Improving Budyko curve-based estimates of long-term water partitioning using hydrologic signatures from GRACE

Water Resources Research

Volumn 52, Issue 7, 2016, Pages 5537-5554

  Fang, Kuai ^a   Shen, Chaopeng ^a   Fisher, Joshua B ^b   Niu, Jie ^c,d  

^a The Pennsylvania State University   (United States)

^b CALIFORNIA INSTITUTE OF TECHNOLOGY   (United States)

^c LAWRENCE BERKELEY NATIONAL LABORATORY   (United States)

^d UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

amplitude; Budyko curve; evapotranspiration; GRACE; GRDC; water partitioning

Indexed keywords

CATCHMENTS; DATA FUSION; EVAPOTRANSPIRATION; SNOW; UNCERTAINTY ANALYSIS; WATER SUPPLY;

AMPLITUDE; BUDYKO CURVE; GRACE; GRDC; WATER PARTITIONING;

DIGITAL STORAGE;

AMPLITUDE; ANNUAL VARIATION; AUTOCORRELATION; DATA SET; EVAPOTRANSPIRATION; GRACE; HYDROLOGICAL RESPONSE; LONG-TERM CHANGE; PARTITIONING; PRECIPITATION (CLIMATOLOGY); RUNOFF; SEASONALITY; WATER STORAGE;

UNITED STATES;

EID: 84979752724     PISSN: 00431397     EISSN: 19447973     Source Type: Journal    
DOI: 10.1002/2016WR018748     Document Type: Article

References (77)

