Paper No. JAWRA-08-0075-P of the Journal of the American Water Resources Association (JAWRA). Discussions are open until six months from print publication.
A Streamflow Forecasting Framework using Multiple Climate and Hydrological Models1
Article first published online: 15 MAY 2009
© 2009 American Water Resources Association
JAWRA Journal of the American Water Resources Association
Volume 45, Issue 4, pages 828–843, August 2009
How to Cite
Block, P. J., Souza Filho, F. A., Sun, L. and Kwon, H.-H. (2009), A Streamflow Forecasting Framework using Multiple Climate and Hydrological Models. JAWRA Journal of the American Water Resources Association, 45: 828–843. doi: 10.1111/j.1752-1688.2009.00327.x
- Issue published online: 29 JUL 2009
- Article first published online: 15 MAY 2009
- Received April 22, 2008; accepted December 17, 2008.
- surface water hydrology;
- computational methods;
Abstract: Water resources planning and management efficacy is subject to capturing inherent uncertainties stemming from climatic and hydrological inputs and models. Streamflow forecasts, critical in reservoir operation and water allocation decision making, fundamentally contain uncertainties arising from assumed initial conditions, model structure, and modeled processes. Accounting for these propagating uncertainties remains a formidable challenge. Recent enhancements in climate forecasting skill and hydrological modeling serve as an impetus for further pursuing models and model combinations capable of delivering improved streamflow forecasts. However, little consideration has been given to methodologies that include coupling both multiple climate and multiple hydrological models, increasing the pool of streamflow forecast ensemble members and accounting for cumulative sources of uncertainty. The framework presented here proposes integration and offline coupling of global climate models (GCMs), multiple regional climate models, and numerous water balance models to improve streamflow forecasting through generation of ensemble forecasts. For demonstration purposes, the framework is imposed on the Jaguaribe basin in northeastern Brazil for a hindcast of 1974-1996 monthly streamflow. The ECHAM 4.5 and the NCEP/MRF9 GCMs and regional models, including dynamical and statistical models, are integrated with the ABCD and Soil Moisture Accounting Procedure water balance models. Precipitation hindcasts from the GCMs are downscaled via the regional models and fed into the water balance models, producing streamflow hindcasts. Multi-model ensemble combination techniques include pooling, linear regression weighting, and a kernel density estimator to evaluate streamflow hindcasts; the latter technique exhibits superior skill compared with any single coupled model ensemble hindcast.