United States

Department of 

Agriculture

Forest Service

Northern 

Research Station

Research Paper NRS-3

Factors Affecting Large Peakflows 

on Appalachian Watersheds: 

Lessons from the Fernow 

Experimental Forest 

James N. Kochenderfer

Mary Beth Adams

Gary W. Miller

J. David Helvey
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September 2007 Fax: (740)368-0152

Abstract

Data collected since 1951 on the Fernow Experimental Forest near Parsons, West Virginia, 

and at a gaging station on the nearby Cheat River since 1913 were used to evaluate factors 

affecting large peakflows on forested watersheds. Treatments ranged from periodic partial 

cuts to complete deforestation using herbicides. Total storm precipitation and average storm 

precipitation intensity were the most significant variables affecting peakflows, and were far 

more important than timber harvesting activites. Since January 1913, of the 20 highestranked peakflows on the Cheat River at the Parsons gaging site, 11 have occurred since 

1984 during a period of limited timber harvesting. These results support earlier findings that 

forests do not prevent floods and that prudent forest harvesting operations do not increase 

large flood peakflows.

Cover Photos

Top: stormflow from a 3,355-acre forested watershed on the Fernow Experimental Forest 

after 1.33 inches of rain fell in March on 8 inches of snow. Bottom: baseflow from the 

watershed in May at the same location.

The Authors

JAMES N. KOCHENDERFER and MARY BETH ADAMS are, respectively, research forester 

(retired), and, project leader/research soil scientist with the U.S. Forest Service’s Northern 

Research Station at Parsons, West Virginia.

GARY W. MILLER is a research forester with the Northern Research Station at Morgantown, 

West Virginia.

J. DAVID HELVEY (retired) was a project leader and research hydrologist with the 

Northeastern Forest Experiment Station at Parsons, West Virginia.

Manuscript received for publication 1 March 2007
1

INTRODUCTION

A common misconception, often repeated by the lay 

public and professional land managers, is that fl oods are 

associated with timber harvesting. As far back as 1863, 

G.P. Marsh proposed in “Man and Nature” that fl ooding 

is affected by forest clearing (Hewlett and Doss 1984). 

The controversy over the infl uence of forest harvesting on 

streamfl ow regulation continued into the early part of the 

20th century (Dodds 1969). Despite a dearth of scientifi c 

evidence at the time, conservationist Gifford Pinchot, 

when asked at a Congressional hearing whether fl oods 

along southern Appalachian rivers were traceable to the 

denuding of forests, replied: “Directly, directly...It is 

a perfectly clear cut proposition” (House of Represent. 

1907).

The possible relationship between forest clearing and 

fl ooding gained national attention in March 1907 when 

heavy rains in the headwaters of the Monongahela River 

were blamed for fl ooding at Pittsburgh, Pennsylvania. 

The fl ooding caused millions of dollars in property 

damage (McKim 1970), and fl ooding was attributed to 

excessive cutting and burning of forests in the watersheds 

of the Allegheny and Monongahela Rivers. After 

reviewing the historical records, Kite1

 concluded that 

the Cheat River, a major tributary of the cut and burned 

watersheds, apparently contributed little to the fl ood. His 

conclusion is supported by a lack of evidence of fl ooding 

in community newspapers, such as the Parsons Advocate, 

during March 1907. This small community was located 

on a fl ood plain at the convergence of two rivers forming 

the Cheat River, the second largest tributary of the 

Monongahela River, downstream from some of the most 

severely denuded forest land in the East.

On the other side of the forest-fl ood issue was Colonel 

H. M. Chittenden (1909), a member of the U.S. Army 

Corps of Engineers. He disagreed with Pinchot, arguing 

that “periods of long-continued, wide spread and heavy 

precipitation alone cause great fl oods in large rivers.” 

Chittenden added that during such periods, “The 

forest bed becomes completely saturated, its storage 

capacity exhausted, and it has no more power to restrain 

fl oods than the open country itself.” He proposed 

that engineering solutions such as reservoirs, levees, 

the clearing of river channels, and raising grades were 

necessary for fl ood protection.

Although the controversy surrounding the degree of 

control that forests exert on streamfl ow would continue 

for many years (Dodds 1969, Anderson et al. 1976), 

on March 1, 1911, Congress passed the Weeks Law, 

which authorized “the purchase of forested, cut-over, or 

denuded lands within the watersheds of navigable streams 

necessary to the regulation of the fl ow of navigable 

streams.” This law authorized government agencies 

to purchase land after state consent, which eventually 

became the national forests of the Eastern United States.

During the next several decades, forest research stations 

were established to better understand how to manage 

forests to achieve multiple objectives, including a 

dependable supply of clean water. As part of this effort, 

watershed research was initiated in 1951 on the Fernow 

Experimental Forest near Parsons, West Virginia 

(Fig. 1). Results from watershed studies at such research 

stations including the Fernow refuted commonly held 

concepts about forests and water. For example, the once 

popular notion that cutting trees causes springs to dry 

up (e.g., Maxwell 1894) and reduces streamfl ow was 

disproved (e.g., Hibbert 1967, Hornbeck et al. 1993). 

Other research demonstrated that cutting trees actually 

increases streamfl ow temporarily, particularly during 

growing seasons, because water losses to interception and 

1

Kite, Steven J. 1997. A short history of fl ooding in the Cheat 

River Basin in West Virginia and Pennsylvania. Unpublished 

report on fi le at the U.S. Forest Service, Timber and Watershed 

Laboratory, Parsons, West Virginia. 
2

transpiration are reduced, thereby increasing soil moisture 

and streamfl ow. Still other research demonstrated that 

reforestation of abandoned farmland on large drainage 

basins in Massachusetts (Patric and Gould 1976) and 

the southern Piedmont (Trimble et al. 1987) reduced 

streamfl ow.

Some of this research also focused on peak stormfl ows 

and fl ooding. Perhaps the most widely quoted study on 

the relationships between forests and fl oods was that of 

Lull and Reinhart (1972). They concluded that:

“The forest, because of its full occupancy, 

provides for any site a maximum opportunity for 

controlling runoff from fl ood-producing rainfalls; 

even so, the forest cannot prevent fl oods.”

•

“Infi ltration capacities of the forest fl oor 

are almost everywhere greater than rainfall 

intensities.”

“The forest is the best of all possible natural 

cover for minimizing overland fl ow, runoff, and 

erosion.”

“With reasonable care, the forest can be cut with 

little detriment to its site-protective capacity.”

Hewlett (1982a) supported those fi ndings, stating that 

“forest operations do not seriously increase fl ood fl ows in 

major streams.”

