Diagnosing an artificial trend in NLDAS-2 afternoon precipitation

Journal of Hydrometeorology

Volumn 18, Issue 4, 2017, Pages 1051-1070

  Ferguson, Craig R ^a   Mocko, David M ^b,c  

^a STATE UNIVERSITY OF NEW YORK   (United States)

^b NASA GODDARD SPACE FLIGHT CENTER   (United States)

^c SCIENCE APPLICATIONS INTERNATIONAL CORPORATION   (United States)

Author keywords

Changepoint analysis; Forcing; Hydrology; Land surface model; Time series; Trends

Indexed keywords

DATA ASSIMILATION; LAND SURFACE; PRECIPITATION (CLIMATOLOGY); RAINFALL; TIME SERIES; TREND ANALYSIS;

MISSISSIPPI RIVER; UNITED STATES;

EID: 85017349649     PISSN: 1525755X     EISSN: 15257541     Source Type: Journal    
DOI: 10.1175/JHM-D-16-0251.1     Document Type: Article

References (100)

1. Andreadis, K. M., E. A. Clark, A. W. Wood, A. F. Hamlet, and D. P. Lettenmaier, 2005: Twentieth-century drought in the conterminous United States. J. Hydrometeor., 6, 985-1001, doi:10.1175/JHM450.1.
2. Bai, J., and P. Perron, 2003: Computation and analysis of multiple structural change models. J. Appl. Econ., 18, 1-22, doi:10.1002/jae.659.
3. Bárdossy, A., and H. J. Caspary, 1990: Detection of climate change in Europe by analyzing European atmospheric circulation patterns from 1881 to 1989. Theor. Appl. Climatol., 42, 155-167, doi:10.1007/BF00866871.
4. Beaulieu, C., J. Chen, and J. L. Sarmiento, 2012: Change-point analysis as a tool to detect abrupt climate variations. Philos. Trans. Roy. Soc. London, 370A, 1228-1249, doi:10.1098/rsta.2011.0383.
5. Benjamini, Y., and Y. Hochberg, 1995: Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. Roy. Stat. Soc., 57B, 289-300.
6. Blenkinsop, S., E. Lewis, S. C. Chan, and H. J. Fowler, 2017: Quality-control of an hourly rainfall dataset and climatology of extremes for the UK. Int. J. Climatol., 37, 722-740, doi:10.1002/joc.4735.
7. Busuioc, A., and H. von Storch, 1996: Changes in the winter precipitation in Romania and its relation to the large-scale circulation. Tellus, 48A, 538-552, doi:10.1034/j.1600-0870.1996.t01-3-00004.x.
8. Cai, W. J., and Coauthors, 2014: Increasing frequency of extreme El Niño events due to greenhouse warming. Nat. Climate Change, 4, 111-116, doi:10.1038/nclimate2100.
9. Changnon, S. A., 2000: El Niño 1997-1998: The Climate Event of the Century. Oxford University Press, 232 pp.
10. Charba, J. P., A. W. Harrell III, and A. C. Lackner III, 1992: A monthly precipitation amount climatology derived from published atlas maps: Development of a digital database. TDL Office Note 92-7, NOAA/NWS, 20 pp. [Available online at http://www.nws.noaa.gov/mdl/pubs/Documents/OfficeNotes/OfficeNote1992-07.pdf.]
11. Chen, M. Y., W. Shi, P. P. Xie, V. B. S. Silva, V. E. Kousky, R. W. Higgins, and J. E. Janowiak, 2008: Assessing objective techniques for gauge-based analyses of global daily precipitation. J. Geophys. Res., 113, D04110, doi:10.1029/2007JD009132.
12. Compo, G. P., and P. D. Sardeshmukh, 2010: Removing ENSO-related variations from the climate record. J. Climate, 23, 1957-1978, doi:10.1175/2009JCLI2735.1.
13. Compo, G. P., and Coauthors, 2011: The Twentieth Century Reanalysis Project. Quart. J. Roy. Meteor. Soc., 137, 1-28, doi:10.1002/qj.776.
