Finding Potential Sites for Small-Scale Hydro Power in Uganda: a ...

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There are a set of tolerances used to adjust the smoothing of input data and theremoving of sinks in the drainage enforcement process. Tolerance number one (tol1)reflects the accuracy and density of the elevation points. Data points, which blockdrainage by no more than this tolerance, are removed. This should be set to half of thecontour interval when using contour data (Johnston et al., 2001). Tolerance number two(horizontal_std_err) represents the amount of error inherent in the process of convertingpoint, line, and polygon elevation data into a regularly spaced grid. It is scaled by theprogram depending on the local slope at each data point and the grid cell size. Largervalues will cause more data smoothing, resulting in a more generalized output grid.Smaller values will cause less data smoothing, resulting in a sharper output grid, which ismore likely to contain spurious sinks and peaks. The third tolerance (vertical_std_err)represents the amount of random error in the z-values of the input data. The defaultvalues for horizontal and vertical standard errors are very robust and have been testedwith a wide variety of data sources. If the interpolation results are inferior, you shouldaccording to Johnston et al. (2001) check for errors in the input data before changing thedefault values. We therefore let the tolerances be default except for the first tolerance(tol1), which we set to 15 meters, half the input data contour interval.4.2.3.3 SinksA sink is a cell or set of spatially connected cells whose neighbouring cells all have ahigher elevation value, which means that there is no drainage from the sink. Naturallyoccurring sinks in elevation data with a cell size of 10 meters or larger are rare except forglacial or karst areas, and generally can be considered errors. As the cell size decreases,the number of sinks in a data set often increases (Johnston et al., 2001). Spurious sinksand local depressions in DEMs are a significant source of problems in hydrologicalapplications. Sinks may be caused by incorrect or insufficient data, or by an interpolationtechnique that does not enforce surface drainage (Hutchinson and Dowling, 1991). Toeliminate possible sinks in the interpolated DEM, we used the ArcGIS function “FILL”.4.2.3.4 Quality Assesment of DEMsThe quality of a derived DEM can vary greatly depending on the source data and theinterpolation technique. The desired quality depends on the application for which theDEM is to be used. Since most applications of DEMs depend on representations ofsurface shape and drainage structure, absolute measures of elevation errors do notprovide a complete assessment of DEM quality.38
A number of graphical techniques for assessing data quality have been developed. Theseare non-classical measures of data quality that offer means of confirmatory data analysiswithout the use of accurate reference data (Wilson and Gallant, 2000). The following twotechniques have been used in the study:o When deriving contours from a DEM, it can provide a reliable check on terrainstructure since their position, aspect and curvature depend directly on theelevation, aspect and plan curvature respectively of the terrain. Derived contoursare a useful tool to measure interpolation quality. (Wilson and Gallant, 2000).o By computing shaded relief images of the DEM, it allows to do a rapid visualinspection of the DEM for local anomalies that show up as bright or dark spots.This technique can indicate both random and systematic errors, for example edgematching as well as terracing problems (Hunter and Goodchild, 1995, Wilson andGallant, 2000).To create reference data to be able to evaluate the accuracy of the absolute elevation ofthe DEM, four contour lines, all containing approximately 300 vertices, from the contourlayer were excluded before the interpolation. Two lines from a flat area and two from asteep were visually chosen in ArcGIS. We calculated Root Mean Square Prediction(RMS) error using the reference data and the interpolated DEM values as input inEquation 4.1. RMS-error indicates modeled points mean deviation from their origin bycalculating the deviations of points from their true position, summing up themeasurements, and then taking the square root of the sum.RMS =n∑i=1( I − R )ini2(4.1)Where I equals the interpolated values, R equals the reference values and n the numberof values (Johnston et al., 2001).4.2.4 Permanent Rivers with ElevationInput data to our algorithm is a layer with rasterized rivers, where each river cell containsthat position’s absolute elevation values. In order to receive needed data, we extractedthe permanent rivers, from the river layer, because our study is not applicable where39
Page 1: Seminar series nr 121Finding Potent
Page 5: AbstractOver 2 billion people, most
Page 9: AcknowledgmentsFirst of all, we wou
Page 12 and 13: 5 Result ..........................
Page 14 and 15: For the specific case of Uganda, a
Page 17 and 18: 2 Study AreaThe main reason for us
Page 19 and 20: In the 1960’s Uganda was one of t
Page 21 and 22: 2.1.3 TopographyMost of Uganda is s
Page 23: Figure 2.4 The highest volcano in K
Page 26 and 27: e able to manually write the data,
Page 28 and 29: Depending on the appearance of the
Page 30 and 31: o Then the water flows from the for
Page 32 and 33: and generator is 65% and 80% effici
Page 34 and 35: Table 3.2 Capacity and production o
Page 36 and 37: Table 3.5 Small-scale hydropower ca
Page 38 and 39: technology was supplied to an area
Page 40 and 41: component to all of these issues, g
Page 42 and 43: The Rural Electrification project t
Page 44 and 45: The closer the interviewer is to th
Page 46 and 47: Over this area we bought the follow
Page 48 and 49: The primary erosive force determini
Page 52 and 53: ivers are seasonal, since the hydro
Page 54 and 55: (a) (b) (c)Figure 4.8 An example sh
Page 56 and 57: 4.2.6 Post Processing of Output Dat
Page 58 and 59: A = ( b ⋅ h)2(4.3)Where A equals
Page 60 and 61: elinquish because of too expensive
Page 62 and 63: The first step when performing the
Page 64 and 65: more obvious in their geographic co
Page 66 and 67: By using a smaller area it is possi
Page 68 and 69: 5.3 Summary of Resultso The general
Page 70 and 71: projects are supposed to be finance
Page 72 and 73: Sinks are a reoccurring problem whe
Page 74 and 75: UTM Zone 36N projection. The study
Page 76 and 77: As described in chapter 3.3.3 all r
Page 78 and 79: and if good financing possibilities
Page 80 and 81: Hutchinson M. F., Dowling T. I., 19
Page 83 and 84: 9 Appendix9.1 Interview: Rural Elec
Page 85 and 86: 9.3 Digital DataAll data layers are
Page 87 and 88: Frame creationThis is made in order
Page 89 and 90: time2=clock;%starts timer on clock
Page 91 and 92: next(1,2)=(y-1);cell_height=(highes
Page 93 and 94: endendif height>=20;%if the accumul
Page 95: Site 16 (147486,78619)Site 20 (1560

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