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Fixation of atmospheric CO2 in terrestrial vegetation, and subsequent export and deposition of terrestrial plant organic matter in marine sediments is an important component of the global carbon cycle, yet it is difficult to quantify. This is partly due to the lack of understanding of relevant processes and mechanisms responsible for organic-matter transport throughout a landscape. Here we present a new approach to identify terrestrial plant organic matter source areas, quantify contributions and ascertain the role of ecologic, climatic, and geomorphic controls on plant wax export in the Arun River catchment spanning the world's largest elevation gradient from 205 to 8,848 m asl, in eastern Nepal. Our approach takes advantage of the distinct stable hydrogen

We review different regression models related to water quality that incorporate spatial aspects in their model. Spatial aspects refer to the location of different sites and are usually characterized by the distance between different points and directions by which they are related to each other. We focus on spatial lag and error, spatial eigenvector-based, geographically weighted regression, and spatial-stream-network-based models. We evaluated different studies using these methods based on how they dealt with clustering (spatial autocorrelation) of response variables, incorporated those clustering in the error (residual spatial autocorrelation), used multi-scale processes, and improved the model performance. The water-quality-based regression modeling approaches are shifting from straight-line distance-based spatial relations to upstream–downstream relations. Calculation of spatial autocorrelation and residual spatial autocorrelation was dependent upon the type of spatial regression used. The weights matrix is used as available in the software and most of the studies did not attempt to modify it. Different scale processes like certain distance from rivers versus consideration of entire watersheds are dealt with separately in most of the studies. Generally, the capacity of the predictor variables to predict the response variable significantly improves when spatial regressions are used. We identify new research directions in terms of spatial considerations, weights matrix construction, inclusion of multi-scale processes, and identification of predictor variables in such models.