hydroclimatic variability in the rocky mountains

11
WATER RESOURCES BULLETIN VOL. 27, NO.5 AMERICAN WATER RESOURCES ASSOCIATION OCTOBER 1991 HYDROCLIMATIC VARIABILITY IN THE ROCKY MOUNTAINS' David Changnon, Thomas B. McKee, and Nolan J. Doesken2 ABSTRACT: The spatial and temporal variability of hydroclimatic elements were investigated in the central and northern Rocky Mountains (Colorado, Idaho, Montana, Utah, and Wyoming) during the 1951-1985 period. The three hydroclimatic elements studied were total water-year (October 1-September 30) streamllow (ST), winter (October 1-March 31) accumulated precipitation (PR), and April 1 snowpack (SN). An analysis of 14 virgin watersheds showed wide spatial djfferences in the temporal variability of SN, PR, and ST, and these were found to be caused largely by basin exposure to moist air flows. The more stable (low variability) basins were those exposed to prevailing northerly to westerly flow, while unstable (high variability) basins were exposed to occasional southwesterly to southeasterly moist flow. Snowpack was the better indicator of ST in 11 of the 14 watersheds, explaining 37 to 87 percent of the ST variance. Analysis of the spatial variability, based on all SN and PR data from across the study area, revealed 11 discrete climatic regions. Both SN and PR exhibited coherent regions of stable and unstable temporal variability. The average variability between stable and unstable regions differed by a factor of two, and the differences were best explained by the exposure of the mountain barrier to moist air flows. (KEY TERMS: hydroclimatic variability; snowpack; streamflow; watershed; aspect; winter precipitation; snowmelt runoff.) INTRODUCTION Each year the available water stored in the winter snowpack of the Rocky Mountains provides much of the usable surface water in the western United States. Much of the economic and environmental wel- fare of the West is affected by the availability of this water (Weiss, 1982) stored in the winter snowpack that is derived from winter precipitation. These surface-water resources are controlled directly by cli- matic factors. Because winter precipitation, winter snowpack, and annual streamfiow are interrelated elements and part of the hydrologic cycle and climate system, they are referred to as hydroclimatic ele- ments. Recent periods of drought and excessive runoff tied to wide fluctuations or variability in winter precipita- tion and snowpack have created major problems for those who design facilities for and manage the distri- bution of the available surface water. Understanding the hydroclimatic variability of the Rocky Mountain region is particularly important because winter pre- cipitation stored in the winter snowpack represents 85 percent of the area's total streamfiow (Grant and Kahan, 1974). Few studies have examined the rela- tionships of winter snowpack variability to stream- flow variability in this region (Meko and Stockton, 1984; Peterson et al., 1987) or the existing connec- tions between physical variables and hydroclimatic variability in the Rocky Mountains (Barry, 1981). The major objective of this research was to define and explain the magnitude of the variability in key hydroclimatic elements in a number of watersheds and across a large region of the Rockies where much of the western surface water is produced. DEVELOPMENT OF DATABASE FOR HYDROCLIMATIC RESEARCH IN THE ROCKIES The five-state study region (Colorado, Idaho, Mon- tana, Utah, and Wyoming) located in the central and northern Rockies, was chosen because it represents the water source for six large watersheds in the United States. The streamfiow from this region is supplied predominantly from higher elevations Paper No. 91093 of the Water Resources Bulletin. Discussions are open until June 1, 1992. 2Respectively, Regional Research Climatologist, Southeast Regional Climate Center, South Carolina Water Resources Commission, 1201 Main St., Suite 1100, Columbia, South Carolina 29201; Professor of Atmospheric Sciences, Colorado State Climatologist, Dept. of Atmospheric Sciences, Colorado State University, Ft. Collins, Colorado 80523; and Assistant State Climatologist for Colorado, Dept. of Atmospheric Sciences, Colorado State University, Ft. Collins, Colorado 80523. 733 WATER RESOURCES BULLETIN

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Page 1: HYDROCLIMATIC VARIABILITY IN THE ROCKY MOUNTAINS

WATER RESOURCES BULLETINVOL. 27, NO.5 AMERICAN WATERRESOURCES ASSOCIATION OCTOBER 1991

HYDROCLIMATIC VARIABILITY IN THE ROCKY MOUNTAINS'

David Changnon, Thomas B. McKee, and Nolan J. Doesken2

ABSTRACT: The spatial and temporal variability of hydroclimaticelements were investigated in the central and northern RockyMountains (Colorado, Idaho, Montana, Utah, and Wyoming) duringthe 1951-1985 period. The three hydroclimatic elements studiedwere total water-year (October 1-September 30) streamllow (ST),winter (October 1-March 31) accumulated precipitation (PR), andApril 1 snowpack (SN). An analysis of 14 virgin watersheds showedwide spatial djfferences in the temporal variability of SN, PR, andST, and these were found to be caused largely by basin exposure tomoist air flows. The more stable (low variability) basins were thoseexposed to prevailing northerly to westerly flow, while unstable(high variability) basins were exposed to occasional southwesterlyto southeasterly moist flow. Snowpack was the better indicator ofST in 11 of the 14 watersheds, explaining 37 to 87 percent of the STvariance.

