• Boardman, H. P., 1936: The effect of soil-absorption on snow-survey forecasting of stream-flow. Trans., Amer. Geophys. Union,17, 534–537.

  • Brekke, L. D., , Garen D. , , Werner K. , , and Laurine D. , 2010: Projecting climate change impacts on seasonal water supply forecasting error. Preprints, 18th Conf. on Applied Climatology/22nd Conf. on Climate Variability and Change/24th Conf. on Hydrology, Atlanta, GA, Amer. Meteor. Soc., J15.4. [Available online at https://ams.confex.com/ams/90annual/techprogram/paper_162386.htm.]

  • Clyde, G. D., 1940: Soil-moisture studies as an aid in forecasting runoff from snow-cover. Trans., Amer. Geophys. Union,21, 871–873.

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

    • Search Google Scholar
    • Export Citation
  • Fiering, M. B., 1965: An optimization scheme for gaging. Water Resour. Res., 1, 463470, doi:10.1029/WR001i004p00463.

  • Garen, D. C., 1992: Improved techniques in regression-based streamflow volume forecasting. J. Water Resour. Plann. Manage., 118, 654670, doi:10.1061/(ASCE)0733-9496(1992)118:6(654).

    • Search Google Scholar
    • Export Citation
  • Garen, D. C., , and Pagano T. C. , 2007: Statistical techniques used in the VIPER water supply forecasting software. NRCS-USDA Engineering-Snow Survey and Water Supply Forecasting Tech. Note 210-2, 18 pp. [Available online at http://www.wcc.nrcs.usda.gov/ftpref/downloads/factpub/wsf/technotes/Tech_note_statistical_techniques_in_Viper.pdf.]

  • Hall, D. K., , Riggs G. A. , , and Salomonson V. V. , 2006: MODIS/Terra Snow Cover 8-Day L3 Global 0.05deg CMG Version 5, Oct 2000 to Nov 2010. National Snow and Ice Data Center, Boulder, CO, digital media. [Available online at http://nsidc.org/data/mod10c2.html.]

  • Hartman, R. K., , and Henkel A. F. , 1994: Modernization of statistical procedures for water supply forecasting. Proc. 62nd Annual Western Snow Conf., Santa Fe, NM, Western Snow Conference, 104–114. [Available online at http://www.westernsnowconference.org/sites/westernsnowconference.org/PDFs/1994Hartman.pdf.]

  • Helms, D., , Phillips S. E. , , and Reich P. F. , Eds., 2008: The History of Snow Survey and Water Supply Forecasting: Interviews with U.S. Department of Agriculture Pioneers. U.S. Department of Agriculture, 306 pp.

    • Search Google Scholar
    • Export Citation
  • Koster, R. D., , Guo Z. , , Yang R. , , Dirmeyer P. A. , , Mitchell K. , , and Puma M. J. , 2009: On the nature of soil moisture in land surface models. J. Climate, 22, 43224335, doi:10.1175/2009JCLI2832.1.

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

    • Search Google Scholar
    • Export Citation
  • Lakshmi, V., , Piechota T. , , Narayan U. , , and Tang C. , 2004: Soil moisture as an indicator of weather extremes. Geophys. Res. Lett., 31, L11401, doi:10.1029/2004GL019930.

    • Search Google Scholar
    • Export Citation
  • Landres, P., , Alderson J. , , and Parsons D. J. , 2003: The challenge of doing science in wilderness: Historical, legal, and policy context. George Wright Forum, Vol. 20 (3), George Wright Society, 4249. [Available online at http://www.fs.fed.us/rm/pubs_other/rmrs_2003_landres_p001.pdf.]

    • Search Google Scholar
    • Export Citation
  • Lea, J., , and Harms D. , 2011: Developing NRCS SNOTEL and SCAN soil moisture parameters for water supply forecasting applications. Proc. 79th Annual Western Snow Conf., Stateline, NV, 109–112. [Available online at http://www.westernsnowconference.org/sites/westernsnowconference.org/PDFs/2011Lea.pdf.]

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

    • Search Google Scholar
    • Export Citation
  • Livneh, B., , Rosenberg E. A. , , Lin C. , , Nijssen B. , , Mishra V. , , Andreadis K. M. , , Maurer E. P. , , and Lettenmaier D. P. , 2013: A long-term hydrologically based dataset of land surface fluxes and states for the conterminous United States: Update and extensions. J. Climate, doi:10.1175/JCLI-D-12-00508.1, in press.

    • Search Google Scholar
    • Export Citation
  • Mahanama, S., , Livneh B. , , Koster R. , , Lettenmaier D. , , and Reichle R. , 2012: Soil moisture, snow, and seasonal streamflow forecasts in the United States. J. Hydrometeor., 13, 189203, doi:10.1175/JHM-D-11-046.1.

