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Seasonal Inflow Forecasts Using Gridded Precipitation and Soil Moisture Information: Implications for Reservoir Operation

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Abstract

Reservoir inflow forecasts are important for guiding reservoir operation. This study proposes an integrated framework of incorporating different forms of seasonal inflow forecasts in identifying the optimal releases policy. Gridded precipitation forecasts from climate models have been widely used for forecasting inflow. Both precipitation forecasts and soil moisture estimates are used as predictors to provide one-season-ahead reservoir inflow forecasts by constructing a regression problem. Principal component analysis is used to reduce the dimension of the regression problem, and a Bayesian regression technique is employed to generate various forms of inflow forecasts such as deterministic, probabilistic and ensemble forecasts. Two optimization models are constructed to couple with different forms of inflow forecasts. The first model aims to maximize hydropower generation and the second one aims to minimize end-of-season reservoir storage deviation from the target storage. Both single-value inflow and ensemble forecasts are incorporated to find the optimal water releasing policy considering inflow uncertainty and end-of-season reservoir storage requirement. The proposed methodology is demonstrated for Huangcai Reservoir in southern China. Bayesian regression technique shows good performance of seasonal inflow forecasts with a Pearson correlation of 0.8 and rank probability score of 0.4, which outperforms climatology. The coupling of ensemble inflow forecasts and optimization models provides water managers a set of release policies considering inflow uncertainty.

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References

  • Anghileri D, Voisin N, Castelletti A, Pianosi F, Nijssen B, Lettenmaier DP (2016) Value of long-term streamflow forecasts to reservoir operations for water supply in snow-dominated river catchments. Water Resour Res 52(6):4209–4225

    Article  Google Scholar 

  • Arsenault R, Latraverse M, Duchesne T (2016) Water Resour Manag 30:4363. https://doi.org/10.1007/s11269-016-1425-4

    Article  Google Scholar 

  • Arsenault R, Latraverse M, Duchesne T (2016) An Efficient Method to Correct Under-Dispersion in Ensemble Streamflow Prediction of Inflow Volumes for Reservoir Optimization. Water Resour Manag 30(12): 4363-4380. https://doi.org/10.1007/s11269-016-1425-4

  • Bartolini P, Salas JD (1993) Modeling of streamflow processes at different time scales. Water Resour Res 29(8):2573–2587

    Article  Google Scholar 

  • Block PJ, Souza Filho FA, Sun L, Kwon HH (2009) A streamflow forecasting framework using multiple climate and hydrological models 1. JAWRA J Am Water Resour Assoc 45(4):828–843

    Article  Google Scholar 

  • Bowman AW, Azzalini A (1997) Applied smoothing techniques for data analysis: the kernel approach with S-plus illustrations. Oxford University Press, New York

    Google Scholar 

  • Chen L, Singh VP, Lu W, Zhang J, Zhou J, Guo S (2016) Streamflow forecast uncertainty evolution and its effect on real-time reservoir operation. J Hydrol. https://doi.org/10.1016/j.jhydrol.2016.06.015

  • D’Ambrosio C, Lodi A, Martello S (2010) Piecewise linear approximation of functions of two variables in MILP models. Oper Res Lett 38:39–47. https://doi.org/10.1016/j.orl.2009.09.005

    Article  Google Scholar 

  • Day GN (1985) Extended streamflow forecasting using NWSRFS. J Water Resour Plan Manag 111(2):157–170

    Article  Google Scholar 

  • Dumedah G, Coulibaly P (2013) Evolutionary assimilation of streamflow in distributed hydrologic modeling using in-situ soil moisture data. Adv Water Resour 53:231–241

    Article  Google Scholar 

  • Fan Y, Dool H (2004) Climate prediction center global monthly soil moisture data set at 0.5 resolution for 1948 to present. J Geophys Res Atmos 109(D10):D10102

    Article  Google Scholar 

  • Golembesky K, Sankarasubramanian A, Devineni N (2009) Improved drought management of falls Lake reservoir: role of multimodel streamflow forecasts in setting up restrictions. J Water Resour Plan Manag 135(3):188–197

