In bad waters: Water year classification in nonstationary climates

Sarah E. Null1 and Joshua H. Viers2

Received 22 June 2012; revised 12 November 2012; accepted 9 January 2013.

[1] Water year indices and drought indices are helpful for categorizing water years intosimilar types, allowing water managers and policymakers to quantify years, visualizevariability, and guide water operations. Many water management decisions, such asenvironmental flow requirements and water supply allocations, are based on water year typedesignations. They vary by region and index, but are defined by runoff in the current wateryear compared to average historical runoff, with numerical thresholds categorizing yeartypes. California’s Sacramento Valley and San Joaquin Valley Indices are used as casestudies to examine how climate change affects indices. Modeled streamflow for 1951–2099from the climate-forced Variable Infiltration Capacity hydrologic model estimate potentialchanges in runoff and water year type frequency. We show that the frequency of water yeartypes changes significantly with climate change and different strategies to adapt water yearclassification indices to climate change affect water allocations as much as the impacts fromchanging hydroclimatic conditions.

Citation: Null, S. E., and J. H. Viers (2013), In bad waters: Water year classification in nonstationary climates, Water Resour. Res.,49, doi:10.1002/wrcr.20097.

1. Introduction

[2] Water management frameworks that were designedassuming stationary climate conditions will be increasinglydifficult to implement in nonstationary climates and presenta barrier to climate change adaptation. Given that manywater allocation decisions are based on arbitrary thresholdsof historical data that are stationary and limited in duration,we must ask: will status quo approaches, which use fixedmanagement prescriptions based on hydrologic stationarity,be used in a future with changing hydroclimatic condi-tions? Will such approaches result in ad hoc and reaction-ary management solutions poorly suited to meet complexwater demands? Or will adaptive, anticipatory solutions beenacted to more equitably sustain water resources in a non-stationary environment?

[3] These questions are particularly pressing for waterresources management of large rivers. Existing research hasshown that climate warming is expected to change the tim-ing, magnitude, frequency, and form of precipitation,impacting water supply [Milly et al., 2008; Barnett et al.,2008; Rajagopalan et al., 2009], hydropower generation[Viers, 2011; Madani and Lund, 2009], and flood protection[Zhu et al., 2007; Kundzewicz et al., 2008], as well as spe-cies, ecosystems, and ecosystem services [Dudgeon et al.,

2006; Shaw et al., 2011]. Water allocation frameworks andenvironmental regulatory laws were designed assuming cli-matic stationarity, and more research is needed for adaptingthem for climate warming [Miller et al., 1997; Cohen et al.,2000]. This is a notable information gap as water managersmust respond to changing hydroclimatic conditions to sus-tain human and environmental water demands.

[4] Water year classification systems and hydrologicindices are common for water planning and managementbecause they simplify complex hydrology into a single, nu-merical metric that can be used in rule-based decision mak-ing [Dracup et al., 1980; Heim, 2002; Quiring, 2009].Estimated unimpaired runoff for a water year is categorizedby year type, such as wet, dry, or normal, compared to his-torical averages. Year type classification is tied to waterresources planning, helping to answer the question ofwhether there is ‘‘enough’’ water [Redmond, 2002], andallocations for various water uses are adjusted based onwater year type (WYT) designation. WYTs inform waterallocation decisions for water supply, hydroelectric powergeneration, reservoir storage, and environmental protection[Simpson et al., 2004]. Many drought and water year indi-ces exist, including the Palmer Drought Severity Index[McKee et al., 1993], Standard Precipitation Index [McKeeet al., 1993], Surface Water Supply Index [Shafer andDezman, 1982], Reclamation Drought Index [Weghorst,1996], and deciles [Gibbs and Maher, 1967]. All indices com-pare current year streamflow to the historical average andmay not capture changing trends in nonstationary climates.

[5] In this paper, we illustrate this problem using Califor-nia’s Sacramento and San Joaquin Valley Indices as case stud-ies to evaluate: (1) how water year classification indices mayfunction with climate change, (2) how indices can be updatedfor changing hydroclimatic conditions, and (3) how antici-pated changes affect allocations to competing water users.

1Department of Watershed Sciences, Utah State University, Logan,Utah, USA.

2Center for Watershed Sciences, University of California, Davis, Cali-fornia, USA.