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

    • Search Google Scholar
    • Export Citation
  • Maurer, E. P., , Wood A. W. , , Adam J. C. , , and Lettenmaier D. P. , 2002: A long-term hydrologically based dataset of land surface fluxes and states for the conterminous United States. J. Climate, 15, 32373251, doi:10.1175/1520-0442(2002)015<3237:ALTHBD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • McCuen, R. H., 1985: Statistical Methods for Engineers. Prentice-Hall, 439 pp.

  • Miller, D. A., , and White R. A. , 1998: A conterminous United States multilayer soil characteristics data set for regional climate and hydrology modeling. Earth Interact., 2, doi:10.1175/1087-3562(1998)002<0001:ACUSMS>2.3.CO;2.

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

    • Search Google Scholar
    • Export Citation
  • Mishra, A. K., , and Coulibaly P. , 2009: Developments in hydrometric network design: A review. Rev. Geophys., 47, RG2001. doi:10.1029/2007RG000243.

    • Search Google Scholar
    • Export Citation
  • Molotch, N. P., , and Bales R. C. , 2005: Scaling snow observations from the point to the grid element: Implications for observation network design. Water Resour. Res., 41, W11421, doi:10.1029/2005WR004229.

    • Search Google Scholar
    • Export Citation
  • Morin, G., , Fortin J. P. , , Sochanska W. , , Lardeau J. P. , , and Charbonneau R. , 1979: Use of principal component analysis to identify homogenous precipitation stations for optimal interpolation. Water Resour. Res., 15, 18411850, doi:10.1029/WR015i006p01841.

    • Search Google Scholar
    • Export Citation
  • Pagano, T. C., , Garen D. C. , , Perkins T. R. , , and Pasteris P. A. , 2009: Daily updating of operational statistical seasonal water supply forecasts for the western U.S. J. Amer. Water Resour. Assoc., 45, 767778, doi:10.1111/j.1752-1688.2009.00321.x.

    • Search Google Scholar
    • Export Citation
  • Pardo-Igúzquiza, E., 1998: Optimal selection of number and location of rainfall gauges for areal rainfall estimation using geostatistics and simulated annealing. J. Hydrol., 210, 206220, doi:10.1016/S0022-1694(98)00188-7.

    • Search Google Scholar
    • Export Citation
  • Peck, E. L., , and Schaake J. C. , 1990: Network design for water supply forecasting in the West. J. Amer. Water Resour. Assoc., 26, 8799, doi:10.1111/j.1752-1688.1990.tb01354.x.

    • Search Google Scholar
    • Export Citation
  • Perkins, T. R., , Marron J. K. , , and Goodbody A. G. , 2010: ArcGIS technique to evaluate the SNOTEL data network. Proc. Second Joint Federal Interagency Conf., Las Vegas, NV, 11 pp. [Available online at http://acwi.gov/sos/pubs/2ndJFIC/Contents/1F_tperkins.pdf.]

  • Rodríguez-Iturbe, I., , and Mejía J. M. , 1974: The design of rainfall networks in time and space. Water Resour. Res., 10, 713728, doi:10.1029/WR010i004p00713.

    • Search Google Scholar
    • Export Citation
  • Rosenberg, E. A., , Wood A. W. , , and Steinemann A. C. , 2011: Statistical applications of physically based hydrologic models to seasonal streamflow forecasts. Water Resour. Res., 47, W00H14, doi:10.1029/2010WR010101.

    • Search Google Scholar
    • Export Citation
  • Schaefer, G. L., , Cosh M. H. , , and Jackson T. J. , 2007: The USDA Natural Resources Conservation Service Soil Climate Analysis Network (SCAN). J. Atmos. Oceanic Technol., 24, 20732077, doi:10.1175/2007JTECHA930.1.

    • Search Google Scholar
    • Export Citation
  • Speers, D. D., , Rockwood D. M. , , and Ashton G. D. , 1996: Snow and snowmelt. Hydrology Handbook, 2nd ed. ASCE Manuals and Reports on Engineering Practice, No. 28, American Society of Civil Engineers, 437–476.

  • Tangborn, W. V., 1980: A model to forecast short-term snowmelt runoff using synoptic observations of streamflow, temperature, and precipitation. Water Resour. Res., 16, 778786, doi:10.1029/WR016i004p00778.

    • Search Google Scholar
    • Export Citation
  • Tsintikidis, D., , Georgakakos K. P. , , Sperfslage J. A. , , Smith D. E. , , and Carpenter T. M. , 2002: Precipitation uncertainty and raingauge network design within Folsom Lake watershed. J. Hydrol. Eng., 7, 175184, doi:10.1061/(ASCE)1084-0699(2002)7:2(175).

