Modelling Uncertainty in Watershed Divides from SRTM and GDEM

Laura Poggio1 and Pierre Soille2
1. The Macaulay Land Use Research Institute, Aberdeen, United Kingdom
2. Joint Research centre, European Commission, Ispra, Italy
1. poggio@macaulay.ac.uk; 2. pierre.soille@jrc.ec.europa.eu

Abstract: Watersheds are considered important units in many environmental decision-making processes. The delineation of watersheds using digital elevation models (DEMs) is common and presents many advantages. However it is very sensitive to the uncertainty of the elevation datasets used. The main aim of this work is to use a probabilistic approach to extract watersheds divides on two widely available datasets in order to estimate the uncertainty. Hundred simulations of each of the input dataset were generated using a Monte Carlo probabilistic approach. The watershed divides were delineated from each iteration. The different iterations were combined to produce a cumulative probability surface representing how many times a cell was part of a watershed divide. The preliminary results showed a high uncertainty in most of the test area. The highest uncertainty was related to small sub-watershed of low Strahler order streams. For both the considered datasets, the modelling of the elevation errors improved the delineation process, providing important additional information.

Keywords: digital elevation models, simulations, Strahler orders, probabilistic.

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