Remote sensing is a less costly and reasonably accurate technology for monitoring andrnmodelling river systems. However, the coarseness of remote sensing data together with therndynamic inherent optical properties of the variables constrained its application. Thus, thernoverall purpose of this research was to propose the spectral unmixing approach for buildingrnremote sensing model and parametrizing the physically distributed hydrologic model, soilrnand water assessment tool (SWAT), for estimating sediment concentration and evaluatingrnimpact of climate and sediment management changes.rnLaboratory, time series in-situ and space remote sensing data analyses were triangulated tornconstruct linear spectral unmixing analysis (LSUA) and compared with the conventionalrn(empirical) remote sensing models. The models were constructed in laboratory experimentsrnusing sediment types sampled from the Tekeze and Tsirare Riverbeds deposited sedimentsrnand then tested to estimate the daily SSCs from in-situ and space remote sensing data andrnevaluated against the observed SSCs in the Rivers. To enhance the LSUA model accuracyrninto the mixed pixels of the moderate-resolution imaging spectroradiometer (MODIS)rnimages, a new approach called Double-stage-LSUA (DLSUA) was proposed. In this case,rnLSUA was applied at two stages that LSUA in the first stage was used to unmix the pixelsâ€˜rnreflectance into respective macro endmembersâ€˜ (rockbare-land and turbid water)rnreflectance and the LSUA in the next stage was used to determine spectral mixingrncoefficients (SMCs) of the constituents in the turbid water (micro components includingrnpure water and sediment) was proposed. Finally, the SSCs of the Rivers were simulated byrninserting the computed SMCs into the LSUA model generated in the laboratory. The LSUArnapproach was also tested to monitor the spatial variability of a vegetation parameter of soilrnerosion and sediment (C-factor) which is the required parameter in most sedimentrnestimating hydrologic models. The spatial minimum C-factor of the upper Tekeze Riverrnbasin was first mapped using the LSUA technique from the Landsat images and tested itsrnaccuracy using time series field monitoring. Average C-factor was integrated intornhydrological response units (HRUs) of SWAT. This differs from the conventional approachrnwhere the C-factors have been integrated into land-use type units of SWAT. The LSUArnintegrated SWAT was demonstrated in evaluating climate and sediment managementrnchange scenarios on sediment yield. The goodness-of-fit indices including Nash-Sutclifferncoefficient (NSE), Coefficient of determination (R2rn), Root Mean Square of Error (RMSE),rnRoot mean Square of error- observations standard deviation Ratio (RSR) and Percent Biasrn(PBIAS) were used to evaluate the performance of the model outputs.rnThe application of LSUA approach to finer (ground) and coarser (MODIS) resolutionsrnremote sensing data for modelling variability of SSCs of the Tekeze Rivers were performedrnaveragely at Rrn2rn= 0.92 with RMSE = Â±0.75g/l and Rrn2rn= 0.83 with RMSE Â±9.96,rnrespectively. These performances were relatively good compared to the simulations usingrnthe conventional empirical regression remote sensing model performed at R2rn=0.78 with rnixrnRMSE = Â±6.76g/l and R2rn= 0.74 with RMSE Â±16.2, respectively. The success of applyingrnthe LSUA approach was not only for the direct estimation of SSCs, but it was alsornsuccessful for determining the spatial variation of C-factor values within and among landuse types. The demonstration in the upper Tekeze basin showed that the use of thernminimum C-factor map produced using LSUA and integrating it into HRUs of SWATrnimproved the fit between the predicted and the measured sediment yield. The coefficientsrnincluding NSE, PBIAS, RSR and R2rnfor sediment yield were 0.72, 0.39, 34.2 & 0.68,rnrespectively, when the C-factor values were for the land-use type units of SWAT. When thernC-factor was for the HRUs in SWAT, the corresponding values were 0.84, 0.23, 10, &rn0.89. The average rainfall and temperature over the basin experienced neither significantrnincreasing nor decreasing trends in the time scales. In contrast, trend analyses of differentrnvariables on the simulated sediment yield from the upper Tekeze basin have shown arnsignificant increasing trend. Moreover, the sediment concentration simulation using thernLSUA-SWAT shows that applying filter strips, stone bunds, and reforestation or integrationrnof these scenarios reduced the current sediment yields by different rates (Ave. 9-38%).rnThe LSUA approach has found to be effective in generating relatively accurate andrnuniversal models working with both ground-based (finer-resolution), and space-basedrn(coarser-resolution) remote sensing data from river systems. LSUA was also effective inrndetermining the variability of C-factor among and within landuse types. The successfulrnintegration of the C-factor values into HRUs enhances the sensitivity of SWAT to thernspatial variability of C-factor and then sediment yield. Therefore, the current study impliedrnthat prior studying and considering the inherent optical properties of endmembers duringrnanalysis is important to enhance remote sensing technology for modelling and monitoringrnsediment concentrations. The continuous and significant increasing trend of sedimentrnconcentration in the basin irrespective of the insignificant and non trending changes ofrnclimate variables has implied that the changes in catchment characteristics over timernincluding changes in land use and/or land cover in the basin are the governing factors.rnMoreover, the sediment yield in the basin varies with the changes in sediment managementrntype. Hence, though further calibration and validation are needed, the LUSA and itsrnintegration to hydrologic models (eg. SWAT) approach can support decision-makingrnconcerning the SSCs variability and impacts of climate change and managementrnalternatives at the river basin scale better than the conventional approaches.