ANALYSIS OF DATA

Data collected for the purpose of predicting the effect of man's activities on erosion and sedimentation are used either in developing empirical relationships or as input to existing mathematical models. Careful examination of the data is necessary to determine if any is un- reliable and should be discarded. In analysing the data, statistical methods usually selected are one or a combination of more than one of either (1) graphical analysis, (2) correlations by using a desk-type calculator, or (3) regression analysis when more than two independent variables are involved by use of more sophisticated digital computer.

A good scientific data collection and analysis programme involving statistical methods require the data to be well organised and summarised for analysing and ultimately displayed characteristics such as drainage area, slope, geology, soil classification, and vegetative cover vary spatially but are invariant with respect to time. The deposits of sediment in a reservoir are spatially non-homogeneous because of the settling characteristics of sediment in a reservoir. An evaluation of the homogeneous and non-homogeneous characteristic of the data is a first check on the reliability of the data. The non-homogeneous characteristics would consistent records because of upstream reservoirs or diversion dams. If inconsistencies are disclosed by the double-mass technique and supported by a check on changes in observation pro- cedures, gauge location, or discharge measuring section, then appropriate adjustments to the data are justified.

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IO I Id I 1111 I Id I Id I IIll

IO 100 1000 10000 100000

A = DRAINAGE AREA IN Km2

Fig. 3.16 Sediment yield from reservoir survey data from semi-arid climate in the United States. (Bureau of Reclamation, USA).

0 100 200 300 400 500 600 700 800

CHANNEL DISTANCE IN METERS -UPSTREAM -

Fig. 3.17 Longitudinal profile showing gully movement on Dry Creek, Nebraska (Survey data, from V.I. Dvorak). (U.S. Department of Agriculture, June 1967).

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-- .---..-.-

In evaluating the relative accuracy of the data collected, the technique used in collect- ing the data must be reviewed. In the case of precipitation data, a good network of rain gau- ges in the catchment area is of prime importance. The same would apply to a stream flow net- work to assure proper representative sampling along with location where runoff data are needed.

Statistical analysis by either the desk-type calculator or sophisticated digital computer can be used to evaluate the relative accuracy of data. For most hydrologic data involving rain- fall, runoff, and sediment transport, a graphical regression analysed analytiaally provides the relative accuracy of the regression in terms of a computed correlation coefficient. In general, a sample of 30 items or more is needed to define a distribution in order to define a confidence band from which conclusions and reasonable decisions are made with regard to the data.

Because of the present capabilities to simulate stream flow hydrographs, flow-simulation models for analysing runoff, storage, sediment transport, sediment deposition and erosion by use of sophisticated digital computers will eventually prove to be useful techniques in studies of the erosion and sedimentation processes. These flow and sediment-simulation models when properly calibrated with ground truth data on runoff and sediment will help improve data col- equation by least-square regression. When more than two variables are involved, the graphical method becomes more difficult to interpret. The most common equations involving two variables are as follows:

Numerous equations have been written for mathematically modelling sediment erosion from interfluvial areas. Most of these related the many factors discussed in section 2.2, Interflu- vial Areas, with measured rates of either sheet or rill erosion. Because of the complexity and difficulty in defining the many factors controlling soil erosion in the interfluvial area, the equations available are considered to be only approximations. Probably the most comprehensive analysis of erosion from the interfluvial area was the work done by Wischmeier and Smith (1965) involves suspended sediment samples, bed material samples, and channel hydraulic measurements which require several graphical and least-square regression analyses. The first is the plot- ting of all the suspended sediment samples for computing a suspended sediment rating curve for depicting the fine material transported in suspension. The criteria for number of samples re- quired to adequately define a rating curve is dependent on hydrologic conditions but should

cover a 5 year period to ensure samples at sufficiently high discharges. Suspended sediment data are either plotted as sediment concentration/stream flow or sediment discharge/stream flow and usually on a logarithmic plot. Stream flow or discharge in cubic metres per second may be on a time basis of either instantaneous or daily, but many investigators have used a monthly or

annual basis for both stream flow and sediment load. Some of the limitations of the rating curve technique are described by Walling (1977) presenting the results of a study on estimating errors. An example of the suspended sediment rating curve is shown in figure 3.18.

suspension. The criteria for number of samples required to adequately define a rating curve is dependent on hydrologic conditions but should cover a 5 year period to ensure samples at sufficiently high discharges. Suspended sediment data are either plotted as sediment concen- tration/stream flow or sediment discharge/stream flow and usually on a logarithmic plot. Stream flow or discharge in cubic metres per second may be on a time basis of either instantaneous or sive characteristic data in multivariant regression analysis for defining soil erosion, gully erosion, or channel movement. The universal Soil-Loss Equation presented in Appendix 3 is a

Other regime-type relationships for defining stable channels have been developed with Kennedy (1895), Blench (1969), Miller and Borland (1963), all useful in analysing channel work new capacity curve and sediment deposition profile as schematically illustrated in figure 3.23.

The difference in capacity curves shown in figure 3.23 is the volume of sediment accumulation in the reservoir. Trap efficiencies can be estimated from empirical relationships such as developed by Brune (1953), figure 3.24 or by Churchill (1948), figure 3.25.

On many reservoirs any data collected on the delta accumulation in the backwater reaches where the coarser size sediments first deposit are analysed and used to project changes on existing deltas and predict delta development on proposed reservoirs. Methods for analysing delta development involve estimating the topset slope from reservoir surveys with measured

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Fig. 3.18