Diagnosis of insidious data disasters

Water Resources Research

Volumn 51, Issue 5, 2015, Pages 3815-3827

  Lundquist, Jessica D ^a   Wayand, Nicholas E ^a   Massmann, Adam ^a,b   Clark, Martyn P ^c   Lott, Fred ^a,d   Cristea, Nicoleta C ^a  

^a University of Washington   (United States)

^b STATE UNIVERSITY OF NEW YORK   (United States)

^c NATIONAL CENTER FOR ATMOSPHERIC RESEARCH   (United States)

^d King County   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ATMOSPHERIC TEMPERATURE; QUALITY ASSURANCE; STREAM FLOW;

DATA COLLECTION; DISTRIBUTED HYDROLOGY SOIL VEGETATION MODELS; FIELD OBSERVATIONS; MODEL SIMULATION; MULTIPLE-MODELING; OBSERVATIONAL ERRORS; SIERRA NEVADA , CALIFORNIA; SURFACE TEMPERATURES;

ERRORS;

DATA QUALITY; ERROR ANALYSIS; HYDROLOGICAL MODELING; SIMULATION; STREAMFLOW; SURFACE TEMPERATURE; WATER BUDGET;

CALIFORNIA; SNOQUALMIE PASS; TUOLUMNE RIVER; UNITED STATES; WASHINGTON [UNITED STATES];

EID: 85027934847     PISSN: 00431397     EISSN: 19447973     Source Type: Journal    
DOI: 10.1002/2014WR016585     Document Type: Article

References (80)

