Evaluation of a coupled event-driven phenology and evapotranspiration model for croplands in the United States northern Great Plains

Journal of Geophysical Research Atmospheres

Volumn 118, Issue 11, 2013, Pages 5065-5081

  Kovalskyy, V ^a   Henebry, G M ^a   Roy, D P ^a   Adusei, B ^a,d   Hansen, M ^a,d   Senay, G ^b   Mocko, D M ^c  

^a SOUTH DAKOTA STATE UNIVERSITY   (United States)

^b U S GEOLOGICAL SURVEY   (United States)

^c NASA GODDARD SPACE FLIGHT CENTER   (United States)

^d UNIVERSITY OF MARYLAND   (United States)

Author keywords

comparison of modeled ET; phenology in ET

Indexed keywords

BIOLOGY; CROPS; DATA PROCESSING; DISTRIBUTION FUNCTIONS; ESTIMATION; EVAPOTRANSPIRATION; MEAN SQUARE ERROR; NASA; RADIOMETERS; REFLECTION; REMOTE SENSING; SATELLITE IMAGERY; VEGETATION; WATER SUPPLY;

BIDIRECTIONAL REFLECTANCE DISTRIBUTION FUNCTIONS; COMPARISON OF MODELED ET; DAILY ACTUAL EVAPOTRANSPIRATIONS; MODERATE RESOLUTION IMAGING SPECTRORADIOMETER; NORMALIZED DIFFERENCE VEGETATION INDEX; NORTH AMERICAN LAND DATA ASSIMILATION SYSTEMS; PHENOLOGY IN ET; REMOTE SENSING DATA ASSIMILATIONS;

FORESTRY;

CLIMATE FORCING; CLIMATE MODELING; ERROR ANALYSIS; ESTIMATION METHOD; EVAPOTRANSPIRATION; HETEROGENEITY; NDVI; PHENOLOGY; REMOTE SENSING; SPATIAL ANALYSIS; WATER BUDGET;

GREAT PLAINS; UNITED STATES;

EID: 84880253683     PISSN: None     EISSN: 21698996     Source Type: Journal    
DOI: 10.1002/jgrd.50387     Document Type: Article

References (75)

