Map and map database of susceptibility to
slope failure by sliding and earthflow
in the Oakland area, California

By Richard J. Pike, Russell W. Graymer, Sebastian Roberts,
Naomi B. Kalman, and Steven Sobieszczyk



Pamphlet to accompany

Miscellaneous Field Studies Map MF-2385

Version 1.0














2001

U.S. Department of the Interior
U.S. Geological Survey

CONTENTS

Abstract	2
Introduction	3
Acknowledgments	3
Oakland's landslide hazard	3
Prior mapping of susceptibility	4
Scope, assumptions, method	5
Input to the susceptibility model	6
	Geology	6
	Old landslide deposits	6
	Recent landslides	10
	Topography and slope	11
A model of landslide susceptibility	13
	Spatial frequency of prior failure	13
	Susceptibility between old landslide deposits	16
	Susceptibility within old landslide deposits	16
	Tests of the susceptibility model	19
The susceptibility map	21
Applications and limitations	23
Future research	23
References cited	25
Appendix - the digital map database	32
	Introduction	32
	Spatial resolution	33
	Overview of the digital database	33
	Database contents	34
	Revisions	35
	Obtaining the database files	35
	Opening the database files	36
	Importing the ARC export files	37


ABSTRACT
Map data that predict the varying likelihood of landsliding can help public 
agencies make informed decisions on land use and zoning.  This map, prepared in a 
geographic information system from a statistical model, estimates the relative 
likelihood of local slopes to fail by two processes common to an area of diverse 
geology, terrain, and land use centered on metropolitan Oakland.  The model 
combines the following spatial data: (1) 120 bedrock and surficial geologic-map 
units, (2) ground slope calculated from a 30-m digital elevation model, (3) an 
inventory of 6,714 old landslide deposits (not distinguished by age or type of 
movement and excluding debris flows), and (4) the locations of 1,192 post-1970 
landslides that damaged the built environment.  The resulting index of likelihood, 
or susceptibility, plotted as a 1:50,000-scale map, is computed as a continuous 
variable over a large area (872 km2) at a comparatively fine (30 m) resolution.  
This new model complements landslide inventories by estimating susceptibility 
between existing landslide deposits, and improves upon prior susceptibility maps 
by quantifying the degree of susceptibility within those deposits.  
Susceptibility is defined for each geologic-map unit as the spatial frequency 
(areal percentage) of terrain occupied by old landslide deposits, adjusted locally 
by steepness of the topography.  Susceptibility of terrain between the old 
landslide deposits is read directly from a slope histogram for each geologic-map 
unit, as the percentage (0.00 to 0.90) of 30-m cells in each one-degree slope 
interval that coincides with the deposits.  Susceptibility within landslide 
deposits (0.00 to 1.33) is this same percentage raised by a multiplier (1.33) 
derived from the comparative frequency of recent failures within and outside the 
old deposits.  Positive results from two evaluations of the model encourage its 
extension to the 10-county San Francisco Bay region and elsewhere.  A similar map 
could be prepared for any area where the three basic constituents, a geologic map, 
a landslide inventory, and a slope map, are available in digital form.  Added 
predictive power of the new susceptibility model may reside in attributes that 
remain to be explored-among them seismic shaking, distance to nearest road, and 
terrain elevation, aspect, relief, and curvature.
 
INTRODUCTION


In the decade since the 1989 Loma Prieta earthquake devastated California's San 
Francisco Bay region, some 350 natural disasters nationwide have resulted in aid 
of over $20 billion at the Federal level alone.  This assistance has become so 
costly that mitigation, rather than post-disaster response, is regarded as the way 
to break the spiraling damage-and-loss cycle (Mileti, 1999; Platt and others, 
1999).  With Congressional support, the Federal Emergency Management Agency (FEMA) 
established a pre-disaster mitigation fund to create Project Impact, a partnership 
between government agencies and the private sector to develop disaster-resistant 
communities throughout the country (FEMA, 1998).  By applying scientific expertise 
and information, the project seeks to reduce the escalating financial and human 
losses that result from repeated natural disasters.  The city of Oakland was 
chosen as one of the first seven pilot communities for Project Impact (fig. 1, on 
map), and adjacent Berkeley followed in 2000.  The U.S. Geological Survey (USGS) 
joined the California Office of Emergency Services (OES), the California Division 
of Mines and Geology (CDMG), and FEMA in a partnership to increase the resistance 
of this urban area to rainfall-induced and seismically generated landslides and 
other types of earthquake hazards.
Slope failure is an expensive problem across the 10-county San Francisco Bay 
region (Nilsen, Taylor, and Dean, 1976).  Damage from landslides alone in the El 
Nio storms of 1997-98 totaled $160 million in direct costs (about $20 million in 
Oakland's Alameda County) and several times that amount in indirect costs, such as 
lost business and wages and increased commuting distances and road-maintenance 
budgets (Pike and others, 1998; Godt, 1999).  Only the epicentral location of the 
Loma Prieta earthquake in a thinly populated area during a dry season prevented 
equally serious losses from landsliding.  As development of San Francisco Bay area 
hillsides continues and land values rise accordingly, the economic consequences of 
slope instability will increase.  Moreover, public policy on landslide hazard and 
risk is so primitive that insurance against most slope failures is not available 
and the disputes that arise tend to be resolved only through litigation 
(Stromberg, 1967; Olshansky and Rodgers, 1987; Howell and others, 1999).  As the 
basis for mitigating the landslide hazard, most land-use planning currently relies 
on either a simple engineering model of slope stability or maps showing ground 
slope and locations of previous failures.  
One obstacle to better public policy is the scarcity of maps showing the 
likelihood of landsliding over large areas at medium to fine scales (1:50,000 to 
1:5,000).  Such maps are rare because slope instability-like earthquake and other 
hazardous natural phenomena (Oreskes, 2000)-is very difficult to predict in space 
as well as time (Haneberg, 2000).  Until recently, maps that delimit the probable 
extent of flooding or earthquake shaking in the San Francisco Bay region have had 
few counterparts that delineate the landslide hazard.  Only a generalized map of 
the entire Bay area (Nilsen and Wright, 1979) and one county-scale map (Brabb and 
others, 1972) have been prepared by USGS.  Medium-scale maps by CDMG outline a 
single hazard zone for earthquake-triggered landslides and carry a regulatory 
mandate but provide little detail (Wilson and others, 2000).  Finer-scale, more 
detailed CDMG maps indicate areas of potential rainfall- or seismically-induced 
landsliding and general slope instability but cover small areas (Haydon, 1995; 
Wills and Majmundar, 2000).  
This report describes an experiment to develop a quantitative technique for 
estimating the relative predisposition, or susceptibility, to certain types of 
landsliding over large areas.  The result is a susceptibility map of the extended 
Oakland Project Impact area and surrounding hillsides, where slope failure has 
long posed a hazard (Beaty, 1956; Stromberg, 1967; Nilsen, Taylor and Brabb, 1976; 
Nilsen and Wright, 1979; Coe and others, 1999).  Pike and others (1999a, b) 
reported earlier results from this experiment, and other USGS work for Project 
Impact in the East Bay has evaluated methods to predict landslides triggered by 
earthquakes on the northern Hayward Fault (Miles and Keefer, 2000).  The 
information generated by these studies will assist CDMG, FEMA, OES, and other 
public agencies in making informed planning and zoning decisions for the San 
Francisco Bay region.


ACKNOWLEDGMENTS
Reviews by Kevin Schmidt, Earl Brabb, and Scott Graham and remarks by Mark Reid 
improved drafts of this report.

OAKLAND'S LANDSLIDE HAZARD


Excluding water, the greater Oakland area examined here occupies 872 km2 on the 
east side of San Francisco Bay, in the Coast Ranges province of central California 
(fig. 1).  The study area comprises seven 7.5-minute quadrangles: Richmond, 
Briones Valley, Oakland East and West, Las Trampas Ridge, San Leandro, and 
Hayward.  Climate and the remaining native vegetation are Mediterranean, the 
highest elevation is 617 m, and slopes at 30-m resolution are as steep as 50 on 
bedrock outcrops.  The northeastern three-fourths of the area is in steep upland 
ridges and valleys, conspicuously elongated NW-SE and underlain by Mesozoic to 
upper Tertiary sedimentary rocks (Graymer, 2000).  The active Hayward Fault Zone 
separates the upland (also cut by many small faults) from a flat-lying alluvial 
plain that borders San Francisco Bay to the southwest.  Quaternary deposits veneer 
the floors of the larger inland valleys and overlie down-dropped upland rocks in 
the lowland plains (Helley and Graymer, 1997a, b).  The bordering lowland is 
almost completely urbanized; much of the gentler upland also is settled, mainly in 
suburbs, which continue to spread into the hills.  The study area is primarily 
residential land (34 percent), forest (28 percent) and rangeland (15 percent); the 
balance is in diverse urban uses (Association of Bay Area Governments (ABAG), 
1996).
Terrain forms and material deposits created by landsliding (Varnes, 1978) are 
prominent in the hills in and east of Oakland (Radbruch and Weiler, 1963; Radbruch 
and Case, 1967; Radbruch, 1969).  Most of the large landslide deposits that have 
been mapped in the area (Nilsen, 1975) are presumed to have resulted from 
earthflow and translational or rotational sliding.  Both types of failure involve 
surficial mantle and bedrock down to depths of a meter to over 50 m, distort the 
ground surface when they move, and remain as recognizable masses that can persist 
for thousands of years (Varnes, 1978; Nilsen and Wright, 1979; Keefer and Johnson, 
1983; Wentworth and others, 1997).  Although slides and earthflows usually move 
slowly and thus seldom threaten life directly (for a Bay region exception, see 
Cotton and Cochrane, 1982), they can cause serious property damage.  When they 
move, in response to such changes as increased pore-water pressure, earthquake 
shaking, added load (graded fill), or removal of downslope support (road cuts), 
they can offset roads, destroy foundations, and break underground gas pipes and 
water mains as well as override property.  By landslides or slides in this report 
we mean only these larger failures and their deposits, at least 60 m in the 
longest dimension and covering a few hectares to several square kilometers.
This report does not address debris flow, a process reflecting a qualitatively 
different set of geologic, hydrologic, topographic, and meteorological controls 
and commonly leaving deposits less than 60 m across.  This exception has important 
public-safety implications, because areas that may not be highly prone to sliding 
and earthflow may be quite susceptible to debris flow, and vice-versa (Haydon, 
1995).  Commonly termed "mudslides", debris flows are small, shallow mixtures of 
water, soil, and other debris that mobilize suddenly during locally heavy rains 
(Ellen, 1988; Dietrich and others, 1993; Wilson and Jayko, 1997) and flow rapidly 
downslope.  Although debris-flow deposits can be extensive and persistent, most 
failures leave thin, ephemeral fans that do not markedly distort the ground 
surface (Varnes, 1978).  Over time, debris flows may recur in the same location.  
They are common in the Oakland hills and elsewhere around San Francisco Bay but 
were not identified explicitly in the set of old landslide deposits analyzed here.  
Because their recognizable features are quickly obscured by vegetation growth, few 
debris flows are shown on inventory maps prepared before the San Francisco Bay 
region's severe 1982 winter (Ellen, 1988).