14. Cosgrove, B. A., and Coauthors, 2003: Real-time and retrospective forcing in the North American Land Data Assimilation System (NLDAS) project. J. Geophys. Res., 108, 8842, doi:10.1029/2002JD003118.
15. Cressman, G. P., 1959: An operational objective analysis system. Mon. Wea. Rev., 87, 367-374, doi:10.1175/1520-0493(1959)087<0367:AOOAS>2.0.CO;2.
16. Crook, A. G., 1977: SNOTEL: Monitoring climatic factors to predict water supplies. J. Soil Water Conserv., 32, 294-295.
17. Dai, A., 1999: Recent changes in the diurnal cycle of precipitation over the United States. Geophys. Res. Lett., 26, 341-344, doi:10.1029/1998GL900318.
18. Dai, A., F. Giorgi, and K. E. Trenberth, 1999: Observed and model-simulated diurnal cycles of precipitation over the contiguous United States. J. Geophys. Res., 104, 6377-6402, doi:10.1029/98JD02720.
19. Daly, C., R. P. Neilson, and D. L. Phillips, 1994: A statistical-topographic model for mapping climatological precipitation over mountainous terrain. J. Appl. Meteor., 33, 140-158, doi:10.1175/1520-0450(1994)033<0140:ASTMFM>2.0.CO;2.
20. Dirmeyer, P. A., and Coauthors, 2012: Simulating the diurnal cycle of rainfall in global climate models: Resolution versus parameterization. Climate Dyn., 39, 399-418, doi:10.1007/s00382-011-1127-9.
21. Erlingis, J. M., and A. P. Barros, 2014: A study of the role of daytime land-atmosphere interactions on nocturnal convective activity in the Southern Great Plains during CLASIC. J. Hydrometeor., 15, 1932-1953, doi:10.1175/JHM-D-14-0016.1.
22. Fan, Y., H. M. Van den Dool, D. Lohmann, and K. Mitchell, 2006: 1948-98 U.S. hydrological reanalysis by the Noah land data assimilation system. J. Climate, 19, 1214-1237, doi:10.1175/JCLI3681.1.
23. Ferguson, C. R., and G. Villarini, 2012: Detecting inhomogeneities in the Twentieth Century Reanalysis over the central United States. J. Geophys. Res., 117, D05123, doi:10.1029/2011JD016988.
24. Ferguson, C. R., and G. Villarini, 2014: An evaluation of the statistical homogeneity of the Twentieth Century Reanalysis. Climate Dyn., 42, 2841-2866, doi:10.1007/s00382-013-1996-1.
25. Ferguson, C. R., J. Sheffield, E. F. Wood, and H. L. Gao, 2010: Quantifying uncertainty in a remote sensing-based estimate of evapotranspiration over continental USA. Int. J. Remote Sens., 31, 3821-3865, doi:10.1080/01431161.2010.483490.
26. Findell, K. L., and E. A. B. Eltahir, 2003a: Atmospheric controls on soil moisture-boundary layer interactions. Part I: Framework development. J. Hydrometeor., 4, 552-569, doi:10.1175/1525-7541(2003)004<0552:ACOSML>2.0.CO;2.
27. Findell, K. L., and E. A. B. Eltahir, 2003b: Atmospheric controls on soil moisture-boundary layer interactions. Part II: Feedbacks within the continental United States. J. Hydrometeor., 4, 570-583, doi:10.1175/1525-7541(2003)004<0570:ACOSML>2.0.CO;2.
28. Glahn, H. R., T. L. Chambers, W. S. Richardson, and H. P. Perrotti, 1985: Objective map analysis for the local AFOS MOS Program. NOAA Tech. Memo. NWS TDL 75, 34 pp. [Available online at http://www.nws.noaa.gov/mdl/pubs/Documents/TechMemo/TechMemo75.pdf.]
