Snowpack trends in the contributing region to the Central Valley of California. James Bethune Senior Integrative Exercise March 10, 2010

Transcription

1 Snowpack trends in the contributing region to the Central Valley of California James Bethune Senior Integrative Exercise March 1, 21 Submitted in partial fulfilment of the requirement for a Bachelor of the Arts degree from Carleton College, Northfield, Minnesota

2 Table of Contents Introduction... 1 Data... 4 Grace Total Water Storage Anomaly... 6 Spatial and Temporal Variability in Snowpack Trends... 9 Local SWE Controls...14 Decadal-Scale Climatic Controls...18 Discussion...24 Drought Trends vs. Long-term Trends in Snowpack...24 Implications for Water Management...25 GRACE as a Monitoring Tool...27 Summary and Conclusion...28 Acknowledgements...29 References cited...3

3 Snowpack trends in the contributing region of the Central Valley of California James Bethune Carleton College Senior Integrative Exercise March 1, 21 Advisors Mary Savina, Carleton College Department of Geology James Famiglietti, University of Irvine Department of Earth Systems Science Remote sensing analyses by the Gravity Recovery and Climate Experiment (GRACE) indicate total water storage in the Sacramento and San Joaquin basins of Central California has decreased at a rate of 38 mm/yr of equivalent water height during the 249 water year timperiod. Drought conditions persisted from 269, providing a unique opportunity to show how drought conditions correspond to water resources in the Central Valley, a region dominated by irrigation-dependent agriculture. As of August 29, the drought had resulted in a total reduction of 31.3 km 3 of water, a volume roughly equivalent to Lake Mead. Of this decline, only 2 mm/yr are attributable to changes in snow-water equivalent (SWE). However, this low regional trend masks strong local signals of averaging - 12 mm/yr in the southern Sierra Ranges. There was no consistent SWE level trend in the northern Sierras, though the pattern of snow accumulation and melt changed, with increasing snow accumulation peaks and snowless increased at a rate of 14 days per year. SWE levels in the north correlate well with temperature, while SWE trends in the southern Sierras correlate more strongly with precipitation. These SWE trends are out of phase with longerterm (196s-present) trends in which SWE levels decreased in the northern Sierras, but increased in the southern Sierras, though the relative controls on SWE were found to be same as in other models. The drastic negative precipitation anomaly associated with the drought likely explains the discrepancy between the SWE trends calculated by this study and the long-term trends. Keywords: Snow-water equivalent, Central Valley, Sierra-Nevada Ranges, climate, GRACE

4 1 INTRODUCTION The contributing region to the Central Valley comprises California's largest hydrologic system, encompassing 74, km 2 in the more northern Sacramento Basin and 8, km 2 in the southern San Joaquin-Tulare Basin system (Fig. 1). The valley produces 8% of the nation s agricultural output by value (Faunt, 29), but is heavily dependant on irrigation due to its perennial water deficit and particularly long, dry summers. Snowpack in the west-draining Sierras represents 4% of the local fresh water supply for the valley (CDWR, 1998), and acts as a storage reservoir to modulate the seasonal cycle of runoff, providing water during the agriculturally active summer months (Andreadis and Lettenmaier, 29). Large-scale snowpack models indicate that snowpack levels in the western United States have been declining since the 196s (Mote, 26; Jefferson et al., 28). This recent decline in snowpack may be controlled by an extended warm phase of the Pacific Decadal Oscillation (PDO), lasting from 1977 to the late 199s, causing warmer temperatures over the north-western United States (Van Kirke and Naman, 28). Others have suggested that global climate change is responsible for these trends (Knowles and Cayan, 24). Whatever the control, the results have been increases in the percentage of precipitation falling as rain, increased runoff during the winter months, and increased snowpack melt in the early spring (Van Kirk and Naman, 28). These trends are not likely to reverse, as the Parallel Climate Model projects that under the business as usual greenhouse gas emissions scenario there will be a positive temperature shift of 2 C in California over the course of the 21 st century (Dai et al., 21). The effects climate change will have on precipitation are less clear, though

5 Sacramento R '"W 12 '"W 4 '"N 4 '"N Sacramento Basin Sierra Nevada Mountains Central Valley Stream San Joaquin R. Central Valley Stream Gauge Elevation (m) 4273 San Joaqin Basin '"N 35 '"N Kilometers 12 '"W Figure 1. Map of California, showing the Sacramento and San Joaquin rivers and contributing basins. The Sacramento-San Joaquin boundary divides the contributing region roughly in half, with 74, km2 in the more northern Sacramento Basin and 8, km2 in the more southern San Joaquin. The basins are underlain by a 3 arc second digital elevation model (DEM), light and dark shading represents high and low elevations, respectively. The DEM shows the Coastal Ranges to the west, the topographic low of the Central Valley, and the Sierra Nevada Ranges to the east. Notice the greater elevations of the Sierras contributing to the San Joaquin Basin.