PRIOR MAPPING OF SUSCEPTIBILITY


Because one clue to the location of future landsliding is the distribution of past 
movement, maps that show landslide deposits are helpful in predicting the hazard 
(Liang and Belcher, 1958; Cleveland, 1971; Nilsen, Taylor, and Dean, 1976; Nilsen 
and Wright, 1979; Cotton and Cochrane, 1982; Wieczorek, 1984; Haydon, 1995; 
Atkinson and Massari, 1998; Baum and others, 1998; Jennings and Siddle, 1998).  
Such inventory maps do not necessarily distinguish recent movements, but in any 
one year some of the mapped slides-or more commonly, portions of them-may become 
active (Baum and others, 1998; Jayko and others, 1998; Keefer and others, 1998).  
New landslides in previously unfailed areas also occur, especially where the 
terrain has been modified by grading (Wentworth and others, 1987).  Landslide 
deposits are well mapped in the San Francisco Bay region.  Over 70,000 old 
(historic but undocumented) and ancient (prehistoric) landslide masses 
(collectively, hereafter, designated "old") are shown on mostly 1:24,000-scale 
inventory maps by USGS and CDMG that individually cover small portions of the 
region (Nilsen and Wright, 1979; Pike, 1997; a few of the approximately 90 maps 
have scales of 1:12,000 and 1:62,500).  These paper maps are being recompiled by 
USGS as computer databases at 1:24,000 scale and released for each county as 
1:62,500-scale plots (Roberts and others, 1998, 1999).  Digital coverage is not 
yet available for the entire Bay area.
A landslide inventory reveals the extent of past movement and the probable locus 
of some future activity within those slide deposits, but it does not indicate the 
likelihood of failure for the much larger area between mapped deposits (Brabb, 
1995).  Blanc and Cleveland (1968) were among the first to devise a spatially 
complete expression of the landslide hazard for an area in southern California.  
Based on experience with slope failure, they qualitatively synthesized several of 
the major controls on slope stability and mapped eight estimated degrees of site 
susceptibility.  Blanc and Cleveland's second innovation was to rank the area's 
geologic units in four groups of decreasing susceptibility and to assign existing 
landslide deposits to the highest rank.  Van Horn (1972) advanced the concept, 
incorporating four classes of ground slope and adding terrain other than mapped 
landslide masses to the highest of four categories of stability.  Because they 
rely on personal expertise, both maps are subjective and difficult to compare; the 
results are not repeatable nor the procedures of compilation transferable.  
Cleveland (1971) advocated a more quantitative approach to synthesizing multiple 
controls on slope failure and listed several candidate variables.  Earlier, Jones 
and others (1961) had statistically correlated landsliding in northeast Washington 
with three of these site factors: geologic materials, terrain slope, and moisture 
content.  
Efforts by CDMG to map areas of potential slope instability as a guide to land 
development in the San Francisco Bay region date to Rogers and Armstrong (1971).  
These relative geologic stability (now relative landslide potential) maps 
typically show four zones of varied susceptibility that carry no regulatory 
mandate (Wills and Majmundar, 2000).  The zones were interpreted qualitatively by 
synthesizing field-based studies of rock type, geologic structure, topography, and 
prior landslide activity as well as potential seismic and rainfall trigger 
mechanisms.  Four such maps cover about half the Oakland Project Impact area 
(Bishop and others, 1973; Haydon, 1995, Majmundar, 1996a, b).  CDMG currently 
publishes landslide-zone maps funded by FEMA and OES under the Seismic Hazards 
Mapping Act (CDMG, 1997) to accompany maps of earthquake shaking (Haydon and 
others, 2000) and liquefaction (Petersen and others, 2000).  Determined in part by 
adapting methods developed at the USGS, the map by Wilson and others (2000) 
depicts a single discontinuous zone subject to seismically-induced landsliding for 
part of the Project Impact area.  This map is an official document that requires 
site mitigation prior to any development, as defined in Public Resources Code 
Section 2693c (CDMG, 1997).  
In the earliest USGS attempt to estimate the landslide hazard continuously over a 
region, Brabb and others (1972) extended the concept of Blanc and Cleveland (1968) 
by combining the distribution of old slide deposits, bedrock map-units, and ground 
slope in a semi-quantitative model of landslide susceptibility (Brabb, 1995).  
Their test area was the entire San Mateo County, across San Francisco Bay from 
Oakland.  Newman and others (1977) automated this three-parameter model, and Roth 
(1983) found its results internally consistent.  Using the same three variables 
and the overlay technique pioneered by McHarg (1969), Nilsen and Wright (1979) 
manually compiled a generalized susceptibility map for the nine-county San 
Francisco Bay region (less Santa Cruz Co.) at 1:125,000 scale.  Whatever their 
virtues, however, none of the maps described above were compiled from high-quality 
slope data or distinguished the varied likelihood of future failure within mapped 
slide deposits.
Geographic information systems (GIS) technology has freed susceptibility modeling 
from many of its qualitative limitations, and the computer-mapping of landslide 
susceptibility has spread worldwide (Aniya, 1985; Carrara and others, 1992, 1995; 
Brunori and others, 1996; Fernndez and others, 1996; Soeters and van Westen, 
1996; van Westen and others, 1997; Cross, 1998; Dhakal and others, 2000).  
Although the multi-parameter estimation of susceptibility now is common, its 
effectiveness is limited in four respects: (1) the resulting maps usually cover 
small areas (Mora and Vahrson, 1994; Miller, 1995; Massari and Atkinson, 1999); 
(2) the maps require detailed information, some of it on many parameters, that can 
not be obtained economically for large areas (Jger and Wieczorek, 1994; Miller, 
1995; Fernndez and others, 1996); (3) the maps express susceptibility 
qualitatively, on an ordinal (high, moderate, low) scale (Aniya, 1985; Hylland and 
Lowe, 1997; Turrini and Visintainer, 1998); and (4) the maps lack transparency, in 
that they result from complex computer-analyses wherein input parameters can be 
difficult to correlate with landslides occurrence and other field observations 
(Dhakal and others, 2000).  The experiment reported here addresses these 
shortcomings.

SCOPE, ASSUMPTIONS, AND METHOD


In this report we develop a numerical index of landslide susceptibility and map it 
over an extensive area at a comparatively high resolution.  The empirical model 
estimates relative susceptibility of a site to the two types of deep failure that 
are most easily recognized from aerial-photograph surveys of large areas of the 
San Francisco Bay region (Nilsen, 1975): slides and earthflows or a combination of 
them (Varnes, 1978).  Our approach improves the statistical method of Brabb and 
others (1972), adapts it to 100 percent digital implementation, and calculates 
susceptibility as a continuous variable.  The three essential sets of data-maps 
showing geologic units, evidence of old and recent landsliding, and terrain slope-
exist within USGS in machine-readable form or require little data-gathering beyond 
digitizing existing maps of landslide deposits.  One expression of the new index 
number, this map, is a possible prototype for quantitative landslide-
susceptibility coverage of the 10-county San Francisco Bay region.  The 
statistical approach should be applicable to any area for which the three sets of 
data are available.
Because no guidelines for mapping the regional likelihood of deep-seated 
landsliding have been established, all susceptibility models are based on 
assumptions-in this case six.  They follow from our realization that waiting for 
the data ideally suited to treating the problem as an engineering exercise would 
delay, perhaps indefinitely, the preparation of this map for Oakland (Cleveland, 
1971).  Pike and others (1994) reached a similar conclusion in mapping the 
susceptibility of earthquake-induced soil liquefaction in Monterey County from 
available USGS data.  A more physically based model of landsliding in Oakland 
would require geotechnical measurements of the material properties of bedrock and 
surficial mantle as well as better definition of the surficial mantle (Neuland, 
1976; Wieczorek, 1984; Ellen and Wentworth, 1995; Miller, 1995).  Geotechnical 
data are unlikely to be acquired in the necessary detail for each geologic unit 
(many of which are heterogeneous) over the large area required for a regional 
assessment of susceptibility.  Our six assumptions, however uncertain, are as 
follows:
(1) Failures under climatic conditions that differ from those now prevailing can 
be manipulated statistically to suggest the locus of future landsliding.
(2) The areal percentage of past failure in a geologic unit reflects the 
cumulative effects of physical conditions and processes that induce landsliding.  
(3) Deposits from past landslides may be used to infer the location of both 
deposits and source areas of future failures.
(4) Geology, landslide deposits, and ground slope serve collectively as a proxy 
for engineering data to estimate susceptibility.
(5) The susceptibility of old landslide deposits to later movement exceeds the 
susceptibility of areas between them.  
(6) The higher likelihood of future movement within old deposits can be estimated 
from an analysis of recent failures.
Our method departs from that pioneered by Brabb and others (1972) in several 
respects.  We subdivide each geologic unit into much finer, one-degree, intervals 
of ground slope, and calculate susceptibility for each interval.  Our topographic 
data also are more accurate and our unit-cell size is smaller.  Like Brabb and 
others (1972) we treat old landslide deposits differently from the terrain between 
them, but rather than assigning the maximum susceptibility value to the entire 
mass (Blanc and Cleveland, 1968), we derive a range of values for the cells in old 
deposits-by using recent failures to model within-slide susceptibility (Sls) as a 
variant of that between deposits (S).
To prepare the statistical map of susceptibility we divided the land portion of 
the Oakland study area into 969,003 rectangular cells 30 meters on a side, 
corresponding to spacing of the gridded elevation array (Graham and Pike, 1997, 
1998a), and created a series of constituent raster-grid maps at this resolution.  
The digital maps are of geology, presence or absence of old landslide deposits, 
ground slope, and a point-location sample of documented post-1970 landslides.  
Similar digital maps were compiled for the adjacent Niles quadrangle, where the 
lithology is similar to that in the study area, to test the susceptibility model.  
Calculations were carried out in the GRID module of version 7.1.2 of ARC/INFO, a 
commercial GIS, on a SUN/Solaris UNIX computer, using a two-step algorithm written 
in Arc Macro Language.


INPUT TO THE SUSCEPTIBILITY MODEL
Geology


Hillside materials are a major, if not the dominant, site control over the larger 
and deeper types of slope failure worldwide (Pachauri and Pant, 1992; Mora and 
Vahrson, 1994; Hylland and Lowe, 1997; Cross, 1998; Massari and Atkinson, 1999; 
Irigaray and others, 1999; Dhakal and others, 2000).  In the San Francisco Bay 
region, the varied prevalence of landsliding with rock type and geologic structure 
is well documented (Cleveland, 1971; Brabb and others, 1972; Nilsen, Taylor, and 
Dean, 1976; Bechini, 1993; Haydon, 1995).  This control is evident in the greater 
Oakland area (table 1), for example, where landslides are conspicuous in two major 
geologic units with high clay content, the Miocene Orinda (Tor) and Briones (Tbr) 
Formations, but rare in two other major units, the Cretaceous Oakland Conglomerate 
(Ko) and the Redwood Canyon Formation (Kr) (Radbruch and Case, 1967; Radbruch, 
1969; Keefer and Johnson, 1983; Bechini, 1993).  
The map area is underlain by 120 diverse lithologic units.  Figure 2 (on map), a 
36 km2 region within the Oakland East quadrangle, exemplifies the complex geology.  
The data on which our model is based are from a new digital database of revised 
geology, compiled at 1:24,000 scale, that integrates the published and unpublished 
maps of prior workers with new mapping and field checking (Graymer, 2000).  Rocks 
of the uplands in and east of Oakland comprise 100 bedrock units occupying 62 
percent of the study area.  They belong to several large fault-bounded blocks that 
contain unique stratigraphic sequences of typically middle-upper Mesozoic to upper 
Tertiary sedimentary rocks (Graymer, 2000).  The 100 hillside units (including the 
10 that are queried in table 1) range in areal extent from less than 0.01 km2 
(Tbgl, limestone member of the middle-upper Miocene Briones Formation) to 90 km2 
(Tus, unnamed upper Miocene sedimentary and volcanic rocks).  Old landslides occur 
so widely in these 100 hillside formations that their deposits are mapped in all 
except eight small units.  Of these eight units only one (fg, Franciscan Complex 
greenstone) occupies more than 450 30-m grid cells (table 1).  The 20 geologic 
units (one is queried) that are unconsolidated Quaternary deposits comprise 38 
percent of the study area, largely in flatland terrain, and contain few old 
landslides (table 1).  Only five of these 20 units host old failures: alluvial fan 
and fluvial deposits (Qpoaf, Qpaf, and Qhaf), Holocene stream channel deposits 
(Qhsc), and artificial fill (af).