More than 90 years after the Pinchot-Chittenden debate, 

and despite much signifi cant research, the perceived 

relationship between forests and fl oods continues to exert 

•

•

•

Figure 1.—U.S. Geological Survey gaging station on the Cheat River and the Fernow 

Experimental Forest.
3

a major infl uence on land-management decisions in the 

United States. Recent fl oods in West Virginia have again 

raised concerns that land-use activities such as timber 

harvesting are infl uencing the frequency and magnitude 

of fl ood events. This is not surprising because forests 

currently cover 78 percent of the State (Griffi th and 

Widmann 2003) and timber harvesting is widespread, 

occurring on an estimated 235,000 acres in 2005, 

according to the West Virginia Division of Forestry.

Since the impacts of various kinds of timber harvesting 

and other land uses on streamfl ow have been evaluated 

on gaged watersheds on the Fernow Experimental 

Forest since 1951, we felt that an evaluation of Fernow 

data for large storms would provide timely and useful 

information. We assembled a data set of peak streamfl ows 

for the 50 largest storms between 1951 and 2004 for 

seven watersheds on the Fernow (http://www.fs.fed.us/

ne/parsons/webdata/data/downloads). Although much of 

our present knowledge about forest hydrology has been 

obtained from experimental watersheds (Hewlett et al. 

1969), caution should be used when extrapolating results 

from studies evaluating timber harvesting impacts on 

peakfl ows from small (fewer than 100 acres) experimental 

watersheds to large river basins containing thousands of 

acres. Entire research watersheds typically are subjected 

to the same treatment in short timespans so that the 

impacts of various forest practices on the quantity and 

quality of streamfl ow can be evaluated (e.g., Reinhart et 

al. 1963). In reality, entire watersheds of major streams 

are not subjected to these same intense land-use practices. 

Patterns of land ownership and multiple management 

objectives ensure a variety of uncoordinated management 

activities on larger watersheds. For example, timber 

harvesting activities, which occur on about 2 percent of 

the forest land in West Virginia each year, usually are 

confi ned to small portions of larger watersheds. A review 

of West Virginia Division of Forestry Timber Harvest 

Notifi cation forms submitted between 2000 and 2006 on 

the 462,080-acre Cheat River watershed above Parsons 

revealed that annual harvesting occurred on less than 2 

percent of the watershed.

Also, such harvests typically remove only part of the 

vegetation (selection or diameter-limit harvests). 

The effect of harvesting will differ from those due to 

deforestation or conversion to other land uses (Hornbeck 

1973). The regrowth of eastern forests is so rapid 

and vigorous that the effects of timber harvesting on 

streamfl ow diminish rapidly (Hornbeck et al. 1993). In 

this paper we focus on the impacts of forest management 

activities on peakfl ows and fl ooding. Our objectives were 

to: 1) use peakfl ow data from the Fernow Experimental 

Forest to evaluate dominant factors believed to affect 

peak streamfl ow from forested watersheds; and 

2) compare results from small (Fernow) and large 

watersheds (Cheat River basin) with respect to fl ooding 

and peakfl ows.

STUDY AREAS AND HISTORICAL USE

The Fernow Experimental Forest is located in the 

unglaciated Allegheny Plateau region of north-central 

West Virginia (Fig. 1). Topography in this region of the 

Allegheny Mountains is characterized by steep mountains 

and narrow valleys. The average elevation of the study 

area is about 2,500 feet. The predominant soil is Calvin 

silt loam with moderate erosion hazard (Losche and 

Beverage 1967) underlain with fractured sandstone and 

shale of the Hampshire formation. Soil depth usually 

is less than 3 feet. The fi rst harvesting activity occurred 

between 1905 and 1910. A that time, most smaller trees 

and species such as sugar maple (Acer saccharum Marsh), 

American beech (Fagus grandifolia Ehrh.), and black 

birch (Betula lenta L.) were considered unmerchantable 

and often were left standing. Today, dominant tree 

species include various oaks (Quercus spp.), yellow-poplar 

(Liriodendron tulipifera L.), and sugar maple.

Research on forest hydrology began on the Fernow in 

1951 when streamfl ow and precipitation measurements 

began on watersheds 1 through 5 (Fig. 2, Table 1). Data 

were collected continuously while the watersheds were 

undisturbed during a 6-year calibration period to develop 

relationships between the watersheds. In 1957-58, studies 

were initiated to evaluate the effects of four cutting 

practices on streamfl ow (Reinhart et al. 1963). Watershed 

4 (WS4) has not been cut since the original 1905-10 

harvesting, and serves as the reference watershed. Data 

collection began on WS6 and WS7 in 1956.
4

Figure 2.—Watersheds on the Fernow Experimental Forest used in this study.

A wide range of treatments has been applied to the 

Fernow watersheds over the years (Table 1), ranging 

from none or minimal treatments (reference WS4), 

partial cutting (WS2, WS5), commercial clearcut with 

no best management practices (BMPs) (WS1), and 

normal silvicultural clearcutting harvests using BMPs 

(WS3), to clearcutting followed by herbicide treatments 

that rendered the watersheds (WS6, WS7) barren of 

vegetation for several years. Gaps in the records represent 

periods before the construction of gaging stations or 

periods when they were inactive.

The Cheat River watershed above the Parsons gaging 

station (Fig. 1) contains 722 square miles (462,080 
5

acres) of drainage area. Elevations range from 1,600 to 

4,861 feet at Spruce Knob, the highest point in West 

Virginia. Annual precipitation averages about 53 inches. 

The original forest included extensive dense stands of 

red spruce (Picea rubra L.) and eastern hemlock (Tsuga 

canadensis L.) above an elevation of 3,000 feet (Fig. 3). 

It was estimated that the original spruce forest in West 

Virginia contained nearly 500,000 acres (Hopkins 1891), 

including 140,000 acres of spruce forests on the Cheat 

River watershed. Since 72 percent of the Cheat watershed 

above the Parsons gaging station is above 3,000 feet in 

elevation, it is reasonable to assume that the original 

spruce and hemlock forests covered at least half of the 

watershed. A forest inventory in 2000 indicated that the 

two counties (Tucker and Randolph) that comprise most 

of the Cheat River watershed above Parsons are about 86-

percent forested (Griffi th and Widmann 2003).

