Hydrologic Variability of the Cosumnes River Floodplain...SEPTEMBER 2006 SUGGESTED CITATION Booth, Eric, Jeffrey Mount, and Joshua Viers. Hydrologic Variability of the Cosumnes River

eScholarship provides open access, scholarly publishingservices to the University of California and delivers a dynamicresearch platform to scholars worldwide.

Peer Reviewed

Title:Hydrologic Variability of the Cosumnes River Floodplain

Journal Issue:San Francisco Estuary and Watershed Science, 4(2)

Author:Booth, Eric, University of California, DavisMount, Jeff, University of California, DavisViers, Joshua H., University of California, Davis

Publication Date:2006

Publication Info:San Francisco Estuary and Watershed Science, John Muir Institute of the Environment, UC Davis

Permalink:http://escholarship.org/uc/item/71j628tv

Keywords:Cosumnes River, environmental flow, flood regime, floodplains, floods, hydrologic analysis,instream flow, restoration, watershed

Abstract:Natural floodplain ecosystems are adapted to highly variable hydrologic regimes, which includeperiodic droughts, infrequent large floods, and relatively frequent periods of inundation. To moreeffectively manage water resources and maintain ecosystem services provided by floodplains –and associated aquatic, riparian, and wetland habitats – requires an understanding of seasonaland inter-annual hydrologic variability of floodplains. The Cosumnes River, the largest river onthe west-slope Sierra Nevada mountains without a major dam, provides a pertinent test caseto develop a systematic classification of hydrologic variability. By examining the dynamics of itsrelatively natural flow regime, and a 98-year streamflow record (1908 – 2005), we identified 12potential flood types. We identified four duration thresholds, defined as short (S), medium (M), long(L), and very long (V). We then intersected the flood duration division by three magnitude classes,defined as small-medium (1), large (2), and very large (3). Of the 12 possible flood types createdby this classification matrix, the Cosumnes River streamflow record populated 10 such classes. Toassess the robustness of our classification, we employed discriminant analysis to test class fidelitybased on independent measures of flood capability, such as start date. Lastly, we used hierarchicaldivisive clustering to classify water years by flood type composition resulting in 8 water year types.The results of this work highlight the significant seasonal and inter-annual variability in naturalflood regimes in Central Valley rivers. The construction of water impoundment and flood control

http://escholarship.org




http://escholarship.org/uc/jmie_sfews

http://escholarship.org/uc/jmie_sfews?volume=4;issue=2

http://escholarship.org/uc/search?creator=Booth%2C%20Eric

http://escholarship.org/uc/search?creator=Mount%2C%20Jeff

http://escholarship.org/uc/search?creator=Viers%2C%20Joshua%20H.

http://escholarship.org/uc/item/71j628tv

eScholarship provides open access, scholarly publishingservices to the University of California and delivers a dynamicresearch platform to scholars worldwide.

structures has significantly altered all aspects of the flood pulse. Restoring floodplain ecosystemservices will require re-establishing key elements of these historic flood regimes in order to achieveregional restoration goals and objectives.





SAN FRANCISCO ESTUARY & WATERSHED SCIENCE

ABSTRACT Natural floodplain ecosystems are adapted to highlyvariable hydrologic regimes, which include periodicdroughts, infrequent large floods, and relatively fre-quent periods of inundation. Effectively managingwater resources and maintaining ecosystem servicesprovided by floodplains—and associated aquatic, ripar-ian, and wetland habitats—requires an understandingof seasonal and inter-annual hydrologic variability offloodplains. The Cosumnes River, the largest river onthe west-slope Sierra Nevada mountains without amajor dam, provides a pertinent test case to develop asystematic classification of hydrologic variability. Byexamining the dynamics of its relatively natural flowregime, and a 98-year streamflow record (1908 –2005), we identified 12 potential flood types. We iden-tified four duration thresholds, defined as short (S),medium (M), long (L), and very long (V). We thenintersected the flood duration division by three magni-tude classes, defined as small-medium (1), large (2),and very large (3). Of the 12 possible flood types cre-ated by this classification matrix, the Cosumnes River

streamflow record populated 10 such classes. To assessthe robustness of our classification, we employed dis-criminant analysis to test class fidelity based on inde-pendent measures of flood capability, such as startdate. Lastly, we used hierarchical divisive clustering toclassify water years by flood type composition result-ing in eight water year types. The results of this workhighlight the significant seasonal and inter-annualvariability in natural flood regimes in Central Valleyrivers. The construction of water impoundment andflood control structures has significantly altered allaspects of the flood pulse. Restoring floodplain ecosys-tem services will require re-establishing key elementsof these historic flood regimes in order to achieveregional restoration goals and objectives.

KEYWORDS Cosumnes River, environmental flow, flood regime,floodplains, floods, hydrologic analysis, instream flow,restoration, watershed

Hydrologic Variability of the Cosumnes River Floodplain(This paper is one in a series of interdisciplinary papers dedicated to documenting the restoration of the lower Cosumnes River, a tribu-tary to the San Francisco Bay-Delta. These papers represent the findings of several California Bay-Delta Authority projects fundedthrough the Ecosystem Restoration Program that focus on linked hydrogeomorphic and ecological processes in order to evaluate andguide ecosystem conservation and restoration efforts throughout the region.)

Eric G. Booth,* Jeffrey F. Mount, Joshua H. ViersUniversity of California, Davis

*Corresponding author: [email protected]

Booth et al.: Hydrologic Variability of the Cosumnes River Floodplain

1

SEPTEMBER 2006

SUGGESTED CITATIONBooth, Eric, Jeffrey Mount, and Joshua Viers.Hydrologic Variability of the Cosumnes RiverFloodplain. San Francisco Estuary and WatershedScience. Vol. 4, Issue 2 [September 2006]. Article 2.

http://repositories.cdlib.org/jmie/sfews/vol4/iss2/art2

INTRODUCTION

BackgroundFloodplains are among the most productive anddiverse ecosystems on Earth; they are also some of themost impacted and at risk ecosystems globally(Tockner and Stanford 2002), affected by myriadanthropogenic stressors and consumptive demands.Natural floodplain ecosystems are adapted to highlyvariable hydrologic regimes, which include periodicdroughts, infrequent catastrophic floods, and relativelyfrequent periods of inundation (Lytle and Poff 2004).Hydrologic variability acts to disturb and/or reset vari-ous biotic populations within aquatic, riparian, andwetland ecosystems, acting as an essential complementto ecological processes (e.g., nutrient cycling) in main-taining complex ecosystem pathways, which in turnpromote high biodiversity and biological integrity(Power et al. 1995; Ward and Stanford 1995).Furthermore, the connectivity of floodplains to riversystems is a critical linkage that creates and maintainsa mosaic of habitats for primary productivity (Ahearnet al. in review), the reproductive cycle of fishes(Ribeiro et al. 2004; Sommer et al. 2004), nesting andforaging of birds (Saab 1999), and regeneration ofriparian vegetation (Tabacchi et al. 1998).

Effective long-term maintenance of floodplain ecosys-tem services—especially large ecosystem recoveryefforts, such as those undertaken by the CaliforniaBay-Delta Authority—requires an improved under-standing of the spatiotemporal variability within aflow regime including the types and frequencies offloods within the regime. The ‘natural flow’ paradigm(Poff et al. 1997; Bunn and Arthington 2002), a syn-thetic approach to better reconciling competingdemands between environment and society, empha-sizes the importance of natural intra-annual and inter-

annual variability in river flows. This paradigm main-tains that hydrologic variability is essential for main-taining ecological integrity, which includes the self-sustaining products and processes of ecosystems thatprovide social and economic services to humans. Thegoal of maintaining ecological integrity of riverecosystems has led to research that attempts to charac-terize flow regimes using available streamflow dataand to apply these characterizations to river manage-ment activities.