6 3 over the past 3 years precipitation has increased throughout the Central Valley and contributing Sierras (Faunt, 29). The individual influences temperature and precipitation have on snowpack can be isolated because these two variables are not well-correlated during the cold season in western North America (Cayan, 1996). Modeling results indicate that snowpack levels in low to moderate elevations (from 1 to 22 m) negatively correlate with temperature, but only weakly correlate with precipitation (Mote, 26; Cayan, 25). These elevations tend to stay at or just below the freezing point, requiring only slight increases in temperature to drastically reduce the amount of snowfall and the duration of snowpack. Conversely, snowpack in high elevations (>22 m) correlates well with precipitation but does not correlate with temperature, because high elevations are generally cold enough that precipitation falls as snow (Mote, 26). Using the Sacramento-San Joaquin drainage divide to delimit northern and southern Sierras, snowpack across the northern Sierras has reflected the regional decreasing trend, but the snowpack in the southern Sierras has increased substantially over the past 6 years, likely driven by a moderate increase in precipitation over the past century (Hamlet et al., 25), combined with the high elevations that make the southern Sierra snowpack less sensitive to increases in temperature. Snowmelt timing controls water availability timing, and to a large extent how much water is lost to evapotranspiration. Under present day conditions, peak snow-pack development occurs on April 1 (Jefferson et al., 28), though throughout the Pacific Northwest, from the Cascades of Washington to the northern Sierras of California, the

7 4 local spring snowmelt peak has shifted earlier in the season between several days to as much as two weeks, and the average summer recession length has increased by 17 days since 195 (Jefferson et al., 28). Recent drought conditions in the valley have lasted from 26 until the present, and are causing widespread crop failures. At the time of this writing, the most recent annual drought summary released by the USGS was in January of 29, a quarter of the way through the final water year of this study (CDWR, Water Conditions Fact Sheet, 29). The drought summary put average surface water reservoir storage at 68% of average, and the Sacramento and San Joaquin River flow volume at the bottom 1% of the historical range. This drought is generally understood to be one of the worst on record (CDWR, Water Conditions Fact Sheet, 29). The goals of this study are two-fold: 1) to integrate snowpack trends into an understanding of the total water storage trend across the region, and 2) to characterize the trends presented in Sierra snowpack volume during the 24 to 29 water years. The study period may be too short to draw conclusions about long-term trends in the Sierra snowpack, but is useful in illustrating the behavior of snow-water resources during the drought. DATA The total water storage anomaly (TWSA) signal was captured with the remote sensing Gravity Recovery and Climate Experiment (GRACE), a dual satellite system in circumpolar orbit that continually measures gravity anomalies as proxies for changes in water storage. GRACE cannot differentiate between sources of mass change, but because GRACE measures

8 5 high magnitude mass changes over relatively short time intervals, the primary sources of the GRACE signal are assumed to be changes in water storage reservoirs, primarily groundwater (GW), surface water (SW), soil moisture (SM), and snow-water equivalent (SWE) (Yeh et al., 26). TWSA GW SM SW SWE, (1) Data from the satellites are reported in equivalent water height, representing a volume of water equal to the reported height multiplied by the area of the extracted region. The satellites have an accuracy of 1.5 cm of equivalent water height on basins 15, km 2, or greater (Tapley et al., 24), and a temporal resolution of one month. Data falls one month short of the 249 water years study period, as it was not yet available for September of 29. Snowpack data were acquired from the National Snow and Ice Data Center (NSIDC; and represent a combination of remotely sensed, field collected, and modeled data. The data are reported in mm of snow-water equivalent (SWE) height, a measure of the liquid-volume held by the snowpack. The data are available in daily time series grids from 23 to present, each covering the extent of the United States at 1 km resolution. Because of the size of the datasets and the length of time associated with their analysis, much of the analysis was performed on data that had been averaged by month. When daily data were required, as in the snowmelt timing analyses, the data were clipped to the extent of the west-draining Sierras. The USGS provided the Digital Elevation Model in 3 arc second resolution, equivalent to about 1.86 km (USGS, Vertical error is less than 15 m. Elevations along a profile are sampled with aerial photography at 8-1% of the

