HESS Hydrology and Earth System Sciences HESS Hydrol. Earth Syst. Sci. 1607-7938 Copernicus Publications Göttingen, Germany 10.5194/hess-17-2131-2013 Predictability of Western Himalayan river flow: melt seasonal inflow into Bhakra Reservoir in northern India Pal I. 1 2 Lall U. 3 Robertson A. W. 2 Cane M. A. 4 Bansal R. 5 Department of Civil Engineering, University of Colorado, Denver, CO 80204, USA International Research Institute for Climate and Society, The Earth Institute at Columbia University, Palisades, NY 10964, USA Department of Earth and Environmental Engineering, Columbia University, New York, NY 10027, USA Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY 10964, USA Bhakra Beas Management Board, Punjab, India 05 06 2013 17 6 2131 2146 Copyright: © 2013 I. Pal et al. 2013 This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/ This article is available from https://hess.copernicus.org/articles/17/2131/2013/hess-17-2131-2013.html The full text article is available as a PDF file from https://hess.copernicus.org/articles/17/2131/2013/hess-17-2131-2013.pdf

Snowmelt-dominated streamflow of the Western Himalayan rivers is an important water resource during the dry pre-monsoon spring months to meet the irrigation and hydropower needs in northern India. Here we study the seasonal prediction of melt-dominated total inflow into the Bhakra Dam in northern India based on statistical relationships with meteorological variables during the preceding winter. Total inflow into the Bhakra Dam includes the Satluj River flow together with a flow diversion from its tributary, the Beas River. Both are tributaries of the Indus River that originate from the Western Himalayas, which is an under-studied region. Average measured winter snow volume at the upper-elevation stations and corresponding lower-elevation rainfall and temperature of the Satluj River basin were considered as empirical predictors. Akaike information criteria (AIC) and Bayesian information criteria (BIC) were used to select the best subset of inputs from all the possible combinations of predictors for a multiple linear regression framework. To test for potential issues arising due to multicollinearity of the predictor variables, cross-validated prediction skills of the best subset were also compared with the prediction skills of principal component regression (PCR) and partial least squares regression (PLSR) techniques, which yielded broadly similar results. As a whole, the forecasts of the melt season at the end of winter and as the melt season commences were shown to have potential skill for guiding the development of stochastic optimization models to manage the trade-off between irrigation and hydropower releases versus flood control during the annual fill cycle of the Bhakra Reservoir, a major energy and irrigation source in the region.

 References Akaike, H.: A new look at the statistical model identification, IEEE T. Automat. Contr., 19, 716–723, 1974. Araghinejad, S., Burn, D. H., and Karamouz, M.: Long-lead probabilistic forecasting of stream flow using ocean atmospheric and hydrological predictors, Water Resour. Res., 42, W03431, <a href="http://dx.doi.org/10.1029/2004WR003853">https://doi.org/10.1029/2004WR003853</a>, 2006. Archer, D.: Contrasting hydrological regimes in the upper Indus basin, J. Hydrol., 274, 198–210, 2003. Barlow, M. A. and Tippett, M. K.: Variability and Predictability of Central Asia River Flows: Antecedent Winter Precipitation and Large-Scale Teleconnections, J. Hydrometeorol., 9, 1334–1349, 2008. Bracken, C., Rajagopalan, B., and Prairie, J.: A multisite seasonal ensemble streamflow forecasting technique, Water Resour. Res., 46, W03532, <a href="http://dx.doi.org/10.1029/2009WR007965">https://doi.org/10.1029/2009WR007965</a>, 2010. Buckland, S. T., Burnham, K. P., and Augustin, N. H.: Model selection: An integral part of inference, Biometrics, 53, 603–618, 1997. Burnham, K. P. and Anderson, D. R.: Model Selection and Multimodel Inference: A Practical Information Theoretic Approach, New York: Springer-Verlag, 2002. Chiew, F. H. S., Zhou, S. L., and McMahon, T. A.: Use of seasonal streamflow forecasts in water resources management, J. Hydrol., 270, 135–144, 2003. Dimri, A. P. and Dash, S. K.: Wintertime climatic trends in the western Himalayas, Clim. Change, 111, 775–800, 2012. Fowler, H. J. and Archer, D. R.: Conflicting Signals of Climatic Change in the Upper Indus Basin, J. Climate, 19, 4276–4293, 2006. Gamiz-Fortis, S., Pozo-Vazquez, D., Trigo, R. M., and Castro-Diez, Y.: Quantifying the predictability of winter river flow in Iberia. Part II: seasonal predictability, J. Climate, 21, 2503–2518, 2008. Garen, D. C.: Improved techniques in regression-based streamflow volume forecasting, J. Water Resour. Plann. Manage., 118, 654–670, 1992. Grafen, A. and Hails, R.: Modern Statistics for the Life Sciences. Oxford: Oxford University Press, 351&nbsp;pp., 2002. Hagedorn, R., Doblas-Reyes, F. J., and Palmer, T. N.: The rationale behind the success of multi-model ensembles in seasonal forecasting. Part I: Basic concept, Tellus A, 57, 219–233, 2005. Jain, S. K., Kumar, N., and Ahmed, T.: SLURP model and GIS for estimation of runoff in a part of Satluj catchment, India, Hydrol. Sci., 43, 875–884, 1998. Jain, S. K., Goswami, A., and Saraf, A. K.: Assessment of snowmelt runoff using remote sensing and effect of climate change on runoff, Water Resour. Manage., 24, 1763–1777, 2010. Johnson, J. B. and Omland, K. S.: Model selection in ecology and evolution, Trends Ecol. Evol., 19, 101–108, 2004. Kattelmann, R.: Role of snowmelt in generating streamflow during spring in East Nepal, Snow and Glacier Hydrology (Proceedings of the Kathmandu Symposium, November 2002), IAHS Publication No. 218, 103–111, 1993. Krishnamurti, T. N., Kishtawal, C. M., LaRow, T. E., Bachiochi, D. R., Zhang, Z., Williford, C. E., Gadgil, S., and Surendran, S.: Improved weather and seasonal climate forecasts from multi-model superensemble, Science, 285, 1548–1550, 1999. Krishnamurti, T. N., Kishtawal, C. M., Zhang, Z., LaRow, T. E., Bachiochi, D. R., Williford, C. E., Gadgil, S., and Surendran, S.: Multi-model ensemble forecasts for weather and seasonal climate, J. Climate, 13, 4196–4216, 2000. Kwon, H. H., Souza Filho, F. A., Sun, L., Block, P., Lall, U., and Dirceu, S.: Assessment of uncertainty of hydrologic model and climate forecast model in Northern Brazil, Hydrol. Process., 26, 3875–3885, 2012. Li, H., Luo, L., Wood, E. F., and Schaake, J.: The role of initial conditions and forcing uncertainties in sea- sonal hydrologic forecasting, J. Geophys. Res., 114, D04114, <a href="http://dx.doi.org/10.1029/2008JD010969">https://doi.org/10.1029/2008JD010969</a>, 2009. Loader, C.: Statistics and Computing: Local Regression and Likelihood, Springer, New York, 1999. Makhdoom, M. T. A. and Solomon, S. I.: Attempting flow forecasts of the Indus river, Pakistan using remotely sensed snow cover data, Nord. Hydrol., 17, 171–184, 1986. Materia, S., Dirmeyer, P. A., Guo, Z., Alessandri, A., and Navarra, A.: The sensitivity of simulated river discharge to land surface representation and meteorological forcings, J. Hydrometeorol., 11, 334–351, 2010. McLeod, A. I. and Xu, C.: bestglm: Best Subset GLM, available at:&nbsp;<a href="http://cran.r-project.org/web/">http://cran.r-project.org/web/</a> packages/bestglm/vignettes/bestglm.pdf, 2010. Miller, A. J.: Subset Selection in Regression. Chapman & HallCRC, Boca Raton, 2nd Edn, 2002. Owosina, A.: Methods for assessing the space and time variability of groundwater data, M.S. Thesis, Utah State University, Logan, Utah, 1992. Pal, I., Lall, U., Robertson, A., Cane, M., and Bansal, R.: Diagnostics of Western Himalayan Satluj River flow: Warm season (MAM/JJAS) inflow into Bhakra Dam in India, J. Hydrol., 478, 132–147, 2013. Rajagopalan, B. and Lall, U.: A Nearest Neighbor Bootstrap Resampling Scheme for Resampling Daily Precipitation and other Weather Variables, Water Resour. Res., 35, 3089–3101, 1999. Rajagopalan, B., Lall, U., and Zebiak, S.: Optimal categorical climate forecasts through multiple GCM ensemble combination and regularization, Mon. Weather Rev., 130, 1792–1811, 2002. Rajagopalan, B., Grantz, K., Regonda, S., Clark, M., and Zagona, E.: Ensemble streamflow forecasting: Methods and Applications, in: Advances in Water Science Methodologies, edited by: Aswathanarayana, U., Taylor and Francis, Netherlands, 2005. Rajeevan, M., Bhate, J., Kale, J. D., and Lal, B.: Development of a high resolution daily gridded rainfall data for the Indian region, India Meteorological Department, Met. Monograph Climatology No. 22/2005, 26&nbsp;pp., 2005. Ramamoorthy, A. S. and Haefner, H.: Runoff modeling and forecasting of river basins, and Himalayan snowcover information system (HIMSIS). Hydrology for the Water Management of Large River Basins (Proceedings of the Vienna Symposium), IAHS Publ. no.