Old Landslide Deposits


Evidence of past landsliding often is cited as an indicator of future movement in 
the Bay region (Cleveland, 1971; Nilsen, 1973a, b; Nilsen and Turner, 1975; 
Wieczorek, 1984; Haydon, 1995; Keefer and others, 1998).  Our susceptibility model 
is based on an inventory of over 6,700 landslide deposits compiled within the 
Project Impact area by T.N. Nilsen (Nilsen, 1973a, b; Nilsen, 1975).  Figure 3, on 
map, the same sub-area in the Oakland East quadrangle as figure 2, shows part of 
this inventory.  Nilsen mapped deposits, exclusive of the difficult-to-identify 
upslope crowns and head scarps, at 1:24,000 scale by reconnaissance 
stereointerpretation of aerial photographs, the most recent of which were taken in 
1968 at scales of 1:20,000 and 1:30,000.  Because slope processes, especially for 
older landslides, are not interpreted reliably from aerial photographs alone none 
of the deposits are attributed by type of movement (Varnes, 1978) or causal agent 
(rainfall- versus earthquake-induced).  Nor were landslide identifications 
verified by field checking.  Detailed, fine-scale maps that report the mode of 
failure for every slide mapped are rare in the San Francisco Bay region and cover 
very small areas (Radbruch and Weiler, 1963; Wieczorek, 1984).  More recent 
1:24,000-scale map inventories cover some of the study area but lie outside the 
Oakland and Berkeley city limits (Haydon, 1995; Majmundar, 1996a, b). 






Nilsen identified landslide deposits on the basis of landscape features known to 
characterize the various types of failure (Liang and Belcher, 1958; Ritchie, 
1958).  Among these features are (1) small isolated ponds, lakes, and other closed 
depressions; (2) abundant natural springs; (3) abrupt and irregular changes in 
slope and drainage pattern; (4) hummocky and irregular surfaces; (5) smaller, 
younger landslide deposits that form within older, larger deposits; (6) steep, 
arcuate scarps at the upper edge of the deposit; (7) irregular soil and vegetation 
patterns; (8) tilted trees and other signs of disturbed vegetation; and (9) 
abundant level areas within steep slopes.  Nilsen (1973a, b) noted fewer of these 
characteristics in smaller deposits.  Complex landslide masses, resulting from 
combinations of different styles of downslope movement, are perhaps the most 
common type mapped.  Deposits varied in appearance from clearly discernible, 
unweathered and uneroded topographic features to indistinct, highly weathered and 
eroded forms recognizable only by characteristic topographic configurations.  Many 
medium-size and all of the largest slide masses had coalesced from smaller 
deposits that Nilsen (1973a) did not identify individually.  Coalesced slides were 
mapped as one deposit and are so treated here.  Finally, because Nilsen mapped 
landslide deposits remotely and not all of his identifications were definitive, he 
ascribed two levels of confidence to his interpretations.  Deposits were queried 
where identification was uncertain. 
We compiled Nilsen's (1973a, b) landslide deposits as a digital database.  His 
original linework inked on 1:24,000-scale Mylar greenlines (Nilsen, 1975) was 
scanned (400 dots per inch), converted automatically from raster to vector form, 
imported into ARC/INFO, edited to remove all information except landslide 
polygons, and combined into one file.  The paper-map inventory had not been wholly 
edge-matched.  Where needed, we adjusted landslide-deposit outlines to align 
across adjacent quadrangles.  The few deposits designated by Nilsen as 
"identification uncertain", and so indicated in the database and by a different 
color on derivative maps (Roberts and others, 1999), were not treated differently.  
Because landslide inventories are inherently incomplete, our inclusion here of 
false positives-Nilsen's identifications, both certain and uncertain, that are not 
landslide deposits (T. McCrink, personal communication, 2000)-is more likely to 
make up the deficiency of other deposits missed by Nilsen than to exaggerate or 
otherwise misrepresent the true extent of slope failure.  



	             Table 2.  Prevalence of old landslide deposits, by type of land 
use

	         Land-use			     Number of	Percentage of each land-use
              category			   slide deposits	category that is slide 
deposit *

Forest land	3197	18 %
Rangeland	2246	26
Residential land	935	10
Vacant & mixed-urban open	217	8
Commercial	27	4
Education	26	5
Wetland	24	5
Infrastructure	13	3
Agriculture	8	3
Industrial	7	7
Extensive outdoor recreation	7	16
Sparsely vegetated	5	8
Military	2	5
Mixed residential-commercial	0	2
Mixed residential-industrial	0	2
Public institutions	0	1
Hospitals	0	0

 Association of Bay Area Governments (1996)
* limited to area most likely to fail-above 90m elevation and 10 slope







Nilsen's data well represent the extent of prior landsliding in the Project Impact 
area, despite errors in his inventory and its dissimilarities with other mapping.  
In addition to the operational constraints of time and airphoto availability, 
quality, scale, date, and season, landslide inventories are sensitive to variance 
in mapper experience and personal idiosyncrasies.  The topographic expression of 
unstable slopes is complex and subtle, and not all mappers apply the same criteria 
or interpret them similarly.  Some observers map both the landslide scar and the 
deposit rather than only the deposit; others excel at recognizing landslides only 
in certain types of bedrock, terrain, or vegetative cover.  These factors result 
in different inventories of the same area by different mappers.  To compare such 
differences numerically, we made a GIS analysis of the landslide-deposit 
inventories by Nilsen (1975) and Majmundar (1996b) for the Las Trampas Ridge 
quadrangle (fig. 1).  (This is not intended as an unbiased test; Majmundar used 
Nilsen's map and had later photos.)  Nilsen mapped 48 km2 of landslide deposits 
and Majmundar 39 km2, of which 35 km2 overlap.  Nilsen thus included 90 percent of 
the total area mapped by Majmundar plus another 13 km2, whereas Majmundar included 
72 percent of Nilsen's total area plus an added 4  km2.  Of the 2,196 individual 
deposits identified by Nilsen, 1,933 (88 percent) coincide at least partially with 
one of the 1,803 mapped by Majmundar, whereas 1,708 (95 percent) of Majmundar's 
deposits coincide with one of Nilsen's.
The 6,714 mapped landslide deposits occupy 12 percent of the study area, and 320 
of them lie within the Oakland city limit.  Most landslides are in hills to the 
northeast, cover 19 percent of this upland area (defined as the 100 non-Quaternary 
units in table 1), and cluster in elongate patterns that align with the regional 
NW-SE topographic trend.  The deposits vary in size from about 1,100 m2 to 4 km2 
and from 60 m to 5 km in the longest dimension for the largest of the many 
coalesced masses, although most are less than 100 m long (fig. 3).  Individual 
deposits range widely in average ground slope (2 to 44; mean approximately 20) 
and shape, complexity of planimetric form tending to increase with size from 
ellipses to convoluted outlines.  Estimated times of initial movement are 
uncertain and vary from 35 years to 100,000's of years before present, although 
elevated precipitation in the late Pleistocene has been hypothesized for smaller, 
shallow landslides in the San Francisco Bay area (Reneau and others, 1986).  Some 
slide deposits moved or continue to move over long periods, and many large masses 
host smaller recent landslides.  The presumed types of movement, translational and 
rotational block slide and earthflow, and contributing causes of instability, 
rainfall and seismic shaking, are undetermined for individual landslide deposits.  
Many past slope failures underlie areas of present human occupancy or land likely 
to be developed.  Although 81 percent of the 6,714 landslide deposits in the study 
area are located in forest and rangeland (much of it in parks) and thus pose a 
limited present or potential hazard, 935 deposits occupy approximately 15 percent 
of all residential areas (as of 1995) and at least 75 additional deposits occur in 
other urban areas (table 2).  The landslides in residential areas occupy 10 
percent of the region we believe most prone to instability, terrain above an 
elevation of 90 m and a slope of 10.


Recent Landslides


Portions of old landslide deposits in the San Francisco Bay region often are 
destabilized by earthquake (Keefer and others, 1998) or heavy rainfall (Pike and 
others, 1998).  Barring a return to the climatic conditions under which they 
formed, perhaps in the late Pleistocene (Reneau and others, 1986) or an earlier 
glacial-nonglacial transition, most large old slides that were triggered by high 
rates of precipitation are unlikely to again move in their entirety.  Smaller 
deposits and parts of large masses, however, frequently remobilize (Nilsen, 
Taylor, and Dean, 1976; Keefer and Johnson, 1983; Baum and others, 1998; Jayko and 
others, 1998), and for the purpose of hazard recognition should be considered 
dormant rather than relict (extinct).  Accordingly, old landslide deposits 
commonly are judged more susceptible to future movement than are unfailed areas of 
comparable slope that lie outside them (Nilsen and Wright, 1979; Cotton and 
Cochrane, 1982; Wieczorek, 1984; Haydon, 1995).  This potential for added risk 
needs to be incorporated into susceptibility estimates.  The magnitude of risk is 
difficult to determine, but can be approximated from the occurrence of recent 
movements within the older slides. 
Although landsliding remains common throughout the Project Impact area, the only 
available data from which to modify within-slide susceptibility are 1,192 post-
1970 rainfall-induced failures that damaged the built environment.  The 1,192 
landslides are recorded in 1:62,500-scale maps by Nilsen and Turner (1975) and 
Nilsen, Taylor, and Brabb (1976); unpublished information compiled by F.A. Taylor 
in the 1970's; and post-1998 El Nio observations for Alameda and Contra Costa 
Counties (Coe and others, 1999; Graymer and Godt, 1999)-931, 105, and 156 
landslides, respectively.  Because outlines of most of these failures were not 
recorded, their locations were compiled digitally as point data and assigned to 
the center of the nearest 30-meter cell (fig. 3).  Of the 1,192 post-1970 
landslides, 183 (15 percent) reactivated older deposits mapped by Nilsen (1975).                                                                          
Distribution of the 1,192 recent slides among the 120 geologic units largely 
mimics the varied prevalence of old landslide deposits (table 1), a consistency we 
judge sufficient for these recent data to be used in the model.  Although more of 
the post-1970 landslides are recorded in the larger units, which suggests some 
role of chance in their occurrence, table 1 shows that the pattern of slope 
instability is not random.  Thirty-six geologic units have six or more recent 
failures.  Of these 36 units, the five bedrock formations that host the largest 
numbers of post-1970 slides (totaling 40 percent of the 1,192 failures) all have 
high mean frequencies (>0.20) of old slide deposits (table 1): the widespread 
unnamed unit Tus (n=178 recent slides), upper and lower members of the Mulholland 
Formation (Tmll, n=85 slides; Tmlu, n=70), the Orinda Formation (Tor, n=83), and 
undivided Franciscan Complex mlange (KJfm, n=66).  These five susceptible 
hillside units, moreover, contain 60 percent of the 183 post-1970 failures that 
also occur in old landslide masses (see table 1).  Similarly, the three Quaternary 
flatland units that recorded the highest numbers of post-1970 failures-younger 
Pleistocene alluvial fan deposits (Qpaf, n=87 slides), older Pleistocene alluvial 
fan deposits (Qpoaf, n=26), and Holocene alluvial fan deposits Qhaf (n=38)-also 
are the three surficial units that have the most area in old landslide deposits 
(>250 cells per unit; see table 1).  Table 1 reveals further that only seven of 
the 183 recent slides that also occur on old slide deposits are in Quaternary 
geologic materials, all in Qpoaf, the most susceptible Quaternary unit.  Recent 
failures tend to be rare in the geologic units that contain the fewest old 
landslide deposits; none of the 28 units in table 1 that have six or fewer cells 
on old landslide masses hosted any post-1970 failures.

