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Landslide Susceptibility in the Tully Valley Area,
Finger Lakes Region, New York

USGS Open-File-Report 94-615 (On-line version)

Stefan Jäger & Gerald F. Wieczorek (addresses)


This report is preliminary and has not been reviewed for conformity with U.S. Geological Survey editorial standards (or with the North American Stratigraphic Code). Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government.


Setting Methodology
Basic Data Analyses


As a consequence of a large landslide in the Tully Valley, Onondaga County, New York, an investigation was undertaken to determine the factors responsible for the landslide in order to develop a model for regional landslide susceptibility. The April 27, 1993 Tully Valley landslide occurred within glacial lake clays overlain by till and colluvium on gentle slopes of 9-12 degrees. The landslide was triggered by extreme climatic events of prolonged heavy rainfall combined with rapid melting of a winter snowpack. A photoinventory and field checking of landslides within a 415 km2 study area, including the Tully Valley, revealed small recently-active landslides and other large dormant prehistoric landslides, probably Pleistocene in age. Similar to the larger Tully Valley landslide, the smaller recently-active landslides occurred in red, glacial lake clays very likely triggered by seasonal rainfall. The large dormant landslides have been stable for long periods as evidenced by slope denudational processes that have modified the landslides. These old and ancient landslides correspond with proglacial lake levels during the Pleistocene, suggesting that either inundation or rapid drainage was responsible for triggering these landslides. A logistic regression analysis was performed within a Geographic Information System (GIS) environment to develop a model of landslide susceptibility for the Tully Valley study area. Presence of glacial clays, slope angle, and glacial lake levels were used as explanatory variables for landslide incidence. The spatial probability of landsliding, categorized as low, moderate and high, is portrayed within 90-m square cells on the susceptibility map.


On April 27, 1993, a landslide severely damaged three homes near the town of LaFayette in the Tully Valley, 24 km (15 miles) south of Syracuse, New York (Fig. 1). Four additional homes had to be evacuated. This landslide, which occurred after heavy precipitation of 190 mm (7.5 in) during April in conjunction with melting of a winter snowpack, led to considerable concern among the local authorities as well as the population about the stability of the slopes in the Finger Lakes region. The Tully Valley landslide was the largest in New York in the past 75 years (Fickies, 1993). The scarp is approximately 450 m (1500 feet) wide and the landslide measures 600 m (1800 feet) from crown to toe (Fig. 2) . The toe of the landslide overrode Tully Farms Road and destroyed 22 hectares (55 acres) of farmland (Fickies, 1993). This landslide can be classified as a rapid slump-earth flow according to the terminology of Varnes (1978). Material involved in this landslide mainly consists of red lake clay deposits of glacial origin, covered by glacial till and colluvium of varying thickness.

Figure 1. Location map

Figure 1: Location (fast)
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An initial reconnaissance revealed that the Tully Valley landslide was not a unique geologic event in this area; immediately north of this landslide a similar scarp indicates another landslide of the same type and roughly the same size that is probably at least 200 years old. In a cooperative effort, Stefan J„ger of the Department of Geography at the University of Heidelberg, Germany and Gerald F. Wieczorek of the U.S. Geological Survey, undertook to examine landslide susceptibility and causative factors in a study area of approximately 415 km2 around Otisco Lake, Tully Valley and Butternut Valley (Fig. 1).

The initial step of the investigation was the preparation of a landslide inventory based on interpretation of aerial photographs. This inventory was field checked and modified on the basis of field examination. The landslide inventory and other geologic data sets were digitized and converted into spatial coverages for statistical analyses within a Geographic Information System (GIS) environment and for development of a landslide susceptibility model. The final results are displayed as a map of landslide susceptibility for the Tully Valley study area, but the results could be extended to areas of similar geologic conditions.

Figure 2. Photograph of Tully valley landslide
Figure 2: Photograph of the Tully Valley landslide, taken from helicopter shortly after its occurrence on April 27, 1994.
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The Tully Valley is located in the northern end of the Allegheny Plateau at the drainage divide of the Susquehanna and the St. Lawrence Rivers. Lower Devonian shales and limestones dip gently to the south throughout the area. Several stages of late Pleistocene glaciation have left both prominent erosional and depositional glacial features in this area (Andrews and Jordan, 1978).