1. Arora, V. K. (2002), The use of the aridity index to assess climate change effect on annual runoff, J. Hydrol., 265(1–4), 164–177, doi:10.1016/S0022-1694(02)00101-4.
2. Berghuijs, W. R., R. A. Woods, and M. Hrachowitz (2014a), A precipitation shift from snow towards rain leads to a decrease in streamflow, Nat. Clim. Change, 4(7), 583–586, doi:10.1038/nclimate2246.
3. Berghuijs, W. R., M. Sivapalan, R. A. Woods, and H. H. G. Savenije (2014b), Patterns of similarity of seasonal water balances: A window into streamflow variability over a range of time scales, Water Resour. Res., 50, 5638–5661, doi:10.1002/2014WR015692.
4. Budyko, M. I. (1948), Evaporation Under Natural Conditions, Gidrometeorizdat, Jerusalem.
5. Buermann, W. (2002), Analysis of a multiyear global vegetation leaf area index data set, J. Geophys. Res., 107(D22), 4646, doi:10.1029/2001JD000975.
6. Carmona, A. M., M. Sivapalan, M. A. Yaeger, and G. Poveda (2014), Regional patterns of interannual variability of catchment water balances across the continental U.S.: A Budyko framework, Water Resour. Res., 50, 9177–9193, doi:10.1002/2014WR016013.
7. Chen, X., N. Alimohammadi, and D. Wang (2013), Modeling interannual variability of seasonal evaporation and storage change based on the extended Budyko framework, Water Resour. Res., 49, 6067–6078, doi:10.1002/wrcr.20493.
8. Chen, Y., et al. (2014), Comparison of satellite-based evapotranspiration models over terrestrial ecosystems in China, Remote Sens. Environ., 140, 279–293, doi:10.1016/j.rse.2013.08.045.
9. Clark, M. P., et al. (2015), Improving the representation of hydrologic processes in Earth System Models, Water Resour. Res., 51, 5929–5956, doi:10.1002/2015WR017096.
10. Döll, P., H. Müller Schmied, C. Schuh, F. T. Portmann, and A. Eicker (2014), Global-scale assessment of groundwater depletion and related groundwater abstractions: Combining hydrological modeling with information from well observations and GRACE satellites, Water Resour. Res., 50, 5698–5720, doi:10.1002/2014WR015595.
11. Duan, Q., et al. (2006), Model Parameter Estimation Experiment (MOPEX): An overview of science strategy and major results from the second and third workshops, J. Hydrol., 320(1–2), 3–17, doi:10.1016/j.jhydrol.2005.07.031.
12. Ek, M. B., K. E. Mitchell, Y. Lin, E. Rogers, P. Grunmann, V. Koren, G. Gayno, and J. D. Tarpley (2003), Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model, J. Geophys. Res., 108(D22), 8851, doi:10.1029/2002JD003296.
13. Emery, W. J., and R. E. Thomson (2004), Data Analysis Methods in Physical Oceanography, Elsevier, Amsterdam, Netherlands.
14. Falcone, J. (2011), GAGES-II: Geospatial Attributes of Gages for Evaluating Streamflow, U.S. Geol. Surv., Reston, Va. [Available at http://water.usgs.gov/lookup/getspatial?gagesII_Sept2011.]
15. Famiglietti, J. S., M. Lo, S. L. Ho, J. Bethune, K. J. Anderson, T. H. Syed, S. C. Swenson, C. R. de Linage, and M. Rodell (2011), Satellites measure recent rates of groundwater depletion in California's Central Valley, Geophys. Res. Lett., 38, L03403, doi:10.1029/2010GL046442.
16. Fatichi, S., et al. (2016), An overview of current applications, challenges, and future trends in distributed process-based models in hydrology, J. Hydrol., 537, 45–60, doi:10.1016/j.jhydrol.2016.03.026.
17. Fisher, J. B., K. P. Tu, and D. D. Baldocchi (2008), Global estimates of the land-atmosphere water flux based on monthly AVHRR and ISLSCP-II data, validated at 16 FLUXNET sites, Remote Sens. Environ., 112(3), 901–919, doi:10.1016/j.rse.2007.06.025.
18. Fox, J. (2008), Applied Regression Analysis and Generalized Linear Models, 2nd ed., SAGE, Los Angeles, Calif.
19. Fu, B. P. (1981), On the calculation of the evaporation from land surface [in Chinese], Sci. Atmos. Sin., 5(1), 23–31.
20. Gentine, P., P. D'Odorico, B. R. Lintner, G. Sivandran, and G. Salvucci (2012), Interdependence of climate, soil, and vegetation as constrained by the Budyko curve, Geophys. Res. Lett., 39, L19404, doi:10.1029/2012GL053492.