The most traditional method for characterizing thehydrologic variability of a river is flood frequencyanalysis. Flood frequency analysis produces dischargerecurrence intervals to calculate an exceedance proba-bility distribution based on an observed streamflowrecord. While these types of analyses are critical forengineering flood control infrastructure, they do notaccurately distinguish different flood events based onduration or timing. Important ecosystem services, suchas primary productivity and juvenile fish rearing, areoften more dependent upon the duration and timing offloodplain inundation than the magnitude or frequen-cy of the event. In other words, ecological complexityis reliant upon the strength of biotic-abiotic interac-tions, which are time-dependent processes.

The discharge-duration-frequency method (Javelle etal. 2002; Javelle et al. 2003) attempts to include dura-tion in flood frequency analysis but, as with all floodfrequency analyses, only the largest events of the yearare considered. By ignoring the smaller, more frequentflooding events many critical aspects of the floodregime are left out of the analysis.

Recent attempts to characterize flow regimes havedeveloped a suite of hydrologic indices, primarilyaimed at quantifying variability. For example, theIndicators of Hydrologic Alteration (IHA) method(Richter et al. 1996) combined with the Range ofVariability Approach (RVA) (Richter et al. 1997) calcu-lates 32 ecologically significant hydrologic indices foreach year of record and recommends a range of vari-ability for each index to set as a goal for water man-agers. In addition to the 32 IHA parameters, 34Environmental Flow Components (EFCs) parametersare calculated based on statistics for five distinct flowtypes: low flows, extreme low flows, high flow pulses,

San Francisco Estuary and Watershed Science, 4(2), Article 2 (2006)

2


small floods, and large floods (Postel and Richter2003). This method has been used extensively byresource agencies throughout the world (TNC 2005).

Other examples include a study by Olden and Poff(2003), who highlight patterns of redundancy for 171hydrologic indices (including the IHA parameters) andrecommend a condensed, non-redundant set of hydro-logic indices that characterize different aspects of theflow regime for different river systems. To similarends, Harris et al. (2000) present a method to classifywater years by the shape and size of the annualhydrograph for several British rivers, using four shapeclasses, timing of the peak flow(s), and four magnitudeclasses. Finally, Stewardson and Gippel (2003) intro-duce the Flow Events Method (FEM), which empha-sizes the importance of temporal variability within thefunctional relationship between hydrology and ecolo-gy. After identifying important ecological processes,the FEM characterizes specific flow events and theirdistribution in time. All of these methods build on theunderstanding of the ‘natural flow’ paradigm and areusually applied as functional analysis tools whenestablishing environmental river flow requirements(Acreman and Dunbar 2004).

Specifically related to floodplains, Benke et al. (2000)quantified the inundation dynamics of a floodplain inthe southeastern U.S. coastal plain over a 58-year timeperiod. Although important hydrologic indices werecalculated for each year of record, no specific typolo-gy of floods or years was given to characterize theinter-annual variability of floodplain hydrology.

ObjectivesThe objectives of this study are to integrate elementsof these previous studies and to characterize thehydrologic variability of the lower Cosumnes River byanalyzing a 98-year streamflow record (1908 – 2005).Located in central California on the west slope of theSierra Nevada, this river is unique in that it possessesa relatively undisturbed hydrograph. Our workexpands on the concept introduced by Harris et al.(2000) by defining individual flood types as one offour duration and three magnitude class combinations,which are based on significant geomorphic and hydro-logic thresholds for the Cosumnes River system. We

calculate frequency of occurrence for each flood typeand then employ discriminant analysis to test typefidelity based on independent measures of flood capa-bility, such as a flood’s start date. Lastly, we used hier-archical divisive clustering to classify water years byflood type composition (i.e., frequency of flood typeswithin the water year).

Our methodology also expands on the IHA method bylooking within the ranges of variability for floodevents and quantifying how often certain types offloods and water years occur. This knowledge of thenatural frequency of certain flood events should aidwater managers in the future. If the goal of manage-ment is to sustain ecosystem services of floodplains,the frequency of certain flood and water year types inthe future should be roughly similar to what has beenquantified in the previous 98 years. The describedmethodology, however, is limited to characterizing thehydrologic variability once the river connects with itsfloodplain and not during flows that remain in-chan-nel. Finally, while similar techniques could be used onother rivers, this specific method only typifies onelowland segment of river in central California.

Study AreaThe Cosumnes River watershed (Figure 1), locatedsoutheast of Sacramento, drains a 1989 km2 areastarting at 2,300 m in the Sierra Nevada mountainrange and draining into the Mokelumne River at anelevation of 2 m above sea level. Water from theCosumnes River ultimately flows into the SanFrancisco Bay–Delta. It is one of the few unim-pounded rivers flowing from the Sierra Nevada Rangeinto the Central Valley. With the exception of loss ofbase flow in the summer and fall (Fleckenstein et al.in press), the Cosumnes maintains a relatively unim-paired hydrograph. Average annual precipitation inthe upper watershed (based on data from a meteoro-logical station at Fiddletown, CA from 1939-2004) is926 mm and in the lowlands (based on data from ameteorological station at Elliott, CA from 1927-1992)447 mm, with the majority of precipitation occurringbetween December and March (NCDC 2006). This win-ter precipitation is typically in the form of snow above1,500 m; however, only 16% of the watershed isabove 1,500 m; thus, winter rainfall plays a much


3

Figure 1. Map of Cosumnes River watershed showing locations of Cosumnes River Preserve (CRP) floodplain,Michigan Bar streamflow gage, McConnell streamflow gage, Fiddletown precipitation gage, and Elliott precipitation gage.

Precipitation and Streamflow in the Cosumnes River watershed

0

5

10

15

20

25

30

35

40

OCT NOV DEC JAN FEB MAR APR MAY JUN JUL AUG SEP

Mea

n M

onth

ly P

reci

pita

tion

(cm

)M

ean

Mon

thly

Str

eam

flow

(m3 /s

)

mean monthly precipitation mean monthly streamflow

Figure 2. Mean monthly precipitation (from meteorological station at Fiddletown, CA) and mean monthlystreamflow at Cosumnes River at Michigan Bar.

Figure 1. Map of Cosumnes River watershed showing locations of Cosumnes River Preserve (CRP) floodplain,Michigan Bar streamflow gage, McConnell streamflow gage, Fiddletown precipitation gage, and Elliott precipitationgage.

Figure 2. Mean monthly precipitation (from meteorological station at Fiddletown, CA) and mean monthly streamflowat Cosumnes River at Michigan Bar.

SEPTEMBER 2006


4


more significant role in runoff compared to otherrivers draining the western Sierra Nevada mountainswith higher elevation watersheds (USACE 1965).Figure 2 shows the mean monthly precipitation (fromthe Fiddletown station) and the mean monthly stream-flow (at Michigan Bar) for the watershed.

Overall, temperature plays an important role in SierraNevada flood regimes by determining whether stormprecipitation will contribute more runoff or moresnowpack (Dettinger 2005). This temperature is drivenby orographic effects as well as by the geographic ori-gin and direction of the storm system. For example,circulations that transport exceptionally warm andmoist air from the subtropics and tropics into the cen-tral Sierra Nevada mountains are often responsible forwarm storms with intense precipitation that lead tolarge floods.

