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@online{lamontagne_synthetic_2017,
	title = {Synthetic streamflow generation},
	url = {https://waterprogramming.wordpress.com/2017/02/07/synthetic-streamflow-generation/},
	abstract = {A recent research focus of our group has been the development and use of synthetic streamflow generators.  There are many tools one might use to generate synthetic streamflows and it may not be obv…},
	titleaddon = {Water Programming: A Collaborative Research Blog},
	author = {Lamontagne, Jon},
	urldate = {2020-11-01},
	date = {2017-02-07},
	langid = {english},
	file = {Snapshot:C\:\\Users\\david.dorchies\\Zotero\\storage\\72FIK27E\\synthetic-streamflow-generation.html:text/html}
}

@article{giuliani_scalable_2018,
	title = {Scalable Multiobjective Control for Large-Scale Water Resources Systems Under Uncertainty},
	volume = {26},
	issn = {1558-0865},
	doi = {10.1109/TCST.2017.2705162},
	abstract = {Advances in modeling and control have always played an important role in supporting water resources systems planning and management. Changes in climate and society are now introducing additional challenges for controlling these systems, motivating the emergence of complex, integrated simulation models to explore key causal relationships and dependences related to uncontrolled sources of variability. In this brief, we contribute a massively parallel implementation of the evolutionary multiobjective direct policy search method for controlling large-scale water resources systems under uncertainty. The method combines direct policy search with nonlinear approximating networks and a hierarchical parallelization of the Borg multiobjective evolutionary algorithm. This computational framework successfully identifies control policies that address both the presence of multidimensional tradeoffs and severe uncertainties in the system dynamics and policy performance. We demonstrate the approach on a challenging real-world application, represented by the optimal control of a network of four multipurpose water reservoirs in the Red River basin in Northern Vietnam, under observed and synthetically generated hydrologic conditions. Results show that the reliability of the computational framework in finding near-optimal solutions increases with the number of islands in the adopted hierarchical parallelization scheme. This setting reduces the vulnerabilities of the designed solutions to the system's uncertainty and improves the discovery of robust control policies addressing key system performance tradeoffs.},
	pages = {1492--1499},
	number = {4},
	journaltitle = {{IEEE} Transactions on Control Systems Technology},
	author = {Giuliani, M. and Quinn, J. D. and Herman, J. D. and Castelletti, A. and Reed, P. M.},
	date = {2018-07},
	note = {Conference Name: {IEEE} Transactions on Control Systems Technology},
	keywords = {Water resources, Optimization, hydrology, Optimal control, Stochastic processes, Linear programming, water supply, robust control, rivers, adopted hierarchical parallelization scheme, Borg multiobjective evolutionary algorithm, complex simulation models, computational framework, environmental management, evolutionary computation, evolutionary multiobjective direct policy search method, integrated simulation models, large-scale water resources systems, massively parallel implementation, Multiobjective control, multipurpose water reservoirs, optimal control, Pareto optimisation, policy performance, reservoirs, robust control policies, scalable multiobjective control, Search problems, system dynamics, system performance tradeoffs, Uncertainty, water resources systems, water resources systems planning},
	file = {Giuliani et al. - 2018 - Scalable Multiobjective Control for Large-Scale Wa.pdf:C\:\\Users\\david.dorchies\\Zotero\\storage\\XDP5CMZD\\Giuliani et al. - 2018 - Scalable Multiobjective Control for Large-Scale Wa.pdf:application/pdf;IEEE Xplore Abstract Record:C\:\\Users\\david.dorchies\\Zotero\\storage\\GVAMBJUG\\7959085.html:text/html}
}

@article{zatarain_salazar_balancing_2017,
	title = {Balancing exploration, uncertainty and computational demands in many objective reservoir optimization},
	volume = {109},
	issn = {0309-1708},
	url = {http://www.sciencedirect.com/science/article/pii/S0309170817305419},
	doi = {10.1016/j.advwatres.2017.09.014},
	abstract = {Reservoir operations are central to our ability to manage river basin systems serving conflicting multi-sectoral demands under increasingly uncertain futures. These challenges motivate the need for new solution strategies capable of effectively and efficiently discovering the multi-sectoral tradeoffs that are inherent to alternative reservoir operation policies. Evolutionary many-objective direct policy search ({EMODPS}) is gaining importance in this context due to its capability of addressing multiple objectives and its flexibility in incorporating multiple sources of uncertainties. This simulation-optimization framework has high potential for addressing the complexities of water resources management, and it can benefit from current advances in parallel computing and meta-heuristics. This study contributes a diagnostic assessment of state-of-the-art parallel strategies for the auto-adaptive Borg Multi Objective Evolutionary Algorithm ({MOEA}) to support {EMODPS}. Our analysis focuses on the Lower Susquehanna River Basin ({LSRB}) system where multiple sectoral demands from hydropower production, urban water supply, recreation and environmental flows need to be balanced. Using {EMODPS} with different parallel configurations of the Borg {MOEA}, we optimize operating policies over different size ensembles of synthetic streamflows and evaporation rates. As we increase the ensemble size, we increase the statistical fidelity of our objective function evaluations at the cost of higher computational demands. This study demonstrates how to overcome the mathematical and computational barriers associated with capturing uncertainties in stochastic multiobjective reservoir control optimization, where parallel algorithmic search serves to reduce the wall-clock time in discovering high quality representations of key operational tradeoffs. Our results show that emerging self-adaptive parallelization schemes exploiting cooperative search populations are crucial. Such strategies provide a promising new set of tools for effectively balancing exploration, uncertainty, and computational demands when using {EMODPS}.},
	pages = {196--210},
	journaltitle = {Advances in Water Resources},
	shortjournal = {Advances in Water Resources},
	author = {Zatarain Salazar, Jazmin and Reed, Patrick M. and Quinn, Julianne D. and Giuliani, Matteo and Castelletti, Andrea},
	urldate = {2020-11-01},
	date = {2017-11-01},
	langid = {english},
	keywords = {Uncertainty, Direct policy search, Multi-objective evolutionary optimization, Multi-purpose reservoir control, Parallel strategies},
	file = {Zatarain Salazar et al. - 2017 - Balancing exploration, uncertainty and computation.pdf:C\:\\Users\\david.dorchies\\Zotero\\storage\\4N5CAARW\\Zatarain Salazar et al. - 2017 - Balancing exploration, uncertainty and computation.pdf:application/pdf}
}

