Estimation of Areal Mean Rainfall in Remote Areas Using B-SHADE Model
This study presented a method to estimate areal mean rainfall (AMR) using a Biased Sentinel Hospital Based Area Disease Estimation (B-SHADE) model, together with biased rain gauge observations and Tropical Rainfall Measuring Mission (TRMM) data, for remote areas with a sparse and uneven distribution...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2016-01-01
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| Series: | Advances in Meteorology |
| Online Access: | http://dx.doi.org/10.1155/2016/7643753 |
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| _version_ | 1832550463867715584 |
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| author | Tao Zhang Baolin Li Jinfeng Wang Maogui Hu Lili Xu |
| author_facet | Tao Zhang Baolin Li Jinfeng Wang Maogui Hu Lili Xu |
| author_sort | Tao Zhang |
| collection | DOAJ |
| description | This study presented a method to estimate areal mean rainfall (AMR) using a Biased Sentinel Hospital Based Area Disease Estimation (B-SHADE) model, together with biased rain gauge observations and Tropical Rainfall Measuring Mission (TRMM) data, for remote areas with a sparse and uneven distribution of rain gauges. Based on the B-SHADE model, the best linear unbiased estimation of AMR could be obtained. A case study was conducted for the Three-River Headwaters region in the Tibetan Plateau of China, and its performance was compared with traditional methods. The results indicated that B-SHADE obtained the least estimation biases, with a mean error and root mean square error of −0.63 and 3.48 mm, respectively. For the traditional methods including arithmetic average, Thiessen polygon, and ordinary kriging, the mean errors were 7.11, −1.43, and 2.89 mm, which were up to 1027.1%, 127.0%, and 358.3%, respectively, greater than for the B-SHADE model. The root mean square errors were 10.31, 4.02, and 6.27 mm, which were up to 196.1%, 15.5%, and 80.0%, respectively, higher than for the B-SHADE model. The proposed technique can be used to extend the AMR record to the presatellite observation period, when only the gauge data are available. |
| format | Article |
| id | doaj-art-0313e0c12e6a4ec785fef82dd5d01186 |
| institution | Kabale University |
| issn | 1687-9309 1687-9317 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Meteorology |
| spelling | doaj-art-0313e0c12e6a4ec785fef82dd5d011862025-02-03T06:06:36ZengWileyAdvances in Meteorology1687-93091687-93172016-01-01201610.1155/2016/76437537643753Estimation of Areal Mean Rainfall in Remote Areas Using B-SHADE ModelTao Zhang0Baolin Li1Jinfeng Wang2Maogui Hu3Lili Xu4State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaThis study presented a method to estimate areal mean rainfall (AMR) using a Biased Sentinel Hospital Based Area Disease Estimation (B-SHADE) model, together with biased rain gauge observations and Tropical Rainfall Measuring Mission (TRMM) data, for remote areas with a sparse and uneven distribution of rain gauges. Based on the B-SHADE model, the best linear unbiased estimation of AMR could be obtained. A case study was conducted for the Three-River Headwaters region in the Tibetan Plateau of China, and its performance was compared with traditional methods. The results indicated that B-SHADE obtained the least estimation biases, with a mean error and root mean square error of −0.63 and 3.48 mm, respectively. For the traditional methods including arithmetic average, Thiessen polygon, and ordinary kriging, the mean errors were 7.11, −1.43, and 2.89 mm, which were up to 1027.1%, 127.0%, and 358.3%, respectively, greater than for the B-SHADE model. The root mean square errors were 10.31, 4.02, and 6.27 mm, which were up to 196.1%, 15.5%, and 80.0%, respectively, higher than for the B-SHADE model. The proposed technique can be used to extend the AMR record to the presatellite observation period, when only the gauge data are available.http://dx.doi.org/10.1155/2016/7643753 |
| spellingShingle | Tao Zhang Baolin Li Jinfeng Wang Maogui Hu Lili Xu Estimation of Areal Mean Rainfall in Remote Areas Using B-SHADE Model Advances in Meteorology |
| title | Estimation of Areal Mean Rainfall in Remote Areas Using B-SHADE Model |
| title_full | Estimation of Areal Mean Rainfall in Remote Areas Using B-SHADE Model |
| title_fullStr | Estimation of Areal Mean Rainfall in Remote Areas Using B-SHADE Model |
| title_full_unstemmed | Estimation of Areal Mean Rainfall in Remote Areas Using B-SHADE Model |
| title_short | Estimation of Areal Mean Rainfall in Remote Areas Using B-SHADE Model |
| title_sort | estimation of areal mean rainfall in remote areas using b shade model |
| url | http://dx.doi.org/10.1155/2016/7643753 |
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