Table 1.—Treatment history and some characteristics of seven Fernow watersheds used in the study

WS Treatment Treatment date

Basal-area 

cut Aspect Area

percent acres

1 Clearcut to 6 inches d.b.h., except culls 5/57-6/58 74 NE 74.4

Fertilized with 500 lb/acre urea 5/71

2 Diameter-limit

 Cut trees ≥17.0 inches d.b.h. 6/58-11/58 37 S 38.3

 Cut trees ≥17.0 inches d.b.h. 8/72 16

 Cut trees ≥17.0 inches d.b.h. 1/78 3

 Cut trees ≥17.0 inches d.b.h. 5/88-7/88 24

 Cut trees ≥17.0 inches d.b.h. 9/96 10

3 Intensive selection cut, including cull trees > 5 inches d.b.h. 10/58-2/59 13 S 84.7

Repeat treatment 9/63-10/63 8

0.4-acre patch cuttings totaling 5.6 acres, cut down to 5 inches, 

1- to-5-inch. stems sprayed with herbicide 

7/68-8/68 6

Clearcut to 1 inch d.b.h., except for a partially cut 7.4-acre shade 

strip along the stream channel

7/69-5/70 91

Shade strip clearcut 11/72 9

Ammonium sulfate fertilizer applied 12/89 - present

4 No treatment/natural recovery since 1905 0 SE 95.7

5 Single-tree selection

Select trees ≥ 11.0 inches d.b.h. 8/58-12/58 21 NE 90.0

Select trees ≥ 11.0 inches d.b.h. 2/68-5/68 14

Select trees ≥ 11.0 inches d.b.h. 1/78-6/78 17

Select trees ≥ 11.0 inches d.b.h. 2/83-3/83 4

Select trees ≥ 11.0 inches d.b.h. 2/88-5/88 23

Select trees ≥ 11.0 inches d.b.h. 4/98-11/98 22

6 Lower 27.5 acres clearcut 3/64-10/64 51 S 55.2

Maintained barren with herbicides 5/65-10/69

Upper 27.5 acres clearcut 10/67-2/68 49

Entire watershed maintained barren with herbicides 5/68-10/69

Planted with Norway spruce 3/73

Aerially sprayed with herbicides 8/75, 8/80

7 Upper 30 acres clearcut 11/63-3/64 49 E 59.9

Maintained barren with herbicides 5/64-10/69

Lower 30 acres clearcut 10/66-3/67 51

Entire watershed maintained barren with herbicides 5/67-10/69

Natural recovery 10/69 to present
6

In the mid-1880s, loggers began harvesting timber in 

the Cheat River watershed (Fansler 1962); this largescale logging continued until the early 1920s (Fig. 4). 

Clarkson (1964) described the cutting as heavy, resulting 

in large accumulations of dried slash. Sparks from steampowered logging equipment often set fi re to the highly 

infl ammable slash. These fi res resulted in the conversion 

of large areas of spruce forest to fi re cherry (Prunus 

pensylvanica L.), blackberry (Rubus spp.), and bracken 

fern (Pteridium aquilinum L.). In some areas, deep humus 

was burned down to bedrock (Fig. 5) (Brooks 1911). The 

effects of wildfi res remain evident in the high plateaus of 

Tucker and Randolph Counties within the Cheat River 

watershed. Merely cutting these original forests without 

burning would not have infl icted the long-term damage 

associated with burning because undamaged soil, spruce 

regeneration, and some spruce seed sources would have 

remained after cutting. Although some of this land was 

planted by U.S. Forest Service crews in the early 1930s 

(Fig. 6), much of the high-elevation land formerly 

occupied by red spruce and hemlock includes inferior 

stands of hardwoods today (Hornbeck and Kochenderfer 

1998). The area of West Virginia occupied with forests 

dominated by red spruce has shrunk from the original 

estimate of 500,000 acres to the current estimate of less 

than 50,000 acres (Griffi th and Widmann 2003).

Figure 3.—This logging railroad was 

constructed in 1913 through a dense red 

spruce stand in the Cheat River watershed 

(photo courtesy of Dr. Roy B. Clarkson).
7

Figure 4.—The Babcock Lumber Co. logging Blackwater Canyon in the Cheat River watershed in 1910 

(photo courtesy of David F. Strahin).

Figure 5.—Fire, which often followed logging in the red spruce-hemlock, destroyed the thick organic layer 

that once covered exposed roots and rocks (National Archives photo).
8

METHODS

Data Acquisition and Analyses

Fernow Storms Data Set

On the Fernow, stream discharge used for these 

evaluations was measured with 120° V-notch weirs. 

Precipitation is measured with recording gages and 

standard 8-inch rain gages (Adams et al. 1994). Annual 

precipitation averages 58 inches and is distributed evenly 

between dormant (November 1 to April 30) and growing 

seasons (May 1 to October 31). Annual streamfl ow from 

WS4 averages 26 inches: 6 inches during the growing 

season and 20 inches during the dormant season.

Data from seven Fernow watersheds with the longest 

period of record were used in this study (Fig. 2, Table 

1). Streamfl ow records were examined for the reference 

watershed (WS4) to determine the 50 largest peakfl ows 

since record collection began in May 1951. Peakfl ows on 

the other six watersheds also were determined for these 

storm dates. These 50 largest storms were chosen because 

they have the greatest potential to infl uence downstream 

fl ooding. We also were trying to increase the number of 

observations available for statistical analysis, though the 

“optimum” number of storms for such statistical analyses 

is unknown. Precipitation measured during each storm, 

taken from recording rain gages, also includes snow that 

may have been on the ground at the time of each event; 

about 38 percent of the storms were infl uenced by snow. 

Meteorological records from the Fernow were used to 

determine the snow contribution for each event.

The total storm precipitation was measured with 

the Fork Mountain rain gage, the gage closest to the 

watersheds. As a result, the precipitation amount, 

independent of snow, is the same for each watershed for 

a single storm event. Because of the proximity of the 

watersheds and because most of these storms are unlikely 

to be limited geographically summer thunderstorms that 

could vary in precipitation delivery among watersheds 

(34 percent of the storms occurred during the growing 

season), this single value is believed to be a reasonable 

representation of total storm precipitation. Antecedent 

Figure 6.—Forest Service tree planting crew in the early 1930s in the Cheat River watershed near Davis, 

West Virginia (photo courtesy of Monongahela National Forest). 
9

precipitation for each watershed was determined by 

the Thiessen polygon method (Hewlett 1982b), which 

weights rain-gage catch by the area each gage represents, 

and was calculated for 7 and 14 days before each storm.

Nine variables that were hypothesized to affect peakfl ow 

were recorded or estimated for each of the seven 

watersheds. These are related to both the character of 

the storm event and to a variety of watershed physical 

features: These include:

1. AVG. INTENSITY—Average storm intensity, in 

inches per hour (in/hr), is total storm precipitation 

(inches) divided by storm duration (hours); average 

storm intensity ranged from 0.02 to 0.92 in/hr.

2. BASAL AREA—Estimated total stand basal area in 

square feet per acre (ft2

/acre) of woody vegetation 

growing on the watersheds at the time of each 

storm. This variable refl ects the average density of 

woody vegetation on each watershed.

3. SKIDROAD LENGTH—Total length of 

skidroads (miles) on each watershed at the time of 

each storm event.

4. LOGGING INTERVAL—Time interval (years) 

on each watershed since logging activity for each 

storm event. This variable describes the recency of 

the timber-cutting effect.