21. Gerrits, A. M. J., H. H. G. Savenije, E. J. M. Veling, and L. Pfister (2009), Analytical derivation of the Budyko curve based on rainfall characteristics and a simple evaporation model, Water Resour. Res., 45, W04403, doi:10.1029/2008WR007308.
22. Global Runoff Data Center (2011), Watershed boundaries of GRDC Stations, GRDC in the Bundesanstalt fuer Gewaesserkunde, 56068 Kolenz, Germany. [Available at http://grdc.bafg.de.].
23. Gupta, H. V., T. Wagener, and Y. Liu (2008), Reconciling theory with observations: Elements of a diagnostic approach to model evaluation, Hydrol. Processes, 22(18), 3802–3813, doi:10.1002/hyp.6989.
24. Hrachowitz, M., et al. (2013), A decade of Predictions in Ungauged Basins (PUB)—A review, Hydrol. Sci. J., 58(6), 1198–1255, doi:10.1080/02626667.2013.803183.
25. Huang, Z., Y. Pan, H. Gong, P. J.-F. Yeh, X. Li, D. Zhou, and W. Zhao (2015), Sub-regional scale groundwater depletion detected by GRACE for both shallow and deep aquifers in North China Plain, Geophys. Res. Lett., 42, 1791–1799, doi:10.1002/2014GL062498.
26. Huffman, G. J., R. F. Adler, P. Arkin, A. Chang, R. Ferraro, A. Gruber, J. Janowiak, A. McNab, B. Rudolf, and U. Schneider (1997), The Global Precipitation Climatology Project (GPCP) Combined Precipitation Dataset, Bull. Am. Meteorol. Soc., 78(1), 5–20, doi:10.1175/1520-0477(1997)078<0005:TGPCPG>2.0.CO;2.
27. Istanbulluoglu, E., T. Wang, O. M. Wright, and J. D. Lenters (2012), Interpretation of hydrologic trends from a water balance perspective: The role of groundwater storage in the Budyko hypothesis, Water Resour. Res., 48, W00H16, doi:10.1029/2010WR010100.
28. Landerer, F. W., and S. C. Swenson (2012), Accuracy of scaled GRACE terrestrial water storage estimates, Water Resour. Res., 48, W04531, doi:10.1029/2011WR011453.
29. Keppel, G., and T. D. Wickens (2004), Design and Analysis: A Researcher's Handbook, 4th ed., Pearson Prentice Hall, Upper Saddle River, N. J.
30. Li, D. (2014), Assessing the impact of interannual variability of precipitation and potential evaporation on evapotranspiration, Adv. Water Resour., 70, 1–11, doi:10.1016/j.advwatres.2014.04.012.
31. Li, D., M. Pan, Z. Cong, L. Zhang, and E. Wood (2013), Vegetation control on water and energy balance within the Budyko framework, Water Resour. Res., 49, 969–976, doi:10.1002/wrcr.20107.
32. Li, H.-Y., M. Sivapalan, F. Tian, and C. Harman (2014), Functional approach to exploring climatic and landscape controls of runoff generation: 1. Behavioral constraints on runoff volume, Water Resour. Res., 50, 9300–9322, doi:10.1002/2014WR016307.
33. Lo, M.-H., J. S. Famiglietti, P. J.-F. Yeh, and T. H. Syed (2010), Improving parameter estimation and water table depth simulation in a land surface model using GRACE water storage and estimated base flow data, Water Resour. Res., 46, W05517, doi:10.1029/2009WR007855.
34. Long, D., Y. Shen, A. Sun, Y. Hong, L. Longuevergne, Y. Yang, B. Li, and L. Chen (2014a), Drought and flood monitoring for a large karst plateau in Southwest China using extended GRACE data, Remote Sens. Environ., 155, 145–160, doi:10.1016/j.rse.2014.08.006.
35. Long, D., L. Longuevergne, and B. R. Scanlon (2014b), Uncertainty in evapotranspiration from land surface modeling, remote sensing, and GRACE satellites, Water Resour. Res., 50, 1131–1151, doi:10.1002/2013WR014581.
36. McCabe, M. F., A. Ershadi, C. Jimenez, D. G. Miralles, D. Michel, and E. F. Wood (2015), The GEWEX LandFlux project: Evaluation of model evaporation using tower-based and globally-gridded forcing data, Geosci. Model Dev. Discuss., 8(8), 6809–6866, doi:10.5194/gmdd-8-6809-2015.
37. Milly, P. C. D. (1994), Climate, soil water storage, and the average annual water balance, Water Resour. Res., 30(7), 2143–2156, doi:10.1029/94WR00586.
38. Miralles, D. G., et al. (2015), The WACMOS-ET project – Part 2: Evaluation of global terrestrial evaporation data sets, Hydrol. Earth Syst. Sci. Discuss., 12(10), 10651–10700, doi:10.5194/hessd-12-10651-2015.
