Estimation of Intensity-Duration-Frequency (IDF) Curves from Large Scale Atmospheric Dataset by Statistical Downscaling

Abstract

The study proposes a new approach that combined statistical downscaling, bias correction, and disaggregation of rainfall techniques in order to derive the IDF curve from large scale atmospheric reanalysis data. The applied methodology details the NCEP/NCAR reanalysis dataset being downscaled by an ANN-based approach to estimate the daily rainfall of Izmir. The annual maximum rainfall series of the study area were sampled from the daily downscaled rainfall series. The sampled daily maximum rainfalls were then bias-corrected by the quantile mapping method and disaggregated into the annual maximum standard-duration rainfall heights regarding the rainfalls' scale-invariant properties. Finally, the IDF curves of the study area were determined by using the disaggregated rainfall heights. The results confirmed that the IDF curves dependent on short-duration extreme rainfall heights could be reasonably estimated from the large-scale atmospheric variables using the statistical downscaling approach.

Keywords

• Statistical downscaling
• bias correction
• quantile mapping
• rainfall disaggregation
• IDF curve

References

1. Kalnay, E. et al., The NCEP/NCAR 40-Year Reanalysis Project, Bull. Am. Meteorol. Soc., vol. 77, no. 3, pp. 437–471, 1996.
2. Dee, D. P. et al., The ERA-Interim Reanalysis: Configuration and Performance of the Data Assimilation System, Q. J. R. Meteorol. Soc., vol. 137, no. 656, pp. 553–597, 2011.
3. Wang, A. and Zeng, X., Evaluation of Multireanalysis Products with in Situ Observations over the Tibetan Plateau, J. Geophys. Res. Atmos., vol. 117, no. 5, 2012.
4. Fu, G., Charles, S. P., Timbal, B., Jovanovic, B., and Ouyang, F., Comparison of NCEP-NCAR and ERA-Interim over Australia, Int. J. Climatol., vol. 36, no. 5, pp. 2345–2367, 2016.
5. Mooney, P. A., Mulligan, F. J., and Fealy, R., Comparison of ERA-40, ERA-Interim and NCEP/NCAR Reanalysis Data with Observed Surface Air Temperatures over Ireland, Int. J. Climatol., vol. 31, no. 4, pp. 545–557, 2011.
6. Druyan, L. and Fulakeza, M., Downscaling Reanalysis over Continental Africa with a Regional Model: NCEP versus ERA Interim Forcing, Clim. Res., vol. 56, no. 3, pp. 181–196, 2013.
7. Tolika, K., Maheras, P., Flocas, H. A., and Arseni-Papadimitriou, A., An Evaluation of a General Circulation Model (GCM) and the NCEP–NCAR Reanalysis Data for Winter Precipitation in Greece, Int. J. Climatol., vol. 26, no. 7, pp. 935–955, 2006.
8. Plummer, D. A., Caya, D., Frigon, A., Côté, H., Giguère, M., Paquin, D., Biner, S., Harvey, R., and De Elia, R., Climate and Climate Change over North America as Simulated by the Canadian RCM, J. Clim., vol. 19, no. 13, pp. 3112–3132, 2006.