5. TOTAL STORM PRECIP—the precipitation 

(inches), including snow, measured during each 

storm event without a 6-hour precipitation-free 

period. It ranged from 1.37 to 6.06 inches.

6. 14 day-AP—Total 14-day antecedent weighted 

precipitation (inches) measured on each watershed 

prior to each storm event.

7. MDA streamfl ow—Mean daily antecedent 

streamfl ow in cubic feet/second/square mile (csm) 

recorded for each watershed on the day before 

each storm event.

8. ELEV—Maximum elevational difference (in feet) 

is the difference between the highest and lowest 

elevations on each watershed.

9. SNOW—Snow accumulation on the ground 

at the time of the storm (an indicator variable). 

Water equivalent of snow ranged from 0.47 to 3.0 

inches.

The ranges of values for the variables that differed among 

watersheds is shown in Table 2. Other variables such as 

1-hour storm intensity and 7-day weighted watershed 

antecedent precipitation were considered but excluded 

because they were duplicative or correlated with these 

variables. Data are available at http://www.fs.fed.us/ne/

parsons/webdata/data/downloads.

Statistical Analyses

To help identify factors with the greatest infl uence on 

peak streamfl ows, we developed nine candidate models 

Table 2.—Range of values for variables used in multiple regression analysis, by watershed, Fernow Experimental Forest 

Variable WS1 WS2 WS3 WS4 WS5 WS6 WS7

BASAL AREA 

(ft2

/acre)

2.1 - 138.4 60.8 - 0 111.1 0.0 -151.8 100.1 - 158.2 87.3 - 116.4 0.0 - 139.1 0.00 - 119.4

SKIDROAD 

LENGTH (mi)

0.0 -3.22 0.0 - 1.05 0.0 - 2.10 0.00 0.00 -2.80 0.00 - 1.28 0-.00 - 1.27

LOGGING 

INTERVAL 

(years)

1 - 52 1 - 53 0 - 53 49 - 97 0 - 53 0 - 59 0 - 59

14 day-AP 

(inches)

0.97 - 6.88 0.81 - 6.88 0.67 - 6.95 0.69 - 7.20 0.73 - 6.26 0.69 - 6.53 0.73 - 6.64

MDA streamfl ow 

(csm)

0.15 - 24.02 0.17 - 34.32 0.23 - 22.97 0.29 - 23.42 0.29 - 26.84 0.18 - 29.99 0.29 - 29.58

ELEV (ft) 640 320 400 405 348 240 480
10

based on hypotheses about the relationship between 

peakfl ows and the other variables (Table 3). Flow from 

the watersheds was standardized to cubic feet per second 

(cfs) per 100 acres to account for size differences among 

watersheds. The dependent variable is standardized 

peakfl ow (cfs/100 acres) and the explanatory variables 

are related to the storm events and physical parameters 

on the watersheds. The peakfl ows from the various 

watersheds expressed on a unit-area basis generally were 

consistent (Table 4). For comparison, descriptive statistics 

are also provided for peakfl ows recorded only during the 

pretreatment calibration period (Table 4).

The nine candidate models were evaluated using 

multimodel regression inference techniques described 

by Burnham and Anderson (1998). These models, 

representing hypotheses developed using expert 

judgment, were compared to determine a most plausible 

model. Each model was analyzed separately using 

multiple regression to derive Akaiki information criterion 

and to determine goodness of fi t for each model. The 

Akaiki coeffi cients were then used to calculate Akaiki 

weights and a most plausible model was selected. This 

method is believed to be superior to stepwise regression 

techniques when the number of possible models 

Table 3.—Candidate models used to evaluate hypotheses about influences on peak storm flows

Model Independent variable

Total Storm 

 Precip. 

TOTAL STORM PRECIP

Snow model TOTAL STORM PRECIP Snow (an indicator variable, yes or no)

Storm Intensity AVG. INTENSITY

Precipitation 

 amount and

 intensity

TOTAL STORM PRECIP AVG. INTENSITY

Soil wetness 14 day- AP MDA Streamfl ow TOTAL STORM PRECIP

Simplest soil 

 wetness

TOTAL STORM PRECIP 14 day- AP

Road SKID ROAD LENGTH LOGGING INTERVAL TOTAL STORM PRECIP AVG. INTENSITY MDA Streamfl ow

Watershed 

 characteristics 

LOGGING INTERVAL BASAL AREA TOTAL STORM PRECIP ELEV 

Trout 

 Fisherman’s 

BASAL AREA LOGGING INTERVAL AVG. INTENSITY TOTAL STORM PRECIP 14 day- AP 

MDA Streamfl ow 

Table 4.—Average standardized peakflows (csm/100 acres), standard deviation, and number 

of storms, Fernow Experimental Forest (pretreatment calibration descriptive statistics refer to 

storms occurring before 1957) 

50 storms data set Pretreatment calibration storm data set

Watershed Mean SD n Mean SD n

WS4 10.32 4.39 50 9.935 3.70 14

WS1 11.31 5.37 50 10.95 5.16 14

WS2 12.29 5.29 43 12.04 6.04 14

WS3 11.06 5.09 50 10.00 4.05 14

WS5 13.37 6.10 34 11.25 4.36 14

WS6 11.06 2.17 46 9.74 4.81 10

WS7 11.78 4.68 46 10.99 4.20 10
11

exceeds sample size. Uncertainty in model selection is 

reduced signifi cantly by the candidate model process. 

These models produce output that is interpreted more 

readily from a hydrologic sense than one developed by 

a statistical technique that builds models by arbitrary 

selection of parameters.

Cheat River Basin Data

Data on peak discharge for the gaging site on the Cheat 

River near Parsons (U.S. Geol. Surv. 1995, 2002; Ward 

et al. 1997, 2001; http://nwis.waterdata.usgs.gov/wv/

nwis/peak) were examined to determine the largest peak 

discharges recorded or estimated from July 6, 1844 

through September 2001 (Tables 5, 6). Streamfl ow 

measurements began at this site in January 1913; 

hourly streamfl ow data for the four largest storm events 

recorded since 1913 at the gaging site on the Cheat 

River were used to plot hydrographs for these events.2

Earlier peakfl ows were estimated from USGS Water Data 

Reports. Precipitation data for Parsons (1899 to 1950) 

were obtained from the National Climatic Data Center 

(2000). Precipitation data collected at the U.S. Forest 

Service Timber and Watershed Laboratory at Parsons 

since 1951 were used in these analyses.

2

Data furnished by Ronald Enaldi, U.S. Geological Survey, 

Charleston, West Virginia.

Table 5.—Dates and peak flow rates for the six largest flood events observed at the 

Parsons gaging site on the Cheat River.