1. Anderson, E. A., (1976), A point energy and mass balance model of a snow cover, NOAA Tech. Rep. NWS 19, U.S. Department of Commerce, 150 pp., Silver Spring, Md.
2. Andréassian, V., C. Perrin, E. Parent, and, A. Bárdossy, (2010), Editorial-The court of miracles of hydrology: Can failure stories contribute to hydrological science?, Hydrol. Sci. J., 55 (6), 849-856.
3. Apogee (2014), Apogee Instruments Owner's Manual: Infrared Radiometer. [Available at http://www.apogeeinstruments.com/content/SI-100manual.pdf.]
4. Augustine, J. A., J. J. DeLuisi, and, C. N. Long, (2000), SURFRAD-A national surface radiation budget network for atmospheric research, Bull. Am. Meteorol. Soc., 81 (10), 2341-2357, doi: 10.1175/1520-0477(2000)081 <2341:SANSRB>2.3.CO;2.
5. Beven, K., and, I. Westerberg, (2011), On red herrings and real herrings: Disinformation and information in hydrological inference, Hydrol. Processes, 25 (10), 1676-1680.
6. Beven, K., P. J. Smith, and, A. Wood, (2011), On the colour and spin of epistemic error (and what we might do about it), Hydrol. Earth Syst. Sci. Discuss., 8 (3), 5355-5386.
7. Burges, S. J., (2011), Invited perspective: Why I am an optimist, Water Resour. Res., 47, W00H11, doi: 10.1029/2010WR009984.
8. California-Nevada River Forecast Center (2007), April-July Volume Water Supply Forecasts, 2007 Verification, technical report. California-Nevada River Forecast Center, Sacramento, Calif. [Available at http://www.cnrfc.noaa.gov/products/water-supply/2007/2007-verification.pdf.]
9. Clark, M. P., D. Kavetski, and, F. Fenicia, (2011), Pursuing the method of multiple working hypotheses for hydrological modeling, Water Resour. Res., 47, W09301, doi: 10.1029/2010WR009827.
10. Clark, M. P., et al., (2015a), A unified approach to process-based hydrologic modeling: 1. Modeling concept, Water Resour. Res., 51, doi: 10.1002/2015WR017198.
11. Clark, M. P., et al., (2015b), A unified approach for process-based hydrologic modeling: 2. Model implementation and example applications, Water Resour. Res., 51, doi: 10.1002/2015WR017200.
12. Crawford, N. H., and, R. K. Linsley, (1966), Digital simulation in hydrology: Stanford watershed model IV, Tech. Rep. 39, 210 pp., Dep. of Civ. Eng., Stanford Univ., Stanford, Calif.
13. Cristea, N. C., J. D. Lundquist, S. P. Loheide, C. S. Lowry, C. E. Moore, and, M. S. Raleigh, (2013), Modeling how vegetation cover affects climate change impacts on streamflow timing and magnitude in the snowmelt-dominated upper Tuolumne Basin, Sierra Nevada, Hydrol. Processes, 28, 3896-3918, doi: 10.1002/hyp.9909.
14. Daly, C., R. P. Neilson, and, D. L. Phillips, (1994), A statistical-topographic model for mapping climatological precipitation over mountainous terrain, J. Appl. Meteorol., 33, 140-158, doi: 10.1175/1520-0450(1994)033<0140:ASTMFM>2.0.CO;2.
15. Daly, C., M. Halbleib, J. I. Smith, W. P. Gibson, M. K. Doggett, G. H. Taylor, J. Curtis, and, P. P. Pasteris, (2008), Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States, Int. J. Climatol., 28, 2031-2064, doi: 10.1002/joc.1688.
16. Doyle, A. C., (1890), The Sign of the Four, 100 pp., Spencer Blackett, London, U. K.
17. Dunne, T., and, R. D. Black, (1970a), An experimental investigation of runoff production in permeable soils, Water Resour. Res., 6, 478-490, doi: 10.1029/WR006i002p00478.
18. Dunne, T., and, R. D. Black, (1970b), Partial area contributions to storm runoff in a small New England watershed, Water Resour. Res., 6, 1296-1311, doi: 10.1029/WR006i005p01296.
19. Feyen, L., J. A. Vrugt, B. O. Nuallain, J. van der Knijff, and, A. De Roo, (2007), Parameter optimisation and uncertainty assessment for large-scale streamflow simulation with the LISFLOOD model, J. Hydrol., 332, 276-289, doi: 10.1016/j.jhydrol.2006.07.004.
