HR: 11:45h
AN: H32C-06    [Abstracts]
TI: Accounting for Uncertainty Propagation: A Streamflow Forecasting Framework using Multiple Climate and Hydrological Models
AU: * Block, P J
EM: pblock@iri.columbia.edu
AF: International Research Institute for Climate and Society (IRI), 61 Route 9W Monell, Palisades, NY 10964, United States
AU: Souza Filho, F
EM: assis@iri.columbia.edu
AF: International Research Institute for Climate and Society (IRI), 61 Route 9W Monell, Palisades, NY 10964, United States
AU: Sun, L
EM: sun@iri.columbia.edu
AF: International Research Institute for Climate and Society (IRI), 61 Route 9W Monell, Palisades, NY 10964, United States
AU: Kwon, H
EM: hk2273@columbia.edu
AF: Columbia University, Department of Earth and Environmental Engineering 918 SW Mudd Hall 500 West 120th Street, New York, NY 10027, United States
AB: Water resources planning and management efficacy is subject to capturing inherent uncertainties stemming from climatic and hydrological inputs and models. Accounting for and properly dealing with these propagating uncertainties remains a formidable challenge. Streamflow forecasts, critical in reservoir operation and water allocation decision-making, fundamentally contain uncertainties arising from assumed initial conditions, model structure, and modeled processes. 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 (GCM), multiple regional climate models, and numerous hydrological models to improve streamflow forecasting and characterize system uncertainty 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 GCM and regional models, including dynamical and statistical models, are integrated with the Sacramento Soil Moisture Accounting and SMAP (Soil Moisture Accounting Procedure) hydrological models. Precipitation hindcasts from the GCM are downscaled via the regional models and fed into the hydrological models, producing streamflow hindcasts. Multi-model ensemble combination techniques include pooling, least squares regression, and a kernel density estimator to evaluate streamflow hindcasts and assess structural uncertainty of climate and hydrological models; the latter technique exhibits slightly superior skill compared to any single coupled model ensemble hindcast.
DE: 1816 Estimation and forecasting
DE: 1860 Streamflow
DE: 1873 Uncertainty assessment (3275)
SC: Hydrology [H]
MN: 2007 Fall Meeting