Landslides in glacial lake deposits of New York

The glacial geology and geomorphology of the Finger Lakes region have been studied for more than a century (Fairchild, 1898; 1899; von Engeln, 1928). A study of the glacial geology is important for the recognition of glacial landforms and distinction of landslides. A review of the geological literature indicates relatively few investigations of landslide processes in the Finger Lakes region. Newland (1909, 1916) investigated landslides in clays and unconsolidated sediments of the Hudson River Valley, an area covered by glacial Lake Albany, which is about 240 km (150 mi) east of the study area. More recently Dunn and Banino (1977) have described slope stability problems with Lake Albany clays. Robak and Fickies (1983) prepared a landslide susceptibility map for the Hudson River Valley. Fickies and Brabb (1989) prepared a landslide inventory map of New York State from historical reports at a scale of 1:500,000 which identified four small recently-active landslides within the Tully Valley study area.

Glacial lake history in the Tully Valley study area

The glacial lake history in the Tully Valley study area is closely related to the lake history of the other adjacent Finger Lakes valleys. During the Late Pleistocene different stages of advance and retreat of the Wisconsin ice sheet blocked northward drainage forming proglacial lakes between the ice margin and moraines deposited further south. Based mainly on the work of Fairchild (1898, 1899, 1934a, 1934b), who published more than 100 papers related to the glacial lake history of central New York, Grasso (1970) published a detailed description of the proglacial lake sequence in the Tully Valley. Blagbrough (1951) studied the lake sequence in Otisco Valley in relation to red clay deposits.

Two types of glacial lakes covered the Tully Valley area. During stages of stagnant ice margins north of the valley, water backed up into the Tully Valley that connected with larger lakes to the northwest, these lake stages are referred to by various names, e.g. Newberry, Warren, and Dana (Fairchild, 1898). During stages of ice advance further southward, smaller lakes formed which were more locally controlled within individual valleys. To the south the drainage of the Tully Valley was blocked by the Valley Heads moraine, a prominent ridge near the Tully Lakes (Fig. 1). When the ice front melted back from the Valley Heads Moraine at Tully, Lake Cardiff was formed in Tully Valley south of the ice front and north of the moraine (Grasso, 1970). Glacial Lake Otisco existed in the next valley to the west. The shoreline of these two contemporaneous lakes within the study area is shown as Lake Otisco-Cardiff on Plate 5. Subsequently, glacial lake levels lowered as the glacial ice retreated further northward and outlets drained to adjacent valleys. Lake Heath Grove and First Lake Marietta were subsequent lakes in the study area (Plate 5).

Plate 5:
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These glacial lakes trapped sediment eroded by the ice as well as carried by the rivers. Thick deposits of fine-grained sediments, including red clays, are common throughout the Finger Lakes region in areas covered by glacial lakes; however, in many places these glacial lake deposits are mantled by colluvium. Near the intersection of Tully Farms Road and Nickols Road, immediately south of the Tully Valley landslide the massive red clay deposits are at least 21 m (70 ft) thick. Although the beds of these lake clays are predominantly horizontal they may have a slight tilt where they lapped along the valley edge. Laboratory tests indicate that these lacustrine "clays" are mostly clayey silts of moderate plasticity (Bill Kappel, U.S. Geological Survey, 1993, written commun.).

Glacial lakes and late Pleistocene landslide processes

The occurrence of landslides in the study area is influenced by at least several causative factors, including the location of previous glacial lakes. Glacial lake deposits, particularly clays, are generally highly susceptible to landsliding. The surface of rupture of the Tully Valley landslide was within red, glacial, lake-clay beds overlain by colluvium. Many of the recently-active landslides observed in the study area occurred within red clay beds, although most of these landslides were smaller and shallower than the Tully Valley landslide. The generally low shear strength of clay facilitates sliding on even moderate slopes. The initial surface slope of the crown area of the Tully Valley landslide was between 9 and 12 degrees, within the range of reported friction angles of clays (Skempton, 1964).

The sensitivity of clays, the ratio of peak to remolded strength, is an important factor influencing the behavior of glacial clays. The Tully Valley clays had only slight to medium sensitivity (1.7-3.9), and exhibited limited mobility near the toe of the landslide where they flowed around a house and over Tully Farms Road. In contrast some glacial marine clays of Canada and Scandinavia with high sensitivity (8 to >64) are subject to sudden collapse and mobilization into very rapidly moving landslides (Karlsrud et al., 1984; Tavenas, 1984).