    Article  Google Scholar 

  • Goor Q, Kelman R, Tilmant A (2011) Optimal multipurpose-multireservoir operation model with variable productivity of hydropower plants. J Water Resour Plan Manag. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000117

  • Grantz K, Rajagopalan B, Clark M, Zagona E (2005) A technique for incorporating large-scale climate information in basin-scale ensemble streamflow forecasts. Water Resour Res 41(10):W10410

    Article  Google Scholar 

  • Hamlet AF, Lettenmaier DP (1999) Columbia River streamflow forecasting based on ENSO and PDO climate signals. J Water Resour Plan Manag 125(6):333–341

    Article  Google Scholar 

  • Hsu K, Gupta HV, Sorooshian S (1995) Artificial neural network modeling of the rainfall-runoff process. Water Resour Res 31(10):2517–2530

    Article  Google Scholar 

  • Huang J, van den Dool HM, Georgarakos KP (1996) Analysis of model-calculated soil moisture over the United States (1931–1993) and applications to long-range temperature forecasts. J Clim 9(6):1350–1362

    Article  Google Scholar 

  • Johnell A, Lindström G, Olsson J (2007) Deterministic evaluation of ensemble streamflow predictions in Sweden. Hydrol Res 38(4-5):441–450

    Article  Google Scholar 

  • Kalra A, Ahmad S (2009) Using oceanic-atmospheric oscillations for long lead time streamflow forecasting. Water Resour Res 45(3):W03413

    Article  Google Scholar 

  • Lettenmaier PD, Wood FE (1993) In: Maidment D (ed) Hydrological forecasting chapter 26 in handbook of hydrology. McGraw-Hill, New York

    Google Scholar 

  • Li S, Goddard L (2005) Retrospective forecasts with ECHAM4.5 AGCM IRI. Technical report, 05-02 December. International Research Institute for Climate and Society, University of Columbia, New York

    Google Scholar 

  • Lima CHR, Lall U (2010) Climate informed monthly streamflow forecasts for the Brazilian hydropower network using a periodic ridge regression model. J Hydrol 380(3-4):438–449

    Article  Google Scholar 

  • Loucks DP, Beek E, Stedinger JR, Dijkman JPM, Villars MT (2005) Water resources systems planning and management. Delft Hydraulics, The Netherland

    Google Scholar 

  • Maurer EP, Lettenmaier DP (2004) Potential effects of long-lead hydrologic predictability on Missouri River main-stem reservoirs. J Clim 17(1):174–186

    Article  Google Scholar 

  • Moradkhani H, Meier M (2010) Long-lead water supply forecast using large-scale climate predictors and independent component analysis. J Hydrol Eng 15(10):744–762

    Article  Google Scholar 

  • Najafi MR, Moradkhani H, Piechota TC (2012) Ensemble streamflow prediction: climate signal weighting methods vs. climate forecast system reanalysis. J Hydrol 442:105–116

    Article  Google Scholar 

  • Nwaogazie IL (1987) Comparative analysis of some explicit-implicit streamflow models. Adv Water Resour 10(2):69–77

    Article  Google Scholar 

  • Piechota TC, Dracup JA (1999) Long-range streamflow forecasting using El Nino-southern oscillation indicators. J Hydrol Eng 4(2):144–151

    Article  Google Scholar 

  • Roeckner E, Arpe K, Bengtsson L, Christoph M, Claussen M, Dümenil L, Schulzweida U (1996) The atmospheric general circulation model ECHAM-4: model description and simulation of present-day climate. Report 218. Max-Planck-Institut für Meteorologie, Hamburg

    Google Scholar 

  • Salas JD, Delleur JW, Yevjevich V, Lane WL (1980) Applied modeling of hydrologic time series. Water Resources Publication, Littleton

    Google Scholar 

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

    Article  Google Scholar 

  • Schwanenberg D, Fan FM, Naumann S et al (2015) Water Resour Manag 29:1635. https://doi.org/10.1007/s11269-014-0899-1