20. Freeze, R. A., (1978), Mathematical models of hillslope hydrology, in Hillslope Hydrology, edited by, M. J. Kirkby, pp. 177-226, John Wiley, Hoboken, N. J.
21. Gandin, L. S., (1988), Complex quality control of meteorological observations, Mon. Weather Rev., 116 (5), 1137-1156, doi: 10.1175/1520-0493(1988)116<1137:CQCOMO>2.0.CO;2.
22. Goswami, M., and, K. M. O'Connor, (2010), A "monster" that made the SMAR conceptual model "right for the wrong reasons," Hydrol. Sci. J., 55 (6), 913-927.
23. Gupta, H. V., S. Sorooshian, and, P. O. Yapo, (1998), Toward improved calibration of hydrologic models: Multiple and noncommensurable measures of information, Water Resour. Res., 34, 751-763, doi: 10.1029/97WR03495.
24. Gupta, H. V., T. Wagener, and, Y. Liu, (2008), Reconciling theory with observations: Elements of a diagnostic approach to model evaluation, Hydrol. Processes, 22, 3802-3813, doi: 10.1002/hyp.6989.
25. Gupta, H. V., C. Perrin, G. Blöschl, A. Montanari, R. Kumar, M. Clark, and, V. Andréassian, (2014), Large-sample hydrology: A need to balance depth with breadth, Hydrol. Earth Syst. Sci., 18, 463-477, doi: 10.5194/hess-18-463-2014.
26. Holländer, H. M., et al., (2014), Impact of modellers' decisions on hydrological a priori predictions, Hydrol. Earth Syst. Sci., 18, 2065-2085, doi: 10.5194/hess-18-2065-2014.
27. Hey, T., (2012), The Fourth Paradigm-Data-Intensive Scientific Discovery, E-Science and Information Management-Communications in Computer and Information Science, vol. 317, 1 pp., doi: 10.1007/978-3-642-33299-9-1.
28. Horton, R. E., (1931), The field, scope, and status of the science of hydrology, Eos Trans. AGU, 12, 189-202.
29. Hoyt, W. G., et al., (1936), Studies of relations of rainfall and run-off in the United States, U.S. Geol. Surv. Water Supply Pap. 772, 301 pp., U.S. Gov. Print. Off., Washington, D. C.
30. James, L. D., and, S. J. Burges, (1982), Selection, calibration, and testing of hydrologic models, in Hydrologic Modeling of Small Watersheds, Hydrol. Model. Monogr., edited by, C. T. Haan, H. P. Johnson, and, D. L. Brakensiek, chap. 11, pp. 437-472, Am. Soc. of Agric. Eng., St. Joseph, Mich.
31. Jordan, R., (1991), A one-dimensional temperature model for a snow cover, Tech. Doc. SNTHERM.89, 49 pp., U.S. Army Corps of Eng. Cold Reg. Res. and Eng. Lab., Hanover, N. H.
32. Kampf, S. K., and, S. J. Burges, (2010), Quantifying the water balance in a planar hillslope plot: Effects of measurement errors on flow prediction, J. Hydrol., 380, 191-202, doi: 10.1016/j.jhydrol.2009.10.036.
33. Kauffeldt, A., S. Halldin, A. Rodhe, C. Y. Xu, and, I. K. Westerberg, (2013), Disinformative data in large-scale hydrological modeling, Hydrol. Earth Syst. Sci., 17 (7), 2845-2857.
34. Killeen, T., and, A. Blatecky, (2011), Dear colleague letter: The "Earth Cube"-Towards a National Data Infrastructure for Earth System Science, NSF 11-065. NSF, Arlington, Va. [Available at http://www.nsf.gov/pubs/2011/nsf11065/nsf11065.jsp?org=NSF.]
35. Kirchner, J. W., (2006), Getting the right answers for the right reasons: Linking measurements, analyses, and models to advance the science of hydrology, Water Resour. Res., 42, W03S04, doi: 10.1029/2005WR004362.
36. Kuczera, G., (1996), Correlated rating curve error in flood frequency inference, Water Resour. Res., 32, 2119-2127, doi: 10.1029/96WR00804.
37. Langbein, W. B., and, J. V. B. Wells, (1955), The water in the rivers and creeks, in Water: The Yearbook of Agriculture, edited by, A. Stefferud, pp. 52-62, U.S. Dep. of Agric., Washington, D. C.
38. Loheide, S. P., II, and, J. D. Lundquist, (2009), Snowmelt-induced diel fluxes through the hyporheic zone, Water Resour. Res., 45, W07404, doi: 10.1029/2008WR007329.
39. Lott, F., (2008), Adaptability of snowmelt models to changing climate in complex terrain, master thesis, 82 pp., Univ. of Wash, Seattle, Wash. [Available at http://depts.washington.edu/mtnhydr/publications/LottMSCEthesis2008final.pdf.]