Corresponding author: S. E. Null, Department of Watershed Sciences,Utah State University, 5210 Old Main Hill, Logan, UT 84322, USA.(sarah.null@usu.edu)

©2013. American Geophysical Union. All Rights Reserved.0043-1397/13/10.1002/wrcr.20097

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WATER RESOURCES RESEARCH, VOL. 49, 1–12, doi:10.1002/wrcr.20097, 2013

[6] Previous research indicates that the distribution ofWYTs is not stationary through time [Booth et al., 2006;VanRheenen et al., 2004; Vicuna, 2006]. Existing studieshave suggested changing WYT thresholds to maintain his-torical distribution of WYTs for human water uses [Milleret al., 1997; VanRheenen et al., 2004] or changing theseasonal weight coefficients in water year indices to reflectclimate-driven changes in inflow timing [Vicuna, 2006].Other research has focused on improved understanding ofthe effects of El Nino-Southern Oscillation events orincluding the paleoclimatic record in hydrologic indices[Anderson et al., 2001; Verdon-Kidd and Kiem, 2010].There has been little research on climate change impacts toenvironmental flows and adaptation strategies, except forgeneral agreement that competition could increase for min-imum instream flow allocations [VanRheenen et al., 2004;Meyer et al., 1999], increasing economic costs of environ-mental requirements [Harou et al., 2010].

2. Background

2.1. California’s Sacramento–San Joaquin Bay Delta

[7] The Sacramento and San Joaquin Rivers drain thewest slope of the Sierra Nevada mountain range, thenmerge and flow through the Sacramento–San Joaquin BayDelta (Bay Delta) to the Pacific Ocean (Figure 1). Meas-ured historical (1906–2009) average annual flow for bothbasins is 29,600 million cubic meters (mcm), with the Sac-ramento River contributing approximately three fourths ofthe flow. No observed long-term trend of increasing ordecreasing discharge is evident for the Sacramento Riverusing measured data from 1906 to 2009 (p¼ 0.773) or for theSan Joaquin River using measured data from 1901 to 2009(p¼ 0.285) (Figure 2). These rivers provide approximately

43% of California’s total average annual surface runoff andprovide drinking water for about 24 million residents. Thisregion has a Mediterranean-montane climate and a notablyvariable hydrology, where interannual variability is less pre-dictable than seasonal or geographic variability.

[8] The Bay Delta serves as the primary water wheel forCalifornia, and is ground zero for extreme sociopoliticalconflict over water transfer schemes [Connick and Innes,2003], habitat degradation [Light and Marchetti, 2007],and failure to proactively manage California’s waterresources [Null et al., 2012]. The state relies on pumpingfacilities in the Bay Delta to divert water south to urbanand agricultural users. This necessitates continuous fresh-water purging of the seasonally brackish delta. The BayDelta is an environmentally sensitive area, which provides

Figure 1. Sacramento and San Joaquin watersheds with gage locations.

Figure 2. Observed discharge for the SVI (1906–2009)and SJI (1901–2009). p values reflect the probability of thelinear trend is nonstationary.

NULL AND VIERS: WATER YEAR CLASSIFICATION IN NONSTATIONARY CLIMATES

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habitat for fish and wildlife (some species are protected underthe state and federal Endangered Species Acts), and holdspublic trust value for common use (State Water ResourcesControl Board (SWRCB), http://www.waterboards.ca.gov/waterrights/board_decisions/adopted_orders/decisions/d1600_d1649/wrd1641_1999dec29.pdf, 2000). Adequate environ-mental water allocations are needed to protect habitat andbiodiversity, and regulatory requirements sometimes drivewater management in this water scarce region. Thus, it isnot surprising that for decades the Bay Delta has been thesource of lawsuits, headaches, and hand-wringing [Connickand Innes, 2003].

2.2. Water Year Indexing

[9] The Sacramento Valley Index (SVI) and San JoaquinValley Index (SJI) were designed with estimated unim-paired historical hydrology and are used in a complex andevolving water delivery allocation scheme shaped by opera-tional constraints, regulatory restrictions, and objectivedemands (SWRCB, online, 2000). Numerical thresholdsseparate WYTs, set by weighted winter and spring runoffvolume for major rivers, as well as the previous year’s index(Table 1). The indices were intended to divide runoff intowet, above normal, below normal, dry, and critical catego-ries (originally weighted approximately 30%, 20%, 20%,15%, and 15%), respectively, of the historic record [Califor-nia Department of Water Resources (CDWR), 1989, 1991](Figure 3). The SVI was developed by the SWRCB in 1989from a previously existing Sacramento River classificationscheme, and the SJI was developed in 1991 [CDWR, 1989,1991].2.2.1. Sacramento Valley Index

[10] The SVI (also known as the ‘‘4 River Index’’ and the‘‘40-30-30 Index’’) uses the sum of calculated monthly unim-paired runoff from the following gages: Sacramento Riverabove Bend Bridge, Feather River at Oroville, Yuba Rivernear Smartsville, and American River below Folsom Lake(California Data Exchange Center (CDEC), http://cdec.water.ca.gov/cgi-progs/iodir/WSIHIST, 2010) (Figure 1). Itis calculated using equation (1), and year type classificationis based on the thresholds in Table 1. The term for the previ-ous year’s index is a proxy for carryover reservoir storage onsystem capability [CDWR, 1989].

