Research Article of American Journal of Geographical Research and Reviews
Evaluation of Seasonal Streamflow Forecasting
Shailesh Kumar Singh
National Institute of Water and Atmospheric Research, Christchurch, New Zealand
Long-term streamflow forecasts are essential for optimal management of water resources for various demands, including irrigation, fisheries management, hydropower production and flood warning. In this paper, a probabilistic forecast framework based on Ensemble Streamflow Prediction (ESP) technique is presented, with the basic assumption that future weather patterns will reflect those experienced historically. Hence, past forcing data (input to hydrological model) can be used with the current initial condition of a catchment to generate an ensemble of flow predictions. The present study employs the ESP-based approach using the TopNet hydrological model. The objective of this present paper is to evaluate and assess the uncertainty due to initial condition of the catchments and forcing (meteorological input to the model) data for (ESP) based streamflow forecasting using the TopNet hydrological model in New Zealand catchments. An ensemble of streamflow predictions which provide probabilistic hydrological forecasts, reflecting the intrinsic uncertainty in climate, with lead time up to three months is presented for the four catchments on New Zealand’s South Island. Verification of the forecast over the period 2000-2010 indicates a Ranked Probability Skill Score of 23% to 69% (over climatology) across the four catchments. In general, improvement in ESP forecasting skill over climatology is greatest in summer for all catchments studied. The major uncertainty associated with ESP forecast is combination of uncertainty due to initial state and climate forcing. The analysis indicates that the sensitivity of flow forecast to initial condition uncertainty depends on the hydrological regime experienced by the basin during the forecast period. On average, the relative importance of initial condition is greatest within two weeks to months of the start of the simulation for all catchment and all season. After this time period uncertainty in forecast is mainly due to uncertainty in forcing data. Finding of this study can be valuable tool for water resource managements.
Keywords: Long-term forecast, Streamflow, ESP, Probabilistic forecast
How to cite this article:
Shailesh Kumar Singh. Evaluation of Seasonal Streamflow Forecasting. American Journal of Geographical Research and Reviews, 2018; 1:4. DOI:10.28933/AJGRR-2017-12-3001
1 Bandaragoda, C., Tarboton, D.G., Woods, R., 2004. Application of TOPNET in the distributed model intercomparison project. Journal of Hydrology, 298(1-4): 178-201.
2 Bárdossy, A., Singh, S.K., 2008. Robust estimation of hydrological model parameters. Hydrology and Earth System Sciences, 12(6): 1273-1283.
3 Chen, J., Brissette, F.P., 2015. Combining Stochastic Weather Generation and Ensemble Weather Forecasts for Short-Term Streamflow Prediction. Water Resources Management, 29(9): 3329-3342. DOI:10.1007/s11269-015-1001-3
4 Clark, M.P. et al., 2011. Representing spatial variability of snow water equivalent in hydrologic and land-surface models: A review. Water Resources Research, 47(7): W07539. DOI:10.1029/2011wr010745
5 Day, G.N., 1985. Extended streamflow forecasting using NWSRFS. Journal of Water Resources Planning and Management, 111(2): 157-170.
6 Epstein, E.S., 1969. A Scoring System for Probability Forecasts of Ranked Categories. Journal of applied meteorology, 8(6): 985-987. DOI:10.1175/1520-0450(1969)008<0985:assfpf>2.0.co;2
7 Garen, D.C., 1992. Improved techniques in regression-based streamflow volume forecasting. Journal of Water Resources Planning and Management, 118(6): 654-670.
8 Hendrikx, J. et al., 2009. Simulations of seasonal snow in New Zealand: past and future, Proceedings of the 9th International Conference on Southern Hemisphere Meteorology and Oceanography, http://www.bom.gov. au/events/9icshmo/manuscripts/M1715_Hendrikx.pdf, Melbourne, pp. 11.
9 Murphy, A.H., 1969. On the ranked probability skill score. Journal of applied meteorology, 8(6): 988–989.
10 Newsome, P.F.J., Wilde, R.H., Willoughby, E.J., 2000. Land Resource Information System Spatial Data Layers. Landcare Research Technical Report No.84p
11 NIWA, 2016. Seasonal Climate Outlook. https://www.niwa.co. nz/climate/sco, NIWA, New Zealand.
12 Paiva, R., Collischonn, W., Bonnet, M., de Gonçalves, L., 2012. On the sources of hydrological prediction uncertainty in the Amazon. Hydrol. Earth Syst. Sci, 16: 3127-3137.
13 Piechota, T.C., Chiew, F.H., Dracup, J.A., McMahon, T.A., 1998. Seasonal streamflow forecasting in eastern Australia and the El Niño–Southern Oscillation. Water Resources Research, 34(11): 3035-3044.
14 Purdie, J.M., Bardsley, W.E., 2010. Seasonal prediction of lake inflows and rainfall in a hydro‐electricity catchment, Waitaki river, New Zealand. International Journal of Climatology, 30(3): 372-389.
15 Robertson, D., Pokhrel, P., Wang, Q., 2013. Improving statistical forecasts of seasonal streamflows using hydrological model output. Hydrology and Earth System Sciences, 17(2): 579-593.
16 S. Thompson, G.I., Gapare, N., 2003. New Zealand Land Cover Database Version 2 – Illustrated Guide to Target Classes. In: Environment, M.f.t. (Ed.), pp. 126.
17 Singh, S.K., 2016. Long-term Streamflow Forecasting Based on Ensemble Streamflow Prediction Technique: A Case Study in New Zealand. Water Resources Management, 30(7): 2295-2309.
18 Singh, S.K., Dutta, S., 2017. Observational uncertainty in hydrological modelling using data
19 depth. Global NEST Journal, 19(3): 489-497
20 Snelder, T.H., Biggs, B.J.F., 2002. Multiscale River Environment Classification for water resources Managements1. JAWRA Journal of the American Water Resources Association, 38(5): 1225-1239.
21 Svensson, C., 2016. Seasonal river flow forecasts for the United Kingdom using persistence and historical analogues. Hydrological Sciences Journal, 61(1): 19-35. DOI:10.1080/02626667.2014.992788
22 Tait, A., Henderson, R., Turner, R., Zheng, X., 2006. Thin plate smoothing spline interpolation of daily rainfall for New Zealand using a climatological rainfall surface. International Journal of Climatology, 26(14): 2097-2115.
23 Tait, A., Woods, R., 2007. Spatial interpolation of daily potential evapotranspiration for New Zealand using a spline model. Journal of Hydrometeorology, 8(3): 430-438.
24 Twedt, T.M., Burnash, R.J., Ferral, R.L., 1978. Extended streamflow prediction during the California drought, Pro ceedings, Western Snow Conference.
25 Wood, A.W., Lettenmaier, D.P., 2008. An ensemble approach for attribution of hydrologic prediction uncertainty. Geophysical Research Letters, 35(14): L14401.
26 Wood, A.W., Schaake, J.C., 2008. Correcting errors in streamflow forecast ensemble mean and spread. Journal of Hydrometeorology, 9(1): 132-148.
27 Woods, R.A., 2009. Analytical model of seasonal climate impacts on snow hydrology: Continuous snowpacks. Advances in Water Resources, 32(10): 1465-1481. DOI:10.1016/j.advwatres.2009.06.011
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