9 6 eventual resolution, then interpolated to fill in gaps. Temperature and precipitation data were obtained from the PRISM Climate Group (PRISM; The data cover the 249 water years in monthly resolution. Data are gridded in cells representing the monthly average value over a 4 km by 4 km area Temperature values are the average of daily maximum temperature estimates, reported in degrees C *1, while precipitation values represent monthly total precipitation, reported in mm*1. Streamflow was acquired from the United States Geological Survey (USGS) website [ Gauge sites on both the Sacramento and San Joaquin rivers were the furthest downstream available, so as to represent runoff from the maximal contributing area (Fig. 1). Evapotranspiration data were provided by Eric Rosenberg and Qiuhong Tang (Civil and Environmental Engineering, University of Washington). The data were modeled using the Moderate Resolution Imaging Spectroradiometer (MODIS) from satellite measurements at.5 degree resolution across the continental US. GRACE TOTAL WATER STORAGE ANOMALY The small size and narrow geometry of the Central Valley region created doubt as to whether GRACE could accurately isolate its gravity anomaly. To verify the accuracy of the GRACE signal, the monthly total water storage change (TWSC) was computed (Eq. 2) and compared with the sum of the primary water fluxes, precipitation (P), evapotranspiration (ET), and streamflow (Q) data: TWSC=TWSA(currentmonth) - TWSA(previousmonth) (2)

10 7 S t=p-et-q (3) If GRACE is accurately measuring the TWSA then the total change in water storage as measured by GRACE should equal S t as calculated by the sum of the modeled and measured fluxes. The results were qualitatively compared on a time series, and it was determined that although there is noise present in the data, the GRACE signal captures the magnitude and timing of the summed fluxes (Fig. 2), giving confidence to proceed with the TWSA trend analysis. The raw GRACE TWSA dataset contains a well defined harmonic annual signal, representing winter water-storage highs and summer water-storage lows (Fig. 3a), but because the dataset does not represent a complete integer number of annual cycles a trend must be fit to the anomaly, not the raw data. To conceptualize this fact, imagine fitting a trend line on a half-period of a cosine function. To mimic the annual signal, a harmonic function was created as a sum of sine and cosine curves: M f ( t) A [ cos(2 ) sin(2 )] A f t B f t k k k k (4) k 1 The annual signal was then subtracted from the raw data (Fig. 3a), leaving a time series of the TWSA anomaly, that represents the deviation of each monthly anomaly measurement from the expected anomaly at that month. The TWSA anomaly time series shows two distinct phases (Fig. 3b), increasing for the first year, then decreasing until the end of the studied time period. This is consistent in timing with the onset of the regional drought. Even with the initial increase, the TWSA anomaly decreased at a rate of 38 mm/yr over the studied period (Fig. 3B), though this trend is driven in part by 25 being one of the wettest water years on record (CDWR, Drought Assessment, 28). Due to the short time GRACE has been in operation it is difficult to

11 8 (mm/moth) Modeled Water Fluxes 3 A P 2 1 ET Q Year (mm) B TWSC: GRACE vs. P - ET - Q GRACE P-ET-Q Figure 2. GRACE TWSC signal vs. Modeled flux components of water cycle. A.) Water flux (mm/month) components of precipitation (P), evapotranspiration (ET), and streamflow (Q) plotted individually in a time series covering August 23-June 28. B.) Total water storage change from water balance (P-ET-Q) and GRACE derived total water storage change (TWSC) in general agreement in timing and magnitude of annual flux cycles. (mm) (mm) GRACE TSWA and derived annual signal TWSA Annual Signal Year B A Year GRACE TWSA with annual signal removed TWSA Trend = -38 mm/yr Year Figure 3. A.) GRACE Total Water Storage Anomaly (TWSA) time series over the combined Sacramento and San Joaquin Basins. Dashed line represents the annual signal present in the TWSA. B.) TWSA with annual signal removed, with trend line showing an average decrease of 38 mm/yr in TWSA during the 249 water years.