Analysis of the spatial variability, based on all SN and PR datafrom across the study area, revealed 11 discrete climatic regions.Both SN and PR exhibited coherent regions of stable and unstabletemporal variability. The average variability between stable andunstable regions differed by a factor of two, and the differenceswere best explained by the exposure of the mountain barrier tomoist air flows.(KEY TERMS: hydroclimatic variability; snowpack; streamflow;watershed; aspect; winter precipitation; snowmelt runoff.)

INTRODUCTION

Each year the available water stored in the wintersnowpack of the Rocky Mountains provides much ofthe usable surface water in the western UnitedStates. Much of the economic and environmental wel-fare of the West is affected by the availability of thiswater (Weiss, 1982) stored in the winter snowpackthat is derived from winter precipitation. Thesesurface-water resources are controlled directly by cli-matic factors. Because winter precipitation, wintersnowpack, and annual streamfiow are interrelated

elements and part of the hydrologic cycle and climatesystem, they are referred to as hydroclimatic ele-ments.

Recent periods of drought and excessive runoff tiedto wide fluctuations or variability in winter precipita-tion and snowpack have created major problems forthose who design facilities for and manage the distri-bution of the available surface water. Understandingthe hydroclimatic variability of the Rocky Mountainregion is particularly important because winter pre-cipitation stored in the winter snowpack represents85 percent of the area's total streamfiow (Grant andKahan, 1974). Few studies have examined the rela-tionships of winter snowpack variability to stream-flow variability in this region (Meko and Stockton,1984; Peterson et al., 1987) or the existing connec-tions between physical variables and hydroclimaticvariability in the Rocky Mountains (Barry, 1981).

The major objective of this research was to defineand explain the magnitude of the variability in keyhydroclimatic elements in a number of watershedsand across a large region of the Rockies where muchof the western surface water is produced.

DEVELOPMENT OF DATABASE FORHYDROCLIMATIC RESEARCH IN THE ROCKIES

The five-state study region (Colorado, Idaho, Mon-tana, Utah, and Wyoming) located in the central andnorthern Rockies, was chosen because it representsthe water source for six large watersheds in theUnited States. The streamfiow from this region issupplied predominantly from higher elevations

Paper No. 91093 of the Water Resources Bulletin. Discussions are open until June 1, 1992.2Respectively, Regional Research Climatologist, Southeast Regional Climate Center, South Carolina Water Resources Commission, 1201

Main St., Suite 1100, Columbia, South Carolina 29201; Professor of Atmospheric Sciences, Colorado State Climatologist, Dept. of AtmosphericSciences, Colorado State University, Ft. Collins, Colorado 80523; and Assistant State Climatologist for Colorado, Dept. of AtmosphericSciences, Colorado State University, Ft. Collins, Colorado 80523.

733 WATER RESOURCES BULLETIN

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Changnon, McKee, and Doesken

through winter precipitation and accumulated snow-pack followed by melting and runoff in the spring andearly summer. The six large watersheds include theMissouri, the Arkansas, the Rio Grande, theColorado, the Great Basin, and the Columbia. Thethree primary data elements for study of hydroclimat-ic variability in this five-state region are precipita-tion, snowpack, and streamfiow. Each of theseelements is related to climate. However, these ele-ments are observed and archived by various federalagencies and used for a variety of purposes. Thedevelopment of a combined database requires thatdata sources be identified, data acquired and checkedfor quality and completeness, individual sites chosen,and limitations of the data recognized.

Data Sources

Each of the three historical observations chosen forstudy were acquired from three different federalagencies. Monthly precipitation records were acquiredfrom the National Climatic Data Center, monthlysnow course measurements were acquired from theSoil Conservation Service, and monthly streamfiowrecords were acquired from the United StatesGeological Survey. The study period of 195 1-1985 waschosen for analysis because data studies revealed itwas the period with the largest number of continuallymonitored snowpack sites and precipitation stations.

April 1 measurements of snow water equivalentwere greater than other measurements taken throughthe winter; thus, the April 1 snowpack (SN) was cho-sen to represent the snowpack each year throughoutthe study. The winter accumulation period was arbi-trarily chosen to begin October 1, the start of thehydrologic year. The October 1-March 31 precipitation(PR) represented the accumulating period up toApril 1, the time of the maximum average snowpackmeasurement, to allow for consistent comparison withSN. The output of the hydrologic cycle in this studywas the streamfiow. Total water-year (October 1-September 30) streamfiow (ST) was chosen to com-pare with PR and SN.

