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Article
Article Type:
Research Article

Decomposition of Sources of Errors in Monthly to Seasonal Streamflow Forecasts in a Rainfall–Runoff Regime

Tushar Sinha Department of Civil, Construction, and Environmental Engineering, North Carolina State University, Raleigh, North Carolina

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A. Sankarasubramanian Department of Civil, Construction, and Environmental Engineering, North Carolina State University, Raleigh, North Carolina

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Amirhossein Mazrooei Department of Civil, Construction, and Environmental Engineering, North Carolina State University, Raleigh, North Carolina

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Print Publication:
01 Dec 2014
DOI:
https://doi.org/10.1175/JHM-D-13-0155.1
Page(s):
2470–2483
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Abstract

Despite considerable progress in developing real-time climate forecasts, most studies have evaluated the potential in seasonal streamflow forecasting based on ensemble streamflow prediction (ESP) methods, utilizing only climatological forcings while ignoring general circulation model (GCM)-based climate forecasts. The primary limitation in using GCM forecasts is their coarse resolution, which requires spatiotemporal downscaling to implement land surface models. Consequently, multiple sources of errors are introduced in developing real-time streamflow forecasts utilizing GCM forecasts. A set of error decomposition metrics is provided to address the following questions: 1) How are errors in monthly streamflow forecasts attributed to various sources such as temporal disaggregation, spatial downscaling, imprecise initial hydrologic conditions (IHCs), climatological forcings, and imprecise forecasts? and 2) How do these errors propagate with lead time over different seasons? A calibrated Variable Infiltration Capacity model is used over the Apalachicola River at Chattahoochee in the southeastern United States. The model is forced with a combination of daily precipitation forcings (temporally disaggregated observed precipitation, spatially downscaled and temporally disaggregated observed precipitation, ESP, ECHAM4.5 forecasts, and observed) and IHCs [simulated and climatological ensemble reverse ESP (RESP)] but with observed air temperature and wind speed at ⅛° resolution. Then, errors in forecasting monthly streamflow at up to a 3-month lead time are decomposed by comparing the forecasted streamflow to simulated streamflow under observed forcings. Results indicate that the errors due to temporal disaggregation are much higher than the spatial downscaling errors. During winter and early spring, the increasing order of errors at a 1-month lead time is spatial downscaling, model, temporal disaggregation, RESP, large-scale precipitation forecasts, and ESP.

Corresponding author address: Tushar Sinha, 2501 Stinson Dr., Box 7908, Raleigh, NC 27695-7908. E-mail: tsinha@ncsu.edu

Abstract

Despite considerable progress in developing real-time climate forecasts, most studies have evaluated the potential in seasonal streamflow forecasting based on ensemble streamflow prediction (ESP) methods, utilizing only climatological forcings while ignoring general circulation model (GCM)-based climate forecasts. The primary limitation in using GCM forecasts is their coarse resolution, which requires spatiotemporal downscaling to implement land surface models. Consequently, multiple sources of errors are introduced in developing real-time streamflow forecasts utilizing GCM forecasts. A set of error decomposition metrics is provided to address the following questions: 1) How are errors in monthly streamflow forecasts attributed to various sources such as temporal disaggregation, spatial downscaling, imprecise initial hydrologic conditions (IHCs), climatological forcings, and imprecise forecasts? and 2) How do these errors propagate with lead time over different seasons? A calibrated Variable Infiltration Capacity model is used over the Apalachicola River at Chattahoochee in the southeastern United States. The model is forced with a combination of daily precipitation forcings (temporally disaggregated observed precipitation, spatially downscaled and temporally disaggregated observed precipitation, ESP, ECHAM4.5 forecasts, and observed) and IHCs [simulated and climatological ensemble reverse ESP (RESP)] but with observed air temperature and wind speed at ⅛° resolution. Then, errors in forecasting monthly streamflow at up to a 3-month lead time are decomposed by comparing the forecasted streamflow to simulated streamflow under observed forcings. Results indicate that the errors due to temporal disaggregation are much higher than the spatial downscaling errors. During winter and early spring, the increasing order of errors at a 1-month lead time is spatial downscaling, model, temporal disaggregation, RESP, large-scale precipitation forecasts, and ESP.

