COMBINED EFFECTS OF RESERVOIR OPERATIONS AND CLIMATE

RIVER RESEARCH AND APPLICATIONS

River Res. Applic. (2014)

Published online in Wiley Online Library(wileyonlinelibrary.com) DOI: 10.1002/rra.2749

COMBINED EFFECTS OF RESERVOIR OPERATIONS AND CLIMATE WARMING ONTHE FLOW REGIME OF HYDROPOWER BYPASS REACHES OF CALIFORNIA’S

SIERRA NEVADA

D. E. RHEINHEIMERa* AND J. H. VIERSa,b

a Center for Watershed Sciences, University of California, Davis, California, USAb School of Engineering, University of California, Merced, California, USA

ABSTRACT

Alterations to flow regimes from regulation and climatic change both affect the biophysical functioning of rivers over long time periods andlarge spatial areas. Historically, however, the effects of these flow alteration drivers have been studied separately. In this study, results fromunregulated and regulated river management models were assessed to understand how flow regime alterations from river regulation differunder future climate conditions in the Sierra Nevada of California, USA. Four representative flow alteration metrics—mean annual flow,low flow duration, centroid timing and mean weekly rate of decrease—were calculated and statistically characterized under historical andfuture unregulated and regulated conditions over a 20-year period at each of the eight regulated river locations below dams across the SierraNevada. Future climatic conditions were represented by assuming an increase in air temperature of 6 °C above historical (1981–2000) airtemperatures, with no change in other meteorological conditions. Results indicate that climate warming will measurably alter some aspectsof the flow regime. By comparison, however, river regulation with business-as-usual operations will alter flow regimes much more thanclimate warming. Existing reservoirs can possibly be used to dampen the anticipated effects of climate warming through improved opera-tions, though additional research is needed to identify the full suite of such possibilities. Copyright © 2014 John Wiley & Sons, Ltd.

key words: flow alteration; dams; climate change; metrics; reservoir operations; flow regime; Sierra Nevada

Received 16 October 2013; Revised 30 January 2014; Accepted 28 February 2014

INTRODUCTION

The natural flow regime in rivers is now widely consideredessential to sustaining natural riverine ecosystems, as itsupports a wide range of abiotic and biotic conditions andprocesses that native ecosystems have adapted to (Poffet al., 1997; Arthington et al., 2010). The flow regime isconsidered a ‘master variable’ as it affects riverine ecosys-tems directly (e.g. hydraulic habitat) and indirectly (e.g.physiology of individuals) (Kiernan and Moyle, 2012).Indirect control mechanisms involve interactions with otherenvironmental factors, such as sediment and air temperature(Caissie, 2006; Yarnell et al., 2010), which are also vital to ariver’s ecological functioning. Though the flow regime isonly one factor influencing a river’s ecosystem (Olden andNaiman, 2010), it is well established that the alteration offlow regimes often has negative consequences to nativefreshwater species and ecosystem functioning. As reviewedby Bunn and Arthington (2002), some of the many negativeeffects include alteration to substrate dynamics and habitatformation, alteration of biogeochemical cycles, promotion

*Correspondence to: D. E. Rheinheimer, Center for Watershed Sciences,University of California, Davis, California, USA.E-mail: [email protected]

Copyright © 2014 John Wiley & Sons, Ltd.

of invasion by non-native species, and disruption of pheno-logical cues. Improving management of the flow regimealone remains a critical challenge (see Petts, 2009).Both reservoirs and climate change alter flow regimes.

The specific effects of reservoirs, dam operations and waterdiversions on flow regimes vary widely (Grantham et al.,2010). The variability is dependent not only on the watermanagement scheme and specifics of the built infrastructurebut also the antecedent environmental conditions and arange of context-dependent factors. Climate change affectsunregulated flow regimes primarily by altering the hydro-logic cycle (Heino et al., 2009). Reservoirs and climatechange also affect other river conditions that are partiallymediated by flows. For example, both reservoirs and climatechange affect stream temperatures (Null et al., 2013b).Effects of water quantity and quality changes on ecosystemsmay be non-additive. For example, Jager et al. (1999)showed that the combination of climate change-inducedshifts in run-off timing and increases in stream temperatureaffected coldwater fish in California’s Sierra Nevada innon-additive ways. In this paper, we focus exclusively onthe flow regime.Re-operating reservoirs, whereby a reservoir’s operating

rules are changed, is increasingly seen as a way to mitigatesome of the harmful effects of dams (Richter and Thomas,

