The Value of Accurate High-Resolution and Spatially Continuous Snow Information to Streamflow Forecasts

Dongyue Li Department of Geography, University of California, Los Angeles, Los Angeles, California

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Dennis P. Lettenmaier Department of Geography, University of California, Los Angeles, Los Angeles, California

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Steven A. Margulis Department of Civil and Environmental Engineering, University of California, Los Angeles, Los Angeles, California

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Konstantinos Andreadis Department of Civil and Environmental Engineering, University of Massachusetts Amherst, Amherst, Massachusetts

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Abstract

Previous studies have shown limited success in improving streamflow forecasting for snow-dominated watersheds using physically based models, primarily due to the lack of reliable snow water equivalent (SWE) information. Here we use a hindcasting approach to evaluate the potential benefit that a high-resolution, spatiotemporally continuous, and accurate SWE reanalysis product would have on the seasonal streamflow forecast in the snow-dominated Sierra Nevada mountains of California if such an SWE product were available in real time. We tested the efficacy of a physically based ensemble streamflow prediction (ESP) framework when initialized with the reanalysis SWE. We reinitialized the SWE over the Sierra Nevada at the time when the Sierra Nevada had domain-wide annual maximum SWE for each year in 1985–2015, and on 1 February of the driest years within the same period. The early season forecasts on 1 February provide valuable lead time for mitigating the impact of drought. In both experiments, initializing the ESP with the reanalysis SWE reduced the seasonal streamflow forecast errors; compared with existing operational statistical forecasts, the peak-annual SWE insertion and the 1 February SWE insertion reduced the overall root-mean-square error of the seasonal streamflow forecasts by 13% and 23%, respectively, over the 13 major rivers draining the Sierra Nevada. The benefits of the reanalysis SWE insertion are more pronounced in areas with greater snow accumulation, while the complex snow and runoff-generation processes in low-elevation areas impede the forecasting skill improvement through SWE reinitialization alone.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Dennis P. Lettenmaier, dlettenm@ucla.edu

Abstract

Previous studies have shown limited success in improving streamflow forecasting for snow-dominated watersheds using physically based models, primarily due to the lack of reliable snow water equivalent (SWE) information. Here we use a hindcasting approach to evaluate the potential benefit that a high-resolution, spatiotemporally continuous, and accurate SWE reanalysis product would have on the seasonal streamflow forecast in the snow-dominated Sierra Nevada mountains of California if such an SWE product were available in real time. We tested the efficacy of a physically based ensemble streamflow prediction (ESP) framework when initialized with the reanalysis SWE. We reinitialized the SWE over the Sierra Nevada at the time when the Sierra Nevada had domain-wide annual maximum SWE for each year in 1985–2015, and on 1 February of the driest years within the same period. The early season forecasts on 1 February provide valuable lead time for mitigating the impact of drought. In both experiments, initializing the ESP with the reanalysis SWE reduced the seasonal streamflow forecast errors; compared with existing operational statistical forecasts, the peak-annual SWE insertion and the 1 February SWE insertion reduced the overall root-mean-square error of the seasonal streamflow forecasts by 13% and 23%, respectively, over the 13 major rivers draining the Sierra Nevada. The benefits of the reanalysis SWE insertion are more pronounced in areas with greater snow accumulation, while the complex snow and runoff-generation processes in low-elevation areas impede the forecasting skill improvement through SWE reinitialization alone.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Dennis P. Lettenmaier, dlettenm@ucla.edu
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  • Adam, J. C., and D. P. Lettenmaier, 2003: Adjustment of global gridded precipitation for systematic bias. J. Geophys. Res., 108, 4257, https://doi.org/10.1029/2002JD002499.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ali, G., D. Tetzlaff, C. Soulsby, and J. J. McDonnell, 2012: Topographic, pedologic and climatic interactions influencing streamflow generation at multiple catchment scales. Hydrol. Processes, 26, 38583874, https://doi.org/10.1002/hyp.8416.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Andreadis, K. M., P. Storck, and D. P. Lettenmaier, 2009: Modeling snow accumulation and ablation processes in forested environments. Water Resour. Res., 45, W05429, https://doi.org/10.1029/2008WR007042.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bales, R. C., N. P. Molotch, T. H. Painter, M. D. Dettinger, R. Rice, and J. Dozier, 2006: Mountain hydrology of the western United States. Water Resour. Res., 42, W08432, https://doi.org/10.1029/2005WR004387.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Beighley, R. E., T. Dunne, and J. M. Melack, 2005: Understanding and modeling basin hydrology: interpreting the hydrogeological signature. Hydrol. Processes, 19, 13331353, https://doi.org/10.1002/hyp.5567.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Berg, A. A., and K. A. Mulroy, 2006: Streamflow predictability in the Saskatchewan/Nelson River basin given macroscale estimates of the initial soil moisture status. Hydrol. Sci., 51, 642654, https://doi.org/10.1623/hysj.51.4.642.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Beven, K. J., 2001: Rainfall-Runoff Modeling: The Primer. Wiley, 488 pp.