Rank Flood date Peakfl ow (cfs) Source

1 November 5, 1985 170,000 Ward et al. (2001)

2 January 19, 1996 90,100 Ward et al. (1997)

3 July 6, 1844 85,000 Ward et al. (2001)

4 October 15, 1954 81,200 U.S. Geological Survey (1995)

5 February 9, 1994 78,300 U.S. Geological Survey (1995)

6 July 10, 1888 71,000 Ward et al. (2001)

Table 6.—Peak discharge for 20 largest storms on Watershed 4, 1951 to 2000, and on Cheat 

River, 1913 to 2000

Watershed 4 Cheat River near Parsons

Rank Date Peakfl ow Rank Date Peakfl ow

cfs cfs

1 11/4/1985 25.44 1 11/5/1985 170000

2 2/9/1994 18.41 2 1/19/1996 90100

3 10/15/1954 18.06 3 10/15/1954 81000

4 7/19/1996 17.59 4 2/9/1994 78300

5 6/6/1981 17.18 5 2/19/2000 57800

6 1/19/1996 15.88 6 5/8/1994 52700

7 5/7/1994 15.58 7 7/19/1996 52500

8 2/10/1957 15.49 8 3/19/1963 52400

9 2/19/2000 13.33 9 3/12/1917 50500

10 5/24/1968 13.19 10 5/17/1996 50000

11 7/29/2001 11.87 10 7/31/1996 50000

12 3/5/1963 11.67 12 9/6/1996 46600

13 3/6/1967 11.35 13 3/7/1967 44000

14 7/31/1996 11.24 14 5/28/1956 42900

15 12/22/1970 10.99 15 2/4/1932 42800

16 5/28/1956 10.55 16 5/26/1990 42500

17 8/11/1984 9.97 17 3/29/1924 41100

18 4/30/1966 9.90 18 10/9/1976 40900

19 1/22/1959 9.67 19 11/29/1985 39600

20 3/21/1962 9.40 20 1/22/1917 37500
12

RESULTS AND DISCUSSION

Fernow Large Peakfl ows

The 50 largest peakfl ows on WS4 since 1951 ranged 

from 5.93 cfs (December 31, 1989) to 25.44 cfs 

(November 4, 1985), with an average value of 9.87 cfs 

(SD = 4.20). Storm precipitation ranged from 1.37 

inches for the February 24, 1977 storm to 6.06 inches 

for the November 4, 1985 storm, with average storm 

precipitation of 3.06 inches (SD = 0.98). Note that while 

the largest storm peakfl ow (November 4, 1985) was 

associated with the highest precipitation amount, this 

relationship does not always hold as some of the smaller 

peakfl ows also were associated with high precipitation 

amounts. Of these 50 storms, 17 occurred in the growing 

season and 33 during the dormant season.

Factors Affecting Peakfl ows on the 

Fernow Watersheds

Results of the candidate model analyses suggest that 

the most plausible model is Precipitation amount 

and intensity, which includes the variables TOTAL 

STORM PRECIP and AVG. INTENSITY (Table 

7). We hypothesized that these two meteorological 

variables would be more important than the watershed 

characteristics and management activities for the largest 

storms. During large storms, the water-holding capacity 

of the soil (watershed) may be exceeded and streamfl ows 

can increase rapidly as a result. Although forests use 

large amounts of water compared with most other land 

uses, interception and transpiration are greatly reduced 

in hardwood forests during dormant seasons. Soilmoisture defi cits are greatly reduced and similar during 

dormant seasons in both cut and uncut hardwood forests 

(Troendle 1970). Growing-season moisture defi cits also 

may be reduced temporarily when a suffi cient number 

of trees is cut on forested watersheds, because moisture 

losses from transpiration and interception are reduced. 

Thus, streams draining forested watersheds are more 

responsive to precipitation during periods when soilmoisture defi cits are low and storage space for additional 

water is limited (as during the dormant season).

Also, since infi ltration rates of forest soil usually exceed 

rainfall rates, there is virtually no overland fl ow on forest 

land (Patric 1976). When a steep 64-acre watershed was 

clearcut on the Fernow, overland fl ow occurred only on 

skid roads with no water control structures (Reinhart 

1964). Stuart and Edwards (2006) concluded that timber 

harvesting has little effect on water resources because 

canopy removal and normal harvesting activities do not 

signifi cantly affect the function of the forest fl oor. Bates 

(2000) concluded that during most storms, even after 

harvesting, subsurface fl ow remains the primary 

mechanism for stream generation. In our analyses, total 

storm precipitation alone explained 39 percent of the 

variability in the model; adding average storm intensity 

explained another 4 percent. By comparison, adding 

basal area and logging interval rather than average storm 

intensity explained less than 1 percent of additional 

variability.

Note that most of the storms occurred more than 5 years 

after the most recent logging disturbance: 84, 60, 62, 76, 

and 76 percent for WS1, WS2, WS3, WS6, and WS7, 

Table 7.—Results of multiple regression analyses

Model R2

Probability of 

greater F AIC

Akaike 

weights Ranking

Total Storm Precip. 0.391 < .0001 845.506 0.000

Snow 0.465 < .0001 807.113 0.031 4

Storm intensity 0.039 0.0005 987.185 0.000

Precip amount 

 and intensity

0.430 0.0001 800.579 0.822 1

Soil wetness 0.396 < .0001 846.688 0.000

Simplest soil 

 wetness

0.391 < .0001 847.276 0.000

Road 0.431 < .0001 806.368 0.045 3

Watershed 

 characteristics

0.397 < .0001 848.466 0.000

Trout Fisherman’s 0.437 < .0001 804.780 0.101 2
13

respectively. For WS5, 35 percent of the storms occurred 

more than 5 years after logging; the percentage was 100 

for WS4, which had not been logged since 1910. About 

30 percent of the storms occurred within the fi rst 3 years 

after logging disturbance. This illustrates the inherent 

diffi culty of evaluating streamfl ow response to extreme 

storms. Since these large storm events occur randomly 

at unpredictable times, their occurrence rarely coincides 

with watershed treatments on experimental watersheds 

and they cannot be replicated in controlled experiments.

The relatively low R2

 values we observed also may result 

from other factors that differ among watersheds or 

that were not measured in our analyses. For example, 

differences in the variable source area, which could vary 

with soil depth or stream channel morphology, were 

not quantifi ed and may have improved our ability to 

predict peakfl ows. Although soil depth is assumed to 

be reasonably uniform across these watersheds, some 

differences are likely. Because of these minor differences 

as well as with differences in stream channel morphology, 

the source area for streamfl ow generation may differ 

among these otherwise homogeneous watersheds. Aspect, 

which can affect snowmelt, was not quantifi ed in our 

analysis.

We conclude that storm characteristics explained a 

considerable amount of variability in peakfl ow while 

disturbance history characteristics explained little or none 

of the variability in peakfl ow, and that the meteorological 

variables (storm precipitation characteristics) were the 

most important in explaining the variability of large peak 

stormfl ows from these forested watersheds even when 

different impacts of timber harvesting are considered.