D. E. RHEINHEIMER AND J. H. VIERS

2007; Konrad et al., 2012). We anticipate that re-operatingreservoirs may also be necessary to help ecosystems adaptto climatic changes. Reservoir re-operation for climatechange adaptation might include, for example, instream flowrequirements (IFRs) to maintain historical rates of flowrecession below a dam (Rheinheimer et al., 2013) orreleases to maintain water temperatures within an acceptablerange (Cummings et al., 2013). Because extensive regula-tion can hamper a riverine ecosystem’s capacity to adaptto hydro-climatic changes, Palmer et al. (2008) suggestedthat rivers already regulated with dams will likely needgreater ecosystem management interventions compared withundammed rivers. We further anticipate that in some in-stances, a well-operated reservoir might be more favourablethan an unregulated river under climate change, as there aresome limitations to the ability of freshwater species to adaptto anticipated environmental changes (Lytle and Poff,2004). This latter idea is broadly consistent with ecosystemmanagement generally and notions that ecosystem managerswill need to take a more active role in helping species adaptto climate change.To re-operate reservoirs for improved ecosystem manage-

ment in the context of changing hydro-climatic regimes, abetter understanding of the individual and combined effectsof existing regulation schemes and anticipated climatechange can be beneficial, particularly at the regional scale(Poff et al., 2010; Pittock and Hartmann, 2011). Some havenoted that non-climatic anthropogenic stressors such asdams and diversions are exacerbated by climate change overbroad spatial areas (Palmer et al., 2008; Heino et al., 2009;Wenger et al., 2011). However, we found no study thatexplored the effects of both climate change and reservoiroperations on river flow in specific rivers.In this study, we quantified the independent and com-

bined effects of river regulation and climate change onstreamflow below seasonal storage/diversion reservoirs ofthe western Sierra Nevada, California, USA, using flowcharacterization metrics. Firstly, river regulation and climatechange in the Sierra Nevada are described. Secondly,methods used, including the selection of study sites and flowcharacterization metrics, are described. Thirdly, metricvalues and their statistical descriptors are presented.Finally, policy and management implications of this studyfor re-operating reservoirs are discussed. Key limitationsof this work are also discussed.

THE SIERRA NEVADA

The Sierra Nevada is a 600-km north–south-oriented moun-tain range in California and Nevada, USA (Figure 1). Simi-lar to other Mediterranean-montane hydro-climatic regions,the Sierra Nevada experiences cool, wet winters and warm,


dry summers. Most precipitation has historically been storedas snowpack, with most run-offs during the spring snowmeltperiod. This hydro-climatic system results in highly sea-sonal flows predictable in timing, though not in magnitude.Similar with other Mediterranean regions, water in theSierra Nevada is also highly regulated. Several large, low-elevation, multi-purpose reservoirs with multi-year storagecapture and store large amounts of water from the SierraNevada to provide the majority of water to California’s vastwater supply network (Hundley, 2001). However, mostreservoirs in the Sierra Nevada are at higher elevations,mostly used as seasonal storage with diversions for high-head hydropower. This hydropower constitutes the bulk ofthe region’s 8800MW of generation capacity (Rheinheimeret al., 2014). Of the 58 high-elevation reservoirs consideredby Rheinheimer et al. (2014), 81% are storage/diversionreservoirs.The Sierra Nevada’s native aquatic species have adapted

to the Mediterranean seasonal fluctuations and hydrologicalextremes, where annual summer drought stresses biologicalsystems and winter and spring floods disturb physicalsystems (Grantham et al., 2010; Yarnell et al., 2010). Nativeaquatic species in the Sierra Nevada, and California moregenerally, have become severely threatened by numerousanthropogenic stressors, including river regulation, landuse practices and invasive species, among many others(Grantham et al., 2010; Moyle et al., 2011; Katz et al.,2012). Dams have been particularly ecologically harmful,with fragmentation of habitat, general degradation of habitatquality and alterations to downstream water quantity andquality (Grantham et al., 2010; Katz et al., 2012). Thestorage/diversion reservoirs considered in this study havesuppressed or eliminated spring snowmelt recession flows,which are particularly ecologically important in the SierraNevada (Yarnell et al., 2010). Many of regional fish speciesalready are highly vulnerable to hydro-climatic change(Moyle et al., 2013). The combined effect of hydrologicalteration with climatic change poses many unknowns toecosystem managers.Most high-elevation hydropower systems in the Sierra