Evaluation of Data

The data for all potential SN sites and PR stationsin the five-state region, with a period of record from1951 to 1985, were examined. Each record was exam-ined thoroughly to identify station relocation orchanges in observation technique.

Figures 1 and 2 show the distribution of PR sta-tions and SN sites that had complete and consistent

WATER RESOURCES BULLETIN 734

records for the 35-year period. A total of 266 PR sta-tions and 275 SN sites are included. A completedescription of all sites used in this study is describedin Changnon et al. (1990). An examination of Fig-ures 1 and 2 reveals that the two types of observa-tions were not generally co-located. PR stations havebeen placed to monitor climate and are spatially dis-tributed and located where volunteers can make dailyobservations. Although there is a fairly uniform spa-tial distribution, there is a distinct bias toward mea-surements in valley locations and lower elevations.SN sites have been specifically designed to monitorsnow for the projection of seasonal streamfiow andthey are usually located at higher elevations wherethe snow is heaviest. Snow course sites are usuallymeasured once a month.

The number of ST gauges selected for studyinghydroclimatic variability was limited by the availabil-ity of within basin data throughout the RockyMountains. Watersheds were chosen using a basinselection criteria to examine the variability of eachelement, PR, SN, and ST, as well as to compare thevalues within each watershed.

Figure 1. Distribution of 266 PR Stations Used in Study.

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Hydroclimatic Variability in the Rocky Mountains

Figure 2. Distribution of 275 SN Sites Used in Study.

Physical Variables

The physical variables influencing hydroclimaticvariability in the five-state region are remarkablydiverse. The study stratified the SN and PR data intodescriptive categories, including latitude, elevation,and aspect. These descriptors were identified for eachstation and watershed. Latitude is related primarilyto the large-scale atmospheric circulation and stormtracks that vary with time. Elevation is importantbecause wetter locations are generally highe. Aspectdefines the direction of the inflowing air that causes

vertical motion. Aspect is important at all latitudesbecause the vertical motion caused by the terrain isoften a greater influence on precipitation productionthan the lifting associated with synoptic-scale storms.

Table 1 shows the distribution of 266 PR stationsand 275 SN sites listed by approximate latitude(states from north to south) and elevation categories.One observation is that PR stations and SN sites arelocated at higher elevations in Colorado than inMontana or Idaho. This is related to the general ele-vation trend in the Rocky Mountains such thatColorado has the highest average elevation.

Comparison of elevations of the PR stations andSN sites reveals that only 16 percent of the 266 PRsites are above 2000m elevation, while 78 percent ofthe 275 SN sites are above this level. The concentra-tion of PR stations at elevations lower than SN sitesconfirms that PR and SN generally do not sample thesame portions of the total water supply. However,they are not independent because they monitor simi-lar storm systems. A summary of the advantages anddisadvantages related to each type of data is given inTable 2. Based on characteristics of the three hydro-climatic elements described in this section, all threeelements were identified as useful for analyzing vari-ous water resource issues in the West.

HYDROCLIMATIC VARIABILITYCHARACTERISTICS OF ROCKY

MOUNTAIN WATERSHEDS

One way to understand the relationships betweenclimatic and hydrologic elements in the western U.S.is by studying individual watersheds (Gleick, 1987;Cayan and Peterson, 1990). In this study, a group ofwatersheds was identified and analyzed to describethe spatial and temporal hydroclimatic variability

TABLE 1. Number of PR Stations and SN Sites by Elevation Rangefor the 1951-1985 Period in Each State.

Elevation(meters) PR

IdahoSN

MoPR

ntanaSN

WyoPR

mingSN

UtahPR SN

CobPR

radoSN PR

TotalSN

0—500 2 (0) 0 (0) 0 (0) 0 (0) 0 (0) 2 (0)501—1000 11 (1) 46 (0) 0 (0) 0 (0) 0 (0) 57 (1)

1001—1500 11 (5) 36 (5) 10 (0) 9 (0) 18 (0) 84 (10)1501—2000 12 (25) 11 (23) 14 (1) 21 (1) 22 (0) 80 (50)2001—2500 0 (23) 2 (31) 7 (26) 3 (10) 20 (0) 32 (90)2501—3000 0 (5) 0 (2) 0 (21) 0 (13) 9 (40) 9 (81)3001—3500 0 (0) 0 (0) 0 (5) 0 (3) 2 (35) 2 (43)

TOTAL 36 (59) 95 (61) 31 (53) 33 (27) 71 (75) 266 (275)

735 WATER RESOURCES BULLETIN

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Changnon, McKee, and Doesken

characteristics of individual watersheds, and throughcomparisons, determine the factors that contributedto the variability among the watersheds.

TABLE 2. Advantages and Disadvantages ofPR, SN, and ST Observations.

Element Advantages Disadvantages

PR + Greater areal coverageat low elevations,

— Variable pointobservations.

+ Includes data throughyear.

— Snowfall observationsinaccurate.

+ Captures wet winterswith large area,

— Few sites in highelevation.

