|The Himalayan Dilemma: Reconciling Development and Conservation (UNU, 1989, 295 pages)|
|6. The Himalayan-lowland interactive system: do land-use changes in the mountains affect the plains?|
Lauterburg analysed the work in Nepal by Nelson (1980), Laban (1978, 1979), and others. Nelson, for instance, attempted to determine the general status of degradation of all Nepalese watersheds by visually estimating a 'watershed condition index.' 'This 'relates the current state of soil erosion in an area in comparison with the soil erosion estimated for that area under natural or well managed conditions' (Nelson, 1980:2). This study shows a significant concentration of heavily degraded areas (accelerated erosion) in the central and lower Siwaliks and in the Kathmandu area.
Lauterburg reassesses Laban's landslide count from a light aircraft (Laban, 1979, and see above, pp. 106-7). This provides us with a Natural Landslide Index, which is obtained by dividing the number of landslides occurring within forested areas by the total number of landslides. This approach suffers from the uncertainty of being able to distinguish between 'natural' forests and degraded forests (lopped, partly grazed, etc.).
A complementary approach is the use of data on suspended sediment load in rivers. By assuming that the sediment load of several rivers is measured accurately over a significant number of years, the total (average) amount of sediment yield per annum divided into the total area of the watershed will provide the so-called Specific Suspended Sediment Delivery (SSD) of a catchment. This, as discussed in Chapter 5, does not include all material eroded within a watershed, since much is left in temporary storage and does not enter the fluvial system. In addition, there were no available data for bedload or solutes. However, as long as the watersheds under comparison are of approximately the same size and form (that is, their Sediment Delivery Ratios are similar) the SSD does provide a useful comparative unit. Table 5.1 gave the SSD rates (actually denudation rates) calculated in this way for Nepal. Figure 6.1 shows the Suspended Sediment Delivery for most of the Himalayan Region. However, this map has been compiled from very different sources which cover different time periods. Thus the SSD data for the Indus watershed was collected in the late-1950s (Ahmad, 1960) and may be higher today. The somewhat higher denudation rates than those given in Table 5.1 are due to the use of estimated suspension data for Figure 6.1, especially for eastern Nepal.
Despite these obvious limitations, together with actual gaps in data availability, a broad-brush comparison may still be useful. Examination of the three macro-regions, the Indus, Ganges, and Brahmaputra watersheds, demonstrates immediately that SSD rates in the Nepalese Himalaya are much higher than the other two regions. Such a map (Figure 6.1), if updated with dependable information, would be extremely valuable. Unfortunately, this cannot be done at the present time. Another problem with this approach is that, whatever we learn about the current erosion rates, there is no time perspective. If an attempt is to be made to assess the relative, or absolute, proportion of total SSD due to human rather than natural (geophysical) processes, we would need to know the change in sediment load over the past hundred years or so. Moreover, no attempt has been made to estimate the stabilizing input by man through terrace construction and their effective maintenance.
As indicated in Chapter 1, the conservationist and scientific literature is replete with qualitative estimates of landscape degradation in specific small areas - this is largely what set in motion the Theory of Himalayan Environmental Degradation - but no attempt has been made to produce quantitative data nor, especially, to link them with the large-scale processes occurring on the flood plains and deltas of the major trunk streams.
In contrast, Figure 6.2 shows the lithological erodibility (specifically, the susceptibility to weathering and erosion of the bedrock) of the Karnali watershed in Nepal divided simply into low, medium, and high erodibility. The close relation between Ethology and susceptibility to erosion is apparent. Of particular significance, the largest area of 'high erodibility' is situated in the high Himalayan zone which has a very low population density. However, this map shows susceptibility to erosion and not actual sediment yield. Rambabu et al.'s (1978) map of annual erosivity in northern India and Nepal (Figure 6.3) is more useful. This is based on a regression equation which calculates erosivity from monthly or annual rainfall data, incorporating 30-minute maximum rainfall intensities and total kinetic energy.
The construction of iso-erosivity maps normally depends on availability of an extensive network of stations that record short-term rainfall intensities as well as the catastrophic climatic events with very long recurrence intervals. As mentioned above there is a great shortage of these kinds of data for the Himalaya and the problems associated with this approach have been discussed in Chapter 5. Nevertheless, we concur with Lauterburg's conclusion that, compared with the other great mountain systems of the world, the Himalaya experience very intense erosivity and high probability of catastrophic highintensity rains, and are at a very severe risk of climatic erosion. Even in making this very general statement, however, we must once again emphasize the gaps in available data, as well as poor data quality.
The discussion of the ratio of natural (geophysical) erosion to that caused by human intervention (accelerated erosion) is taken a step further by Lauterburg. He compares maps of natural erosion risk (Ethology and climatic factors) with maps indicating the state of watershed degradation and landslide frequency in Nepal (Figures 6.4, 6.5 and 6.6). Figure 6.4 indicates susceptibility to erosion according to a combination of Ethology and climatic factors with data assembled on a grid. Figure 6.5 demonstrates watershed condition, incorporating Nelson's (1980) and Laban's (1979) determinations of landslide incidence induced by human intervention. Finally, Figure 6.6, which combines data from Figures 6.4 and 6.5, identifies those areas in Nepal where human activities have had very high, high, and moderate impacts on watershed degradation.
The data sets used in the compilation of Figures 6.4-6.6 include quantitative and semi-quantitative information as well as subjective estimates. There are also significant gaps in the data base. Figure 6.6 is principally a qualitative estimation of risk of watershed degradation. As anticipated in the previous discussion, there is a heavy concentration of 'very high impact,¹ which is defined as low natural risk and high actual watershed degradation, in the central Siwaliks and the Kathmandu Valley and adjacent Middle Mountains. The much-discussed heavy damage in the Tamur and Arun watersheds in eastern Nepal does not appear and the Middle Mountains west of Pokhara are largely blank. The implication is, of course, that according to this approach, human impact (negative) must be quite low.
The approach of Lauterburg is interesting and, if more data can be accumulated, future construction of such maps could provide a valuable guide to land reclamation and watershed protection policy development. The present attempt, however, while giving some useful indicators, is not only limited because of data availability and accuracy, but cannot take into account the indigenous land-reclamation efforts of the farmers. Nevertheless, and regardless of gaps in data availability, Figure 6.6 creates the impression that the areas of high human impact in Nepal are very restricted. We are still left with the impression, however, that considerable areas of Nepal are in a condition of potential instability whereby heightened subsistence-farming pressures, or reduced maintenance of agricultural terraces, could lead to a rapid and dramatic increase in watershed degradation.