The 1,192 recent landslides, which damaged road cuts, graded fill, and other 
manmade works (Nilsen and Turner, 1975, p. 6-7), are not ideal data to use in 
modeling within-slide susceptibility, for in several respects they little resemble 
the slide deposits mapped previously.  These post-1970 landslides (many of which 
include scarps and crowns in addition to their deposits) are much smaller than 
most of the old slide masses mapped by Nilsen, none was generated by earthquake, 
the positional accuracy of those located from broad-scale maps exceeds the 30-m 
size of the grid cells, and-most important-the true incidence of fresh failures 
within old landslide deposits cannot be known because of an anthropogenic bias: 
hillside development increases the susceptibility of normally stable geologic 
materials to slope failure.  
The 1,192 damaging post-1970 landslides are failures of cut-and-fill grading in 
urbanized areas, many of them in such otherwise safe materials as alluvial fan 
(Qpaf, Qhaf, and Qpoaf; n=151 slides), the Garrity unit (Tcgl, n=31), and the 
Novato Quarry terrane (Kfn, n=27).  (Note the spatial coincidence of recent slides 
with local roads in unit Kfn in figure 3.)  Table 1 shows that of the 1,192 recent 
landslides only 675 occurred in the 60 more-susceptible units (those with >10 
percent of area in old landslide deposits), whereas 517 were in the 60 less-
susceptible units (< 10 percent area in old deposits).  Moreover, only 15 percent 
(n=183) of the recent failures occurred in the pre-1970 landslide deposits mapped 
by Nilsen; the remaining 1,009 were in terrain that had not moved previously, 
including Quaternary materials.  These 1,009 post-1970 slides thus overrepresent 
both the urbanized lowland bordering the Hayward Fault and developed parts of the 
adjacent hills (fig. 4, on map).  An unbiased sample, which would include the many 
non-damaging (but currently unmapped) recent failures in the undeveloped uplands 
east of Oakland, would require a substantial effort toward which resources have 
yet to be directed by public agencies.  

Topography and Slope


Configuration of the land surface (fig. 4) is related to the processes that 
continue to shape San Francisco Bay region hillsides (Radbruch and Weiler, 1963; 
Cleveland, 1971; Waltz, 1971; Nilsen, Taylor, and Dean, 1976; Nilsen and Wright, 
1979), as well as other areas worldwide (Mora and Vahrson, 1994; Massari and 
Atkinson, 1999; Irigaray and others, 1999).  So diagnostic to the expert observer 
is the topographic expression of all but the oldest landslide deposits (see list 
of features above, under Old Landslide Deposits), that the extent of much past 
failure can be identified from a visual appraisal of the terrain at scales as 
coarse as 1:30,000 (Brabb and Pampeyan, 1972; Nilsen, 1973a, b).  Few of the 
geomorphic features that characterize a landslide or tract of landslide-prone 
terrain (Liang and Belcher, 1958; Ritchie, 1958) translate readily into measures, 
parameters from which a numerical index of susceptibility can be computed.  Among 
these few measures is surface gradient, or slope, the first derivative of 
elevation with respect to the horizontal and arguably the single most important 
descriptor of ground-surface form (Evans, 1972; Pike, 1988).  The relation between 
slope and landsliding is complex (Lanyon and Hall, 1983; Wentworth and others, 
1987; Pike, 1988; Dietrich and others, 1993; Montgomery and Dietrich, 1994).  To 
evaluate the role of topography in landsliding in the Project Impact area, we 
calculated slope for each 30-m grid cell.  
Our data are from a recent slope map of the 10-county San Francisco Bay region 
computed from a digital elevation model (DEM), a rectangular matrix of terrain 
heights spaced at a constant interval (Graham and Pike, 1998b).  Slope, in one-
degree increments, is the maximum rate of change for the central 30-m cell within 
each 3 _ 3 subgrid of cells.  The full San Francisco Bay region set of about 
35,000,000 cells was assembled from 204 smaller DEM's derived from individual 7.5' 
quadrangles (Graham and Pike, 1997, 1998a).  The 7.5' DEM's were created by 
interpreting elevations from map contours (U.S. Geological Survey, 1993) rather 
than by prior, less accurate, techniques.  Accordingly, the new DEM is free of the 
striped artifacts that marred shaded-relief portrayals and generated erroneous 
analyses from older 30-m data (Graham and Pike, 1997).  However, the source 
contours portray topography mapped from the late 1940's to the early 60's, before 
much of the subsequent residential development (Stromberg, 1967); the DEM does not 
capture the myriad cut-and-fill changes that have since altered many of the local 
hillsides and thereby increased their potential susceptibility to failure.  (We 
discuss possible implications of this problem later in the report.)  Because it is 
derived from the same contour maps, a newer 10-m USGS DEM of the region does not 
increase the detail relevant to our susceptibility model over that in the 30-m 
data, although it yields a smoother-appearing shaded-relief image (fig. 4) of the 
sample area in figures 2 and 3.
The slopes of failed and unfailed terrain in the study area differ dramatically, 
as shown by the histograms in figures 5A and B.  Slopes on old landslide deposits 
are near-normally distributed (fig. 5B) and peak at about 16-17, whereas 
unfailed slopes (fig. 5A) are strongly skewed toward low values, with the mode at 
zero.  Much of this contrast reflects the widespread coastal flatlands that hosted 
few old landslides.  The distribution of slope for the 1,192 cells that hosted 
recent landslides (fig. 5C) has the same near-normal shape as that for cells on 
the old deposits in figure 5B, although the mode (approximately 12) is lower by 
about 4.  Figures 5A and B do not express the exact relation between slope and 
prior landsliding, however, because Nilsen (1973a, b) did not identify the entire 
area affected by each old failure.  The steeper slopes on terrain occupied by 
landslide crowns and head scarps upslope of the deposits are not included in 
figure 5B, which would otherwise extend to higher slope values.  The magnitude of 
this deficiency is unknown, and we propose no arbitrary correction.  The missing 
steep slopes must affect both the derivative histograms in figure 6 and the 
susceptibility map; this undersampling of older steep ground that failed (scarps) 
infers too low a susceptibility for all steep terrain, not just terrain upslope 
from mapped deposits.









A














B














C









Figure 5. Distribution of ground slope in the Oakland Project Impact area shown by 
histograms of 30-m grid cells.  A, stable terrain that has not failed.  B, terrain 
underlain by old landslide deposits.  C, terrain underlying recent (post-1970) 
landslides that damaged the built environment (fig. 3).
A MODEL OF LANDSLIDE SUSCEPTIBILITY
Spatial Frequency of Prior Failure


Overall susceptibility of a geologic unit to slope failure can be expressed 
numerically as the percentage of the unit's area that falls within existing 
landslide deposits (Brabb and others, 1972).  This percentage, or average spatial 
frequency, is highly variable.  Computed in table 1 as the number of 30-m cells in 
landslide deposits divided by all cells in the geologic unit, spatial frequency in 
the Oakland area ranges from 82 percent (820 of 1002 cells) in the Alcatraz 
terrane (Kfa), a subunit of the Franciscan Complex, to less than 1 percent in 26 
different units.  The more susceptible formations are among the 100 bedrock units 
in the greater Oakland hills; the unconsolidated Quaternary units are far less 
susceptible.  Twenty-seven hillside units, covering one-fourth of the area, each 
are at least one-fourth in landslide deposits and thus quite likely to host slopes 
that will mobilize in the future.  By contrast, less than one percent of the area 
underlain by the 20 Quaternary flatland units in table 1 is on landslide masses 
(1,817 of 364,030 cells, mostly in the widespread younger Pleistocene alluvial fan 
deposits Qpaf).  The highest spatial frequency of landslide deposits among 
Quaternary units occurs in Qpoaf, older Pleistocene alluvial fan (8 percent).  The 
10 queried units in table 1 that have nonqueried counterparts all differ from (and 
eight are higher than) the nonqueried units in areal prevalence of landslide 
deposits, which indicates that the lithologic contrasts distinguishing queried 
units have real consequences with respect to potential for slope instability.  
Mapping the mean values of spatial frequency in table 1 alone yields a generalized 
map of landslide susceptibility (not shown), but its usefulness is limited by the 
lack of detail.
The whole-unit spatial frequencies in table 1 can be refined considerably by 
ground slope.  Blanc and Cleveland (1968), Radbruch (1970, p. 4), Brabb and others 
(1972), Roth (1983), and Bechini (1993), among others, noted that prevalence 
(defined in various ways) of non-debris-flow landsliding in the San Francisco Bay 
area and elsewhere in California increases with terrain slope, but only up to a 
maximum value, commonly 15 to as much as 35, depending on mode of failure and 
the underlying lithology.  (Such forms of mass-movement as rock falls, topples, 
and shallow landslides occur in the steeper areas; debris-flow source areas, for 
example, peak at about 30 mean slope; Ellen, 1988.)  Similar slope maxima are 
documented elsewhere (Lanyon and Hall, 1983; Jger and Wieczorek, 1994; Brunori 
and others, 1996; Wjcik, 1997; Hylland and Lowe, 1997; Jennings and Siddle, 1998; 
Irigaray and others, 1999).  
We examined in detail the correlation of spatial frequency of old landslide 
deposits with ground slope in the Oakland study area and found the relation 
systematically nonlinear.  Histograms in figure 6 give five examples of what is 
fundamentally a near-normal distribution of slope failure.  Figure 6A shows the 
entire Project Impact area, for which the mean spatial frequency is 12 percent.  
The rising segment of figure 6A, which includes well over half the grid cells that 
contain old landslide deposits, describes a positive correlation 

between landslide frequency and slope that confirms much of the predictive 
capability of slope observed previously.  The spatial frequency of deposits 
departs from the area's 12 percent overall mean, increasing in one-degree 
increments from 0 percent at zero slope to about 23 percent at 19.  However, the 
prevalence of slide and earthflow deposits declines on steeper slopes, to about 8 
percent at 33 and remains constant thereafter.  This constancy persists only up 
to the highest values of slope, because of a sampling effect: the histogram 
becomes increasingly irregular as the number of cells diminishes with increasing 
slope.  The few steep cells in figure 6A, for example (7, 2, and 3 cells at slopes 
of 44, 48, and 49), yield unrealistically high frequencies, whereas slopes of 
45-47 host no deposits and thus have frequencies of zero.  The true spatial 
frequency of old landslide deposits on the steepest slopes remains unknown, but 
probably is low and variable from unit to unit.
All but the 32 smallest geologic units in table 1 follow a bell-shaped relation 
similar overall to that in figure 6A.  Figure 6D describes the most extensive unit 
in the study area, Tus, the unnamed late Miocene sedimentary and volcanic rocks.  
Figure 6D resembles figure 6A, but shows a much higher frequency of landsliding, 
averaging 36 percent in old deposits (table 1).  Spatial frequency for unit Tus 
rises from 2 percent at zero slope to 43 percent at a slope of about 20 and 
thereafter diminishes to about 10 percent.  Although frequencies for the steepest 
slopes in Tus vary irregularly, as they do for steep slopes in the entire study 
area (fig. 6A) as well as in other individual units, these few aberrant values 
(two at 38) have little effect on the resulting statistics of susceptibility for 
this unit.  Compared with unit Tus, the Hambre Sandstone (unit Th, fig. 6B) and 
younger Pleistocene alluvial fan (Qpaf, fig. 6C) have much lower spatial 
frequencies of old landslide deposits.  Also, histograms for the 32 smallest units 
(< 450 cells) tend to be more rectangular than normal, as exemplified by the 
Lafayette Tuff in figure 6E (which shows the numbers of cells comprising each 
histogram bar).  The bell-shaped distribution of spatial frequency with respect to 
ground slope is not unique to old landslide deposits, as shown in figure 6F, a 
histogram of the number-frequency of the 1,192  recent landslides with respect to 
slope.
The wide range of spatial frequencies in the histograms sampled in figures 6B-E 
confirms that the mean values in table 1 are unrepresentative of the hazard 
locally and thus cannot be used to model the likelihood of failure throughout a 
geologic unit.  Accordingly, the 969,003 susceptibilities extracted from the 
spatial frequency of old landslide deposits were calculated to reflect the 
variability of these frequencies with ground slope in each of the 120 geologic 
units.  We obtained susceptibility both between (n = 852,643 grid cells) and 
within (n = 116,360 cells) landslide masses in the Project Impact area (table 3).  
Because some of the latter values exceed 1.00, all 969,003 susceptibilities are 
expressed as a decimal rather than a percentage.




