@article {80541,
	title = {Hydrological modelling using proxies for gauged precipitation and temperature},
	journal = {Hydrological Processes},
	volume = {31},
	year = {2017},
	note = {Hydrological modeling is plagued by incomplete datasets due to {\textquotedblleft}short time, inadequate spatial coverage, missing data and biases from various causes . . . The development of alternative datasets for using as proxies for inadequate/missing weather data represents a key research area{\textquotedblright} (from the Abstract).  In this paper, 6 alternative datasets are evaluated for 12 watersheds in Canada and the United States.  The authors find that the North American Regional Reanalysis was the alternative dataset with the best performance. Specific and standard calibration techniques are discussed.},
	month = {2017, Oct. 30},
	pages = {p.3881-3897},
	keywords = {Umpqua River Basin, hydrology, climate, precipitation, temperature, mathematical modeling},
	doi = {10.1002/hyp.11304},
	author = {Chen, Jie and Brissette, Fran{\c c}ois P.}
}
@article {80546,
	title = {Using raw regional climate model outputs for quantifying climate change impacts on hydrology},
	journal = {Hydrological Processes},
	volume = {31},
	number = {24},
	year = {2017},
	note = {This is a look at regional climate models (RCMs) and their use in predicting the responses of watersheds to climate change.  In this paper, three {\textquotedblleft}RCMs driven by three different general circulation models are used to investigate the uncertainty of hydrological simulations associated with the choice of a bias-corrected or nonbias-corrected RCM simulation. The results indicate that the uncertainty envelope is generally watershed and indicator dependent.{\textquotedblright}  In order to allow users to make the most informed decisions, the authors recommend giving both the bias-corrected and nonbias-corrected data.},
	month = {2017, Nov. 30},
	pages = {p.4398-4413},
	keywords = {Umpqua River Basin, Yellowstone River Basin, Yampa River Basin, Manic 5 River Basin,  hydrology, climate, precipitation, temperature, mathematical modeling},
	doi = {DOI: 10.1002/hyp11368},
	author = {Chen, Jie and Brissette, Fran{\c c}ois P. and Liu, Pan and Xia, Jun}
}
@article {52146,
	title = {Finding appropriate bias correction methods in downscaling precipitation for hydrologic impact studies over North America},
	journal = {Water Resources Research},
	volume = {49},
	year = {2013},
	note = {This is a technical discussion of how hydrologic models can be improved by various statistical techniques. The authors ran raw regional climate model precipitation data with a hydrologic simulation, and then tried different methods of correcting input precipitation data for ten different North American rivers.   It turns out that the Umpqua River raw regional climate model data worked well with the hydrologic model, and additional corrections were not needed for the simulation to approximate reality. },
	pages = {p.4187-4205},
	keywords = {hydrology, mathematical modeling, precipitation, Umpqua River},
	doi = {doi:10.1002/wrcr.20331},
	author = {Chen, Jie and Brissette,Francois and Chaumont, Diane and Braun, Marco}
}