  • Bohn, T. J., B. Livneh, J. W. Oyler, S. W. Running, B. Nijssen, and D. P. Lettenmaier, 2013: Global evaluation of MTCLIM and related algorithms for forcing of ecological and hydrological models. Agric. For. Meteor., 176, 3849, https://doi.org/10.1016/j.agrformet.2013.03.003.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Burnash, R. J. C., R. L. Ferral, and R. A. McGuire, 1973: A generalized streamflow simulation system: Conceptual models for digital computers. Joint Federal and State River Forecast Center, U.S. National Weather Service, and California Department of Water Resources Tech. Rep., 204 pp.

  • Cloke, H. L., and F. Pappenberger, 2009: Ensemble flood forecasting: A review. J. Hydrol., 375, 613626, https://doi.org/10.1016/j.jhydrol.2009.06.005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Crawford, N. H., 1962: The synthesis of continuous streamflow hydrographs on a digital computer. Dept. of Civil Engineering Tech. Rep. 12, Stanford University, 121 pp.

  • Day, G. N., 1985: Extended streamflow forecasting using NWSRFS. J. Water Resour. Plann. Manage., 111, 157170, https://doi.org/10.1061/(ASCE)0733-9496(1985)111:2(157).

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dettinger, M. D., and D. R. Cayan, 1995: Large-scale atmospheric forcing of recent trends toward early snowmelt runoff in California. J. Climate, 8, 606623, https://doi.org/10.1175/1520-0442(1995)008<0606:LSAFOR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dickinson, R. E., 1984: Modeling evapotranspiration for three-dimensional global climate models. Climate Processes and Climate Sensitivity, Geophys. Monogr., Vol. 29, Amer. Geophys. Union, 58–72.