    Article  Google Scholar 

  • Steinschneider S, Brown C (2012) Dynamic reservoir management with real-option risk hedging as a robust adaptation to nonstationary climate. Water Resour Res 48(5):W05524

    Article  Google Scholar 

  • Schwanenberg D, Fan FM, Naumann S, Kuwajima JI, Montero RA, Reis AA (2015) Short-Term Reservoir Optimization for Flood Mitigation under Meteorological and Hydrological Forecast Uncertainty. Water Resour Manag 29(5): 1635-1651. https://doi.org/10.1007/s11269-014-0899-1

  • Vrugt JA, Ter Braak CJF, Clark MP, Hyman JM, Robinson BA (2008) Treatment of input uncertainty in hydrologic modeling: doing hydrology backward with Markov chain Monte Carlo simulation. Water Resour Res 44, W00B09, https://doi.org/10.1029/2007WR006720

  • Wang H, Arumugam S and Ranjithan RS (2011). Integration of Climate and Weather Information for Improving 15-Day-Ahead Accumulated Precipitation Forecasts. Journal of Hydrometeorology https://doi.org/10.1175/JHM-D-11-0128.1

  • Wang H, Reich B, Lim YH (2013) A Bayesian approach to probabilistic streamflow forecasts. J Hydroinf 15(2):381–391

    Article  Google Scholar 

  • Wei W, Watkins DW (2011) Probabilistic streamflow forecasts based on hydrologic persistence and large-scale climate signals in Central Texas. J Hydroinf 13(4):760–774

    Article  Google Scholar 

  • Werner K, Brandon D, Clark M, Gangopadhyay S (2004) Climate index weighting schemes for NWS ESP-based seasonal volume forecasts. J Hydrometeorol 5(6):1076–1090

    Article  Google Scholar 

  • Wood AW, Lettenmaier DP (2006) A test bed for new seasonal hydrologic forecasting approaches in the western United States. Bull Am Meteorol Soc 87(12):1699–1712

    Article  Google Scholar 

  • Wood AW, Schaake JC (2008) Correcting errors in streamflow forecast ensemble mean and spread. J Hydrometeorol 9(1):132–148

    Article  Google Scholar 

  • Xu K, Brown C, Kwon HH, Lall U, Zhang J, Hayashi S, Chen Z (2007) Climate teleconnections to Yangtze river seasonal streamflow at the three gorges dam, China. International Journal of Climatology: A Journal of the Royal Meteorological Society 27(6):771–780

    Article  Google Scholar 

  • Zealand CM, Burn DH, Simonovic SP (1999) Short term streamflow forecasting using artificial neural networks. J Hydrol 214(1-4):32–48

    Article  Google Scholar 

  • Zhao T, Cai X, Yang D (2011) Effect of streamflow forecast uncertainty on real-time reservoir operation. Adv Water Resour 34(4):495–504

    Article  Google Scholar 

  • Zhao Q, Cai X, Li Y (2019) Determining inflow forecast horizon for reservoir operation. Water Resour Res 55:4066–4081

    Google Scholar 

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Authors and Affiliations

  1. School of Hydraulic Engineering, Changsha University of Science & Technology, Changsha, 410114, Hunan, China

    Yuannan Long & Changbo Jiang

  2. Key Laboratory of Dongting Lake Aquatic Eco-Environmental Control and Restoration of Hunan Province, Changsha, 410114, Hunan, China

    Yuannan Long, Hui Wang & Changbo Jiang

  3. Key Laboratory of Water-Sediment Sciences and Water Disaster Prevention of Hunan Province, Changsha, 410114, Hunan, China

    Yuannan Long & Changbo Jiang

  4. Hunan Hydro & Power Design Institute, Changsha, 410007, Hunan, China

    Shang Ling

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  1. Yuannan Long
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  2. Hui Wang
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  4. Shang Ling
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Correspondence to Yuannan Long.

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Long, Y., Wang, H., Jiang, C. et al. Seasonal Inflow Forecasts Using Gridded Precipitation and Soil Moisture Information: Implications for Reservoir Operation. Water Resour Manage 33, 3743–3757 (2019). https://doi.org/10.1007/s11269-019-02330-8

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