Nevada are river diversion projects, consisting of a reservoirfrom which water is diverted for high-head generationelsewhere. Reaches below hydropower diversion reservoirsare termed ‘bypass reaches’. IFRs exist for all bypassreaches, mostly as defined in licenses issued by the USFederal Energy Regulatory Commission. Nearly all IFRsin this region are for bypass reaches. To date, most IFRshave been relatively simple, typically consisting of a simpleminimum instream flow (MIF) requirement. Some MIFsvary seasonally and/or by water year type.The Sierra Nevada’s hydro-climatic system is anticipated to

change substantially with global climate warming, similar toother Mediterranean regions (Klausmeyer and Shaw, 2009).


DOI: 10.1002/rra

N Feather R,L Almanor (ALM)

N Yuba R,New BullardsBar Res. (NBB) S Yuba R,

L Spaulding (SPG)

Rubicon R,Hell Hole Res. (HHL)

M Stanislaus R,Donnels Res. (DON)

Tuolumne R,Hetch Hetchy Res. (HTH)

San Joaquin R,Mammoth Pool Res. (MPL)

Kings R,Wishon Res. (WIS)

N = NorthM = MiddleS = SouthR = RiverL = Lake

Figure 1. Study area. This figure is available in colour online at wileyonlinelibrary.com/journal/rra

FLOW REGIME WITH DAMS AND CLIMATE WARMING

California’s climate is expected to warm from about 1.5 to6 °C above the 1961–1990 mean by the end of the century,with the greatest warming in the Sierra Nevada (Hayhoeet al., 2004). Downscaled CMIP3 projections generally indi-cate a slightly drier future (Cayan et al., 2008; Franco et al.,2011). A warmer climate will reduce winter precipitation assnow, increasing the frequency and magnitude of rain-drivenflows and causing earlier snowmelt. This will result in earlierrun-off, reduced spring and summer snowmelt, and otherchanges (Vicuna et al., 2007; Null et al., 2010). With climatewarming, hydropower operators will continue to maximizeelectricity generation during summer months for peak


electricity demand, yet operations will be modified some-what to minimize winter spill, resulting in concomitantchanges in flows below reservoirs (Madani and Lund,2010; Vicuña et al., 2011; Rheinheimer et al., 2014).

METHODS

Many methods have been developed to characterize flowregimes and their deviations (Olden and Poff, 2003). Forinstance, the Indicators of Hydrologic Alteration (IHA)method (Richter et al., 1996) has been widely used to


DOI: 10.1002/rra


quantify changes in pre-dam and post-dam statistical de-scriptors of the magnitude, frequency, duration, timing andrate of change of various flow conditions at different timescales, for single and multiple rivers (e.g. Magilligan andNislow, 2005and Yan et al., 2010). IHA and similarapproaches assume that increasing deviations from histori-cal, natural mean and variability in flow metrics increasinglyharm the idealized ‘natural’ ecosystem, based on the naturalflow regime paradigm (Poff et al., 1997).We used an analytical framework conceptually similar to

IHA, though not the specific metrics. Because there aremany metrics (indicators) used in IHA applications andsimilar methods, they be hydrologically redundant (Oldenand Poff, 2003; Gao et al., 2009) or ecologically redundant(Yang et al., 2008; Jowett and Biggs, 2009). Furthermore,there may be nonlinear relationships between flow metricalterations and biotic responses. Regardless of these limita-tions, the IHA (or similar) analytical framework is effectivefor quantifying flow alteration for a first approximation ofenvironmental impacts of regulation. The IHA analyticalframework also can be used to assess any kind of flowregime change, including those due to climate change(Gibson et al., 2005; Mathews and Richter, 2007).With four scenarios and at each of the eight sites, we

computed four ecologically important annual flow metrics,as described in the succeeding text. Scenarios consisted ofdifferent management and climate states: regulated andunregulated (management) and historical and future (climate).We then compared differences between scenarios, primarilyunregulated and regulated with a historical climate, historicaland future without regulation and unregulated/historical andregulated/future. To do so, we first tested metrics for statisticalsignificance of differences between scenarios. We thencomputed the mean and coefficient of variation of eachmetric for each scenario and site and compared the resultsamong scenarios.

