Hortonian runoff closure relations for geomorphologic response units: evaluation against field data

Vannametee, E.; Karssenberg, Derek; Hendriks, M.R.; Bierkens, Marc F. P.

doi:10.5194/hess-17-2981-2013

Cited by 2 publications

(2 citation statements)

References 55 publications

(71 reference statements)

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“…Landform units at this scale can be chosen as model units in a hydrological model (e.g. Vannametee et al, 2013) or as functional units for planning and management (e.g. Arattano et al, 2010).…”

Section: Field Mapping Of Landformsmentioning

confidence: 99%

“…Delineation of the response units used in the semi-distributed hydrological modeling is in many studies based on landform components and geomorphological features as they generally represent areas with hydrological similarities due to internal homogeneity of morphometric and physical properties (e.g. Tilch et al, 2002;Güntner and Bronstert, 2004;Uhlenbrook et al, 2004;Pluntke et al, 2013;Vannametee et al, 2013). Thus, an automated landform map can be incorporated into a hydrological modeling framework to provide information on the model units and model parameterization (e.g.…”

Section: Future Perspectivesmentioning

confidence: 99%

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Semi-automated mapping of landforms using multiple point geostatistics

et al. 2014

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This study presents an application of a multiple point geostatistics (MPS) to map landforms. MPS uses information at multiple cell locations including morphometric attributes at a target mapping cell, i.e. digital elevation model (DEM) derivatives, and non-morphometric attributes, i.e. landforms at the neighboring cells, to determine the landform. The technique requires a training data set, consisting of a field map of landforms and a DEM. Mapping landforms proceeds in two main steps. First, the number of cells per landform class, associated with a set of observed attributes discretized into classes (e.g. slope class), is retrieved from the training image and stored in a frequency tree, which is a hierarchical database. Second, the algorithm visits the non-mapped cells and assigns to these a realization of a landform class, based on the probability function of landforms conditioned to the observed attributes as retrieved from the frequency tree. The approach was tested using a data set for the Buëch catchment in the French Alps. We used four morphometric attributes extracted from a 37.5-m resolution DEM as well as two non-morphometric attributes observed in the neighborhood. The training data set was taken from multiple locations, covering 10% of the total area. The mapping was performed in a stochastic framework, in which 35 map realizations were generated and used to derive the probabilistic map of landforms. Based on this configuration, the technique yielded a map with 51.2% of correct cells, evaluated against the field map of landforms. The mapping accuracy is relatively high at high elevations, compared to the mid-slope and low-lying areas. Debris slope was mapped with the highest accuracy, while MPS shows a low capability in mapping hogback and glacis. The mapping accuracy is highest for training areas with a size of 7.5-10% of the total area. Reducing the size of the training images resulted in a decreased mapping quality, as the frequency database only represents local characteristics of landforms that are not representative for the remaining area. MPS outperforms a rulebased technique that only uses the morphometric attributes at the target mapping cell in the classification (i.e. one-point statistics technique), by 15% of cell accuracy.

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Section: Field Mapping Of Landformsmentioning

confidence: 99%