An Efficient Approach for Estimating Streamflow Forecast Skill Elasticity

Louise Arnal Department of Geography and Environmental Science, University of Reading, and European Centre for Medium-Range Weather Forecasts, Shinfield Park, Reading, United Kingdom

Search for other papers by Louise Arnal in
Current site
Google Scholar
PubMed
Close
,
Andrew W. Wood Research Applications Laboratory, NCAR, Boulder, Colorado

Search for other papers by Andrew W. Wood in
Current site
Google Scholar
PubMed
Close
,
Elisabeth Stephens Department of Geography and Environmental Science, University of Reading, Reading, United Kingdom

Search for other papers by Elisabeth Stephens in
Current site
Google Scholar
PubMed
Close
,
Hannah L. Cloke Department of Geography and Environmental Science, and Department of Meteorology, University of Reading, Reading, United Kingdom

Search for other papers by Hannah L. Cloke in
Current site
Google Scholar
PubMed
Close
, and
Florian Pappenberger European Centre for Medium-Range Weather Forecasts, Shinfield Park, Reading, and School of Geographical Sciences, University of Bristol, Bristol, United Kingdom

Search for other papers by Florian Pappenberger in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

Seasonal streamflow prediction skill can derive from catchment initial hydrological conditions (IHCs) and from the future seasonal climate forecasts (SCFs) used to produce the hydrological forecasts. Although much effort has gone into producing state-of-the-art seasonal streamflow forecasts from improving IHCs and SCFs, these developments are expensive and time consuming and the forecasting skill is still limited in most parts of the world. Hence, sensitivity analyses are crucial to funnel the resources into useful modeling and forecasting developments. It is in this context that a sensitivity analysis technique, the variational ensemble streamflow prediction assessment (VESPA) approach, was recently introduced. VESPA can be used to quantify the expected improvements in seasonal streamflow forecast skill as a result of realistic improvements in its predictability sources (i.e., the IHCs and the SCFs)—termed “skill elasticity”—and to indicate where efforts should be targeted. The VESPA approach is, however, computationally expensive, relying on multiple hindcasts having varying levels of skill in IHCs and SCFs. This paper presents two approximations of the approach that are computationally inexpensive alternatives. These new methods were tested against the original VESPA results using 30 years of ensemble hindcasts for 18 catchments of the contiguous United States. The results suggest that one of the methods, end point blending, is an effective alternative for estimating the forecast skill elasticities yielded by the VESPA approach. The results also highlight the importance of the choice of verification score for a goal-oriented sensitivity analysis.

Denotes content that is immediately available upon publication as open access.

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/JHM-D-16-0259.s1.

© 2017 American Meteorological Society.

Corresponding author: Louise Arnal, l.l.s.arnal@pgr.reading.ac.uk; louise.arnal@ecmwf.int

Abstract

Seasonal streamflow prediction skill can derive from catchment initial hydrological conditions (IHCs) and from the future seasonal climate forecasts (SCFs) used to produce the hydrological forecasts. Although much effort has gone into producing state-of-the-art seasonal streamflow forecasts from improving IHCs and SCFs, these developments are expensive and time consuming and the forecasting skill is still limited in most parts of the world. Hence, sensitivity analyses are crucial to funnel the resources into useful modeling and forecasting developments. It is in this context that a sensitivity analysis technique, the variational ensemble streamflow prediction assessment (VESPA) approach, was recently introduced. VESPA can be used to quantify the expected improvements in seasonal streamflow forecast skill as a result of realistic improvements in its predictability sources (i.e., the IHCs and the SCFs)—termed “skill elasticity”—and to indicate where efforts should be targeted. The VESPA approach is, however, computationally expensive, relying on multiple hindcasts having varying levels of skill in IHCs and SCFs. This paper presents two approximations of the approach that are computationally inexpensive alternatives. These new methods were tested against the original VESPA results using 30 years of ensemble hindcasts for 18 catchments of the contiguous United States. The results suggest that one of the methods, end point blending, is an effective alternative for estimating the forecast skill elasticities yielded by the VESPA approach. The results also highlight the importance of the choice of verification score for a goal-oriented sensitivity analysis.

Denotes content that is immediately available upon publication as open access.

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/JHM-D-16-0259.s1.

© 2017 American Meteorological Society.