9. Fu, G., Liu, Z., Charles, S. P., Xu, Z., and Yao, Z., A Score-Based Method for Assessing the Performance of GCMs: A Case Study of Southeastern Australia, J. Geophys. Res. Atmos., vol. 118, no. 10, pp. 4154–4167, 2013.
10. Jiang, D., Tian, Z., and Lang, X., Reliability of Climate Models for China through the IPCC Third to Fifth Assessment Reports, Int. J. Climatol., vol. 36, no. 3, pp. 1114–1133, 2016.
11. Wilby, R. L., Hay, L. E., and Leavesley, G. H., A Comparison of Downscaled and Raw GCM Output: Implications for Climate Change Scenarios in the San Juan River Basin, Colorado, J. Hydrol., vol. 225, no. 1–2, pp. 67–91, 1999.
12. Fistikoglu, O. and Okkan, U., Statistical Downscaling of Monthly Precipitation Using NCEP/NCAR Reanalysis Data for Tahtali River Basin in Turkey, J. Hydrol. Eng., vol. 16, no. 2, pp. 157–164, 2011.
13. Brands, S., Gutiérrez, J. M., Herrera, S., and Cofiño, A. S., On the Use of Reanalysis Data for Downscaling, J. Clim., vol. 25, no. 7, pp. 2517–2526, 2012.
14. Sachindra, D. A. and Perera, B. J. C., Statistical Downscaling of General Circulation Model Outputs to Precipitation Accounting for Non-Stationarities in Predictor-Predictand Relationships, PLoS One, vol. 11, no. 12, p. e0168701, 2016.
15. Chen, H., Xu, C. Y., and Guo, S., Comparison and Evaluation of Multiple GCMs, Statistical Downscaling and Hydrological Models in the Study of Climate Change Impacts on Runoff, J. Hydrol., vol. 434–435, pp. 36–45, 2012.
16. Okkan, U. and Fistikoglu, O., Evaluating Climate Change Effects on Runoff by Statistical Downscaling and Hydrological Model GR2M, Theor. Appl. Climatol., vol. 117, no. 1, pp. 343–361, 2014.
17. Liu, J., Yuan, D., Zhang, L., Zou, X., and Song, X., Comparison of Three Statistical Downscaling Methods and Ensemble Downscaling Method Based on Bayesian Model Averaging in Upper Hanjiang River Basin, Adv. Meteorol., pp. 1–12, 2016.
18. Murphy, J., An Evaluation of Statistical and Dynamical Techniques for Downscaling Local Climate, J. Clim., vol. 12, no. 8, pp. 2256–2284, 1999.
19. Trzaska, Y. and Schnarr, E., A Review of Downscaling Methods for Climate Change Projections (USAID, 2014), 2014.
20. Khan, M. S., Coulibaly, P., and Dibike, Y., Uncertainty Analysis of Statistical Downscaling Methods, J. Hydrol., vol. 319, no. 1–4, pp. 357–382, 2006.
21. Schoof, J. T., Pryor, S. C., and Robeson, S. M., Downscaling Daily Maximum and Minimum Temperatures in the Midwestern USA: A Hybrid Empirical Approach, Int. J. Climatol., vol. 27, no. 4, pp. 439–454, 2007.
22. Benestad, R. E., Hanssen-Bauer, I., and Førland, E. J., An Evaluation of Statistical Models for Downscaling Precipitation and Their Ability to Capture Long-Term Trends, Int. J. Climatol., vol. 27, no. 5, pp. 649–665, 2007.
23. Cannon, A. J., Sobie, S. R., and Murdock, T. Q., Bias Correction of GCM Precipitation by Quantile Mapping: How Well Do Methods Preserve Changes in Quantiles and Extremes?, J. Clim., vol. 28, no. 17, pp. 6938–6959, 2015.
24. Mendoza, P. A., Mizukami, N., Ikeda, K., Clark, M. P., Gutmann, E. D., Arnold, J. R., Brekke, L. D., and Rajagopalan, B., Effects of Different Regional Climate Model Resolution and Forcing Scales on Projected Hydrologic Changes, J. Hydrol., vol. 541, pp. 1003–1019, 2016.
25. Karmacharya, J., Jones, R., Moufouma-Okia, W., and New, M., Evaluation of the Added Value of a High-Resolution Regional Climate Model Simulation of the South Asian Summer Monsoon Climatology, Int. J. Climatol., vol. 37, no. 9, pp. 3630–3643, 2017.
26. Sun, Y., Wendi, D., Kim, D. E., and Liong, S. Y., Deriving Intensity–Duration–Frequency (IDF) Curves Using Downscaled in Situ Rainfall Assimilated with Remote Sensing Data, Geosci. Lett., vol. 6, no. 1, p. 17, 2019.
27. Christensen, J. H., Boberg, F., Christensen, O. B., and Lucas-Picher, P., On the Need for Bias Correction of Regional Climate Change Projections of Temperature and Precipitation, Geophys. Res. Lett., vol. 35, no. 20, 2008.
