Location: Southwest Watershed Research Center
Title: Towards global coverage of gridded parameterization for CLImate GENerator (CLIGEN)Author
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Fullhart, Andrew |
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PONCE-CAMPOS, G. - University Of Arizona |
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Meles, Menberu |
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MCGEHEE, R. - Iowa State University |
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WEI, H. - University Of Arizona |
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Armendariz, Gerardo |
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BURNS, I.S. - Us Geological Survey (USGS) |
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Goodrich, David - Dave |
Submitted to: Big Earth Data
Publication Type: Peer Reviewed Journal Publication Acceptance Date: 11/28/2023 Publication Date: 12/26/2023 Citation: Fullhart, A.T., Ponce-Campos, G., Meles, M.B., McGehee, R., Wei, H., Armendariz, G.A., Burns, I., Goodrich, D.C. 2023. Towards global coverage of gridded parameterization for CLImate GENerator (CLIGEN) . Big Earth Data. 8(1):142-165. https://doi.org/10.1080/20964471.2023.2291215. DOI: https://doi.org/10.1080/20964471.2023.2291215 Interpretive Summary: Global climate datasets derived from climate models and satellite-based systems are increasingly available to researchers. However, this data is often not usable in several USDA computer modeling applications related to hydrology, soil erosion, and rainfall-runoff simulations. Commonly, global climate data has significant systematic bias and may be at the wrong spatial scale expected by a model application. Furthermore, the data is often difficult to access and may be provided in the wrong format. To solve these issues, global coverage of climate data was processed and prepared to be compatible with the USDA climate driver model, CLIGEN, which creates synthetic climate time series useful for several applications. Time series generated by CLIGEN are statistically representative of long-term observations and reproduce key weather dynamics that existing global climate datasets often mischaracterize. Improving the availability of CLIGEN data solves an issue for many researchers who may wish to use USDA models but lack appropriate climate data. In particular, the coverage of CLIGEN data has been expanded internationally to achieve near-global coverage. This has involved the development of CLIGEN coverage for 79% of global land mass, providing useable climate data to many parts of the globe where data was previously unavailable. In doing so, this gives the potential for USDA models to have an integral role in international projects and bring more widespread use of modeling for environmental assessments, as well as a variety of other scientific applications. Technical Abstract: Stochastic weather generators create time series that reproduce key weather dynamics present in long-term observations. These synthetic time series are used as climate drivers in model simulations and other applications. The dataset detailed herein is a large-scale gridded parameterization for CLImate GENerator (CLIGEN) that fills spatial gaps in the coverage of existing regional CLIGEN parameterizations, thereby obtaining near-global availability of combined coverages. This dataset primarily covers countries north of 40° latitude with 0.25° spatial resolution. The coverage is largely characterized by cold climates with a wide range of other climate types being represented. Various CLIGEN parameters were estimated based on 20-year records from four popular global climate products. Precipitation parameters were statistically downscaled to estimate point-scale values, while point-scale temperature and solar radiation parameters were approximated by direct calculation from high resolution datasets. Surrogate parameter values were used in some cases, such as with wind parameters. Cross-validation was done to assess the downscaling approach for six precipitation parameters using known point-scale values from ground-based CLIGEN parameterizations. These parameter values were derived from daily accumulation records at 7,280 stations and high temporal resolution records at 609 stations. Cross-validation showed that two critical parameters, monthly average storm accumulation and maximum 30-minute intensity, have RMSE values of 1.48 mm and 4.67 mm hr-1, respectively. Cumulative precipitation and the annual number of days with precipitation occurrence were both within 5% of ground-based parameterizations. This dataset improves the global availability of climate drivers, particularly in ungauged basins, enabling a variety of climate applications. |