Spatial interpolation of climate variables in Northern Germany—Influence of temporal resolution and network density

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dc.identifier.uri http://dx.doi.org/10.15488/3383
dc.identifier.uri http://www.repo.uni-hannover.de/handle/123456789/3413
dc.contributor.author Berndt, C.
dc.contributor.author Haberlandt, Uwe
dc.date.accessioned 2018-05-23T08:43:24Z
dc.date.available 2018-05-23T08:43:24Z
dc.date.issued 2018
dc.identifier.citation Berndt, C.; Haberlandt, U.: Spatial interpolation of climate variables in Northern Germany - Influence of temporal resolution and network density. In: Journal of Hydrology: Regional Studies 15 (2018), S. 184-202. DOI: https://doi.org/10.1016/j.ejrh.2018.02.002
dc.description.abstract Study region: Region in Lower Saxony (North Germany) covered by the measuring range of the weather radar device located near Hanover (approx. 50.000 m2). Study focus: This study investigates the performance of various spatial interpolation techniques for climate variables. Meteorological observations are usually recorded as site-specific point information by weather stations and estimation accuracy for unobserved locations depends generally on station density, temporal resolution, spatial variation of the variable and choice of interpolation method. This work aims to evaluate the influence of these factors on interpolation performance of different climate variables. A cross validation analysis was performed for precipitation, temperature, humidity, cloud coverage, sunshine duration, and wind speed observations. Hourly to yearly temporal resolutions and different additional information were considered. New hydrological insights: Geostatistical techniques provide a better performance for all climate variables compared to simple methods Radar data improves the estimation of rainfall with hourly temporal resolution, while topography is useful for weekly to yearly values and temperature in general. No helpful information was found for cloudiness, sunshine duration, and wind speed, while interpolation of humidity benefitted from additional temperature data. The influences of temporal resolution, spatial variability, and additional information appear to be stronger than station density effects. High spatial variability of hourly precipitation causes the highest error, followed by wind speed, cloud coverage and sunshine duration. Lowest errors occur for temperature and humidity. eng
dc.language.iso eng
dc.publisher Amsterdam : Elsevier B.V.
dc.relation.ispartofseries Journal of Hydrology: Regional Studies 15 (2018)
dc.rights CC BY-NC-ND 4.0 Unported
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Climate data eng
dc.subject Geostatistics eng
dc.subject Interpolation eng
dc.subject Kriging eng
dc.subject.ddc 550 | Geowissenschaften ger
dc.subject.ddc 690 | Hausbau, Bauhandwerk ger
dc.title Spatial interpolation of climate variables in Northern Germany—Influence of temporal resolution and network density eng
dc.type article
dc.type Text
dc.relation.issn 2214-5818
dc.relation.doi https://doi.org/10.1016/j.ejrh.2018.02.002
dc.bibliographicCitation.volume 15
dc.bibliographicCitation.firstPage 184
dc.bibliographicCitation.lastPage 202
dc.description.version publishedVersion
tib.accessRights frei zug�nglich


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