29. GMAO, 2015: MERRA-2 tavg1_2d_flx_Nx: 2d, 1-hourly, time-averaged, single-level, assimilation, surface flux diagnostics V5.12.4. Goddard Earth Sciences Data and Information Services Center, accessed 29 July 2016, doi:10.5067/7MCPBJ41Y0K6.
30. Gochis, D. J., and Coauthors, 2016: Operational Hydrometeorological Predictions in Mountain Watersheds using the New National Water Model Implementation of the WRF-Hydro Modeling System. 17th Conf. on Mountain Meteorology, Burlington, VA, Amer. Meteor. Soc., 15.6. [Available online at https://ams.confex.com/ams/17Mountain/webprogram/Paper296619.html.]
31. Guo, Z. C., and Coauthors, 2006: GLACE: The Global Land-Atmosphere Coupling Experiment. Part II: Analysis. J. Hydrometeor., 7, 611-625, doi:10.1175/JHM511.1.
32. Henderson-Sellers, A., A. J. Pitman, P. K. Love, P. Irannejad, and T. H. Chen, 1995: The Project for Intercomparison of Land Surface Parameterization Schemes (PILPS): Phases 2 and 3. Bull. Amer. Meteor. Soc., 76, 489-503, doi:10.1175/1520-0477(1995)076<0489:TPFIOL>2.0.CO;2.
33. Higgins, R. W., J. Janowiak, and J. P. Yao, 1996: A gridded hourly precipitation data base for the United States (1963-1993). NCEP/Climate Prediction Center Atlas 1, NOAA, 47 pp. [Available online at http://www.cpc.ncep.noaa.gov/research_papers/ncep_cpc_atlas/1/cover.html.]
34. Higgins, R. W., W. Shi, E. S. Yarosh, and R. Joyce, 2000: Improved United States precipitation quality control system and analysis. NCEP/Climate Prediction Center Atlas 7, NOAA, 40 pp. [Available online at http://www.cpc.ncep.noaa.gov/research_papers/ncep_cpc_atlas/7/toc.html.]
35. Hirsch, R. M., J. R. Slack, and R. A. Smith, 1982: Techniques of trend analysis for monthly water quality data. Water Resour. Res., 18, 107-121, doi:10.1029/WR018i001p00107.
36. Janjic, Z. I., 2003: A nonhydrostatic model based on a new approach. Meteor. Atmos. Phys., 82, 271-285, doi:10.1007/s00703-001-0587-6.
37. Joyce, R. J., J. E. Janowiak, P. A. Arkin, and P. P. Xie, 2004: CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. J. Hydrometeor., 5, 487-503, doi:10.1175/1525-7541(2004)005<0487:CAMTPG>2.0.CO;2.
38. Kendall, M. G., 1975: Rank Correlation Methods. 4th ed. Griffin, 202 pp.
39. Khaliq, M. N., T. B. M. J. Ouarda, P. Gachon, L. Sushama, and A. St-Hilaire, 2009: Identification of hydrological trends in the presence of serial and cross correlations: A review of selected methods and their application to annual flow regimes of Canadian rivers. J. Hydrol., 368, 117-130, doi:10.1016/j.jhydrol.2009.01.035.
40. Klein, S. A., X. N. Jiang, J. Boyle, S. Malyshev, and S. C. Xie, 2006: Diagnosis of the summertime warm and dry bias over the U.S. Southern Great Plains in the GFDL climate model using a weather forecasting approach. Geophys. Res. Lett., 33, L18805, doi:10.1029/2006GL027567.
41. Koster, R. D., and Coauthors, 2006: GLACE: The Global Land-Atmosphere Coupling Experiment. Part I: Overview. J. Hydrometeor., 7, 590-610, doi:10.1175/JHM510.1.
42. Lee, M. I., and Coauthors, 2007: An analysis of the warm-season diurnal cycle over the continental United States and northern Mexico in general circulation models. J. Hydrometeor., 8, 344-366, doi:10.1175/JHM581.1.