Historically, the lower Cosumnes River was a dynam-ic, low-gradient, multi-channel anastomosing systemdominated by frequent avulsions and regular inunda-tion of the floodplain during winter and spring(Florsheim and Mount 2002). This floodplain can beclassified as a low-energy cohesive floodplain (C2)according to the genetic floodplain classification pre-sented by Nanson and Croke (1992). For the last 200years, the Cosumnes River has been impacted by arange of land use activities. Hydraulic mining andgrazing increased erosion throughout the upperwatershed and provided a source of sediment to thedownstream channel and floodplain. Once thehydraulic mining sediment source was eliminated inthe early 1900s, channel incision, initiated due tolevee construction, occurred throughout the previouslyaggraded bed (Vick et al. 1997). This incision hasoccurred in all alluvial reaches of the river except thefarthest downstream section near its tributary junctionwith the Mokelumne River (Florsheim and Mount2003). Widespread conversion of the floodplainforests and wetlands of the lower Cosumnes to agri-cultural fields took place during the early 1900s,although some patches of remnant riparian forestremain. Today, the river is confined to a single chan-nel and remains almost entirely disconnected from itsfloodplain except during high flows when levees arebreached.

The lowland river-floodplain reference site is arestored floodplain located on the Cosumnes RiverPreserve (CRP), which is managed by a coalition ofstate, federal, and non-profit organizations, such asThe Nature Conservancy California. Restoration of theagricultural fields along the river was achievedthrough breaching of levees during the 1990s. Sincerestoration of connectivity, the channel and the flood-plain have undergone considerable topographicchange due to localized deposition and scour duringflood events (Florsheim and Mount 2002). Thisrestoration effort has increased habitat heterogeneitydue to the colonization of tree and herbaceous vegeta-tion on floodplain sand deposits.

METHODS

Streamflow RecordA continuous daily record of discharge data (Figure 3)for the Cosumnes River at Michigan Bar from 1908 –2005 was acquired from the U.S. Geological Survey’sNational Water Information System (USGS 2005).Although the gage at Michigan Bar (MHB) is locatedapproximately 50 km upstream of the floodplain refer-ence site on the Cosumnes River Preserve, it accuratelypredicts flood conditions on the floodplain due to verylittle streamflow being added downstream of MichiganBar. This point is illustrated by comparing annualstreamflow volumes at MHB with the Cosumnes Riverat McConnell gage (MCC) that was rated by the USGSfor discharge from 1941 – 1982 (USGS 2005). MCC islocated approximately 11 km upstream of the CRPfloodplain and is a very good predictor of streamflowat the CRP because there are no tributaries in betweenthe two sites. Excluding three drought years (1961,1976, and 1977), the mean of the ratio of November –June streamflow volume between MHB and MCC is0.94. Therefore, on average, 94% of the streamflowvolume during the flood season that passes MCC alsopassed MHB. This 6% difference is acceptable for ouranalysis since MHB data are used only as a proxy forflooding on the CRP floodplain.

The MHB streamflow record was also analyzed for sta-tionarity because many surrounding basins in theSierra Nevada exhibit trends in variables such as cen-


5

SEPTEMBER 2006

ter of mass of annual flow and maximum annual flowdue to changes in climatic conditions since 1950.Stewart et al. (2005) found that the center of mass ofannual flow has come earlier since 1948 forsnowmelt-dominated watersheds throughout the west-ern U.S. They attributed this trend to warmer winterand spring air temperatures. Similarly, the NationalResearch Council (NRC) (1999) found that larger floodevents were concentrated after 1950 in each of thestreamflow records for the American, Feather, Merced,Mokelumne, Stanislaus, Tuolumne, and Yuba rivers.For the Cosumnes River, we found no trend for thecenter of mass of annual flow, which is most likelybecause the Cosumnes River is a lower elevationwatershed with less snowpack compared to most west-slope Sierran rivers; snowmelt is therefore not as largeof a contributor to the annual streamflow volume.However, we performed a 51-year moving windowanalysis of the MHB streamflow record, whichrevealed an increasing trend related to maximum

annual flow. As in surrounding basins, the larger floodevents have occurred post-1950 in the Cosumnes Riverbasin. To determine this change in stationarity, we firstcalculated the moving mean (MM) and moving standarddeviation (MSD) of the annual maximum daily flowtime series. Subsequently, we applied maximum likeli-hood estimation, using a log-normal distribution, todetermine the moving 100-year annual maximum dailyflow event (Q100) (Figure 4). To test for significant dif-ferences in trend, the series was bisected and the differ-ences in means of the MM, MSD, and Q100 for eachhalf were tested using the two-sample t-test withunequal variances. The first and second half means foreach parameter were significantly different (p = 0.01)from each other (Table 1). We believe that the observedincreasing trend in the moving mean, moving standarddeviation, and moving 100-year daily flood in theCosumnes River streamflow record is noteworthy andone that is considered throughout this analysis.

0

200

400

600

800

1000

1200

1400

1600

1800

2000

1907

1910

1913

1916

1919

1922

1925

1928

1931

1934

1937

1940

1943

1946

1949

1952

1955

1958

1961

1964

1967

1970

1973

1976

1979

1982

1985

1988

1991

1994

1997

2000

2003

2006

Mea

n D

aily

Str

eam

flow

(m3 /s

)

Figure 3. Hydrograph of daily discharge data of Cosumnes River at Michigan Bar (WY1908-2005)Figure 3. Hydrograph of daily discharge data of Cosumnes River at Michigan Bar (WY1908-2005)


6


In a similar concern, the streamflow record was ana-lyzed for alteration using the Indicators of HydrologicAlteration (IHA) analysis (Richter et al. 1996). The IHAanalysis yielded medians and coefficients of dispersionfor 68 hydrologic parameters for a period before andafter a “disturbance,” which in this case was defined

as the middle of the record. The only significant (95%confidence interval) changes related to floods on theCosumnes were for the coefficients of dispersion forJanuary, April, May, and June monthly discharge and30-day and 90-day annual maximum discharges. Foreach of these five parameters, coefficients of disper-sion were larger post-1956. The idea that dischargeswere more variable in the period after 1956 is impor-tant and could be related to climate changes. However,other pertinent characteristics of the streamflow recorddo not show a similar trend and can be consideredconsistent with a stationary time series.

Flood TypingField observations of the floodplain at the CosumnesRiver Preserve since 1998 suggest a floodplain con-nectivity threshold of 25 m3/s at MHB (Florsheim etal. in press). This threshold corresponds to approxi-

0

200

400

600

800

1000

1200

1400

1600

1800

2000

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

Water Year

100-

year

Ann

ual P

eak

Dai

ly F

low

(m3 /s

),A

n nu a

l Pea

k D

aily

Flo

w (m

3 /s)

0

50

100

150

200

250

300

350

Mov

ing

Mea

n an

d St

anda

rd D

evia

tion

Figure 4. Plot showing the annual peak daily flow time series (red line) and trends in the moving mean (black line with circle markers), standard deviation (black line with x-markers), and 100-year annual maximum daily flow(black line with no markers) of the Cosumnes River at Michigan Bar (WY 1907 - 2005) that was calculated byfitting the log-normal distribution using maximum likelihood estimation. Vertical line shows the mid-point of the record.

Figure 4. Plot showing the annual peak daily flow time series (red line) and trends in the moving mean (black linewith circle markers), standard deviation (black line with x-markers), and 100-year annual maximum daily flow (blackline with no markers) of the Cosumnes River at Michigan Bar (WY 1907 - 2005) that was calculated by fitting the log-normal distribution using maximum likelihood estimation. Vertical line shows the mid-point of the record.

Water Year range MM mean MSD mean Q100 mean 1907 – 1955 (N = 24) 251.0 m3/s 206.2 m3/s 1041.2 m3/s

1956 – 2005 (N = 25) 267.5 m3/s 269.7 m3/s 1335.2 m3/s

Degrees of Freedom 38.1 28.3 28.5 t 0.01 2.712 2.763 2.763

t* 5.014 7.124 7.187

Table 1. Means of the 51-year moving-window mean (MM),standard deviation (MSD), and 100-year annual maximum dailyflow for the first and second halves of the annual maximumdaily flow record and results from a two-sample t-test withunequal variances showing the means to be significantly differ-ent (p = 0.01) for each parameter.