28. Teutschbein, C. and Seibert, J., Bias Correction of Regional Climate Model Simulations for Hydrological Climate-Change Impact Studies: Review and Evaluation of Different Methods, J. Hydrol., vol. 456–457, pp. 12–29, 2012.
29. Singh, V. K. and Kumar, D., Downscaling Daily Precipitation over the Upper Shivnath Basin: A Comparison of Three Statistical Downscaling Techniques, Int. J. Curr. Microbiol. Appl. Sci., vol. 9, no. 1, pp. 1676–1688, 2020.
30. Goly, A., Teegavarapu, R. S. V., and Mondal, A., Development and Evaluation of Statistical Downscaling Models for Monthly Precipitation, Earth Interact., vol. 18, no. 18, pp. 1–28, 2014.
31. Hung, N. Q., Babel, M. S., Weesakul, S., and Tripathi, N. K., An Artificial Neural Network Model for Rainfall Forecasting in Bangkok, Thailand, Hydrol. Earth Syst. Sci., vol. 13, no. 8, pp. 1413–1425, 2009.
32. Mailhot, A., Duchesne, S., Caya, D., and Talbot, G., Assessment of Future Change in Intensity-Duration-Frequency (IDF) Curves for Southern Quebec Using the Canadian Regional Climate Model (CRCM), J. Hydrol., vol. 347, no. 1–2, pp. 197–210, 2007.
33. Mirhosseini, G., Srivastava, P., and Stefanova, L., The Impact of Climate Change on Rainfall Intensity-Duration-Frequency (IDF) Curves in Alabama, Reg. Environ. Chang., vol. 13, no. SUPPL.1, pp. 25–33, 2013.
34. Hofer, M., Mölg, T., Marzeion, B., and Kaser, G., Empirical-Statistical Downscaling of Reanalysis Data to High-Resolution Air Temperature and Specific Humidity above a Glacier Surface (Cordillera Blanca, Peru), J. Geophys. Res. Atmos., vol. 115, no. 12, 2010.
35. Giorgi, F., Hewitson, B., Arritt, R., Gutowski, W., Knutson, T., and Landsea, C., Regional Climate Information—Evaluation and Projections, Clim. Chang. 2001 Sci. bases, 2001.
36. Wilby, R. L., Wigley, T. M. L., Conway, D., Jones, P. D., Hewitson, B. C., Main, J., and Wilks, D. S., Statistical Downscaling of General Circulation Model Output: A Comparison of Methods, Water Resour. Res., vol. 34, no. 11, pp. 2995–3008, 1998.
37. Karl, T. R., Wang, W.-C., Schlesinger, M. E., Knight, R. W., and Portman, D., A Method of Relating General Circulation Model Simulated Climate to the Observed Local Climate. Part I: Seasonal Statistics, J. Clim., vol. 3, no. 10, pp. 1053–1079, 1990.
38. Kidson, J. W. and Thompson, C. S., A Comparison of Statistical and Model-Based Downscaling Techniques for Estimating Local Climate Variations, J. Clim., vol. 11, no. 4, pp. 735–753, 1998.
39. Chen, H., Guo, J., Xiong, W., Guo, S., and Xu, C.-Y., Downscaling GCMs Using the Smooth Support Vector Machine Method to Predict Daily Precipitation in the Hanjiang Basin, Adv. Atmos. Sci., vol. 27, no. 2, pp. 274–284, 2010.
40. Anandhi, A., Srinivas, V. V., Kumar, D. N., and Nanjundiah, R. S., Role of Predictors in Downscaling Surface Temperature to River Basin in India for IPCC SRES Scenarios Using Support Vector Machine, Int. J. Climatol., vol. 29, no. 4, pp. 583–603, 2009.
41. Wetterhall, F., Bárdossy, A., Chen, D., Halldin, S., and Xu, C. Y., Statistical Downscaling of Daily Precipitation over Sweden Using GCM Output, in Theoretical and Applied Climatology, May 2009, vol. 96, no. 1–2, pp. 95–103.
42. Dayon, G., Boé, J., and Martin, E., Transferability in the Future Climate of a Statistical Downscaling Method for Precipitation in France, J. Geophys. Res. Atmos., vol. 120, no. 3, pp. 1023–1043, 2015.
43. Vu, M. T., Aribarg, T., Supratid, S., Raghavan, S. V., and Liong, S. Y., Statistical Downscaling Rainfall Using Artificial Neural Network: Significantly Wetter Bangkok?, Theor. Appl. Climatol., vol. 126, no. 3–4, pp. 453–467, 2016.
44. Hu, Y., Maskey, S., and Uhlenbrook, S., Downscaling Daily Precipitation over the Yellow River Source Region in China: A Comparison of Three Statistical Downscaling Methods, Theor. Appl. Climatol., vol. 112, no. 3–4, pp. 447–460, 2013.