— Doesn't account forother atmosphericelements duringwinter.

SN + Located in maximumsnow zone.

— Variable pointobservations.

+ Integrates severalatmospheric elementsthrough winter, such asevaporation, wind,temperature, andprecipitation,

— Few sites at lowelevations to monitorwet years.

— April 1 date isvariable relative tomelt.

ST + Actual water to exitbasin,

— Few basins havenatural flow.

+ Integrates all hydrologicand geologic processes.

— Variability not alldue to climate.

— Basincharacteristicsvary in all basins.

Watershed Selection Criteria

The selection of a set of watersheds adequate tostudy the climate variability across a wide region wasan initial priority. The procedures used includedselecting a number of candidate watersheds,- develop-ing a basin selection criteria, and identifying the finalset of watersheds that qualified for detailed study.

Over 600 basins of various sizes, monitored by ST,are located within the boundaries of the five-stateregion. Several physical characteristics of the regionand of the watersheds within the region made thor-ough study of all watersheds an unproductive task.Based on discussions with state USGS offices, a groupof watersheds with high quality long-term data wereselected. Thirty-one of these watersheds had less than

WATER RESOURCES BULLETIN 736

25 percent of their flow diverted, did not receivetransbasin diversions, and were initially selected ascandidate watersheds.

The watershed selection criteria were developed toyield a group of basins with similar physical charac-teristics. However, it was important that the criterianot be too restrictive; rather, it should be flexiblebecause no one basin is exactly like another in theRocky Mountain region (Pilgrim, 1983). Four majorcriteria, described below, were developed to judge the31 watersheds.

The amount of water diverted from the naturalrunoff was the first limiting factor examined in water-shed selection. Meko and Stockton's study (1984)selected watersheds if less than 7 percent of the totalflow was used for irrigation and less than 7 percentwas used for reservoir capacity. In this study, water-sheds were selected if less than 10 percent of the totalflow was diverted for any reason such as transmoun-tam diversion, reservoir storage, or irrigation.

Initially, pairs of watersheds, one located up-wind(air ascending) and another down-wind (air descend-ing) of a mountain barrier, and along the mountainchain in the study region, were chosen to study theimpact of large winter synoptic patterns on surface-water supplies east and west of the ContinentalDivide (C.D.). However, because the mountain chainvaries in width and topography, not all watershedscould be selected in pairs. Watersheds that were geo-graphically situated in locations where the up-windflow was favorable for precipitation and was notimpeded by other local mountain ranges passed thissecond selection criterion.

The third selection criterion was elevation.Watersheds selected were located at or above an ele-vation where snowmelt runoff was the most impor-tant contributor to the annual streamfiow (Cayan andPeterson, 1990). The watershed elevation criterionvaries with latitude and climate regime; consequently,no fixed elevation minimum was applied throughoutthe region.

Once the first three watershed criteria were met,questions regarding the data availability and qualitywere addressed. Because of the sparse networks ofNWS cooperative stations, USGS streamflow gauges,and SCS snow courses, only a limited number ofwatersheds had all three data sets available. Mostdata sets for watershed analyses included records forthe study period 1951 through 1980. A longer periodof study was not chosen because the availability ofhydroclimatic data before 1951 or after 1980 dimin-ished in many watersheds and would have limited thenumber of watersheds for analysis to less than ten.PR and SN data located just outside the watershedbut in the same general climatic region weresometimes used to get all three measures. Other

Page 5: HYDROCLIMATIC VARIABILITY IN THE ROCKY MOUNTAINS

Hydroclimatic Variability in the Rocky Mountains

information about each watershed's subregions suchas slope and aspect, vegetation, and soil types wereconsidered too complex for this study and were notused as criteria for selecting watersheds.

The four major criteria resulted in 14 watershedsfor analyses. The watersheds are listed in Table 3 bynumber and their locations appear in Figure 3.

Figure 3. The Locations of 14 Selected WatershedsUsed in the Rocky Mountain Study Region.

Small dots indicate the location of the 275 SN sites.Dashed line represents the Continental Divide.

TABLE 3. Fourteen Selected Watersheds.

Number onFigure Watershed State

1 Swiftcurrent Montana2 Coeur d'Alene Idaho3 Nevada Montana4 Boulder Montana5 Big Wood Idaho6 Wind Wyoming7 Green Wyoming8 Smith's Fork Wyoming9 Bear Utah

10 West Fork Duchesne Utah11 Muddy Creek Utah12 North Fork Colorado Colorado13 St. Vram Colorado14 San Juan Colorado

Determination of Hydroclimatic Variability in theWatersheds

First, a time series for each of the three hydrocli-matic elements in each of the 14 watersheds was pre-pared. Examples of the time series for the San JuanWatershed and the Coeur d'Alene Watershed appearin Figure 4. These reveal the differences in the tempo-ral variability of each element. The absolute valuesfor each hydroclimatic element had been normalized(scaled) to their median.