7

SEPTEMBER 2006

mately the one-year flood using the methodologydescribed in Bulletin 17B (IACWD 1982) on dailystreamflow data at MHB from 1907-2004. For the pur-poses of this analysis, a flood event—a flow that con-nects the lower Cosumnes River channel to the flood-plain through overland flow—was defined to be anyperiod when the flow at MHB is above 25 m3/s.Disconnection is defined as the day when flow at MHBreaches 25 m3/s following a flood event. Since leveebreaches currently control connectivity between thechannel and floodplain, this threshold does notdescribe actual historical flood conditions. Rather, thisthreshold only describes how the current floodplainwould function if subjected to the range of dischargeconditions experienced over the past 98 years. Sincethe Cosumnes floodplain is highly altered, this thresh-old provides only an approximation of historic condi-tions and is not intended to be used to simulate flowconditions prior to land conversion. This study pro-vides an analysis of flood patterns, rather than a simu-lation of historic conditions.

Using the 98-year discharge record observed from thecontinuous flow gage at MHB, we separated 479 floodevents (using the 25 m3/s threshold) and calculated

the following statistics for each event: startdate, end date, flood duration, peak daily dis-charge, mean daily discharge, disconnectionperiod before flood, disconnection period afterflood, antecedent floodplain volume, totalflood volume, and number of flood peaks. Weplotted flood duration versus peak daily dis-charge for each flood event (Figure 5), dis-playing the corresponding season in color.Using the empirical hydrogeomorphic thresh-olds described below, we delineated four dura-tion classes, which were defined as short (S)(<7 days), medium (M) (7-20 days), long (L)(21-70 days), and very long (V) (>70 days). Wethen superimposed three magnitude classes,defined as small-medium (1) (<100 m3/s),large (2) (100-400 m3/s), and very large (3)(>400 m3/s), creating 12 possible flood types.

The ten flood types shown in Figure 5 indicate whereobserved gage data met our typing criteria—two typeswere absent in the period of record.

Boundary thresholds for each flood type were definedbased on hydrologic and geomorphic characteristics ofthe floodplain-channel interface and the watershed.Floods with durations greater than 71 days dominatedthe entire flood season and had peak daily dischargesgreater than 100 m3/s (approximately the 1.5-yearflood). This magnitude threshold corresponds to obser-vations from floodplain monitoring, in which peak dailydischarges above 100 m3/s transport new sediment (i.e.,sand) onto the floodplain (Florsheim et al. in press).These empirical observations also guided the definitionof an upper magnitude threshold; flood events with apeak daily discharge above 400 m3/s (slightly less thanthe 5-year flood) inundate and fill the entire floodplainand create substantial geomorphic disturbance.

A majority (63%) of the floods less than seven days induration were formed with one peak and most likelycorresponded with one significant precipitation event. Amajority (75%) of the floods with a duration greaterthan seven days were composite events (more than onepeak) and most likely corresponded with more than oneprecipitation event. Floods lasting less than 21 days didnot occupy a large fraction of the flood season but dif-fered considerably from the single-event floods.

Duration vs Magnitude vs Timing

10

100

1000

10000

1 10 100 1000

Flood Duration (days)

Peak

Dai

ly F

low

(m3 /s

)

Fall Winter Early Spring Late Spring

S1

S2

M1

M2

M3

L1

L2

L3

V2

V3

Figure 5. Plot of 479 events on Cosumnes River at Michigan Bar(WY1908-2005) separated into nine flood types. Fall = OCT, NOV;Winter = DEC, JAN, FEB; Early Spring = MAR, APR; Late Spring= MAY, JUN


8


Flood Frequency & Flood Type FidelityIn order to quantify the frequency of occurrence ofeach flood type, the flood event record was separatedinto water years (October 1 – September 30). Thenumber of occurrences of each flood type during theperiod of record and the empirical frequency of aflood type occurring in any given yearwere calculated.

Discriminant analysis was used to validatethe statistical uniqueness of each floodtype. This technique predicts whether ornot a particular flood event belongs withinits assigned flood type by looking at inde-pendent variables aside from the ones thatwere used for classification (e.g., peak dailydischarge and flood duration). Three vari-ables (start date, mean daily discharge, andnumber of peaks) were used to evaluatehow rigorously the classification distin-guished flood events. The stepwise methodof discriminant analysis was used withprior probabilities proportional to the num-ber of floods in each flood type.

Water Year ClassificationFinally, to classify water year types, hierarchical clus-

ter analysis was used to differentiate yearsbased on the number of days each flood typeoccurred during each water year. Hierarchicalcluster analysis groups similar observationsinto clusters based on their distance apart fromone another. In other words, the closer twoobservations are to each other the more likelythey will be clustered together. This analysiscan be performed using N number of variables(i.e., distance is calculated in N-dimensions). Inthis case, each observation was a water year,and the number of variables coincided withten flood types (N = 10). The value given toeach variable was the number of days eachflood type was present in the water year. Forexample, in water year 1978 an M2 floodevent was present for 27 days (Figure 6). Using

the number of days each flood type was present in agiven water year instead of only the number of floodscorresponding with each type allowed us to separateout the longer events from the shorter events and givemore credence to flood duration. Ward’s minimumvariance method was used to calculate distancebetween objects. This method uses the distancebetween two clusters as the ANOVA sum of squaresbetween the two clusters added over all the variables.

Water Year 1978

0

25

50

75

100

125

150

175

12/11 12/26 1/10 1/25 2/9 2/24 3/11 3/26 4/10 4/25 5/10 5/25

Date

Mea

n D

aily

Str

eam

flow

(m3 /s

)

S1 S2 M2 M1 L2 daily flowFlood type

S1

S2

M1

M2

M3

L1

L2

L3

V2

V3

# of days

1

3

10

27

0

0

57

0

0

0

Figure 6. Mean daily streamflow (cm) on the Cosumnes River atMichigan Bar for water year 1978 shown with flood types andnumber of days each flood type is present.

Flood Duration Peak Flow Start # of emp freq emp freq emp freq Type

Duration (days)

Magnitude(m3/s) Season occur. 1 or more 2 or more 3 or more

S1 short < 7 small to med < 100 All seasons 278 0.91 0.72 0.54

S2 short < 7 large 100-400 Fall to Early Spring 31 0.29 0.03 0.00

M1 medium 7-20 small to med < 100 Winter to Late

Spring 42 0.33 0.09 0.01

M2 medium 7-20 large 100-400 Winter 44 0.36 0.07 0.02

M3 medium 7-20 very large > 400 Fall to Winter 5 0.05 0.00 0.00

L1 long 21-70 small to med < 100 Early Spring 20 0.18 0.02 0.00

L2 long 21-70 large 100-400 Winter to Early Spring 31 0.28 0.04 0.00

L3 long 21-70 very large > 400 Winter 12 0.11 0.01 0.00

V2 very long > 70 large 100-400 Winter to Early Spring 10 0.10 0.00 0.00

V3 very long > 70 very large > 400 Winter to Early Spring 6 0.06 0.00 0.00

2 or 3 large-very large > 100 139 0.66 0.47 0.22

3 very large > 400 23 0.21 0.02 0.00

L or V long-very long > 20 79 0.58 0.22 0.00

V very long >70 16 0.16 0.00 0.00

ALL 479 0.94 0.88 0.83

Table 2. Nine flood types of the Cosumnes River and eachrespective duration, magnitude, timing, and empirical frequencyin any given year.


9

SEPTEMBER 2006

RESULTS

Flood TypingWe created a generalized framework for hydrologiccharacterization of the Cosumnes River using a combi-nation of flood duration and magnitude. Using empiri-cal hydrogeomorphic thresholds, we typed 479 floodsinto 10 classes, of the possible 12, for all observedevents in the 98-year streamflow record—Table 2describes each of the 10 flood types. The relationshipbetween magnitude (peak flow), duration, and timing(season) for all events is shown by respective classes

in Figure 5. No flood events occupied the short dura-tion, very large magnitude type and the very longduration, small-medium magnitude type classes, aswas expected due to the positive relationship betweenflood magnitude and duration. An example of how theflood types are defined in a given year is shown inFigure 6.

