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Improving Distributed Runoff Prediction in Urbanized Catchments with Remote Sensing based Estimates of Impervious Surface Cover

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ABSTRACT

The amount and intensity of runoff on catchment scale are strongly determined by the presence of impervious land-cover types, which are the predominant cover types in urbanized areas. This paper examines the impact of different methods for estimating impervious surface cover on the prediction of peak discharges, as determined by a fully distributed rainfall-runoff model (WetSpa), for the upper part of the Woluwe River catchment in the southeastern part of Brussels. The study shows that detailed information on the spatial distribution of impervious surfaces, as obtained from remotely sensed data, produces substantially different estimates of peak discharges than traditional approaches based on expert judgment of average imperviousness for different types of urban land use. The study also demonstrates that sub-pixel estimation of imperviousness may be a useful alternative for more expensive high-resolution mapping for rainfall-runoff modelling at catchment scale.

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Related in: MedlinePlus

Mean absolute error (MAECj) for vegetation for different intervals of reference proportions and for different levels of aggregation (black: 30 m, grey: 60 m, white: 90 m).
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f6-sensors-08-00910: Mean absolute error (MAECj) for vegetation for different intervals of reference proportions and for different levels of aggregation (black: 30 m, grey: 60 m, white: 90 m).

Mentions: The mean absolute error (MAECj) for impervious surfaces and vegetation, which are the two dominant classes in the image, is around 10% (Table 2). Aggregation to cell sizes of 60 m and 90 m reduces this error to 7.5 % and 6.1 % for impervious surfaces, and to 7.2 % and 5.9 % for vegetation. Residual analysis (Figure 5, Figure 6) indicates that the mean absolute error for impervious surfaces and vegetation is the highest (up to 25 % or more) for heterogeneous pixels, where the reference proportion is close to 50 %. Pixels with high or low proportions of impervious surfaces or vegetation (<10 % or >90 %) have much smaller errors (between 5 % and 10 %).


Improving Distributed Runoff Prediction in Urbanized Catchments with Remote Sensing based Estimates of Impervious Surface Cover
Mean absolute error (MAECj) for vegetation for different intervals of reference proportions and for different levels of aggregation (black: 30 m, grey: 60 m, white: 90 m).
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3927506&req=5

f6-sensors-08-00910: Mean absolute error (MAECj) for vegetation for different intervals of reference proportions and for different levels of aggregation (black: 30 m, grey: 60 m, white: 90 m).
Mentions: The mean absolute error (MAECj) for impervious surfaces and vegetation, which are the two dominant classes in the image, is around 10% (Table 2). Aggregation to cell sizes of 60 m and 90 m reduces this error to 7.5 % and 6.1 % for impervious surfaces, and to 7.2 % and 5.9 % for vegetation. Residual analysis (Figure 5, Figure 6) indicates that the mean absolute error for impervious surfaces and vegetation is the highest (up to 25 % or more) for heterogeneous pixels, where the reference proportion is close to 50 %. Pixels with high or low proportions of impervious surfaces or vegetation (<10 % or >90 %) have much smaller errors (between 5 % and 10 %).

View Article: PubMed Central - PubMed

ABSTRACT

The amount and intensity of runoff on catchment scale are strongly determined by the presence of impervious land-cover types, which are the predominant cover types in urbanized areas. This paper examines the impact of different methods for estimating impervious surface cover on the prediction of peak discharges, as determined by a fully distributed rainfall-runoff model (WetSpa), for the upper part of the Woluwe River catchment in the southeastern part of Brussels. The study shows that detailed information on the spatial distribution of impervious surfaces, as obtained from remotely sensed data, produces substantially different estimates of peak discharges than traditional approaches based on expert judgment of average imperviousness for different types of urban land use. The study also demonstrates that sub-pixel estimation of imperviousness may be a useful alternative for more expensive high-resolution mapping for rainfall-runoff modelling at catchment scale.

No MeSH data available.


Related in: MedlinePlus