    • Search Google Scholar
    • Export Citation
  • Volkmann, T. H. M., , Lyon S. W. , , Gupta H. V. , , and Troch P. A. , 2010: Multicriteria design of rain gauge networks for flash flood prediction in semiarid catchments with complex terrain. Water Resour. Res., 46, W11554, doi:10.1029/2010WR009145.

    • Search Google Scholar
    • Export Citation
  • Wood, A. W., 2007: The effects of climate change on water supply forecasting in the Feather River basin. Preprints, Fourth Annual California Climate Change Conf., Sacramento, CA, California Energy Commission.

  • Wood, A. W., , and Lettenmaier D. P. , 2008: An ensemble-based approach for the attribution of streamflow prediction uncertainty. Geophys. Res. Lett., 35, L14401, doi:10.1029/2008GL034648.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 61 61 7
Full Text Views 1 1 0
PDF Downloads 2 2 0

Informing Hydrometric Network Design for Statistical Seasonal Streamflow Forecasts

View More View Less
  • 1 Department of Civil and Environmental Engineering, University of Washington, Seattle, Washington, and Hazen and Sawyer, PC, New York, New York
  • | 2 NOAA/NWS Northwest River Forecast Center, Portland, Oregon
  • | 3 Department of Civil and Environmental Engineering, and Evans School of Public Affairs, University of Washington, Seattle, Washington, and Scripps Institution of Oceanography, La Jolla, California
© Get Permissions Rent on DeepDyve
Restricted access

Abstract

A hydrometric network design approach is developed for enhancing statistical seasonal streamflow forecasts. The approach employs gridded, model-simulated water balance variables as predictors in equations generated via principal components regression in order to identify locations for additional observations that most improve forecast skill. The approach is applied toward the expansion of the Natural Resources Conservation Service (NRCS) Snowpack Telemetry (SNOTEL) network in 24 western U.S. basins using two forecasting scenarios: one that assumes the currently standard predictors of snow water equivalent and water year-to-date precipitation and one that considers soil moisture as an additional predictor variable. Resulting improvements are spatially and temporally analyzed, attributed to dominant predictor contributions, and evaluated in the context of operational NRCS forecasts, ensemble-based National Weather Service (NWS) forecasts, and historical as-issued NRCS/NWS coordinated forecasts. Findings indicate that, except for basins with sparse existing networks, substantial improvements in forecast skill are only possible through the addition of soil moisture variables. Furthermore, locations identified as optimal for soil moisture sensor installation are primarily found in regions of low to mid elevation, in contrast to the higher elevations where SNOTEL stations are traditionally situated. The study corroborates prior research while demonstrating that soil moisture data can explicitly improve operational water supply forecasts (particularly during the accumulation season), that statistical forecasts are comparable in skill to ensemble-based forecasts, and that simulated hydrologic data can be combined with observations to improve statistical forecasts. The approach can be generalized to other settings and applications involving the use of point observations for statistical prediction models.

Corresponding author address: Eric A. Rosenberg, Hazen and Sawyer, PC, 498 Seventh Avenue, New York, NY 10018. E-mail: erosenberg@hazenandsawyer.com

Abstract

A hydrometric network design approach is developed for enhancing statistical seasonal streamflow forecasts. The approach employs gridded, model-simulated water balance variables as predictors in equations generated via principal components regression in order to identify locations for additional observations that most improve forecast skill. The approach is applied toward the expansion of the Natural Resources Conservation Service (NRCS) Snowpack Telemetry (SNOTEL) network in 24 western U.S. basins using two forecasting scenarios: one that assumes the currently standard predictors of snow water equivalent and water year-to-date precipitation and one that considers soil moisture as an additional predictor variable. Resulting improvements are spatially and temporally analyzed, attributed to dominant predictor contributions, and evaluated in the context of operational NRCS forecasts, ensemble-based National Weather Service (NWS) forecasts, and historical as-issued NRCS/NWS coordinated forecasts. Findings indicate that, except for basins with sparse existing networks, substantial improvements in forecast skill are only possible through the addition of soil moisture variables. Furthermore, locations identified as optimal for soil moisture sensor installation are primarily found in regions of low to mid elevation, in contrast to the higher elevations where SNOTEL stations are traditionally situated. The study corroborates prior research while demonstrating that soil moisture data can explicitly improve operational water supply forecasts (particularly during the accumulation season), that statistical forecasts are comparable in skill to ensemble-based forecasts, and that simulated hydrologic data can be combined with observations to improve statistical forecasts. The approach can be generalized to other settings and applications involving the use of point observations for statistical prediction models.

Corresponding author address: Eric A. Rosenberg, Hazen and Sawyer, PC, 498 Seventh Avenue, New York, NY 10018. E-mail: erosenberg@hazenandsawyer.com
Save