On average, the first flood of the season occurs inearly January (mean and median of all years withfloods is January 6). Subsequent winter floods havelarger peak to duration ratios due to higher intensityrainfall inputs compared with spring floods, whichhave smaller peak to duration ratios due most likely tohigher snowmelt contributions (Figure 5). The wettestmonth of the year based on total streamflow volume isMarch (median = 7.0 x 107 m3), with April andFebruary slightly drier (Figure 7). However, peak daily

Mon

thly

Str

eam

flow

Vol

ume

(m^3

)

0e+0

1.000e+8

2.000e+8

3.000e+8

4.000e+8

5.000e+8

1-OCT 2-NOV 3-DEC 4-JAN 5-FEB 6-MAR 7-APR 8-MAY 9-JUN

WY Month

Pea

kD

aily

Flo

w(c

ms)

100

80

70

60

50

40

30

20

1000

800

700

600

500

400

300

200


WY month

Figure 7. Monthly water volumes for 98-year streamflow recordfor Cosumnes River at Michigan Bar.

Figure 8. Peak daily flow versus starting month with box plotsfor all 479 flood events.

g y g p

Floo

d D

urat

ion

(day

s)

1

108

6

4

3

2

10080

60

40

30

20


Water Year Month

250

500

750

1000

1250

# of

occ

urre

nces


Figure 9. Flood duration versus starting month with box plots forall 479 flood events.

Figure 10. Frequency distribution of days of flooding per wateryear month.


10

flows of the separated flood events are highest inFebruary (median = 81 m3/s), with March and Januaryapproximately 20 m3/s lower (Figure 8). Flood dura-tions, as shown in Figure 9, are longest when floodingstarts in February (median = 6 days), with March justslightly shorter. Figure 10 shows the frequency distri-bution of all days of flooding over the 98-year record;occurrences steadily increase until the peak in April.Since precipitation declines substantially in April, thispeak in the number of flood days is most likely due tosnowmelt. Finally, nearly two-thirds of all floods inthe period of record started in January, February, orMarch.

Since each flood event within a flood type is different,average hydrographs were determined for each floodtype to show the general shape of the flood hydro-graph. Figure 11 shows each average hydrograph withplus or minus one standard deviation from the meanto show variability for each day of flooding. Eachaverage hydrograph shows a peak within several daysof the start date and then gradually decreases overtime. The peaks are more noticeable in the flood typeswith higher peak discharges relative to duration such

as types S2, M2, M3, L2, L3, and V3. The flood typeswith smaller magnitudes relative to duration (S1, M1,L1, and V2) tend to have a more sustained and con-stant discharge over the flood period. Variability tendsto decrease with duration within each flood typebecause of the decrease in sample size (n) of floodswith longer durations (e.g., for L1, n = 20 at 23 days,but n = 2 at 47 days).

Several of the same flood statistics used to describeindividual flood events were also used to describeeach flood type, including the following variables:number of peaks, start date, duration, peak flow, meanflow, and flow volume (Figure 12). Start dates showroughly the same temporal trend observed in Figure 5(i.e., timing) but with specific start dates for eachflood type. The general trend is that floods with higherpeak flows and shorter durations tend to be earlier inthe season as compared to floods with smaller peakflows and longer durations that occur later in the sea-son. The differences in early season versus late seasonflood types is exemplified by type M3, which onlyoccurs in fall and winter, and type L1, which primarilyoccurs in spring due to snowmelt. Types L and V tend

Type S1

010203040506070

0 2 4 6

Day of Flood

Mea

n D

aily

Dis

char

ge (m

3 /s)

MeanQ MeanQ+SD MeanQ-SD

Type S2

0

50

100

150

200

0 2 4 6

Day of Flood

Mea

n D

aily

Dis

char

ge (m

3 /s)


Type M1

010203040506070

0 5 10 15 20

Day of Flood

Mea

n D

aily

Dis

char

ge (m

3 /s)


Type M2

0

50

100

150

200

0 5 10 15 20

Day of Flood

Mea

n D

aily

Dis

char

ge (m

3 /s)


Type M3

0

200

400

600

800

0 5 10 15 20

Day of Flood

Mea

n D

aily

Dis

char

ge (m

3 /s)


Type L1

010203040506070

0 20 40 60

Day of Flood

Mea

n D

aily

Dis

char

ge (m

3 /s)


Type L2

0

50

100

150

200

0 20 40 60

Day of Flood

Mea

n D

aily

Dis

char

ge (m

3 /s)


Type L3

0

200

400

600

800

0 20 40 60

Day of Flood

Mea

n D

aily

Dis

char

ge (m

3 /s)


Type V2

0

50

100

150

200

0 50 100 150

Day of Flood

Mea

n D

aily

Dis

char

ge (m

3 /s)


Type V3

0

200

400

600

800

0 50 100 150

Day of Flood

Mea

n D

aily

Dis

char

ge (m

3 /s)


Figure 11. Average hydrographs representing each flood type.


11

SEPTEMBER 2006

to start later in the season while types S and M canbegin at any time throughout the flood season.

Discriminant Analysis

Discriminant analysis is an eigenvector technique thatquantifies the degree of association between independ-ent variables, maximally separating a fixed number ofclasses (McCune and Grace 2002). Our objective was tomaximize among class variation, in this case acrossflood types, relative to within class variation, or withinflood types. As a statistical technique, discriminantanalysis is focused on multivariate structure and mis-classifications of a priori classes (McCune and Grace2002), allowing us to gauge the efficacy of our typingthresholds.

We used three independent measures of flood compo-sition in discriminant analysis to objectively quantify

the strength of association of floods within our a priorithresholds. Using flood start date, mean daily dis-charge during the flood, and number of peaks within aflood as independent, multivariate classifiers and withclass odds proportional to observed, discriminantanalysis showed that 27.6% of the flood events weremisclassified. However, 57% of misclassified eventswere predicted to be S1 flood types (n = 75), which isthe most highly populated flood type. Of these mis-classified events—ones predicted to be S1—over 50% (n= 41) were initially defined as M1 events.

This relatively high misclassification rate is most likelydue to the descriptive nature of the original classifica-tion; the misclassification rate was also quite high(23%) when using dependent variables (i.e., peak flowand duration) instead of the independent variables(i.e., start date, average daily discharge, and number of

Figure 12. Flood type statistics (flood type code, # of occurrences, mean # of peaks, mean start date (WY fraction and date), mean floodduration, mean peak flow, mean average flow, mean storm volume.


12


peaks) within the discriminant analysis. In other words,rigid vertical and horizontal thresholds (Figure 5) wereneeded to describe the magnitude and durationboundaries for each flood type. However, statistically-based techniques (e.g., discriminant analysis) do notuse such rigid boundaries to group similar data pointsbut instead use n-dimensional multivariate space fordetermining class separation. The results of our dis-criminant analysis suggested that of all a priorithresholds for flood typing, the demarcation betweenS1 and M1 events (seven-day duration) does not pos-sess high discriminatory power. Furthermore, work byGallo et al. (in review) suggests that complete flood-plain mixing of waterborne constituents happens infloods greater than five days in duration. Therefore,our results, while useful and indicative of hydrologicalprocesses, should be viewed in the context of ourintent: to provide a meaningful composite of floodtypes throughout the streamflow record and theirprevalence within water years.

Water Year Typing & Cluster AnalysisUsing the classified flood types, we performed a hierar-chical cluster analysis to yield eight distinct water yeartypes (Figure 13). Water year types were named numeri-cally based on increasing median annual streamflowvolumes (Figure 14). Of the 98 water years typed, WYT-1 possessed the greatest number (n = 32), representing

33% of the record. WYT-7 was the least populat-ed water year type (n = 4) using this method, with4% of the years on record. The number of occur-rences and empirical frequencies of the differentwater year types is given in Table 3.

