 |
| Titel |
Geostatistical interpolation of daily rainfall at catchment scale: the use of several variogram models in the Ourthe and Ambleve catchments, Belgium |
| VerfasserIn |
S. Ly, C. Charles, A. Degré |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1027-5606
|
| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 15, no. 7 ; Nr. 15, no. 7 (2011-07-18), S.2259-2274 |
| Datensatznummer |
250012892
|
| Publikation (Nr.) |
 copernicus.org/hess-15-2259-2011.pdf |
|
|
|
|
|
| Zusammenfassung |
| Spatial interpolation of precipitation data is of great importance for
hydrological modelling. Geostatistical methods (kriging) are widely applied
in spatial interpolation from point measurement to continuous surfaces. The
first step in kriging computation is the semi-variogram modelling which
usually used only one variogram model for all-moment data. The objective of
this paper was to develop different algorithms of spatial interpolation for
daily rainfall on 1 km2 regular grids in the catchment area and to
compare the results of geostatistical and deterministic approaches. This
study leaned on 30-yr daily rainfall data of 70 raingages in the hilly
landscape of the Ourthe and Ambleve catchments in Belgium (2908 km2).
This area lies between 35 and 693 m in elevation and consists of river
networks, which are tributaries of the Meuse River. For geostatistical
algorithms, seven semi-variogram models (logarithmic, power, exponential,
Gaussian, rational quadratic, spherical and penta-spherical) were fitted to
daily sample semi-variogram on a daily basis. These seven variogram models
were also adopted to avoid negative interpolated rainfall. The elevation,
extracted from a digital elevation model, was incorporated into multivariate
geostatistics. Seven validation raingages and cross validation were used to
compare the interpolation performance of these algorithms applied to
different densities of raingages. We found that between the seven variogram
models used, the Gaussian model was the most frequently best fit. Using
seven variogram models can avoid negative daily rainfall in ordinary
kriging. The negative estimates of kriging were observed for convective more
than stratiform rain. The performance of the different methods varied
slightly according to the density of raingages, particularly between 8 and
70 raingages but it was much different for interpolation using 4 raingages.
Spatial interpolation with the geostatistical and Inverse Distance Weighting
(IDW) algorithms outperformed considerably the interpolation with the
Thiessen polygon, commonly used in various hydrological models. Integrating
elevation into Kriging with an External Drift (KED) and Ordinary Cokriging
(OCK) did not improve the interpolation accuracy for daily rainfall.
Ordinary Kriging (ORK) and IDW were considered to be the best methods, as
they provided smallest RMSE value for nearly all cases. Care should be taken
in applying UNK and KED when interpolating daily rainfall with very few
neighbourhood sample points. These recommendations complement the results
reported in the literature. ORK, UNK and KED using only spherical model
offered a slightly better result whereas OCK using seven variogram models
achieved better result. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|