SVI ¼ 0:4� current April–July runoffÞðþ ð0:3�current October–March runoffÞþ 0:3� previous year0s indexÞ:ð

(1)

2.2.2. San Joaquin Valley Index[11] The SVI and SJI were intentionally given different

weights on each segment of the index to account for snow-

melt-dominated runoff and occasional large winter floodsthat provide less water deliveries in the San Joaquin basin[CDWR, 1991]. San Joaquin watersheds are generallyhigher elevation with geology that limits infiltration ofgroundwater. The SJI (or the ‘‘60-20-20 Index’’) uses thesum of unimpaired runoff from Stanislaus River belowGoodwin Dam, Tuolumne River below La Grange Dam,Merced River below Merced Falls, and San Joaquin Riverinflow to Millerton Lake (CDEC, online, 2010) (Figure 1).It is calculated using equation (2), and year type thresholdsare based on the values in Table 1.

SJI ¼ 0:6� current April–July runoffÞðþ ð0:2�current October–March runoffÞþ 0:2� previous year0s indexÞ:ð

(2)

2.3. Historical Water Year Thresholds

[12] For planning purposes, year types are set by forecastsbeginning in February (and updated monthly through May),although for this study we use calculated unimpaired runoff(CDEC, online, 2010) or modeled data. Values of the SVIand SJI account for geographic variation in streamflow, sothe SVI has greater thresholds than the SJI (Table 1). The his-torical relative frequency of year types also varies slightlybetween the SVI and SJI (Figure 3). For example, the thresh-old for critically dry year types falls at the 13th percentile ofthe observed period of record for Sacramento Valley stream-flow, but at the 17th percentile for San Joaquin Valleystreamflow. Operationally, this means there is a slightlyhigher chance that any year will be critically dry in the SanJoaquin Valley, and more environmental flow is allocatedfrom Sacramento Valley rivers than the San Joaquin rivers.The opposite is true for dry and below-normal year types.

2.4. Water Allocation

[13] Generally, the SVI and SJI determine WYT for Cal-ifornia’s two largest water projects, the State Water Project

Table 1. Sacramento Valley Index and San Joaquin Valley Index Year Type Classification Thresholds in Millions of Cubic Meters(mcm) and Millions of Acre-Feet (maf)

Water Year Type Sacramento Valley Index mcm (maf) San Joaquin Valley Index mcm (maf)

Wet �11,348 (�9.2) �4,687 (�3.8)Above normal >9,621 to <11,348 (>7.8 to <9.2) >3,824 to <4,687 (>3.1 to <3.8)Below normal >8,018 to �9,621 (>6.5 to �7.8) >3,084 to �3,824 (>2.5 to �3.1)Dry >6,661 to �8,018 (>5.4 to �6.5) >2,590 to �3,084 (>2.1 to �2.5)Critical �6,661 (�5.4) �2,590 (�2.1)

Figure 3. Percentile of WYT with observed data (1906–2000).

NULL AND VIERS: WATER YEAR CLASSIFICATION IN NONSTATIONARY CLIMATES

3

(SWP) and the federally funded Central Valley Project(CVP) to allocate water for out-of-stream agricultural usersin the Bay Delta, environmental flows, and export limits towater users south of the Bay Delta (SWRCB, online, 2000).Environmental flow objectives for the region include BayDelta outflow, flow-dependent salinity and water tempera-ture objectives, and instream flows for rivers in the Sacra-mento and San Joaquin watersheds. The SVI is the mostimportant for managing the Bay Delta, although the SJIimpacts environmental flow objectives and the ‘‘EightRiver Index’’ uses both Sacramento and San Joaquin sys-tem runoff to determine salinity in Suisun Bay. The SVIand SJI were designed with the understanding that watershortages would occur in critical year types (M. Roos, per-sonal communication, 2011). In reality, water scarcityexists in critical, dry, and below normal year types duringAugust, September, and October (SWRCB, online, 2000).The SVI and SJI directly influence water policy in the statethrough regulatory restrictions and directly affect dozens offederal, state, and local agencies [Simpson et al., 2004].

3. Methods

3.1. Climate-Forced Hydrology

[14] Downscaled, climate-forced streamflow estimatesare from the Variable Infiltration Capacity (VIC) model, alarge-scale, distributed, physically based hydrologic modelthat balances surface energy and water over a grid [Maureret al., 2002; Liang et al., 1994]. VIC uses subgrid represen-tation for vegetation, soils, and topography to retain localvariability for partitioning precipitation into runoff andinfiltration and uses nonlinear representation for simulatingbaseflow. Data were downscaled using bias correction andspatial downscaling (BCSD), a statistical downscalingmethod that preserves monthly climate patterns betweencourse and fine resolutions [Maurer and Hidalgo, 2008].Water routing was postprocessed to estimate streamflow atriver outlets using an algorithm developed by Lohmannet al. [1996] [as cited in Cayan et al., 2008]. Parameteriza-tion for deriving streamflow is identical to that used byVanRheenen et al. [2004] for the Sacramento–San Joaquinbasin. VIC is commonly used to assess hydrologic effects ofclimate change in the western United States [VanRheenen

et al., 2004; Maurer et al., 2002; Cayan et al., 2008; Vicunaet al., 2007].