45. Tatli, H., Nüzhet Dalfes, H., and Sibel Menteş, A Statistical Downscaling Method for Monthly Total Precipitation over Turkey, Int. J. Climatol., vol. 24, no. 2, pp. 161–180, 2004.
46. Tareghian, R. and Rasmussen, P. F., Statistical Downscaling of Precipitation Using Quantile Regression, J. Hydrol., vol. 487, pp. 122–135, 2013.
47. Harpham, C. and Wilby, R. L., Multi-Site Downscaling of Heavy Daily Precipitation Occurrence and Amounts, J. Hydrol., vol. 312, no. 1–4, pp. 235–255, 2005.
48. Mendes, D. and Marengo, J. A., Temporal Downscaling: A Comparison between Artificial Neural Network and Autocorrelation Techniques over the Amazon Basin in Present and Future Climate Change Scenarios, Theor. Appl. Climatol., vol. 100, no. 3, pp. 413–421, 2010.
49. ASCE, Artificial Neural Networks in Hydrology. II: Hydrologic Applications, J. Hydrol. Eng., vol. 5, no. 2, pp. 124–137, 2000.
50. Nourani, V., Razzaghzadeh, Z., Baghanam, A. H., and Molajou, A., ANN-Based Statistical Downscaling of Climatic Parameters Using Decision Tree Predictor Screening Method, Theor. Appl. Climatol., vol. 137, no. 3–4, pp. 1729–1746, 2019.
51. Coulibaly, P., Dibike, Y. B., and Anctil, F., Downscaling Precipitation and Temperature with Temporal Neural Networks, J. Hydrometeorol., vol. 6, no. 4, pp. 483–496, 2005.
52. Sennikovs, J. and Bethers, U., Statistical Downscaling Method of Regional Climate Model Results for Hydrological Modelling, 2009.
53. Ines, A. V. M. and Hansen, J. W., Bias Correction of Daily GCM Rainfall for Crop Simulation Studies, Agric. For. Meteorol., pp. 44–53, 2006.
54. Piani, C., Haerter, J. O., and Coppola, E., Statistical Bias Correction for Daily Precipitation in Regional Climate Models over Europe, Theor. Appl. Climatol., vol. 99, no. 1–2, pp. 187–192, 2010.
55. Johnson, F. and Sharma, A., Accounting for Interannual Variability: A Comparison of Options for Water Resources Climate Change Impact Assessments, Water Resour. Res., vol. 47, no. 4, 2011.
56. Huff, F. A. and Angel, J. R., Rainfall Frequency Atlas of the Midwest. Bulletin (Illinois State Water Survey) No. 71, 1992.
57. Nguyen, V. T. V., Nguyen, T. D., and Wang, H., Regional Estimation of Short Duration Rainfall Extremes, Water Sci. Technol., vol. 37, no. 11, pp. 15–19, 1998.
58. Borga, M., Vezzani, C., and Dall Fontana, G., Regional Rainfall Depth-Duration-Frequency Equations for an Alpine Region, Nat. Hazards, vol. 36, no. 1–2, pp. 221–235, 2005.
59. Bonaccorso, B., Brigandì, G., and Aronica, G. T., Regional Sub-Hourly Extreme Rainfall Estimates in Sicily under a Scale Invariance Framework, Water Resour. Manag., vol. 34, no. 14, pp. 4363–4380, 2020.
60. Huff, F. A., Time Distribution of Rainfall in Heavy Storms, Water Resour. Res., vol. 3, no. 4, pp. 1007–1019, 1967.
61. Al Mamun, A., bin Salleh, M. N., and Noor, H. M., Estimation of Short-Duration Rainfall Intensity from Daily Rainfall Values in Klang Valley, Malaysia, Appl. Water Sci., vol. 8, no. 7, p. 203, 2018.
62. Anilan, T., Yüksek, Ö., Saka, F., and Orgun, E., Rainfall Intensity-Duration-Frequency Analysis in Turkey, with the Emphasis of Eastern Black Sea Basin, Tek. Dergi, vol. 33, no. 4, 2022.
63. Chow, T. V., Maidment, D. R., and Larry, W. M., “Applied hydrology.” Water Resources Handbook; McGraw-Hill. New York, NY, USA, 1988.
64. Chowdhury, R., Alam, M. J., Das, P., and Alam, M. A., Short Duration Rainfall Estimation of Sylhet: IMD and USWB Method, J. Indian Water Work. Assoc., vol. 39, no. 4, pp. 285–292, 2007.
65. Jahnvi, M., Bhatt, P., Gandhi, H. M., and Gohil, K. B., Generation of Intensity Duration Frequency Curve Using Daily, J. Int. Acad. Res. Multidiscip., vol. 2, no. 2, pp. 717–722, 2014.
66. AlHassoun, S. A., Developing an Empirical Formulae to Estimate Rainfall Intensity in Riyadh Region, J. King Saud Univ. - Eng. Sci., vol. 23, no. 2, pp. 81–88, 2011.