Corresponding author: Louise Arnal, l.l.s.arnal@pgr.reading.ac.uk; louise.arnal@ecmwf.int

Supplementary Materials

    • Supplemental Materials (PDF 5.31 MB)
Save
  • Baroni, G., and S. Tarantola, 2014: A general probabilistic approach for uncertainty and global sensitivity analysis of deterministic models: A hydrological case study. Environ. Modell. Software, 51, 2634, doi:10.1016/j.envsoft.2013.09.022.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bierkens, M. F. P., and L. P. H. van Beek, 2009: Seasonal predictability of European discharge: NAO and hydrological response time. J. Hydrometeor., 10, 953968, doi:10.1175/2009JHM1034.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cherry, J., H. Cullen, M. Visbeck, A. Small, and C. Uvo, 2005: Impacts of the North Atlantic Oscillation on Scandinavian hydropower production and energy markets. Water Resour. Manage., 19, 673691, doi:10.1007/s11269-005-3279-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chiew, F. H. S., S. L. Zhou, and T. A. McMahon, 2003: Use of seasonal streamflow forecasts in water resources management. J. Hydrol., 270, 135144, doi:10.1016/S0022-1694(02)00292-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Clark, M. P., M. C. Serreze, and G. J. McCabe, 2001: Historical effects of El Niño and La Niña events on the seasonal evolution of the montane snowpack in the Columbia and Colorado River basins. Water Resour. Res., 37, 741757, doi:10.1029/2000WR900305.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cloke, H. L., F. Pappenberger, and J.-P. Renaud, 2008: Multi-method global sensitivity analysis (MMGSA) for modelling floodplain hydrological processes. Hydrol. Processes, 22, 16601674, doi:10.1002/hyp.6734.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cloke, H. L., F. Pappenberger, P. Smith, and F. Wetterhall, 2017: How do I know if I’ve improved my continental scale flood early warning system? Environ. Res. Lett., 12, 044006, doi:10.1088/1748-9326/aa625a.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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).

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Demargne, J., and Coauthors, 2014: The science of NOAA’s operational Hydrologic Ensemble Forecast Service. Bull. Amer. Meteor. Soc., 95, 7998, doi:10.1175/BAMS-D-12-00081.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Flato, G. M., 2011: Earth system models: An overview. Wiley Interdiscip. Rev.: Climate Change, 2, 783800, doi:10.1002/wcc.148.

  • Hamlet, A. F., D. Huppert, and D. P. Lettenmaier, 2002: Economic value of long-lead streamflow forecasts for Columbia River hydropower. J. Water Resour. Plann. Manage., 128, 91101, doi:10.1061/(ASCE)0733-9496(2002)128:2(91).

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kwon, H.-H., C. Brown, K. Xu, and U. Lall, 2009: Seasonal and annual maximum streamflow forecasting using climate information: Application to the Three Gorges Dam in the Yangtze River basin, China. Hydrol. Sci. J., 54, 582595, doi:10.1623/hysj.54.3.582.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, H., L. Luo, E. F. Wood, and J. Schaake, 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
  • Lilburne, L., and S. Tarantola, 2009: Sensitivity analysis of spatial models. Int. J. Geogr. Inf. Sci., 23, 151168, doi:10.1080/13658810802094995.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lins, H. F., 2012: USGS Hydro-Climatic Data Network 2009 (HCDN-2009). USGS Fact Sheet 2012-3047, 4 pp. [Available online at http://pubs.usgs.gov/fs/2012/3047/.]

    • Crossref
    • Export Citation
  • Luo, L., and E. F. Wood, 2007: Monitoring and predicting the 2007 U.S. drought. Geophys. Res. Lett., 34, L22702, doi:10.1029/2007GL031673.

  • MacLeod, D., H. Cloke, F. Pappenberger, and A. Weisheimer, 2016: Evaluating uncertainty in estimates of soil moisture memory with a reverse ensemble approach. Hydrol. Earth Syst. Sci., 20, 27372743, doi:10.5194/hess-20-2737-2016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mendoza, P. A., A. W. Wood, E. A. Clark, E. Rothwell, M. P. Clark, B. Nijssen, L. D. Brekke, and J. R. Arnold, 2017: An intercomparison of approaches for improving predictability in operational seasonal streamflow forecasting. Hydrol. Earth Syst. Sci. Discuss., doi:10.5194/hess-2017-60.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Paiva, R. C. D., W. Collischonn, M. P. Bonnet, and L. G. G. de Gonçalves, 2012: On the sources of hydrological prediction uncertainty in the Amazon. Hydrol. Earth Syst. Sci., 16, 31273137, doi:10.5194/hess-16-3127-2012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pappenberger, F., M. Ratto, and V. Vandenberghe, 2010: Review of sensitivity analysis methods. Modelling Aspects of Water Approach Directive Implementation, P. A. Vanrolleghem, Ed., IWA Publishing, 191–265.

  • Regonda, S. K., B. Rajagopalan, M. Clark, and E. Zagona, 2006: A multimodel ensemble forecast approach: Application to spring seasonal flows in the Gunnison River basin. Water Resour. Res., 42, W09404, doi:10.1029/2005WR004653.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Saltelli, A., S. Tarantola, and F. Campolongo, 2000: Sensitivity analysis as an ingredient of modeling. Stat. Sci., 15, 377395, doi:10.1214/ss/1009213004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Saltelli, A., S. Tarantola, F. Campolongo, and M. Ratto, 2004: Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models. John Wiley & Sons, 218 pp.