[15] This application of VIC uses a 1/8� spatial grid and adaily timestep (later aggregated to a monthly timestep) for1951–2099 water years. Twelve VIC runs were analyzed,with climate input data from six general circulation models(GCMs) for the A2 and B1 emissions scenarios (Figure 4).These models and emissions scenarios are consistent withresearch from California Energy Commission’s climatechange research center [Cayan et al., 2012; CaliforniaEnergy Commission (CEC), http://www.climatechange.ca.gov/research/index.html, 2012]. Modeled water years areseparated into three time periods and GCMs simulated his-torical conditions from 1951 to 2000 to represent interannualvariability [Cayan et al., 2008]. Here 2001–2050 representsthe near-term future, and 2051–2099 are far-term estimatesof runoff conditions. Water years (October–September) areused throughout this paper.

[16] Differences between emissions scenarios are due touncertainty in human actions such as population growthand greenhouse gas (GHG) emissions, while differences inGCMs are from uncertainty in climate models such as rep-resentation of physical processes and sensitivity to GHGforcings. The A2 scenario has more severe climate change,assuming maximum carbon dioxide (CO2) emissions of850 parts per million (ppm), continuously increasing globalpopulation, and slow economic growth. The B1 scenario ismore moderate, assuming maximum CO2 emissions of 550ppm, global population that peaks mid-century and laterdeclines, and global sustainability solutions that introduceresource-efficient technology [Intergovernmental Panel onClimate Change, 2000]. Other potential future changes(such as land cover change) are ignored here.

3.2. Statistical Analysis

[17] Nonparametric Kruskal–Wallis and Wilcoxon testswere used to determine whether differences in mean runoffbetween modeled and observed data or different modeledtime periods were statistically significant. Distributions ofthe modeled historical 1951–2000 datasets (modeled A2and B1 simulations) were tested against observed historicaldata for the same time period. Modeled distributions fromGCM-driven VIC simulations for each emissions scenario

Figure 4. Climate scenarios, GCMs, and modeled time periods.

NULL AND VIERS: WATER YEAR CLASSIFICATION IN NONSTATIONARY CLIMATES

4

were pooled in statistical analyses and some figures to cap-ture uncertainty associated with individual climate models.Pairwise comparisons were used to determine whethersimulated SVI and SJI indices changed through time forsimulated 1951–2000, 2001–2050, and 2051–2099 timeperiods. Lastly, we examined frequency of WYTs for dif-ferent time periods with chi-square tests using observedvalues for expected frequency. Statistical analyses wereconducted using R [R Core Team, 2012].

4. Results

4.1. Water Year Index Response to Climate Change

[18] We evaluated the response of water year indices toclimate change using a multiple model, multiple emissionsscenario approach (Figure 4). To determine whether modelrun distributions (n¼ 12, 6 GCMs and 2 emissions scenar-ios) were different than historical observations for the1951–2000 modeled historical period for each basin, weused nonparametric Kruskal–Wallis tests with adjustmentsfor multiple comparisons where modeled water year indexvalues were considered treatments and observed historicaldata were considered controls (kruskalmc procedure)[Giraudoux, 2012]. Using a two-tailed test at the p¼ 0.001significance level, we found no significant differencesbetween modeled and observed index values and concludethat the modeled index values represent measured conditions.

[19] To assess whether GCM-driven, VIC-modeled futuredischarge and corresponding WYT index values were statisti-cally different from the modeled historical equivalent, wecompared modeled results for near-term (2001–2050) and far-term (2051–2099) time periods against each other and tomodeled historical conditions (1951–2000) for each basin andemissions scenarios, aggregating GCMs as ensembles. Non-parametric Wilcoxon rank sum tests, adjusted for multiplecomparisons and false discovery [Benjamini and Hochberg,1995], were applied to each pairwise comparison (Table 2).Index values for both basins and both emission scenarioswere statistically different between historical and far term, aswell as near term and far term (Table 2). Only the A2 scenariofor SJI was significantly different in the historical versus nearterm (p¼ 0.0002).

