SNAMP water research SNAMP water team UC Merced Topics covered Objectives, goals & overview What & why the water component of SNAMP Pre-treatment Observations Water Quality Water Quantity Modeling & Scenarios: : post-treatment predictions RHESSys Calibration Treatment Scenarios Following lunch SWEEP Water Research Proposed treatments Potential effect on snowpack/water yield 1
Topics covered Objectives, goals & overview What & why the water component of SNAMP Pre-treatment Observations Water Quality Water Quantity Modeling & Scenarios: : post-treatment predictions RHESSys Calibration Treatment Scenarios Following lunch SWEEP Water Research Proposed treatments Potential effect on snowpack/water yield Adaptive Management Framework USFS: Change management direction as needed USFS: Plan projects with existing management direction (ROD) Analyze & model expected environmental affects Propose adjustments to management? Adaptive Management USFS: Implement projects as treatments Analyze & recalibrate models Observe & measure Adaptive management must be a participatory process that engages scientists, stakeholders, and managers in a long-term relationship grounded in shared learning about the ecosystem and society. UC Science Team 2
Last Chance Sugar Pine Big Sandy Bear Trap Frazier Speckerman Vegetation properties for Sugar Pine, derived from Lidar Reconstruction of forest Spatial team 3
Mountain Hydrology Fluxes Research Questions evapotranspiration precipitation Where & when is water stored? How is it routed through the forested catchments? snowmelt infiltration What effects do forest treatments have on water quality, quantity (yield), storage & routing through the catchments? runoff sublimation ground & surface water exchange What is the transferability of 1 km 2 watersheds to fireshed response? Hypothesis 1 Fuels treatments will reduce Leaf Area Index (forest cover) As LAI decreases, snow accumulation on the ground will increase, while evapotranspiration (ET) and snow retention in late spring will decrease. 4
fuel Treatments & Energy Balance Fuels treatments: LAI & gaps & solar radiation Lower LAI: interception Gap size & spacing: control snow accumulation & melt timing A change in snow accumulation will be seen in the magnitude of peak stream flow Changes in snow retention, will be observed in the recession limb of the hydrograph & the soil moisture curves Hypotheses 2 Changes in ET will affect both the timing & magnitude of lateseason baseflow. 5
Hypothesis 3 Changes in water quality will be a function of changes in discharge Increased turbidity will be a function of stream discharge as opposed to hillslope erosion Hypothesis 4 Using hydrologic models, physiographic & hydroclimatic thresholds can be defined linking area treated with aquatic effects & impacts on forest water cycle Hydrologic models & spatial data will enable extending responses to the larger watershed & fireshed scales 6
Topics covered Objectives, goals & overview What & why the water component of SNAMP Pre-treatment Observations Water Quality Water Quantity Modeling & Scenarios RHESSys Calibration Treatment Scenarios Pretreatment Observations Oct 2007 Sept 2011 4 years Meteorological 3 years Snow depth (distributed) 2 years Soil moisture (distributed) Stream Water Quality Stream Level & Discharge 7
Temp Cond DO Turbidity Precip & Discharge Water Quality: Speckerman Snow Depth water chemistry varies with discharge Speckerman Frazier 8
Controls on Conductivity Base flow High flow evaporation surface runoff precipitation on channel evaporation groundwater input groundwater input Common caused of increase: Groundwater input, contact w/ soil & evaporation Surface runoff and precipitation on channel have dilution effect Water chemistry changes with water Sources Stable water isotopes 2 H 18 O 9
Stable Isotopes Lighter (vapor) Heavier (liquid) Elevational trends for water chemistry Elevation 2 H Latitude 18 O 10