The primary flood type in WYT-1, the driest wateryear type, is S1 with very few other types. S1 andS2 floods are the only dominant types in WYT-2.L1 flood types are the major constituent in WYT-3; whereas M2 and L2 events dominate WYT-4,but with several M1 floods also present. WYT-5consists of mostly V2 floods in combination withL3 floods, in contrast to WYT-6, which primarilyconsists of L3 floods. WYT-7 is primarily M3floods; but several V2 and V3 events are presentas well. The wettest water year type, WYT-8, islargely dominated by the very large magnitude,very long duration V3 floods.

WYT-5

WYT-7

WYT-3

WYT-4

WYT-8

WYT-2

WYT-6

WYT-1

WYT-2

WYT-6

WYT-1

WYT-3

WYT-4

WYT-8

WYT-5

WYT-7

driest water year type, S1 dominant but no other flood types usu. present

large floods (L3 dominant) in early part of year but dry in later part of year

relatively dry year, S1 and S2 dominant with a few M1, M2, L1, L2 events

latest center of mass, L1 dominant with several S1, M1, M2 events present

M2 and L2 dominant with several M1 events present

wettest water year type, V3 dominant, relatively late center of mass

M3 dominant in early part of year with several V2 and V3 events present

V2 dominant with several L3 events present, relatively late center of mass

Figure 13. Hierarchical clustering of similar water years andgeneral descriptions of each water year type.

tota

l ann

ual w

ater

vol

ume

(km

^3)

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

WYT-1 WYT-2 WYT-3 WYT-4 WYT-5 WYT-6 WYT-7 WYT-8

Water Year Type

Figure 14. Total annual water volume (km^3) for each wateryear type.

Water Year Type

# of occurrences

empirical frequency

WYT-1 32 0.33 WYT-2 15 0.15 WYT-3 10 0.10 WYT-4 20 0.20 WYT-5 7 0.07 WYT-6 5 0.05 WYT-7 4 0.04 WYT-8 5 0.05

Table 3. Water Year Types with number of occurrences andempirical frequency based on 98-year streamflow record.


13

SEPTEMBER 2006

We also calculated the center of flow mass for eachwater year type in addition to generating average

hydrographs. The center of flow mass for each wateryear is shown in Figure 15 and shows a wide range ofvalues associated with each water year type. The typewith the earliest center of mass is WYT-6, which islargely dominated by early-season L3 floods. WYT-3has the latest center of mass because of the largeinfluence of the late-season L1 floods and relativelyfew floods in the early season. Average hydrographsfor each water year type are shown in Figure 16.Changes in the center of flow mass, total streamflowvolume, and overall magnitude for each water yeartype are visually apparent.

DISCUSSION AND SUMMARYBased on the general hydrologic characteristics givenby separating flood types, the lowland Cosumnes River

cent

er o

f mas

s (W

Y da

y)

110

120

130

140

150

160

170

180

190

200

210

WYT-1 WYT-2 WYT-3 WYT-4 WYT-5 WYT-6 WYT-7 WYT-8

Water Year Type

Figure 15. Day of water year that corresponds with the centerof flow mass for each water year type.

WYT-1

050

100150200250300350400

0 25 50 75 100 125 150 175 200 225 250 275 300 325 350

Day of Water Year

Stre

amflo

w(m

3 /s)

mean mean+SDmean-SD center of mass

WYT-2

050

100150200250300350400

0 25 50 75 100 125 150 175 200 225 250 275 300 325 350

Day of Water Year

Stre

amflo

w(m

3 /s)


WYT-3

050

100150200250300350400

0 25 50 75 100 125 150 175 200 225 250 275 300 325 350

Day of Water Year

Stre

amflo

w (m

3 /s)


WYT-4

050

100150200250300350400

0 25 50 75 100 125 150 175 200 225 250 275 300 325 350

Day of Water Year

Stre

amflo

w (m

3 /s)


WYT-5

050

100150200250300350400

0 25 50 75 100 125 150 175 200 225 250 275 300 325 350

Day of Water Year

Stre

amflo

w (m

3 /s)


WYT-6

050

100150200250300350400

0 25 50 75 100 125 150 175 200 225 250 275 300 325 350

Day of Water Year

Stre

amflo

w (m

3 /s)


WYT-7

050

100150200250300350400

0 25 50 75 100 125 150 175 200 225 250 275 300 325 350

Day of Water Year

Stre

amflo

w(m

3 /s)


WYT-8

050

100150200250300350400

0 25 50 75 100 125 150 175 200 225 250 275 300 325 350

Day of Water Year

Stre

amflo

w(m

3 /s)


Figure 16. Average annual hydrographs for each water year type.


14


floodplain experiences two distinct periods of flood-ing. The first period, occurring roughly fromNovember to February, is comprised of floods thattend to be flashier and have larger peak flows (Figure8), but sustained flooding is not as common duringthis period as in the second period. This early periodalso yields smaller monthly streamflow volumes(Figure 7) based on less days of flooding (Figure 10).The second period, occurring roughly from March toMay, contains smaller peak flows (Figure 8) comparedto the first period, but days of flooding are moreabundant (Figure 10). Therefore, the second periodyields a much larger amount of streamflow volume(Figure 7). These two distinct periods of the flood sea-son are most likely due to later-season snowmelt con-tributions and larger shallow groundwater inputs inthe second period from sources earlier in the season.

This bi-seasonal effect is also reflected in the differ-ence in mean start date for types M1 and L1 versustypes M2, M3, L2, and L3 (Figure 12). For the formergroup, the combined mean start date is March 24, andfor the latter group, the combined mean start date isJanuary 28. This nearly two month difference betweenthese groups of flood types shows the bi-seasonaleffect and supports the effectiveness of this methodol-ogy at recognizing certain hydrologic phenomena spe-cific to this watershed.

Empirical frequencies over the period of record werealso calculated for each flood type and several floodtype combinations (Table 2) to see how often certainfloods occurred in the historical record. Types 2 and 3,which consist of the floods that can transport sandonto the floodplain, occur at least once in approxi-mately two out of every three years and twice in halfof the years. The very large magnitude type 3 floodsoccur at least once in one out of every five years onaverage. The long duration flood types (L and V) occurat least once in roughly six out of every ten years.

The flood type classification along with the flood sta-tistics determined for each of the 479 flood events onrecord can also be used to test the potential long-termfrequency of certain biological phenomena observedon the lowland Cosumnes River floodplain. Ahearn etal. (in review) examined the importance of flood pulseinteraction on the floodplain and its influence on

energy and nutrient subsidies between the floodplainand the river. They showed that floodplain waters thathave intermediate residence times yield high levels ofprimary productivity, principally as algal biomass.Flood pulses displace this residual floodplain waterfrom the floodplain back into the river, feeding riverfood webs. Ahearn et al. (in review) defined “produc-tivity pumps” as floods that optimize algal subsidiesfrom the floodplain to the river channel. Productivitypumps occur when floods are separated by periods offloodplain draining (no inflow) that last 5-25 days.

The five-day lower draining period boundary is basedon sampling that shows significantly increasedChlorophyll-a concentrations (a proxy for algal pro-ductivity) after at least five days of draining; the 25-day upper disconnection period boundary is based onthe floodplain being too empty for significant produc-tion subsidies to occur. These conditions are typicallyassociated with S and M-type floods. Based on histori-cal data, at least one productivity pump floodoccurred, on average, in two out of every three years,and at least two effective floods occurred in roughlyhalf of the years. The relatively high frequency of pro-ductivity pumping floods may reflect how much of arole floodplains play in providing a source of produc-tive water to downstream areas such as the SanFrancisco Bay-Delta. Tributaries to the Delta, such asthe Cosumnes River, are the largest source of organiccarbon (Jassby and Cloern 2000) and represent animportant research topic for management of this com-plex ecosystem.