  A





  B





  C






Figure 6.  Spatial frequency of slope failure by sliding and earthflow, shown by 
ratio of number of 30-m cells on old landslide deposits to number of all cells, as 
a function of ground slope in 1 intervals.  A, Oakland Project Impact area.  B, 
Hambre Sandstone (map unit Th).  C, Pleistocene alluvial fan deposits (Qpaf).  




  D









  E








  F







Figure 6 (continued).  D, unnamed Tertiary sedimentary and volcanic rocks (Tus).  
E, tuff at Lafayette of Wagner (1978) (Tlt), shown at larger scale (0.13 is mean 
frequency; small numbers are cells for the 17 slope intervals).  F, 1,192 post-
1970 damaging landslides (one cell per slide).  See text for discussion. 

	Table 3.  Distribution of grid cells and their susceptibility values Sls and 
S, by hillside           	          or flatland sites and by location within or 
between old landslide deposits

	Terrain and Geology				        Number of 30-m grid cells*

							All	  Sls - within      S - between
							cells	  slide masses     slide masses

Hillsides:  100 bedrock units		604,973	114,543	490,430

Flatlands:  20 surficial units		364,030	     1,817	362,213

Entire region:  all 120 units		969,003	116,360	852,643

       * excluding cells on inland water bodies



Susceptibility Between Old Landslide Deposits


Susceptibility S for grid cells not underlain by old landslide masses (87 percent 
of the study area, 81 percent of the Oakland hills, table 3) is estimated directly 
from the histograms that show the spatial frequency of old landslide deposits with 
respect to ground slope.  S is calculated for each slope interval.  In geologic 
unit Tus, for example, where 35 percent of the 30-m cells sloping at 10 are 
located on the old landslide masses (fig. 6D), every cell in unit Tus with a slope 
of 10 is assigned that same susceptibility S, of 0.35.  In the less-susceptible 
Hambre Sandstone (unit Th, fig. 6B), by contrast, only 8 percent of the cells in 
the 10 slope interval lie on mapped slide masses, whereupon an S of 0.08 is 
assigned to all 10 cells in the Hambre.  Values of S, determined slope interval-
by-slope interval, are unique to each slope value in each of the 120 units.  The 
resulting distribution of susceptibility for the 852,643 cells that lie between 
mapped landslide deposits is shown in figure 7A.  The values are severely skewed, 
even in the log domain, and range from S = 0.00 for 300,000 cells in predominantly 
flat-lying surficial units to S = 0.90 for 14 cells in unit Kfa, Franciscan 
Alcatraz terrane.  


Susceptibility Within Old Landslide Deposits


Obtaining susceptibility Sls for the smaller fraction of the Oakland area that is 
underlain by landslide deposits is more complex.  We first calculated raw 
susceptibility S for the 116,360 cells within old slide masses, by the same 
procedure as for cells between them.  The resulting log frequency distribution 
(fig. 7B) has many (by 1 to 3 orders of magnitude) fewer cells in the lowest (0.00 
to 0.25) S range and is more symmetric than the histogram for cells between 
deposits (fig. 7A).  The highest value of S on landslide masses is 1.00, for 70 
cells: 15 cells in unit Kfa, Franciscan Alcatraz terrane, seven or eight cells 
each in three small units (locally mapped sedimentary rocks, Tusl; silica-
carbonate and altered serpentinite, sc; and the Cierbo Sandstone, Tc), and one to 
four cells each in 17 other geologic units in table 1 (not identified).  
To estimate the higher susceptibilities that characterize old landslide deposits 
Sls we raised these 116,360 values of S by a factor a, based on the spatial 
frequency of recent (post-1970) failures in the East Bay, 
    a = (#histls / Als) / (#histnls / Anls),
where #histls and #histnls are the numbers of recent failures within and outside 
old landslide deposits, respectively, and Als and Anls are the areas (in number of 
cells) of old deposits and the terrain between them. From this correction, (183 / 
116,360) / (1,009 / 852,643) or 1.33, recent landslides in the study area are 
about 1/3 more likely to occur within old landslide deposits than between them.  
(This number differs from the 1.05 correction computed over the smaller total area 
in a more complex model by Pike and others, 1999a, b).  In the absence of historic 
data on landsliding for each geologic unit, we applied the 1.33 multiplier to all 
units.  The highest value of Sls is 1.33, for the same 70 cells in unit Kfa and 
the 20 other units mentioned above.  The shape of the resulting frequency 
distribution of susceptibility (fig. 7C) is essentially that for the raw S values 
(fig. 7B).  Figure 7D combines figures 7A and 7C to yield the distribution of 
susceptibility for all 969,003 grid cells plotted on the map.
The 33 percent higher incidence of post-1970 landslides on old slide deposits is 
of the same magnitude cited previously for the East Bay, about 55 -70 percent in 
the more susceptible geologic units (Nilsen, Taylor, and Dean, 1976, p. 19), but 
may be low.  Keefer and others (1998, fig. 4), for example, found that 18 of the 
20 largest slides triggered in the Santa Cruz Mountains by the 1989 Loma Prieta 
earthquake occurred within areas that had failed previously (>55 percent spatial 
overlap)-not necessarily an invalid comparison despite the difference in 
triggering mechanisms.  





A










B













Figure 7.  Distribution of susceptibility to slope failure by sliding and 
earthflow in the Oakland Project Impact area, shown by log number of grid cells as 
a function of modeled index number.  A, Between old landslide deposits (S).  B, 
Within old landslide deposits-uncorrected (S).  



C

















D










Figure 7 (continued).  C, Within old deposits _ 1.33 correction factor (Sls).  D, 
Final result for entire map area (A + C; S + Sls).  See text for discussion.
Tests of the Susceptibility Model


Susceptibility to future failure by deep-seated sliding and earthflow, 
particularly estimates derived from old landslide deposits, is difficult to 
verify.  ("Verify" probably is too strong a term.  The extent to which earth-
science predictions from a computer model can be evaluated, let alone validated, 
is a matter of much current debate; Oreskes, 2000.)  Not only are the types of 
landslides addressed here less readily predicted than debris flows (statistical 
curves that relate rainfall intensity to its duration, of the sort cited by 
Haneberg, 2000, p. 214, do not exist for large, deep slides), but debris-flow 
prediction (Dietrich and others, 1993) is easier to test by virtue of being more 
DEM-dependent and based on landslide-occurrence data that are recent and frequent.  
(Also, similar triggering conditions can be assumed for all debris flows that 
occur in one storm.)  Despite this comparative disadvantage and the limited data 
available, two evaluations were devised for the new susceptibility model, one 
outside the Project Impact area and one within.  
We computed the susceptibility index for an adjacent area with similar geology, 
the Niles 7.5-minute quadrangle southeast of the Hayward quadrangle, and compared 
the results with the prevalence of old landslide deposits in that area (Nilsen, 
1973b, 1975).  S or Sls was obtained for each 30-m cell in the Niles quadrangle, 
unit by geologic unit, by comparing the corresponding ground-slope value 
calculated from the Niles DEM with the appropriate slope:spatial frequency 
histogram (for example, fig. 6) derived in the Project Impact area.  Figure 8A 
shows that the spatial frequency of 30-m cells coinciding with landslide deposits 
in the Niles quadrangle increases with the modeled susceptibility, from zero to 
100 percent.  The histogram maxima at values of 0.20-0.30 and 0.40-0.50 reflect 
steep slopes on the widespread and landslide-prone Briones Formation (Tbr).  While 
this test is not wholly independent-the model is predicated on the correlation of 
high susceptibility with high spatial frequency of old landslide deposits 
(although in a different area than those in the test)-the positive correlation in 
figure 8A indicates that the model has identified potentially unstable terrain in 
the Niles quadrangle.

	    Table 4.  Correspondence of recent landslide activity to modeled 
landslide susceptibility,	         showing the contrast between less hazardous and 
more hazardous geologic units

     Modeled				    Number of damaging post-1970 landslides		
  susceptibility			
   for grid cells		Undifferentiated		Differentiated by mean spatial 
frequency
   that contain		by geologic unit		      of old landslides on geologic 
units*
1,192 post-1970
     landslides     					lower (< 0.10)		higher (>0.10)

	0.00 - 0.09	514	438	76
	0.10 - 0.19	180	68	112
	0.20 - 0.29	112	10	102
	0.30 - 0.39	147	0	147
	0.40 - 0.49	149	1	148
	0.50 - 1.20	      90  	     0  	    90  	
		1,192	675	517

	* from table 1




We also examined susceptibility estimates for the 1,192 post-1970 landslides-their 
30-m grid cells were anticipated to be highly susceptible (defined provisionally 
here as values of Sls and S >0.30).  This expectation is not realized in the raw 
counts of the 1,192 recent failures, only 386 of which occur in cells where 
modeled susceptibility exceeds 0.30 (column 2 in table 4).  Rather than indicating 
failure of the model, however, table 4 demonstrates the importance of 
anthropogenically influenced landsliding in Oakland, where human disturbance of 
the landscape has made formerly safe terrain hazardous.  
To draw this distinction, we divided the 120 geologic units in table 1 into two 
groups, more hazardous and less hazardous, at 0.10, the median spatial frequency 
of old landslides.  The resulting columns 3 and 4 of table 4 reveal a marked 
difference in the distribution of recent landslides with respect to the geologic 
units in which they occurred.  Many of the 675 post-1970 slides in column 3 (in 
geologic units having a mean spatial frequency < 0.10) that are on grid cells with 
low modeled susceptibilities (0.00 - 0.29) are failures of graded cut-and-fill 
slopes in geologic units on flat-lying terrain in which pre-development failure 
was rare: alluvial fan (Qpaf, Qhaf, Qpoaf), the Garrity unit (Tcgl), and Novato 
Quarry terrane (Kfn).  Few of these units (cited above) have modeled 
susceptibilities that exceed 0.30.  In contrast, most of the 517 post-1970 
landslides in column 4 of table 4 (in geologic units having a mean spatial 
frequency >0.10) occurred in the five hillside units known to be susceptible from 
their high frequency of old landslide deposits (also mentioned above): the 
Mulholland (Tmlu, Tmll) and Orinda (Tor) Formations, Franciscan Complex mlange 
(KJfm), and the large unnamed unit (Tus).  