40. Louis, J.-F., (1979), A parametric model of vertical eddy fluxes in the atmosphere, Boundary Layer Meteorol., 17 (2), 187-202.
41. Lowry, C., J. Deems, S. Loheide, and, J. D. Lundquist, (2010), Linking snowmelt derived recharge and groundwater flow in a high elevation meadow system, Sierra Nevada Mountains, California, Hydrol. Processes, 24, 2821-2833.
42. Lowry, C., S. P. Loheide, C. Moore, and, J. D. Lundquist, (2011), Groundwater controls on vegetation composition and patterning in mountain meadows, Water Resour. Res., 47, W00J11, doi: 10.1029/2010WR010086.
43. Lundquist, J. D., and, S. P. Loheide, (2011), How evaporative water losses vary between wet and dry water years as a function of elevation in the Sierra Nevada, California and critical factors for modeling, Water Resour. Res., 47, W00H09, doi: 10.1029/2010WR010050.
44. Lundquist, J. D., J. Minder, P. J. Neiman, and, E. Sukovich, (2010), Relationships between barrier jet heights, orographic precipitation gradients, and streamflow in the Northern Sierra Nevada, J. Hydrometeorol., 11, 1141-1156, doi: 10.1175/2010JHM1264.1.
45. Lundquist, J. D., A. K. Massmann, J. Stimberis, and, N. E. Wayand, (2014), Surface meteorological and snow observations at Snoqualmie Pass, Univ. of Wash. Seattle, Wash. [Available at http://hdl.handle.net/1773/25611.]
46. Mahrt, L., (1987), Grid-averaged surface fluxes, Mon. Weather Rev., 115, 1550-1560.
47. McMillan, H., T. Krueger, and, J. Freer, (2012), Benchmarking observational uncertainties for hydrology: Rainfall, river discharge and water quality, Hydrol. Processes, 26, 4078-4111, doi: 10.1002/hyp.9384.
48. Medawar, P. B., (1979), Advice to a Young Scientist, Basic Books, 109 pp., N. Y. [Available at http://www.albany.edu/~scifraud/data/sci-fraud-2927.html.]
49. Meek, D. W., and, J. L. Hatfield, (1994), Data quality checking for single station meteorological databases, Agric. For. Meteorol., 69, 85-109.
50. Meinzer, O. E., (Ed.) (1949), Hydrology (McGraw-Hill 1942), 712 pp., Dover, N. Y.
51. Mellor, M., (1977), Engineering properties of snow, J. Glaciol., 19 (81), 15-66.
52. Mizukami, N., and, M. B. Smith, (2012), Analysis of inconsistencies in multi-year gridded quantitative precipitation estimate over complex terrain and its impact on hydrologic modeling, J. Hydrol., 428-429, 129-141.
53. Montanari, A., and, G. Di Baldassarre, (2013), Data errors and hydrological modelling: The role of model structure to propagate observation uncertainty, Adv. Water Resour., 51, 498-504.
54. Montanari, A., and, D. Koutsoyiannis, (2012), A blueprint for process-based modeling of uncertain hydrological systems, Water Resour. Res., 48, W09555, doi: 10.1029/2011WR011412.
55. Mulvany, T. J., (1851), On the use of self-registering rain and flood gauges, Trans. Inst. Civ. Eng. Ireland, 4, 1-8.
56. Oberg, K., and, D. S. Mueller, (2007), Validation of streamflow measurements made with acoustic Doppler current profilers, J. Hydraul. Eng., 133 (12), 1421-1432, doi: 10.1061/(ASCE)0733-9429(2007)133:12(1421).
57. Philip, J. R., (1975), Some remarks on science and catchment prediction, in Prediction in Catchment Hydrology, edited by, T. G. Chapman, and, F. X. Dunin, pp. 23-30, Aust. Acad. of Sci., Canberra.
58. Pielke, R., Sr,., et al. (2007), Documentation of uncertainties and biases associated with surface temperature measurement sites for climate change assessment, Bull. Am. Meteorol. Soc., 88, 913-928, doi: 10.1175/BAMS-88-6-913.
59. Rasmussen, R., et al., (2012), How well are we measuring snow: The NOAA/FAA/NCAR winter precipitation test bed, Bull. Am. Meteorol. Soc., 93, 811-829, doi: 10.1175/BAMS-D-11-00052.1.
60. Refsgaard, J. C., and, J. R. Hansen, (2010), A good-looking catchment can turn into a modeller's nightmare, Hydrol. Sci. J., 55 (6), 899-912.