Unregulated historical and future streamflow data

Unregulated run-off data were generated from the weeklyhydrologic model of the western Sierra Nevada developedby Young et al. (2009) using the Water Evaluation andPlanning System (WEAP; Yates et al., 2005). Young et al.(2009) approximated regional warming by increasing airtemperatures uniformly by 2, 4 and 6 °C above the1981–2001 water year (WY; October–September) historicalrecord, without changing precipitation. We used the 6 °Cwarming scenario to represent the ‘future’ climate andlimited the period of record to WY1981–2000. This periodincluded the wettest year on record (1983), the flood yearof record (1997) and a 5-year drought (1988–1992), repre-senting the climatic extremes typical of this region and pro-viding the necessary variance for model simulations.


Regulated historical and future streamflow data

Regulated flow data were from a water management opera-tions simulation model coincident with but computationallyindependent from the hydrologic model (less WY2001) de-scribed by Rheinheimer et al. (2014). The operations model,also developed with WEAP, was applied by Rheinheimeret al. (2014) with run-off from the hydrologic model forhistorical and future climates. The operations model assumedhistorical (business-as-usual) water and hydropower demandunder future climate conditions. To approximate historicalhydropower operations, the operations model released weeklywater volumes for hydropower linearly proportional to re-gional annual water availability. Expressed mathematically,the per-powerplant diversion rule was

QHP ¼ mt�WYI þ bt (1)

where QHP is the hydropower release (m3), WYI is a regional

water year supply index (m3/year) and mt and bt are weeklyconstants calculated empirically from historical diversions.This rule is sensitive to changes in annual run-off magnitudesbut not changes in run-off timing. To account for increasingwinter run-off anticipated with climate warming and increasedpotential winter spill, a second rule was imposed to maximizehydropower generation during times of spill. This additionalrule allows for some operational adaptation to changes inrun-off timing, but lack of perfect hydrologic foresight resultsin suboptimal adaptation.For hydropower turbine flow simulation, the model is

better suited for trend analysis (see Rheinheimer et al.,2014). However, for flows at IFR locations, which are belowmedium-size storage/diversion dams and fairly predictable,the operations model is well suited for comparative assess-ments of flows at the native (weekly) time step.

Regulated flow locations

We chose eight representative regulated sites across thewestern Sierra Nevada (Figure 1 and Table I) on the basis offlow regulation type, representation of operations andgeographic location (i.e. latitude and elevation). Selected siteswere directly below medium-size reservoirs (79.8–1191million m3). All reservoirs considered here store and divertwater for hydropower and some water supply, with bypassreaches below the reservoir. One reservoir (New BullardsBar) is also used for flood control. Collectively, these reser-voirs represent almost half (46%) of the total capacity of allreservoirs in the Sierra Nevada above two million m3. Fourof these eight are among the seven largest high-elevationreservoirs in the western Sierra Nevada.Flows at the IFR locations generally consist of spill,

which is typically high, or MIFs, which are typically low.Though some management schemes include occasional


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Table I. Characteristics of study locations

Location (river) Reservoir name Latitude, longitudeDam crest

elevation (m)Mean annual run-off(MAR) (million m3)

Reservoir capacity(million m3)

Historical storage(% MAR)

N Feather R L Almanor (ALM) 40.17, �121.09 1376 653 1149 164%N Yuba R New Bullards Bar (NBB) 39.39, �121.14 599 1991 1191 42%S Yuba R L Spaulding (SPG) 39.33, �120.64 1529 567 92.6 10%Rubicon R Hell Hole (HHL) 39.06, �120.41 1417 573 256 6%M Stanislaus R Donnells (DON) 38.33, �119.96 1500 406 79.8 10%Tuolumne R Hetch Hetchy (HTH) 37.95, �119.79 1162 638 444 47%San Joaquin R Mammoth Pool (MPL) 37.32, �119.32 1024 1454 148 5%N Kings R Wishon (WSN) 37.00, �118.97 1917 308 159 27%

