344
Views
0
CrossRef citations to date
0
Altmetric
Data Article

Towards global coverage of gridded parameterization for CLImate GENerator (CLIGEN)Open Data

ORCID Icon, , , , , , & show all
Pages 142-165 | Received 31 Jul 2023, Accepted 28 Nov 2023, Published online: 26 Dec 2023

References

  • Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A., & Hegewisch, K. C. (2018). TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Scientific Data, 5(170191), 1–12. https://doi.org/10.1038/sdata.2017.191
  • Agricultural Research Service. (2023). CLIGEN [Software]. https://www.ars.usda.gov/midwest-area/west-lafayette-in/national-soil-erosion-research/docs/wepp/cligen/
  • Bayley, T., Elliot, W., Nearing, M. A., Guertin, D. P., Johnson, T., Goodrich, D., & Flanagan, D. (2010). Modeling erosion under future climates with the WEPP model. In Proceedings of the 2nd Joint Federal Interagency Conference on Sedimentation and Hydrologic Modeling, Las Vegas, NV, 27 June - 1 July, (pp. 1–12).
  • Beck, H. E., Zimmermann, N. E., McVicar, T. R., Vergopolan, N., Berg, A., & Wood, E. F. (2018). Present and future Köppen-Geiger climate classification maps at 1-km resolution. Scientific Data, 5(1), 180214. https://doi.org/10.1038/sdata.2018.214
  • Chen, J., Chen, H., & Guo, S. (2018). Multi-site precipitation downscaling using a stochastic weather generator. Climate Dynamics, 50(5–6), 1975–1992. https://doi.org/10.1007/s00382-017-3731-9
  • Diamond, H. J., Karl, T. R., Palecki, M. A., Baker, C. B., Bell, J. E., Leeper, R. D., Easterling, D. R., Lawrimore, J. H., Meyers, T. P., Helfert, M. R., Goodge, G., & Thorne, P. W. (2013). US climate reference network after one decade of operations: Status and assessment. Bulletin of the American Meteorological Society, 94(4), 485–498. https://doi.org/10.1175/BAMS-D-12-00170.1
  • Feng, Q., Engel, B. A., Flanagan, D. C., Huang, C. H., Yen, H., & Yang, L. (2019). Design and development of a web-based interface for the agricultural policy environmental eXtender (APEX) model. Environmental Modelling & Software, 111, 368–374. https://doi.org/10.1016/j.envsoft.2018.09.011
  • Flanagan, D. C., McGehee, R. P., & Srivastava, A. (2020). Evaluation of different precipitation inputs to WEPP, ASABE Annual International Virtual Meeting, St. Joseph, Michigan, U.S.A., July 12-15, 2020, https://doi.org/10.13031/aim.202000740
  • Friedman, J. H. (2002). Stochastic gradient boosting. Computational Statistics & Data Analysis, 38(4), 367–378. https://doi.org/10.1016/S0167-9473(01)00065-2
  • Fullhart, A. T., Nearing, M. A., Armendariz, G., & Weltz, M. A. (2021). Climate benchmarks and input parameters representing locations in 68 countries for a stochastic weather generator, CLIGEN. Earth System Science Data, 13(2), 435–446. https://doi.org/10.5194/essd-13-435-2021
  • Fullhart, A., Nearing, M., & Weltz, M. (2021). Temporally downscaling precipitation intensity factors for Köppen climate regions in the United States. Journal of Soil and Water Conservation, 76(1), 39–51. https://doi.org/10.2489/jswc.2021.00156
  • Fullhart, A., Ponce-Campos, G. E., Meles, M. B., McGehee, R. P., Armendariz, G., Oliveira, P. T., Almeida, C. N., de Araújo, J. C., Nel, W., & Goodrich, D. C. (2022). Gridded 20-year climate parameterization of Africa and south America for a stochastic weather generator (CLIGEN). Big Earth Data, 7(2), 1–26. https://doi.org/10.1080/20964471.2022.2136610
  • Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27. https://doi.org/10.1016/j.rse.2017.06.031
  • Hernandez, M., Nearing, M. A., Al-Hamdan, O. Z., Pierson, F. B., Armendariz, G., Weltz, M. A., Spaeth, K., Williams, J. C., Nouwakpo, S. K., Goodrich, D. C., Unkrich, C. L., Nichols, M. H., & Holifield-Collins, C. D. (2017). The rangeland hydrology an erosion model: A dynamic approach for predictiong soil loss on rangelands. Water Resources Research, 53(11), 9368–9391. https://doi.org/10.1002/2017WR020651
  • Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., & Villaume, S.… Thépaut, J. N. (2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730), 1999–2049. https://doi.org/10.1002/qj.3803
  • Huffman, G. J., Bolvin, D. T., Nelkin, E. J., & Tan, J. (2015). Integrated multi-satellite retrievals for GPM (IMERG) technical documentation. NASA/GSFC Code.
