WATER RESOURCES RESEARCH, VOL. 46, W05513, doi: /2008wr007650, 2010

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1 Click Here for Full Article WATER RESOURCES RESEARCH, VOL. 46,, doi: /2008wr007650, 2010 A combined water balance and tree ring approach to understanding the potential hydrologic effects of climate change in the central Rocky Mountain region Stephen T. Gray 1 and Gregory J. McCabe 2 Received 12 December 2008; revised 19 October 2009; accepted 1 December 2009; published 6 May [1] Models suggest that average temperatures in the central Rocky Mountains will increase by >3 C over the next century, while precipitation may remain within late Holocene boundaries. This study investigates the potential hydrologic effects of such warming when combined with the full range of precipitation variability experienced over the past millennium. Using the upper Yellowstone drainage as a test case, a water balance model is constructed to estimate river discharge from precipitation and temperature inputs (r = 0.85 versus observed). The model then was run using tree ring precipitation estimates for and A.D. combined with (1) average observed temperatures ; (2) reconstructed Northern Hemisphere temperatures since 1177; or (3) Intergovernmental Panel on Climate Change temperature projections for 2025, 2050, and Discharge estimates generated by driving the model with tree ring precipitation for and observed temperatures served as a baseline for comparisons with other climate runoff scenarios. All combinations of the various temperature scenarios and pre 1911 precipitation resulted in mean discharge below the gauge period baseline. Projected temperatures for 2050 and 2100 produced the lowest mean discharge at 85% and 76% of baseline, respectively. Combining observed temperatures with the paleoprecipitation scenarios created numerous multidecadal periods with discharge <85% of baseline. Discharge during these same multidecadal droughts declined an additional 16 34% under the temperature regimes. Likewise inherent multidecadal precipitation variability adds a large degree of nonstationarity to the climate change responses seen in discharge estimates. While this combined tree ring and water balance scenario exercise does not provide precise forecasts for future conditions, these results suggest that a 1 3 C warming could have major negative effects on water availability in the upper Yellowstone. These results also indicate that twentieth century observations paint an incomplete and potentially overly optimistic picture of regional water supplies. Citation: Gray, S. T., and G. J. McCabe (2010), A combined water balance and tree ring approach to understanding the potential hydrologic effects of climate change in the central Rocky Mountain region, Water Resour. Res., 46,, doi: /2008wr Introduction [2] Hydrologic effects related to climate change resulting from increasing concentrations of atmospheric greenhouse gases are an important concern of water mangers in the western United States [Hoerling and Eischeid, 2007; McCabe and Wolock, 2007; Barnettetal., 2008; Barnett and Pierce, 2009]. Future temperatures are generally expected to rise from 1 C to 3 C by midcentury and 2 6 C by 2100 [Intergovernmental Panel on Climate Change 1 Department of Civil and Architectural Engineering, University of Wyoming, Laramie, Wyoming, USA. 2 U.S. Geological Survey, Denver Federal Center, Denver, Colorado, USA. Copyright 2010 by the American Geophysical Union /10/2008WR (IPCC), 2007]. Predicted warming alone would bring major changes to drought status in the western United States. In one study, Hoerling and Eischeid [2007] showed how modest increases in temperature would affect Palmer Drought Severity Index (PDSI) values for the western United States, primarily through increased evapotranspiration (ET). According to these analyses, a 1.4 C rise in temperature would result in average PDSI values for the West on par with the 1950s drought, and a 2.8 C increase would yield average PDSIs equivalent to the extreme dry years of Moreover, modeled changes in PDSI would occur even in the absence of major precipitation change. [3] Other studies have looked specifically at the effects of rising temperatures on surface water supplies in portions of the central Rocky Mountains [e.g., Wolock and McCabe, 1999; Christensen et al., 2004; Christensen and Lettenmaier, 2007; McCabe and Wolock, 2007; Barnett et al., 2008; Barnett and Pierce, 2009], a region 1of13

2 Figure 1. The upper Yellowstone River (UYR) study area. The map on the left shows the outline of the UYR drainage where precipitation was reconstructed, and the location of the Yellowstone River at Corwin Springs, Montana, gauge that was analyzed in the water balance study. The map on the right shows the location of the study area in relation to the Yellowstone and Missouri River drainages at large. that incorporates parts of five states (Montana, Wyoming, Colorado, Utah, and Idaho) and the headwaters of three major river systems: the Colorado, Yellowstone/Missouri, and Snake/Columbia. While focused primarily on the Colorado River basin, all of these studies point to the potential for significant decreases in streamflow and reservoir storage across the region. As in assessments for the western United States at large, such simulation exercises tend to highlight the negative effects of increasing ET on water availability. In the central Rocky Mountain region, direct warming related ET impacts would be further exacerbated by decreasing snowpack and other temperature driven changes in the rate or timing of runoff from high elevations areas. [4] Further assessments of net climate change effects on water resources have been complicated by uncertainty related to future precipitation scenarios. On the basis of both model output and an understanding of large scale components of the climate system, the current consensus is moving toward