A Correlation–Scale–Threshold Method for Spatial Variability of Rainfall
Rainfall data at fine spatial resolutions are often required for various studies in hydrology and water resources. However, such data are not widely available, as their collection is normally expensive and time-consuming. A common practice to obtain fine-spatial-resolution rainfall data is to employ...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-01-01
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| Series: | Hydrology |
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| Online Access: | https://www.mdpi.com/2306-5338/6/1/11 |
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| author | Bellie Sivakumar Fitsum M. Woldemeskel Rajendran Vignesh Vinayakam Jothiprakash |
| author_facet | Bellie Sivakumar Fitsum M. Woldemeskel Rajendran Vignesh Vinayakam Jothiprakash |
| author_sort | Bellie Sivakumar |
| collection | DOAJ |
| description | Rainfall data at fine spatial resolutions are often required for various studies in hydrology and water resources. However, such data are not widely available, as their collection is normally expensive and time-consuming. A common practice to obtain fine-spatial-resolution rainfall data is to employ interpolation schemes to derive them based on data available at nearby locations. Such interpolation schemes are generally based on rainfall correlation or distance between stations. The present study proposes a combined rainfall correlation-spatial scale-correlation threshold method for representing spatial rainfall variability. The method is applied to monthly rainfall data at a resolution of 0.25 × 0.25 latitude/longitude across Australia, available from the Tropical Rainfall Measuring Mission (TRMM 3B43 version). The results indicate that rainfall dynamics in northern and northeastern Australia have far greater spatial correlations when compared to the other regions, especially in southern and southeastern Australia, suggesting that tropical climates generally have greater spatial rainfall correlations when compared to temperate, oceanic, and continental climates, subject to other influencing factors. The implications of the outcomes for rainfall data interpolation and the rain gauge monitoring network are also discussed, especially based on results obtained for ten major cities in Australia. |
| first_indexed | 2024-04-13T05:52:29Z |
| format | Article |
| id | doaj.art-b7e148ef0c174333b475c424e2d6bc02 |
| institution | Directory Open Access Journal |
| issn | 2306-5338 |
| language | English |
| last_indexed | 2024-04-13T05:52:29Z |
| publishDate | 2019-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Hydrology |
| spelling | doaj.art-b7e148ef0c174333b475c424e2d6bc022022-12-22T02:59:44ZengMDPI AGHydrology2306-53382019-01-01611110.3390/hydrology6010011hydrology6010011A Correlation–Scale–Threshold Method for Spatial Variability of RainfallBellie Sivakumar0Fitsum M. Woldemeskel1Rajendran Vignesh2Vinayakam Jothiprakash3UNSW Water Research Centre, School of Civil and Environmental Engineering, The University of New South Wales, Sydney, NSW 2052, AustraliaSchool of Civil and Environmental Engineering, The University of New South Wales, Sydney, NSW 2052, AustraliaVel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology, Avadi, Chennai 600 062, Tamil Nadu, IndiaDepartment of Civil Engineering, Indian Institute of Technology Bombay, Powai, Mumbai 400 076, Maharashtra, IndiaRainfall data at fine spatial resolutions are often required for various studies in hydrology and water resources. However, such data are not widely available, as their collection is normally expensive and time-consuming. A common practice to obtain fine-spatial-resolution rainfall data is to employ interpolation schemes to derive them based on data available at nearby locations. Such interpolation schemes are generally based on rainfall correlation or distance between stations. The present study proposes a combined rainfall correlation-spatial scale-correlation threshold method for representing spatial rainfall variability. The method is applied to monthly rainfall data at a resolution of 0.25 × 0.25 latitude/longitude across Australia, available from the Tropical Rainfall Measuring Mission (TRMM 3B43 version). The results indicate that rainfall dynamics in northern and northeastern Australia have far greater spatial correlations when compared to the other regions, especially in southern and southeastern Australia, suggesting that tropical climates generally have greater spatial rainfall correlations when compared to temperate, oceanic, and continental climates, subject to other influencing factors. The implications of the outcomes for rainfall data interpolation and the rain gauge monitoring network are also discussed, especially based on results obtained for ten major cities in Australia.https://www.mdpi.com/2306-5338/6/1/11rainfallspatial variabilitycorrelationscalethresholdAustralia |
| spellingShingle | Bellie Sivakumar Fitsum M. Woldemeskel Rajendran Vignesh Vinayakam Jothiprakash A Correlation–Scale–Threshold Method for Spatial Variability of Rainfall Hydrology rainfall spatial variability correlation scale threshold Australia |
| title | A Correlation–Scale–Threshold Method for Spatial Variability of Rainfall |
| title_full | A Correlation–Scale–Threshold Method for Spatial Variability of Rainfall |
| title_fullStr | A Correlation–Scale–Threshold Method for Spatial Variability of Rainfall |
| title_full_unstemmed | A Correlation–Scale–Threshold Method for Spatial Variability of Rainfall |
| title_short | A Correlation–Scale–Threshold Method for Spatial Variability of Rainfall |
| title_sort | correlation scale threshold method for spatial variability of rainfall |
| topic | rainfall spatial variability correlation scale threshold Australia |
| url | https://www.mdpi.com/2306-5338/6/1/11 |
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