N=North, M=Middle, S = South, R = river, L = lake.


pulse flows, most selected sites have basic MIFs (oftenseasonally variable) as operational constraints. Accuracy inoperations representation was determined by visual inspec-tion of log-transformed observed and simulated regulatedflows for five of the eight sites (Figure 2). Accuracy at thethree other sites were not assessed because of lack ofobserved data but were assumed to have similar accuracybecause of similarity in operational characteristics.

Flow metrics

We selected flow metrics for their ecological importance inthe region, representation of flow features, hydrologic inde-pendence, information parsimony and robustness to our

Figure 2. Observed and simulated flows (log scale) at five of theeight study sites, water years 1993–1996


modelling domain. On the basis of these objectives, we chosefour flow metrics: mean annual flow (MAF), annual low flowduration (LFD), mean weekly rate of decrease (RoD), andcentroid timing (CT), described in the succeeding text. Thesemetrics are broadly consistent with the work of others (e.g.Clausen and Biggs, 2000; Olden and Poff, 2003) for ecologi-cal sensitivity and reduction of redundancy. We provide indi-cations of ecological importance, though a full review ofimportance, which has been covered by others (see Petts,2009), is beyond the scope of this paper. Specific to the SierraNevada, Yarnell et al. (2010) provide a comprehensive reviewof the ecological implications of changes in spring snowmeltflows, a fundamental flow component substantially affectedby both climate change and regulation.

Mean annual flow. Mean annual flow is defined as

MAF ¼ 1N

XT

t¼1

Qt (2)

where Qt is flow volume during time step t, T is the totalnumber of time steps in the study period and N is thenumber of years in the study period. MAF is a measure ofthe total amount of water available for distributionthroughout the year. It is the metric most directly affectedby diversions, yet its ecological significance is mediatedby the shape of the hydrograph within the year.

Low flow duration. Low flow duration is the number orpercent of weeks per year below the historical 10% non-exceedance flow under unimpaired conditions:

LFD ¼ count Qtð Þ∀Qt≤Q0:1hist (3)

where Q0:1hist is the historical 10% non-exceedance flow. LFD

affects degree of biological competition for hydraulic habitatand the water temperature regime. Higher LFD is expectedto be particularly ecologically harmful when coupled withclimate warming, which will detrimentally increase stream


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Figure 3. Flowrate (log scale) below Mammoth Pool Reservoirwith different management (unregulated and regulated) andclimate (historical and future) scenarios for 1 October 1992–30

September 1993


temperatures in unregulated rivers (Null et al., 2013b),though reservoirs might help reduce stream temperatureswith warming (Null et al., 2013a).

Rate of decrease. Rate of decrease is the mean change inweekly flowrate, limited to weeks when flowrate decreases:

RoD ¼ 1T

XT

t¼1

Qt � Qt�1

Qt�1

��

��∀Qt ≤Qt�1 (4)

where terms are as previously defined. Higher RoD indicateshigher and/or more frequent rapid decreases in flow. In theSierra Nevada, RoD is particularly ecologically importantduring the spring snowmelt period (Yarnell et al., 2010) andis substantially increased with regulation after high spillevents (Rheinheimer et al., 2013). Such increases altergeomorphic functions such as substrate sorting and candisrupt animal reproductive cycles (Yarnell et al., 2010).

Centroid timing. Centroid timing is the time (week) of thecenter of mass of the annual hydrograph:

CT ¼ whereXCT

t¼1

Qt ¼12

XT

t¼1

Qt (5)

where terms are as previously defined. CT indicates the overalltiming of annual run-off and is particularly influenced by highflow events or floods. Timing of run-off affects communitystructure generally (Kiernan and Moyle, 2012). High flowrun-off timing can also change the timing of bed scour,which, in conjunction with other concomitant flow changes,can affect salmonid reproduction (Jager et al., 1999).