  • Shukla, S., and D. P. Lettenmaier, 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, 35293538, doi:10.5194/hess-15-3529-2011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shukla, S., J. Sheffield, E. F. Wood, and D. P. Lettenmaier, 2013: On the sources of global land surface hydrologic predictability. Hydrol. Earth Syst. Sci., 17, 27812796, doi:10.5194/hess-17-2781-2013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Singla, S., J. P. Céron, E. Martin, F. Regimbeau, M. Déqué, F. Habets, and J. P. Vidal, 2012: Predictability of soil moisture and river flows over France for the spring season. Hydrol. Earth Syst. Sci., 16, 201216, doi:10.5194/hess-16-201-2012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Slater, L. J., G. Villarini, and A. A. Bradley, 2017: Evaluation of the skill of North-American Multi-Model Ensemble (NMME) global climate models in predicting average and extreme precipitation and temperature over the continental USA. Climate Dyn., doi:10.1007/s00382-016-3286-1, in press.

    • Search Google Scholar
    • Export Citation
  • Staudinger, M., and J. Seibert, 2014: Predictability of low flow—An assessment with simulation experiments. J. Hydrol., 519, 13831393, doi:10.1016/j.jhydrol.2014.08.061.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • van Dijk, A. I. J. M., J. L. Peña-Arancibia, E. F. Wood, J. Sheffield, and H. E. Beck, 2013: Global analysis of seasonal streamflow predictability using an ensemble prediction system and observations from 6192 small catchments worldwide. Water Resour. Res., 49, 27292746, doi:10.1002/wrcr.20251.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Viel, C., A.-L. Beaulant, J.-M. Soubeyroux, and J.-P. Céron, 2016: How seasonal forecast could help a decision maker: An example of climate service for water resource management. Adv. Sci. Res., 13, 5155, doi:10.5194/asr-13-51-2016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Welles, E., S. Sorooshian, G. Carter, and B. Olsen, 2007: Hydrologic verification: A call for action and collaboration. Bull. Amer. Meteor. Soc., 88, 503511, doi:10.1175/BAMS-88-4-503.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wood, A. W., and D. P. Lettenmaier, 2006: A test bed for new seasonal hydrologic forecasting approaches in the western United States. Bull. Amer. Meteor. Soc., 87, 16991712, doi:10.1175/BAMS-87-12-1699.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wood, A. W., and D. P. Lettenmaier, 2008: An ensemble approach for attribution of hydrologic prediction uncertainty. Geophys. Res. Lett., 35, L14401, doi:10.1029/2008GL034648.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wood, A. W., A. Kumar, and D. P. Lettenmaier, 2005: A retrospective assessment of National Centers for Environmental Prediction climate model–based ensemble hydrologic forecasting in the western United States. J. Geophys. Res., 110, D04105, doi:10.1029/2004JD004508.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wood, A. W., T. Pagano, and M. Roos, 2016b: Tracing the origins of ESP. HEPEX, accessed 24 October 2016. [Available online at https://hepex.irstea.fr/tracing-the-origins-of-esp/.]

  • Yossef, N. C., H. Winsemius, A. Weerts, R. van Beek, and M. F. P. Bierkens, 2013: Skill of a global seasonal streamflow forecasting system, relative roles of initial conditions and meteorological forcing. Water Resour. Res., 49, 46874699, doi:10.1002/wrcr.20350.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yuan, X., 2016: An experimental seasonal hydrological forecasting system over the Yellow River basin—Part 2: The added value from climate forecast models. Hydrol. Earth Syst. Sci., 20, 24532466, doi:10.5194/hess-20-2453-2016.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yuan, X., E. F. Wood, J. K. Roundy, and M. Pan, 2013: CFSv2-based seasonal hydroclimatic forecasts over the conterminous United States. J. Climate, 26, 48284847, doi:10.1175/JCLI-D-12-00683.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yuan, X., E. F. Wood, and Z. Ma, 2015: A review on climate-model-based seasonal hydrologic forecasting: Physical understanding and system development. Wiley Interdiscip. Rev.: Water, 2, 523536, doi:10.1002/wat2.1088.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yuan, X., F. Ma, L. Wang, Z. Zheng, Z. Ma, A. Ye, and S. Peng, 2016: An experimental seasonal hydrological forecasting system over the Yellow River basin—Part 1: Understanding the role of initial hydrological conditions. Hydrol. Earth Syst. Sci., 20, 24372451, doi:10.5194/hess-20-2437-2016.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 1100 211 24
PDF Downloads 656 102 3