C

CC

"I=C

C

CC

NORMALIZED TIME SERIESSAN JUAN BASIN, COLORAOO

Differences between the two watersheds are easilyidentified by comparing the ranges about the median(set at 1.0). For example, Figure 4 shows all elementsin the San Juan watershed are more variable thanthose in the Coeur d'Alene with the San Juan water-shed values ranging from 0.20 to 2.40, while those in

737 WATER RESOURCES BULLETIN

Figure 4. Coincident Time Series of Normalized Precipitation(October-March), April 1 Snowpack, and Total

Water-Year Streamilow for the San Juan Watershedin Colorado and Coeur d'Alene in Idaho.

Page 6: HYDROCLIMATIC VARIABILITY IN THE ROCKY MOUNTAINS

Changnon, McKee, and Doesken

the Coeur d'Alene watershed ranged from 0.30 to1.70.

A measure of variability was needed to comparebasins and determine the magnitude of variability forthe three hydroclimatic elements. The measure ofvariability (MOV) was defined from the cumulativeprobability distribution of each data set as:

MOV = [X(80%) — X(20%)}/X(50%)

where X represents the SN, PR, or ST value for eachnonexceedence probability. This value is similar to thecoefficient of variation (CV), defined as the standarddeviation (s) divided by the mean (m):

CV=s/m

extreme values, those with nonexceedence probabili-ties <20 percent and >80 percent, are not consideredin the computation of MOV, whereas all values in adata sample influence both the mean and the stan-dard deviation.

The range of variability values calculated for the 14watersheds was: (1) PR values ranged from 0.38 to0.90, (2) SN from 0.35 to 0.80, and (3) ST from 0.20 to

(1) 0.83. The mean values of variability for the 14 water-sheds was 0.58 for PR, 0.56 for SN, and 0.53 for ST.

The small difference between the means and thecomparable ranges of the three MOVs for these inter-related hydroclimatic elements suggested that anyone of the three elements was a good indicator of themagnitude of variability for most watersheds.However, in seven watersheds, the MOV value for oneof the three hydroclimatic elements was consideredanomalous. These anomalous MOV values could notbe explained easily, which suggests some uncertaintyin using any one of the three hydroclimatic elementsindividually when comparing differences in variabili-ty from one watershed to another. Therefore, threeMOV values for each watershed were averaged to get.a single index of variability because: (1) the threehydroclimatic elements were interrelated, (2) theuncertainty associated with using just one of thehydroclimatic MOV values existed, and (3) it was eas-ier to compare one MOV value for each watershedrather than three. These average variability values,or indices, for the 14 watersheds appear in Table 4with the watersheds ranked by their averaged mea-sure of variability. Physical characteristics of eachwatershed are shown.

(2)

The CV has been used widely in hydroclimatic studiesfor regions in other parts of the world (Conrad, 1941;Longley, 1952; Hershfield, 1962). The MOV in thisstudy retains the central 60 percent of the observa-tions, while 2CV contains the central 68 percent of anormal distribution.

The decision to use the measure of variability wasbased on four issues. First, the MOV is essentially thetypically linear portion of the cumulative distributioncurve between nonexceedence probabilities of 0.2 and0.8. Second, the nonexceedence probabilities used inthe MOV are not as dependent on the distribution ofthe data as is the standard deviation (Medina andMielke, 1985; Faiers, 1989). Third, the median alwaysrepresents the central point of a distribution. Fourth,

TABLE 4. Characteristics of 14 Watersheds.

Watershed

LocationRelativeto C.D.*

AverageMeasure ofVariability

Aspect ofWatershed

WatershedSize

(km2)

Elevation ofST Gauge

(m)

SwiftcurrentCoeur d'AleneNevadaSmith's ForkNorth Fork ColoradoBearBoulderGreenSt. VramWindBig WoodMuddy CreekWest Fork DuchesneSan Juan

EastWestWestWestWestWestEastWestEastEastWestWestWestWest

0.380.400.440.440.450.450.550.560.620.640.680.410.710.76

NorthWestWestWestWestNorthwestNortheastSouthEastSoutheastSouthSoutheastSouthSouthwest

802310

300427137445

13781212549601

1658272161720

1481640

142020272667242812372276161321911614195122002149

C.D. = Continental Divide.

WATER RESOURCES BULLETIN 738

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Hydroclimatic Variability in the Rocky Mountains

Analysis of these data helped identify the surfacephysical variable important in explaining the differ-ences in variability between watersheds. For example,six of the 14 watersheds had an average measure ofvariability less than 0.55 and all had a westerly ornortherly aspect. The aspect reflects the exposure ofeach watershed to moist air flow, local lifting, andpotential for precipitation development. The othereight watersheds with larger variability had easterlyor southerly aspects. This indicated that watershedaspect influences the hydroclimatic variability. Thewatersheds with a measure of variability less than0.55 were classified as stable (STA), while those withvariability values equal to or greater than 0.55 wereunstable (UST). The basin's position relative to theC.D., the watershed size, and watershed elevationhad less influence on variability than did aspect.Aspect has been recognized for many years as animportant climate control affecting the magnitude ofprecipitation in mountainous areas (Spreen, 1947;Peck and Brown, 1962); however, its role in influenc-ing hydroclimatic variability has not been docu-mented.