Brewer et al. (1982) performed similar analyses but with 

data from 1952-78 snow-free periods from WS4. They 

found that rainfall depth and initial streamfl ow rate 

accounted for 87 percent of the variation in peakfl ow. 

Their preferred models generally underestimated larger 

peakfl ows. A graph of predicted versus observed peak 

fl ows for WS4 based on the Precipitation Amount and 

Intensity model (Fig. 7) suggests that while there is 

considerable scatter around the equal peakfl ow line (the 

1:1 line), this model predicted peakfl ows reasonably well 

over most of the range of fl ows.

0

5

10

15

20

25

30

0 5 10 15 20 25 30

Predicted Peak Discharge (cfs/100 acres)

Observed Peak Dischrage (cfs/100 acres)

1:1 line

Figure 7.—Comparison of 

predicted peakfl ows (using 

total precipitation and 

average storm intensity) 

vs. observed peakfl ows, 

Watershed 4 on the Fernow 

Experimental Forest.
14

Snow can be an important component of storms during 

the dormant season, so including an indicator variable 

for snow accumulation on the ground (Snow model) 

increased the R2

 over that of the Total Storm Precip. 

model. Indeed, the Snow model had the highest R2

 (Table 

7), suggesting that storms that include snowmelt behave 

differently than those not infl uenced by snow. A model 

that included the variables TOTAL STORM PRECIP, 

AVG. INTENSITY and SNOW was the most plausible 

in a post-hoc exploratory analysis, providing additional 

evidence for the importance of snow in dormant-season 

peakfl ows. The infl uence of a snowpack on peakfl ow was 

demonstrated in the storm of January 19, 1996. This 

storm would not have been included among the top 10 

(Table 6) without the additional water derived from 18 

inches of snow (2.39 inches of water equivalent) on the 

ground before the storm. The storm on February 9, 1994, 

also was infl uenced by a snowpack as 5 inches of snow 

(0.5 inch of water equivalent) was on the ground prior to 

the storm.

Two of the three largest stormfl ows are related to 

hurricanes that deposited signifi cant amounts of rain 

in a relatively short period and during the dormant 

season. The storm in November 1985 was the result of 

Hurricane Juan, and the storm in October 1954 was 

related to Hurricane Hazel. Large rainfall events over 

wide areas often are associated with hurricanes in the 

central Appalachians. The presence of forest cover did 

not appreciably reduce fl ood peaks in the Shenandoah 

River watershed of Virginia during an extreme rainfall 

event in 1996 caused by Hurricane Fran (SturdevantRees et al. 2001). Lull and Reinhart (1972) pointed 

out that fl oods from forested watersheds are common 

and provided numerous examples of record peakfl ows 

from well forested watersheds in the Eastern United 

States. Kochenderfer et al. (1997) showed that while 

timber harvesting can increase growing-season stormfl ow 

volumes and peakfl ow rates of small storms temporarily, 

these increases do not have important downstream 

implications, that is, they generally are not associated 

with fl ooding.

Stormfl ows and peakfl ows were increased during the 

growing season after forest clearing in New Hampshire 

but stormfl ows that occurred after soil-moisture recharge 

in the fall and before spring snowmelt were unaffected 

(Hornbeck 1973). The increased response on the cleared 

watershed during the growing season was attributed to 

reduced transpiration and interception, which caused 

wetter soils and reductions in moisture storage capacity. 

In a paired watershed experiment in which the hydrologic 

impacts of clearcutting and site preparation on the 

Georgia Piedmont were evaluated, it was found that small 

stormfl ows generated under dry antecedent conditions 

could be increased by 50 percent or more but that 

larger fl ood discharges, which usually occur under wet 

conditions, are little affected (Hewlett and Doss 1984). 

Results of a watershed study in which forest-harvest and 

site-preparation treatments in the Ouachita Mountains 

of Arkansas were evaluated showed that stormfl ow and 

peakfl ow for a large, 100-year event were not signifi cantly 

increased by the treatments (Miller et al. 1988). In 

evaluating the effects of clearcutting on stormfl ow 

hydrographs in Missouri, Settergren and Krstansky 

(1987) concluded that clearcutting may increase total 

stormfl ow yields and peakfl ows, but that the magnitudes 

are not important with respect to fl ood fl ow generation.

Largest Cheat River Peakfl ows

The Cheat River has experienced major fl oods since the 

arrival of European settlers in that area in the 1700s. The 

fi rst recorded fl ood in July of 1844 did little damage to 

the settlements, perhaps because the area was sparsely 

populated at that time. The next recorded fl ood, in 

July 1888, was much more damaging even though 

the estimated peak discharge was lower than the fl ood 

in 1844. According to Fansler (1962): “The fl ood of 

July 1888 must have been the highest ever to come 

to the Cheat River ... the whole valley from Parsons 

to Rowlesburg was under water. Horses, cattle, sheep, 

and hogs were washed away.” However, the available 

evidence indicates that the November 1985 fl ood was 

much greater and many times more destructive than any 

experienced since the area was settled. Peakfl ow rates at 

the Parsons gaging site on the Cheat River for the six 

largest fl oods observed since settlement are shown in 

Table 5. These storms also are ranked in descending order 

according to the maximum peakfl ows recorded for each 

event. Peak discharge for 1844 and 1888 were estimated 

from fl oodmarks and datum in use prior to August 17, 

1944 (Ward et al. 2001).
15

A major storm occurred on July 17, 1907, but it was 

excluded from this analysis because no estimate of 

peakfl ow was available at the Parsons gaging site. The 

aerial extent of this storm is unknown but the 4.7 

inches of rain recorded by National Weather observer 

S.W. Swisher, on that date at Parsons (Natl. Clim. Data 

Cent. 2000) were similar to the 4.3 inches recorded at 

Parsons during the storm in October 1954. Similar peak 

discharges were reported for these storm events on the 

Cheat River at Rowlesburg (U.S. Army Corps of Eng. 

1963). The fl ood of November 5, 1985, was the largest 

event recorded on the Cheat River (Kite and Linton 

1993). Peakfl ow for this storm was nearly twice that 

recorded for the next largest storm (Table 5).

Comparison of Peak Discharge on the Cheat River 

and WS4 Watersheds

Peak discharges for the 20 largest storms for the Cheat 

River basin near Parsons and WS4 are shown in Table 

6. Only four of the largest storms on the Cheat River at 

the Parsons gaging site occurred before 1951 when the 

Fernow watersheds, including WS4, were gaged. Except 

for the sixth-ranked storm (January 19, 1996), only the 

four highest-ranked storms on WS4 resulted in major 

downstream fl ooding on the Cheat River. There are both 

differences and similarities in rankings but the 1985 

storm was the largest on both watersheds, and the storm 

on October 15, 1954 ranked third on each list. A major 

difference in ranking occurred with the storm on January 

19, 1996, which ranked sixth on WS4 but second on 

the Cheat River watershed at the Parsons gaging site. 

