The late Pleistocene pluvial history of Surprise Valley, CaliforniaDaniel E. Ibarra1*, Anne E. Egger2, Kate Maher1

1 Dept. of Geological and Environmental Sciences, Stanford University; 2 Dept. of Geological Sciences, Central Washington University*Corresponding Author: danieli@stanford.edu

References AcknowledgementsTufa U-series geochronology was conducted in the Stanford ICP-MS/TIMS Laboratory, with support from Dr. Caroline Harris, Dr. Karrie Weaver, Kim Lau, Miguel Cruz, and Conni De Masi. Tufa radiocarbon dating was conducted by Beta Analytic, Inc. in Miami, FL. Dr. Guangchao Li assisted with cation analyses at Stanford’s Environmental Measurements 1 Laboratory and Dr. David Mucciarone assisted with stable isotope analyses in Stanford’s Stable Isotope Biogeochemistry Laboratory. Funding for geochemical analyses was provided by NSF grant EAR 0921134 to Professor Kate Maher.

Airborne LIDAR data presented here was collected by the National Center for Airborne Laser Mapping (NCALM) with funding from a grant to Professor Anne Egger, Dr. Jonathan Glen and Corey Ippolito from NASA’s UAS-Enabled Earth Science program.

David Medeiros assisted with volume, surface area and basin delineation calculations using ArcGIS at the Stanford Geospatial Center.

We gratefully acknowledge support from local landowners, the Bureau of Land Management, and business owners in Surprise Valley. Sabina Kraushaar (USGS Intern) provided !eld assistance and support during the 2011 !eld season.

Introduction Regional Synthesis

Pleistocene Lake Surprise

Shoreline Tufa Geochronology Basin-scale Hydrologic Modelling

CMIP5/PMIP3 Model-Data Comparison

Implications and Future Work

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0 50 100 150 200 500 1000 1500 0.2 0.4 0.6Lake Surface Area (km2)Lake Volume (km3) Hydrologic Index (Z)

Lake

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el (m

)

Elevation (m.a.s.l.)

Figure 11. Determination of Lake Surprise hypsometric data using modern topography. (A) Using the ArcGIS hydrology toolbox, the USGS National Elevation Dataset DEM with a resolution of 1/3 arc-second (~10 m) was analyzed to calculate the basin size, lake surface area and volume. This also con!rmed from modern topography that Surprise Valley is a closed, inward draining basin. B) Volume vs. depth, surface area vs. depth and hydrologic index (Z) vs. depth. Z = Surface Area/Tributary Area = Runo"/Net Lake Evaporation. (Mi#in and Wheat, 1979; Reheis, 1999)

(A) (B)

Figure 5. 2-D model of the !exural response of the crust to loading by the lake highstand. Two models were constructed: approximately E-W across the lake, and N-S across the valley (not shown). This provides an absolute maximum constraint on the isostatic rebound a"ecting modern elevations of paleoshorelines. For a deep, narrow valley the E-W model is likely closer to the real $exure that occurred during the Last Glacial Maximum. (Modeled using OSXFlex2D Version 3.3; Cardoza, 2012)

> 20

0 m

Figure 3. Map of Surprise Valley, CA.- Paleoshorelines are preserved on the east side of the valley.- Shoreline tufa samples were collected from four localities on the east side of the valley.- Two di"erent highstand elevations have been previously proposed (Reheis, 1999; Irwin and Zimbelman, 2012).- Erosional relationship with the Bidwell Landslide indicates that the lake highstand was ~17 ka (Elder and de la Fuente, 2009).- Basin bedrock is primarily volcanic (basalt and lahars).

Figure 2. Middle Lake Paleoshoreline Set - Surprise Valley, CA. Paleoshorelines are well-preserved from late Pleistocene pluvial lakes across the Basin and Range. Dating of shoreline materials (e.g., tufa) places absolute constraints on both past climatic/hydrologic changes and basin-scale Quaternary deformation caused by faulting and isostasy.

Figure 1. Western US Pluvial Lakes and Glaciers (right). Surprise Valley, CA (red) is a small, inward draining, hydrologically closed basin in the northwest Basin and Range.

Figure 4. Airborne LIDAR data of the Middle Lake Shorelines was processed to remove vegetation to produce a digital elevation model (DEM) with a 0.5 m resolution. Distinct shorelines are visible in the LIDAR data on the east side of the valley. Tufa sample locations (yellow) with radiocarbon ages are shown.

Figure 10. Lake Surprise lake levels based on radiocarbon ages (left). Combining sample ages from the four localities in the basin records the transgression and regression of Lake Surprise during the last deglaciation. The radiocarbon ages were calibrated using IntCal09 (Reimer et al., 2009).