Corresponding author address: Tushar Sinha, 2501 Stinson Dr., Box 7908, Raleigh, NC 27695-7908. E-mail: tsinha@ncsu.edu
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  • Ajami, N. K., Duan Q. , and Sorooshian S. , 2007: An integrated hydrologic Bayesian multimodel combination framework: Confronting input, parameter, and model structural uncertainty in hydrologic prediction. Water Resour. Res., 43, W01403, doi:10.1029/2005WR004745.

    • Search Google Scholar
    • Export Citation

  • Barnston, A. G., Mason S. J. , Goddard L. , Dewitt D. G. , and Zebiak S. E. , 2003: Multimodel ensembling in seasonal climate forecasting at IRI. Bull. Amer. Meteor. Soc., 84, 1783–1796, doi:10.1175/BAMS-84-12-1783.

    • Search Google Scholar
    • Export Citation

  • Betts, R. A., Cox P. M. , Lee S. E. , and Woodward F. I. , 1997: Contrasting physiological and structural vegetation feedbacks in climate change simulations. Nature, 387, 796–799, doi:10.1038/42924.

    • Search Google Scholar
    • Export Citation

  • Caldwell, P., 2010: California wintertime precipitation bias in regional and global climate models. J. Appl. Meteor. Climatol., 49, 2147–2158, doi:10.1175/2010JAMC2388.1.

    • Search Google Scholar
    • Export Citation

  • Cherkauer, K. A., and Lettenmaier D. P. , 2003: Simulation of spatial variability in snow and frozen soil. J. Geophys. Res.,108, 8858, doi:10.1029/2003JD003575.

  • Cocke, S., LaRow T. E. , and Shin D. W. , 2007: Seasonal rainfall prediction over the southeast United States using the Florida State University nested regional spectral model. J. Geophys. Res., 112, D04106, doi:10.1029/2006JD007535.

    • Search Google Scholar
    • Export Citation

  • Day, G. N., 1985: Extended streamflow forecasting using NWSRFS. J. Water Resour. Plann. Manage., 111, 157–170, doi:10.1061/(ASCE)0733-9496(1985)111:2(157).

    • Search Google Scholar
    • Export Citation

  • Devineni, N., Sankarasubramanian A. , and Ghosh S. , 2008: Multimodel ensembles of streamflow forecasts: Role of predictor state in developing optimal combinations. Water Resour. Res., 44, W09404, doi:10.1029/2006WR005855.

    • Search Google Scholar
    • Export Citation

  • Di Luca, A., de Elía R. , and Laprise R. , 2012: Potential for added value in precipitation simulated by high-resolution nested regional climate models and observations. Climate Dyn., 38, 1229–1247, doi:10.1007/s00382-011-1068-3.

    • Search Google Scholar
    • Export Citation

  • Duan, Q., Ajami N. K. , Gao X. , and Sorooshian S. , 2007: Multimodel ensemble hydrologic prediction using Bayesian model averaging. Adv. Water Resour., 30, 1371–1386, doi:10.1016/j.advwatres.2006.11.014.

    • Search Google Scholar
    • Export Citation

  • Franz, K. J., Hartmann H. C. , Sorooshian S. , and Bales R. , 2003: Verification of National Weather Service ensemble streamflow predictions for water supply forecasting in the Colorado River basin. J. Hydrometeor., 4, 1105–1118, doi:10.1175/1525-7541(2003)004<1105:VONWSE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Goddard, L., Barnston A. G. , and Mason S. J. , 2003: Evaluation of the IRI’s “net assessment” seasonal climate forecasts: 1997–2001. Bull. Amer. Meteor. Soc., 84, 1761–1781, doi:10.1175/BAMS-84-12-1761.