The water year type classification also has the abilityto analyze the frequency of certain ecological phe-nomena but on an annual time-scale. While the classi-fication adequately distinguishes different water yearsand results in relative frequencies of these differentwater year types, more research is needed to moreaccurately describe the ecological differences betweenwater year types. Continued collection of field-basedobservations that have been going on since 2001 willstrengthen our approach by allowing for explicit link-age to temporal variability in ecological processes. Asan example of this connection, WYT-7 contains atleast one M3 flood, which will most likely create newbare ground in the form of sand deposits, and sub-stantial late-season flooding. Using the Recruitment


15

SEPTEMBER 2006

Box Model (Amlin and Rood 2002), the combinationof new bare ground and late-season flooding providesa very favorable condition for the recruitment of cot-tonwood trees. Conversely, WYT-6, while containingseveral large early-season floods (the four highestannual maximum daily flows on record all occur inWYT-6), has a relatively dry spring period and there-fore is likely responsible for poor fish recruitment(Crain et al. 2004) as they cannot access the floodplainfor spawning and rearing.

The distribution of certain water year types throughoutthe period of record also illuminated the previouslymentioned observation of the inconsistency of certainaspects of the streamflow record with a stationary timeseries. Two water year types with very different char-acteristics showed opposite patterns in distributionover the period of record. WYT-3 describes a year witha relatively dry winter but a relatively wet spring.Intuitively, this water year type would occur whenwinter rainfall does not generate large flood eventsbut enough snowmelt and/or abundant spring precipi-tation leads to inundation of the floodplain into thelate spring. This water year type was much moreprevalent in the first half of the period of record ascompared to the second half (Figure 17). By 1950,80% of WYT-3 years had occurred. Conversely, theopposite trend is shown for WYT-6. This water yeartype consists of a year with a very wet winter (four of

the highest daily flows in the record occur in thiswater year type) but a relatively dry spring that doesnot lead to inundation of the floodplain into the latespring. WYT-6 is much more prevalent in the latterhalf of the record—it did not occur until water year1956. These two opposite trends are consistent withthe hypothesis of a rising snow-rainfall transition line,leading to larger winter floods and diminishing thelater snowmelt-dominated part of the hydrograph dueto increased winter and spring air temperatures sincethe mid-twentieth century (Stewart et al. 2005).

While restoration of aquatic ecosystems continues torise in popularity and importance due to the recogni-tion of the valuable services they provide, the ques-tion of how much water individual ecosystems needremains largely unanswered (Richter et al. 2003).Concurrently, the demand for water continues toincrease as human populations grow, which compelswater managers to increase regulation of rivers andstreams. Therefore, as more complex water resourcesissues surface, managers need to be informed aboutthe degree of variability that these systems criticallyneed to continue to provide ecosystem services tohumans. The promotion of natural variability is nowrecognized as a central theme in the implementationof successful river restoration projects (Wohl et al. 2005).

Organizing flood events and water years into similartypes will allow managers to visualize this variabilitymore effectively. While climate will ultimately drivethe frequency at which these important floods occur,as a watershed becomes more regulated, the watermanagers will increasingly become more responsiblefor maintaining the natural frequencies of specificflood types and water year types. For example, thenatural frequency of very large magnitude and longduration events (e.g., greater than 25-year recurrenceinterval) will still occur in modestly regulated systems,but the frequency of smaller events will often be con-trolled by water regulators. A wide range of hydrolog-ic events is responsible for maintaining the ecologicalintegrity of aquatic ecosystems by resetting ecologicalsuccession during large floods, providing ecologicalcues, and discouraging the persistence of non-nativespecies that are not adapted to natural conditions(Stewardson and Gippel 2003). By knowing roughlythe natural frequencies of specific flood types and

WYT-3

WYT-6

Figure 17. Cumulative frequency of water year types for WYT-3(blue line) & WYT-6 (red line) for entire period of record. Blackline shows a constant cumulative frequency.


16


water year types in the recent past, water managerswill be able to more accurately provide these aquaticecosystems with the variability they require to exist.

Flow or flood regimes of any river, stream, or flood-plain could be characterized using this methodologyas long as interaction between hydrologic, geomor-phic, and ecological processes is well understood forthe system under analysis. Inputs to the method arethe daily streamflow record and a number of hydroge-omorphic thresholds in terms of magnitude and dura-tion of flooding. Huh et al. (2005) suggest that at least40 years of streamflow record are necessary to effec-tively characterize hydrologic variability. Given suffi-cient input data, outputs to the method (i.e., a givennumber of flood types and water year types alongwith corresponding frequencies) will aid in character-izing the historical hydrologic variability and planningfor future sustainable ecosystem management. Similarto the IHA method (Richter et al. 1996; Richter et al.1997; Postel and Richter 2003), separate analysescould be performed before and after a large-scalehydrologic modification is introduced to the system(e.g., a large dam) to determine how the frequencies ofcertain flood types and water year types haveresponded to this disturbance.

Although this method has potential for helping watermanagers provide a more naturally variable hydrolog-ic regime, several limitations exist. The implicitassumption of constant flood magnitude thresholds(i.e., the 25 m3/s, 100 m3/s, and 400 m3/s values) overthe period of record may not be able to adequatelyhandle the effects of a changing riverine system. Riverand floodplain systems are inherently dynamic andhydrogeomorphic thresholds (e.g., floodplain connec-tivity) are constantly changing. Future research willdetermine whether or not the floodplain connectivitythreshold has changed substantially since 1908 due tothe construction of levees along the river.

The assumption made when recommending that thenatural frequencies of hydrologic phenomena in thefuture should be similar to those in the recent past (i.e.,the last 100 years) is that the ecosystem adapted underhistoric conditions will also exist in the near future.Changes in climatic conditions will undoubtedly alterthese frequencies, and the question of whether or not

these ecosystems can adapt to these changes in hydro-logic variability will need to be examined. However, thetrue value of the methodology presented is the idea thatsubstantial hydrologic variability must be present topreserve the integrity of such aquatic ecosystems. Thequestion of how similar this variability is to historicalvariability will need to continue to be examined.

ACKNOWLEDGEMENTSResearch on the Cosumnes River floodplain was madepossible by a grant from the CALFED Bay DeltaEnvironmental Restoration Program (Award # ERP-01-NO1) and a fellowship awarded to Jeffrey Oppermanfrom the CALFED Bay Delta Science Program. Theauthors are grateful for cooperation with theCosumnes River Preserve, The Nature Conservancy –California, and the Cosumnes Research Group. Wewould also like to acknowledge Erika Gallo, BillRainey, Dylan Ahearn, and Ken Potter for providinginsightful comments and information throughout ouranalysis.

REFERENCESAcreman, M. and Dunbar, M. J. 2004. Defining envi-ronmental river flow requirements - a review.Hydrology and Earth System Sciences 8(5):861-876.

Ahearn, Dylan S., Viers, Joshua H., Mount, Jeffrey F.and Dahlgren, Randy A. in review. Priming the pro-ductivity pump: Flood pulse driven trends in suspend-ed algal biomass distribution across a restored flood-plain. Freshwater Biology.

Amlin, N. M. and Rood, S. B. 2002. Comparative toler-ances of riparian willows and cottonwoods to water-table decline. Wetlands 22(2):338-346.

Benke, A. C., Chaubey, I., Ward, G. M. and Dunn, E. L.2000. Flood pulse dynamics of an unregulated riverfloodplain in the southeastern US coastal plain.Ecology 81(10):2730-2741.

Bunn, S. E. and Arthington, A. H. 2002. Basic princi-ples and ecological consequences of altered flowregimes for aquatic biodiversity. EnvironmentalManagement 30(4):492-507.


