1 Spatial Modelling of the Terrestrial Environment - Georeferencial

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Near Real-Time Modelling of Regional Scale Soil Erosion 167 Figure 8.3 Sediment transport in the Lake Tanganyika catchment for the second decad of April 1998. Black polygons are lakes, dark grey indicates high-sediment delivery and white lowsediment delivery, (a) the entire catchment: the black line indicates the extent of the catchment, (b) the northern lake region: to the right of the letter A are the River Rusizi sub-catchments that provide the highest sediment inputs to the lake during this period highlights a paradox when using remote sensing to estimate both rainfall and vegetation cover from AVHRR imagery. When it is actually raining, and erosion is occurring, remote estimation of rainfall is possible but that of vegetation cover is not. Figure 8.3(a) shows the total monthly sediment transport results for the Lake Tanganyika region for the second decad of April 1998, when the catchment received up to 270 mm rainfall. At this scale the reader gets a picture of the erosion-prone areas; however, the fine detail of the sediment transport in the channel network cannot be seen. Figure 8.3(b) is zoomed in on the region of Rwanda, Burundi and eastern Zaire that shows the highest amounts of sediment transport to the lake during this decad. The figure depicts both hillslope and channel sediment transport. The dark regions are areas of high-hillslope erosion and subsequent sediment transport and the networks overlying and surrounding these blocks are areas where sediments transported from the hillslopes concentrate in river channels. Estimates of sediment yield of individual rivers during April (Plate 9) suggest that 43 rivers produced significant sediment inputs into the lake, with the majority occurring in the second decad. The Burundi sub-catchments of the River Rusizi appear to produce most of the sediment providing 17% of the total sediment yield. High erosion in this catchment can be attributed to relatively steep slopes (0.64 m/m, or 33%), high-overland flow (4 mm), and most importantly low-vegetation cover (7%). Though the model appears to work quite well when routing sediment into many parts of Lake Tanganyika, it suggests that the Malagarasi River, the catchment’s largest watercourse, produces no sediment, and
168 Spatial Modelling of the Terrestrial Environment very little sediment reaches Lake Victoria, though this lake is known to suffer sedimentation problems. This seems to illustrate a deficiency in the sediment delivery model when it is applied to large catchments that contain a considerable amount of flood plain. Problems with the sediment routing can, to a certain extent, be evaluated by remote sensing of the lake sediment plumes as it is possible to determine if sediment plumes are associated with rivers that the sediment delivery ratio method predicts produce no sediment. AVHRR and ATSR-2 imagery has clearly identified large near-surface sediment plumes emanating from a number of rivers in the catchement (Figures 8.4–8.6), and in the case of AVHRR even other rift valley lakes (Figure 8.4). A number of plumes are visible on the eastern side of the lake (Figures 8.4–8.6). The most extensive being the Malagarasi River plume, which is observed in all images, and consistently covers hundreds of kilometres. Patterns displayed by the Malagarasi River plumes closely match the wind-driven lake currents modelled by Huttula et al. (1997). The sediment yield modelling suggests that the Malagarasi River provides no sediment inputs to the lake, so there is a big discrepancy here. The study by Tiercelin and Mondegeur (1991) initially appears to supports that of the sediment routing. They suggest that Malagarasi River deposits much of its sediment in upstream swamplands and also traps sediment in its delta system. Thus, the fact that AVHRR and ATSR-2 imagery clearly identifies many large plumes is somewhat puzzling. However, Tiercelin and Mondegeur’s (1991) study is based on sediment core analysis of lake sediments that indicates that the central Lake Tanganyika basin has a lower sedimentation rate than that of the north where the Ruzizi River flows into the lake. It is possible that there is not much depth of sediment in the middle basin of the lake because the Malagarasi River sediment plume is transported towards the north under prevailing near-surface currents, and dispersed over a wide area, therefore creating less observable deposition in the middle basin. Thus, it is possible that the large buoyant plumes of the Malagarasi River could cause an underestimation in the significance of this river as a sediment contributor to the lake because plume dispersal causes its sediments to be deposited over a wide area. Only a few plumes can be seen in the northern basin where the highest sediment yields were predicted. Plumes are detected at the Ruzizi River mouth on only one occasion over the monitoring period (4 March 1998), whereas plumes can be detected from the Mugere or Ntahangwa on three dates (22 March 1998, 4 March 1998, 24 April 1998). It is interesting to note that the Ruzizi River does not have many large visible plumes, while modelling results estimated that it is responsible for a large amount of sediment input into the lake. Field measurements suggest that it transports a great deal of sediment (Sebahene et al., 1999) and that the plume sinks rapidly presumably because its water is more dense than the lake. Because of this, remote sensing is unlikely to be effective at monitoring the dispersal of what appears to be the most important source of sediments into the lake. 8.4 Conclusion The results show that regional scale erosion modelling can provide a general picture of the source areas of erosion in the Lake Tanganyika catchment, and estimates of the quantity of sediment transported into the lake. Previous spatial studies of lake sediment inputs have relied on the interpretation of satellite imagery (and topographic maps) to assess the amount of forest in a catchment, in order to determine those catchments most likely
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Spatial Modelling of the Terrestria
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Copyright C○ 2004 John Wiley & So
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Contents List of Contributors Prefa
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Contributors Jonathan L. Bamber Sch
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List of Contributors xi George Perr
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xiv Preface in theory and applicati
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2 Spatial Modelling of the Terrestr
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Editorial: Spatial Modelling in Hyd
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Editorial: Spatial Modelling in Hyd
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2 Modelling Ice Sheet Dynamics with
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Modelling Ice Sheet Dynamics by Sat
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36 Spatial Modelling of the Terrest
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4 Using Coupled Land Surface and Mi
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Coupled Land Surface and Microwave
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100 Spatial Modelling of the Terres
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Editorial: Terrestrial Sediment and
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Editorial: Terrestrial Sediment and
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6 Remotely Sensed Topographic Data
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Remotely Sensed Topographic Data fo
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Page 204 and 205: PART III SPATIAL MODELLING OF URBAN
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11 Modelling the Impact of Traffic
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Modelling the Impact of Traffic Emi
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PART IV CURRENT CHALLENGES AND FUTU
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268 Index Bi-spectral InfraRed Dete
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270 Index floodplain maps, UK 92 fl
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272 Index Long-Term Ecological Rese
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274 Index snow depth measurement ne
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276 Index urban land use in remotel
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1 Spatial Modelling of the Terrestrial Environment - Georeferencial

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