The DOI matches its Crossref record; 101 of the 101 references it cites were found in a registry or an index.
Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling
Open accessAbstract
Abstract. We undertook a comprehensive evaluation of 22 gridded (quasi-)global (sub-)daily precipitation (P) datasets for the period 2000–2016. Thirteen non-gauge-corrected P datasets were evaluated using daily P gauge observations from 76 086 gauges worldwide. Another nine gauge-corrected datasets were evaluated using hydrological modeling, by calibrating the HBV conceptual model against streamflow records for each of 9053 small to medium-sized ( < 50 000 km2) catchments worldwide, and comparing the resulting performance. Marked differences in spatio-temporal patterns and accuracy were found among the datasets. Among the uncorrected P datasets, the satellite- and reanalysis-based MSWEP-ng V1.2 and V2.0 datasets generally showed the best temporal correlations with the gauge observations, followed by the reanalyses (ERA-Interim, JRA-55, and NCEP-CFSR) and the satellite- and reanalysis-based CHIRP V2.0 dataset, the estimates based primarily on passive microwave remote sensing of rainfall (CMORPH V1.0, GSMaP V5/6, and TMPA 3B42RT V7) or near-surface soil moisture (SM2RAIN-ASCAT), and finally, estimates based primarily on thermal infrared imagery (GridSat V1.0, PERSIANN, and PERSIANN-CCS). Two of the three reanalyses (ERA-Interim and JRA-55) unexpectedly obtained lower trend errors than the satellite datasets. Among the corrected P datasets, the ones directly incorporating daily gauge data (CPC Unified, and MSWEP V1.2 and V2.0) generally provided the best calibration scores, although the good performance of the fully gauge-based CPC Unified is unlikely to translate to sparsely or ungauged regions. Next best results were obtained with P estimates directly incorporating temporally coarser gauge data (CHIRPS V2.0, GPCP-1DD V1.2, TMPA 3B42 V7, and WFDEI-CRU), which in turn outperformed the one indirectly incorporating gauge data through another multi-source dataset (PERSIANN-CDR V1R1). Our results highlight large differences in estimation accuracy, and hence the importance of P dataset selection in both research and operational applications. The good performance of MSWEP emphasizes that careful data merging can exploit the complementary strengths of gauge-, satellite-, and reanalysis-based P estimates.
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The ERA‐Interim reanalysis: configuration and performance of the data assimilation system
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., et al. (2011) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.828 · Cited by 24,480
River flow forecasting through conceptual models part I — A discussion of principles
Nash, J. E., & Sutcliffe, J. V. (1970) · Journal of Hydrology · doi:10.1016/0022-1694(70)90255-6 · Cited by 23,674
WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas
Fick, S. E., & Hijmans, R. J. (2017) · International Journal of Climatology · doi:10.1002/joc.5086 · Cited by 16,088
Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations
Moriasi, D. N., Arnold, J. G., Liew, M. W. V., Bingner, R. L., Harmel, R. D., & Veith, T. L. (2007) · Transactions of the ASABE · doi:10.13031/2013.23153 · Cited by 13,446
Chai, T., & Draxler, R. R. (2014) · Geoscientific Model Development · doi:10.5194/gmd-7-1247-2014 · Cited by 7,178
Huffman, G. J., Bolvin, D. T., Nelkin, E. J., Wolff, D. B., Adler, R. F., Gu, G., Hong, Y., Bowman, K. P., & Stocker, E. F. (2007) · Journal of Hydrometeorology · doi:10.1175/jhm560.1 · Cited by 6,767
Self-citationGupta, H. V., Kling, H., Yilmaz, K. K., & Martinez, G. F. (2009) · Journal of Hydrology · doi:10.1016/j.jhydrol.2009.08.003 · Cited by 6,758
Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L., Hoell, A., et al. (2015) · Scientific Data · doi:10.1038/sdata.2015.66 · Cited by 6,362
The NCEP Climate Forecast System Reanalysis
Saha, S., Moorthi, S., Pan, H.-L., Wu, X., Wang, J., Nadiga, S., Tripp, P., Kistler, R., Woollen, J., Behringer, D., et al. (2010) · Bulletin of the American Meteorological Society · doi:10.1175/2010bams3001.1 · Cited by 5,136
The JRA-55 Reanalysis: General Specifications and Basic Characteristics
KOBAYASHI, S., OTA, Y., HARADA, Y., EBITA, A., MORIYA, M., ONODA, H., ONOGI, K., KAMAHORI, H., KOBAYASHI, C., ENDO, H., et al. (2015) · Journal of the Meteorological Society of Japan. Ser. II · doi:10.2151/jmsj.2015-001 · Cited by 4,760
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MSWEP V2 Global 3-Hourly 0.1° Precipitation: Methodology and Quantitative Assessment
Beck, H. E., Wood, E. F., Pan, M., Fisher, C. K., Miralles, D. G., van Dijk, A. I. J. M., McVicar, T. R., & Adler, R. F. (2019) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-17-0138.1 · Cited by 1,411
Self-citationGlobal and Regional Trends and Drivers of Fire Under Climate Change
Jones, M. W., Abatzoglou, J. T., Veraverbeke, S., Andela, N., Lasslop, G., Forkel, M., Smith, A. J. P., Burton, C., Betts, R. A., van der Werf, G. R., et al. (2022) · Reviews of Geophysics · doi:10.1029/2020rg000726 · Cited by 1,332
High-resolution (1 km) Köppen-Geiger maps for 1901–2099 based on constrained CMIP6 projections
Beck, H. E., McVicar, T. R., Vergopolan, N., Berg, A., Lutsko, N. J., Dufour, A., Zeng, Z., Jiang, X., van Dijk, A. I. J. M., & Miralles, D. G. (2023) · Scientific Data · doi:10.1038/s41597-023-02549-6 · Cited by 917
Self-citationSheffield, J., Wood, E. F., Pan, M., Beck, H., Coccia, G., Serrat‐Capdevila, A., & Verbist, K. (2018) · Water Resources Research · doi:10.1029/2017wr022437 · Cited by 561
Self-citationBeck, H. E., Pan, M., Miralles, D. G., Reichle, R. H., Dorigo, W. A., Hahn, S., Sheffield, J., Karthikeyan, L., Balsamo, G., Parinussa, R. M., et al. (2021) · Hydrology and Earth System Sciences · doi:10.5194/hess-25-17-2021 · Cited by 380
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Metadata
- Authors
- Beck, H. E., Vergopolan, N., Pan, M., Levizzani, V., van Dijk, A. I. J. M., Weedon, G. P., Brocca, L., Pappenberger, F., Huffman, G. J., & Wood, E. F.
- Venue
- Hydrology and Earth System Sciences
- Publisher
- Copernicus GmbH
- Published
- 2017-12-08
- Volume
- 21
- Issue
- 12
- Pages
- 6201-6217
- ISSN
- 1027-5606 (ISSN-L)
- 1607-7938
- OpenAlex
- W2771159036
- Cited by
- 891
- References
- 101
- FWCI
- 38.61
- Type
- journal article
- Language
- en
- Open access
- gold
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