loads to land use by assigning EMCs. 1. Streamflow / IVZ Runoff 2. Fine Sediment 3. Phosphorus (associated) 4. Nitrogen species Snow Telemetry Data LTIMP/ USGS Flow Gages LTIMP, Storm Data, Literature LTIMP Monitoring Gages 5

6 N Subwatershed Delineation Lake Tahoe Basin was subdivided into 184 discrete subwatersheds Delineation based on: elevation (topographic data) stream connectivity location of flow and water quality monitoring stations Each subwatershed is modeled with one representative stream Stream segments distinguished from intervening zones Lake Tahoe Streams Subwatersheds Kilometers

7 Individual layers used to create TMDL land use layer 1. TRPA land parcels 2. Ski run delineations 3. Delineated campgrounds 4. IKONOS hard-cover impervious grid 5. Roads and Trails Forest Service roads and trails CTC roads and trails Nevada State Parks roads and trails 6. LTBMU Harvested Forest Boundaries 7. LTBMU & CTC forest fire boundaries (natural and prescribed)

8 Parcel Boundary vs. Actual Land Use Original Campground Parcel Boundary Revised Campground Land use

9 Parcel Boundary vs. Actual Land Use Before After Parcel Right-of-way Hard-Cover Impervious

10

11

12 Weather Data Requirements Land Temp Snow * Water Sediment Reaches Water Heat Gen. Qual. Required Optional Precipitation Pot. ET Air Temperature Wind Speed Solar Radiation Dewpoint Temp. Cloud Cover * LSPC uses an Energy Balance Snow Method

13 Snow Simulation Schematic Evaporation from Snow Pack Precipitation Snow or Rain Atmospheric Conditions Total Snow Pack Snow Ice Liquid Ground

14 SNOTEL Temperature Data QA/QC Number of Impaired Hours per Day Original Hourly SNOTEL Temperature (Fallen Leaf) Corrected Temperature /1/1995 7/1/1995 1/1/1996 7/1/1996 1/1/1997 7/1/1997 1/1/1998 7/1/1998 1/1/1999 7/1/1999 1/1/2000 7/1/2000 1/1/2001 7/1/2001 1/1/2002 7/1/2002 1/1/2003 7/1/2003 1/1/2004 7/1/2004 1/1/2005 Average Temperature (Deg F) Data Quality (Impaired Hours) Developed QA/QC protocol for estimating missing/impaired data and disaggregating NRCS min/max temperature data to hourly

15 Site-Specific Temperature Lapse Rate Observed lapse rate = Deg F/foot (40-60% lower than default HSPF lapse rates)

16 Potential Evapotranspiration Methods Ward Creek SNOTEL (WRDC1) Computed using default coefficients for each method

17 Potential Evapotranspiration Methods Milwaukee Mitchell Field Airport Computed using default coefficients for each method

18 Modeled vs. Historical Reference ET Data: Model: in/year in/year

19 Evapotranspiration in Tahoe Basin Relatively low dewpoints & low humidity leads to a higher temperature/dewpoint gradient and therefore higher evaporation potential Wind and Solar Radiation are also influential Penman considers their added influence on PEVT In other regions of the country, the three methods may yield more similar PEVT predictions The Tahoe basin s weather patterns are distinct characteristics of the watershed that when factored together, tend to predict higher potential evapotranspiration

20 Ward Creek Subwatershed Focus Area for Results Snow at Elev ft (SWS 8060) Flow Calibration at USGS

21 Model Testing, Calibration, Validation Hydrology Snow simulation Land processes and parameters Stream hydraulics Sediment Erosion & sediment yield Instream settling and resuspension Water Quality Total and dissolved nutrients Sediment associated nutrients

22 10 Snow Simulation ( ) Rainfall volume (in) LSPC snowfall volume (in) LSPC air temperature (Deg F) LSPC snowfall temp (Deg F) SNOTEL temperature (Deg F) 80 Volume (in) O-96 A-97 O-97 A-98 O-98 A-99 O-99 A-00 O-00 A-01 O-01 A-02 O-02 A-03 O-03 A-04 O Temperature (Deg F) Volume (in) LSPC snow water equivalent (in) LSPC snowfall volume (in) Snowmelt volume(in) LSPC snowpack water yield (in) Total land outflow - PERO (in) SNOTEL snow water equivalent (in) O-96 A-97 O-97 A-98 O-98 A-99 O-99 A-00 O-00 A-01 O-01 A-02 O-02 A-03 O-03 A-04 O-04 Snow water equivalent (in)