High pore water pressure reduces shear strength and is a major mechanism responsible for triggering landslides, as observed in glacial clays of the Cincinnati area (Fleming et al., 1981). Ohlmacher and Baskerville (1991) described landslides in fine-grained glacial deposits of Lake Hitchcock, Vermont triggered by elevated pore-water pressures during heavy precipitation. After the Tully Valley landslide movement, water rapidly flowed from the main scarp forming ponds on the body of the landslide; artesian pressures existed below the lacustrine clays and high pressures also existed in the overlying colluvium (Bill Kappel, U.S. Geological Survey, 1994, written commun.).

The filling and rapid emptying of Pleistocene glacial lakes could have triggered landslides in the Finger Lakes region. Raising ground water levels within a hillside during filling of a lake increases pore-water pressures and reduces slope stability (Jones et al., 1961). Likewise, rapid drawdown of a lake can temporarily reduce slope stability by temporarily leaving high pore pressures in slow draining materials (Lambe and Whitman, 1969; Schuster, 1979). The initial filling of Yellowtail Reservoir, Montana, and the Panama Canal, were cited by Lane (1967) as examples in which large landslides were triggered by initial raising of the water levels on natural or cut slopes. The Mayunmarca landslide of April 25, 1974, blocked the Mantaro River in Peru, and the rising water level behind the landslide dam triggered landslides along the shores of the lake (Lee and Duncan, 1975). Sudden breaching of the Mayunmarca landslide dam and rapid drawdown of the lake level triggered additional landslides along the banks of the lake (R.L. Schuster, 1992, U.S. Geological Survey, personal commun.). Drainage of some glacial lakes in the Finger Lakes region was believed to be quite rapid as evidenced by channels cut into bedrock (Hand, 1978).

The Valley Heads recession began by 14,000 BP (Muller and Calkin, 1993). Landslides triggered by rising or falling glacial lake levels in the study area are Late Pleistocene in age, the youngest lakes in this area being gone by about 12,000 years BP (Fullerton, 1980). Consequently, the oldest landslides in the study area probably date to a maximum of about 14,000 years BP, after the beginning of northward retreat of the ice margin from the highest elevations in this region. Any older Pleistocene landslide features would probably have been greatly modified or totally removed by overriding ice. Landslides which occurred between about 10,000 and 14,000 yr BP have undergone subsequent slope modification under a presumably wet and cold Late Pleistocene climate which distinguishes them geomorphically from more recent Holocene landslides. These Late Pleistocene landslides are identified as ancient landslides (Plate 1) and are unlikely to be fully reactivated because the environmental conditions that led to their occurrence no longer exist. However, under certain conditions, such as undercutting by streams or construction, or by extreme climatic events, these ancient dormant landslides could be partially or fully reactivated.


Based on the landslide inventory, field observation and literature review we selected glacial clays, levels of former glacial lakes, and degree of slope steepness as the most significant independent or explanatory variables to use in a statistical procedure to model landslide susceptibility. We used a logistic regression analysis to model landslide susceptibility following the approach of Bernknopf et al. (1988), Shu-Quiang and Unwin (1992) and Dikau and Jä„ger (1994). Logistic regression is used to model the probability of a dichotomous variable, i.e. a dependent or response variable with only two possible values. A logistic regression can be compared with an ordinary least squares regression. The difference is that the logit-transformation, or logit, of the dependent variable is used. The logit transformation is calculated by log[p/(1-p)], p being the probability. In the case of a dichotomous variable ordinary least squares regression is not applicable because the requirement of constant variance of the error is not met in the case of a binary variable (Bahrenberg et al., 1992). A logistic regression eliminates this problem by using the logit transformation. The analysis was performed on a tabulation of data that were digitized within a GIS, in our case the Geographic Resources Analysis Support System (GRASS ). For the statistical analysis and model derivation the Statistical Analysis System (SAS ) was used.

Basic Data

Landslide Inventory

An inventory of landslides within the Tully Valley study area (Plate1) was prepared from photointerpretation of 360 black and white areal photos taken in 1991 at a scale of 1:10,000. Some of the glacial landforms in the area, such as hummocky till deposits, can be misinterpreted for slope-movement deposits. Consequently, a field check of features interpreted as landslides was necessary and carried out during a two-week period in late April and early May of 1994. Each suspected landslide in the preliminary inventory was examined in the field for diagnostic features, such as a main scarp, toe, etc. (Varnes, 1978), and either verified or rejected. Approximately 10% of the suspected landslides were rejected in the field; another 10% additional landslides were added to the inventory, mostly small active/recently-active landslides that had occurred since the 1991 photos. A few small active/recently-active landslides were discovered in the field within the map area of Plate 1. but slightly outside the boundary of photo coverage; they are included on Plate 1, but are not utilized in the statistical analyses. Despite heavy vegetative cover, several large landslides that appeared questionable on the air photos were easily distinguishable in the field.