-120°0'

-120°0'

41°30'

41°0'

Proposed Lake Surprise highstand elevations 1567 m (Reheis, 1999) 1545 m (Irwin and Zimbelman, 2012)

Tufa Sample Shoreline Transects

Bidwell landslide

26th Pacific Climate WorkshopMarch 3-6 2013, Pacific Grove, CA

Distance (km)-100 -37.5 25 87.5 150

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ght o

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)

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Elastic thickness of crust = 32 kmYoung modulus = 30 GPaPoisson ratio = 0.25Density = 3300 g/cm3

Greatest e"ect = 11 m

East West

Motivation- To test climate model reconstruction of the hydrologic cycle through model-data intercomparison at the Last Glacial Maximum (~21 ka).- To produce a lake level reconstruction from a previously unexplored basin.- To investigate the utility of radiocarbon and U-series geochronology to shoreline tufa deposits.

Surprise Valley

Chewaucan Basin

Lake Lahontan

0 100 200 300 400 500 Meters

SVDI12-T10: 14.95 ± 0.2814.52 ± 0.36

10.71 ± 0.1421.03 ± 0.2

Middle Lake

21.81 ± 0.56

Figure 9. Example isochron plots for SVDI12-T10 (elevation = 1517 m, location in Fig. 4) for the Total Sample Dissolution (TSD) method. Error-weighted linear regressions are calculated using Isoplot’s “York!t” function (Ludwig, 2003a;b). The slopes of the error-weighted linear regressions provide the detrital corrected calculation of (230Th/238U)authigenic and (234U/238U)authigenic needed to determine the sample’s isochron age. All analytical errors and regression error bands are 2σ. The paired radiocarbon age of this sample is 14.95 ± 0.28 (ka cal BP).

CNRM-CM5 MRI-CGCM3 NCAR-CCSM4PMIP3/CMIP5 Precipitation Anomaly (Last Glacial Maximum - Modern, Annual)

Precipitation Anomaly (m

m/yr)

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Figure 13. LGM anomaly maps as predicted by three climate models from the PMIP3/CMIP5 ensemble. Monthly climatologies were calculated from 300 to 1000 year model runs. CNRM-CM5, MRI-CGCM3 and NCAR-CCSM4 are three of the higher resolution models available from the PMIP3/CMIP5 ensemble. Precipitation is not bias corrected. Modern simulations are the pre-industrial (1850 AD) control runs. (Paleoclimate Model Intercomparison Project 3, PMIP3 Database and boundary condition description: http://pmip3.lsce.ipsl.fr/)

Weather Station CompositeNCAR-CCSM4 CNRM-CM5 FGOALS-g2 IPSL-CM5A-LRMIROC-ESM MPI-ESMMRI-CGCM3

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Figure 14. Normalization of climate model precipitation output. Due to the large spread in absolute precipitation values, seven models from the PMIP3/CMIP5 ensemble were normalized to the percent of annual rainfall.

Raw Climate Model Precipitation Normalized Precipation

BSE Detrital Corrected Age vs 14C AgeTSD Isochron Age vs 14C Age

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Figure 8. Plot of paired 230Th-U and 14C ages from the same sample. 230Th-U ages are calculated using the BSE detrital correction and TSD isochron age method (when suitable). BSE detrital corrected ages are the error weighted average of 1 to 5 samples. TSD isochron ages are constructed from isochrons of 3 to 7 coeval samples (e.g. Fig. 9).

Middle Lake

Lower Lake

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Accommodation ZoneUpper LakeMiddle LakeLower Lake

Anomalous Age? Anomalous Age?

Regression

Rapid Transgression?

ObjectiveObtain paired radiocarbon and 230Th-U ages on ~15 shoreline tufa from Surprise Valley. Combining two geochronologic approaches will help constrain potential radiocarbon reservoir e"ects for 14C ages, as well as detrital correction constraints on 230Th-U ages.