12 9 estimate how anomalously high water storage was in 25, or how much this affects the water storage trend. The TWSA was broken into its storage components of surface water, groundwater, soil moisture, and snow-water equivalent. Because it is difficult to measure the actual groundwater storage levels, groundwater was calculated as residual by rearranging the water storage equation: GW TWSA SWE SM SW, (5) The results show that during the 249 water years, 25 mm/yr were lost from groundwater, 9 mm/yr were lost to surface water, and 2 mm/yr were lost from each of SWE and soil moisture (Famiglietti, 29). SPATIAL AND TEMPORAL VARIABILITY IN SWE TRENDS As with the TWSA, the raw SWE signal contains an annual cycle that reflects the seasonal highs and lows of snowpack values during the course of a water year (Fig. 4a). Unlike the TWSA, the annual snowpack cycle does not represent a simple harmonic function because it contains a length of time each year in which the SWE levels are. To compute the SWE anomaly the raw monthly SWE data was averaged into a time series representing an average SWE-year for the study period, which was then subtracted from the raw data. The SWE anomaly was calculated for each grid cell in the dataset, fitted with a trend, and then remapped (Fig. 5). The study area holds two distinct regions of SWE trends, that of losing SWE in the southern Sierras and stable SWE in the north Sierras, roughly separated by the San Joaquin-Sacramento Basin boarder. The Central Valley and Coastal Ranges do not receive enough snow to drive a significant SWE trend. To find the average SWE trend

13 1 SWE (mm) A SWE - raw + annual signal Annual signal SWE level Year Sacamento Basin B SWE Anomaly (mm) SWE Anomaly SWE Anomaly Trend ( mm/yr) Year SWE (mm) C SWE- raw + annual signal Annual signal SWE level Year San Joaquin Basin D SWE Anomaly (mm) Year Figure 4. Snow-water equivalent data time series. A.) & C.) Average SWE height over the Sacramento and San Joaquin basins respectively. The derived annual signals represent average SWE years within the respective datasets. B.) & D.) SWE anomalies, found by subtracting the average SWE year from the raw data. Across the San Joaquin and Sacramento basins, snowpack of 27 was anomalously low. Despite this similarity, average SWE levels in the Sacramento show no general trend, while SWE levels in the San Joaquin are decreasing at a rate of -12 mm/yr. Data from the National Operational Hydrologic Remote Sensing Center (NOHRSC) SWE Anomaly Anomaly Trend (-12 mm/yr)

14 11 associated with these two regions, time series of average SWE-loss were calculated for each basin individually using the daily SWE data. The San Joaquin lost an average of 12 mm/yr in SWE from 249. Over the same time period, the Sacramento presented no SWE level trend, though the magnitude of maximum SWE pack increased (Fig. 4). A monthly time series does not have the temporal resolution to achieve meaningful results in analyses of SWE melt timing and snowless day counts. Instead daily SWE data were used for these analyses. To ensure that the localized signals presented by the monthly SWE trend analysis were preserved, the SWE melt timing analyses were preformed for on SWE data averaged over the southern and northern Sierras individually. Snowless days were tabulated by counting the number of days per year with average SWE equal to, and then plotted in a time series (Fig. 6). Snowless days increased throughout the Sierras, by 14 days/yr in the north (R 2 =.82) and 6 days/yr in the south (R 2 =.1), though the poor fit in the south calls this trend into question. The timing of spring runoff was examined by first calculating daily dswe/dt (use equation format) and summing the negative values for the two month of March and June. Assuming that snowpack did not melt on days that modeled increases in SWE, the result yields the magnitude of snowmelt by month. Plots of June SWE-melt against March SWEmelt show that the two are inversely correlated (R 2 =.8) in both the Sacramento and the San Joaquin Sierras (Fig. 7). The slope of the trend fitted to the southern Sierra data is steeper, signifying more snowmelt is lost in June per unit of snowmelt in March. Snowmelt in March and June shows no temporal trend (Fig. 8). This is surprising as one would expect snowpack in June to decrease in drought years, as increased summer recessions intrude further into June. Snowmelt in March could either increase due to earlier

15 '"W 122 '"W 121 '"W 12 '"W 119 '"W 118 '"W 42 '"N 42 '"N 41 '"N 41 '"N 4 '"N 4 '"N 39 '"N 39 '"N 38 '"N 38 '"N 37 '"N 37 '"N Sacramento Basin 36 '"N San Joaquin Basin 36 '"N SWE Trend (mm/yr) '"N Kilometers 35 '"N 123 '"W 122 '"W 121 '"W 12 '"W 119 '"W 118 '"W Figure 5. Map of snow-water equivalent (SWE) trends (mm/yr), calculated from monthly SWE data. Dark and light colors denote losing and gaining SWE trends. White regions in the valley are missing data. SWE trends are concentrated in the Sierra Ranges. There is higher SWE loss in the San Joaquin Basin as compared to those in the Sacramento Basin.

16 A Snowless days in the northern Sierras Snowless days y = 14*x + 5 Snowless days (SWE=) Snowless days (SWE=) B Snowless days y = 6.2*x + 22 Snowless days in the southern Sierras Figure 6. Number of snowless days in the A.) northern Sierras and the B.) southern Sierras. Both basins show increasing snowless days, though the trend is cleaner in the north (R 2 =.82), and steeper at 14 days/yr. In the south the trend is 6 days/yr, but the poor fit of the trend makes this result questionable (R 2 =.1).