    • Crossref
    • Export Citation
  • Diffenbaugh, N. S., D. L. Swain, and D. Touma, 2015: Anthropogenic warming has increased drought risk in California. Proc. Natl. Acad. Sci. USA, 112, 39313936, https://doi.org/10.1073/pnas.1422385112.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dozier, J., 2011: Mountain hydrology, snow color, and the fourth paradigm. Eos, Trans. Amer. Geophys. Union, 92, 373374, https://doi.org/10.1029/2011EO430001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hamman, J. J., B. Nijssen, T. J. Bohn, D. R. Gergel, and Y. Mao, 2018: The Variable Infiltration Capacity model version 5 (VIC-5): Infrastructure improvements for new applications and reproducibility. Geosci. Model Dev., 11, 34813496, https://doi.org/10.5194/gmd-11-3481-2018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • He, M., M. Russo, and M. Anderson, 2016a: Predictability of seasonal streamflow in a changing climate in the Sierra Nevada. Climate, 4, 57, https://doi.org/10.3390/cli4040057.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • He, M., and Coauthors, 2016b: Verification of ensemble water supply forecasts for Sierra Nevada watersheds. Hydrology, 3, 35, https://doi.org/10.3390/hydrology3040035.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hogan, J. F., and J. D. Blum, 2003: Tracing hydrologic flow paths in a small forested watershed using variations in 87Sr/86Sr,[Ca]/[Sr],[Ba]/[Sr] and δ18O. Water Resour. Res., 39, 1282, https://doi.org/10.1029/2002WR001856.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kormos, P. R., D. Marks, J. P. McNamara, H. P. Marshall, A. Winstral, and A. N. Flores, 2014: Snow distribution, melt and surface water inputs to the soil in the mountain rain–snow transition zone. J. Hydrol., 519, 190204, https://doi.org/10.1016/j.jhydrol.2014.06.051.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Koster, R. D., S. P. Mahanama, B. Livneh, D. P. Lettenmaier, and R. H. Reichle, 2010: Skill in streamflow forecasts derived from large-scale estimates of soil moisture and snow. Nat. Geosci., 3, 613616, https://doi.org/10.1038/ngeo944.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kumar, S. V., and Coauthors, 2014: Assimilation of remotely sensed soil moisture and snow depth retrievals for drought estimation. J. Hydrometeor., 15, 24462469, https://doi.org/10.1175/JHM-D-13-0132.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lettenmaier, D. P., D. Alsdorf, J. Dozier, G. J. Huffman, M. Pan, and E. F. Wood, 2015: Inroads of remote sensing into hydrologic science during the WRR era. Water Resour. Res., 51, 73097342, https://doi.org/10.1002/2015WR017616.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, D., M. L. Wrzesien, M. Durand, J. Adam, and D. P. Lettenmaier, 2017a: How much runoff originates as snow in the western United States, and how will that change in the future? Geophys. Res. Lett., 44, 61636172, https://doi.org/10.1002/2017GL073551.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, D., M. Durand, and S. A. Margulis, 2017b: Estimating snow water equivalent in a Sierra Nevada watershed via spaceborne radiance data assimilation. Water Resour. Res., 53, 647671, https://doi.org/10.1002/2016WR018878.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liang, X., D. P. Lettenmaier, E. F. Wood, and S. J. Burges, 1994: A simple hydrologically based model of land surface water and energy fluxes for general circulation models. J. Geophys. Res., 99, 14 41514 428, https://doi.org/10.1029/94JD00483.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, F., C. Hunsaker, and R. C. Bales, 2013: Controls of streamflow generation in small catchments across the snow–rain transition in the Southern Sierra Nevada, California. Hydrol. Processes, 27, 19591972, https://doi.org/10.1002/hyp.9304.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, Y., C. D. Peters-Lidard, S. V. Kumar, K. R. Arsenault, and D. M. Mocko, 2015: Blending satellite-based snow depth products with in situ observations for streamflow predictions in the Upper Colorado River Basin. Water Resour. Res., 51, 11821202, https://doi.org/10.1002/2014WR016606.