Relationship of Hydroclimatic Elements

Correlation coefficients Cr-values) were determinedfor PR to ST, SN to ST, and SN to PR in the 14 select-ed watersheds to measure the degree of temporalrelationship within each watershed. The differencesin r-values within each watershed (Table 5) could beexplained by: (1) differences in small scale climatecontrols in each watershed, (2) the elevational sitingof the instruments within the basin, and (3) the factthat the three elements do not sample precipitationcomparably in all watersheds.

The range of PR to ST correlation coefficients (r-values) for the selected 14 watersheds was +0.42 to+0.88; the range of r-values for SN to ST was +0.61 to+0.93, and the range for SN to PR was +0.36 to +0.90.The watersheds, as ranked by their average MOV(Table 4) are listed with the respective r-values inTable 5.

SN had a better relationship to ST than did PR in11 of the 14 watersheds. The overall average of thePR to ST r-values was +0.67, while the average of theSN to ST was +0.78. The average SN to ST r-valuesfor stable watersheds (STA), unstable watersheds

TABLE 5. Correlation Coefficients of SN to ST, PR to ST, and SN to PR for 14 Watersheds.All r-values are positive.

WatershedStability

Designator SN vs. ST PR vs. ST SN vs. PR

Swiftcurrent STA 0.66 0.70 0.68Coeur d'Alene STA 0.79 0.88 0.75Nevada STA 0.66 0.72 0.79Smith's Fork STA 0.93 0.69 0.76NorthFork Colorado STA 0.75 0.74 0.90Bear STA 0.70 0.55 0.59Boulder USTE 0.69 0.58 0.36Green USTS 0.90 0.64 0.62St. Vram USTE 0.61 0.59 0.82Wind USTE 0.91 0.42 0.44Big WoodMuddy CreekWest Fork Duchesne

USTSUSTEUSTS

0.930.760.74

0.830.540.67

0.820.410.72

San Juan USTS 0.87 0.83 0.79

AverageAverage for STA WatershedsAverage for USTS WatershedsAverage for USTE Watersheds

0.780.750.860.74

0.670.710.740.53

0.680.740.740.51

NOTE:STA = stable,USTE = unstable easterly exposure.USTS = unstable southerly exposure.

739 WATER RESOURCES BULLETIN

Page 8: HYDROCLIMATIC VARIABILITY IN THE ROCKY MOUNTAINS

with easterly exposure (USTE), and unstable water-sheds with southerly exposure (USTS) are all greaterthan those for PR to ST or SN to PR.

For STA and USTS watersheds the average corre-lations are similar and generally high (r> 0.70) sug-gesting that in both types of these watersheds the SNsites and PR stations are exposed to similar precipita-tion events. The predominant upper air flow duringthe winter is westerly with long-wave troughs andridges steering the winds into northerly or southerlydirections. In both STA and USTS watersheds, the PRstations and SN sites experience precipitation fromsimilar upslope precipitation events throughout thewatersheds. However, in USTE watersheds, the SN toST correlations are much higher than the PR to ST r-values, suggesting that two different types of precipi-tation events, one affecting high elevation SN sitesassociated with the predominant winter upper airflow, and another affecting low elevation PR sites andassociated with an easterly upsiope flow. In the USTEwatersheds, high elevation SN data represents STdata better than does low elevation PR data. Overall,SN was identified as a better indicator of ST than PR,and SN appears to be the better monitor of winter cli-mate in the Rocky Mountains.

SPATIAL PATTERNS OF WINTER PRECIPITATIONAND APRIL 1 SNOWPACK VARIABILITY

The temporal analyses of hydroclimatic variabilityin the 14 watersheds revealed that variabilityappeared to be explained best by aspect. To confirmthis finding and evaluate its significance, spatial vari-ability characteristics for all PR stations and SN siteswere examined.

Computing a Measure of Variability and IdentifyingSpatial Patterns

The MOV was computed for the 275 SN sites and266 PR stations in the five-state region that had 35years of data (1951-1985). The SN MOV valuesranged from 0.22 (low variability) to 1.19 (high vari-ability). The PR MOV values ranged from 0.28 to1.25. Although the elevation distributions of SN andPR locations are very different, both data sets showeda similar range in variability. The next step was toidentify any spatial patterns of variability for SN andPR.