  • Hussein, E. A., Ghaziasgar, M., Thron, C., Vaccari, M., & Bagula, A. (2021). Basic statistical estimation outperforms machine learning in monthly prediction of seasonal climatic parameters. Atmosphere, 12(5), 539. https://doi.org/10.3390/atmos12050539
  • Körner, P., Kronenberg, R., Genzel, S., & Bernhofer, C. (2018). Introducing gradient boosting as a universal gap filling tool for meteorological time series. Meteorologische Zeitschrift, 27(5), 369. https://doi.org/10.1127/metz/2018/0908
  • McGehee, R. P., Flanagan, D. C., & Srivastava, P. (2020). WEPPCLIFF: A command-line tool to process climate inputs for soil loss models. Journal of Open Source Software, 5(49), 2029. https://doi.org/10.21105/joss.02029
  • Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R. D., & Veith, T. L. (2007). Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 50(3), 885–900. https://doi.org/10.13031/2013.23153
  • Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830.
  • PRISM Climate Group. (2023). PRISM (parameter-elevation regressions on Independent Slopes model) climate group. Oregon State University. http://www.prism.oregonstate.edu
  • Rodell, M., Houser, P. R., Jambor, U. E. A., Gottschalck, J., Mitchell, K., Meng, C. J., Arsenault, K., Cosgrove, B., Radakovich, J., Bosilovich, M., Entin, J. K., Walker, J. P., Lohmann, D., & Toll, D. (2004). The global land data assimilation system. Bulletin of the American Meteorological Society, 85(3), 381–394. https://doi.org/10.1175/BAMS-85-3-381
  • Rui, H., Beaudoing, H., & Loeser, C. (2021). README document for NASA GLDAS version 2 data products, Goddard Earth Sciences data and information Services center (GES DISC). NASA Goddard Space Flight Center.
  • Salmerón, R., García, C. B., & García, J. (2018). Variance inflation factor and condition number in multiple linear regression. Journal of Statistical Computation and Simulation, 88(12), 2365–2384. https://doi.org/10.1080/00949655.2018.1463376
  • Sparks, N. J., Hardwick, S. R., Schmid, M., & Toumi, R. (2018). IMAGE: A multivariate multi-site stochastic weather generator for European weather and climate. Stochastic Environmental Research and Risk Assessment, 32(3), 771–784. https://doi.org/10.1007/s00477-017-1433-9
  • Srivastava, A., Flanagan, D. C., Frankenberger, J. R., & Engel, B. A. (2019). Updated climate database and impacts on WEPP model predictions. Journal of Soil and Water Conservation, 74(4), 334–349. https://doi.org/10.2489/jswc.74.4.334
  • Tadono, T., Ishida, H., Oda, F., Naito, S., Minakawa, K., & Iwamoto, H. (2014). Precise global DEM generation by ALOS PRISM. ISPRS Annals of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 2, 71–76. https://doi.org/10.5194/isprsannals-II-4-71-2014
  • Wang, W., Flanagan, D. C., Yin, S., & Yu, B. (2018). Assessment of CLIGEN precipitation and storm pattern generation in China. Catena, 169, 96–106. https://doi.org/10.1016/j.catena.2018.05.024
  • Wang, W., Yin, S., Flanagan, D. C., & Yu, B. (2018). Comparing CLIGEN-generated storm patterns with 1-minute and hourly precipitation data from China. Journal of Applied Meteorology and Climatology, 57(9), 2005–2017. https://doi.org/10.1175/JAMC-D-18-0079.1
  • Wang, W., Yin, S., Yu, B., & Wang, S. (2021). CLIGEN parameter regionalization for China. Earth System Science Data, 13(6), 2945–2962. https://doi.org/10.5194/essd-13-2945-2021
  • Wischmeier, W. H., & Smith, D. D. (1965). Predicting rainfall erosion losses in the Eastern U.S.: A guide to conservation planning. In Agricultural Research Service (p. 47). US Gov. Print Office.
  • Yin, S., Nearing, M. A., Borrelli, P., & Xue, X. (2017). Rainfall erosivity: An overview of methodologies and applications. Vadose Zone Journal, 16(12), 1–16. https://doi.org/10.2136/vzj2017.06.0131
  • Yu, B. (2002). Using CLIGEN to generate RUSLE climate inputs. Transactions of the ASAE, 45(4), 993. https://doi.org/10.13031/2013.9952
  • Zhao, Y., Nearing, M. A., & Guertin, D. P. (2019). A daily spatially explicit stochastic rainfall generator for a semi-arid climate. Canadian Journal of Fisheries and Aquatic Sciences, 574, 181–192. https://doi.org/10.1016/j.jhydrol.2019.04.006
  • Zhao, Y., Nearing, M. A., & Guertin, D. P. (2021). Modeling hydrologic responses using multi-site and single-site rainfall generators in a semi-arid watershed. International Soil & Water Conservation Research, 10(2), 177–187. https://doi.org/10.1016/j.iswcr.2021.09.003