Headwaters make up the majority of any given stream network, yet, they are poorly mapped. A solution to this is to model the stream networks from high resolution digital elevation models. Matthews Correlation Coefficient (MCC) for a modelled stream network was 0.463 while the best topographical maps of today, had an MCC of 0.387. A residual analysis showed that 15 % of the errors could be explained by variability in runoff, quaternary deposits, local topography and location.
Headwaters make up the majority of any given stream network, yet, they are poorly mapped. A...
Review status: this preprint was under review for the journal HESS. A final paper is not foreseen.
The importance of better mapping of stream networks using high resolution digital elevation models – upscaling from watershed scale to regional and national scales
Anneli M. Ågrenand William LidbergAnneli M. Ågren and William LidbergAnneli M. Ågrenand William Lidberg
Received: 18 Jan 2019 – Accepted for review: 28 Jan 2019 – Discussion started: 04 Feb 2019
Abstract. Headwaters make up the majority of any given stream network, yet, they are poorly mapped. A solution to this is to model the stream networks from a high resolution digital elevation model. Selecting the correct stream initiation threshold is key, but how do you do that on a national scale across physiographic regions? Here the Swedish landscape is used as a test bench to investigate how the mapping of small stream channels (< 6 m width) can be improved. The best modelled stream channel network was generated by pre-processing the DEM, calculating the accumulated flow, extracting a stream network using a stream initiation threshold of 2 ha. The Matthews Correlation Coefficient (MCC) for the 2 ha stream channel network was 0.463 while the best available maps of today, the Swedish property map (1 : 12 500) had an MCC of 0.387. A residual analysis of the 2 ha network show that there is additional improvements to be made by adapting the model to local conditions, as 15 % of the over and underestimation could be explained by the variability in runoff, quaternary deposits, local topography and location. The most accurate stream channel network had a length 4.5 times longer the currently mapped stream network, demonstrating how important accurate stream networks is for upscaling aquatic and climate research.
Headwaters make up the majority of any given stream network, yet, they are poorly mapped. A solution to this is to model the stream networks from high resolution digital elevation models. Matthews Correlation Coefficient (MCC) for a modelled stream network was 0.463 while the best topographical maps of today, had an MCC of 0.387. A residual analysis showed that 15 % of the errors could be explained by variability in runoff, quaternary deposits, local topography and location.
Headwaters make up the majority of any given stream network, yet, they are poorly mapped. A...