Statistical differences in flow metrics

We used the non-parametric Wilcoxon signed-rank test(Hollander and Wolfe, 1973) to determine whether flowmetric time series were statistically different betweenpaired climate and management scenarios for each IFRsite. This test statistically compares the absolute sum ofsigned ranks (W score) of time series pairs. The nullhypothesis of no difference between metrics from pairedscenarios is rejected if the test indicated a statistically sig-nificant difference (p< 0.05).

RESULTS

To demonstrate the effect of management and regulation onhydrographs, Figure 3 shows modelled flows (log scale) foreach management and climate scenario in the San JoaquinRiver below Mammoth Pool Reservoir for WY1993.Natural run-off is dominated by spring snowmelt. Climatewarming simultaneously decreases snowmelt run-off and


increases winter run-off as more precipitation occurs as rainand snow melts earlier. Regulated flows are characterized byMIFs punctuated by spill events. Climate warming shiftsspill events to earlier in the year, coincident with naturalrun-off changes. Although specific flow regimes and re-sponses to climate change and management vary by site,these patterns are representative of all sites.

Statistical differences in flow metrics

In general, the results of the Wilcoxon signed-rank testsindicate that most changes in management and climateconditions are highly significantly different regardless ofthe treatment (Table II). In only two comparisons were dif-ferences negligible: MAF between the historical and futureclimate with regulated flows (p = 0.98) and RoD betweenthe historical and future climate with unregulated flows(p= 0.06). W scores suggest comparatively non-uniformresponses to treatment scenarios, where a higher W scoreindicates a greater difference. For MAF, extreme differenceswere observed between climate states when unregulated,between management states and with the interaction ofmanagement and climate (unregulated, historical vs. regu-lated, future). There was far less dispersion in W scores forLFD, with the largest treatment effect found in regulated,historical versus regulated, future (W = 2084.0). A very largeclimate effect (W = 12720.0) was observed with unregulatedconditions for CT. When combined with the small compara-tive difference in future, regulated versus future, unregulatedconditions (W= 478.0) and large interaction effect of climateand regulation (W=8911.0), this points towards an over-whelming effect of reservoir operations on flow timing.Whereas the climate effect of RoD with unregulated condi-tions remains equivocal (p=0.06), significant yet moderateeffects remain for all other comparisons.

Flow metrics

Mean annual flow (Figure 4). With a historical climate,regulation decreased average MAF across sites by 40–98%,with a 79% decrease on average across all sites (Figure 4).Reductions were mostly due to off-stream diversions for


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Table II. Wilcoxon signed-rank test results for each flow regime metric with treatments by management (unregulated and regulated), climate(historical and future) and the interaction of management and climate compared with the natural, historical scenario

Metric Treatment Management Climate W p-value

Mean annual run-off Climate Unregulated Historical versus future 12287.0 <2e-16Regulated Historical versus future 4074.0 0.982

Management Unregulated versus regulated Historical 12880.0 <2e-16Unregulated versus regulated Future 12880.0 <2e-16

Management × climate Unregulated versus regulated Historical versus future 12880.0 <2e-16Low flow duration Climate Unregulated Historical versus future 951.0 3.4E-15

Regulated Historical versus future 2084.0 5.3E-05Management Unregulated versus regulated Historical 188.5 <2e-16

Unregulated versus regulated Future 463.5 <2e-16Management × climate Unregulated versus regulated Historical versus future 191.5 <2e-16

Centroid timing Climate Unregulated Historical versus future 12720.0 <2e-16Regulated Historical versus future 6144.0 4.4E-15

Management Unregulated versus regulated Historical 2852.0 8.1E-05Unregulated versus regulated Future 478.0 <2e-16

Management × climate Unregulated versus regulated Historical versus future 8911.0 4.1E-12Rate of decrease Climate Unregulated Historical versus future 5357.0 0.0649

Regulated Historical versus future 1791.0 7.5E-08Management Unregulated versus regulated Historical 2986.0 4E-09

Unregulated versus Regulated Future 1486.0 <2e-16Management × climate Unregulated versus regulated Historical versus future 1390.0 <2e-16


hydropower and other uses. Climate warming reducedunregulated MAF by 1–12%, with 6% on average (see alsoNull et al., 2010). The combined effects of regulation(diversions) and climate warming appear to be fairly similarcompared with diversions alone, with half of the siteshaving less reduction over historical unregulated flows witha future climate and the other half having greater reduction.MAF decreases with both regulation and climate warmingranged from 51% to 97% and 80% on average.