1. Abramowitz, G., R. Leuning, M. Clark, and, A. Pitman, (2008), Evaluating the performance of land surface models, J. Clim., 21 (21), 5468-5481, doi: 10.1175/2008JCLI2378.1.
2. Anderson, M. C., et al. (2011), Mapping daily evapotranspiration at field to continental scales using geostationary and polar orbiting satellite imagery, Hydrol. Earth Syst. Sci., 15 (1), 223-239, doi: 10.5194/hess-15-223-2011.
3. Busetto, L., M. Meroni, and, R. Colombo, (2008), Combining medium and coarse spatial resolution satellite data to improve the estimation of sub-pixel NDVI time series, Remote Sens. Environ., 112 (1), 118-131, doi: 10.1016/j.rse.2007.04.004.
4. Campo, L., F. Castelli, D. Entekhabi, and, F. Caparrini, (2009), Land-atmosphere interactions in a high resolution atmospheric simulation coupled with a surface data assimilation scheme, Nat. Hazard. Earth Syst. Sci., 9 (5), 1613-1624, doi: 10.5194/nhess-9-1613-2009.
5. Chang, J., M. C. Hansen, K. Pittman, M. Carroll, and, C. DiMiceli, (2007), Corn and soybean mapping in the United States using MODIS time-series data sets, Agron. J., 99 (6), 1654-1664, doi: 10.2134/agronj2007.0170.
6. Debaeke, P., J. P. Caussanel, J. R. Kiniry, B. Kafiz, and, G. Mondragon, (1997), Modelling crop: Weed interactions in wheat with ALMANAC, Weed Res., 37 (5), 325-341, doi: 10.1046/j.1365-3180.1997.d01-55.X.
7. de Beurs, K. M., and, G. M. Henebry, (2004), Land surface phenology, climatic variation, and institutional change: Analyzing agricultural land cover change in Kazakhstan, Remote Sens. Environ., 89, 497-509, doi: 10.1016/j.rse.2003.11.006.
8. de Beurs, K. M., and, G. M. Henebry, (2005), A statistical framework for the analysis of long image time series, Int. J. Remote Sens., 26 (8), 1551-1573, doi: 10.1080/01431160512331326657.
9. de Beurs, K. M., and, G. M. Henebry, (2010), Spatio-temporal statistical methods for modelling land surface phenology, in Phenological Research, edited by, I. L. Hudson, and, M. R. Keatley, pp. 177-208, Springer, Netherlands.
10. de Beurs, K. M., C. K. Wright, and, G. M. Henebry, (2009), Dual scale trend analysis for evaluating climatic and anthropogenic effects on the vegetated land surface in Russia and Kazakhstan, Environ. Res. Lett., 4, 045012, doi: 10.1088/1748-9326/4/4/045012.
11. Dickinson, R. E., M. Shaikh, R. Bryant, and, L. Graumlich, (1998), Interactive canopies for a climate model, J. Clim., 11 (11), 2823-2836, doi: 10.1175/1520-0442(1998)011<2823:ICFACM>2.0.CO;2.
12. Fisher, J. I., J. F. Mustard, and, M. A. Vadeboncoeur, (2006), Green leaf phenology at Landsat resolution: Scaling from the field to the satellite, Remote Sens. Environ., 100 (2), 265-279, doi: 10.1016/j.rse.2005.10.022.
13. Foley, J. A., S. Levis, M. H. Costa, W. Cramer, and, D. Pollard, (2000), Incorporating dynamic vegetation cover within global climate models, Ecol. Appl., 10 (6), 1620-1632, doi: 10.1890/1051-0761(2000)010[1620:IDVCWG]2.0.CO;2.
14. Friedl, M. A., et al. (2002), Global land cover mapping from MODIS: Algorithms and early results, Remote Sens. Environ., 83 (1-2), 287-302, doi: 10.1016/S0034-4257(02)00078-0.
15. Gao, Z. Q., C. S. Liu, W. Gao, and, N. B. Chang, (2010), A coupled remote sensing and the Surface Energy Balance with Topography Algorithm (SEBTA) to estimate actual evapotranspiration under complex terrain, Hydrol. Earth Syst. Sci. Discuss., 7 (4), 4875-4924, doi: 10.5194/hessd-7-4875-2010.
16. Godfrey, C. M., and, D. J. Stensrud, (2010), An empirical latent heat flux parameterization for the Noah land surface model, J. Appl. Meteorol. Climatol., 49 (8), 1696-1713, doi: 10.1175/2010JAMC2180.1.
17. Godfrey, C., D. Stensrud, and, L. Leslie, (2007), A new latent heat flux parameterization for land surface models, paper presented at Preprints of 21st Conference on Hydrology, American Meteorological Society, San Antonio, TX, Jan15-18, 2007.
18. Henebry, G. M., (2010), Land surface phenology as an integrative diagnostic for landscape modeling, paper presented at LANDMOD2010, Montpellier, France, http://www.symposcience.org/exl-doc/colloque/ART-00002394.pdf.
19. Hopkins, A. D., (1918), Periodical events and natural law as guides to agricultural research and practice, in Monthly Weather Review Supplement, edited, Government Printing Office, Washington, DC.