A







B















Figure 8.  Histograms showing tests of the susceptibility model.  A, Spatial 
frequency of old landslide deposits in the Niles quadrangle (SE of the Hayward 
quadrangle, fig. 1) as a function of modeled susceptibility; small numbers are 
grid-cell counts in each shaded bar.  B, Spatial frequency of post-1970 damaging 
landslides in the study area, shown by number of slides (normalized by total grid 
cells with same index value) as a function of modeled susceptibility.  For raw 
counts of post-1970 landslides see table 4.  White portion of each bar: low-
susceptibility geologic units; black portion: high-susceptibility units.  See text 
for discussion.

Finally, the spatial frequency of the 1,192 post-1970 failures in figure 8B mimics 
the positive test results for the Niles quadrangle in figure 8A.  The number of 
these recent landslides in figure 8B, normalized by the number of cells in each 
susceptibility interval, increases with values of the susceptibility index.  Post-
1970 landslides on low-hazard geologic units (white part of each histogram bar in 
figure 8B, according to the < 0.10 spatial-frequency criterion established above) 
are less prevalent with increasing model susceptibility, whereas recent failures 
on landslide-prone units (black parts of the bars, by the >0.10 spatial-frequency 
criterion) show the expected positive correlation.  

The two evaluations suggest that our model or some variant of it, with due 
allowance for the destabilizing effects of grading in Quaternary deposits, can 
generate regional estimates of relative susceptibility, both within and outside 
the area in which the procedure was developed.  In its present form the model is 
most applicable to bedrock formations in upland terrain.  The model can be tested 
further as additional landslide inventories, such as those prepared recently by 
CDMG, become available in digital form.  A robust test of the second type 
described here will require larger numbers of recent failures in different 
geologic units, as well as a sample unbiased toward damaging slides in developed 
terrain.  The necessary data can come only from systematic mapping of all 
landslides regardless of their effects on the built environment.  An alternative 
test might be designed to evaluate susceptibility modeling only for developed 
areas in Quaternary deposits.  Table 5 summarizes some strengths and weaknesses of 
the current model and the map derived from it.  Two of the shortcomings noted in 
table 5 reflect the data available for the Oakland area rather than the model 
itself.




		Table 5.  Summary evaluation of the susceptibility model and map

		STRENGTHS					WEAKNESSES

Susceptibility is a continuous variable			  Index number is relative 
only
Method is 100 percent quantitative			  Some causative factors omitted
High resolution (that of DEM)			  Requires large database
Spatial coverage is 100 %			  Requires all-digital input
Can be applied to large areas			*Landslide inventory is not
Model is computationally transparent				attributed or field-
checked
Technique is exportable			*Slope data predate urbanization

	*the Oakland application only




THE SUSCEPTIBILITY MAP


The 969,003 values of susceptibility for all cells between (S) and within (Sls) 
old landslide deposits (fig. 7D) are combined in one digital file and displayed on 
the map.  The base information of topography, drainage, and culture is from an 
earlier 1:125,000-scale map (Aitken, 1997).  Overall, our map resembles its sole 
predecessor, the 1:125,000-scale regional slope-stability map of Nilsen and Wright 
(1979), but differs greatly in detail.  The map shows the continuous range of 
susceptibility, in decimal units, divided into eight intervals that broaden with 
increasing susceptibility and decreasing number of cells.  The unequal intervals 
were chosen, after inspection of the frequency distribution (fig. 7D) and test 
plots (not shown), to emphasize the spatial variability of the index and yield a 
balanced-appearing map.
We have not assigned exact levels of landslide hazard to these susceptibility 
intervals, represented on the map by eight colors, other than an increasing 
susceptibility with warmth of tone and saturation of color, from gray to red.  
Which intervals to designate safe or dangerous is subjective and remains a matter 
of interpretation, as no direct means of calibration is available at this time.  
However, from field study that has long identified such geologic units as the 
Briones and Orinda Formations (Radbruch and Case, 1967; Radbruch, 1969; Keefer and 
Johnson, 1983; Bechini, 1993) as highly likely to fail (their mean spatial 
frequencies are 0.27-0.28, table 1), it appears that susceptibility values at 
least as low as 0.20 (yellow on the map) indicate areas of potential instability-
depending on the strength and duration of triggering events.  Dense clusters of 
values exceeding 0.30 (light orange on the map) are likely to contain areas of 
concern.  Table 6 shows the distribution of the Project Impact area among the 
eight intervals, for the entire study area as well as its more susceptible 
hillside terrain (see table 3).  We have not simplified the map into a few large 
"zones", convenient as these might be for regulatory applications.  Because 
landsliding is a localized phenomenon, we find the concept of broad hazard zones 
unrealistic.



	         Table 6.  Distribution of modeled susceptibility values on map

					          Percentage of map area     	
          Index of
    Susceptibility			Entire		Hillside geologic 
					  area  		     units only      	

	< 0.01  (0)	37	%		00 %
	0.01-0.049	17		27
	0.05-0.099	  9		14
	0.10-0.199	 14		23
	0.20-0.299	  9		14
	0.30-0.399	  7		11
	0.40-0.549	  6		10
	   > 0.55	  1		  1



The map's spatial patterns are not random, but rather reveal a systematic relation 
between slope instability and contrasts in geology and the resulting NW-SE-
trending topography, particularly the distribution of steep, high ridges.  The 
susceptibility values that indicate the greatest potential hazard (>0.30) are 
conspicuous in steep terrain underlain by such geologic units as the lower member 
of the Mulholland Formation (Tmll), the Neroly Sandstone? (Tn?), the mudstone 
member of the Siesta Formation (Tst), an Eocene shale and claystone unit (Tshc), 
the widespread unnamed Tertiary sedimentary and volcanic unit (Tus), and also the 
Orinda (Tor) and Briones (Tbr) Formations.  Table 6 shows that roughly 15 to 20 
percent of the greater Oakland area is characterized by susceptibility values 
greater than 0.30.  However, lower susceptibilities (in the 0.10-0.29 range) are 
common in Franciscan Complex mlange (KJfm) and other units known from field study 
(also evident by their high spatial frequencies in table 1) to be quite 
susceptible to sliding and earthflow.  Some of the most susceptible localities in 
the map area are in the western Briones Valley quadrangle, the northern Oakland 
West quadrangle, the east-central Las Trampas Ridge quadrangle, and in the south-
central Richmond quadrangle near the Hayward Fault.  Most, but not all, locations 
on the map with the highest potential for slope failure are steep and thinly 
settled, commonly in parkland or areas unlikely to be developed.
A potential public-safety issue highlighted by this map is the location of 
residential dwellings in terrain likely to be unstable.  Comparison of ABAG (1996) 
land-use data with this map (table 7) shows the percentage of residential housing 
in each of the eight levels of likely failure, for both the entire Project Impact 
area and the more susceptible hillsides within it (see table 3).  Although most of 
the greater Oakland region lies within terrain having susceptibility-index values 
less than 0.05, 15 percent of the residential housing in hillside areas (covering 
nearly 20 km2) is situated on terrain within the three highest levels of potential 
failure (>0.30).  One of the larger such convergences is located on the eastern 
slopes of Las Trampas Ridge in the town of Danville, west of San Ramon Creek.  
This residential area is underlain by an extensive landslide complex mapped by 
Nilsen that suggests highly susceptible rocks (unit Tn?; mean spatial frequency of 
old landslides in table 1 = 0.63) possibly related to the Neroly Sandstone (Tn)-
the main body of which is much less susceptible (mean spatial frequency of old 
landslides in table 1 = 0.12).  Hazard mapping by CDMG also ranked this area in 
its least-stable category (Majmundar, 1996b).


	   Table 7.  Distribution of susceptibility values in land 
		     occupied by residential housing *

				      % Area in residential housing
    Index of
Susceptibility		      Entire	       Hillside geologic
				   study area	            units only	

< 0.01  (0)	53	%		  00 %
0.01-0.049	20			43
0.05-0.099	  7			15
0.10-0.199	  8			17
0.20-0.299	  4			  9
0.30-0.399	  4			  9
0.40-0.549	  3			  5
   > 0.55	  1			  1

	* Land-use data from ABAG (1996) 

APPLICATIONS AND LIMITATIONS


This map is a prototype for quantitative landslide-susceptibility coverage of the 
10-county San Francisco Bay region.  The digital geologic map (Wentworth, 1997) 
and slope data (Graham and Pike, 1998b) needed for this next step already exist, 
as well as some of the requisite landslide inventories (Pike, 1997) now available 
as digital-map files (Roberts and others, 1998, 1999).  However, this map and 
similar products of GIS technology can aid in reducing the risk created by slope 
instability in the San Francisco Bay region only if they are used.  It is the 
communication of susceptibility information to the public agencies and officials 
positioned to apply it, as well as guidance in its application, that ensures the 
success of such programs as Project Impact (Kockelman, 1979; Highland, 1997; Pike 
and others, 1998; Howell and others, 1999).  The susceptibility model and map 
described here and projected research may be judged less by the quality or 
sophistication of the work than by whether their eventual use led to improved 
public safety or contributed to better land-use decisions (Olshansky and Rodgers, 
1987; Mileti, 1999; Platt and others, 1999).
The information on this map is intended principally to assist public agencies in 
making planning decisions for the development or zoning of hillside locales within 
the greater Oakland area.  Secondary uses include emergency preparedness through 
training exercises and dissemination of information on areas most likely to become 
unstable during an earthquake or after a season or succession of seasons of high 
rainfall.  A successful example elsewhere in the San Francisco Bay area of the 
first application is the landslide-susceptibility map prepared by Brabb and others 
(1972) for San Mateo County.  In 1973  and 1976, that County enacted ordinances 
that limited the density of development in geologically hazardous areas (defined 
as the three highest-susceptibility categories of the 1972 map) to as little as 
one dwelling unit per 16 hectares and required geologic reports and review by the 
County Geologist before any development was allowed.  Over the last 25 years, 
these ordinances have been expanded in area and strengthened in their execution 
(Brabb, 1995).  Kockelman (1979 and references therein) gives more examples of the 
application of USGS map products by public agencies in the San Francisco Bay 
region.  
Because any prediction in the earth sciences is difficult at best (Oreskes, 2000) 
and hazard modeling is subject to great uncertainty (Haneberg, 2000), this map 
should be used with its limitations in mind.  Most locations on the map that have 
high values of susceptibility are more likely to fail than locations with low 
values but almost certainly include areas that are not hazardous.  More important 
from the viewpoint of public safety, most areas on the map showing low 
susceptibility are less prone to failure than areas of high values but are not 
without hazard.  Some low susceptibility locales slope steeply and are subject to 
other types of failure.  For example, landslide deposits less than 60 m in the 
longest dimension, which are common throughout the study area, were not included 
in the inventory on which this map is based.  Many small failures may have 
resulted from debris flow, and the map does not depict that hazard.  
(Susceptibility to debris flow may be evaluated by computer methods differing from 
that devised here; Dietrich and others, 1993; Montgomery and Dietrich, 1994; Ellen 
and others, 1997; Campbell and others, 1998; Campbell and Chirico, 1999; Pack and 
others, 1999).  Also, because landslide crowns and head scarps were not included 
in Nilsen's inventory and thus not incorporated into our model, terrain upslope of 
old deposits may be more susceptible than shown on this map.  Finally, many deep-
seated landslides simply cannot be predicted.  Such failures result from chance 
blockage of surface drainage or other consequences of hillside development, as 
well as from the random operation of natural processes.
The caveats accompanying landslide inventories and susceptibility maps published 
by USGS and CDMG bear repeating (see also the comment on spatial resolution in the 
section, Digital Map Database, below).  Geologic and climatic changes over the 
last few hundred thousand years have altered conditions of slope stability since 
initial movement of many (most?) of the observed landslide deposits in the Oakland 
area.  Despite its 30-m level of detail our assessment is regional, based on a 
reconnaissance inventory that does not incorporate the composition or type of 
movement of individual landslide deposits.  Our map estimates the relative 
importance of landsliding and, thus, overall stability, but it is only a guide to 
the likelihood of future movement.  No frequency of failure per unit time is 
implied.  In gauging the severity of slope instability, this map should be used 
together with local information on land use and soil and groundwater conditions.
Because detailed site investigations, under Chapters 7.5 and 7.8 of Division 2 of 
the California Public Resources Code, are required to judge the stability of a 
specific locale, this map is not a substitute for a report by a licensed 
engineering geologist or soils engineer (CDMG, 1997).  This map does not carry the 
regulatory mandate of the official State of California landslide map (Wilson and 
others, 2000).  Areas believed susceptible to landslide activity should be studied 
carefully before planning any development.  In addition, surficial deposits other 
than landslides may pose construction problems and require a site investigation 
(Nilsen, 1973a, b; Nilsen and Wright, 1979).  To gain a complete understanding of 
natural hazards, Oakland-area officials should consult maps that show 
susceptibility to debris flow, flooding, wildfire, earthquake ground-motion, soil 
liquefaction, and seismically induced slope failure (Haydon and others, 2000; 
Petersen and others, 2000).  