43. Lettenmaier, D. P., E. F. Wood, and J. R. Wallis, 1994: Hydro-climatological trends in the continental United States, 1948-88. J. Climate, 7, 586-607, doi:10.1175/1520-0442(1994)007<0586:HCTITC>2.0.CO;2.
44. Liang, X. Z., L. Li, A. Dai, and K. E. Kunkel, 2004: Regional climate model simulation of summer precipitation diurnal cycle over the United States. Geophys. Res. Lett., 31, L24208, doi:10.1029/2004GL021054.
45. Lin, Y., and K. E. Mitchell, 2005: The NCEP stage II/IV hourly precipitation analyses: Development and applications. 19th Conf. on Hydrology, San Diego, CA, Amer. Meteor. Soc., 1.2. [Available online at https://ams.confex.com/ams/Annual2005/techprogram/paper_83847.htm.]
46. Lin, Y., M. E. Baldwin, K. E. Mitchell, E. Rogers, and G. DiMego, 2001: Spring 2001 changes to the NCEP Eta Analysis and Forecast System: Assimilation of observed precipitation. 18th Conf. Weather Analysis Forecasting/14th Conf. on Numerical Weather Prediction, Fort Lauderdale, FL, Amer. Meteor. Soc., J2.7. [Available online at https://ams.confex.com/ams/WAF-NWP-MESO/techprogram/paper_23306.htm.]
47. Liu, L., Z. X. Xu, and J. X. Huang, 2012: Spatio-temporal variation and abrupt changes for major climate variables in the Taihu basin, China. Stochastic Environ. Res. Risk Assess., 26, 777-791, doi:10.1007/s00477-011-0547-8.
48. Livezey, R. E., and W. Y. Chen, 1983: Statistical field significance and its determination by Monte Carlo techniques. Mon. Wea. Rev., 111, 46-59, doi:10.1175/1520-0493(1983)111<0046:SFSAID>2.0.CO;2.
49. Livneh, B., E. Rosenberg, C. Lin, B. Nijssen, V. Mishra, K. Andreadis, E. Maurer, and D. Lettenmaier, 2013: A long-term hydrologically based dataset of land surface fluxes and states for the conterminous United States: Update and extensions. J. Climate, 26, 9384-9392, doi:10.1175/JCLI-D-12-00508.1.
50. Livneh, B., T. J. Bohn, D. W. Pierce, F. Munoz-Arriola, B. Nijssen, R. Vose, D. R. Cayan, and L. Brekke, 2015: A spatially comprehensive, hydrometeorological data set for Mexico, the U.S., and southern Canada 1950-2013. Sci. Data, 2, 150042, doi:10.1038/sdata.2015.42.
51. Luo, Y., E. H. Berbery, and K. E. Mitchell, 2005: The operational Eta model precipitation and surface hydrologic cycle of the Columbia and Colorado basins. J. Hydrometeor., 6, 341-370, doi:10.1175/JHM435.1.
52. Mallakpour, I., and G. Villarini, 2016: A simulation study to examine the sensitivity of the Pettitt test to detect abrupt changes in mean. Hydrol. Sci. J., 61, 245-254, doi:10.1080/02626667.2015.1008482.
53. Mann, H. B., 1945: Nonparametric tests against trend. Econometrica, 13, 245-259, doi:10.2307/1907187.
54. Matsui, T., D. Mocko, M. I. Lee, W. K. Tao, M. J. Suarez, and R. A. Pielke, 2010: Ten-year climatology of summertime diurnal rainfall rate over the conterminous U.S. Geophys. Res. Lett., 37, L13807, doi:10.1029/2010GL044139.
55. Maurer, E. P., A. W. Wood, J. C. Adam, D. P. Lettenmaier, and B. Nijssen, 2002: A long-term hydrologically based dataset of land surface fluxes and states for the conterminous United States. J. Climate, 15, 3237-3251, doi:10.1175/1520-0442(2002)015<3237:ALTHBD>2.0.CO;2.
56. McPhaden, M. J., 1999: El Niño: The child prodigy of 1997-98. Nature, 398, 559-562, doi:10.1038/19193.