Plate 1: Landslide inventory map, based on aerial photography and field work. Landslide types are indicated by the following abbreviations: ef, earth flow, es, earth slide, ds, debris slide, df, debris flow, bs, block slide, mrs, multiple rotational slide, u, undefined. The study area represents the area covered by aerial photographs. Landslides outside the study area were not included for the susceptibility evaluation but are shown here for completeness. The numbers in the corners are UTM eastings and northings.
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Based on field inspection the type and relative age of each landslide was categorized. We chose three relative age classes: active/recently active, old, and ancient. While there are no explicit rules for classification of landslide age, the decisions can be based on the degree of modification of typical landslide features (McCalpin, 1984; Wieczorek, 1984; Jibson and Keefer, 1988). No absolute dating, such as historical documentation, dendrochronology or radiocarbon-age determination was available for calibration of landslide age in this area. The chronological age of active/recently-active landslides probably ranges from currently active during this season (zero years) to an arbitrary age of about 200 years, approximately representing the period of historical occupation in this area. The chronological ages of old (200-10,000 years) and ancient (10,000- 14,000 years) were selected to distinguish Holocene from Late Pleistocene features.

The inventory includes 73 total landslides, of which 22% (16) were classified as active/recently-active, 52% (38) fall in the category old, and 26% (19) are termed ancient. The proportion of area covered by the different landslide age groups differs significantly from the frequency (Table 1). Ancient landslides covered the largest proportion of area, 227 hectares (562 acres), 170 hectares (421 acres) are covered by old landslides and only 60 hectares (150 acres) by recent/recently-active landslides. Old and ancient landslides (87% of the combined landslide area) are more significant than active/recently-active landslides (13% of landslide area) to the susceptibility model because the statistical analyses are based on areal extent.

Landslide Age Group Number % (Landslide area)
active/recently active 16 13
old 38 37
ancient 19 50

Table 1: Distribution of landslide age by number and percentage of total area involved in landsliding.


Digital Elevation Model (DEM)

U.S. Geological Survey Digital Elevation Models (DEMs) of the eastern half of the Elmira 2ø sheet provided the topography in the form of a regularly spaced 90-m array of elevation points. A 30-m spacing available from DEMs of 7.5' quadrangles would have been preferable to use for developing the susceptibility model, but was not available for the study area.


Soil information within Onondaga County was acquired from maps made at a scale of 1:20,000 by the Soil Conservation Service of the United States Department of Agriculture (USDA, 1977). Soil descriptions that accompanied these maps identified those soil series which had a high clay content in the C-horizon related to glacial lake deposits. Since this soil mapping only characterizes the upper one to two meters (3-6 feet), deeper soils are not represented. If clay underlies several meters of colluvium, as in the case of the Tully Valley landslide, the clay would not be identified on the maps. Despite this shortcoming, the maps were useful because they showed the minimum spatial extent of clay deposits (Plate 4). The following clayey soil series were included: Collamer Series, Lakemont Series, Niagara Series, Odessa Series, Rhinebek Series, Shoharie Series and Williamson Series (USDA, 1977). In the development of the susceptibility model the soil layer is treated as a single category, i.e. a binary variable: (1) for glacial clays or (0) for any other soil.

Plate 4 Map
Plate 4:
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Slope angle

Slope steepness is a major contributing factor to the occurrence of landslides closely related to many other factors including the soil thickness, climate, hydrology, lithology, structure and geomorphic history. Most assessments of regional landslide hazard (potential, susceptibility, or probability) utilize slope angle as one of several important independent variables (Brabb et al., 1972; Carrara, 1983; Campbell and Bernknopf, 1993; Dikau and Jäger, 1994). A categorization of slope steepness (0-5°; 6-10°; 11-15°; 16-25°; and >25°) for the study area was determined using GRASS and displayed using ARC/INFO in Plate 3.

