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Reducing uncertainty in stochastic streamflow generation and reservoir sizing by combining observed, reconstructed and projected streamflow

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Abstract

Reservoir sizing is one of the most important aspects of water resources engineering as the storage in a reservoir must be sufficient to supply water during extended droughts. Typically, observed streamflow is used to stochastically generate multiple realizations of streamflow to estimate the required storage based on the Sequent Peak Algorithm (SQP). The main limitation in this approach is that the parameters of the stochastic model are purely derived from the observed record (limited to less than 80 years of data) which does not have information related to prehistoric droughts. Further, reservoir sizing is typically estimated to meet future increase in water demand, and there is no guarantee that future streamflow over the planning period will be representative of past streamflow records. In this context, reconstructed streamflow records, usually estimated based on tree ring chronologies, provide better estimates of prehistoric droughts, and future streamflow records over the planning period could be obtained from general circulation models (GCMs) which provide 30 year near-term climate change projections. In this study, we developed paleo streamflow records and future streamflow records for 30 years are obtained by forcing the projected precipitation and temperature from the GCMs over a lumped watershed model. We propose combining observed, reconstructed and projected streamflows to generate synthetic streamflow records using a Bayesian framework that provides the posterior distribution of reservoir storage estimates. The performance of the Bayesian framework is compared to a traditional stochastic streamflow generation approach. Findings based on the split-sample validation show that the Bayesian approach yielded generated streamflow traces more representative of future streamflow conditions than the traditional stochastic approach thereby, reducing uncertainty on storage estimates corresponding to higher reliabilities. Potential strategies for improving future streamflow projections and its utility in reservoir sizing and capacity expansion projects are also discussed.

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References

  • Cook ER, Jacoby GC (1983) Potomac river streamflow since 1730 as reconstructed by tree rings. J Climate Appl Meteor 22:1659–1672

    Article  Google Scholar 

  • Das Bhowmik R, Sankarasubramanian A, Sinha T, Mahinthakumar G, Kunkel K, Patskoski J (2017) Multivariate downscaling approach preserving cross-correlations across climate variable for projecting hydrologic fluxes. J Hydrometeorol. doi:10.1175/JHM-D-16-0160.1

    Google Scholar 

  • Devineni N, Sankarasubramanian A (2010) Improved categorical winter precipitation forecasts through multimodel combinations of coupled GCMs. Geophys Res Lett 37:L24704. doi:10.1029/2010GL044989

    Article  Google Scholar 

  • Devineni N, Lall U, Pederson N, Cook E (2013) A tree-ring-based reconstruction of delaware river basin streamflow using hierarchical Bayesian regression. J Climate 26:4357–4374

    Article  Google Scholar 

  • Gangopadhyay S, Harding BL, Rajagopalan B, Lukas JJ, Fulp TJ (2009) A nonparametric approach for paleohydrologic reconstruction of annual streamflow ensembles. Water Resour Res 45:W06417. doi:10.1029/2008WR007201

    Article  Google Scholar 

  • Ghil M et al (2002) Advanced spectral methods for climatic time series. Rev Geophys 40(1):1003. doi:10.1029/2000RG000092

    Article  Google Scholar 

  • Gleick PH (1987) Regional hydrologic consequences of increases in atmospheric carbon dioxide and other trace gases. Clim Change 10(2):137–161

    Article  CAS  Google Scholar 

  • Gleick PH, Chalecki EL (1999) The impact of climatic changes for water resources of the Colorado and Sacramento-San Joaquin river systems. J Am Water Resour Assoc 35(6):1429–1441

    Article  Google Scholar 

  • Hanak E, Lund JR (2012) Adapting California's water management to climate change. Clim Change 111(1):17–44. doi:10.1007/s10584-011-0241-3

    Article  Google Scholar 

  • Hargreaves GH, Samni ZA (1982) Estimation of potential evapotranspiration. J Irrig Drainage Div Proc Am Soc Civil Eng 108:223–230

    Google Scholar 

  • Hawkins E, Sutton R (2009) The potential to narrow uncertainty in regional climate predictions. Bull Amer Meteor Soc 90:1095–1107

    Article  Google Scholar 

  • Henley BJ, Thyer MA, Kuczera G, Franks SW (2011) Climate-informed stochastic hydrological modeling: incorporating decadal-scale variability using paleo data. Water Resour Res 47:W11509. doi:10.1029/2010WR010034

    Article  Google Scholar 

  • Hidalgo HG, Piechota TC, Dracup JA (2000) Alternative principal components regression procedures for dendrohydrologic reconstructions. Water Resour Res 36(11):3241–3249. doi:10.1029/2000WR900097