57. Mesinger, F., and Coauthors, 2006: North American Regional Reanalysis. Bull. Amer. Meteor. Soc., 87, 343-360, doi:10.1175/BAMS-87-3-343.
58. Miller, C. J., and Coauthors, 2001: Controlling the false-discovery rate in astrophysical data analysis. Astron. J., 122, 3492-3505, doi:10.1086/324109.
59. Mitchell, K. E., and Coauthors, 2004: The multi-institution North American Land Data Assimilation System (NLDAS): Utilizing multiple GCIP products and partners in a continental distributed hydrological modeling system. J. Geophys. Res., 109, D07S90, doi:10.1029/2003JD003823.
60. Mo, K. C., L. C. Chen, S. Shukla, T. J. Bohn, and D. P. Lettenmaier, 2012: Uncertainties in North American Land Data Assimilation Systems over the contiguous United States. J. Hydrometeor., 13, 996-1009, doi:10.1175/JHM-D-11-0132.1.
61. Molod, A., L. Takacs, M. Suarez, and J. Bacmeister, 2015: Development of the GEOS-5 atmospheric general circulation model: Evolution from MERRA to MERRA2. Geosci. Model Dev., 8, 1339-1356, doi:10.5194/gmd-8-1339-2015.
62. NASA, 2016: Land Data Toolkit: LDT 7.1 users' guide. NASA GSFC Doc., 129 pp. [Available online at https://modelingguru.nasa.gov/servlet/JiveServlet/previewBody/2555-102-3-6450/LDT_usersguide.pdf.]
63. Nearing, G. S., D. M. Mocko, C. D. Peters-Lidard, S. V. Kumar, and Y. L. Xia, 2016: Benchmarking NLDAS-2 soil moisture and evapotranspiration to separate uncertainty contributions. J. Hydrometeor., 17, 745-759, doi:10.1175/JHM-D-15-0063.1.
64. Pan, M., H. B. Li, and E. Wood, 2010: Assessing the skill of satellite-based precipitation estimates in hydrologic applications. Water Resour. Res., 46, W09535, doi:10.1029/2009WR008290.
65. Pettitt, A. N., 1979: A non-parametric approach to the change-point problem. Appl. Stat., 28, 126-135, doi:10.2307/2346729.
66. Pitman, A. J., and A. Henderson-Sellers, 1998: Recent progress and results from the Project for the Intercomparison of Landsurface Parameterization Schemes. J. Hydrol., 212-213, 128-135, doi:10.1016/S0022-1694(98)00206-6.
67. Pitman, A. J., and Coauthors, 1999: Key results and implications from phase 1(c) of the Project for Intercomparison of Land-Surface Parametrization Schemes. Climate Dyn., 15, 673-684, doi:10.1007/s003820050309.
68. Pohlert, T., 2015: Trend: Non-parametric trend tests and change-point detection, version 0.0.1. R package. [Available online at https://www.rdocumentation.org/packages/trend/versions/0.0.1.]
69. Poli, P., and Coauthors, 2013: The data assimilation system and initial performance evaluation of the ECMWF pilot re-analysis of the 20th-century assimilating surface observations only (ERA-20c). ERA Rep. Series 14, ECMWF, 62 pp. [Available online at http://www.ecmwf.int/en/elibrary/11699-data-assimilation-system-and-initial-performance-evaluation-ecmwf-pilot-reanalysis.]
70. Prein, A. F., and Coauthors, 2015: A review on regional convection-permitting climate modeling: Demonstrations, prospects, and challenges. Rev. Geophys., 53, 323-361, doi:10.1002/2014RG000475.
71. R Core Development Team, 2011: R: A language and environment for statistical computing. R Foundation for Statistical Computing. [Available online at http://www. R-project.org/.]