U-Th Age Detrital Correction~5 coeval samples were analyzed for Th and U isotopes on the MC-ICP-MS at Stanford University. Due to high detrital Th (which results in excess initial 230Th) we have employed two methods for the detrital correction: - Bulk Solid Earth (BSE) single sample correction method. (e.g. Ludwig and Titterington, 1994; Maher et al., 2007) - Total Sample Dissolution (TSD) isochron method (e.g. Fig. 9). (e.g. Ku et al., 1998; Blard et al., 2011)

2.4

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6 7 8 9 10 11 12(238U/232Th)

(230 Th

/232 Th

)

Sample: SVDI12-T10a/b (n=7)Slope = 0.277 ± 0.025 = (230Th/238U)authigenic

Intercept = 0.78 ± 0.21 = (230Th/232Th)detrital

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Sample: SVDI12-T10a/b (n=7)

Slope = 1.884 ± 0.096 = (234U/238U)authigenic

(238U/232Th)

(234 U/

232 Th

)

Isochron Age = 17.23 ± 1.74 ka

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Secular Equilibrium

Highest elevation samples

Figure 7. Comparison of tufa (234U/238U)initial ratios with modern waters and carbonates. Most calculated (234U/238U)initial ratios lie within the range of measured modern sample values. Surface playa sediments were sequentially leached (1:1 MΩ H2O followed by NaOAc) to obtain exchangeable and carbonate fractions.

Figure 6. 230Th-U evolution diagram of BSE detrital Th corrected (230Th/238U) vs. (234U/238U). Detrital Th is corrected using Isoplot (Ludwig, 2003a;b) assuming a 230Th/232Th ratio equal to that of bulk silicate earth (4.46 x 106 ± 2.23 (2σ)) and a 232Th/238U of 3.8 ± 1.9 (2σ). Changes in the calculated (234U/238U)initial ratios (Fig. 7) may reveal changes in lake U chemistry, basin-scale hydrology and residence times at di"erent lake levels.

Implications- Lake Surprise’s late Pleistocene history was characterized by low to medium lake levels during the LGM rising to a peak highstand at 14.9 ka to 16.5 ka. This evidence is consistent with other nearby large (Lahontan) and smaller (Chewaucan) lake systems.- Oxygen isotopes and basin geometry, along with the paleo lake level data, suggest that minimal increases (<50%) in precipitation, coupled with decreased evaporation, created conditions to maintain pluvial Lake Surprise during the LGM and deglaciation.

Future Work- Completion of U-series measurements will help constrain detrital Th correction.- Examination of LIDAR data to map individual shorelines along the length of the valley and look for potential o"set by faults.- Development of a non-steady-state stable isotope model to more accurately and quantitatively determine the changes in precipitation and evaporation during latest late Pleistocene lake cycle.- Incorporation of additional PMIP3/CMIP5 models as model outputs are made available.

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Figure 12. Calculated increases in precipitation based on the Hydrologic Index (Z) and stable isotopes. Percent of precipitation increase relative to modern are calculated for three scenarios using steady-state mass balance and isotope equations below. 0%, 10% and 25% net lake evaporation decrease. Key assumptions:- Runo" (R) to Precipitation (P) ratio in basin tributary remains constant- ~6 °C decrease in Mean Annual Temperature- Annual average incoming rainwater δ18Orain = -13.5 ‰ (δ18OVSMOW) (Online Isotopes in Precipitation Calculator - Bowen and Revenaugh, 2003)

- Average atmospheric vapor δ18Oatm = -21‰ (δ18OVSMOW) (Hostetler and Benson, 1994)

- Minimal change in annual average relative humidity compared to modern observations (RH ≈ 58%)

Hydrologic Index (Z) Model (Mi#in and Wheat, 1979; Reheis, 1999)

Isotope Model (Steady State) (adapted from Li, 1995)

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Figure 15. Comparison of PMIP3/CMIP5 ensemble to the stable isotope model calculations for changes in precipitation (LGM-Modern). Seven climate models were included in the ensemble average. Due to the inconsistencies in absolute precipitation predicted by the models (Fig 14), we compare the percent change in annual precipitation between the LGM and modern. All calculations for LGM (19-26 ka) dated tufa samples (n=7) were included. Error bars are 1σ.