Low flow duration (Figure 5). With a historical climate,unregulated sites all started with the same mean LFD of5.2weeks per year, by definition (Figure 5). Regulationalways either greatly increased or greatly decreased LFD,depending on the LFD definition relative to the relativelyuniform MIFs. Under historical, unregulated conditions,

Figure 4. Mean annual flow (log scale) boxplots by scenarioU = unregulated, R = regulated, 0 (grey) = historical climate, 6

(white) = future climateFigure 5. Low flow duration boxplots by scenario; U=unregulatedR= regulated, 0 (grey) = historical climate, 6 (white) = future climate


;

LFD ranged from zero to 30weeks, with fewer low flowweeksin wet years and more in dry years. With a historical climate,regulation increased LFD by 26–46weeks in most locations,to about 38weeks on average. In contrast to other reservoirs,Donnells Reservoir (M Stanislaus R) substantially decreasedLFD. Without regulation, climate warming increased LFDfor unregulated flows to about 5–10weeks (10weeks onaverage). With regulation, LFD decreased slightly withwarming at all locations except M Stanislaus R, likely due toincreased winter spill. Even with this slight decrease, LFDwith warming and regulation was still much greater (37weekson average) than with warming alone.

Centroid timing (Figure 6). Historically, CT spanned themonths of April and May (weeks 23–34). Regulation witha historical climate delayed CT at most sites, by about

River Res. Applic. (2014

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,

)

Figure 6. Centroid timing boxplots by scenario; U = unregulated,R= regulated, 0 (grey) = historical climate, 6 (white) = future climate


1–2weeks on average, because of reservoirs storing anddiverting earlier winter run-off. Some reservoirs alsoreleased MIFs that were higher than natural low flows in latesummer, shifting CT to earlier in the year and counteractingthe effect of delaying CT. Regulation also modestly increasedCT variability. Warming consistently shifted unregulated CTto earlier in the year, here by 5–11weeks, or about 8weekson average. In all cases except one, CT with both warmingand regulation was between CT with regulation alone andwarming alone. Thus, regulation helped keep CT closer towhat it was historically, though still earlier than if unregulated.

Rate of decrease (Figure 7). With a historical climate,regulation increased mean RoD from about 20% per weekfor all locations to about 50% per week among alllocations except one. RoD, which is predictably stableunder a natural flow regime, became more variable withregulation. Regulation increased RoD from between 10%and 64% per week. Warming slightly increased RoD inunregulated rivers, by about 0.7% per week on average.With warming, RoD decreased during the snowmelt periodbecause of decreasing snowmelt volume but increased inthe winter with higher precipitation-driven events,resulting in little net effect. Though warming had little neteffect on mean RoD in unregulated rivers, warmingincreased mean RoD by about 6% per week in regulated

Figure 7. Mean weekly rate of decrease boxplots by scenario;U = unregulated, R = regulated, 0 (grey) = historical climate, 6

(white) = future climate


rivers. The combined effect of warming and regulation inthis case was worse than the sum of individual effects.

DISCUSSION

Several important policy and management implications canbe drawn from these results. Most importantly, reservoirscan be used to create downstream flow regimes that are bothless ecologically harmful than current management practicesand that can help buffer against climate change effects. Thatinstream flows need to be improved for better riverineecosystem management is widely recognized, particularlyfor the Sierra Nevada. In previous work, we showed thatsuch improvements, which could include reshaping thehydrograph rather than simply releasing more water, mighthave relatively modest hydropower costs if optimally managed(Rheinheimer et al., 2013).That reservoirs might be used to help adapt ecosystems to

climate warming is reflected by the lesser relative change inflow metrics with warming under regulated conditions thanunder unregulated conditions. However, re-operating reser-voirs specifically for climate adaptation is a relatively newconcept that has not been explored much in the literature.IFR releases are highly managed and mostly stationary overthe long term. Any nonstationarity is a result of spillnonstationarity and instances where IFRs explicitly changewith water year type (see Null and Viers, 2013). With betterhydrologic foresight, reservoirs might better minimize higherwinter spill. New IFR rules for climate change adaptationwould need to appropriately balance long-term ecosystemand energy management goals, with a clear understanding ofhow costs (or benefits) of climate change would be sharedbetween ecological and energy systems in the future. Thistype of adaptation would also require new policy approachesto dam licensure and regulation (Viers, 2011).Though further work is needed to propose specific new

management rules, general management possibilities forpotential climate change adaptation can be inferred fromthe flow metric results, as follows.