72. Reichle, R. H., R. D. Koster, G. J. M. de Lannoy, B. A. Forman, Q. Liu, S. P. P. Mahanama, and A. Touré, 2011: Assessment and enhancement of MERRA land surface hydrology estimates. J. Climate, 24, 6322-6338, doi:10.1175/JCLI-D-10-05033.1.
73. Renard, B., and Coauthors, 2008: Regional methods for trend detection: Assessing field significance and regional consistency. Water Resour. Res., 44, W08419, doi:10.1029/2007WR006268.
74. Renka, R. J., 1988: Multivariate interpolation of large sets of scattered data. ACM Trans. Math. Software, 14, 139-148, doi:10.1145/45054.45055.
75. Rogers, E., T. Black, D. Deaven, G. DiMego, Q. Zhao, M. Baldwin, N. Junker, and Y. Lin, 1996: Changes to the operational 'early' Eta analysis/forecast system at the National Centers for Environmental Prediction. Wea. Forecasting, 11, 391-413, doi:10.1175/1520-0434(1996)011<0391:CTTOEA>2.0.CO;2.
76. Ruane, A. C., 2010: NARR's atmospheric water cycle components. Part II: Summertime mean and diurnal interactions. J. Hydrometeor., 11, 1220-1233, doi:10.1175/2010JHM1279.1.
77. Sato, T., H. Miura, M. Satoh, Y. N. Takayabu, and Y. Q. Wang, 2009: Diurnal cycle of precipitation in the tropics simulated in a global cloud-resolving model. J. Climate, 22, 4809-4826, doi:10.1175/2009JCLI2890.1.
78. Sen, P. K., 1968: Estimates of the regression coefficient based on Kendall's tau. J. Amer. Stat. Assoc., 63, 1379-1389, doi:10.1080/01621459.1968.10480934.
79. Seneviratne, S. I., T. Corti, E. L. Davin, M. Hirschi, E. B. Jaeger, I. Lehner, B. Orlowsky, and A. J. Teuling, 2010: Investigating soil moisture-climate interactions in a changing climate: A review. Earth-Sci. Rev., 99, 125-161, doi:10.1016/j.earscirev.2010.02.004.
80. Serreze, M. C., M. P. Clark, R. L. Armstrong, D. A. McGinnis, and R. S. Pulwarty, 1999: Characteristics of the western United States snowpack from Snowpack Telemetry (SNOTEL) data. Water Resour. Res., 35, 2145-2160, doi:10.1029/1999WR900090.
81. Shafran, P. C., J. Woolen, W. Ebisuzaki, W. Shi, Y. Fan, R. Grumbine, and M. Fennessy, 2004: Observational data used for assimilation in the NCEP North American Regional Reanalysis. 14th Conf. on Applied Climatology, San Diego, CA, Amer. Meteor. Soc., 1.4. [Available online at http://ams.confex.com/ams/pdfpapers/71689.pdf.]
82. Shepard, D. S., 1984: Computer mapping: The SYMAP interpolation algorithm. Spatial Statistics and Models, G. L. Gaile and C. J. Willmott, Eds., D. Reidel, 133-145.
83. Song, H.-J., C. R. Ferguson, and J. K. Roundy, 2016: Land-atmosphere coupling at the Southern Great Plains Atmospheric Radiation Measurement (ARM) field site and its role in anomalous afternoon peak precipitation. J. Hydrometeor., 17, 541-556, doi:10.1175/JHM-D-15-0045.1.
84. Tao, W. K., and Coauthors, 2013: Precipitation intensity and variation during MC3E: A numerical modeling study. J. Geophys. Res. Atmos., 118, 7199-7218, doi:10.1002/jgrd.50410.
85. Theil, H., 1950: A rank-invariant method of linear and polynomial regression analysis. Proc. K. Ned. Akad. Wet., 53, 386-392, 512-525, 1397-1412.
86. Tramblay, Y., S. El Adlouni, and E. Servat, 2013: Trends and variability in extreme precipitation indices over Maghreb countries. Nat Hazard Earth Sys, 13, 3235-3248, doi:10.5194/nhess-13-3235-2013.
