Water & Sierra Nevada forests What do we know & what do we not know? Martha Conklin, Sierra Nevada Research Institute, UC Merced Introduction: Montane water balances Background: Forests and snow SSCZO Photo courtesy SSCZO Collaborators: R Bales, UC Merced; S. Glaser, UC Berkeley; & many others

1. Introduction: Montane Water Balances & Forests This in an artists rendition of a “critical zone observatory”. Note the satellite to gather spatial data. The point measurements to measure water table and groundwater flux. The staff gauge in the stream to measure height of water and the snow depth sensor to the snow side of the stream. Hidden in the grass is a short flux tower to measure evapotranspiration and respiration. Also at the tree-grass interface there are soil moisture sensors. Note that the artist shows both the vegetation and the weathered bedrock below the surface. Image by Jenny Parks, courtesy SSCZO

Basic annual montane water balance MODIS-derived map of Sierra Nevada snow cover, courtesy Robert Rice = + Disconnect between source catchments and the downstream recharge = rim dams Note the satellite image is for MODIS on a cloud free day. White is snow (500 m2 pixels); gray is partial snow, black is no snow. If one is using shorter timestep, Precipitation = Evapotranspiration + Runoff + Change in Storage The change in storage can be positive or negative. Runoff refers to stream discharge. Note in this equation all terms need to have same units. We often use depth to water. Photos courtesy SSCZO Precipitation = Evapotranspiration + Runoff (Evapotranspiration is mainly water use by vegetation)

What elevations provide the most snowmelt? 9000 7000 6000 10000 5000 12000 11000 8000 feet Rice, R. and R. C. Bales. 2013. An assessment of snow cover in major river basins of Sierra Nevada Network parks and potential approaches for long-term monitoring. Natural Resource Technical Report NPS/SIEN/NRTR—2013/800. National Park Service, Fort Collins, Colorado. Determined from analysis of MODIS satellite data. Provides snow-covered area. Robert Rice summed the snow covered area for the Merced R basin. Gray bars indicate area at each elevation. Remember that mountains are cone-like with less area at the top. The temperatures are colder at the top, but is less area to accumulate snow. The red, black and blues lines with symbols represent the fraction of snowmelt that comes from each elevation. Note the most snowmelt comes from elevations above 2700 m (or 9000 ft). Fraction of annual snowmelt by elevation band in Merced River basin. From Rice & Bales, 2013.

Snowpack loss & water storage: 30-yr horizon for the Sierra Nevada snowpack annual storage 14 MAF 13.5 MAF 11 MAF Rim dams are the dams that hold back the water from Sierra Nevada rivers. They are the main reservoirs for the state. – from dwr reports. C footprint and operation costs. Have under 2 yrs water supply in rim dams – heavily reliant on CA snowpack. Sacramento Valley storage San Joaquin Valley storage Likely loss of ~3.5 MAF of snowpack storage in next 1-3 decades MAF: million acre feet Storage data from CA Department of Water Resources

2. Background: Trees & snow accumulation Image by Jenny Parks, courtesy SSCZO

Tree density affects snow accumulation Trees block low-angle winter sun, retarding snowmelt … … but intercept snowfall, some of which sublimates (< 20%) … … and emit longwave radiation that melts snow (see tree wells) … Photo courtesy SSCZO

Thinned unit w/ control in background Right now the Sierra Nevada forests are overgrown due to fire suppression. There are many small trees and fewer large trees. The density of small trees makes the forest vulnerable to “crown” fires, with the small trees acting as ladders to bring the fire up to the crown of the large trees. Forest thinning is about reducing the density of smaller trees. In this photo, the overgrown forest is in the background. The thinned forest is in the foreground. Forest restoration/thinning about 50% of biomass. Note that the largest trees are left – restoration to a forest will be more resistant to severe fires. Thinned unit w/ control in background Stanislaus-Tuolumne Experimental Forest Photo by Eric Knapp, USFS

Measuring forest effects on snow accumulation This is the plan for the forest treatment where there were 1) control (no thinning), 2) evenly thinned (trees eqi-distance apart)– no big gaps 3) variable spacing to see effect of gap size. The result was on average more snow accumulated in sites that were thinned. Note the data are noisy, and this was done during a low snow year as California had a 4 year drought was this was done. Map made by Stanislaus-Tuolumne Experimental Forest, USFS. 1200 measurements

3. UC Research: Forest Evapotranspiration & Runoff Image by Jenny Parks, courtesy SSCZO

University of California catchment field sites Yosemite NP SNAMP American R & SNAMP MODIS image 600 1200 1800 2400 3000 Elev., m San Joaquin Experimental Range 400 m Shorthair Creek 2700 m CZO P301 2000 m Soaproot Saddle 1100 m E-W transect of flux towers A series of flux towers measuring water vapor and CO2 to estimate GPP & Respiration and ET. The towers are usually as tall or taller than the tallest vegetation (typically trees). This transect was designed to capture different ecosystems. CZO sites Figure courtesy SSCZO

SSCZO conceptual model We have a conceptual model of the elevational transect that relates the productivity of the forest with weathering depth (regolith depth). Note both the transition in tree species and the regolith thickness as one goes up in elevation. We have the most intensive instruments at 2000 m (Providence catchment). Figure courtesy SSCZO. Pore-to-plot and catena illustrations by Jenny Parks.