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Livneh, B., E. A. Rosenberg, C. Lin, B. Nijssen, V. Mishra, K. M. Andreadis, E.P. Maurer, and D.P. 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, 93849392, https://doi.org/10.1175/JCLI-D-12-00508.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lohmann, D., E. Raschke, B. Nijssen, and D. P. Lettenmaier, 1998: Regional scale hydrology: I. Formulation of the VIC-2L model coupled to a routing model. Hydrol. Sci. J., 43, 131141, https://doi.org/10.1080/02626669809492107.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lundquist, J. D., P. J. Neiman, B. Martner, A. B. White, D. J. Gottas, and F. M. Ralph, 2008: Rain versus snow in the Sierra Nevada, California: Comparing Doppler profiling radar and surface observations of melting level. J. Hydrometeor., 9, 194211, https://doi.org/10.1175/2007JHM853.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lundquist, J. D., M. Hughes, B. Henn, E. D. Gutmann, B. Livneh, J. Dozier, and P. Neiman, 2015: High-elevation precipitation patterns: Using snow measurements to assess daily gridded datasets across the Sierra Nevada, California. J. Hydrometeor., 16, 17731792, https://doi.org/10.1175/JHM-D-15-0019.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Luo, L., and Coauthors, 2003: Validation of the North American land data assimilation system (NLDAS) retrospective forcing over the southern Great Plains. J. Geophys. Res., 108, 8843, https://doi.org/10.1029/2002JD003246.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mahanama, S. P., R. D. Koster, R. H. Reichle, and L. Zubair, 2008: The role of soil moisture initialization in subseasonal and seasonal streamflow prediction—A case study in Sri Lanka. Adv. Water Resour., 31, 13331343, https://doi.org/10.1016/j.advwatres.2008.06.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mao, Y., B. Nijssen, and D. P. Lettenmaier, 2015: Is climate change implicated in the 2013–2014 California drought? A hydrologic perspective. Geophys. Res. Lett., 42, 28052813, https://doi.org/10.1002/2015GL063456.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Margulis, S. A., M. Girotto, G. Cortés, and M. Durand, 2015: A particle batch smoother approach to snow water equivalent estimation. J. Hydrometeor., 16, 17521772, https://doi.org/10.1175/JHM-D-14-0177.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Margulis, S. A., G. Cortés, M. Girotto, and M. Durand, 2016: A Landsat-era Sierra Nevada snow reanalysis (1985–2015). J. Hydrometeor., 17, 12031221, https://doi.org/10.1175/JHM-D-15-0177.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Marks, D., T. Link, A. Winstral, and D. Garen, 2001: Simulating snowmelt processes during rain-on-snow over a semi-arid mountain basin. Ann. Glaciol., 32, 195202, https://doi.org/10.3189/172756401781819751.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maurer, E. P., and D. P. Lettenmaier, 2003: Predictability of seasonal runoff in the Mississippi River basin. J. Geophys. Res., 108, 8607, https://doi.org/10.1029/2002JD002555.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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, 32373251, https://doi.org/10.1175/1520-0442(2002)015<3237:ALTHBD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McNamara, J. P., D. Chandler, M. Seyfried, and S. Achet, 2005: Soil moisture states, lateral flow, and streamflow generation in a semi-arid, snowmelt-driven catchment. Hydrol. Processes, 19, 40234038, https://doi.org/10.1002/hyp.5869.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Milly, P. C., J. Betancourt, M. Falkenmark, R. M. Hirsch, Z. W. Kundzewicz, D. P. Lettenmaier, and R. J. Stouffer, 2008: Stationarity is dead: Whither water management? Science, 319, 573574, https://doi.org/10.1126/science.1151915.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Moradkhani, H., and S. Sorooshian, 2009: General review of rainfall-runoff modeling: model calibration, data assimilation, and uncertainty analysis. Hydrological Modelling and the Water Cycle, Springer, 1–24, https://doi.org/10.1007/978-3-540-77843-1_1.