slightly wetter conditions (5 10% increase) in the Pacific Northwest, drier conditions (10 15% decrease) in the southwestern United States, and a break even ( 5 to +5% change) scenario for the central Rocky Mountain region [IPCC, 2007]. Extreme precipitation events may also increase, and the amount of precipitation change may vary with elevation, aspect, and season. Still the errors associated with all of these precipitation related predictions are large, even when advanced downscaling approaches are applied. These efforts are further hampered by instrumental records and climate model output that may not capture the full range of precipitation variability in a given region [Cayan et al., 1998; Cook and Evans, 2000; IPCC, 2007]. In particular, observations and simulations may not include important aspects of decadal to multidecadal variability, and they are often not long enough to capture extremes of drought duration, magnitude, and intensity. [5] Tree rings and other high resolution paleoproxies offer a means to broaden the range of precipitation scenarios used in climate impact assessments. Tree rings in particular offer annually resolved records of precipitation that can span many centuries to millennia, and when analyzed properly they can show decadal to multidecadal variability in rich detail [Gray et al., 2003]. Tree ring records are especially well suited for providing information on system state, e.g., runs of dry/wet years, regime shifts, etc., that may not be captured in instrumental records or plausibly reproduced in general circulation model (GCM) based simulations [Prairie et al., 2008]. Long tree ring records from the central Rocky Mountain region also include a wealth of interesting events such as the Medieval Climate Anomaly (a time of regional warming and drying that shares some similarities with predicted 21st century climatic changes [Cook et al., 2004]) that can serve as test cases for understanding water supply vulnerabilities and other natural resource challenges. Likewise these characteristics make tree rings an ideal platform for assessing assumptions related to the stationarity of regional hydroclimates [Milly et al., 2008], and they provide essential long term context for observational data sets and baselines used in planning. [6] Despite their potential benefits, tree ring reconstructions and other high resolution paleo data sets have rarely been used as direct inputs to the types of simulation models that are commonly applied to natural resource management. In this paper a novel combination of existing dendroclimatic reconstruction techniques and water balance modeling are used to investigate potential climate change effects on water availability in the central Rocky Mountain region. Using the test case of the upper Yellowstone River (UYR; Figure 1), an investigation is performed of how, when paired with the full range of precipitation variability over the past millennium, future temperature increases might alter streamflow in the region. Using this same approach we explore the sta- 2of13

3 Table 1. Descriptive Statistics for the Individual Site Chronologies Used in the Reconstruction Chronology Species Elevation (m) Time Span (years A.D.) Number of Trees Number of Series (radii) Average Series Length (Years) Interseries Correlatoin (R) Anderson Ridge East a (ARE) Pinus flexilis Mount Everts b (MEV) Psuedotsuga menziesii Salmon River Valley b (SAL) Psuedotsuga menziesii Yellow Mountain Ridge b (YMR) Psuedotsuga menziesii a Watson et al. [2009]. b International Tree Ring Data Bank ( tionarity, as mediated by variations in precipitation, of potential streamflow responses to warming, and examine the suitability of instrumental observations as a baseline for planning and assessments of future water availability. 2. Data and Methods 2.1. Upper Yellowstone River Basin [7] The upper Yellowstone River (UYR) basin lies primarily within the boundaries of Yellowstone National Park and encompasses portions of northwestern Wyoming and southwestern Montana. As defined here (Figure 1), the basin extends northward from the Yellowstone River headwaters in the Absaroka Mountain range to the U.S. Geological Survey gauging station at Corwin Springs, Montana (USGS ID ). The UYR has an area of almost 6800 km 2 [Ladd, 2008] and is covered in a mix of coniferous forest, high alpine, and sagebrush steppe vegetation types. Elevations within the basin range from over 3700 m near its southern headwaters to 1548 m at the Corwin Springs gauge. Temperatures are generally cool, with average wintertime lows near 20 C and average summertime highs 25 C. Precipitation is highly variable across the basin with the mountains in the south averaging >1000 mm/yr and some lower elevation areas in the north averaging <300 mm/ yr. The seasonal distribution of precipitation varies across the UYR basin, with high elevations receiving maximum precipitation in December through February, while lower elevations tend to have a late spring or early summer (typically May or June) peak. [8] The UYR was selected as the test case for this study because of the availability of existing tree ring data and a recent history of high resolution paleoclimatic reconstruction work in the region [e.g., Graumlich et al., 2003; Gray et al., 2007]. Moreover, hyrdoclimatic modeling of this type requires that flow estimates be calibrated against observational records that have either little or no signature of human impacts or records where the signature of human impacts have been removed. Calibration records must also be of sufficient length to capture a wide range of dry/wet years and several multiyear dry/wet periods. Because it originates in a federally protected wilderness and flows primarily through Yellowstone National Park, the UYR represents one of the least human affected rivers in the 48 contiguous United States. The gauge at Corwin Springs has also been in continuous operation since