CAMELE: Collocation-Analyzed Multi-source Ensembled Land Evapotranspiration Data
<p>Land evapotranspiration (ET) plays a crucial role in Earth's water–carbon cycle, and accurately estimating global land ET is vital for advancing our understanding of land–atmosphere interactions. Despite the development of numerous ET products in recent decades, widely used products st...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2024-04-01
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| Series: | Earth System Science Data |
| Online Access: | https://essd.copernicus.org/articles/16/1811/2024/essd-16-1811-2024.pdf |
| _version_ | 1797212992903839744 |
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| author | C. Li Z. Liu W. Yang Z. Tu J. Han S. Li H. Yang |
| author_facet | C. Li Z. Liu W. Yang Z. Tu J. Han S. Li H. Yang |
| author_sort | C. Li |
| collection | DOAJ |
| description | <p>Land evapotranspiration (ET) plays a crucial role in Earth's water–carbon cycle, and accurately estimating global land ET is vital for advancing our understanding of land–atmosphere interactions. Despite the development of numerous ET products in recent decades, widely used products still possess inherent uncertainties arising from using different forcing inputs and imperfect model parameterizations. Furthermore, the lack of sufficient global in situ observations makes direct evaluation of ET products impractical, impeding their utilization and assimilation. Therefore, establishing a reliable global benchmark dataset and exploring evaluation methodologies for ET products is paramount. This study aims to address these challenges by (1) proposing a collocation-based method that considers non-zero error cross-correlation for merging multi-source data and (2) employing this merging method to generate a long-term daily global ET product at resolutions of 0.1° (2000–2020) and 0.25° (1980–2022), incorporating inputs from ERA5L, FluxCom, PMLv2, GLDAS, and GLEAM. The resulting product is the Collocation-Analyzed Multi-source Ensembled Land Evapotranspiration Data (CAMELE). CAMELE exhibits promising performance across various vegetation coverage types, as validated against in situ observations. The evaluation process yielded Pearson correlation coefficients (<span class="inline-formula"><i>R</i></span>) of 0.63 and 0.65, root-mean-square errors (RMSEs) of 0.81 and 0.73 mm d<span class="inline-formula"><sup>−1</sup></span>, unbiased root-mean-square errors (ubRMSEs) of 1.20 and 1.04 mm d<span class="inline-formula"><sup>−1</sup></span>, mean absolute errors (MAEs) of 0.81 and 0.73 mm d<span class="inline-formula"><sup>−1</sup></span>, and Kling–Gupta efficiencies (KGEs) of 0.60 and 0.65 on average at resolutions of 0.1 and 0.25°, respectively. In addition, comparisons indicate that CAMELE can effectively characterize the multiyear linear trend, mean average, and extreme values of ET. However, it exhibits a tendency to overestimate seasonality. In summary, we propose a reliable set of ET data that can aid in understanding the variations in the water cycle and has the potential to serve as a benchmark for various applications. The dataset is publicly available at <a href="https://doi.org/10.5281/zenodo.8047038">https://doi.org/10.5281/zenodo.8047038</a> (Li et al., 2023b).</p> |
| first_indexed | 2024-04-24T10:51:12Z |
| format | Article |
| id | doaj.art-917cf7b8cc7d466db179beced5d6c833 |
| institution | Directory Open Access Journal |
| issn | 1866-3508 1866-3516 |
| language | English |
| last_indexed | 2024-04-24T10:51:12Z |
| publishDate | 2024-04-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Earth System Science Data |
| spelling | doaj.art-917cf7b8cc7d466db179beced5d6c8332024-04-12T12:17:13ZengCopernicus PublicationsEarth System Science Data1866-35081866-35162024-04-01161811184610.5194/essd-16-1811-2024CAMELE: Collocation-Analyzed Multi-source Ensembled Land Evapotranspiration DataC. Li0Z. Liu1W. Yang2Z. Tu3J. Han4S. Li5H. Yang6State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, ChinaState Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, ChinaState Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, ChinaState Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, ChinaState Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, ChinaCenter for Agricultural Water Research in China, China Agricultural University, Beijing 100083, ChinaState Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China<p>Land evapotranspiration (ET) plays a crucial role in Earth's water–carbon cycle, and accurately estimating global land ET is vital for advancing our understanding of land–atmosphere interactions. Despite the development of numerous ET products in recent decades, widely used products still possess inherent uncertainties arising from using different forcing inputs and imperfect model parameterizations. Furthermore, the lack of sufficient global in situ observations makes direct evaluation of ET products impractical, impeding their utilization and assimilation. Therefore, establishing a reliable global benchmark dataset and exploring evaluation methodologies for ET products is paramount. This study aims to address these challenges by (1) proposing a collocation-based method that considers non-zero error cross-correlation for merging multi-source data and (2) employing this merging method to generate a long-term daily global ET product at resolutions of 0.1° (2000–2020) and 0.25° (1980–2022), incorporating inputs from ERA5L, FluxCom, PMLv2, GLDAS, and GLEAM. The resulting product is the Collocation-Analyzed Multi-source Ensembled Land Evapotranspiration Data (CAMELE). CAMELE exhibits promising performance across various vegetation coverage types, as validated against in situ observations. The evaluation process yielded Pearson correlation coefficients (<span class="inline-formula"><i>R</i></span>) of 0.63 and 0.65, root-mean-square errors (RMSEs) of 0.81 and 0.73 mm d<span class="inline-formula"><sup>−1</sup></span>, unbiased root-mean-square errors (ubRMSEs) of 1.20 and 1.04 mm d<span class="inline-formula"><sup>−1</sup></span>, mean absolute errors (MAEs) of 0.81 and 0.73 mm d<span class="inline-formula"><sup>−1</sup></span>, and Kling–Gupta efficiencies (KGEs) of 0.60 and 0.65 on average at resolutions of 0.1 and 0.25°, respectively. In addition, comparisons indicate that CAMELE can effectively characterize the multiyear linear trend, mean average, and extreme values of ET. However, it exhibits a tendency to overestimate seasonality. In summary, we propose a reliable set of ET data that can aid in understanding the variations in the water cycle and has the potential to serve as a benchmark for various applications. The dataset is publicly available at <a href="https://doi.org/10.5281/zenodo.8047038">https://doi.org/10.5281/zenodo.8047038</a> (Li et al., 2023b).</p>https://essd.copernicus.org/articles/16/1811/2024/essd-16-1811-2024.pdf |
| spellingShingle | C. Li Z. Liu W. Yang Z. Tu J. Han S. Li H. Yang CAMELE: Collocation-Analyzed Multi-source Ensembled Land Evapotranspiration Data Earth System Science Data |
| title | CAMELE: Collocation-Analyzed Multi-source Ensembled Land Evapotranspiration Data |
| title_full | CAMELE: Collocation-Analyzed Multi-source Ensembled Land Evapotranspiration Data |
| title_fullStr | CAMELE: Collocation-Analyzed Multi-source Ensembled Land Evapotranspiration Data |
| title_full_unstemmed | CAMELE: Collocation-Analyzed Multi-source Ensembled Land Evapotranspiration Data |
| title_short | CAMELE: Collocation-Analyzed Multi-source Ensembled Land Evapotranspiration Data |
| title_sort | camele collocation analyzed multi source ensembled land evapotranspiration data |
| url | https://essd.copernicus.org/articles/16/1811/2024/essd-16-1811-2024.pdf |
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