[20] Seasonal discharge simulations for winter (October–March) runoff showed no significant differences, regardlessof time period, basin, or emissions scenario (Table 2). Thisreflects variable, but generally stable, hydroclimatic condi-tions during the winter season. Discharge during the springsnowmelt period (April–June) shows statistically signifi-cant differences between all time periods across all basinsand under all emissions scenarios (p< 0.005) (Table 2).This suggests that index calculations are sensitive tospring discharge and this season may drive hydrologicalnonstationarity through time. For future projections, mod-eled ensembles represent uncertain estimations of futureconditions and may not be truth-centered [Annan andHargreaves, 2011]; however, significant differences infar-term streamflow, driven by spring season runoff, corre-spond to existing climate-induced volume and timing run-off research for this region [VanRheenen et al., 2004;Stewart et al., 2004; Null et al., 2010]. Table 3 givesmodeled average seasonal and annual runoff for the threemodeled time periods. T

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NULL AND VIERS: WATER YEAR CLASSIFICATION IN NONSTATIONARY CLIMATES

5

4.2. Climate Change Affects the Frequency of WaterYear Types

[21] We used chi-square tests of simulated and observedWYT frequencies to determine if the abundance of eachWYT is expected to change through time. In the near term(2001–2050), three or fewer simulations were significantlydifferent than the historical period (p< 0.01) (Table 4).However, the frequencies of four or more of the six GCMswere significantly different from historical frequencies(p< 0.01) (Table 4) when far-term (2051-2100) simula-tions for each basin index were compared with the histori-cal time period. The relative frequency that water years areclassified as each WYT is illustrated with histograms bymodeled time period for SVI (Figure 5) and SJI (Figure 6)(note scale change between figures). Observed data areincluded for the 1951–2000 historical period (Figures 5aand 6a) for visual corroboration of modeled and observeddata. Differences between emissions scenarios (warm huesversus cool hues) are due to uncertainty in human actionssuch as population growth and GHG emissions, while dif-ferences in GCMs (variability within the warm hues or coolhues) are due to uncertainty in climate models, such as rep-resentation of physical processes and sensitivity to radiativeforcings.

[22] Figures 5 and 6 demonstrate that the relative fre-quency of WYTs is expected to shift throughout the 21stcentury. For the SVI, modeling suggests a more even distri-bution of WYTs in each category by the end of the century(Figure 5). Projections from both the A2 and B1 emissionsscenarios indicate the Sacramento Basin will likely havemore dry and critical years, and less normal and wet yearsthroughout the current century (Figure 5, Table 5),although some categories—such as wet years in 2001–2050—suggest considerable uncertainty between individualmodels. By the latter half of the 21st century (2051–2099),6–10% more critical years and 10–12% more dry yearscould occur if water year thresholds remain the same. Themore drastic changes occur if the higher CO2 emissions andincreasing population assumptions of the A2 emissions sce-narios are realized. For the SJI, many more years fall intothe critical category with fewer years in all other year types,particularly toward the end of this century (Figure 6).

Results indicate a 28–35% increase in critical water yearsby the last half of this century, with the larger changes underA2 assumptions (Figure 6, Table 5).

[23] The SVI and SJI are numerical indices, so they cancontinue to be used with severe climate change withoutaltering how indices are calculated. However, WYT classi-fications and threshold definitions will likely become lessrepresentative with climate change, and in fact, may losemeaning if most years fall into the same year type classifi-cation. By the end of this century, the distribution of partic-ular year types is anticipated to be significantly differentfrom the historical record.

Table 3. Modeled Average Annual Flow by Time Period

Index and Data

Average Annual Flow (mcm)

1951–2000 2001–2050 2051–2099

SacramentoAnnual runoff A2 24,781 23,905 22,560

B1 24,694 25,027 22,486October–March runoff A2 14,432 14,901 15,419

B1 14,382 15,641 14,703April–July runoff A2 9,054 7,783 6,056

B1 9,017 8,141 6,649

San JoaquinAnnual runoff A2 7,438 6,784 5,908

B1 7,426 7,167 5,958October–March runoff A2 2,763 2,899 3,084

B1 2,763 3,022 2,800April–July runoff A2 4,293 3,589 2,566

B1 4,293 3,812 2,874

Table 4. Chi-Square Results Test Simulated Future Frequency ofWYTs Against Simulated Historical Frequency for all SVI andSJI GCMs and Emission Scenariosa

ES GCM1951–2000 vs.

2001–20501951–2000 vs.