Providence Ck (2100 m) – instrumentation Figure courtesy SSCZO Providence Ck (2100 m) – instrumentation 3 headwater catchments w/ stream gauges & water-quality measurements 2 met stations 60-m tall flux tower 60-node wireless embedded sensor network 214 EC-TM sensors for volumetric water content 113 MPS sensors for matric potential 57 snow-depth sensors Meadow piezometers & wells Sap-flow sensors Note the numerous soil moisture sensors measuring volumetric water content and matric potential. Matric potential is the suction to remove water and is a measurement of soil moisture content. Met stations are meteorological stations (wind speed, direction, precipitation, relative humidity, air temperature, solar radiation (both incoming through atmosphere and outgoing from ground). Wireless sensor network is network of snow depth sensors and soil moisture sensors to gather spatial heterogeneity of snow accumulation and melt and soil moisture. Piezometers measure the pressure of the groundwater at a certain depth. With two piezometers screened for a small interval collocated at different depths, one can determine the water is recharging or discharging. Wells, fully screened, have instruments (pressure transducers) that record the groundwater depth. Sap flow sensors are placed on trees to determine when they are photosynthesizing. All these instruments contribute to the water budget.

Drilling, deeper wells, additional geophysics in progress Photos courtesy SSCZO We are using soil pits, geoprobe (to dig wells and take soil cores) and ground penetrating radar to estimate soil depth.

Seismic survey results at Providence Note that seismic surveys indicate that the regolith (weathered material above the bedrock including soil) can go done to 30 m. The dark blue indicates bedrock. The least deep regolith is under the meadow. The meadow has stayed wet through the 4 yrs of drought, indicating that it is a groundwater discharge point (as many wet meadows are). Weathered bedrock has about 25% porosity – the regolith has significant water holding capacity.

Evapotranspiration (ET) across a Southern Sierra elevation transect Oak savannah Mixed conifer Red fir 3 Summer moisture deficit Winter dormancy 2 ET, ft per year Sweet spot for mixed conifer 1 3000 6700 10,000 Happy Zone – you have know your forest – in the Sierra Nevada trees thrive between 3000 – 7000 ft. The regolith provides resiliency to climate variability – providing water from deeper strata. Trees have access to that water through roots and a symbiotic relationship with mycorrhizae. In the happy zone, trees photosynthesize and transpire all winter (it is not too cold) and the summers are not too dry (because of the water stored in the regolith). Runoff is generated above that zone – where trees are dormant over winter due to the cold. How will zone extend to right with climate warming? Will we lose part of happy zone at lower elevation? Dashed line is gross primary productivity – parallels ET line. Elevation, ft From Goulden et al., 2012 Photo courtesy SSCZO Mid-elevation forests show neither summer nor winter shutdown: deep rooting & resiliency to moisture stress warmer canopy-level temperatures despite snow

Monthly dry season evapotranspiration at mixed conifer site (Providence Creek) Jun Jul Aug Sep ~7000 ft elev. This forest is in the snow/rain transition. 2009 and 2010 were average snow years, 2011 was an exceptionally wet year and 2012 and 2013 were drought years. Note that ET really starts decreasing during the driest months (Aug and Sept) for all years and particularly in Sept for the drought years. Note resilience of trees to drought (started 2012 – with major response not really noted until Aug/Sept in 2012 – the driest months). After Goulden et al., 2012, courtesy Roger Bales

Monthly dry season evapotranspiration at mixed conifer site (Providence Creek) Jun Jul Aug Sep ~7000 ft elev. The trees are moisture stressed & are vulnerable to insects, disease & fire starting in 2012 Most of the snow falls above this elevation & much of the runoff comes through this forest Q = P – ET Note resilience of trees to drought (started 2012 – with major response not really noted until Aug/Sept in 2012 – the driest months). After Goulden et al., 2012, courtesy Roger Bales

Kings River Basin: Precip = Evapotranspiration + Runoff Precip based on PRISM Runoff by difference ET extended using satellite indices Rain-snow transition Note: we have used the flux tower data to extrapolate to the Kings River Basin using satellite products. Both graphs are presenting runoff over the same elevation. The top graph is the water flux (amount over time). The bottom graph is the amount over area. Top graph: Precipitation is top line – based on PRISM (an interpolation and extrapolation model based on ground-based data. Note high-elevation is totally extrapolation based on lower elevation measurements). ET is measured by flux tower and then used to convert NDVI (from landsat or modis satellites) into ET. The normalized difference vegetation index (NDVI) is a simple graphical indicator that can be used to analyze remote sensing measurements, typically but not necessarily from a space platform, and assess whether the target being observed contains live green vegetation or not. Q = P – ET Bottom graph is area per elevation band, precipitation are the circles. The blue line is the runoff (Q) generated at each elevation. Note that runoff comes from the higher elevations. But remember that mountains are conical and have less area at the top (see slide 4). If the happy zone moves uphill due to climate warming, P-ET will become less (i.e. less runoff) as the ET is higher at higher elevation due to warmer temperatures at higher elevation. Can we do anything? One approach is to thin trees. Fraction of runoff by elevation band From Goulden & Bales, 2014

What we know: science Path forward: Water Security Vegetation removal generally results in more runoff, initially Vegetation regrowth means less runoff Clear cutting or wildfire means more sublimation & earlier snowmelt – runoff could go up or down Less-dense forests (up to a point) can retain snow longer and are more fire-resistant. Colder, snow-dominated areas produce more runoff that lower, rain-dominated areas Path forward: Water Security Sustained forest management that provides measurable benefits for water supply & forest health will require investment, verification, & maintenance Better information is a critical foundation for water security, especially in a warming & more-variable climate