late 1910 and offers one of the longest and most complete flow records in the Central Rockies. Last, the UYR is a major freshwater fishery and the largest single tributary to the upper Missouri River, making it a focus of major conservation efforts and water resources concern Reconstructing UYR Precipitation [9] The initial step of this study was the reconstruction of annual (previous July to current June) precipitation in the UYR study area (Figure 1). This involved a modification of the more spatially extensive Gray et al. [2007] precipitation record. In addition to covering the UYR drainage, Gray et al. [2007] incorporated parts of the Madison, Clarks Fork of the Yellowstone, and Shoshone River drainages. As done by Gray et al. [2007], precipitation was reconstructed on a 12 month time step, with each annual increment beginning in July of the previous year and ending in June of the current year. Both the Douglas fir and limber pines used here produce annual rings from a combination of carbohydrates stored during the previous summer and carbohydrates produced at the start of the current year s growing season [Fritts, 1976]. The reconstruction approach used in this study takes advantage of the timing of these carbon storage and growth phases, thereby maximizing the precipitation to tree ring width relationship. [10] In this study, data from two tree ring sites included by Gray et al. [2007] are used, as well as data from two additional sites used in other studies of regional hydroclimate [i.e., Biondi et al., 1999; Watson et al., 2009]. As shown in prior work, tree ring width at all of these sites is strongly correlated with precipitation and streamflow in the study region, and all of the selected site records extended back to 1200 A.D. or earlier (Table 1). Samples at each site originated from either Douglas fir (Pseudotsuga menziesii) or limber pine (Pinus flexilis). All samples were fully dated, and the age of each ring was verified using standard methods [Stokes and Smiley, 1968; Swetnam et al., 1985; Holmes, 1983]. [11] Processing of the chronologies to remove age size trends [Cook, 1985] and reconstruction methodologies were the same as done by Gray et al. [2007], with two exceptions. In this study, tree growth was calibrated against precipitation estimates derived from the Parameter elevation Regressions on Independent Slopes Model (PRISM [Daly et al., 1994]) rather than precipitation observations averaged across a collection of meteorological stations. Estimates were obtained for 4 km (km) 4 km grid cells ( prism.oregonstate.edu/), and the time series for each cell spanned A.D. PRISM estimates from all grid cells then were combined to produce basin precipitation averages for the UYR. The second difference was that a principal components (PC) regression approach was used to develop the final reconstruction model. PC regression has the advantage of reducing the pool of tree ring site records to discrete, orthogonal modes of common variation, thus avoiding many common pitfalls of multiple linear regression [Woodhouse et al., 2006; Meko and Woodhouse, 2010]. 3of13

4 Figure 2. (a) Mean water year precipitation for the UYR basin (observed) computed from Parameterelevation Regressions on Independent Slopes (PRISM) data for , and mean water year precipitation for the UYR reconstructed from tree ring chronologies. (b) Complete annual precipitation reconstruction covering the years A.D. Gray et al. [2007] also created a PC regression based version of their reconstruction, but their analyses focused on a reconstruction produced via stepwise regression. [12] The final UYR reconstruction incorporated data from all four tree ring sites: ARE, MEV, SAL. and YMR (Table 1). The model itself included the first principal component (PC1) which explains 42% of the common tree growth variability at the four sites to estimate July through June precipitation (July June PPT): July June PPT ¼ 741 þ 68:1 PC1: The resultant model performed well in a variety of verification tests including a leave one out procedure (Table 2), and it explained 47% of the variance in UYR precipitation as estimated by PRISM. Total variance explained was similar to that reported for the Gray et al. [2007] reconstruction of Yellowstone region precipitation (r 2 = 0.52) and in tree ring based reconstructions of precipitation from surrounding regions (e.g., Gray et al. [2004]; r 2 = 0.42). Comparison with PRISM precipitation (Figure 2a) shows that the model tracks year to year changes in overall wet versus dry conditions and multiyear to decadal trends well. The model does generally underestimate precipitation during the wettest years and overestimates during the driest. This variance compression is a common feature of all such regression based reconstructions [Meko and Woodhouse, 2010], but in this case estimates are not significantly biased in either direction. As such, the reconstruction can be thought of as providing a conservative estimate of interannual variability and a strong indicator of overall system state. [13] Using the Durbin Watson test [Draper and Smith, 1998], no statistically significant autocorrelation within the model residuals was found, and the Kolomogorov Smirnov test [Maidment, 1993] showed the resulting reconstruction to be normally distributed. The reconstruction contains significant autocorrelation at a lag of 1 year (r = 0.24), which is similar to the persistence in PRISM precipitation (r = 0.19). The final annual precipitation reconstruction spans the period from 1173 to 1996 A.D. (Figure 2b). 4of13