87. Venables, W. N., and B. D. Ripley, 2002: Modern Applied Statistics with S. 4th ed. Springer, 498 pp.
88. Ventura, V., C. J. Paciorek, and J. S. Risbey, 2004: Controlling the proportion of falsely rejected hypotheses when conducting multiple tests with climatological data. J. Climate, 17, 4343-4356, doi:10.1175/3199.1.
89. Villarini, G., F. Serinaldi, J. A. Smith, and W. F. Krajewski, 2009: On the stationarity of annual flood peaks in the continental United States during the 20th century. Water Resour. Res., 45, W08417, doi:10.1029/2008WR007645.
90. Wallace, J. M., 1975: Diurnal variations in precipitation and thunderstorm frequency over the conterminous United States. Mon. Wea. Rev., 103, 406-419, doi:10.1175/1520-0493(1975)103<0406:DVIPAT>2.0.CO;2.
91. Wijngaard, J. B., A. M. G. K. Tank, and G. P. Konnen, 2003: Homogeneity of 20th century European daily temperature and precipitation series. Int. J. Climatol., 23, 679-692, doi:10.1002/joc.906.
92. Wilks, D. S., 2006: On 'field significance' and the false discovery rate. J. Appl. Meteor. Climatol., 45, 1181-1189, doi:10.1175/JAM2404.1.
93. Wood, A. W., and D. P. Lettenmaier, 2006: A test bed for new seasonal hydrologic forecasting approaches in the western United States. Bull. Amer. Meteor. Soc., 87, 1699-1712, doi:10.1175/BAMS-87-12-1699.
94. Wood, E. F., and Coauthors, 1998: The Project for Intercomparison of Land-Surface Parameterization Schemes (PILPS) phase 2(c) Red-Arkansas River basin experiment: 1. Experiment description and summary intercomparisons. Global Planet. Change, 19, 115-135, doi:10.1016/S0921-8181(98)00044-7.
95. Xia, Y. L., and Coauthors, 2009: NLDAS primary forcing data L4 hourly 0.125 × 0.125 degree V002. Goddard Earth Sciences Data and Information Services Center, accessed 3 August 2016, doi:10.5067/6J5LHHOHZHN4.
96. Xia, Y. L., and Coauthors, 2012a: Continental-scale water and energy flux analysis and validation for the North American Land Data Assimilation System project phase 2 (NLDAS-2): 1. Intercomparison and application of model products. J. Geophys. Res., 117, D03109, doi:10.1029/2011JD016048.
97. Xia, Y. L., and Coauthors, 2012b: Continental-scale water and energy flux analysis and validation for North American Land Data Assimilation System project phase 2 (NLDAS-2): 2. Validation of model-simulated streamflow. J. Geophys. Res., 117, D03110, doi:10.1029/2011JD016051.
98. Xia, Y. L., J. Sheffield, M. B. Ek, J. Dong, N. Chaney, H. Wei, J. Meng, and E. F. Wood, 2014: Evaluation of multi-model simulated soil moisture in NLDAS-2. J. Hydrol., 512, 107-125, doi:10.1016/j.jhydrol.2014.02.027.
99. Yamada, T. J., M. I. Lee, M. Kanamitsu, and H. Kanamaru, 2012: Diurnal characteristics of rainfall over the contiguous United States and northern Mexico in the dynamically downscaled reanalysis dataset (US10). J. Hydrometeor., 13, 1142-1148, doi:10.1175/JHM-D-11-0121.1.
100. Yue, S., P. Pilon, B. Phinney, and G. Cavadias, 2002: The influence of autocorrelation on the ability to detect trend in hydrological series. Hydrol. Processes, 16, 1807-1829, doi:10.1002/hyp.1095.