The California mountain water cycle: knowns & unknowns

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Winfred Howard
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1 The California mountain water cycle: knowns & unknowns Roger Bales Professor & Director Sierra Nevada Research Institute UC Merced NASA-MODIS satellite image

2 Topics in this talk 1. Mountain water cycle 2. Precipitation, snowpack storage & snowmelt 3. Evapotranspiration & runoff 4. Linking better measurements to decision making

3 Topics in this talk 1. Mountain water cycle 2. Precipitation, snowpack storage & snowmelt 3. Evapotranspiration & runoff 4. Linking better measurements to decision making

4 Mountain hydrology fluxes evapotranspiration precipitation Reservoirs: Snowpack storage Soil-water storage snowmelt sublimation infiltration Myths: We can, with a high degree of skill, estimate or predict the magnitude of these fluxes & reservoirs Better hydrologic modeling using existing data sources will yield significant improvement runoff ground & surface water exchange

5 Mountain hydrology fluxes evapotranspiration runoff snowmelt precipitation sublimation ground & surface water exchange infiltration Not a myth: In the history of hydrologic science most of the significant advances have resulted from new measurements (NRC 1991) over the past decade, only modest progress has been made in hydrologic measurements (NRC 1999) There are critical & extensive cyberinfrastructure needs (to) take advantage of advances in measurements (NRC 2008)

6 Mountain water cycle & climate change Warming by +2 to +6 o C drives significant changes: rain-vs-snow storms * snowpack amounts * snowmelt timing * flood risk streamflow timing * low baseflows growing seasons * recharge? drier soil in summer Precipitation changes uncertain Land surface temperatures 5-yr average Departure from mean Already observed (*)

7 Snowpack loss & water storage snowpack annual storage Sacramento Valley storage San Joaquin Valley storage Likely loss of 20-30% of snowpack storage w/ 3 o C temperature increase MAF: million acre feet Data from DWR

8 Topics in this talk 1. Mountain water cycle 2. Precipitation, snowpack storage & snowmelt 3. Evapotranspiration & runoff 4. Linking better measurements to decision making

9 Current operational snow measurements From a regional view, operational snow measurements look like a dense network Main basis for seasonal water supply estimates

10 American River basin Lake Tahoe 2 snow pillows in N. Fork, 1 in Middle Fork, 8 in S. Fork Non-representative network Stations are on flat ground, in clearings, at mid elevations

11 American River basin Water supply outlook: statistically based forecast Uncertainties in forecasts have huge implications for the regional economy, hundreds of millions of dollars annually Seasonal forecasts of water supply are within +20% half of the time Improved measurements will help most in years that depart from the long-term mean

12 Enhancing seasonal water-supply forecasting Ground data Precipitation forecast Empirical & regression methods Volume forecasts Decision making

13 Enhancing seasonal water-supply forecasting Digital elevation model Landcover information Enhanced ground data Satellite snowcover Precipitation forecast Integration & modeling Snowpack, soil moisture, ET estimates Broader decision making Streamflow forecasts

14 Emergent technology for snow measurements

15 Satellite data provide a spatially continuous time series of Sierra Nevada snowpack but how accurate is an issue Satellite remote sensing of snowpack

16 Sierra Nevada fractional snow covered area (SCA) from MODIS MODSCAG algorithm Painter et al., 2008 SCA is binned into 4 classes for ease of viewing Pixel size: 500 m Data available for 2000-present SCA for March 10, 2008

17 Bishop Creek Cottonwood Creek Jan 11, 2008

18 Bishop Creek Cottonwood Creek Feb 12, 2008

19 Bishop Creek Cottonwood Creek Mar 10, 2008

20 Bishop Creek Cottonwood Creek Apr 10, 2008

21 Bishop Creek Cottonwood Creek May 11, 2008

22 Bishop Creek Cottonwood Creek Jun 11, 2008

23 Bishop Creek Cottonwood Creek Jul 8, 2008

24 Two approaches to estimate snow water equivalent (SWE) across a basin fsca Energy balance Met station time series Landscape data T air & T dew Snowmelt Snow vs. rain Precipitation 1. Back calculated 2. Interpolated fsca SWE Local SWE Local SWE t y x Potential SWE Landscape data

25 Catchment area Interpolated Back calculated dry year Fraction of annual snowmelt from various elevations wet year Snowmelt estimate based on interpolation of SWE from existing, ground stations (snow pillows) underestimates snowmelt at highest elevations Elevation band, m Rice et al., in review

26 Snowmelt, m Bishop Kings Reconstructed snowmelt by elevation band for wet, average & dry years Adjacent basins across Sierra Nevada crest Average snowmelt by elevation: 2005, 2007, 2008 Note: precipitation timing not included Bales, in preparation