The calculated MOV values of SN and PR wereplotted as shown in Figures 5a and 5b. The analysisused to identify patterns of differing variability across

WATER RESOURCES BULLETIN 740

Changnon, McKee, and Doesken

a

b

Figure 5. The Measure of Variability Computed for(a) SN Sites and (b) PR Stations Across the

Five.State Region. The 0.55 measure of variabilityisoline separates areas of low variability(stable) and high variability (unstable).

Page 9: HYDROCLIMATIC VARIABILITY IN THE ROCKY MOUNTAINS

Hydroclimatic Variability in the Rocky Mountains

the highly varied topographic regions within theRockies was selected to maintain geographical unityand coherence. On the SN map (Figure 5a) a contourline of 0.55 MOV was drawn to separate regions con-sidered to be stable and unstable sites. This was donebecause (1) the 0.55 value had divided stable (lessvariable, MOV < 0.55) and unstable (more variable,MOV> 0.55) watersheds, and (2) the 0.55 value alsorepresented the mean MOV value for the 275 SNsites. Using the 0.55 isoline led to 11 broad, homoge-neous climatic regions for SN.

Despite the differences in the location of SN sitesand PR stations, the analysis of the PR variabilityvalues (Figure 5b) using the 0.55 MOV isoline alsoidentified the same 11 climatic regions. The similarityin locations of the 0.55 isolines on these two mapsstrengthened the argument for using this type ofanalysis in studies where geographic coherence ofregions must be maintained. An additional climateregion was found for PR stations in the Black Hills ofnortheastern Wyoming, an area where no SN sitesexisted. Here, topographic influences cause a reduc-tion in variability. A similar analysis using the meanand standard deviation to compute the coefficient ofvariation for all PR and SN sites showed similar pat-terns of stable and unstable regions.

These results confirm there are substantial hydro-climatic differences in the spatial variability withinlarge regions, such as the Colorado above GlenCanyon Dam. The results from this analysis differfrom those of Peck and Schaake (1990), which indi-cate an average value of variability can be appliedthroughout large watersheds, such as the Colorado, inthe Rockies.

In each of the 11 regions identified in Figure 5a, aregional average MOV value was computed for SNand PR. Figures 6a and 6b show the regionally aver-aged values for SN and PR, and Figure 6c shows theregions numbered for discussion. SN and PR valuesare similar for each region. Five SN values exactlyagree with six PR regions, all with average MOV val-ues less than 0.55, indicating they are relatively sta-ble or have less interannual variability. Three of thesestable regions lie along and west of the C.D., two liejust east of the C.D., and the remaining stable region(PR only) is in the Black Hills.

Six regions had average MOV values in both SNand PR, greater than 0.55. They were classified asunstable, and thus, are areas with much more vari-able winter precipitation. Three of these are locatedwest of the C.D., while three are located east of theC.D. The regions have similar average MOV for bothSN and PR, indicating that although most measure-ments were taken at different elevational zones in agiven region, they have the same magnitude of vari-ability over the 35-year study period. Note also thatthe values in the unstable regions are nearly doublethose in the stable regions. Because the differencesbetween the stable and unstable areas are so large,and because both SN and PR variability values agreewithin each region, these findings imply that spatialvariability information is very important whenattempting to understand the fluctuations over timein the available water resources in the Rocky Moun-tain region.

Figure 6. The Regionally Average Measure of Variability Values for (a) SN and (b) PR in Stable and Unstable Areas.The regions were numbered for future reference in (c).

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Changnon, McKee, and Doesken

Reasons for Regional Differences in Variability

Several climatic and topographic characteristicscan influence the spatial variability of the hydrocli-matic elements analyzed in this study. The regionaldifferences in variability are difficult to explainbecause physical variables such as latitude, elevation,and aspect to different moisture flows vary widely forthe SN sites and PR stations within the study region.

Average precipitation in the Rocky Mountains gen-erally increases with elevation (Peck and Brown,1962; Marlatt and Riehl, 1963; Rhea, 1978). The vari-ability of precipitation generally decreases withincreasing average annual precipitation (Conrad,1941; Longley, 1952; Hershfield, 1962). Thus, onemight conclude that precipitation variability in moun-tainous terrain should decrease with elevation.However, the relationship of MOV to elevation for the11 climatic regions did not demonstrate this feature.For all regions the values were clustered in groupsrather than in a linear distribution (Changnon et al.,1990). The relationship of site MOV values and eleva-tion explained between 5 and 30 percent of the vari-ance in the various regions.

The median value of SN for sites in each regionwas correlated with MOV and the relationship wasweak. The small amount of variance explained ineach region (0.03 < r2 < 0.38) suggested that the rela-tionship between median values of SN and MOV werenot close and much less than the relationshipsbetween precipitation and elevation found in previousstudies by Longley (1952) and Hershfield (1962).Generally, there was a slight decrease in MOV as themedian value of SN increased at sites in all stableregions and unstable regions west of the C.D. Inunstable regions east of the C.D., there was a greaterdecrease in MOV as the median value of SNincreased. This indicated that as the median SNvalue decreases, variability in unstable regions on theeast side of the C.D. increases more rapidly than thevariability on the west sideof the C.D. This differenceat the unstable SN sites east of the C.D. was believedto be caused by precipitation which "blows-over" thetop of the C.D. during storms, associated with the pre-dominant winter upper air flow which is also affectingareas west of the C.D. Also, the relatively infrequentupslope events east of the C.D. do not always influ-ence the SN sites east of the C.D.

