Comparative analysis of interpolation methods for rainfall mapping in the Faria catchment, Palestine Article Swipe
Sameer Shadeed
,
Anan Jayyousi
,
Abdelhaleem Khader
,
Christian Chwala
,
Harald Kunstmann
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.35552/anujr.a.36.1.2000
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.35552/anujr.a.36.1.2000
The availability of rainfall data is essential for the prediction of extreme rainfall events, hydrological modeling, and the design of water resources systems. In Palestine, the limited available rain gauges and the available rainfall data are insufficient to precisely characterize the areal rainfall distribution over the entire area. Hence, it is essential to find proper methods to utilize the available rainfall data at the existing raingauges to predict rainfall values at certain locations where rainfall measurements are not available. This study aims at identifying the most suitable interpolation method that can be used to produce interpolated rainfall surfaces at the Faria catchment, Palestine. Faria catchment (320 km2), located in the northeastern part of the West Bank, Palestine, is characterized by high temporal and spatial rainfall variations. Six, GIS-based, spatial interpolation methods have been tested by visual observation and cross-validation. These methods are: Inverse Distance Weighted, Spline completely Regularized, Spline with Tension, Ordinary Kriging, Universal Kriging, and Regression. Available seasonal rainfall data recorded at 16 locations for two rainy seasons; 2016/2017 and 2017/2018 were utilized. Results indicate that the Universal Kriging method has slightly a higher correlation coefficient and lower mean relative error than the other methods for the two seasons that make the method the most optimal one for mapping rainfall distribution in the Faria catchment. Moreover, the spatial distribution of raingauges between the two seasons affects the performance of different interpolation methods in the catchment.
Related Topics
Concepts
Kriging
Drainage basin
Multivariate interpolation
Environmental science
Interpolation (computer graphics)
Palestine
Spatial distribution
Hydrology (agriculture)
Geography
Remote sensing
Mathematics
Geology
Statistics
Computer science
Cartography
Bilinear interpolation
Geotechnical engineering
Ancient history
Computer graphics (images)
Animation
History
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.35552/anujr.a.36.1.2000
- https://journals.najah.edu//media/journals/full_texts/1_dKqBprN.pdf
- OA Status
- diamond
- Cited By
- 4
- References
- 12
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4362689526
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4362689526Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.35552/anujr.a.36.1.2000Digital Object Identifier
- Title
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Comparative analysis of interpolation methods for rainfall mapping in the Faria catchment, PalestineWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
-
2022-02-01Full publication date if available
- Authors
-
Sameer Shadeed, Anan Jayyousi, Abdelhaleem Khader, Christian Chwala, Harald KunstmannList of authors in order
- Landing page
-
https://doi.org/10.35552/anujr.a.36.1.2000Publisher landing page
- PDF URL
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https://journals.najah.edu//media/journals/full_texts/1_dKqBprN.pdfDirect link to full text PDF
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://journals.najah.edu//media/journals/full_texts/1_dKqBprN.pdfDirect OA link when available
- Concepts
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Kriging, Drainage basin, Multivariate interpolation, Environmental science, Interpolation (computer graphics), Palestine, Spatial distribution, Hydrology (agriculture), Geography, Remote sensing, Mathematics, Geology, Statistics, Computer science, Cartography, Bilinear interpolation, Geotechnical engineering, Ancient history, Computer graphics (images), Animation, HistoryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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4Total citation count in OpenAlex
- Citations by year (recent)
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2024: 1, 2023: 2, 2022: 1Per-year citation counts (last 5 years)
- References (count)
-
12Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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