2051–2100

SVIA2 CNRMCM3 �2¼ 1.958 �2¼ 5.124

p¼ 0.743 p¼ 0.275A2 GFDLCM21 �2¼ 5.867 �2¼ 16.648

p¼ 0.209 p 5 0.002A2 MIROC32MED �2¼ 29.506 �2¼ 49.333

p 5 0 p 5 0A2 MPIECHAM5 �2¼ 11.89 �2¼ 30.861

p¼ 0.018 p 5 0A2 NCARCSM3 �2¼ 13.97 �2¼ 29.321

p¼ 0.007 p 5 0A2 NCARPCM1 �2¼ 11.325 �2¼ 32.171

p¼ 0.023 p 5 0B1 CNRMCM3 �2¼ 9.397 �2¼ 3.876

p¼ 0.052 p¼ 0.423B1 GFDLCM21 �2¼ 8.711 �2¼ 15.486

p¼ 0.069 p 5 0.004B1 MIROC32MED �2¼ 22.6 �2¼ 44.23

p 5 0 p 5 0B1 MPIECHAM5 �2¼ 14.653 �2¼ 53.067

p 5 0.005 p 5 0B1 NCARCSM3 �2¼ 15.487 �2¼ 12.17

p 5 0.004 p¼ 0.016B1 NCARPCM1 �2¼ 7.638 �2¼ 4.479

p¼ 0.106 p¼ 0.345

SJIA2 CNRMCM3 �2¼ 4.785 �2¼ 29.335

p¼ 0.31 p 5 0A2 GFDLCM21 �2¼ 7.554 �2¼ 37.96

p¼ 0.109 p 5 0A2 MIROC32MED �2¼ 26.837 �2¼ 116.72

p 5 0 p 5 0A2 MPIECHAM5 �2¼ 31.025 �2¼ 28.424

p 5 0 p 5 0A2 NCARCSM3 �2¼ 7.789 �2¼ 34.268

p¼ 0.1 p 5 0A2 NCARPCM1 �2¼ 2.122 �2¼ 2.661

p¼ 0.713 p¼ 0.616B1 CNRMCM3 �2¼ 26.492 �2¼ 22.089

p 5 0 p 5 0B1 GFDLCM21 �2¼ 3.421 �2¼ 28.547

p¼ 0.49 p 5 0B1 MIROC32MED �2¼ 50.267 �2¼ 125.524

p 5 0 p 5 0B1 MPIECHAM5 �2¼ 7.594 �2¼ 46.249

p¼ 0.108 p 5 0B1 NCARCSM3 �2¼ 17.003 �2¼ 6.748

p 5 0.002 p¼ 0.15B1 NCARPCM1 �2¼ 3.956 �2¼ 13.506

p¼ 0.412 p¼ 0.009

ap values given in bold indicate <0.005.

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5. Discussion

5.1. Adapting Water Year Type Indices for ClimateChange

[24] Water allocations are affected by the climate changeimpacts discussed above, as well as strategies to adaptWYT indices to hydroclimatic changes. Maintaining the sta-tus quo of fixed WYT thresholds under nonstationary hydro-climate regimes is likely to result in system-wide inequitiesover the far term and could have severe consequences forwater management. While it is easy to acknowledge thatsome level of adaptation is inevitable, and is likely toinclude a variety of approaches, the scope, direction, andtimeliness of adaptation are less clear. This section presents

two extreme adaptation methods: (1) maintaining historicalWYT thresholds with changing flow regimes for a new dis-tribution of WYT, or (2) maintaining the historical distribu-tion of WYTs by modifying WYT thresholds for changingflow regimes. Other adaptation options exist, such as chang-ing the seasonally weighted coefficients of each index inresponse to climate-driven runoff timing but are not dis-cussed here.

[25] If current WYT thresholds are maintained (proac-tive changes are rarely made in the Bay Delta’s challengingpolitical and regulatory climate, effectively maintaining thestatus quo), substantially more dry and critically dry yearsare anticipated to occur as explained above and further

Figure 5. SVI relative frequency histograms for (a) 1951–2000, (b) 2001–2050, and (c) 2051–2099.

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illustrated with the modeled distribution of WYTs usinghistorical thresholds (Figures 7a and 7b) (black bars showthresholds, and wider bars quantify uncertainty between theA2 and B1 runoff estimates). This would disproportionatelyimpact environmental uses (i.e., Bay Delta outflows arereduced by approximately 36% between wet and dry years,whereas exports and out-of-stream agricultural uses haverelatively constant deliveries among year types (Bay DeltaConservation Plan, http://baydeltaconservationplan.com/Libraries/Dynamic_Document_Library/BDCP_Chapter_2_-_Existing_Ecological_Conditions_2-29-12.sflb.ashx, 2012)).With persistent dry conditions under this scenario, Californiarisks failing to provide adequate baseflow and hydrologicvariability to support various ecosystems, and failing to pro-

tect species and habitat as required by the state and federalESA and the Clean Water Act. Additional regulatory driversinclude oversight by the SWRCB to uphold public trust val-ues, hydropower relicensing through Federal Energy Regula-tory Commission [Viers, 2011], and the emergence of stateinterest in safeguarding public trust values through the Cali-fornia Fish and Game Code [Baiocchi, 1980].

[26] Conversely, WYT thresholds could be redefined toreflect changes in climate, recognizing that the normalyears of the future may resemble the critical or dry years ofthe past century (Figures 8a and 8b). The thresholds deter-mining year types could be lowered to maintain the histori-cal distribution of water years with climate-driven modeleddata [CDWR, 1989, 1991]. For example, for modeled SJI

Figure 6. SJI relative frequency histograms for (a) 1951–2000, (b) 2001–2050, and (c) 2051–2099.