17

SEPTEMBER 2006

Crain, P.K., Whitner, K. and Moyle, P.B. 2004. Use of arestored central California floodplain by larvae ofnative and alien fishes. American Fisheries SocietySymposium 39:125-140.

Dettinger, M. D. 2005. A long-term (~50-yr) historicalperspective on flood-generating winter storms in theAmerican River basin. 2005 California ExtremePrecipitation Symposium. 62-73.

Fleckenstein, Jan H., Niswonger, Richard G. and Fogg,Graham E. in press. River-Aquifer Interactions,Geologic Heterogeneity, and Low Flow Management.Ground Water.

Florsheim, J. L. and Mount, J. F. 2002. Restoration offloodplain topography by sand-splay complex forma-tion in response to intentional levee breaches, LowerCosumnes River, California. Geomorphology 44(1-2):67-94.

Florsheim, J. L. and Mount, J. F. 2003. Changes inlowland floodplain sedimentation processes: pre-dis-turbance to post-rehabilitation, Cosumnes River, CA.Geomorphology 56(3-4):305-323.

Florsheim, J.L., Mount, J.F. and Constantine, C.R. inpress. A geomorphic monitoring and adaptive assess-ment framework to assess the effect of lowland flood-plain river restoration on channel-floodplain sedimentcontinuity. River Research and Applications.

Gallo, E., Dahlgren, R. and Grosholz, E. in review.Biogeochemistry of a California Floodplain as revealedby high resolution temporal sampling. FreshwaterBiology.

Harris, N. M., Gurnell, A. M., Hannah, D. M. and Petts,G. E. 2000. Classification of river regimes: a contextfor hydroecology. Hydrological Processes 14(16-17):2831-2848.

Huh, S. H., Dickey, D. A., Meador, M. R. and Ruhl, K.E. 2005. Temporal analysis of the frequency and dura-tion of low and high streamflow: years of record need-ed to characterize streamflow variability. Journal ofHydrology 310(1-4):78-94.

IACWD (Interagency Committee on Water Data). 1982.Guidelines for determining flood flow frequency. H.Subcommittee.

Jassby, A. D. and Cloern, J. E. 2000. Organic mattersources and rehabilitation of the Sacramento-SanJoaquin Delta (California, USA). Aquatic Conservation-Marine and Freshwater Ecosystems 10(5):323-352.

Javelle, P., Ouarda, TBMJ., Lang, M., Bobee, B., Galea,G. and Gresillon, J. M. 2002. Development of regionalflood-duration-frequency curves based on the index-flood method. Journal of Hydrology 258(1-4):249-259.

Javelle, P., Ouarda, TBMJ. and Bobee, B. 2003. Springflood analysis using the flood-duration-frequencyapproach: application to the provinces of Quebec andOntario, Canada. Hydrological Processes 17(18):3717-3736.

Lytle, D. A. and Poff, N. L. 2004. Adaptation to naturalflow regimes. Trends in Ecology & Evolution19(2):94-100.

McCune, Bruce and Grace, James B. 2002. Analysis ofecological communities. Gleneden Beach, OR, MjMSoftware Design.

Nanson, G. C. and Croke, J. C. 1992. A GeneticClassification of Floodplains. Geomorphology4(6):459-486.

NCDC (National Climatic Data Center). 2006. On-lineprecipitation data. Retrieved May 14, 2006. U.S.Department of Commerce. Updated May 27, 2005.http://www.ncdc.noaa.gov

NRC (National Research Council). 1999. ImprovingAmerican River flood frequency analyses. Washington,D.C., National Academy Press.

Olden, J. D. and Poff, N. L. 2003. Redundancy and thechoice of hydrologic indices for characterizing stream-flow regimes. River Research and Applications19(2):101-121.

Poff, N. L., Allan, J. D., Bain, M. B., Karr, J. R.,Prestegaard, K. L., Richter, B. D., Sparks, R. E. andStromberg, J. C. 1997. The natural flow regime.Bioscience 47(11):769-784.

Postel, Sandra and Richter, Brian 2003. Rivers for Life:Managing Water for People and Nature. Washington,DC, Island Press.


18


Power, M. E., Parker, G., Dietrich, W. E. and Sun, A.1995. How Does Floodplain Width Affect FloodplainRiver Ecology - a Preliminary Exploration UsingSimulations. Geomorphology 13(1-4):301-317.

Ribeiro, F., Crain, P. K. and Moyle, P. B. 2004. Variationin condition factor and growth in young-of-year fishesin floodplain and riverine habitats of the CosumnesRiver, California. Hydrobiologia 527(1):77-84.

Richter, B. D., Baumgartner, J. V., Powell, J. andBraun, D. P. 1996. A method for assessing hydrologicalteration within ecosystems. Conservation Biology10(4):1163-1174.

Richter, B. D., Baumgartner, J. V., Wigington, R. andBraun, D. P. 1997. How much water does a river need?Freshwater Biology 37(1):231-249.

Richter, B. D., Mathews, R. and Wigington, R. 2003.Ecologically sustainable water management: Managingriver flows for ecological integrity. EcologicalApplications 13(1):206-224.

Saab, V. 1999. Importance of spatial scale to habitatuse by breeding birds in riparian forests: A hierarchi-cal analysis. Ecological Applications 9(1):135-151.

Sommer, T. R., Harrell, W. C., Solger, A. M., Tom, B.and Kimmerer, W. 2004. Effects of flow variation onchannel and floodplain biota and habitats of theSacramento River, California, USA. AquaticConservation-Marine and Freshwater Ecosystems14(3):247-261.

Stewardson, M. J. and Gippel, C. J. 2003.Incorporating flow variability into environmental flowregimes using the flow events method. River Researchand Applications 19(5-6):459-472.

Stewart, I. T., Cayan, D. R. and Dettinger, M. D. 2005.Changes toward earlier streamflow timing across west-ern North America. Journal of Climate 18(8):1136-1155.

Tabacchi, E., Correll, D. L., Hauer, R., Pinay, G.,Planty-Tabacchi, A. M. and Wissmar, R. C. 1998.Development, maintenance and role of riparian vege-tation in the river landscape. Freshwater Biology40(3):497-516.

TNC (The Nature Conservancy). Sustainable WatersProgram. 2005. IHA Applications Database. RetrievedDec. 5, 2005, fromhttp://www.freshwaters.org/tools/pdf/iha_apps.pdf.

Tockner, K. and Stanford, J. A. 2002. Riverine floodplains: Present state and future trends. EnvironmentalConservation 29(3):308-330.

USACE. (U.S. Army Corps of Engineers), Sacramento.1965. Flood Plain Information Report, Cosumnes RiverBasin, California.

USGS. (United States Geological Survey). 2005.National Water Information System (NWISWeb) dataavailable on the World Wide Web. Retrieved Oct. 2,2005, from http://waterdata.usgs.gov/nwis.

Vick, Jennifer, Andrews, Elizabeth and Williams,Philip B. 1997. Analysis of opportunities for restoringa natural flood regime. PWA. 1148 prepared for TheNature Conservancy. San Francisco, CA

Ward, J. V. and Stanford, J. A. 1995. The SerialDiscontinuity Concept - Extending the Model toFloodplain Rivers. Regulated Rivers-Research &Management 10(2-4):159-168.

Wohl, E., Angermeier, P. L., Bledsoe, B., Kondolf, G.M., MacDonnell, L., Merritt, D. M., Palmer, M. A., Poff,N. L. and Tarboton, D. 2005. River restoration. WaterResources Research 41(10).


19

Documents

Hydrologic Variability of the Cosumnes River Floodplain...SEPTEMBER 2006 SUGGESTED CITATION Booth, Eric, Jeffrey Mount, and Joshua Viers. Hydrologic Variability of the Cosumnes River