39. Mu, Q., M. Zhao, and S. W. Running (2011), Improvements to a MODIS global terrestrial evapotranspiration algorithm, Remote Sens. Environ., 115(8), 1781–1800, doi:10.1016/j.rse.2011.02.019.
40. Niu, J., C. Shen, S.-G. Li, and M. S. Phanikumar (2014), Quantifying storage changes in regional Great Lakes watersheds using a coupled subsurface-land surface process model and GRACE, MODIS products, Water Resour. Res., 50, 7359–7377, doi:10.1002/2014WR015589.
41. Oleson, K. W., et al. (2013), Technical description of version 4.5 of the Community Land Model (CLM), NCAR/TN-503+STR, NCAR Tech. Note, UCAR/NCAR, Boulder, Colo. [Available at http://www.cesm.ucar.edu/models/cesm1.2/clm/CLM45_Tech_Note.pdf.]
42. Pike, J. G. (1964), The estimation of annual run-off from meteorological data in a tropical climate, J. Hydrol., 2(2), 116–123, doi:10.1016/0022-1694(64)90022-8.
43. Potter, N. J., L. Zhang, P. C. D. Milly, T. A. McMahon, and A. J. Jakeman (2005), Effects of rainfall seasonality and soil moisture capacity on mean annual water balance for Australian catchments, Water Resour. Res., 41, W06007, doi:10.1029/2004WR003697.
44. Reager, J. T., and J. S. Famiglietti (2013), Characteristic mega-basin water storage behavior using GRACE, Water Resour. Res., 49, 3314–3329, doi:10.1002/wrcr.20264.
45. Reager, J. T., B. F. Thomas, and J. S. Famiglietti (2014), River basin flood potential inferred using GRACE gravity observations at several months lead time, Nat. Geosci., 7(8), 588–592, doi:10.1038/ngeo2203.
46. Rodell, M., et al. (2004), The Global Land Data Assimilation System, Bull. Am. Meteorol. Soc., 85(3), 381–394, doi:10.1175/BAMS-85-3-381.
47. Sawicz, K. A., C. Kelleher, T. Wagener, P. Troch, M. Sivapalan, and G. Carrillo (2014), Characterizing hydrologic change through catchment classification, Hydrol. Earth Syst. Sci., 18(1), 273–285, doi:10.5194/hess-18-273-2014.
48. Scanlon, B. R., C. C. Faunt, L. Longuevergne, R. C. Reedy, W. M. Alley, V. L. McGuire, and P. B. McMahon (2012), Groundwater depletion and sustainability of irrigation in the US High Plains and Central Valley, Proc. Natl. Acad. Sci. USA, 109(24), 9320–9325, doi:10.1073/pnas.1200311109.
49. Shen, C., J. Niu, and M. Phanikumar (2013), Evaluating controls on coupled hydrologic and vegetation dynamics in a humid continental climate watershed using a subsurface-land surface processes model, Water Resour. Res., 49, 2552–2572, doi:10.1002/wrcr.20189.
50. Shen, C., J. Niu, and K. Fang (2014), Quantifying the effects of data integration algorithms on the outcomes of a subsurface-land surface processes model, Environ. Model. Software, 59, 146–161, doi:10.1016/j.envsoft.2014.05.006.
51. Shen, C., W. J. Riley, K. M. Smithgall, J. M. Melack, and K. Fang (2016), The fan of influence of streams and channel feedbacks to simulated land surface water and carbon dynamics, Water Resour. Res., 52, 880–902, doi:10.1002/2015WR018086.
52. Shuttleworth (1993), Evaporation, in Handbook of Hydrology, edited by D. Maidment, pp. 4.1–4.53, McGraw-Hill, N. Y.
53. Sivapalan, M., M. A. Yaeger, C. J. Harman, X. Xu, and P. A. Troch (2011), Functional model of water balance variability at the catchment scale: 1. Evidence of hydrologic similarity and space-time symmetry, Water Resour. Res., 47, W02522, doi:10.1029/2010WR009568.
54. Slater, L. J., and M. B. Singer (2013), Imprint of climate and climate change in alluvial riverbeds: Continental United States, 1950-2011, Geology, 41(5), 595–598, doi:10.1130/G34070.1.
55. Swenson, S. (2012), GRACE monthly land water mass grids ASCII release 5.0, Jet Propul. Lab., Pasadena, Calif. [Available at http://dx.doi.org/10.5067/TELND-TX005.]
56. Swenson, S. (2014), GRACE Monthly Mass Grids - Land, Jet Propul. Lab., Pasadena, Calif. [Available at http://grace.jpl.nasa.gov.].
57. Swenson, S., and J. Wahr (2006), Post-processing removal of correlated errors in GRACE data, Geophys. Res. Lett., 33, L08402, doi:10.1029/2005GL025285.