    Article  Google Scholar 

  • Hosking JRM, Wallis JR (1986) Paleoflood hydrology and flood frequency analysis. Water Resour Res 22(4):543–550. doi:10.1029/WR022i004p00543

    Article  Google Scholar 

  • Kaplan A, Cane M, Kushnir Y, Clement A, Blumenthal M, Rajagopalan B (1998) Analyses of global sea surface temperature 1856–1991. J Geophys Res 103:18567–18589

    Article  Google Scholar 

  • Keenlyside NS, Latif M, Jungclaus J, Kornblueh L, Roeckner E (2008) Advancing decadal-scale climate prediction in the North Atlantic sector. Nature 453:84–88

    Article  CAS  Google Scholar 

  • Lall U, Miller CW (1988) An optimization model for screening multipurpose reservoir systems. Water Resour Res 24(7):953–968. doi:10.1029/WR024i007p00953

    Article  Google Scholar 

  • Lettenmaier DP, Brettman KL, Vail LW, Yabusaki SB, Scott MJ (1992) Sensitivity of Pacific Northwest water resources to global warming. Northw Environ J 8(2):265–283

    Google Scholar 

  • Leung L Ruby, Hamlet Alan F, Lettenmaier Dennis P, Kumar Arun (1999) Simulations of the ENSO Hydroclimate Signals in the Pacific Northwest Columbia River Basin. Bull Amer Meteor Soc 80:2313–2329. doi:10.1175/1520-0477

    Article  Google Scholar 

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

    Google Scholar 

  • Li W, Sankarasubramanian A, Sinha T, Ranjithan SR (2016) Role of multimodel combination and data assimilation in improving streamflow prediction over multiple time scales. Stoch Env Res Risk Assess 30(8):2255–2269

    Article  Google Scholar 

  • Mazoorei A, Sinha T, Sankarasubramanian A, Kumar S, Peters-Lidard C (2015) Decomposition of sources of errors in seasonal streamflow forecasting over the US sunbelt. J Geophys Res Atmos 120(23):11809–11825. doi:10.1002/2015jd023687

    Article  Google Scholar 

  • McCabe GJ, Wolock DM (1999) General-circulation-model simulations of future snowpack in the western United States. J Am Water Resour Assoc 35:1473–1484

    Article  Google Scholar 

  • Meehl GA et al (2009) Decadal Prediction, can it be skillful? Bull Am Meteor Soc 90(10):1467–1485

    Article  Google Scholar 

  • Meehl GA et al (2014) Decadal climate prediction: an update from the trenches. Bull Am Meteor Soc 95(2):243–267

    Article  Google Scholar 

  • Milly PCD, Betancourt J, Falkenmark M, Hirsch RM, Kundzewicz ZW, Lettenmaier DP, Stouffer RJ (2008) Climate change: stationarity is dead: whither water management? Science 319:573–574

    Article  CAS  Google Scholar 

  • Mishra Ashok K, Özger Mehmet, Singh Vijay P (2011) Wet and dry spell analysis of Global Climate Model-generated precipitation using power laws and wavelet transforms. Stoch Env Res Risk Assess 25(4):517–535

    Article  Google Scholar 

  • Oh J, Sankarasubramanian A (2012) Climate, Streamflow and water quality interactions over the southeastern US. Hydrol Earth Syst Sci 17:2285–2298

    Article  Google Scholar 

  • Patskoski J, Sankarasubramanian A (2015) Improved reservoir sizing utilizing observed and reconstructed streamflows within a Bayesian combination framework. Water Resour Res. doi:10.1002/2014WR016189

    Google Scholar 

  • Patskoski J, Sankarasubramanian A, Wang H (2015) Reconstructed streamflow using SST and Tree-ring chronologies over the Southeastern United States. J Hydrol 527:761–775

    Article  Google Scholar 

  • Prairie J, Nowak K, Rajagopalan B, Lall U, Fulp T (2008) A stochastic nonparametric approach for streamflow generation combining observational and paleoreconstructed data. Water Resour Res 44(6):W06423. doi:10.1029/2007WR006684

    Article  Google Scholar 

  • Sankarasubramanian A, Vogel RM, Limbrunner JF (2001) Climate elasticity of streamflow in the United States. Water Resour Res 37(6):1771–1781

    Article  Google Scholar 

  • Sankarasubramanian A, Lall U, Espuneva S (2008) Role of retrospective forecasts of GCM forced with persisted SST anomalies in operational streamflow forecasts development. J Hydrometeorol 9(2):212–227

    Article  Google Scholar 

  • Seo SB, Sinha T, Mahinthakumar G, Sankarasubramanian A, Kumar M (2016) Identification of dominant source of errors in developing streamflow and groundwater projections under near-term climate change. J Geophys Res Atmosphere 121(13):7652–7672. doi:10.1002/2016JD025138