17 14 melting or decrease due to perennial decreased snowpack. In summary, decreasing snowpack-duration combined with increasing magnitude of peak snowpack levels, led to no spatially uniform SWE trend in the northern Sierras, though the patterns of both snowpack development and snowmelt changed over the study period (Figs. 4 and 5). In contrast to the results of the northern Sierras, the magnitude of peak snowpack development in the southern Sierras declined over the study period, and the duration of the summer recession increased. These two effects created a regionally strong negative snowpack trend (Fig. 4). LOCAL SWE CONTROLS To isolate the affects precipitation and temperature have on SWE trends, a trend was calculated for each cell in the precipitation and temperature grids over both the northern and southern Sierras (Fig. 9; Fig. 1). The resultant precipitation trend map shows more instances of increasing precipitation over the southern Sierras (average = mm/yr), with large regions of sharply decreasing precipitation adjacent to regions of increasing precipitation. Precipitation trends are more uniformly negative in the northern Sierras (average = -1.8 mm/yr), though isolated pockets of increasing precipitation do occur. The temperature trend map shows that the greatest temperature increases were over the southeast Sierras, where elevations are the highest (average =.5 C/yr). In the highest elevations of the northern Sierras temperature stayed relatively constant (average =.4 C /yr). The precipitation and temperature trend maps were then overlain with the SWE trend map, and zonal statistics were calculated using the grid cells of the control variables as zones (Fig. 11), averaging the SWE trends over a given control variable value. The results show

18 15 June SWE melt (mm) June SWE melt (mm) March SWE melt (mm) A B Northern Sierras Southern Sierras Year y = -.45*x + 58 y = - 1.9*x March SWE melt (mm) Year Figure 7. Total Snow-water Equivalent (SWE) melt in June vs. total SWE melt in March for each studied year in both the A.) north and B.) south Sierras. The steeper slope in the south as compared to the north indicates decreased snowmelt in June per unit of snowmelt in March. R 2 =.8 in both regions. SWE melt (mm) A Northern Sierras March snowloss June snowloss SWE melt (mm) B Southern Sierras March snow-loss June snow-loss Figure 8. Total snow-water equivalent (SWE) loss during June and March of 249 in both the A.) north and B.) south Sierras. March and June signals are out of phase in both the north and south Sierras. There is no temporal trend.

19 '"W 122 '"W 121 '"W 12 '"W 119 '"W 118 '"W 42 '"N 42 '"N 41 '"N 41 '"N 4 '"N 4 '"N 39 '"N 39 '"N 38 '"N 38 '"N 37 '"N 37 '"N Sacramento Basin San Joaquin Basin 36 '"N 36 '"N Precip Trend (mm/yr) '"N '"N Kilometers 123 '"W 122 '"W 121 '"W 12 '"W 119 '"W 118 '"W Figure 9. Map of precipitation trends (mm/yr) in the west-draining Sierras over the 249 water years. Average values are -1.8 mm/yr in the northern Sierras, and mm/yr in the southern Sierras. Data from PRISM.

20 '"W 122 '"W 121 '"W 12 '"W 119 '"W 118 '"W 42 '"N 42 '"N 41 '"N 41 '"N 4 '"N 4 '"N 39 '"N 39 '"N 38 '"N 38 '"N 37 '"N 37 '"N Sacramento Basin 36 '"N San Joaquin Basin 36 '"N Temp Trend (C/yr) '"N Kilometers 35 '"N 123 '"W 122 '"W 121 '"W 12 '"W 119 '"W 118 '"W Figure 1. Map of temperature trends (C/yr) in the west-draining Sierras over the 249 water years. Spatial resolution is 4 km. Average values are.4 C/yr in the north and.5 C/yr in the southern Sierras. Data from PRISM.