The possible relationship of latitude and the spatialpatterns of hydroclimatic variability was examined.Although stable regions were more numerous in thenorthern part of the study area, stable and unstableregions were found at all latitudes in the study area.No strong evidence of a latitudinal effect existed.

The relationship of variability to aspect was exam-ined in the 11 regions, defined by the PR and SNdata, across the five-state region. The prevailing win-ter upper air flow over the study region is from thewest and northwest (Trewartha and Horn, 1980). Thisprevailing flow provides upsiope precipitation forRegions 1, 2, 3, 4, and 5 with a northerly or westerlyaspect and downslope drier winds for Regions 6, 7,and.8 with a southerly aspect and Regions 9, 10, and11 with an easterly aspect. During the winter, infre-quent periods of southerly or easterly upper air flowbring upslope precipitation events to those regionswith southerly or easterly aspects. The precipitationin those regions with their highest mountain rangeexposed to northerly or westerly moist flow is lessvariable (stable). In regions with their highest rangesexposed to moist air flow with a southerly or easterlydirection precipitation is more variable (unstable).These results agreed with the findings for the 14watersheds.

CONCLUSIONS

Water stored in the winter snowpack of the RockyMountains is a critical resource for the West that isdifficult to estimate with accuracy. We sought toimprove the understanding of the variability thatexists in the Rocky Mountains for those who modeland predict streamflow. Hydroclimatic elements wereinvestigated (1) to determine how variable key ele-ments were in the primary water source region, (2) toidentify the spatial and temporal variability charac-teristics and patterns, and (3) to attempt to explainthe variability using surface physical elements. Thestudy covered a 35-year period from 195 1-1985.

A database was developed that incorporated inter-related processes involved with the regions hydrocli-mate. The three elements chosen for study includedtotal water-year streamfiow (ST), winter (October 1-March 31) accumulated precipitation (PR), and April1 snowpack (SN-snow water equivalent).

The results from a watershed analyses showed thatthe range in temporal variability values for 14 virginwatersheds could be explained largely by exposure oraspect to moist air flows. Stable watersheds, thosewith lower temporal variability, had measure of vari-ability values less than 0.55 and were found to bethose exposed to prevailing flows from the north andwest. The more variable, or unstable, watershedswere those exposed to infrequent flows from the southor east.

Correlation analysis between hydroclimatic ele-ments within basins identified SN as the betterindicator of ST in 11 of 14 watersheds in the Rocky

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Hydroclimatic Variability in the Rocky Mountains

Mountains. Streamfiows in many western basins areso affected by man-made diversions that in mostareas it is difficult to use ST as a representative mea-sure of the hydroclimate. Overall, SN was determinedto be the best single monitor of winter climate in theRockies. It explained between 37 and 87 percent ofthe variance found in streamfiow of virgin basins.

Analyses of the spatial variability, based on datafrom 275 snow course sites and 266 cooperativeweather stations in the study area, revealed 11 dis-crete climatic regions. Both SN and PR exhibitedcoherent regions of stable and unstable temporal vari-ability. The average variability between stable andunstable precipitation regions differed by a factor oftwo, and the differences were best explained by thephysical variable, aspect. Large mountain barriers instable regions were exposed to northerly or westerlyair flow, while large mountain barriers in unstableregions were exposed to southerly or easterly air flow.

These results provide a more basic understandingto the region's climate, specifically its variability.Ongoing research will determine whether knowingthat distinct regions of stable and unstable hydrocli-mates exist can be potentially useful to streamfiowmodelers and forecasters. For example, in watershedsthat are not well monitored, identifying the basin in astable region or unstable region may assist in under-standing the amount of hydroclimatic variability thatcould be expected in that watershed. Also, this infor-mation may be useful when choosing watersheds toconduct weather modification (snow making) experi-ments. The success level of the operation may be tiedto the variability of the watershed or region.

ACKNOWLEDGMENTS

This research was supported by the United States GeologicalSurvey under Grant Number 14-08-000l-G1294 and the ColoradoAgricultural Experiment Station. The efforts of William Gray,Roger Pielke, Jose Salas, Bob Jarrett, and Stan Changnon in mak-ing constructive comments on this report are appreciated. Theauthors also wish to thank those in the USGS and SCS that provid-ed assistance in understanding the data problems. Special thanksgo to Ms. Odie Bliss and Ms. Suzy Changnon for help with the draftreport and Ms. Judy Sorbie and Ms. Malynn Fields for drafting sev-eral of the figures.

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