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1951–2000 data, the threshold for critically dry year typesshould be set at about 2097 mcm for the historical distribu-tion of 17% of years to be in the critically dry year type.The threshold would have to be reset between 1110 and1357 mcm for 17% of years to be in the critically dry cate-gory by 2051–2099. If volumetric environmental flowrequirements tied to each WYT remain the same, much ofthe burden of climate change would fall on human wateruses under this scenario because environmental require-ments for wet years would remain unchanged, but futurewet years would be drier than historical wet years. In thiscase, regulatory restrictions could increasingly drive waterpolicy in California. If environmental flow allocations werealso altered to reflect overall drier conditions, water scar-city from climate change could be shared more equitablyamong water uses. Wider black bars in Figure 8 representuncertainty and perhaps model bias in this sort of thresholdchange, which could be explored in greater detail with cli-mate models, downscaling methods, or stochastic parame-terization of VIC.

5.2. Climate Change Adaptation Strategies AffectWater Allocations

[27] Water year classification forms the framework forwater exports through SWP and CVP pumping facilities,out-of-stream agricultural uses in the Bay Delta, and envi-ronmental flow objectives in the Bay Delta and Sierra Ne-vada rivers. We estimated water deliveries as percentages ofhistorical annual flow for wet, above normal, and dry yearsusing values from Bay Delta Conservation Plan (online,2012), with the above normal percentage allocations alsoused for below normal years and dry year percentage alloca-tions applied for critical years. This illustrates how adapta-tion strategies affect water allocations differently usingsimplified deliveries as a percentage of runoff for waterexports, out-of-stream users, and Bay Delta outflow. Adapt-ing water allocation frameworks to climate change is as im-portant as anticipating climate change impacts becauseadaptation strategies directly affect water allocations by shift-ing climate-driven water scarcity and associated economiccosts between water users. Yet, most research focuses on

Table 5. Percentage of Years in Each Water Year Type by Modeled Time Period and Emissions Scenarioa

1951–2000 (%) 2001–2050 (%) 2051–2099 (%)

A2 B1 A2 B1 A2 B1

SVICritical 8.7 8.3 11.3 (2.7) 6.7 (�1.7) 18.4 (9.7) 14.0 (5.6)Dry 7.7 10.0 12.0 (4.3) 15.7 (5.7) 19.4 (11.7) 20.1 (10.1)Below normal 23.3 21.3 23.3 (0.0) 17.3 (�4.0) 18.7 (�4.6) 19.4 (�1.9)Above normal 21.0 22.7 16.7 (�4.3) 20.7 (�2.0) 12.9 (�8.1) 18.4 (�4.3)Wet 39.3 37.7 36.7 (�2.7) 39.7 (2.0) 30.6 (�8.7) 28.2 (�9.4)

SJICritical 26.0 26.0 41.3 (15.3) 35.3 (9.3) 60.9 (34.9) 54.1 (28.1)Dry 13.0 12.3 11.0 (�2.0) 12.7 (0.3) 8.2 (�4.8) 11.9 (�0.4)Below normal 19.3 19.7 15.7 (�3.7) 14.0 (�5.7) 10.5 (�8.8) 10.9 (�8.8)Above normal 13.7 13.3 9.3 (�4.3) 12.0 (�1.3) 8.5 (�5.2) 10.9 (�2.5)Wet 28.0 28.7 22.7 (�5.3) 26.0 (�2.7) 11.9 (�16.1) 12.2 (�16.4)

aValues given in italics are percent change from historical period.

Figure 7. Modeled distribution of water year types using historical thresholds, where black bandsshow uncertainty between A2 and B1 projections for (a) SVI and (b) SJI (C is critically dry, D is dry,BN is below normal, AN is above normal, and W is wet).

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climate change impacts to water resources, while adapta-tions to climate change are underrepresented in the scientificliterature.

[28] Modeled data indicate a reduction of approximately3700 mcm in unregulated inflow to the Bay Delta by theend of the 21st century (ignoring direct precipitation andcontributions from eastside tributaries). Unimpaired esti-mates are used in indices, which also ignore upstreamdiversions and all other water regulation. If WYT thresh-olds are redefined so that the historical distribution of wateryears is preserved (Figure 8), then water reductions are splitbetween exports to central and southern California (�3%),out-of-stream agricultural Bay Delta uses (�2%), and BayDelta environmental outflows (�14%) (Bay Delta outflowslump undeveloped—or uncontrolled—water with environ-mental flow requirements) (Figure 9). However, if currentWYT thresholds are maintained (Figure 7), then the burdenof climate-driven water scarcity falls entirely on environ-mental outflow through the Bay Delta (�16%), while thepercentage of average annual flow to exports (þ2%) and out-of-stream uses (þ4%) increase somewhat to preserve rela-tively constant deliveries in drier years (Figure 9). The WYTframework, and how it could be altered to reflect climatechange, directly affects water winners and losers in the state.