27 Comparison with snow-pillow measurements Elevation adjusted value from reconstructed snowmelt compared to snow-pillow SWE for peak accumulation It is well established that sparse snow-pillow measurements do not accurately represent the amount of snow in a basin

28 Elevation transect Comparison with PRISM data along a NE-SW transect 4-km gridded precipitation data different gradients in snow zone Snowmelt or precip (Oct-Mar), m Precipitation gradients Elevation, m

29 Projected impact of climate warming on snow, Eastern Sierra Nevada based on data analysis Bales, in preparation Difference from present, o C

30 Topics in this talk 1. Mountain water cycle 2. Precipitation, snowpack storage & snowmelt 3. Evapotranspiration & runoff 4. Linking better measurements to decision making

31 3 o C Increase in water yield w/ elevation, from rain to snow dominated Kings River basin 0.1 increase per 350 m Decreasing temperature Increasing snow fraction Decreasing LAI Coarser soils Hunsaker et al., submitted

32 Water balance on regolith Change in storage ΔS S = Rain + Snowmelt ET S Deep_drainage ΔS D = Deep_drainage ET D Streamflow gw_loss Loss = ET S + ET D + ΔS D + gw_loss = Rain + Snowmelt Streamflow ΔS S

33 Water balance on regolith Change in storage ΔS S = Rain + Snowmelt ET S Deep_drainage ΔS D = Deep_drainage ET D Streamflow gw_loss Loss = ET S + ET D + ΔS D + gw_loss = Rain + Snowmelt Streamflow ΔS S

34 (NSF Critical Zone Observatory) Southern Sierra CZO MODIS image Elev., m E-W transect of flux towers San Joaquin Experimental Range 400 m Soaproot Saddle 1100 m NEON to follow same E-W transect Shorthair CZO Creek P m 2000 m Main CZO site N-S transect of research catchments CZO

35 CZOs: a new generation of integrated measurements eddy correlation embedded sensor networks lidar isotopes & ions satellite snowcover low-cost sensors sap flow sediment

36 Providence Creek main CZO instrument cluster Elev m T ave 8.9 o C (2000 m) Annual precip: 1.0 m 130 dy snow 12 mo growing season neither cold nor drought limited White fir w/ sugar & other pines, incense cedar & patchy, dense shrubs Over 400 sensors integrated for basin-scale, spatial measurements

37 cm water Average water-balance components for WY fall winter spring summer Partition annual ET over year using sap flow: P301: 73 cm } P303: 93 cm includes gw_loss high!! Shallow-soil storage insufficient for ET in 4th quarter deep storage Low ET in 2 nd quarter relative to Loss deep storage Bales et al., submitted

38 ET based on transect of CZO flux towers Temperature Summer drought limited Increasing Elevation Growing season Year-round growing season Water availability Winter cold limited

39 Cumulative evapotranspiration Subalpine forest (8900') Midmontane forest (6600') Oak/Pine woodland (1300') Cumulative ET, cm Cumulative Et (mm) Midmontane forest year round ET results in high annual ET Subalpine forest minimal ET in winter reduces annual ET Day since 1/1/09 fall winter spring summer Fall Winter Spring Summer M. Goulden, manuscript in preparation

40 Topics in this talk 1. Mountain water cycle 2. Precipitation, snowpack storage & snowmelt 3. Evapotranspiration & runoff 4. Linking better measurements to decision making

41 Some implications of the deeper water storage Models using max soil depths of <2 m for water storage are underestimating seasonal & annual ET Widely used ecological estimates of water deficit underestimate ET & overestimate water deficit in many locations More buffering of seasonal to inter-annual fluctuations than previously assumed

42 Some implications of steep elevation gradients in ET & runoff Forest management is important for water yield & the timing of snowmelt runoff Downstream beneficiaries have a stake in upstream watershed management Better measurement & process understanding are critical to realizing benefits of management actions

43 Water in a changing climate is at the center of management issues affecting ecosystem services Reduced snowpack, earlier snowmelt & altered hydrology associated with warmer temperatures & altered precipitation patterns will introduce additional uncertainty into Western U.S. water management & affect other ecosystem services Possible responses: More information-intensive decision support Collaborative & integration in planning, management California is trying to do some of both Madden: The Beast that ate the earth

44 Basin-wide deployment of hydrologic instrument clusters American R. basin Strategically place low-cost sensors to get spatial estimates of snowcover, soil moisture & other water-balance components Network & integrate these sensors into a single spatial instrument for waterbalance measurements. in progress

45 Turning unknowns into knows through new water information systems Research support: NSF, NASA, CA-DWR

Water Year 2001 in Northern California: Have the Good Years Ended?

Water Year 2001 in Northern California: Have the Good Years Ended? Water Year 21 in Northern California: Have the Good Years Ended? Maurice Roos Abstract For the second water season in a row, precipitation and snowpack accumulation from October through December were far