5 Table 2. Calibration and Verification Statistics for Reconstructed Annual (July June) Precipitation Reconstruction Start Year F Value Significance of F Value R 2 Adjusted R 2 Predicted (PRESS) R 2a RMSE b (mm) p < a Results of leave one out cross validation [Weisberg, 1985]. PRESS, prediction of sum of squares. b RMSE, root mean squared error of validation UYR Water Balance Model [14] The water balance (WB) model (Figure 3) allocates water among various components of the hydrologic system using a monthly accounting procedure [McCabe and Wolock, 1999; Wolock and McCabe, 1999]. Inputs to the model are mean monthly temperature (T, in C), monthly total precipitation (P, in millimeters), and the latitude (in decimal degrees) of the location of interest. The latitude of the location is used for the computation of day length, which is needed for the computation of potential evapotranspiration (PET). The WB model was calibrated to estimate flows at the U.S. Geological Survey gauging station at Corwin Springs, Montana (USGS ID ). This gauge represents total discharge from the UYR, and as discussed previously, human activities within the watershed have had little effect on flows over the duration of the record. [15] The WB model used in this study, although simple in structure, reliably simulates hydrologic variables (e.g., snow accumulation and melt, soil moisture storage, runoff) on monthly and annual time steps [Calvo, 1986; Mintz and Serafini, 1992; Wolock et al., 1993; Hay and McCabe, 2002]. Of particular importance to this study, Hay and McCabe [2002] reported that the monthly WB model reliably simulated monthly runoff for locations in the mountainous western United States. [16] One of the computations of the WB model is the estimation of the amount of monthly precipitation (P) that is rain (P rain ) or snow (P snow ), in mm. The occurrence of snow is computed by 8 P for T a T snow >< T rain T a P snow ¼ P for T snow < T a < T rain T rain T snow ; [19] Direct runoff (DRO) is subtracted from P rain to compute the amount of remaining precipitation (P remain ): P remain ¼ P rain DRO: Snow storage (snostor) is subject to melt if conditions are such that melting can occur. Thus, for some cases, snow, rain, and snowmelt can occur in the same month. Snowmelt (SM) is computed by a degree day method: SM ¼ ðt air T snow Þd; where SM (mm) is the amount of snow storage that can be melted in a month, a is a melt rate coefficient, and d is the number of days in a month. SM is added to P rain to compute the total liquid water input (P total ) to the soil. [20] Monthly PET is estimated from mean monthly temperature (T) and is defined as the water loss from a large, homogeneous, vegetation covered area that never lacks water [Thornthwaite, 1948]. Thus PET represents the climatic demand for water relative to the available energy. In this water balance, PET is calculated by using the Hamon equation [Hamon, 1961]: PET Hamon ¼ 13:97 d D 2 W t ; where PET Hamon is monthly PET (mm), d is the number of days in a month, D is the mean monthly hours of daylight in >: 0 for T a T rain where P snow is monthly snow fall (mm), P is monthly precipitation (mm), T a is monthly air temperature ( C), T rain is a threshold above which all monthly precipitation is rain, and T snow is a threshold below which all monthly precipitation is snow. When the monthly air temperature is between T rain and T snow, the proportion of precipitation that is snow or rain changes linearly. Snow (P snow ) that occurs during the month accumulates as snow storage (snostor). [17] P rain is computed as P rain ¼ P P snow : [18] Direct runoff (DRO), is runoff (mm) from impervious surfaces, or runoff resulting from infiltration excess overflow. The fraction (drofrac) ofp rain that becomes DRO is specified by the user. The expression for DRO is DRO ¼ P rain drofrac: Figure 3. model. Schematic represenation of the water balance 5of13

6 units of 12 h, and W t is a saturated water vapor density term, in grams per cubic meter, calculated by W t ¼ 4:95 e0:62t 100 where T is the mean monthly temperature ( C) [Hamon, 1961]. [21] Monthly actual evapotranspiration (AET) is computed in two ways, 8 < PET for P total PET AET ¼ ; : P total þ STW for P total < PET where STW is soil moisture storage withdrawal (mm). [22] If P total exceeds PET, then AET is equal to PET and the water in excess of PET replenishes soil moisture storage (ST). When ST is greater than the soil moisture storage capacity (STC), the excess water becomes surplus (S) and is eventually available for runoff. [23] When P total for a month is less than PET, then AET is equal to P total plus the amount of soil moisture that can be withdrawn from storage in the soil. Soil moisture storage withdrawal (STW) linearly decreases with decreasing ST such that as the soil becomes drier, water becomes more difficult to remove from the soil and less is available for AET. [24] STW is computed by first determining the change in soil moisture storage (ST) by ST i ¼ ST i 1 absðp total PETÞ ST i 1 STC where ST i is soil moisture storage (mm) for the current month, ST i 1 is the soil moisture storage (mm) for the previous month. If ST i is less than zero, then ST i is set to zero. STW is computed by STW ¼ ST i 1 ST i : [25] Runoff (RO) is generated from surplus (S), at a specified rate (rfactor). The rfactor parameter determines the fraction of surplus that becomes runoff in a month. The remaining surplus is carried over to the following month to compute total S for that month. Direct runoff (DRO), in millimeters, is added directly to the runoff generated from surplus (RO) to compute total monthly runoff (RO total ), in millimeters. [26] Parameters for the WB model (i.e., drofac, T rain, T snow, a, and rfactor) were determined through an exhaustive search calibration procedure [Hay et al., 2002]. The calibration procedure used combinations of parameter values, selected from a range of values for each parameter, to compute time series of monthly runoff. Time series of monthly runoff were computed for all PRISM grid cells within the UYR and then aggregated to compute monthly runoff for the UYR. The WB estimated time series of monthly runoff was subsequently compared to measured runoff for the UYR for the water years 1911 through [27] The Nash Sutcliffe coefficient of efficiency (E) was used as a measure of agreement between the estimated and measured time series [Nash and Sutcliffe, 1970; Wilcox et al., 1990; Legates and McCabe, 1999]. This statistic has : ; been widely used to evaluate the performance of hydrologic models. It is a useful statistic for model evaluation because it is sensitive to differences in measured and modeled means and variances. The coefficient of efficiency ranges from minus infinity to 1.0, with a value of 1.0 indicating perfect agreement between model and measured values. The statistic is computed as E ¼ 1:0 P N i¼1 P N i¼1 ðo i P i Þ 2 ; 2 O i O where O i are measured values, N is the number of values, P i are predicted values, and the over bar denotes the long term mean of the measured values. [28] The snow model parameter set associated with the highest E statistic (0.83) was chosen for use in this study. The resultant parameter set was drofrac = 0.06, T rain = 5.0 C, T snow = 5.0 C, a = 0.6, and rfactor = 0.5. These values are similar to values used in previous studies. For example, based on previous water balance analyses, 5% (i.e., 0.05) is an appropriate drofrac value for use in the majority of WB applications [Wolock and McCabe, 1999]. Tarboton et al. [1991] reported T rain = 3.3 C and T snow = 1.1 C for use with a monthly time step snow model, and McCabe and Wolock [1999] used T rain = 5.0 C and T snow = 0.0 C to estimate regionally averaged 1 April SWE for the western United States. Additionally, the melt rate coefficient of 0.6 is within the range of values reported by Rango and Martinec [1995]. An rfactor value of 0.5 is commonly used [Wolock and McCabe, 1999]. [29] Using the calibrated parameters and monthly temperature and precipitation inputs from PRISM, we tested the WB model s ability to estimate monthly runoff for the UYR during water years 1911 through The monthly runoff values were summed to produce a water year total runoff time series for the UYR. The water year estimates of UYR runoff (Figure 4) were well correlated with measured flows at the Corwin Springs gauge (r = 0.85, p < 0.001), with a positive bias of 19 mm (5% of mean water year total observed flow), a root mean squared error of 54 mm, and a Nash Sutcliffe statistic of On the basis of these initial results, we found the WB model suitable for use in further scenario testing exercises. We also retained these calibration run or PRISM ONLY estimates for comparison with WB output from runs using additional climate scenarios (see section 2.4) Climate Scenarios for Water Balance Analysis [30] While only total water year flow was considered in subsequent analyses, the WB model requires climate variables to be input on monthly time steps. PRISM temperatures were originally obtained as monthly values, but the annual tree ring precipitation estimates required disaggregation. Yearly values from the tree ring reconstruction were parsed to reflect the percentage of total annual precipitation each month accounted for in the historical ( ) record. May, for example, was historically the wettest month in the UYR, accounting for an average of 10.6% of total annual precipitation. Accordingly, the May value submitted to the WB model in each year of a run would be total annual 6of13

7 Figure 4. Water year runoff in millimeters for the UYR basin, Observed values were obtained from the U.S. Geological Survey gauge at Corwin Springs, Montana. The water balance (WB) PRISM time series was computed using the WB model running on inputs of monthly temperature and precipitation from the PRISM data set, and the WB tree ring time series was computed using tree ring reconstructed precipitation and PRISM temperature estimates. precipitation from the corresponding tree ring estimate multiplied by Analyses of the historical precipitation record showed that the percentage of the annual total occurring in each month varied significantly among dry and wet years, with drought years having a smaller proportion of their precipitation in winter (November January). To account for this wet dry year variation, average monthly percentages under four precipitation regimes (dry, lowest quartile of precipitation values; dry moderate, 2nd quartile; moderate wet, 3rd quartile; and wet, highest quartile), were calculated from the historical record. Values in the tree ring based precipitation reconstruction were divided into these same quartiles, and the corresponding disaggregation schemes were applied. [31] Temperature scenarios were generated in three different ways. First, a baseline temperature scenario was developed by calculating average monthly UYR temperatures over the period. These baseline temperatures were derived from PRISM estimates. Next, climatological averages were modified to reflect projected warming at roughly 2025, 2050, and Warming scenarios were taken from the median seasonal A1B estimates in the IPCC s Fourth Assessment Report [IPCC, 2007]. When averaged over the entire year, the scenarios for 2025, 2050, and 2100 include 0.85 C, 1.7 C, and 3.4 C increases in temperature, respectively. Future temperature scenarios are similar to those used in recent studies of climate change effects on western U.S. hydrology [e.g., McCabe and Wolock, 2007; Hoerling and Eischeid, 2007]. Last, two reconstructions of Northern Hemisphere (NH) temperatures were obtained from the World Data Center for Paleoclimatology ( These proxies, Paleo M [Moberg et al., 2005] and Paleo D [D Arrigo et al., 2006], were chosen because of their overlap with the entire period of record for the UYR precipitation reconstruction. When compared to other available proxy records, both of these NH temperature reconstructions also feature relatively high sample depth (i.e., a large number of site records) prior to 1500 A.D. The two reconstructions differ somewhat in the amount of relative warming and cooling they show during the Medieval Climate Anomaly and Little Ice Age, respectively. Thus both versions were used to create new temperature scenarios that combined the original monthly climatological averages with the reconstructed anomaly for each year in the preinstrumental (before 1895) record. 