20. Ibanez, I., R. B. Primack, A. J. Miller-Rushing, E. Ellwood, H. Higuchi, S. D. Lee, H. Kobori, and, J. A. Silander, (2010), Forecasting phenology under global warming, Philos. Trans. R. Soc. B: Biol. Sci., 365 (1555), 3247-3260, doi: 10.1098/rstb.2010.0120.
21. Iglesias, A., J. Schlickenrieder, D. Pereira, and, A. Diz, (2011), From the farmer to global food production: use of crop models for climate change impact assessment, in Handbook on Climate Change and Agriculture, edited by, R. O. Mendelsohn, and, A. Dinar, p. 49, Edward Elgar Publishing.
22. Jang, K., S. Kang, J. Kim, C. B. Lee, T. Kim, J. Kim, R. Hirata, and, N. Saigusa, (2009), Mapping evapotranspiration using MODIS and MM5 Four-Dimensional Data Assimilation, Remote Sens. Environ., 114 (3), 657-673, doi: 10.1016/j.rse.2009.11.010.
23. Kang, S., W. A. Payne, S. R. Evett, C. A. Robinson, and, B. A. Stewart, (2009), Simulation of winter wheat evapotranspiration in Texas and Henan using three models of differing complexity, Agric. Water Manage., 96 (1), 167-178, doi: 10.1016/j.agwat.2008.07.006.
24. Kiniry, J., M. Schmer, K. Vogel, and, R. Mitchell, (2008), Switchgrass biomass simulation at diverse sites in the Northern Great Plains of the U.S, BioEnergy Res., 1 (3), 259-264, doi: 10.1007/s12155-008-9024-8.
25. Koster, R. D., and, M. J. Suarez, (1996), Energy and water balance calculations in the Mosaic LSM, Tech. Memo Rep. 104606, 59 pp., NASA.
26. Koster, R. D., and, M. J. Suarez, (2003), Impact of land surface initialization on seasonal precipitation and temperature prediction, J. Hydrometeorol., 4 (2), 408-423, doi: 10.1175/1525-7541(2003)4 < 408:IOLSIO > 2.0.CO;2.
27. Koster, R. D., M. J. Suarez, P. Liu, U. Jambor, A. Berg, M. Kistler, R. Reichle, M. Rodell, and, J. Famiglietti, (2004), Realistic initialization of land surface states: Impacts on subseasonal forecast skill, J. Hydrometeorol., 5 (6), 1049-1063, doi: 10.1175/JHM-387.1.
28. Kovalskyy, V., and, G. M. Henebry, (2012a), A new concept for simulation of vegetated land surface dynamics-Part 1: The event driven phenology model, Biogeosci., 9 (1), 141-159, doi: 10.5194/bg-9-141-2012.
29. Kovalskyy, V., and, G. M. Henebry, (2012b), Alternative methods to predict actual evapotranspiration illustrate the importance of accounting for phenology-Part 2: The event driven phenology model, Biogeosci., 9 (1), 161-177, doi: 10.5194/bg-9-161-2012.
30. Kovalskyy, V., D. P. Roy, X. Y. Zhang, and, J. Ju, (2011), The suitability of multi-temporal web-enabled Landsat data NDVI for phenological monitoring-A comparison with flux tower and MODIS NDVI, Remote Sens. Lett., 3 (4), 325-334, doi: 10.1080/01431161.2011.593581.
31. Lawrence, P. J., and, T. N. Chase, (2007), Representing a new MODIS consistent land surface in the Community Land Model (CLM 3.0), J. Geophys. Res., 112, G01023, doi: 10.1029/2006jg000168.
32. Lewis, P., J. Gõmez-Dans, T. Kaminski, J. Settle, T. Quaife, N. Gobron, J. Styles, and, M. Berger, (2012), An Earth observation land data assimilation system (EO-LDAS), Remote Sens. Environ., 120, 219-235, doi: 10.1016/j.rse.2011.12.027.
33. Li, Z.-L., R. Tang, Z. Wan, Y. Bi, C. Zhou, B. Tang, G. Yan, and, X. Zhang, (2009), A review of current methodologies for regional evapotranspiration estimation from remotely sensed data, Sens., 9 (5), 3801-3853, doi: 10.3390/s90503801.
34. Liu, H., J. Yang, C. Drury, W. Reynolds, C. Tan, Y. Bai, P. He, J. Jin, and, G. Hoogenboom, (2011), Using the DSSAT-CERES-Maize model to simulate crop yield and nitrogen cycling in fields under long-term continuous maize production, Nutr. Cycl. Agroecosyst., 89 (3), 313-328, doi: 10.1007/s10705-010- 9396-y.
35. Luo, L., et al. (2003), Validation of the North American land data assimilation system (NLDAS) retrospective forcing over the southern Great Plains, J. Geophys. Res., 108 (D22), 8843, doi: 10.1029/2002jd003246.
36. Manabe, S., (1969), Climate and the ocean circulation, Mon. Weather Rev., 97 (11), 739-774, doi: 10.1175/1520-0493(1969)097 < 0739:CATOC > 2.3.CO;2.