1. Adams K. D., Goebel T., Graf K., Smith G. M., Camp A. J., Briggs R. W. and Rhode D. (2008) Geoarchaeology 23, 608–643. 2. Benson L., Kashgarian M. and Rubin M. (1995) Palaeogeography, Palaeoclimatology, Palaeoecology 117, 1–30. 3. Blard P. H., Sylvestre F., Tripati A. K., Claude C., Causse C., Coudrain A., Condom T., Seidel J.-L., Vimeux F. and Moreau C. (2011) Quaternary Science Reviews 30, 3973–3989. 4. Bowen G. J. and Revenaugh J. (2003) Water Resources Research 39(10), 1299. 5. Cardozo, N., 2012, OSXFlex2D, downloadable from http://www.ux.uis.no/~nestor/work/programs.html. 6. Elder, D. and de la Fuente, J. (2009) Portland, OR, Annual Meeting, Geological Society of America, 41, 7, 325. 7. Hostetler S. W. and Benson L. V. (1994) Limnology and oceanography, 356–364. 8. Irwin R. P. III and Zimbelman J. R. (2012) J. Geophys. Res. 117, E07004–. 9. Ku T. L., Luo S., Lowenstein T. K., Li J. and Spencer R. J. (1998) Quaternary Res 50, 261–275. 10. Li, J. (1995) University of Southern California, PhD Thesis; Licciardi J. M., Clark P. U., Brook E. J., Elmore D. and Sharma P. (2004) Geology 32, 81. 11. Ludwig K. and Titterington D. (1994) Geochimica et Cosmochimica Acta 58, 5031–5042. 12. Ludwig, K.R. (2003a). Reviews in Mineralogy and Geochemistry, 52(1), 631–656. 62. 13. Ludwig, K.R. (2003b). User’s manual for Isoplot 3.00: A geochronological toolkit for microsoft Excel. Berkeley Geochronology Center, Berkeley CA, Special Publication No. 4. 14. Maher K., Wooden J. L., Paces J. B. and Miller D. M. (2007) Quaternary International 166, 15–28. 15. McGee D., Quade J., Edwards R. L., Broecker W. S., Cheng H., Reiners P. W. and Evenson N. (2012) Earth and Planetary Science Letters 351-352, 182–194. 16. Mi#in, M. D., and Wheat, M. M., 1979, Pluvial lakes and estimated pluvial climates of Nevada: Nevada Bureau of Mines and Geology Bulletin 94. 17. Munroe J. S. and Laabs B. J. C. (2012) J. Quaternary Sci. 18. Oviatt C. G., Currey D. R. and Sack D. (1992) Palaeogeography, Palaeoclimatology, Palaeoecology 99, 225–241. 19. Phillips F. M., Zreda M., Plummer M. A., Elmore D. and Clark D. H. (2009) Geol Soc America Bull 121, 1013–1033. 20. Reheis M. (1999) Quaternary Res 52, 196–205. 21. Reheis, M.C., 1999, Extent of Pleistocene lakes in the western Great Basin: U.S. Geological Survey Miscellaneous Field Investigations Map MF-2323 (digital map). 22. Reimer P. J., Baillie M. G., Bard E., Bayliss A., Beck J. W., Blackwell P. G., Ramsey C. B., Buck C. E., Burr G. S. and Edwards R. L. (2009) Radiocarbon; Rosenbaum J. G., Reynolds R. L. and Colman S. M. (2012) Quaternary Res 78, 333–340. 23. Smith G. I. (1984) Quaternary Res 22, 1–17; Thackray G. D., Lundeen K. A. and Borgert J. A. (2004) Geology 32, 225.

Surprise Valley Radiocarbon Ages

Lake Highstands (Munroe and Laabs, 2012)

Chewaucan Basin

Flux

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Lake Lahontan, NV

Searles Lake, CA

Lake Bonneville, UT

Sawtooth Mountains, ID (retreat)

Wallowa Mountains, OR (maximum)

Sierra Nevada, CA(Tioga 1 - 4 advances)

Klamath Lake, OR Glacial Flour Record

(A)

(B)

(C)

(D)

(E)

Figure 16. Comparison of the Lake Surprise lake level record with other western US paleoclimate records. (A) Lake Surprise shoreline tufa radiocarbon ages. (B) Basin and Range lake highstands individually plotted and illustrated as a histogram. Compiled and calibrated by Munroe and Laabs (2012). (C) Additionaly lake level curves plotted as percent of maximum from Lake Bonneville (Oviatt et al., 1992; and others compiled in McGee et al., 2012), Lake Lahontan (Benson et al., 1995; Adams,

2008) and Searles Lake (Smith, 1984). (D) Glacial records: Sierra Nevada, CA glacier advances recorded in glacial moraines (Tioga 1-4) using cosmogenic 36Cl (Phillips et al., 2009), maximal glaciations documented using cosmogenic 10Be ages in the Wallowa Mountains, OR (Licciardi et al., 2004), and glacial retreat recorded by radiocarbon ages in the Sawtooth Mountains, ID (Thackray et al., 2004). (E) Southeast Cascades glacier $our $ux recorded recorded in Klamath Lake, OR (Rosenbaum et al., 2012).

Surprise Valley Tufa Radiocarbon AgesBonneville Lake LevelLahontan Lake LevelSearles Lake LevelHistogram of Lake HighstandsGreat Basin Lake HighstandsGlacial Flower FluxGlacial Termination Ages

Legend

All Age Errors 2σ