3. Results and Discussion [32] In order to begin testing the potential impacts of the selected climate scenarios on runoff, the calibrated WB model was first run using monthly precipitation estimates generated from the tree ring record plus monthly PRISM estimates for UYR temperatures. Runoff based on tree ring precipitation and PRISM temperatures was significantly correlated with observed water year flows at Corwin Springs (r = 0.56, p < 0.001) and the WB flows produced from the original PRISM only (i.e., PRISM precipitation and temperatures) calibration run (r = 0.71, p < 0.001). For the period of overlap with the gage record (i.e., ), average WB runoff based on tree ring precipitation plus PRISM temperatures was slightly wetter than observed runoff (424 mm/yr versus 407 mm/yr, respectively). When compared to average WB runoff generated from the PRISM ONLY calibration run ( ), the tree ring precipitation plus PRISM temperature estimates were essentially the same (425 mm/yr). In the driest years (lowest 10th percentile and below), the tree ring precipitation plus PRISM temperature model produced an average runoff of 298 mm/yr compared to 264 mm/yr and 275 mm/yr for the PRISM ONLY estimates and observed values. During wet 7of13

8 Figure 5. Mean estimated UYR runoff under the seven climate scenarios. Runoff estimates were generated using the water balance approach described in section 2.2. The climate scenarios used were tree ring reconstructed precipitation from 1911 to 1995 A.D. plus climatological average temperatures from 1896 to 1995 (baseline); tree ring reconstructed precipitation from 1176 to 1910 plus climatological average temperatures from 1896 to 1995 (TREE OBS); tree ring reconstructed precipitation plus projected temperatures for 2025, 2050, and 2100 (PROJ 2025, etc.); tree ring reconstructed precipitation from 1176 to 1910 plus reconstructed Northern Hemisphere temperatures from 1176 to 1910 (PALEO M and PALEO D). years (90th percentile and above), tree ring precipitation plus PRISM temperatures resulted in average runoff values that were similar to the observed record (540 versus 544 mm/yr, respectively), and less than the 579 mm/yr average from the WB model running on the PRISM ONLY precipitation and temperature inputs. [33] On the basis of the ability of the original calibrated WB model to capture observed flows and the ability of the WB model running on tree ring precipitation inputs to capture the mean state of the system, we then moved on to investigate the potential effects of selected climate scenarios on UYR discharge. Recognizing the uncertainty in the underlying tree ring precipitation estimates and the limitations of the WB model, our intention in these scenario testing exercises was not to provide precise annual reconstructions of past streamflows. Likewise, output from scenario tests involving predicted temperatures should not be viewed as precise forecasts for future conditions. Our focus was instead to examine how average flows might change under a wide range of precipitation and temperature regimes, and these results should be considered in light of the underlying model uncertainties. [34] For our baseline scenario we selected runoff estimates generated using tree ring precipitation and PRISM temperatures over the period (i.e., the common period between the tree ring records and gauged flow at Corwin Springs). For comparison we combined (1) tree ring reconstructed precipitation for the pregauge period ( ) and climatological average temperatures (TREE OBS); (2) tree ring reconstructed precipitation ( ) and the three future temperature projections (PROJ 2025, etc.); and (3) tree ring reconstructed precipitation ( ) and NH paleotemperatures (PALEO M and PALEO D). The result was a suite of seven climate runoff scenarios. Note that WB estimates begin with the year 1176 rather than 1173 (i.e., the start of the tree ring precipitation reconstruction) because 3 simulation years are required to spin up the model. [35] When considering the effect of the various climate scenarios on long term average runoff, all possible combinations of temperature and pre 1910 tree ring precipitation produced flows that were noticeably lower than the baseline (Figure 5). Running the WB model on the TREE OBS scenario, for example, resulted in a 5.7% decrease in average flow. Despite being C cooler than the average of observed conditions, the two paleotemperature plus paleoprecipitation scenarios generated flows 3 4% below baseline. These results suggest that precipitation and streamflows in the twentieth century were unusually high, and point to both the inherent nonstationarity of regional hydroclimates and potential problems with using instrumental records as the sole source for planning and assessment data. Moreover, when the WB model was run using the TREE OBS scenario, flows during severe (i.e., 10th percentile or less) drought years were 8% lower than similarly ranked years in the baseline (Figure 6), 8of13