6. Major Findings and Implications

[29] Water year typing has inherent limitations. Wateryear indices typically focus on runoff volume, with less em-phasis on timing [Vicuna, 2006], although the SVI and SJIhave seasonally weighted coefficients that incorporate run-off timing. More research is needed to update coefficients toreflect climate-driven shifts in runoff timing. Accuratelydescribing the ecological differences between year types isanother current information gap. Likewise, the volume andtiming of undeveloped water will likely change and meritsfurther research. Separating undeveloped water from man-aged environmental flow allocations would also help quan-tify anticipated flow changes [Null, 2008].

Figure 8. Modeled change to water year classification thresholds using historical percentages of yearsper category, where black bands show uncertainty between A2 and B1 projections for (a) SVI and (b)SJI (C is critically dry, D is dry, BN is below normal, AN is above normal, and W is wet) (note scalechange between figures).

Figure 9. (a) Example Bay Delta water balance, wherehistorical average annual water allocation for Delta Out-flow, Exports, and Consumptive Use are given (mcm), andaverage annual change in allocation for 2051–2099 aregiven in bold values (mcm) for maintaining historicalWYT thresholds (orange columns) and maintaining histori-cal WYT distribution (red columns). Black bars quantifyuncertainty between A2 and B1 emissions scenarios, andall values use the average of A2 and B1 emissions scenar-ios. Arrow and bar size represents flow percentage. (b) Ap-proximate water allocations as a percentage of annual flowestimated from the Bay Delta Conservation Plan (BayDelta Conservation Plan, online, 2012), which ignoresupstream diversions, direct precipitation, and eastside tribu-taries. Bay Delta outflow lumps undeveloped water withsome environmental flows.

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[30] The modeled data used here suggest that 34–38% ofyears may be classified as dry and critically dry years in theSVI, and 66–69% may be classified as dry and criticallydry years in the SJI by the end of the 21st century withoutchanging thresholds values. These findings indicate thathydrologic and drought indices should be adapted forchanging hydroclimate conditions, particularly in large riv-ers which are relied upon for water supply, hydropowergeneration, flood protection, and ecosystem function.Where water allocations are reliant on WYT classifications,deliveries are sensitive to both climate change impacts andadaptations. Hydroclimate changes may not be sufficient toprompt adaptation in the short term, in part, because bimo-dalities in wet and critically dry frequencies provide littleevidence to a public focused on ‘‘normal’’ water years. Inthe long term, maintaining the status quo (historical WYTthreshold values) versus redefining thresholds to reflect thehistorical distribution of WYTs affects which water usersface climate-driven water scarcity. Climate, WYTs, andwater allocation decision making are interrelated.

[31] Extensive studies have been conducted to developadequate environmental flows [Acreman and Dunbar,2004; Arthington et al., 2006] and to determine how muchwater ecosystems need [Richter et al., 1997]. But the qual-ity, accuracy, and utility of WYT indices, like the SVI andSJI, which in practice determine how much water ecosys-tems receive, have yet to be extensively studied with pro-jected hydroclimatic changes. Failing to recognize howprobabilities of year types may shift with climate changeintroduces error and uncertainty into long-term regulatorystability and may not preserve the hydrologic variabilityneeded to maintain ecosystem health.

[32] Aquatic ecosystems depend on hydrologic variabili-ty to preserve function and integrity [Richter et al., 1997].In developed systems, water managers have some responsi-bility to maintain ecological function and health of aquaticand riparian systems with anticipated hydroclimatechanges. In a future where more than half of all years aredesignated as critically dry, larger instream flows may bewarranted to manage hydrologic variability if we are tomaintain existing ecosystems. However, preserving histori-cal assemblages of species may not be the ecosystems wechoose to manage for in the future [Lund et al., 2010]. As asociety, we tend to preserve ecosystems that we are accus-tomed to, although that may not be realistic in a future withsevere climate change [Hanak et al., 2011]. Future condi-tions, as well as unanticipated events such as exotic speciesinvasions, food webs collapse, or changing migration bar-riers, could alter the historical range of species. Changingfrequencies of WYTs may present an opportunity to openlyrecognize that ecosystems are already heavily managed andto more explicitly decide what complement of ecosystems,functions, and species we opt to manage for. We recom-mend an adaptive WYT framework, with a formal processto review year type thresholds periodically to ensure thatwater is allocated according to water rights, regulatoryrequirements, and desired ecosystems.

[33] Acknowledgments. This work was funded by the CaliforniaEnergy Commission and a white paper describing research and results wassubmitted to the funding agency. We would like to thank Maury Roos andJay Lund for critical feedback, Guido Franco for oversight with the CEC

PIER program, and past UC Davis student Danielle Salt for processingdata. We also thank Mary Tyree and the California Nevada ApplicationsProgram/California Climate Change Center (directed by the ClimateResearch Division of Scripps Institute for Oceanography) for sharing VICclimate data, as well as Ed Maurer and Dan Cayan for their advice. Themajority of this research was completed while Sarah Null was a postdoc-toral scholar at UC Davis.

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