    • Search Google Scholar
    • Export Citation

  • Halmstad, A., Najafi M. R. , and Moradkhani H. , 2013: Analysis of precipitation extremes with the assessment of regional climate models over the Willamette River basin, USA. Hydrol. Processes, 27, 2579–2590, doi:10.1002/hyp.9376.

    • Search Google Scholar
    • Export Citation

  • Hamlet, A. F., Huppert D. , and Lettenmaier D. P. , 2002: Economic value of long-lead streamflow forecasts for Columbia River hydropower. J. Water Resour. Plann. Manage., 128, 91–101, doi:10.1061/(ASCE)0733-9496(2002)128:2(91).

    • Search Google Scholar
    • Export Citation

  • Hartmann, H. C., Pagano T. C. , Sorooshian S. , and Bales R. , 2002: Confidence builders: Evaluating seasonal climate forecasts from user perspectives. Bull. Amer. Meteor. Soc., 83, 683–698, doi:10.1175/1520-0477(2002)083<0683:CBESCF>2.3.CO;2.

    • Search Google Scholar
    • Export Citation

  • Hayhoe, K., and Coauthors, 2004: Emissions pathways, climate change, and impacts on California. Proc. Natl. Acad. Sci. USA, 101, 12 422–12 427, doi:10.1073/pnas.0404500101.

    • Search Google Scholar
    • Export Citation

  • Koster, R. D., and Suarez M. J. , 1995: Relative contributions of land and ocean processes to precipitation variability. J. Geophys. Res., 100, 13 775–13 790, doi:10.1029/95JD00176.

    • 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, 613–616, doi:10.1038/ngeo944.

    • Search Google Scholar
    • Export Citation

  • Kumar, N., Lall U. , and Petersen M. R. , 2000: Multisite disaggregation of monthly to daily streamflow. Water Resour. Res., 36, 1823–1833, doi:10.1029/2000WR900049.

    • Search Google Scholar
    • Export Citation

  • Kumar, S., Peters-Lidard C. D. , Eastman J. L. , and Tao W. K. , 2008: An integrated high-resolution hydrometeorological modeling testbed using LIS and WRF. Environ. Model. Software, 23, 169–181, doi:10.1016/j.envsoft.2007.05.012.

    • Search Google Scholar
    • Export Citation

  • Lall, U., and Sharma A. , 1996: A nearest neighbor bootstrap for resampling hydrologic time series. Water Resour. Res., 32, 679–693, doi:10.1029/95WR02966.

    • Search Google Scholar
    • Export Citation

  • Leung, L. R., Qian Y. , Bian X. D. , and Hunt A. , 2003: Hydroclimate of the western United States based on observations and regional climate simulation of 1981–2000. Part II: Mesoscale ENSO anomalies. J. Climate, 16, 1912–1928, doi:10.1175/1520-0442(2003)016<1912:HOTWUS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Leung, L. R., Qian Y. , Bian X. D. , Washington W. M. , Han J. G. , and Roads J. O. , 2004: Mid-century ensemble regional climate change scenarios for the western United States. Climatic Change, 62, 75–113, doi:10.1023/B:CLIM.0000013692.50640.55.

    • Search Google Scholar
    • Export Citation

  • Li, H., Luo L. , Wood E. F. , and Schaake J. , 2009: The role of initial conditions and forcing uncertainties in seasonal hydrologic forecasting. J. Geophys. Res., 114, D04114, doi:10.1029/2008JD010969.

    • Search Google Scholar
    • Export Citation

  • Li, S., and Goddard L. , 2005: Retrospective forecasts with the ECHAM4.5 AGCM. IRI Tech. Rep. 05-02, Earth Institute, Columbia University, Palisades, NY, 16 pp.

  • Li, W., and Sankarasubramanian A. , 2012: Reducing hydrologic model uncertainty in monthly streamflow predictions using multimodel combination. Water Resour. Res., 48, W12516, doi:10.1029/2011WR011380.

    • Search Google Scholar
    • Export Citation

  • 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, 14 415–14 428, doi:10.1029/94JD00483.