FUTURE RESEARCH


Among advances embodied in this map are replacement of manual map-overlay by 
computer sorting, calculation of susceptibility as a continuous variable, high 
spatial resolution, narrow slope intervals, estimation of susceptibility within 
landslide deposits, and availability of the results as a digital file.  Although 
we doubt that the map can be quickly or easily improved upon as a quantitative 
first-order estimate of regional susceptibility to the kind of failures mapped by 
Nilsen (1975), the underlying model need not be static.  Before basing further 
calculations on the model or extending its mapping beyond the Oakland area, it is 
prudent to weigh future courses of model development.  Among these is 
consideration of variables that contribute to sliding and earthflow but are not 
included in the model (Brabb, 1995).  To be practicable, additional data must be 
available over large areas, in digital-map format, and obtainable at modest effort 
and expense.  Twelve candidate variables, mainly topographic parameters and 
generalized properties of geologic materials, are discussed briefly here.  We have 
experimented with some of them.  
(1) Ground-water level, a known trigger of deep landsliding, varies both 
seasonally and cyclically and thus is not strictly an attribute intrinsic to a 
site, but may be more appropriate to a model of landslide probability.  
Nonetheless, we did establish two general relations that may prove useful in 
incorporating this key variable into the susceptibility model.  Mean annual 
precipitation (MAP) does not vary across the Oakland area in sufficient detail to 
provide a good proxy for ground-water level, but MAP does increase with (2) height 
above sea level (Rantz, 1971).  Accordingly, we were not surprised to find that 
the spatial frequency of old landslide deposits in the Project Impact area also 
varies systematically with terrain elevation as expressed by the 30-m DEM (Graham 
and Pike, 1998a).  The bell-shaped distribution of failure frequency with 
elevation (not shown) rises abruptly from 40 m to a maximum at about 210 m and 
tapers off above 600 m.
Because hillsides sheltered from afternoon sun commonly retain more moisture and 
thus may be more prone to instability than slopes facing the sun (Beaty, 1956), 
(3) slope exposure (aspect) in the lineated East Bay topography also can be 
expected to account for unexplained variance in our susceptibility model.  
However, the spatial scale most sensitive to differences in azimuth-probably a 
sub-grid larger than the usual default array of 3 _ 3 cells-has yet to be 
identified.  (4) Relative relief, vertical distance between highest and lowest 
elevations in an area (larger than that within which slope is customarily 
measured), also may supply useful information.  While relief also may have an 
optimal spatial scale at which it influences landsliding, relief measured over a 
sample of any size rarely fails to correlate with slope (other than in such 
localized applications as the fine-scale transects measured by Schmidt and 
Montgomery, 1995) and thus could prove redundant.  
(5) Slope curvature may be important in initiating failures other than debris 
flows, where it plays a critical role (Ellen and others, 1997; Dietrich and 
others, 1993; Pack and others, 1999).  Convergent (concave upward and outward) 
hillslopes concentrate moisture (Richter, 1962; Beven, 1997) and thus may fail 
more readily than divergent (convex) or planar slopes, although exceptions to this 
generalization exist (Wjcik, 1997).  Curvature is measured in profile and plan 
(Waltz, 1971; Evans, 1972), and has been modeled in both modes (Lanyon and Hall, 
1983; Rowbotham and Dudycha, 1998; Irigaray and others, 1999).  We experimented 
with profile curvature, the second derivative of elevation, calculating it for 30-
m DEM cells from a 3 _ 3 sub-grid.  Averaged values of curvature for each of the 
6,714 old slide masses neither usefully described the deposits nor provided 
contrasts in their frequency of occurrence that relate to susceptibility.  The 3 _ 
3 sub-grid is too small to describe the relation of most mapped deposits to the 
gross curvature of surrounding topography, and testing is needed of sample designs 
that differ in size (and perhaps shape; Rowbotham and Dudycha, 1998).  
A still-unexamined use of slope curvature that is likely to aid landslide 
prediction is the quantification of differences between frequency distributions 
pre- and post-failure.  Accurate DEMs are required.  While DEMs can be generated 
for landslide scarps and deposits from detailed surveys post-failure, pre-slide 
topography of comparable accuracy is unavailable.  The usual sources of pre-slide 
DEMs in the Bay area and elsewhere, 1:24,000-scale contour maps, are 
unsatisfactory not only because of insufficient resolution and accuracy but also 
because they predate the landscape changes that resulted from hillside development 
in the 1950's and 1960's.  High-resolution LIght Distance And Ranging (LIDAR ) 
data acquired by airborne scanning pulsed-laser rangefinder are expected to 
eliminate this shortcoming (Ralph Haugerud, 2001, personal communication).  
Although currently expensive, LIDAR-generated DEMs soon will enable slope 
curvature and other terrain parameters to characterize landslide terrain with 
unprecedented accuracy.
Our unpublished tests on materials-properties data interpreted from Ellen and 
Wentworth (1995) show that (6) substrate expansivity correlates with landsliding, 
but this property is related to rock type and thus already is incorporated in the 
present model and map.  While we found (7) bedding-dip direction (Radbruch and 
Weiler, 1963, table 2; Wentworth and others, 1987, p. 642) unrelated to 
landsliding in a sample of the study area, the convergence of bedding orientation 
with terrain slope (Schmidt and Montgomery, 1996 and references therein) warrants 
study in greater detail.  Other site attributes remain unexamined.  Among these 
are (8) bedding thickness and (9) fracture spacing (Ellen and Wentworth, 1995; 
Schmidt and Montgomery, 1996), (10) proximity to the Hayward Fault, and (11) 
vegetation type (now non-native in much of the study area).
The most effective additions to a model of regional susceptibility may not be 
natural site characteristics of Bay area hillsides.  Because prediction of 
landsliding in areas of human habitation is of more immediate societal value than 
estimation of failure in unoccupied pristine terrain, the model needs parameters 
that better reflect susceptibility of slopes that have been modified to build 
residences and other structures.  This enhancement of the model would diminish the 
number of low susceptibilities predicted for the obviously susceptible (by virtue 
of having failed) terrain in figure 8B.  Natural physical controls on slope 
stability are unlikely to be among the candidate variables.  The most promising 
alternative, we believe, is (12) horizontal distance to the nearest road, a 
parameter of proximity to disturbance of slopes by cut-and-fill grading-a major 
contributor to historic landsliding in the study area, particularly in units that 
are otherwise less susceptible (Nilsen and Turner, 1975, p. 6-7).  The coincidence 
of recent damaging slides with suburban roads (fig. 3) supports this view.
Added factors will complicate or force abandonment of our susceptibility model, 
which is limited to three variables in its current implementation.  Such changes 
are not to be undertaken lightly; prospective variables must demonstrate a 
significant contribution to susceptibility.  The simplest approach to adding 
variables is to compute susceptibility for each variable as done here for ground 
slope and then combine the resulting values (weighted where appropriate) into a 
summary susceptibility.  This method would preserve much of the original model's 
transparency.
Multivariate statistics (Cross, 1998; Brunori and others, 1996) is a more 
sophisticated method for adding landslide factors, but the explained variance thus 
gained must be weighed against losing the independent unit-by-unit determination 
of susceptibility, an important conceptual and computational advantage when 
analyzing large areas.  Probit analysis, exemplified by the mapping of 
liquefaction susceptibility in the Monterey Bay area (Pike and others, 1994), is 
one multivariate technique that provides for such non-numerical (categorical) 
information as lithology, vegetation, and land use.  Another multivariate 
technique adapted to landslide susceptibility (Massari and Atkinson, 1999), 
general linear modeling (Francis and others, 1993), also accommodates both 
numerical and non-numerical observations.  Logistic regression, a third 
multivariate statistical tool, also has been applied to landsliding (Mark, 1992; 
Jger and Wieczorek, 1994; Rowbotham and Dudycha, 1998).
Other questions warrant consideration in future development of the model.  For 
example, estimates of susceptibility for old landslide deposits Sls are based on 
the present slopes of those deposits, not the best values from which to predict 
movement because they describe topography that has already failed.  Slope values 
better suited to model predictions would describe topography that has not yet 
failed, but will.  Such measurements, obtainable only from surveyed profiles or 
DEMs of the same terrain both before and after failure has occurred, are 
unavailable for the study area and rare elsewhere (Yanagisawa and Umemura, 1999, 
p. 88).  Recent experiments to find a temporary way around this problem (Pike and 
others, 1999a, b) suggest that a more refined model of susceptibility for sliding 
and earthflow might result from assumptions about (1) terrain geometry pre- and 
post-failure and (2) the coherence of earth materials on steep slopes.  
Implementing these assumptions would elevate susceptibility for the steeper 
terrain in existing landslide masses while not unduly 


raising or reducing it for the gentler terrain in those deposits:
The overall frequency distribution of surface gradient in a landslide deposit, as 
expressed by mean slope, may be an acceptable starting-point from which to 
represent pre-slide topography.  Two arguments favor this view.  First, neither 
our experimental reconstructions of pre-slide terrain surrounding the landslide 
deposits nor our attempts to model the failure surface resulted in more credible 
values of susceptibility than use of the existing topography.  Also, it has yet to 
be shown that pre- and post-slide frequency distributions of slope angle for the 
same piece of topography differ significantly-although the spatial distributions 
of those slopes obviously are not the same.  
Varied stability of the steepest slopes in an area of similar rock may reflect the 
contrasts in rock strength between and within existing landslide deposits.  Steep 
slopes in the coherent bedrock between slides presumably are of low susceptibility 
because the physical properties of rock in undisturbed terrain (for example, 
continuous bedding and low density of fractures) resist failure.  Low 
susceptibility should not be expected for steep slopes on landslide masses, 
however, where the disrupted, weakened rocks have different physical properties 
(discontinuous bedding, many fractures) and are likely to fail again.  
Accordingly, our model might be modified to allow susceptibility to reach its 
maximum values on the steepest terrain within old landslide deposits.  
Finally, the model devised for the new map may contribute to more dynamic, 
scenario-based calculations.  This map reveals the relative susceptibility of the 
greater Oakland area to sliding and earthflow, but not absolute susceptibility-
that is, probability.  A map of landslide probability requires coupling this map 
with historic information on the timing of precipitation amounts, groundwater 
levels, and landslide movements.  Probability maps, in turn, can be combined with 
land-use data to create maps showing risk to the built environment.  As a further 
step, data on economic valuation can be added to estimate the magnitude of 
possible future losses and conduct benefit-cost analyses for both proposed 
development and measures to mitigate the landslide hazard (Nilsen, Taylor and 
Brabb, 1976; Bernknopf and others, 1988).  To fully appraise the slope-instability 
hazard in Oakland or any other area, such derivative maps would need to 
incorporate separate susceptibility calculations for debris flow, soil 
liquefaction, and earthquake-induced landsliding.  