Plate 3 - A map of slope steepness
Plate 3:
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In our statistical analyses slope steepness is not introduced as a slope angle value but as a failure rate relative to particular selected slope categories. Failure rate analysis (Aniya, 1985) compares the relative frequency of an attribute within different population subsets. The failure rate is obtained by dividing each relative frequency for a terrain factor such as slope angle class for landslide and non-landslide cells (% area of slope class with landslides / % area of slope class without landslides). A value greater than 1 indicates that the particular attribute contributes to landsliding. For example (Table 2), for the slope category of 11-15° there would be 1660 cells with landslides for every 1000 cells without landslides (failure rate= 1.66).

We analyzed five slope classes (0-5°; 6-10°; 11-15°; 16-25°; and >25°) for failure rate values for each separate age class of landslides and for the combined data set of all landslides. Although the failure rate values for each age category are given in Table 2, only the failure rate values for the group of all landslides were used subsequently to calculate the susceptibility model because the sample size of landslides in each individual age group was too small to give statistically significant results. The two slope categories 6-10° and 11-15° each had failure rates significantly higher than one, indicating that landslides are overly represented on these slopes. The failure rates in Table 2 also suggest that ancient landslides tend to have occurred on steeper slopes than old and recent landslides, possibly related to the presence of glacial lakes.

Slope angle category All landslides Active/recently active Old Ancient
0-5° 0.7 0.67 1.01 0.48
6-10° 1.54 1.75 1.13 1.79
11-15° 1.66 1.56 0.83 2.31
16-25° 1.18 0.75 0.42 1.85
> 25° 0.0 0.0 0.0 0.0

Table 2: Failure rate values of slope categories for different landslide age groups. No landslides occurred on slopes steeper than 25 degrees.

The results in Table 2 also show that for the entire landslide sample within the Tully Valley study area the steepest slopes are not necessarily the most prone to landsliding and suggest other additional factors influence slope stability.

The accuracy of slope angles measured from a DEM depends
largely upon the spacing; in general, the closer the spacing, the better the slope angles will reflect ground truth. The relatively large 90-m spacing of the DEM available for the Tully Valley study area limits the accuracy of the measured slope angles; as a result, slope values are averaged over large distances and tend to be lower than would be measured in the field within any given cell.


The model was calculated using the CATMOD procedure of the SAS software package (SAS Institute Inc., 1989). CATMOD is a procedure for modeling categorical data and can be used for logistic regression models and log-linear models. The CATMOD software determined the parameter estimates (Table 3) for the logistic regression model.

Table 3: Parameter estimates used to calculate the probability for each cell to be effected by landslides.
FACTOR           CLASS            PARAMETER
                 (failure rate)   ESTIMATE

Slope             0- 5° (0.7)       0.252
Category          6-10° (1.54)     -0.611
                 11-16° (1.66)     -0.693
                 16-25° (1.18)     -0.149
                   >25° (0)         1.201

Soils             1 (clay)          0.435

Otisco-Cardiff    1                 0.449
Heath Grove       1                 0.285
First Lake        1                -0.075

Intercept                          4.2628

The parameter estimates were tested for their significance using the Chi-Square test and were found to be significantly different from zero at a 99% confidence level. The parameter estimates determined in the model included an intercept and a value for each independent variable. The model took the form:

logit=b0+ bSOILS+ bOCL+ bHGL+ bFLM+ bSLOPEFR,

where b0=4.2628 and bi=parameter estimate. There are a limited number of possible combinations (40) of the explanatory variables-soils, lake levels and slope categories. Using the estimated parameters the logit was calculated for the possible combinations. The probability for no landslide was then calculated by p=elogit/(1+elogit) for each row in the contingency table (Table 4).