    • Search Google Scholar
    • Export Citation

  • Liang, X., Lettenmaier D. P. , and Wood E. F. , 1996: One-dimensional statistical dynamic representation of sub-grid spatial variability of precipitation in the two layer Variable Infiltration Capacity model. J. Geophys. Res., 101, 21 403–21 422, doi:10.1029/96JD01448.

    • Search Google Scholar
    • Export Citation

  • Lohmann, D., Raschke E. , Nijssen B. , and Lettenmaier D. P. , 1998a: Regional scale hydrology: I. Formulation of the VIC-2L model coupled to a routing model. Hydrol. Sci. J., 43, 131–142, doi:10.1080/02626669809492107.

    • Search Google Scholar
    • Export Citation

  • Lohmann, D., Raschke E. , Nijssen B. , and Lettenmaier D. P. , 1998b: Regional scale hydrology: II. Application of the VIC-2L model to the Weser River, Germany. Hydrol. Sci. J., 43, 143–158, doi:10.1080/02626669809492108.

    • Search Google Scholar
    • Export Citation

  • Luo, L., and Wood E. F. , 2008: Use of Bayesian merging techniques in a multimodel seasonal hydrologic ensemble prediction system for the eastern United States. J. Hydrometeor., 9, 866–884, doi:10.1175/2008JHM980.1.

    • Search Google Scholar
    • Export Citation

  • Luo, L., Wood E. F. , and Pan M. , 2007: Bayesian merging of multiple climate model forecasts for seasonal hydrological predictions. J. Geophys. Res., 112, D10102, doi:10.1029/2006JD007655.

    • Search Google Scholar
    • Export Citation

  • Mahanama, S., Livneh B. , Koster R. , Lettenmaier D. P. , and Reichle R. , 2012: Soil moisture, snow, and seasonal streamflow forecasts in the United States. J. Hydrometeor., 13, 189–203, 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., and Hidalgo H. G. , 2008: Utility of daily vs. monthly large-scale climate data: An intercomparison of two statistical downscaling methods. Hydrol. Earth Syst. Sci., 12, 551–563, doi:10.5194/hess-12-551-2008.

    • Search Google Scholar
    • Export Citation

  • Maurer, E. P., Wood A. W. , Adam J. C. , Lettenmaier D. P. , and Nijssen B. , 2002: A long-term hydrologically based dataset of land surface fluxes and states for the conterminous Unites States. J. Climate, 15, 3237–3251, doi:10.1175/1520-0442(2002)015<3237:ALTHBD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Maurer, E. P., Lettenmaier D. P. , and Mantua N. J. , 2004: Variability and potential sources of predictability of North American runoff. Water Resour. Res., 40, W09306, doi:10.1029/2003WR002789.

    • Search Google Scholar
    • Export Citation

  • Murphy, J., 1999: An evaluation of statistical and dynamical techniques for downscaling local climate. J. Climate, 12, 2256–2284, doi:10.1175/1520-0442(1999)012<2256:AEOSAD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Pagano, T. C., Hartmann H. C. , and Sorooshian S. , 2001: Using climate forecasts for water management: Arizona and the 1997–1998 El Niño. J. Amer. Water Resour. Assoc., 37, 1139–1153, doi:10.1111/j.1752-1688.2001.tb03628.x.

    • Search Google Scholar
    • Export Citation

  • Prairie, J., Rajagopalan B. , Lall U. , and Fulp T. , 2007: A stochastic nonparametric technique for space–time disaggregation of streamflows. Water Resour. Res., 43, W03432, doi:10.1029/2005WR004721.

    • Search Google Scholar
    • Export Citation

  • Sankarasubramanian, A., Sharma A. , Lall U. , and Espinueva S. , 2008: Role of retrospective forecasts of GCMs forced with persisted SST anomalies in operational streamflow forecasts development. J. Hydrometeor., 9, 212–227, doi:10.1175/2007JHM842.1.