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APPENDIX - THE DIGITAL MAP DATABASE

INTRODUCTION

This report provides digital data and plottable files that depict the relative 
susceptibility to certain types of slope failure in the Oakland area, California, 
at 30-m ground resolution.  Although the susceptibility calculations described in 
the body of the report were derived from digital maps of geology, terrain slope, 
and landslides, only data for the susceptibility map are included here.

The input data on geology, slope, and landslides were compiled and the 
susceptibility calculations performed in ARC/INFO, a commercial geographic 
information system (GIS) (Environmental Research Institute [ESRI], Redlands, 
California), by a program written in Arc Macro Language (AML) for the GRID 
extension of ARC/INFO.

The resulting landslide-susceptibility database consists of a data layer called 
susgrd_utm, described in table 1 below.  The map layer is stored in UTM projection 
(zone 10; see table 2); projection files are included to convert between UTM and 
the state plane projection.  Digital tics define a 7.5-minute grid of latitude and 
longitude.  Contents of the map database are described as susceptibility values in 
30-m grid cells.  Terms in table 3 describe the database fields.


Table 1.  Data layers
	Data layer name			Description

	susgrd_utm			30-meter raster grid containing
						the susceptibility values
   drain				Vector coverage of drainage 
base
culture			Vector coverage of cultural 
base 
   roads				Vector coverage of road 
network base
   hwys				Vector coverage of interstate 
highway base
   hydro				Vector coverage of 
hydrological base




Table 2.  Map projection

	Projection		UTM (universal transverse mercator)
	Units			meters
	Zone			10
	Datum		NAD 27
	Spheroid		Clark 1886








Table 3.  Field definition terms

	ITEM NAME	Name of database field (item)
	WIDTH		Maximum number of digits or
					characters stored
	OUTPUT		Output width
	TYPE			B - binary integer,
				F - binary floating point number,
				N - ASCII floating point number,
				 I - ASCII integer,
				C - ASCII character string
	N.DEC		Number of decimal places maintained
					for floating point numbers



Susceptibility values are stored for individual 30-meter grid cells, and are 
tabulated in the 'count' column (see table 4).


Table 4.  Content of the value attribute table   (SUSGRD_UTM.PAT)
  Item name	Width	Output	Type	N.DEC	    Description

     VALUE	   4	  10	   B	   --	Susceptibility value
     COUNT	   4	  10	   B	   --	Number of cells with 					      
corresponding value


The susceptibility values in table 4 are distributed in the 1:50,000-scale map, 
which contains 1112 rows and 1394 columns.  The number of different susceptibility 
values is 110; the minimum value is 0.0, the maximum 133.0, the mean 13.2, and the 
standard deviation 18.1.  Because the distribution of susceptibility values is 
highly skewed (see text figure 7D), the latter two statistics are given for 
reference only.




SPATIAL RESOLUTION

Uses of this digital map should not violate the spatial resolution of the data.  
Although the digital form of the data removes the physical constraint imposed by 
the scale of a paper map, the detail and accuracy inherent in map scale are also 
present in the digital data.  Because this database was extracted from maps at a 
scale of 1:24,000 and compiled at that same scale, higher-resolution information 
is not present.  Enlargement of the database to scales larger than 1:24,000 will 
not yield greater real detail, although it may reveal fine-scale irregularities 
below the intended resolution of the database.  Similarly, where this database is 
used in combination with other data of higher resolution, the resolution of the 
combined output will be limited by the lower resolution of these data.




OVERVIEW OF THE DIGITAL DATABASE

This report consists of a 1:24,000-scale digital database, a 1:50,000-scale 
digital map image derived from it entitled "Map and Map Database of Susceptibility 
to Slope Failure by Sliding and Earthflow in the Oakland Area, California," and 
supporting files.  The report is stored as several digital files: the spatial data 
as both ARC export (uncompressed) and ARCVIEW shape formats, and the map image as 
both Postscript and PDF formats.  The exported ARC coverages lie in UTM zone 10 
projection and the shape versions are coded in decimal degrees.  This pamphlet, 
which fully describes preparation of the digital map database as well as its 
content and character, is included as Postscript, PDF, and ASCII text files and is 
also available on paper as USGS Miscellaneous Field Studies Map MF-2385.  

A plotted copy of the 1:50,000-scale map can be ordered from a USGS Earth Science 
Information Center or by telephone at 1-888-ASK-USGS.  Any or all of the digital 
files can be obtained over the Internet or by magnetic tape copy, as described at 
the end of this section.  The full versatility of the 1:24,000-scale spatial 
database is realized by importing the ARC export files into ARC/INFO or an 
equivalent GIS.  Other GIS packages, including MapInfo and ARCVIEW, can use either 
the ARC export or shape files.  The Postscript map image can be used for viewing 
or plotting in computer systems with sufficient capacity, and the considerably 
smaller PDF image files can be viewed or plotted in full or in part from Adobe 
ACROBAT software running on Macintosh, PC, or UNIX platforms.





DATABASE CONTENTS

The six sets of digital files comprising the database are encoded in more than one 
format.  Names of the files are unique designators based on the report identifier, 
MF-2385, followed by part numbers (1 through 6 below) and an extension indicating 
file type.  Some files have been bundled using the tar (UNIX Tape Archive) utility 
(.tar extension).  All larger files have been compressed with the gzip utility, 
indicated by the .gz extension.  The files and their identities are as follows:

1. Revision list:  A list of the parts of the report and at what version number of 
the report each was last revised (if at all) followed by a chronological list that 
describes any revisions (see REVISIONS, below).

		MF-2385revs_1a.txt			ASCII file


2. Text:  The pamphlet (this text), which describes the database and how to obtain 
it. 

		MF-2385_2a.txt			ASCII file
		MF-2385_2b.ps			Postscript file
		MF-2385_2c.pdf			PDF file


3. Database of susceptibility values:  ARC GRID containing 30-meter cells.  
Import.aml will name this grid susgrd_utm.

		MF-2385_3.e00.gz		Arc export grid


4. Supporting files for ARC/INFO use, bundled as one tar file:  When opened, the 
tar file yields:

		sp2utm.prj
and
		utm2sp.prj 

which are projection files to convert between the UTM zone 10 projection of the 
database and state plane projection, 

and
		import.aml:    

which is an ASCII script written in Arc Macro Language for converting the ARC 
export files into usable coverages and INFO files that are assigned standard names 
(see IMPORTING THE ARC EXPORT FILES). 

5. Quadrangle index database:  The data files representing lines and polygons of 
the quadrangle index (ARC export and ARCVIEW shape format).  The ARC version also 
includes quadrangle names as annotation.

	MF-2385_5a.e00.gz	ARC export files containing
	MF-2385_5b.e00.gz	lines, polygons, and annotation.
	MF-2385_5c.e00.gz
	MF-2385_5d.e00.gz
	MF-2385_5e.e00.gz

	MF-2385_5.tar.gz	Line and polygon ARCVIEW shape
		files bundled as one tar file.
		When opened, the tar file yields:

	Polygon files:	

MF-2385_5a.dbf	MF-2385_5a.shp	MF-2385_5a.shx	
	MF-2385_5b.dbf       MF-2385_5b.shp       MF-2385_5b.shx
MF-2385_5c.dbf	MF-2385_5c.shp	MF-2385_5c.shx
MF-2385_5d.dbf	MF-2385_5d.shp	MF-2385_5d.shx
MF-2385_5e.dbf	MF-2385_5e.shp	MF-2385_5e.shx	

6. Plot files for the map:  "Map and Map Database of Susceptibility to Slope 
Failure by Sliding and Earthflow in the Oakland Area, California," which measures 
30 by 51 inches when plotted.  

	MF-2385_6a.eps	Postscript file (39.6 MB)
		

	MF-2385_6b.pdf	PDF file (13 MB)  Available
		     on Adobe Acrobat Version 4.0



REVISIONS

Any part of the report (the numbered items described above and listed in the 
revision list MF-2385revs_a.txt) may be changed in the future if needed.  Changes 
could involve, for example, fixing files that don't work, correcting or adding 
landslide, geology, or slope details, or adding new file formats or other 
components.  Any major revision to the input data or the susceptibility model 
would result in a new report.

The report begins at version 1.00.  Any revisions will be noted in the revision 
list and will result in the recording of a new version number for the report.  
Small changes will be indicated by decimal increments and larger changes by 
integer increments in the version number.  Revisions will be announced and 
maintained on the Web page for this report on the Western Region geologic 
publications server (see next section).



OBTAINING THE DATABASE FILES

The database may be obtained at:

		http://pubs.usgs.gov/mf/2002/2385/

or by requesting a CD-R from:

		Oakland Slope Failure Susceptibility Map Database
		c/o Database Coordinator 
		U.S. Geological Survey
		345 Middlefield Road   MS 975
		Menlo Park, CA  94025

The specified files bundled in a compressed tar file will be returned to you on 
the disc. 





OPENING THE DATABASE FILES

Some of the files are packaged as tar files, and the larger files containing the 
databases and images have been compressed with gzip.  Thus, gzip is required to 
uncompress the files, and a tar utility is required to open the tar files.  The 
necessary utilities are available on-line:

Compressed Gzip Files

Files compressed with gzip (those with a .gz extension) can be uncompressed with 
gzip.  The gzip utility converts the compressed file name.gz to its uncompressed 
equivalent name.  The compressed file is replaced by the uncompressed file.  This 
utility is free of charge over the Internet at:

			http://www.gzip.org/


Tar Files (UNIX Tape Archive)

To extract the contents of a tar file, first uncompress it with gzip if the 
extension is .tar.gz.  Once the tar extension is exposed, extract the contents 
with a tar utility.  This utility is included in most UNIX systems.  Tar utilities 
for PC and Macintosh can be obtained free of charge via the Internet from Internet 
Literacy's Common Internet File Formats Web Page:  

			http://www.matisse.net/files/formats.html


WinZip

This commercial package runs on PCs and can deal with both gzip and tar files.  An 
evaluation copy of WinZip for Windows 3.1, 95 and NT can be downloaded from:

			http://www.winzip.com/winzip/




IMPORTING THE ARC EXPORT FILES 

(Note that the export files are ASCII files.  Hold down the shift key when you 
click to download)  

The ARC export files with the extension .e00 can be converted to ARC/INFO vector 
maps (coverages) and INFO files by running the import.aml that is included in the 
database.  This will import the export files and assign standard names (see 
below).  Be sure to uncompress the MF-2385_6a.e00.gz file with the gzip utility 
before running the import.aml.  Run import.aml from the ARC prompt in the 
directory containing the export files:

		ARC: &run import.aml		- run import.aml 

Note that the ARC coverages and separate INFO files will be given standard names:

	MF-2385_3.e00  (Susceptibility Database)  is named		susgrd_utm


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