37. Maruyama, A., and, T. Kuwagata, (2010), Coupling land surface and crop growth models to estimate the effects of changes in the growing season on energy balance and water use of rice paddies, Agricultural and Forest Meteorology, 150 (7-8), 919-930, doi: 10.1016/j.agrformet.2010.02.011.
38. Mearns, L. O,., T. Mavromatis, E. Tsvetsinskaya, C. Hays, and, W. Easterling, (1999), Comparative responses of EPIC and CERES crop models to high and low spatial resolution climate change scenarios, J. Geophys. Res., 104 (D6), 6623-6646, doi: 10.1029/1998jd200061.
39. Meng, C. L., Z. L. Li, X. Zhan, J. C. Shi, and, C. Y. Liu, (2009), Land surface temperature data assimilation and its impact on evapotranspiration estimates from the Common Land Model, Water Resour. Res., 45, W02421, doi: 10.1029/2008wr006971.
40. Miralles, D. G., T. R. H. Holmes, R. A. M. De Jeu, J. H. Gash, A. G. C. A. Meesters, and, A. J. Dolman, (2010), Global land-surface evaporation estimated from satellite-based observations, Hydrol. Earth Syst. Sci. Discuss., 7 (5), 8479-8519, doi: 10.5194/hessd-7-8479-2010.
41. Mitchell, K. E., et al. (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.
42. Monteith, J. L., (1965), Evaporation and environment, paper presented at Symposium Society Experiment. Biology, 1965, Cambridge University Press, London, 19, 205-234.
43. Mu, Q., F. A. Heinsch, M. Zhao, and, S. W. Running, (2007), Development of a global evapotranspiration algorithm based on MODIS and global meteorology data, Remote Sens. Environ., 111 (4), 519-536, doi: 10.1016/j.rse.2007.04.015.
44. Mu, Q., L. A. Jones, J. S. Kimball, K. C. McDonald, and, S. W. Running, (2009), Satellite assessment of land surface evapotranspiration for the pan-Arctic domain, Water Resour. Res., 45, W09420, doi: 10.1029/2008wr007189.
45. Mu, Q., M. Zhao, and, S. W. Running, (2011), Improvements to a MODIS global terrestrial evapotranspiration algorithm, Remote Sens. Environ., 115, 1781-1800, doi: 10.1016/j.rse.2011.02.019.
46. Nagler, P. L., J. Cleverly, E. Glenn, D. Lampkin, A. Huete, and, Z. Wan, (2005), Predicting riparian evapotranspiration from MODIS vegetation indices and meteorological data, Remote Sens. Environ., 94 (1), 17-30, doi: 10.1016/j.rse.2004.08.009.
47. Nagler, T., H. Rott, P. Malcher, and, F. Müller, (2008), Assimilation of meteorological and remote sensing data for snowmelt runoff forecasting, Remote Sens. Environ., 112 (4), 1408-1420, doi: 10.1016/j.rse.2007.07.006.
48. Niu, G.-Y., et al. (2011), The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements, J. Geophys. Res., 116, D12109, doi: 10.1029/2010jd015139.
49. Nkomozepi, T., and, S.-O. Chung, (2012), Assessing the trends and uncertainty of maize net irrigation water requirement estimated from climate change projections for Zimbabwe, Agric. Water Manage., 111 (0), 60-67, doi: 10.1016/j.agwat.2012.05.004.
50. Pitman, A. J., (2003), The evolution of, and revolution in, land surface schemes designed for climate models, Int. J. Climatol., 23 (5), 479-510, doi: 10.1002/joc.893.
51. Prihodko, L., A. S. Denning, N. P. Hanan, I. Baker, and, K. Davis, (2008), Sensitivity, uncertainty and time dependence of parameters in a complex land surface model, Agr. Forest. Meteorol., 148 (2), 268-287, doi: 10.1016/j.agrformet.2007.08.006.
52. Ransom, J., D. Franzen, P. Glogoza, K. Hellevang, V. Hofman, M. McMullen, and, R. Zollinger, (2004), Basics of corn production in North Dakota, North Dakota Extension Service, 20.
53. Rosero, E., Z.-L. Yang, L. E. Gulden, G.-Y. Niu, and, D. J. Gochis, (2009), Evaluating enhanced hydrological representations in Noah LSM over transition zones: Implications for model development, J. Hydrometeorol., 10 (3), 600-622, doi: 10.1175/2009JHM1029.1.
54. Rötzer, T., M. Leuchner, and, A. Nunn, (2010), Simulating stand climate, phenology, and photosynthesis of a forest stand with a process-based growth model, Int. J. Biometeorol., 54 (4), 449-464, doi: 10.1007/s00484-009- 0298-0.
55. Running, S. W., R. R. Nemani, F. A. Heinsch, M. Zhao, M. Reeves, and, H. Hashimoto, (2004), A continuous satellite-derived measure of global terrestrial primary production, Bioscience, 54 (6), 547-560, doi: 10.1641/0006-3568(2004) 054[0547:acsmog]2.0.co;2.