    • Search Google Scholar
    • Export Citation

  • Shukla, S., and Lettenmaier D. P. , 2011: Seasonal hydrologic prediction in the United States: Understanding the role of initial hydrologic conditions and seasonal climate forecast skill. Hydrol. Earth Syst. Sci., 15, 3529–3538, doi:10.5194/hess-15-3529-2011.

    • Search Google Scholar
    • Export Citation

  • Singh, H., and Sankarasubramanian A. , 2014: Systematic uncertainty reduction strategies for developing streamflow forecasts utilizing multiple climate models and hydrologic models. Water Resour. Res., 50, 1288–1307, doi:10.1002/2013WR013855.

    • Search Google Scholar
    • Export Citation

  • Sinha, T., and Sankarasubramanian A. , 2013: Role of climate forecasts and initial conditions in developing streamflow and soil moisture forecasts in a rainfall–runoff regime. Hydrol. Earth Syst. Sci., 17, 721–733, doi:10.5194/hess-17-721-2013.

    • Search Google Scholar
    • Export Citation

  • Slack, J. R., Lumb A. , and Landwehr J. M. , 1993: Hydro-Climatic Data Network (HCDN) streamflow data set, 1874–1988. USGS Water-Resources Rep. 93-4076. [Available online at http://pubs.usgs.gov/wri/wri934076/.]

  • Stern, P. C., and Easterling W. E. , 1999: Making Climate Forecasts Matter. National Academies Press, 192 pp.

  • Wang, H., Sankarasubramanian A. , and Ranjithan R. S. , 2013: Integration of climate and weather information for improving 15-day-ahead accumulated precipitation forecasts. J. Hydrometeor., 14, 186–202, doi:10.1175/JHM-D-11-0128.1.

    • Search Google Scholar
    • Export Citation

  • Wilby, R. L., Hay L. E. , Gutowski W. J. , Arritt R. W. , Takle E. S. , Pan Z. , Leavesley G. H. , and Clark M. P. , 2000: Hydrological responses to dynamically and statistically downscaled climate. Geophys. Res. Lett., 27, 1199–1202, doi:10.1029/1999GL006078.

    • Search Google Scholar
    • Export Citation

  • Wood, A. W., and Lettenmaier D. P. , 2006: A test bed for new seasonal hydrologic forecasting approaches in the western United States. Bull. Amer. Meteor. Soc., 87, 1699–1712, doi:10.1175/BAMS-87-12-1699.

    • Search Google Scholar
    • Export Citation

  • Wood, A. W., Maurer E. P. , Kumar A. , and Lettenmaier D. P. , 2002: Long-range experimental hydrologic forecasting for the eastern United States. J. Geophys. Res., 107, 4429, doi:10.1029/2001JD000659.

    • Search Google Scholar
    • Export Citation

  • Wood, A. W., Leung L. R. , Sridhar V. , and Lettenmaier D. P. , 2004: Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs. Climatic Change, 62, 189–216, doi:10.1023/B:CLIM.0000013685.99609.9e.

    • Search Google Scholar
    • Export Citation

  • Yuan, X., Wood E. F. , Luo L. , and Pan M. , 2011: A first look at Climate Forecast System version 2 (CFSv2) for hydrological seasonal prediction. Geophys. Res. Lett., 38, L13402, doi:10.1029/2011GL047792.

    • Search Google Scholar
    • Export Citation

  • Yuan, X., Liang X.-Z. , and Wood E. , 2012: WRF ensemble downscaling seasonal forecasts of China winter precipitation during 1982–2008. Climate Dyn., 39, 2041–2058, doi:10.1007/s00382-011-1241-8.

    • Search Google Scholar
    • Export Citation

  • Yuan, X., Wood E. F. , Roundy J. K. , and Pan M. , 2013: CFSv2-based seasonal hydroclimatic forecasts over the conterminous United States. J. Climate, 26, 4828–4847, doi:10.1175/JCLI-D-12-00683.1.

    • Search Google Scholar
    • Export Citation
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