61. Reichle, R. H., (2008), Data assimilation methods in the Earth sciences, Adv. Water Resour., 31 (11), 1411-1418.
62. Seibert, J., and, J. J. McDonnell, (2002), On the dialog between experimentalist and modeler in catchment hydrology: Use of soft data for multicriteria model calibration, Water Resour. Res., 38 (11), 1241, doi: 10.1029/2001WR000978.
63. Seyfried, M. S., and, B. P. Wilcox, (1995), Scale and the nature of spatial variability: Field examples having implications for hydrologic modeling, Water Resour. Res., 31, 173-184, doi: 10.1029/94WR02025.
64. Sieck, L. C., S. J. Burges, and, M. Steiner, (2007), Challenges in obtaining reliable measurements of point rainfall, Water Resour. Res., 43, W01420, doi: 10.1029/2005WR004519.
65. Slater, A. G., and, M. P. Clark, (2006), Snow data assimilation via an ensemble Kalman filter, J. Hydrometeorol., 7, 478-493, doi: 10.1175/JHM505.1.
66. Smirnova, T. G., J. M. Brown, S. G. Benjamin, and, D. Kim, (2000), Parameterization of cold-season processes in the MAPS land-surface scheme, J. Geophys. Res., 105, 4077-4086, doi: 10.1029/1999JD901047.
67. Steinacker, R., D. Mayer, and, A. Steiner, (2011), Data quality control based on self-consistency, Mon. Weather Rev., 139, 3974-3991, doi: 10.1175/MWR-D-10-05024.1.
68. Tarboton, D., and, C. Luce, (1996), Utah energy balance snow accumulation and melt model (UEB), in Computer Model Technical Description Users Guide, 64 pp., Utah Water Res. Lab. and USDA For. Serv. Intermed. Res. Stn., Logan, Utah.
69. Theis, C. V., (1967), Aquifer and models, in Symposium on Groundwater Hydrology, edited by, M. A. Marino, pp. 138-148, Am. Water Resour. Assoc., Middleburg, Va.
70. Udall press release (2013), Washington D. C. [Available at http://votesmart.org/publicstatement/835844/udall-nrcs-announce-plan-to-maintain-funding-for-snow-data-water-supply-monitoring-program#.VR76pkLWt74.]
71. United States Army Corps of Engineers (USACE) (1956), Snow Hydrology: Summary Report of the Snow Investigations, Portland, Oreg.
72. USGS (2009). United States Geological Survey Streamflow Information Program, Reston, Va. [Available at http://water.usgs.gov/nsip/history1.html.]
73. Vrugt, J. A., C. J. F. ter Braak, M. P. Clark, J. M. Hyman, and, B. A. Robinson, (2008), Treatment of input uncertainty in hydrologic modeling: Doing hydrology backward with Markov chain Monte Carlo simulation, Water Resour. Res., 44, W00B09, doi: 10.1029/2007WR006720.
74. Western, A., and, R. Grayson, (2000), Soil moisture and runoff processes at Tarrawarra, in Spatial Patterns in Catchment Hydrology, Observations and Modelling, edited by, R. Grayson, and, G. Bloschl, pp. 209-246, Cambridge Univ. Press, Cambridge, U. K.
75. Wigmosta, M. S., and, S. J. Burges, (1997), An adaptive modeling and monitoring approach to describe the hydrologic behavior of small catchments, J. Hydrol., 202, 48-77.
76. Wigmosta, M. S., L. W. Vail, and, D. P. Lettenmaier, (1994), A distributed hydrology-vegetation model for complex terrain, Water Resour. Res., 30, 1665-1679, doi: 10.1029/94WR00436.
77. Wood, E. F., et al., (2011), Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring Earth's terrestrial water, Water Resour. Res., 47, W05301, doi: 10.1029/2010WR010090.
78. World Meteorological Organization (WMO) (2008), Guide to meteorological instruments and methods of observation, Geneva, Switzerland. [Available at http://www.wmo.int/pages/prog/www/IMOP/CIMO-Guide.html.]
79. Yen, Y.-C., (1965), Effective thermal conductivity and water vapor diffusivity of naturally compacted snow, J. Geophys. Res., 70, 1821-1825, doi: 10.1029/JZ070i008p01821.
80. Yilmaz, K. K., H. V. Gupta, and, T. Wagener, (2008), A process-based diagnostic approach to model evaluation: Application to the NWS distributed hydrologic model, Water Resour. Res., 44, W09417, doi: 10.1029/2007WR006716.