AbstractThe wet-dry tropics of Asia support large populations dependent on subsistence agriculture. Sedimentation of water storages is an increasing problem, and governments are relying on catchment management to reduce sediment yield. However sources of sediments are poorly understood. Sediment budgets can be useful in informing catchment management; however the resources and data required to create a sediment budget may be prohibitive. This study presents a multidisciplinary approach to the investigation of sediment sources and the creation of a first-order sediment budget for a catchment in the data-poor wet-dry tropics of eastern Indonesia. The approach integrates results from geospatial analysis, key informant interviews and radionuclide tracer measurements. Free software and imagery were used to demonstrate that geospatial analysis can be achieved without high costs. Surface soil erosion rates were predicted using Revised Universal Soil Loss Equation (RUSLE) and used to map the spatial distribution of relative rates of surface erosion. Key informant interviews enabled the identification of gully erosion sites, many of which were not detectable through spatial analysis. Key informant interviews also contributed to the understanding of drivers and socio-economic impacts of erosion. Radionuclide tracers 137Cs, 210Pb(ex) and 239Pu were used to determine the relative contributions of surface soil to sediment. The dominant sediment sources in the Kambaniru catchment (1227 km2) were surface soils (31%), and the subsurface sources, channel change (23%), gully erosion (8%), and landslides (1%), with an estimated annual sediment load of 1,440,000 t yr-1. This sediment budget showed sub-surface soils were a major source of sediment and this was confirmed by radionuclide tracer results. These results contradict assumptions that underpin Indonesian catchment management policies. The geospatial, interview and field methods applied in this study were effective in identifying, mapping and quantifying sub-surface sediment sources, and can readily be applied generally in areas where data are few and technical capacity and financial resources are low.
Note: Please note that Appendix C is available in hard copy only.
|Date of Award||2015|
|Supervisor||Bronwyn Myers (Supervisor), Rohan Fisher (Supervisor), Robert Wasson (Supervisor) & Guy Stuart Boggs (Supervisor)|