&nbsp;201, 1991. Regonda, S. K., Rajagopalan, B., Clark, M., and Zagona, E.: A multimodel emsemble forecast framework: Application to spring seasonal flows in the Gunnison River Basin, Water Resour. Res., 42, W09404, <a href="http://dx.doi.org/10.1029/2005WR004653">https://doi.org/10.1029/2005WR004653</a>, 2006. Risley, J. C., Gannett, M. W., Lea, J. K., and Roehl Jr., E. A.: An analysis of statistical methods for seasonal flow forecasting in the Upper Klamath River Basin of Oregon and California: US Geological Survey Scientific Investigations Report 2005–5177, 44&nbsp;pp., 2001. Schar, C., Vasilina, L., Pertziger, F., and Dirren, S.: Seasonal runoff forecasting using precipitation from meteorological data assimilation systems, J. Hydrometeorol., 5, 959–973, 2004. Singh, P. and Bengtsson, L.: Hydrological sensitivity of a large Himalayan basin to climate change, Hydrol. Process., 18, 2363–2385, 2004. Singh, P. and Jain, S. K.: Snow and glacier melt in the Satluj river at Bhakra dam in the western Himalayan region, Hydrol. Sci., 47, 93–106, 2002. Singh, P. and Jain, S. K.: Modelling of streamflow and its components for a large Himalayan basin with predominant snowmelt yields, Hydrol. Sci. J., 48, 257–276, 2003. Singh, P. and Kumar, N.: Impact assessment of climate change on the hydrological response of a snow and glacier melt runoff dominated Himalayan River, J. Hydrol., 193, 316–350, 1997. Singh, P. and Quick, M. C.: Streamflow simulation of Satluj river in the Western Himalayas. Snow and Glacier Hydrology (Proceedings of the Kathmandu Symposium, November 2002), IAHS Publication No. 218, 261–271, 1993. Singla, S., Céron, J.-P., Martin, E., Regimbeau, F., Déqué, M., Habets, F., and Vidal, J.-P.: Predictability of soil moisture and river flows over France for the spring season, Hydrol. Earth Syst. Sci., 16, 201–216, <a href="http://dx.doi.org/10.5194/hess-16-201-2012">https://doi.org/10.5194/hess-16-201-2012</a>, 2012. Song, P. and Kroll, C.: The impacts of multicollinearity on small sample hydrologic regional regression, ASCE Conf. Proc., 3713–3722, <a href="http://dx.doi.org/10.1061/41173(414)389">https://doi.org/10.1061/41173(414)389</a>, 2011. Souza, F. A. and Lall, U.: Seasonal to Interannual Ensemble Streamflow Forecasts for Ceara, Brazil: Applications of a Multivariate, Semi-Parametric Algorithm, Water Resour. Res., 39, 1307–1320, 2003. Srivastava, A. K., Rajeevan, M., and Kshirsagar, S. R.: Development of a high resolution daily gridded temperature data set (1969–2005) for the Indian region. NCC Research Report 8, National Climate Centre, India Meteorological Department, Pune, India, 15&nbsp;pp., 2008. Stewart, I. T.: Changes in snowpack and snowmelt runoff for key mountain regions, Hydrol. Process., 23, 78–94, 2009. Tippett, M. K., Barlow, M., and Lyon, B.: Statistical correction of central Southwest Asia winter precipitation simulations, Int. J. Climatol., 23, 1421–1433, 2003. Venables, W. N. and Ripley, B. D.: Modern Applied Statistics with S-PLUS, 2nd Edn, Springer, ISBN 0-387-98214-0, 1997. Wang, E., Zhang, Y., Luo, J., Chiew, F. H. S., and Wang, Q. J.: Monthly and seasonal streamflow forecasts using rainfall-runoff modeling and historical weather data, Water Resour. Res., 47, W05516, <a href="http://dx.doi.org/10.1029/2010WR009922">https://doi.org/10.1029/2010WR009922</a>, 2010. Wedgbrow, C. S., Wilby, R. L., Fox, H. R., and O'Hare, G.: Prospects for seasonal forecasting of summer drought and low river flow anomalies in England and Wales, Int. J. Climatol., 22, 219–236, 2002. Wilby, R. L., Wedgbrow, C. S., and Fox, H. R.: Seasonal predictability of the summer hydrometeorology of the River Thames, UK, J. Hydrol., 295, 1–16, 2004. Wilks, D. S.: Statistical Methods in the Atmospheric Sciences, Academic Press, USA, 2006. Wood, A. W. and Lettenmaier, D. P.: A test bed for new seasonal hydrologic forecasting approaches in the western United States, B. Am. Meteorol. Soc., 8, 1699–1712, 2006. Yadav, R. R., Park, W. K., Singh, J., and Dubey, B.: Do the western Himalayas defy global warming?, Geophys. Res. Lett., 31, L17201, <a href="http://dx.doi.org/10.1029/2004GL020201">https://doi.org/10.1029/2004GL020201</a>, 2004.