9 Figure 6. Same as Figure 5, but for the driest 10th percentile of runoff estimates. and the cooler PALEO M and PALEO D scenarios resulted in 5 6% reductions in severe low flow values compared to baseline. These results further indicate that observational precipitation and stream gauge records may not encompass the full range of droughts that should be considered by water planners. [36] Several additional features of the WB output show that precipitation shifts within the range of late Holocene variability alone could cause major disruptions to the UYR. The most striking example is a modeled low flow event lasting 60 years (Figure 7a). In this particular case we see the mark of a widely documented megadrought that was associated with severe precipitation deficits from roughly 1230 to 1290 A.D. [Cook et al., 2004; Gray et al., 2004, 2007; Meko et al., 2007]. Under the TREE OBS scenario, the lowest 25 year average flows during this Medieval megadrought would have been comparable to 10 year average flows during the 1930s, the worst case decade in the gauge record. However, because of its great length, this Medieval low flow period would have accounted for a cumulative m 3 deficit in UYR flow, as opposed to a m 3 deficit for the 1930s. The combination of tree ring reconstructed precipitation and twentieth century temperatures also produced six other low flow events (i.e., below the longterm TREE OBS mean)lasting 25 to 40+ years. [37] The potential for significant warming related impacts is evident when temperature projections for 2025 to 2100 A.D. are combined with tree ring reconstructed precipitation. Running the WB model on the PROJ 2025 warming scenario (average annual increase of 0.85 C) produced estimated long term flows 11% below baseline, while the PROJ 2050 (+1.7 C) and PROJ 2100 (+3.4 C) scenarios yielded 15% and 24% declines, respectively (Figure 5). Additional comparisons with the original PRISM ONLY and TREE OBS runs show that approximately 5 6% of these losses versus baseline can be ascribed to lower precipitation pre 1910, but the remaining declines are directly attributable to temperature forcing. In the driest years (10th percentile or less) the PROJ 2025 scenario reduced flows an additional 16% below severe drought year levels in the baseline (Figure 6). Using the PROJ 2050 scenario reduced modeled flows in severe drought years by 22%. The PROJ 2100 scenario resulted in severe low flows 34% below the baseline s 10th percentile. [38] Within the context of the WB model, future warming would greatly exacerbate low flows during extended drought events (Figure 7a). Under the PROJ 2050 scenario, for example, a 1.7 C warming would result in long term average flows during the 60 year Medieval megadrought as low as WB estimated flows for 1954, the year with the 7th lowest flows in the baseline scenario. Warming to PROJ 2100 levels would result in flows during this 60 year drought being, on average, as low as those in 1933, the driest single year in the baseline. Future warming would similarly increase the duration of dry episodes. If consecutive years below the 25th percentile are considered, the maximum dry run duration seen in the baseline scenario is 6 years, with an average run of 1.8 years. Applying this same 25th percentile threshold to the TREE OBS estimates yields a maximum dry run of 9 years with an average length of 2.0 years. Under the PROJ 2050 scenario, maximum run length increases to 13 years, with an average of 2.8 years. On the basis of their respective cumulative probability density functions [Salas et al., 1980; Biondi et al., 2005], the chance of any run below the 25th percentile lasting more than 2 years 9of13

10 Figure 7. (a) Twenty five year running means for modeled UYR runoff in millimeters. Estimates are based on water balance model runs using tree ring reconstructed precipitation plus climatological average temperatures (gray line), projected temperatures for 2025 (orange line), projected temperatures for 2050 (red line), and projected temperatures for 2100 (maroon line). The vertical line at 1911 represents the advent of instrumental observations on the UYR, and the gray line post 1911 coincides with the baseline scenario discussed in the text. The solid black line indicates the long term mean runoff for 1176 through 1910 (401 mm), and for 1911 through 1995 (425 mm). (b) Wavelet power spectrum of estimated flows under the PROJ 2050 climate scenario. Plot shows the relative amount of variance (i.e., power) contained in different portions of frequency domain over time. Relative power increases from yellow to orange to red, decreases from green to blue to purple. Concentrations of significant power (p < 0.05 compared to red noise ) are delimited by dashed lines. 10 of 13

11 would increase from 20.3% in the baseline to 28.1% under PROJ [39] Additional WB analyses were used to extend the tree ring precipitation and future temperature scenarios into the late 21st century. When tree ring precipitation from 1911 to 1995 is combined with temperatures from the PROJ 2025 scenario (Figure 7a), the magnitude (but not duration) of the 1930s worst case drought surpasses that of the 13th century Medieval megadrought as modeled under the TREE OBS scenario. When the lack of precipitation in the 1930s is combined with PROJ 2050 and PROJ 2100 temperatures, the WB model produces low flow periods whose magnitude surpasses anything in the TREE OBS estimates. Overall, these simulations suggest that seemingly small amounts of warming would result in a significant intensification of worst case scenario 1930s and 1950s type droughts. The combination of long term precipitation variability and future warming holds the potential for low flow events having durations, magnitudes, and intensities (i.e., magnitude/duration) well outside the range of observations. [40] The original tree ring precipitation reconstruction (Figure 2b) also includes four extended wet periods that produce sustained high flows in the WB estimates (Figure 7a). Two of these high flow periods are driven by twentieth century wetness, and only the pluvials of the early fourteenth and seventeenth centuries generate greater 25 year average flows in the WB model, indicating the unusual nature of the gauge period and calling into question assumptions surrounding observational records as a baseline for planning and analysis. When the PROJ 2025 and PROJ 2050 scenarios are applied, these fourteenth and seventeenth century pluvials provide the only cases when 25 year average flows exceed the baseline mean. Twenty five year average flows never exceed