23 Flow (cfs) Total Daily Rainfall (in) Avg January Observed 1997 Flow (10/1/ year to 9/30/2000 storm ) Avg Modeled Flow (Same Period) Oct-96 Jan-97 Apr-97 Jul-97 Ward Creek Hydrology Calibration (10/1/1996 9/30/2000) Date Flow (cfs) Total Daily Rainfall (in) Avg Observed Flow (10/1/1996 to 9/30/2000 ) Avg Modeled Flow (Same Period) Oct-96 Oct-97 Oct-98 Oct-99 Ward Creek Seasonality Validation (10/1/ /31/2004) Date Daily Rainfall (in) 150 Avg Monthly Rainfall (in) Avg Observed Flow (10/1/1996 to 12/31/2004) Avg Modeled Flow (Same Period) O N D J F M A M J J A S 0 2 Average Monthly Rainfall (in) Observed (25th, 75th) Median Observed Flow (10/1/1996 to 12/31/2004) Modeled (Median, 25th, 75th) 150 O N D J F M A M J J A S 0 2 Flow (cfs) Flow (cfs) Monthly Rainfall (in Monthly Rainfall (in Month Month 16

24 Hydrograph Separation ( )

25 Ward Creek Validation Metrics LSPC Simulated Flow REACH OUTFLOW FROM SUBBASIN 8060 Observed Flow Gage USGS WARD C AT HWY 89 NR TAHOE PINES CA 8.25-Year Analysis Period: 10/1/ /31/2004 Placer County, California Flow volumes are normalized, with total observed as 100 Hydrologic Unit Code Latitude 39 07'56", Longitude '24" NAD27 Drainage area 9.70 square miles Total Simulated In-stream Flow: Total Observed In-stream Flow: Total of simulated highest 10% flows: Total of Observed highest 10% flows: Total of Simulated lowest 50% flows: 4.54 Total of Observed Lowest 50% flows: 4.21 Simulated Summer Flow Volume (months 7-9): 8.49 Observed Summer Flow Volume (7-9): 6.02 Simulated Fall Flow Volume (months 10-12): 5.70 Observed Fall Flow Volume (10-12): 5.59 Simulated Winter Flow Volume (months 1-3): Observed Winter Flow Volume (1-3): Simulated Spring Flow Volume (months 4-6): Observed Spring Flow Volume (4-6): Total Simulated Storm Volume: 7.03 Total Observed Storm Volume: 8.29 Simulated Summer Storm Volume (7-9): 0.54 Observed Summer Storm Volume (7-9): 0.40 Errors (Simulated-Observed) Error Statistics Recommended Criteria Error in total volume: Error in 50% lowest flows: Error in 10% highest flows: Seasonal volume error - Summer: Seasonal volume error - Fall: Seasonal volume error - Winter: Seasonal volume error - Spring: Error in storm volumes: Error in summer storm volumes:

26 Summary of Lessons Learned Customization required when applied to complex, mountainous landscapes with high meteorological variability over relatively small space Subwatershed delineation needs to reflect gradual changes in elevation for good snow prediction Detailed land use and connected imperviousness representation benefits source representation and runoff peak predictability Team building and local researcher and stakeholder involvement improves model quality, builds understanding, and promotes wider acceptance of a proposed modeling approach

27 Summary of Lessons Learned, cont. Elevation change has significant impact on Tahoe weather conditions Precipitation and temperature data quality determines snow simulation quality Modeled temperature lapse rate should be adjusted based on local data to the extent possible Modeled evapotranspiration should always be validated against locally available information Relative humidity, wind movement data should be included when estimating evapotranspiration in relatively arid settings with high orographic influence Good snow and hydrology representation sets the stage for water quality modeling.

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