Table 4: Contingency table of probability values (PR0B) and susceptibility categories (SUSCEPT) for each factor combination. OBS: consecutive observation number, SOILS: soils layer, OCL: Otisco-Cardiff Lake, HGL: Lake Heath Grove, FLM: First Lake Marietta, and SLOPEFR: slope failure rate.
 1     0    0   0   0       0   0.00141    low
 2     0    0   0   0     .70   0.00365    low
 3     0    0   0   0    1.18   0.00544    low
 4     0    0   0   0    1.54   0.00861    low
 5     0    0   0   0    1.66   0.00934    low
 6     0    1   0   0       0   0.00346    low
 7     0    1   0   0     .70   0.00891    low
 8     0    1   0   0    1.18   0.01325    low
 9     0    1   0   0    1.54   0.02086    moderate
10     0    1   0   0    1.66   0.02260    moderate
11     0    1   1   0       0   0.00611    low
12     0    1   1   0     .70   0.01564    low
13     0    1   1   0    1.18   0.02319    moderate
14     0    1   1   0    1.54   0.03630    moderate
15     0    1   1   0    1.66   0.03928    moderate
16     0    1   1   1       0   0.00527    low
17     0    1   1   1     .70   0.01351    low
18     0    1   1   1    1.18   0.02004    moderate
19     0    1   1   1    1.54   0.03144    moderate
20     0    1   1   1    1.66   0.03403    moderate
21     1    0   0   0       0   0.00337    low
22     1    0   0   0     .70   0.00867    low
23     1    0   0   0    1.18   0.01290    low
24     1    0   0   0    1.54   0.02031    moderate
25     1    1   0   0       0   0.00823    low
26     1    1   0   0     .70   0.02100    moderate
27     1    1   0   0    1.18   0.03105    moderate
28     1    1   0   0    1.54   0.04839    moderate
29     1    1   0   0    1.66   0.05231    high
30     1    1   1   0     .70   0.03655    moderate
31     1    1   1   0    1.18   0.05363    moderate
32     1    1   1   0    1.54   0.08250    high
33     1    1   1   0    1.66   0.08892    high
34     1    1   1   1     .70   0.03165    moderate
35     1    1   1   1    1.18   0.04655    moderate
36     1    1   1   1    1.54   0.07190    high
37     1    1   1   1    1.66   0.07757    high

Factor combinations not listed had a zero frequency (hence no probability value). For example, (observation 34 in Table 4) for a cell of glacial clay below levels of all three proglacial lakes within a slope category of 0-5°, the logit and probability are calculated as follows:

logit= 4.2628 - 0.4350 - 0.4486 - 0.2850 + 0.0745 + 0.2520 = 3.4207

p (no landslide) = (e3.4207)/(1+e3.4207) = 0.9683

p (landslide) = 1 - 0.9683 = 0.0317

Note that the negative values of the parameter estimates are used for the soils and lake levels, but not for the slope category which is a continuous independent variable. Based on the probability values, the map (Plate 2) is categorized into three susceptibility classes - low, moderate and high with numerical ranges of probability and respective percentage of area indicated in Table 5.

Plate 2 - Landslide susceptibility map

Plate 2: Landslide susceptibility map based on a logistic regression model. The model is based on a failure rate value for slope steepness, the occurrence of glacial lake clays and three proglacial lake levels (see Text).
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Table 5: Probability ranges, susceptibility categories and extent of aerial coverage within each susceptibility category.

Probability Susceptibility  Study Area (%)

  < 0.02        low              83.75
0.02-0.05     moderate           16.00
  > 0.05        high              0.25


A statistical model was developed to represent the susceptibility to landsliding in a study area in part of Onondaga County, New York. This model represents the spatial probability of landslides, but gives no indication of temporal probability. The resulting map shows the likelihood of being on landslide prone terrain due to landslides of any age. The model represents landslide susceptibility in soils and on slopes similar to the 1993 Tully Valley landslide. Slope steepness, soil type, and previous glacial lake levels were accounted for in a logistic regression model. A very small percentage of the study area (0.25%) falls within the 'high' susceptibility category; however, in addition to the Tully Valley landslide, small recently- active landslides in this area have resulted in property damage, suggesting that landslides are an important hazard and potential risk. This susceptibility map can be used for landslide hazard mitigation by identifying landslide prone areas allowing for avoidance through zoning or other land use policy decision or by engineering mitigation.

With the exception of the Tully Valley landslide, active/recently- active landslides examined in the area were relatively small. Large landslides, similar to the Tully Valley landslide, were probably triggered by extreme hydrologic events. The majority of ancient landslides are large and probably were triggered by rising or rapidly falling proglacial lakes or by climatic extremes of high precipitation during the Late Pleistocene. Human activity, such as slope modification in the form of cutting slopes, have also resulted in small recently-active landslides in conjunction with seasonal rainfall.

This landslide susceptibility map can be used as a planning tool but is not recommended for individual site specific evaluations. Areas within the moderate and high susceptibility categories should require further study by engineering geologists before development to determine the extent of possibly unstable conditions.


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Stefan Jäger
Dept. of Geography,
University of Heidelberg
Im Neuenheimer Feld 348
69120 Heidelberg, Germany

Gerald F. Wieczorek
US Geological Survey,
922 National Center
Reston, VA 20192 logo  Take Pride in America button