the baseline mean when the WB model is run on the PROJ 2100 scenario. Within the context of the WB model only the greatest precipitation experienced over the past 800+ years would be sufficient to overcome warming related reductions of UYR flows. [41] Comparing the interactions between paleoprecipitation and temperature forcing scenarios over time (Figure 7a) points to the inherent nonstationarity of potential responses to climate change. The full UYR precipitation reconstruction contains a number of marked shifts between persistent wet and dry regimes at decadal to multidecadal (D2M) time scales (Figure 2b). When combined with future temperatures, this D2M precipitation variability results in a wide range of UYR flow impacts ranging from conditions near or slightly below baseline (e.g., late fifteenth century and midsixteenth century) to extended excursions far below the baseline mean (early sixteenth century). Subjecting flow estimates to wavelet analysis demonstrates that within this underlying framework of D2M variability the dominant modes of precipitation and WB response are themselves nonstationary. Focusing on the PROJ 2050 output (Figure 7b), flow estimates tend to vary in a wide band from 25 to 45 years, with a second mode ranging from 65 to 80 years. However, significant power may disappear from either of these bands for decades to centuries at a time, and some time intervals (e.g., eighteenth through late nineteenth centuries) contain no significant D2M variability. D2M precipitation variability is likely related to complex interactions between linked ocean atmosphere processes operating across the globe [Gray et al. 2003, 2007; McCabe et al., 2008], and the effects will likely continue or intensify under global climate change scenarios [Goodkin et al., 2008]. If this is the case, such regime like behavior would alternately amplify or dampen the hydrologic impacts of warming. 4. Summary and Conclusions [42] The use of combined paleoreconstruction techniques and WB modeling offers a unique opportunity to explore a wide range of water supply scenarios for the central Rocky Mountain region, even as estimates for future precipitation remain uncertain. As in previous tree ring based studies from the Yellowstone and Missouri River basins [e.g., Gray et al., 2004, 2007] and surrounding areas [e.g., Gray et al., 2003; Cook et al., 2004; Woodhouse et al., 2006; Meko et al., 2007], these analyses show the potential for severe and sustained droughts far outside the range of instrumental observations, even in the absence of human induced climate change. Moreover, this study points to the inherent nonstationarity of regional hyrdoclimates. [43] Nonstationarity, expressed as both regime like behavior in regional discharge and shifts in the dominant time scales of variability, has three critical implications for water resource management. First, a series of unusually wet precipitation regimes and resulting high flow periods were defining features of twentieth century hydroclimate in the central Rockies. As a result, instrumental records may present an overly optimistic view of regional water supplies. Paleoclimatic studies by Cook et al. [2004] also show that in the context of the past 1200 years, the early twentieth century was unusually wet across the western United States, calling into question the relevance of instrumental records over a much larger area. Second, inherent regime like behavior in precipitation and other facets of the climate system could further complicate climate change monitoring, assessment, and planning through its ability to both mask and intensify the impacts of anthropogenic forcing. Last, manifestations of inherent nonstationarity in tree rings and other high resolution paleoclimatic records from western North America present the possibility of rapid climatic transitions that would challenge our capacity for adaptation and mitigation. [44] The analyses incorporating projected temperature trends suggest that median amounts of warming alone (i.e., in the absence of major precipitation change) could significantly reduce average streamflow in the central Rocky Mountain region. Such seemingly modest warming could greatly intensify the effects of any drought seen in the tree ring record or gauge observations. Within the context of our UYR model, only a future precipitation increase on par with the wettest pluvials of the past 800+ years would offset the negative effects of projected temperature change. Though this study does not include any analyses of water rights, reservoir operations, or other aspects of water resource management, it is clear that 1 3 C warming could place a great strain on regional water supplies. [45] Future studies in the UYR and other western U.S. basins need to consider the susceptibility of water resources to changes in precipitation seasonality. The creation of new high resolution seasonal moisture proxies would aid in these analyses. Similar approaches looking at the interacting effects of long term precipitation variability and climatic 11 of 13

12 change on consumptive water use would also be of great value. [46] A growing body of evidence points to recent humaninduced changes in western U.S. hydroclimate, and these changes are likely to continue for decades to come [Barnett et al., 2008; IPCC, 2007]. While inherent aridity makes the West susceptible to any type of climatic change, anthropogenic or otherwise, our historical reliance on observational records combined with an overarching assumption of system stationarity [Milly et al., 2008] have likely increased vulnerability to future change. Assessing water availability in the face of both human induced climate change and a more realistic portrayal of long term precipitation variability is a key challenge for researchers, resource managers, and policy makers alike. [47] Acknowledgments. S. T. 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