21 18 that snowpack in the northern Sierras was more sensitive to changes in temperature (R 2 =.46) than to changes in precipitation (R 2 =.23, Figs. 12, 13). The snowpack of the southern Sierras did not correlate well with either variable, though it was more sensitive to changes in precipitation (R 2 =.23) than changes in temperature (R 2 =.4, Figs. 12, 13). Zonal statistics were calculated averaging the SWE trends in the cells of a Digital Elevation Model. The results, plotted for the northern and southern Sierras individually (Fig. 13), show that both the northern and southern Sierras lose increasing SWE from higher elevations, though the general shape of these trends are markedly different. In the Sacramento SWE losses increase from 15 m to 22 m. Though this trend increases from 22 to 25 m it becomes a looser fit. Above 25 m the data are too variable to support any trend. In the San Joaquin SWE loss increases from 15 to 24 m, stays steady from 24 to 34 m. Above this elevation SWE trends are again highly variable, though there is a weak trend of gaining SWE with increased elevations. DECADAL-SCALE CLIMATIC CONTROLS Previous studies have found that Pacific climate variability is responsible for 1-6% of SWE trends in the western United States (Mote, 26). On the scale of this study, there are two climatic indices that could be driving snowpack trends, the El Nino-Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO). ENSO describes a 5-7 year periodic cycle with warm phases of ENSO that are associated with anomalously cool and wet weather in the southwest USA, including over the southern Sierras, and anomalously dry and warm weather over the northern Sierras (Dettinger, 1995). Cool phases of ENSO have cool wet conditions in the north, and warm dry conditions in the south. ENSO conditions that persist

22 19 Zonal Statistics Zone Raster Value Raster (SWE Trends) The zone raster defines the zonal shapes into which the value raster will be averaged. This study uses precipitation and temperature trend data, as well as elevation data to define zones. The value raster holds the values to be averaged, the SWE trend data in this study. 1 2 Zoned Values Raster Output Table Zone Average Value The overlay operation averages the value raster in the zones defined by the zone raster, then exports the results to a table Figure 11. Schematic illustration showing the computational steps involved in the zonal statistics analyses of local SWE controls, including precipitation temperature and elevation.

23 2 SWE Trends vs. Precipitation Trends 6 4 Sacramento San Joaquin SWE Trend (mm/yr) Precipitation Trend (mm/yr) Figure 12. Plots of averaged SWE trends (mm/yr) against precipitation trends (mm/yr), with both Sacramento (black circles) and San Joaquin (blue squares) data shown. Both data display relatively weak trends (R =.23), but with markedly different patterns. The Sacramento data shows a negative trend at the lowest precipitation trend values, before levelling off to essentially no trend, while the San Joaquin data shows a positive relationship between precipitation and SWE at low precipitation trend values, but becomes variable over the highest precipitation trends. SWE Trends vs. Temperature Trends 3 2 Sacramento San Joaquin SWE Trend (mm/yr) Temperature Trend (C/yr) Figure 13. Plots of averaged SWE trends (mm/yr) against temperature trends (C/yr), with both the Sacramento (black circles) and San Joaquin (blue squares) data shown. Although both datasets display similar patterns, data for the Sacramento shows a moderate correlation (R =.46), but data for the San Joaquin shows no correlation (R =.4).

24 A B Elevation (m) Figure 14. Plots of averaged SWE trends for given elevations in the northern Sierras. Not enough snow falls below 15m to drive a trend, but above 15 m there are 2 distinct regions, displayed in higher resolution in sub figures: A.) marginally decreasing SWE trends from 152 m B.) variable but generally more steeply decreasing SWE trends above 22 m B Average SWE Trend (mm/yr) Average SWE Trend (mm/yr) A Average SWE Trend (mm/yr) Elevation (m) Elevation (m) 21

25 22 4 C Elevation (m) A Average SWE Trend (mm/yr) Average SWE Trend (mm/yr) A B Elevation (m) B C Elevation (m) Average SWE Trend (mm/yr) Average SWE Trend (mm/yr) Elevation (m) Figure 15. Plots of averaged SWE trends against elevations for the southern Sierras. Above 15 m plots shows 3 distinct regions, displayed in higher resolution in sub figures. A.) Decreasing SWE trends (R2 =.75) in the moderate altitudes. B.) Steady SWE trends from 244 m. C.) Highly variable SWE trends above 34 m. Note that the axes are different.

26 23 for longer than 6 months are referred to as El Nino or La Nina events, for warm and cool phases respectively. Previous work has correlated El Nino events with low SWE levels in the northern Sierras and high SWE levels in the southern Sierras, and La Nina events with high SWE levels in the northern Sierras and low SWE levels in the southern Sierras (Dettinger, 1995). Recent ENSO activity includes an El Nino event in the 27 water year and a La Nina event in 28. Instead of increasing SWE levels in the southern Sierras, the 27 El Nino event corresponds to the lowest SWE levels of the studied time period (Fig. 4). The 27 water year in the north Sierras shows a strong negative anomaly, as one would expect. Snowless days also jumped up in 27 (Fig. 6). SWE conditions then swing to the opposite extreme during the La Nina event in 28, reaching the highest positive anomaly of the dataset. In the San Joaquin, instead of lowering the SWE levels, the La Nina event seems to have no effect. Another climatic index of Pacific Ocean climate that exerts control on the northern Sierra snowpack is the Pacific Decadal Oscillation (PDO). Warm phases of the PDO correlate with above average temperatures and precipitation in the Pacific Northwest, extending as far south as the northern Sierras, but not to the southern Sierras (Mote, 26). In general, warm phases are associated with lower SWE levels, particularly at low to moderate elevations (155 m). Starting in 1998 the PDO entered a cool phase lasting only a few years, which was followed by a weak warm phase. If the PDO is controlling SWE patterns during the studied time period there should be a decrease in SWE levels over the northern Sierras, but there was no regional SWE trend in the northern Sierras. This is likely due to a lack of a strong or