    Article  Google Scholar 

  • Singh H, Singha T, Sankarasubramanian A (2014) Impacts of near-term climate change and population growth on within-year reservoir systems. Manuscript submitted for publication

  • Sinha T, Cherkauer KA (2010) Impacts of future climate change on soil frost in the Midwestern United States. J Geophys Res 115(D08105):1–16

    Google Scholar 

  • Sivakumar B (2011) Global climate change and its impacts on water resources planning and management: assessment and challenges. Stoch Env Res Risk Assess 25(4):583–600

    Article  Google Scholar 

  • Sivakumar B and Christakos G (2011) Climate: patterns, changes, and impacts. 443–444

  • Slack JR, Landwehr JM (1992) Hydro-climatic data network (HCDN): a U.S. geological survey streamflow data set for the United States for the study of climate variations, 1874–1988: U.S. Geological Survey Open-File Report 91–129, 193 p

  • Stedigner JR, Taylor MR (1982a) Synthetic streamflow generation, 1, model verification and validation. Water Resour Res 18(4):909–918

    Article  Google Scholar 

  • Stedigner JR, Taylor MR (1982b) Synthetic streamflow generation, 1, effect of parameter uncertainty. Water Resour Res 18(4):909–918

    Article  Google Scholar 

  • Stedinger JR, Cohn TA (1986) Flood frequency analysis with historical and paleoflood information. Water Resour Res 22(5):785–793. doi:10.1029/WR022i005p00785

    Article  Google Scholar 

  • Thomas HA (1981) Improved methods for national water assessment: final report USGS water resources. Contract WR15249270, Harvard University, Cambridge 44

  • Thomas HA Jr, Burden RP (1963) Operations research in water quality management. Harvard Water Resources Group, Cambridge, pp 1–17

    Google Scholar 

  • Trenberth KE, Stepaniak DP (2001) Indices of El Niño evolution. J. Climate 14:1697–1701

    Article  Google Scholar 

  • Valdés JB, Rodríguez-Iturbe I, Vicens GJ (1977) Bayesian generation of synthetic streamflows, 2, the multivariate case. Water Resour Res 132:291–295

    Article  Google Scholar 

  • Vicens GJ, Rodríguez-Iturbe I, Schaake JC Jr (1975) Bayesian generation of synthetic streamflows. Water Resour Res 116:827–838

    Article  Google Scholar 

  • Vogel RM (1988) The value of stochastic stream flow models in overyear reservoir design applications. J Water Resour Res 24(9):1483–1490

    Article  Google Scholar 

  • Vogel RM, Stedinger JR (1987) Generalized storage-reliability-yield relationships. J Hydrol 89:303–327

    Article  Google Scholar 

  • Wood AW, Leung LR, Sridhar V, Lettenmaier DP (2004) Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs. Clim Change 62:189–216. doi:10.1023/B:CLIM.0000013685.99609.9e

    Article  Google Scholar 

  • Woodhouse CA (2001) A tree-ring reconstruction of streamflow for the Colorado front range. JAWRA J Am Water Resour Assoc 37:561–569. doi:10.1111/j.1752-1688.2001.tb05493.x

    Article  Google Scholar 

  • Woodhouse CA, Lukas JJ (2006) Multi-century tree-ring reconstructions of Colorado streamflow for water resource planning. Clim Change. doi:10.1007/s10584-006-9055-0

    Google Scholar 

  • Woodhouse CA, Gray ST, Meko DM (2006) Updated streamflow reconstructions for the Upper Colorado River Basin. Water Resour Res 42:W05415. doi:10.1029/2005WR004455

    Article  Google Scholar 

  • Woodhouse CA, Meko DM, MacDonald GM, Stahle DW, Cook ER (2010) A 1200-year perspective on the 21st century drought in southwestern North America. Proc Natl Acade Sci (PNAS) 107:21283–21288. doi:10.1073/pnas.0911197107

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This work was supported by the United States National Science Foundation CAREER grant CBET-0954405. Any opinions, findings and conclusions or recommendations expressed in this paper are those of the authors and do not reflect the views of the NSF. The authors also thank the Associate Editor and anonymous reviewers whose valuable comments led to significant improvements in the manuscript.

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Correspondence to Jason Patskoski.

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Patskoski, J., Sankarasubramanian, A. Reducing uncertainty in stochastic streamflow generation and reservoir sizing by combining observed, reconstructed and projected streamflow. Stoch Environ Res Risk Assess 32, 1065–1083 (2018). https://doi.org/10.1007/s00477-017-1456-2

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