    • Crossref
    • Export Citation
  • Mote, P. W., S. Li, D. P. Lettenmaier, M. Xiao, and R. Engel, 2018: Dramatic declines in snowpack in the western US. npj Climate Atmos. Sci., 1, 2, https://doi.org/10.1038/s41612-018-0012-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Niu, G. Y., and Coauthors, 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, https://doi.org/10.1029/2010JD015139.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pagano, T., D. Garen, and S. Sorooshian, 2004: Evaluation of official western US seasonal water supply outlooks, 1922–2002. J. Hydrometeor., 5, 896909, https://doi.org/10.1175/1525-7541(2004)005<0896:EOOWUS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Painter, T. H., and Coauthors, 2016: The Airborne Snow Observatory: Fusion of scanning lidar, imaging spectrometer, and physically based modeling for mapping snow water equivalent and snow albedo. Remote Sens. Environ., 184, 139152, https://doi.org/10.1016/j.rse.2016.06.018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pan, M., and Coauthors, 2003: Snow process modeling in the North American Land Data Assimilation System (NLDAS): 2. Evaluation of model simulated snow water equivalent. J. Geophys. Res., 108, 8850, https://doi.org/10.1029/2003JD003994.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rienecker, M. M., and Coauthors, 2011: MERRA: NASA’s modern-era retrospective analysis for research and applications. J. Climate, 24, 36243648, https://doi.org/10.1175/JCLI-D-11-00015.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rodell, M., and Coauthors, 2004: The Global Land Data Assimilation System. Bull. Amer. Meteor. Soc., 85, 381394, https://doi.org/10.1175/BAMS-85-3-381.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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, 93209325, https://doi.org/10.1073/pnas.1200311109.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Scott, D., J. Dawson, and B. Jones, 2008: Climate change vulnerability of the US Northeast winter recreation–tourism sector. Mitig. Adapt. Strategies Global Change, 13, 577596, https://doi.org/10.1007/s11027-007-9136-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shi, X., A. W. Wood, and D. P. Lettenmaier, 2008: How essential is hydrologic model calibration to seasonal streamflow forecasting? J. Hydrometeor., 9, 13501363, https://doi.org/10.1175/2008JHM1001.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sturm, M., M. A. Goldstein, and C. Parr, 2017: Water and life from snow: A trillion dollar science question. Water Resour. Res., 53, 35343544, https://doi.org/10.1002/2017WR020840.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sun, S., J. Jin, and Y. Xue, 1999: A simple snow-atmosphere-soil transfer model. J. Geophys. Res., 104, 19 58719 597, https://doi.org/10.1029/1999JD900305.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Swain, D. L., B. Langenbrunner, J. D. Neelin, and A. Hall, 2018: Increasing precipitation volatility in twenty-first-century California. Nat. Climate Change, 8, 427433, https://doi.org/10.1038/s41558-018-0140-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tanaka, S. K., and Coauthors, 2006: Climate warming and water management adaptation for California. Climatic Change, 76, 361387, https://doi.org/10.1007/s10584-006-9079-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tetzlaff, D., S. K. Carey, J. P. McNamara, H. Laudon, and C. Soulsby, 2017: The essential value of long-term experimental data for hydrology and water management. Water Resour. Res., 53, 25982604, https://doi.org/10.1002/2017WR020838.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Truffer, M., W. D. Harrison, and R. S. March, 2005: Record negative glacier balances and low velocities during the 2004 heatwave in Alaska, USA: Implications for the interpretation of observations by Zwally and others in Greenland. J. Glaciol., 51, 663664, https://doi.org/10.3189/172756505781829016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vano, J. A., B. Nijssen, and D. P. Lettenmaier, 2015: Seasonal hydrologic responses to climate change in the Pacific Northwest. Water Resour. Res., 51, 19591976, https://doi.org/10.1002/2014WR015909.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vicuña, S., J. A. Dracup, and L. Dale, 2011: Climate change impacts on two high-elevation hydropower systems in California. Climatic Change, 109, 151169, https://doi.org/10.1007/s10584-011-0301-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vionnet, V., E. Brun, S. Morin, A. Boone, S. Faroux, P. Le Moigne, E. Martin, and J.-M. Willemet, 2012: The detailed snowpack scheme Crocus and its implementation in SURFEX v7. 2. Geosci. Model Dev., 5, 773791, https://doi.org/10.5194/gmd-5-773-2012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wood, A. W., and J. C. Schaake, 2008: Correcting errors in streamflow forecast ensemble mean and spread. J. Hydrometeor., 9, 132148, https://doi.org/10.1175/2007JHM862.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wood, A. W., T. Hopson, A. Newman, L. Brekke, J. Arnold, and M. Clark, 2016: Quantifying streamflow forecast skill elasticity to initial condition and climate prediction skill. J. Hydrometeor., 17, 651668, https://doi.org/10.1175/JHM-D-14-0213.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, Z., Z. Jiang, J. Li, S. Zhong, and L. Wang, 2012: Possible association of the western Tibetan Plateau snow cover with the decadal to interdecadal variations of northern China heatwave frequency. Climate Dyn., 39, 23932402, https://doi.org/10.1007/s00382-012-1439-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xia, Y., and Coauthors, 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, https://doi.org/10.1029/2011JD016048.

    • Search Google Scholar
    • Export Citation
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