Mean annual flow. Regulation dampens the effect of warmingin most instances but is overall more harmful thanwarming alone. Because flows are already substantiallyreduced compared with natural conditions and are mostlycontrolled by an upstream reservoir, there are ampleopportunities to increase and reshape flows in rivers belowthe reservoirs represented in this study, though there wouldbe inevitable trade-offs with meeting existing water userdemands (Rheinheimer et al., 2013).

Low flow duration. Though LFD increases generally underclimate warming, the effect of regulation is far more extremeas indicated by its variability in low flow conditions.


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Although low flow events could be minimized withalternative flow requirements, MIFs might be lowered ifother components of the flow regime, such as rates ofdecrease, were restored sufficiently to natural-like conditionsso as to recreate the natural variability important for well-functioning ecosystems. However, any decrease in MIFswould need to consider stream temperature effects (see Nullet al., 2013a).

Centroid timing. Reservoir operations can be used to ensurethat the timing of releases more closely matches historicalunregulated conditions. As reservoirs already hedge storagefor non-environmental management objectives (i.e. providingsupply later in the year), this can likely be done with littleadditional effort.

Rate of decrease. With climate warming, reservoirs can beused to maintain historical rates of decrease (Rheinheimeret al., 2013). However, operating for decrease rates mayrequire modifications in infrastructure, which might bedifficult to implement. Furthermore, there are implicit trade-offs between water used for environmental or economicobjectives.This study was limited in several ways. Firstly, an assess-

ment of flow regime changes alone is insufficient to under-stand the comprehensive ecological effects of riverregulation and climate change, which includes increasingstream temperatures, as modified by reservoirs (Null et al.,2013a, 2013b). Inclusion of stream temperatures could yieldvaluable insights. Secondly, specific relationships betweenflow metrics and ecosystems were not considered. Includingsuch mechanistic relationships in hydropower operationsmodels would likely need to focus on individual river sys-tems because of complex, long-term feedbacks betweenecosystems, environmental stochasticity and human manip-ulations (Kiernan and Moyle, 2012). Finally, the operatingrules used for hydropower diversions do not necessarilyreflect optimal decisions. For example, operators usuallyhave greater hydrologic foresight for minimizing spill thanin the operations simulation model. Embedding optimizationmodels into operations would improve modelling accuracy.

CONCLUSION

This study explored the independent and combined effectsof river regulation and climate change below several impor-tant high-elevation, medium-size diversion/storage reser-voirs in California’s Sierra Nevada. Results show thathistorical reservoir management has a much greater effecton flow regimes below storage/diversion reservoirs thanclimate change. For all flow metrics except one (CT), devi-ation from historical, unregulated conditions was greatestwith regulation under a future climate. Though the effects


of regulation were substantial, the flow regimes consideredhere are below reservoirs that follow baseline minimumflow requirements. Re-operating reservoirs provide an op-portunity to explicitly supply the flow regime featuresneeded to sustain downstream ecosystems beyond tradi-tional minimum flow requirements. There are indicationsthat reservoirs also can help ameliorate some of the harm-ful flow effects of warming. However, any such potentialoutcome must be studied in greater detail, with specificengineering studies for specific rivers. Such studies mustconsider other climate change effects, such as increasedstream temperatures or changes in energy demand andconsider more mechanistic relationships between abioticconditions and biotic quality.

ACKNOWLEDGEMENTS

This work was partially supported by the California EnergyCommission Public Interest Energy Research Program. Wealso thank Dave Steindorf of the American WhitewaterAssociation for posing the research questions and the Stock-holm Environment Institute (SEI) for their generous supportof the WEAP application in all modeling efforts needed forthis work.

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COMBINED EFFECTS OF RESERVOIR OPERATIONS AND CLIMATE