Pitfall in Subrogating Slope Maps for
Landslide Hazard Maps
Dr. R.K. Bhandari1, and Kumar M. Weerasinghe2
1Dr. R.K. Bhandari, Head, International Science & Technology Affaris Directorate,
Council of Scientific & Industrial Research,
Rafi Marg, New Delhi, 110001 (India).
2Kumari M. Weearsinghe, Scientist, National Building Research Organization,
99/1 Jawatta Road,
Colombo 05, Sir Lanka.
Abstract:
Slope Gradient, Geology, Hydrology, Soil Overburden, Land-use, and Landform are the major causative factors of slope instability. For landslide hazard mapping in Sir Lanka, the nexus between the observed landslides and the various factor maps was mainly focussed on in deciding the relative contribution of major factors as well as the relative weightings of the sub_faxtors within a factor. This paper limits its scope to the relative importance of various slope classes in a slope map, and their impact on overall assessment of landslide hazard potential. The analysis indicates that the relative weightings assigned to different slope classes based on theoretically estimated sin suave provides a better convergence between
the observed and inferred slope failure based only on field observations do.
Introduction :
Planners, Architects and Engineers continue to read the instability potential of hilly terrenes in terms of their slope gradients, following arbitrary and varied slope gradients, following arbitrary and varied slope classifications. Examples of practical use of landslide hazard maps for planning and development are rare because either such maps, do not exist or they are not readily available to the user and because they are expensive and time consuming to generate. Nearly 1200 sq km. Of the central highlands of Sir Lanka, mapped at a scale of 1:10,000 from every, conceivable angel, has provided a firm basis to discourage the orthodox approach of relying too heavily on instability potential read from slope angles along, without cognizance of the history of landsliding of the area, and without consideration of geology, soil cover, hydrology, landform, land-use and management.
The Landslide Hazard Maps of the Districts of Nuwara Eliya and Badulla in Sri Lanka were calibrated against the landslide inventory map, and field checked for their reliability after going through the process of integration of factor maps of slope, geology, overburden soil, hydrology, landform, land-use and management. It follows therefore that the landslide inventory map and the slope map are both subservient to a landslide hazard map, and can be subrogated for it by the users, only with if they wish to make a heavy compromise.
For landslide hazard mapping, one needs to know the relative contribution of major causative factors as well as the relative weightings of the sub-factors within a factors. The rationale of arriving at objective weightings in the Sri Lankan Project was rooted in the nexus between the observed landslides (read from the landslide inventory map, called the mother map) and the varies factor
maps (Called the daugther maps). The paper restricts its scope to the sensitivity of the relative weightings, assigned to various slope classes in a slope map, and their eventual impact on the assessment of the overall landslide hazard potential.
The slope maps prepared, display five classes (Class 1-5) in the decreasing order of slope angle range. Of these, Slope Class 3 (17o-31o) was found to be associated with the largest number of events of landslides based on the study nearly 863 sites in Nuwara Eliya, and 213 sites in Badulla.
This led the investigators to believe that slope Class 3 was most vulnerable to landslides and should therefore attract the highest billing (weighting) vis-à-vis other slope classes, while assessing the landslide hazard potential. As set of hazard maps were prepared accordingly, only of find a poor match between the landslide hazard maps so produced and the corresponding Landslide inventory Map which reflected ground realities. Further studies revealed that although the incidence of landslide in slope Class 3 are the highest, it was also a fact that area covered under it was the largest. The question of assigning weightings to the different slope classes was therefore re-examined in recondite minutia, using six different criteria discussed in the paper. The results, which show remarkably similar trends, essentially point out two things. The first that the weightings assigned to different slope classes should not be based on the simple observations and observed relationships between slope classes and landslides (usually based on low population of data), and the second that a large population of statistical data is essential to iron-out the differences between the weightings assigned on the basis of field observation and the theoretically assigned weightings.
It is concluded that weightings to the sub-factor within a slope map is best decided in terms of the theoretical estimates rather than by analyses of observed landslide slope class observed landslide slope class relationship for a limited area. By doing so, a better match is found to occur between the observed and inferred instability potentials, when cognizance is taken of all the factors including the slope angle.
Relative Weightings Between the Different Slope Classes:
In order to arrive at the relative weightings appropriate for different slope classes, relationship between slope class and landsliding were studied.
The field observation on 863 landslides in Nuwara Eliya district (Fig 1), and the 213 landslides in the Badulla district were plotted. These observations prompted the investigators to bill the highest weighting to slope class 3 in which preponderance of landslides were observed. The relative weightings assigned to each slope category on this
basis is shown in Table 1. When landslide hazard maps were prepared using these weightings, a very poor match was found between landslide inventory map and the map of inferred instability. It therefore became imperative to look into this aspect more closely.
Figure 1 Variation of landslide frequency with ground slopes
Table 1 Correlation between slope range, assigned slope category and the relative weighting based on observed data and the theoretical cure
| Slope Range |
Assigned Slope Class |
Relative weighting based on |
| Degree |
% |
Field observations |
Theoretical curve |
| > 40° |
> 84° |
1 |
5 |
25 |
| 31°-40° |
60°-84° |
2 |
15 |
16 |
| 17°-31° |
30°-60° |
3 |
25 |
13 |
| 11°-17° |
20°-30° |
4 |
20 |
7 |
| 0°-11° |
0°-20° |
5 |
10 |
5 |
Criteria for Relative Ranking:
The following six criteria were used to study the nexus between observed landslides and slope classes.
1. Criterion 1 (C1)
| C1 =
| Number of Landslides in al Map Unit (Nu)
----------------------------------------------------
Area of that Map Unit (Au)
|
2. Criterion 2 (C2)
| C2 =
| Area of Landslides in a Unit (Au)
---------------------------------------------
Area of that Map Unit (Au) |
3. Criterion 3 (C3)
4. Criterion 4 (C4)
5. Criterion 5 (C5)
| C5 = |
No of Landslides in a Map Unit (Nu)
-----------------------------------------
Total No. of Landslides (Nu)
------------------------------------------
Area of Landslides in a Map Unit (Au)
------------------------------------------
Total No. of Landslides (au)
|
This could be easily done, because the available maps were on a digitized form,
and ARCINFO software was eminently suitable.