58. Tapley, B. D., S. Bettadpur, M. Watkins, and C. Reigber (2004), The gravity recovery and climate experiment: Mission overview and early results, Geophys. Res. Lett., 31, L09607, doi:10.1029/2004GL019920.
59. Troch, P. A., G. Carrillo, M. Sivapalan, T. Wagener, and K. Sawicz (2013), Climate-vegetation-soil interactions and long-term hydrologic partitioning: Signatures of catchment co-evolution, Hydrol. Earth Syst. Sci., 17(6), 2209–2217, doi:10.5194/hess-17-2209-2013.
60. 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.
61. Vogel, R. M., and A. Sankarasubramanian (2003), Validation of a watershed model without calibration, Water Resour. Res., 39(10), 1292, doi:10.1029/2002WR001940.
62. Voss, K. A., J. S. Famiglietti, M. Lo, C. Linage, M. Rodell, and S. C. Swenson (2013), Groundwater depletion in the Middle East from GRACE with implications for transboundary water management in the Tigris-Euphrates-Western Iran region, Water Resour. Res., 49, 904–914, doi:10.1002/wrcr.20078.
63. Wagener, T., G. Blöschl, D. C. Goodrich, H. V. Gupta, M. Sivapalan, Y. Tachikawa, P. A. Troch, and M. Weiler (2013), A synthesis framework for runoff prediction in ungauged basins, in Runoff Prediction in Ungauged Basins. Synthesis Across Processes, Places and Scales, edited by G. Bloschl et al., pp. 11–28, Cambridge Univ. Press, N. Y.
64. Wahr, J. (2004), Time-variable gravity from GRACE: First results, Geophys. Res. Lett., 31, L11501, doi:10.1029/2004GL019779.
65. Wahr, J., S. Swenson, and I. Velicogna (2006), Accuracy of GRACE mass estimates, Geophys. Res. Lett., 33, L06401, doi:10.1029/2005GL025305.
66. Wang, D., and Y. Tang (2014), A one-parameter Budyko model for water balance captures emergent behavior in Darwinian hydrologic models, Geophys. Res. Lett., 41, 4569–4577, doi:10.1002/2014GL060509.
67. Woods, R. A. (2009), Analytical model of seasonal climate impacts on snow hydrology: Continuous snowpacks, Adv. Water Resour., 32(10), 1465–1481, doi:10.1016/j.advwatres.2009.06.011.
68. Xia, Y., et al. (2012), 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.
69. Xu, X., W. Liu, B. R. Scanlon, L. Zhang, and M. Pan (2013), Local and global factors controlling water-energy balances within the Budyko framework, Geophys. Res. Lett., 40, 6123–6129, doi:10.1002/2013GL058324.
70. Yadav, M., T. Wagener, and H. Gupta (2007), Regionalization of constraints on expected watershed response behavior for improved predictions in ungauged basins, Adv. Water Resour., 30(8), 1756–1774.
71. Yang, H., D. Yang, Z. Lei, and F. Sun (2008), New analytical derivation of the mean annual water-energy balance equation, Water Resour. Res., 44, W03410, doi:10.1029/2007WR006135.
72. Yang, H., D. Yang, and Q. Hu (2014), An error analysis of the Budyko hypothesis for assessing the contribution of climate change to runoff, Water Resour. Res., 50, 9620–9629, doi:10.1002/2014WR015451.
73. 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.
74. Zhang, L., W. R. Dawes, and G. R. Walker (2001), Response of mean annual evapotranspiration to vegetation changes at catchment scale, Water Resour. Res., 37(3), 701–708, doi:10.1029/2000WR900325.
75. Zhang, S., H. Yang, D. Yang, and A. W. Jayawardena (2015), Quantifying the effect of vegetation change on the regional water balance within the Budyko framework, Geophys. Res. Lett., 43, 1140–1148, doi:10.1002/2015GL066952.
76. Zhou, M. C., H. Ishidaira, H. P. Hapuarachchi, J. Magome, A. S. Kiem, and K. Takeuchi (2006), Estimating potential evapotranspiration using Shuttleworth–Wallace model and NOAA-AVHRR NDVI data to feed a distributed hydrological model over the Mekong River basin, J. Hydrol., 327(1–2), 151–173, doi:10.1016/j.jhydrol.2005.11.013.
77. Zhou, X., Y. Zhang, Y. Wang, H. Zhang, J. Vaze, L. Zhang, Y. Yang, and Y. Zhou (2012), Benchmarking global land surface models against the observed mean annual runoff from 150 large basins, J. Hydrol., 470–471, 269–279, doi:10.1016/j.jhydrol.2012.09.002.