Precipitation records on WS4 and at the Bearden Knob 

weather station near Davis, West Virginia, indicate that 

about 2 inches of rain fell at both sites between 3 and 

9 a.m. on January 19. The difference in storm response 

probably can be attributed to the greater depth of the 

snowpack on the high-elevation Cheat watershed.

Measurements on January 5 at the Bearden Knob station 

indicated a snow depth of 28 inches versus 18 inches 

measured on WS4 (equivalent to 3.72 and 2.39 inches 

of water, respectively). Temperatures remained above 

freezing at both sites from about 10 a.m. on the 16th 

until 9 a.m. on the 19th when it stopped raining. Average 

wind speed on January 18 and 19 was 10 to 20 miles 

per hour; this also enhanced snowmelt. The melting 

snowpack along with the rainfall were suffi cient to cause 

the Cheat River to fl ood. The dramatic temperature drop 

recorded at Bearden Knob, from 53 oF at 3 a.m. to 20 oF 

at noon peaking time on the Cheat at Parsons resulted 

in a rapid decline in snowmelt and streamfl ow. This 

illustrates how the interaction of meteorological events 

can infl uence peak discharges.

It is interesting that 6 and 7 of the 10 largest peakfl ows 

recorded since 1951 on WS4 and since 1913 at the 

Cheat gaging site, respectively, have occurred since 1984 

(Table 6). Most of the largest peakfl ows were recorded 

during the dormant season for both WS4 and the Cheat 

River when evapotranspiration losses were low. This was 

not unexpected because watersheds usually are wetter and 

more responsive to precipitation during those periods. 

The occurrence of individual large storms is comparable 

between the two watersheds. Seven of the top 10 storms 

on the Fernow also were included in the 10 highest 

ranked storms on the Cheat.

Note the large number of peakfl ows recorded at the 

Parsons gaging station in 1996 (Table 6). Five of the 

20 largest peakfl ows were recorded during that year; 4 

occurred during the growing season. In 1996, 70.4 inches 

of precipitation fell at Parsons, the greatest amount since 

offi cial measurements began in January 1899. Many large 

storms occurred during 1996, reducing soil-moisture 

defi cits (lower storage capacity), thereby making streams 

more responsive to precipitation in 1996 than in years 

with less precipitation.

The storms of October 14, 1954, and November 5, 

1985, are the two largest recorded on Fernow WS4 that 

were unconfounded with snow. A more detailed look 

at these events provides insight into the relationship 

between streamfl ow and precipitation during large storm 

events. The distribution of precipitation in West Virginia 

during the 1954 storm is shown in Figure 8. Rainfall 

amounts probably were greater over limited areas, but 

the rain-gage network was too sparse to defi ne individual 

storm cells. Some hydrologic characteristics of this storm 

are shown in Figure 9. On October 14, rain fell from 

7:30 a.m. until 11 p.m. This storm had the highest 

4-hour rainfall intensity (2.25 inches) of the 10 highest 

ranked storms on WS4. Streamfl ow rose rapidly, peaking 
16

Figure 9.—Hydrologic characteristics of the storm of October 15-16, 1954, from data collected on 

Watershed 4 on the Fernow Experimental Forest.

Figure 8.—Map of West Virginia 

with isohyetal lines showing rainfall 

distribution for the storm that 

occurred on October 15-16, 1954.

at about 6 p.m. Antecedent soil-moisture content was 

low judging from the streamfl ow rate just before the 

storm began (0.04 cfs). The hydrologic response factor, 

defi ned by Hewlett (1967) as the percentage of storm 

rainfall converted to streamfl ow, was about 60 percent. 

Flood damage was severe in the small communities of 

Hambleton and Hendricks. Several houses and basements 

were damaged extensively by fl oodwater, forcing many 

/
17

residents to vacate their homes until the next morning. 

Fortunately, no lives were lost and no homes were washed 

away or damaged beyond repair.

The 1985 storm resulted from a complex sequence of 

meteorological events beginning when Hurricane Juan 

made landfall in the Gulf Coast on October 31 (Colucci 

et al. 1993). The approximate precipitation distribution 

over the study area is shown in Figure 10. Unfortunately, 

rain gages usually are located in areas where people live 

and records for high-elevation remote areas are scarce. 

Although the rainfall patterns between the 1954 and 

1985 storms were similar, with maximum amounts in 

the headwaters of Dry Fork River, the total amounts 

were much greater during the 1985 storm. In the study 

area, initial rainfall intensity was low; less than an inch 

of total rainfall was recorded for November 2 and 3 on 

the Fernow (Fig. 11). At about 9 a.m. on November 4, 

rainfall intensity increased over the entire study area. 

During the next 15 hours, about 5 inches of rain fell 

at the Fernow and more than 7 inches were reported 

at Spruce Knob. Maximum rainfall intensity was not 

unusual (0.50 in/ hr) but an average rainfall rate of 0.34 

in/hr was sustained over a 15-hour period.

Streamfl ow response on WS4 is shown in Figure 11. 

Antecedent soil-moisture content was moderate when 

the intense rainfall began on November 4, as indicated 

by the base fl ow rate of 0.06 cfs. The response factor 

(70 percent) was greater than that recorded for the 1954 

storm. A larger percentage of precipitation was returned 

as streamfl ow under the wetter conditions created by the 

additional 1.4 inches of precipitation in the 1985 storm.

Flood damage from the 1985 storm was many times 

greater than ever experienced before in this region. 

Many homes and businesses were washed away or 

damaged beyond repair (Fig. 12). The rarity of a fl ood 

of this magnitude can be inferred from other bits of 

information. First, several homes and other buildings 

along the Cheat River and its tributaries were washed 

away by the fl oodwaters. Many of these structures had 

stood for more than 100 years. For example, the house 

shown in Figure 13 was under construction adjacent to 

the Cheat River below Parsons during the 1888 fl ood. 

The gentleman in the photograph, Ernest Fitzwater 

(1901-1990) stated that his father told him that while 

he was helping construct the house, the water reached 

the fi rst fl oor and fl oated fl ooring boards that were 

being installed. By contrast, the water level was about 

8 feet higher during the 1985 fl ood. Also, many large 

trees growing along the streams were uprooted by the 

fl oodwaters. Figure 14 shows a red oak tree that grew 

along the Black Fork River just upstream from Parsons. 

Figure 10.—Map of West Virginia 

with isohyetal lines showing rainfall 

distribution for the storm that 

occurred on November 4-5, 1985.
18

Figure 11.—Hydrologic characteristics of the November 1985 storm from data collected on 

Watershed 4 on the Fernow Experimental Forest.