Distinct Sources for the different streams 18 O turbidity peaks will be a function of stream discharge Observations: often largest seasonal flush at initial rains turbidity precip discharge no data Accumulation phase Transport phase Current thinking: there may be seasonal depletion of sediment especially following multiple storm events 11
turbidity peaks will be a function of stream discharge Speckerman Frazier Current analysis: comparing discharge to turbidity to analyze sediment transport patterns Additional findings: Speckerman Snow melt Spring recession Baseflow Temp 14:00 15:00 15:30 Discharge 14:30 18:00 11:45 Snow 12
Summary Water Quality Definite differences in behavior between N & S sites We are working to understand source of waters for different streams Turbidity may not be only a function of discharge, recent event history may also play a role. Hysteresis patterns indicate in-channel areas are dominant sources of sediment Diel cycles suggest a piston-type flow model rather than surface runoff during snow melt. Duncan Peak Met Temp Rad Precip Wind 13
Snow Depth 34 sensors 16 locations +20 new wireless sensors - Last Chance Soil Moisture 89 Sensors 16 Locations 14
Stream discharge 4 Streams Stream discharge 8 Sensors 4 Streams 15
Summary Water Quantity Data: 4 yrs Met, 3 yrs snow, 2 years soil moist/streamflow Approx. 200 continuously recording instruments Good range of annual precipitation/conditions measured % Avg Precip 2008: 70 2009: 70 2010: 100 2011: 120 (wet year) 2012: 65 (dry year) Topics covered Objectives, goals & overview What & why the water component of SNAMP Pre-treatment Observations Water Quality Water Quantity Modeling & Scenarios RHESSys Calibration Treatment Scenarios 16
RHESSys Calibrations Temp & precip met input Soil moisture Snow depth/swe Measured streamflow Rain/Snow met input RHESSys Calibrations CZO KREW P303 Observed & modeled streamflow comparable 17
RHESSys Calibrations: P301 Precip v. Rain/Snow Model efficiency increases from 0.5 to 0.82 Observed & modeled streamflow comparable RHESSys Calibrations: Bear Trap SWE level and timing is comparable Model needs better calibration with observed soil_moist & streamflow 18
Model Scenarios High Precipita6on (174- cm) 8 Change in ET / Streamflow (cm) 6 4 2 0-2 -4-6 -8-10 90% 80% 70% 60% 50% LAI (% Current Level) Monthly Total (cm) 50 40 30 20 10 Precip > ET Precipitation Evapotranspiration 0 Sierra Nevada Adaptive Management Oct Nov DecProject Jan Feb Mar Apr May Jun Jul Aug Sep Model Scenarios Low Precipitation (96-cm) Change in ET / Streamflow(cm) 4 3 2 1 0-1 -2-3 -4 90% 80% 70% 60% 50% LAI (% Current Level) Monthly Total (cm) 35 30 25 20 15 10 5 Precip > ET Precipitation Evapotranspiration 0 Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep 19
Model Scenarios Snow Accumulation/Melt Stream Discharge Snow Water Equivalent (cm) 20 16 12 8 4 50% LAI 60% 70% 80% 90% 100% Stream Discharge (L s -1 ) 200 160 120 80 40 50% LAI 60% 70% 80% 90% 100% 0 0 Mar-29 Apr-05 Apr-12 Apr-19 Apr-26 Oct Dec Feb Apr Jun Aug Temp_melt =.002 * air_temp * (1-0.8 * forest_cover) Model Scenarios Currently canopy density reduction 50% LAI * 100% canopy cover = 50% forest Next canopy cover reduction 100% LAI * 50% canopy cover = 50% forest Processes affected Water cycle (direct) - Interception - Evap/Sublimation - Transpiration Both canopy cover & density reduction Snowmelt (indirect) 75% LAI * 75% canopy cover = 50% forest - Sunlight - Temperature - Wind Future incorporate a more sophisticated snow process model -using high resolution forest structure LiDAR data 20
Model Scenarios: Research Questions Scaling Where & when is water stored? How is it routed through the forested catchments? What effects do forest treatments have on water quality, quantity (yield), storage & routing through the catchments? What is the transferability of 1 km 2 watersheds to fireshed response? Summary Modeling Calibrations: CZO basins near completion SNAMP still needs improvement Scenarios: Preliminary results show increased water in wet & dry years Canopy gaps & detailed snow process next Scaling up to larger watersheds is the final step 21