27 24 consistent signal in the PDO during the studied time period. DISCUSSION Drought Trends vs. Long-Term Trends in Snowpack Though the relative importance of control variables in determining the snowpack trends calculated by this study mirror those found in other models, the results of this study showing decreasing snowpack in the southern Sierras and stable snowpack in the northern Sierras conflict with the regional long-term trends found in previous models (Hamlet et al., 25; Knowles et al., 26; Jefferson et al., 28). It seems a natural starting point to assume that the drought conditions of the study period were responsible for the reversal of snowpack trends. In support of this theory, the precipitation anomaly was strongly negative, and the spatial picture reflects a system in which precipitation trends control the regional snowpack trends (Figs. 1, 11), such that regions more correlated with precipitation trends than temperature trends lost snowpack, but those correlated less with precipitation than with temperature had stable snowpack. But this picture is complicated by two factors. First, there was no appreciable snowpack trend in the temperature-dependant northern Sierras, despite an average temperature trend of.4 C/yr (Fig. 1). Second, the northern Sierras were equally correlated to precipitation changes as the southern Sierras. One explanation for the first factor is that although temperatures climbed during the study period, the change was minor when compared to the shift in precipitation. The average temperature increase calculated by this study of.4 C/yr doubles the.2 C/yr temperature trend calculated by the IPCC (27). Precipitation in the 269 water years was far below average and decreased at an average rate of 9. mm/yr from 249, resulting in

28 cm less precipitation by the end of the study. In a region with normal precipitation as low as 2 cm/yr (Faunt, 29), a year with 5.5 cm less precipitation represents a substantial anomaly. In summary, the temperature trend was elevated over background levels, but the precipitation trend represents a complete turnaround from the long-term trend. The decreasing temperature trends were greater on average in the northern Sierras than in the southern Sierras (-1 mm/yr vs. -7 mm/yr). All else being equal, with equal SWEprecipitation trend correlations across the Sierras, one would expect the northern Sierras to lose more SWE. Both regions correlated poorly with precipitation, so there are likely confounding factors that are not controlled for in this study. Both regions have elevationally dependent snowpack trends, with both the highest rate of snowpack loss and the most total snowpack loss at moderate elevations. Lower elevations do not get enough seasonal snow to drive a trend. Higher elevations were highly variable in their snowpack trends, indicating a stability buffer against increasing temperatures at elevations above 25 m. Knowles and Cayan (24) found the exact range of elevation with the greatest SWE loss to differ between basins, with a SWE loss maximum at 15 m in the northern Sierras but a more distributed pattern of SWE loss in the southern Sierras. This is explained by substantially more area above 2 m in the southern Sierras, though for two regions of similar elevation profiles one would expect the SWE-loss maximum to shift to higher elevations at lower latitudes due to increased temperatures. The differences in temperature trends between basins may have confounded the results of this study. Implications for Water Management If these snowpack trends represent typical drought behavior they can have profound

29 26 implications for water management in the valley. Substantial amounts of water are imported from the Sacramento Basin each year to meet both agricultural and residential demand in the perennially water stressed San Joaquin Basin, but in years of drought the imports fall short of demand (Faunt, 29). That the higher elevations of the southern Sierras are more sensitive to the precipitation changes of a drought only serves to exacerbate the problem, decreasing both the total volume of water falling in the ranges, and the percentage that is stored as snow. When fields are left fallow and farmers are left without work the results are damaging to the economy of California and the food supply of the nation. The increase in snowless days of 14 days/yr in the northern Sierras can have dire consequences for ecosystems and water resources, causing increased evapotranspiration and salinity. In addition to contaminating drinking water, increased salinity stresses crops and impacts sensitive ecosystems, the San Francisco Estuary, for example (Mote, 26). The quantity of water that is lost to evapotranspiration depends largely on how the reservoirs are managed and proved to be beyond the scope of this paper. The snowless day increases presented in this paper far exceed the regional average rate of increasing snowless days.35 days/yr (Jefferson et al., 28). Obviously the trend of increasing snowless days could not continue indefinitely, and the Sierra snowless days plot does seem to level out in the last couple years. Unfortunately, this study period is too short to draw conclusions about the maximum summer recession duration. Fortunately, the high elevations of the southern Sierras buffer the snowpack against the immediate effects of increased the temperatures projected by climate change models. However, warming does make the Sierras more sensitive to droughts. Results of this study show that June snowmelt is disproportionally affected by March snowmelt, shifting

