(4.1)
Overview of chapter
This chapter presents the approach taken and details o f the methodology employed in determining runoff potential in the study area. In particular, it details the nature o f the hydrological
investigations, particularly issues related to the simulation of rainfall. What is known about characteristics of natural rainfall in the study area are presented in Chapter 6 together with other details about the study area. Methods related to the possibilities of ‘upscaling’ point
measurements of runoff potential by systematically classifying and mapping crusting soils and by linking them to spectral areal coverage of the landscape - in the form o f optical remote sensing - is reserved for the subsequent chapter. Chapter 5. This chapter covers the ‘philosophy’ of attempting to substitute rapid, low cost indicators of runoff potential in a suitabihty assessment context for the quantitative but time consuming rainfall simulations, as well as the details of these ‘simple’ tests. The results of the rainfall simulations carried out are presented in Chapter 7, and the results o f the alternative measures of runoff potential in Chapter 8, together with a comparison between the two in terms of the relative runoff potential of the study sites. Details of the study sites themselves are presented in Chapter 6.
This chapter, on a chronological basis, starts with an introduction to the research design, then turns to the role of simple tests as tests o f the wetting effects on sealing in crusting soils, before
addressing the question of rainfall simulation in some detail. Relevant properties of rainfall to be simulated are discussed, the design options for doing so compared and their advantages and disadvantages noted and then the design chosen described and its shortcomings noted with respect to an hypothetical ‘ideal’ design. The calibrations carried out on this design before putting it to use in the field are detailed and the implications of the findings discussed. The protocol employed with each of the two simulator designs used are outlined and then questions o f how to validate the results addressed. Finally, general conclusions are drawn about the approach taken and the instruments employed and the implications for mapping runoff potential highlighted. This leads naturally to the subsequent chapter, where various attempts to map runoff from point
(4.2)
Research design: General approach taken
The general approach taken is illustrated by a number of figures, which will be referred to here below. The approach taken is essentially comparative and data driven. It is based on the principle that to build up an accurate picture o f the environment in general, and the spatial (and temporal) distribution o f runoff potential in particular, it is necessary to employ a range o f techniques, each of which may reveal a different aspect of the phenomenon under study.
This idea is underlain by the principal o f the principle o f weight o f evidence, on the premise that a range of data sets collected with a range of instruments and protocols, when combined, are more likely to highlight possible errors, in the form of deviations from the rest of the datasets, and that, as a statistical principle, errors will to some degree cancel each other out. On the other hand, it is recognised that such an approach can prove difficult for the same reasons; the range of data types, measurement scales, explicit and implicit definitions and class limits employed, temporal
disparities, georeferencing errors etc. create a problem of data integration, data reduction and error assessment, and a GIS analysis was therefore utilised for the final assessment of suitability for water harvesting, as this is an analytical tool well suited for this type of problem.
An inexpensive raster open-architecture GIS widely used in developing countries (Eastman 1995), Idrisi, was chosen for this purpose, and analysis restricted to this software for GIS and image processing purposes, in spite of some limitations. It is much quicker to carry out some routines in other, more expensive software, but it is not considered that such expensive software is likely to be widely available in developing countries.
With a GIS, however, the old adage ‘garbage in, garbage out’ very much holds true, and therefore buttressed by systematic cahbration o f the instruments employed and a critical awareness of the limitation o f the protocols and data sets employed and of any contradictions between them in terms of dates, scale, definitions etc. Such contradictions, however, are a real world situation, and the contradictions between the surveys carried out by the various agencies which have worked in Baringo soon became readily apparent. A transect was chosen down a shallow catena towards Lake Baringo, collecting infiltration and ancillary data over a short sample interval (50 m blocks
every 100 m, over some 3 km), and the picture painted by this data collection exercise contrasted with those presented by a number o f secondary data sets / thematic maps.
Figure 4.1 illustrates the principal data sets collected and possible relationships between them. This is predicated on the logic that the 3.5 m rainfall simulation results (carried out in the second field season) would be the most accurate / reliable measure o f runoff potential, and its central position reflects its status as the reference value (comparison will also be made with the ‘ultimate’ reference value, runoff under natural rainfall, as described in the validation section below). The figure is a quadrat set out along the axes ‘direct / indirect measures o f reflection / absorption’ in an left-right dichotomy and ‘direct / indirect measures o f infiltration / runofT opposed in a top- bottom orientation. This figure provides an overview of the types of data collected and the modes of collection, as well as the nature o f the data in terms o f qualitative or quantitative and the comparisons possible and envisioned between the data sets generated using these instruments / measures and associated protocols. These will be described below for the more important measures.
Figure 4.2 provides an overview o f some possible approaches which one could take in mapping runoff potential, and the comparisons which could be made between the resulting data sets for the purpose of improving the accuracy of the final output, as well as to determine the advantages and disadvantages of each approach. This figure also establishes the relationship between and roles of primary and secondary data sets in the approach chosen for the present study.
Figure 4.3 presents the operational approaches possible for assessing runoff potential integrating ground tmth and earth observation imagery. The central role of a GIS in converting between ‘point’ data (ground measurements, which can effectively be considered to be points, given the resolution of a satellite pixel (20 or 30 m but taken to about one ha to account for georeferencing errors)) and area data. The latter are normally represented cartographically as chloropleth maps; which imply that all points within a given polygon are of equal value, which is rarely the case.
A similar assumption underlies image classification: to put it crudely, if two areas look similar on an image, then they are probably similar on the ground. Applied to the objectives of the present study, this principle would be translated as: if the demarcation o f the study area into homogeneous zones, on the basis of the spectral characteristics of the landscape, corresponds to the classification of the landscape into runoff response units, then one can map runoff potential fi'om remote
Figure 4.1
Possible relationships between data sets
DIRECT MEASURES OF INFILTRATION / RUNOFF
RAINFALL SPRAYERS:
SIMULATION: HAND, BACKPACK
ONE METER DRIP
TYPE RINGS:
FIVE METER SPRAY LARGE, SMALL
TYPE (secondary data)
3.5 METER RAINFALL SIMULATION RESULTS REFERENCE VALUES DIRECT MEASURES OF REFLECTANCE: CONCURRENT FIELD SPECTRO- RADIOMETRY •INDIRECT' MEASURES OF REFLECTANCE: ^ w
POST &IOK ANTE
REMOTE SENSING
QUALITATIVE QUANTITATIVE
MEASURES MEASURES
(for example. (for example.
crust colour) soil strength using
penetrometer)