Figure 12.—Damage along Pennsylvania Avenue in Parsons, West Virginia, from a fl ood that occurred 

on November 4-5, 1985 (photo courtesy of John Warner).
19

Figure 13.—Water barely reached to the fi rst story of this house during the 1888 fl ood on 

the Cheat River. During the fl ood of November 1985, water reached the second fl oor.

Figure 14.—This 130-year-old red oak near Parsons, West Virginia, was uprooted during 

the fl ood of November 1985.
20

A growth-ring count indicated that the tree was 130 years 

old. Before the fl ood, the tree grew on a bank several feet 

above the river edge. Most of the bank was stripped away 

by the fl oodwaters. The magnitude of the 1985 fl ood is 

illustrated in Figure 15, which shows basefl ow on the Dry 

Fork River above Parsons during the summer of 1986. 

The photo shows a chair deposited in a tree behind the 

young swimmer.

Comparison of Storm Hydrographs

Hydrographs are compared in Figures 16 and 17 for 

the four largest storms recorded on WS4 with the same 

storms recorded at the gaging site on Cheat River near 

Parsons. An additional lower ranked storm (January 19, 

1996) also is shown for WS4 so it can be compared with 

the same storm that ranked second at the Cheat River 

gaging site. A hydrograph is not shown for the 1985 

storm on the Cheat River because the gaging station was 

destroyed during that event. Peakfl ow for the 1985 storm 

was estimated from extension of the rating curve above 

55,000 cfs (Ward et al. 2001). Estimated peakfl ow for the 

1985 storm was much greater than peakfl ows recorded 

for the other highest ranked storms on the Cheat 

watershed. It was almost twice as high as that recorded 

for the second highest ranked storm in the Cheat 

watershed (January 19, 1996). On WS4, peakfl ow for 

the 1985 storm was only 28 percent larger than peakfl ow 

for the second highest ranked storm (February 9, 1994). 

The magnitude of peakfl ows for the other top ranked 

storms within both watersheds were similar. On average, 

stormfl ows at the Cheat gaging site peaked about 5 hours 

later than those on Fernow WS4.

Figure 15.—The chair (circled) in 

the tree behind the young swimmer 

was deposited during the November 

1985 fl ood on the Dry Fork River near 

Hendricks, West Virginia.
21

Figure 16.—Hydrographs of the four largest storms recorded on Watershed 4 on the Fernow 

Experimental Forest since measurements began in 1951. A lower ranked storm (January 19, 1996) is 

included for comparison purposes (see Figure 17).

Figure 17.—A peakfl ow indicator line for the storm of November 1985 and hydrographs for the other 

three largest storms recorded at the gaging site on the Cheat River near Parsons, West Virginia, since 

measurements began in 1913.
22

CONCLUSIONS

Results from our analyses and from other researchers 

provide little support for the commonly held view 

that timber harvesting increases the severity of fl oods. 

However, careful forest management, including planning 

and placement of roads, use of BMPs, and thoughtful 

execution of the operations is necessary so that other 

off-site effects, e.g., erosion and sedimentation, are 

minimized or negated. We conclude that:

The total storm precipitation (39 percent) and 

average storm precipitation intensity (4 percent) 

model accounted for most of the variation (43 

percent) in peakfl ow.

Meteorological variables were more important 

than disturbance history in explaining the 

variability in large stormfl ows.

The unexplained variation in our peakfl ow models 

probably refl ects the complex interaction of 

meteorological variables that result in large peakfl ows 

as well as variables such as soil depth, stream channel 

morphology, and watershed exposure, none of which 

were included in our models.

The two highest ranked peakfl ows on both the 

Fernow and Cheat watersheds occurred during 

the dormant season.

Although prolonged snowpacks are uncommon 

in the central Appalachians, snow can be an 

important component of dormant-season peakfl ow.

More than half of the 10 largest peakfl ows recorded 

on these well forested watersheds have occurred 

since 1984 during a period of limited timber 

harvesting and a high percentage of forest cover. 

The storm of 1985 generated record peakfl ows 

on both watersheds though the relative size of 

the 1985 peakfl ow was much larger on the Cheat 

than on the Fernow watersheds.

ACKNOWLEDGMENTS

The authors thank Doug Owens, John Campbell 

(deceased) and Linda Plaugher of the Northern Research 

Station for compiling streamfl ow and precipitation 

data, assisting with literature searches, and typing the 

manuscript; and Ronald D. Evaldi of the U.S. Geological 

•

•

•

•

•

•

•

Survey in Charleston, West Virginia for providing 

streamfl ow data. Frederica Wood provided assistance 

with data management and compilation of these data 

sets. Desta Fekedulegn provided guidance in our early 

analytical discussions. John Stanovick, statistician with 

the Northern Research Station, assisted in the statistical 

analyses. Reviews of the draft manuscript by Jim Vose, 

Jeffrey Wiley, and Bill Gillespie were most helpful and 

appreciated.

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The U.S. Department of Agriculture (USDA) prohibits discrimination in all its programs and activities on 

the basis of race, color, national origin, age, disability, and where applicable, sex, marital status, familial 

status, parental status, religion, sexual orientation, genetic information, political beliefs, reprisal, or because 

all or part of an individual’s income is derived from any public assistance program (Not all prohibited 

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of program information (Braille, large print, audiotape, etc.) should contact USDA’s TARGET Center at 

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of Civil Rights, 1400 Independence Avenue, S.W., Washington, DC 20250-9410, or call (800)795-3272 

(voice) or (202)720-6382 (TDD). USDA is an equal opportunity provider and employer. 

Kochenderfer, James N.; Adams, Mary Beth; Miller, Gary, W.; Helvey, David J. 2007. 

Factors affecting large peakflows on Appalachian watersheds: lessons 

from the Fernow Experimental Forest. Res. Pap. NRS-3. Newtown Square, 

PA: U.S. Department of Agriculture, Forest Service, Northern Research Station. 

24 p.

Data collected since 1951 on the Fernow Experimental Forest near Parsons, West 

Virginia, and at a gaging station on the nearby Cheat River since 1913 were used 

to evaluate factors affecting large peakflows on forested watersheds. Treatments 

ranged from periodic partial cuts to complete deforestation using herbicides. 

Total storm precipitation and average storm precipitation intensity were the most 

significant variables affecting peakflows, and were far more important than timber 

harvesting activites. Since January 1913, of the 20 highest-ranked peakflows on 

the Cheat River at the Parsons gaging site, 11 have occurred since 1984 during a 

period of limited timber harvesting. These results support earlier findings that forests 

do not prevent floods and that prudent forest harvesting operations do not increase 

large flood peakflows.

KEY WORDS: Forest harvesting, floods, watershed research, peakflows
www.nrs.fs.fed.us

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