56. Sabater, J. M., C. Rüdiger, J.-C. Calvet, N. Fritz, L. Jarlan, and, Y. Kerr, (2008), Joint assimilation of surface soil moisture and LAI observations into a land surface model, Agr. Forest. Meteorol., 148 (8-9), 1362-1373, doi: 10.1016/j.agrformet.2008.04.003.
57. Schaaf, C. B., et al. (2002), First operational BRDF, albedo nadir reflectance products from MODIS, Remote Sens. Environ., 83 (1-2), 135-148, doi: 10.1016/S0034-4257(02)00091-3.
58. Senay, G., (2008), Modeling landscape evapotranspiration by integrating land surface phenology and a water balance algorithm, Algorithms, 1 (2), 52-68, doi: 10.3390/a1020052.
59. Senay, G., M. Budde, J. Verdin, and, A. Melesse, (2007), A coupled remote sensing and simplified surface energy balance approach to estimate actual evapotranspiration from irrigated fields, Sens., 7 (6), 979-1000, doi: 10.3390/s7060979.
60. Settle, J., and, N. Campbell, (1998), On the errors of two estimators of sub-pixel fractional cover when mixing is linear, Geosci. Remote Sens. IEEE Trans. on, 36 (1), 163-170, doi: 10.1109/36.655326.
61. Stancalie, G., A. Marica, and, L. Toulios, (2010), Using Earth observation data and CROPWAT model to estimate the actual crop evapotranspiration, Phys. Chem. Earth, Parts A/B/C, 35 (1-2), 25-30, doi: 10.1016/j.pce.2010.03.013.
62. Tucker, C. J., (1979), Red and photographic infrared linear combinations for monitoring vegetation, Remote Sens. Environ., 8 (2), 127-150, doi: 10.1016/0034-4257(79)90013-0.
63. Turner, M. R. J., J. P. Walker, and, P. R. Oke, (2008), Ensemble member generation for sequential data assimilation, Remote Sens. Environ., 112 (4), 1421-1433, doi: 10.1016/j.rse.2007.02.042.
64. Vinukollu, R. K., E. F. Wood, C. R. Ferguson, and, J. B. Fisher, (2011), Global estimates of evapotranspiration for climate studies using multi-sensor remote sensing data: Evaluation of three process-based approaches, Remote Sens. Environ., 115 (3), 801-823, doi: 10.1016/j.rse.2010.11.006.
65. Wegehenkel, M., (2009), Modeling of vegetation dynamics in hydrological models for the assessment of the effects of climate change on evapotranspiration and groundwater recharge, Adv. Geosci., 21, 109-115, doi: 10.5194/adgeo-21-109- 2009.
66. Willmott, C., and, K. Matsuura, (2007), Terrestrial water budget data archive: Monthly time series (1950-1999) 30.
67. Xia, Y., et al. (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. Atmos., 117, D03109, doi: 10.1029/2011jd016048.
68. Xia, Y., et al. (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. Atmos., 117, D03110, doi: 10.1029/2011jd016051.
69. Yan, H., et al. (2012), Global estimation of evapotranspiration using a leaf area index-based surface energy and water balance model, Remote Sens. Environ., 124, 581-595, doi: 10.1016/j.rse.2012.06.004.
70. Yuan, W., et al,. (2010), Global estimates of evapotranspiration and gross primary production based on MODIS and global meteorology data, Remote Sens. Environ., 114 (7), 1416-1431, doi: 10.1016/j.rse.2010.01.022.
71. Zha, T., A. G. Barr, G. van der Kamp, T. A. Black, J. H. McCaughey, and, L. B. Flanagan, (2010), Interannual variation of evapotranspiration from forest and grassland ecosystems in western Canada in relation to drought, Agr. Forest. Meteorol., 150 (11), 1476-1484, doi: 10.1016/j.agrformet.2010.08.003.
72. Zhang, X., M. A. Friedl, C. B. Schaaf, A. H. Strahler, J. C. F. Hodges, F. Gao, B. C. Reed, and, A. Huete, (2003), Monitoring vegetation phenology using MODIS, Remote Sens. Environ., 84 (3), 471-475, doi: 10.1016/S0034-4257(02) 00135-9.
73. Zhang, X., M. A. Friedl, and, C. B. Schaaf, (2006), Global vegetation phenology from Moderate Resolution Imaging Spectroradiometer (MODIS): Evaluation of global patterns and comparison with in situ measurements, J. Geophys. Res., 111, G04017, doi: 10.1029/2006jg000217.
74. Zhang, X., M. A. Friedl, and, C. B. Schaaf, (2009), Sensitivity of vegetation phenology detection to the temporal resolution of satellite data, Int. J. Remote Sens., 30 (8), 2061-2074, doi: 10.1080/01431160802549237.
75. Zhenzhong, Z., P. Shilong, L. Xin, Y. Guodong, P. Shushi, C. Philippe, and, B. M. Ranga, (2012), Global evapotranspiration over the past three decades: Estimation based on the water balance equation combined with empirical models, Environ. Res. Lett., 7 (1), 014026, doi: 10.1088/1748-9326/7/1/014026.