30 27 snowmelt away from the water stressed summers. Even if the long-term precipitation trends resume and precipitation continues to increase, increasing temperatures will increase winter and early spring runoff, such that the total SWE trend could increase even while the pattern of snowpack development and melt actually decreases the availability of snowmelt as a resource. GRACE as a Monitoring Tool GRACE indicates a total water storage decline of -38 mm/yr, but cannot attribute this decline to any specific storage reservoir, nor could it achieve finer resolution than the combined Sacramento-San Joaquin Basins. This and other studies show that the SWE trends over the Sacramento and San Joaquin are out of phase, making snow-water trends aggregated on the level of GRACE resolution less useful for water management. Given the difficulty of measuring or modeling groundwater in the region, and the ease with which groundwater can be extracted from GRACE as residual (Eq. 5), GRACE may be more useful as a groundwater monitoring tool. Unlike regionally out-of-phase snowpack, the Central Valley Aquifer is continuous and texturally homogeneous across both basins (Faunt, 29). Unfortunately, there is evidence that groundwater levels of the San Joaquin and Sacramento basins behave independently, at least on short timescales (Faunt, 29). Recent data show that groundwater levels are stable in the Sacramento Basin, even during times of drought, while groundwater in the San Joaquin loses groundwater during droughts (Faunt, 29). The results of this study may help explain this discrepancy in groundwater stability, given that snowmelt is one of the primary natural sources of surface and ground water

31 28 recharge. Certainly, the decrease in snowpack seen in this study affects downstream reservoirs, though this relationship is complicated by the convoluted nature of water management in the region. In light of the changes in its recharge trends and historical data, it is likely that the San Joaquin Basin is the source of a disproportional amount of the regional water loss. SUMMARY AND CONCLUSION The San Joaquin and Sacramento basins have 31.3 km 3 of water over the past six years, a volume equivalent to Lake Mead. This caused massive agricultural losses and unease in a region beset by water stress even in wet years. Only a small fraction of this water-loss total is attributable to decreases in snowpack, but the low regional trend masks strong localized trends of heavy snowpack losses in the southern sierras. The higher elevations of the southern Sierras make them relatively insensitive to changes in temperature, as compared to the northern Sierras. Both basins were equally sensitive to precipitation trends, but the greater precipitation trends in the San Joaquin resulted in greater snowpack loss over the San Joaquin. But to put these water-loss trends in perspective, over the same time period India lost 4 cm/yr of total water storage, more than an order of magnitude greater than California (Famiglietti, 29). And while California's water problems are dire, there is no reason to believe that the drought trends will extend into the future. In fact, the long-term snowpack trends show increasing snowpack over the Sierras of the San Joaquin Basin. This increases drought resistance, and buffers the long dry summers with a more temporally distributed

32 29 water supply. While snowpack is decreasing in the northern Sierras, precipitation is not. The summer recession may be increasing, but adequate water supplies can be maintained with an appropriate management strategy. The general strategy for surface reservoir management in California is to treat winter and spring runoff as a flood hazard, quickly released in order to free reservoir flood control space (Mote, 26). After April, runoff is managed for redistribution and consumption. Drought years in the San Joaquin see decreased snowmelt totals, with an earlier runoff pulse that is more likely to be managed as flood water, rather than a resource. In light of the increasing summer recession, flexibility needs to be built into this strategy. ACKNOWLEDGMENTS This work was built on a foundation of work performed at UC Irvine over the past five years. Professor James Famiglietti provided the vision, organized the National Science Foundation funding and people. Without him this work would not have possible. Before I knew the project existed, Karli Anderson and Stephanie Ho had already made great strides in deciphering the GRACE results over California. MinHui Lo, JT Reagar, and Caroline delinage spent tremendous amounts of time and exercised great patience in assisting with this project from start to finish. MinHui in particular was always ready to help. My Carleton advisor Mary Savina provided valuable insights on the paper and kindly patiently allowed me to explore all comps options before finding my way to this.

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