@article{quinn_rival_2017,
	title = {Rival framings: A framework for discovering how problem formulation uncertainties shape risk management trade-offs in water resources systems},
	volume = {53},
	rights = {© 2017. American Geophysical Union. All Rights Reserved.},
	issn = {1944-7973},
	url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1002/2017WR020524},
	doi = {10.1002/2017WR020524},
	shorttitle = {Rival framings},
	abstract = {Managing water resources systems requires coordinated operation of system infrastructure to mitigate the impacts of hydrologic extremes while balancing conflicting multisectoral demands. Traditionally, recommended management strategies are derived by optimizing system operations under a single problem framing that is assumed to accurately represent the system objectives, tacitly ignoring the myriad of effects that could arise from simplifications and mathematical assumptions made when formulating the problem. This study illustrates the benefits of a rival framings framework in which analysts instead interrogate multiple competing hypotheses of how complex water management problems should be formulated. Analyzing rival framings helps discover unintended consequences resulting from inherent biases of alternative problem formulations. We illustrate this on the monsoonal Red River basin in Vietnam by optimizing operations of the system's four largest reservoirs under several different multiobjective problem framings. In each rival framing, we specify different quantitative representations of the system's objectives related to hydropower production, agricultural water supply, and flood protection of the capital city of Hanoi. We find that some formulations result in counterintuitive behavior. In particular, policies designed to minimize expected flood damages inadvertently increase the risk of catastrophic flood events in favor of hydropower production, while min-max objectives commonly used in robust optimization provide poor representations of system tradeoffs due to their instability. This study highlights the importance of carefully formulating and evaluating alternative mathematical abstractions of stakeholder objectives describing the multisectoral water demands and risks associated with hydrologic extremes.},
	pages = {7208--7233},
	number = {8},
	journaltitle = {Water Resources Research},
	author = {Quinn, J. D. and Reed, P. M. and Giuliani, M. and Castelletti, A.},
	urldate = {2020-11-01},
	date = {2017},
	langid = {english},
	note = {\_eprint: https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/2017WR020524},
	keywords = {multiobjective optimization, decision making under uncertainty, problem framing, reservoir operations},
	file = {Quinn et al. - 2017 - Rival framings A framework for discovering how pr.pdf:C\:\\Users\\david.dorchies\\Zotero\\storage\\H8X4FHEP\\Quinn et al. - 2017 - Rival framings A framework for discovering how pr.pdf:application/pdf;Snapshot:C\:\\Users\\david.dorchies\\Zotero\\storage\\3FYSR5AH\\2017WR020524.html:text/html}
}

@article{gudmundsson_technical_2012,
	title = {Technical Note: Downscaling {RCM} precipitation to the station scale using statistical transformations – a comparison of methods},
	volume = {16},
	issn = {1607-7938},
	url = {https://hess.copernicus.org/articles/16/3383/2012/},
	doi = {10.5194/hess-16-3383-2012},
	shorttitle = {Technical Note},
	abstract = {Abstract. The impact of climate change on water resources is usually assessed at the local scale. However, regional climate models ({RCMs}) are known to exhibit systematic biases in precipitation. Hence, {RCM} simulations need to be post-processed in order to produce reliable estimates of local scale climate. Popular post-processing approaches are based on statistical transformations, which attempt to adjust the distribution of modelled data such that it closely resembles the observed climatology. However, the diversity of suggested methods renders the selection of optimal techniques difficult and therefore there is a need for clarification. In this paper, statistical transformations for post-processing {RCM} output are reviewed and classified into (1) distribution derived transformations, (2) parametric transformations and (3) nonparametric transformations, each differing with respect to their underlying assumptions. A real world application, using observations of 82 precipitation stations in Norway, showed that nonparametric transformations have the highest skill in systematically reducing biases in {RCM} precipitation.},
	pages = {3383--3390},
	number = {9},
	journaltitle = {Hydrology and Earth System Sciences},
	shortjournal = {Hydrol. Earth Syst. Sci.},
	author = {Gudmundsson, L. and Bremnes, J. B. and Haugen, J. E. and Engen-Skaugen, T.},
	urldate = {2020-12-01},
	date = {2012-09-21},
	langid = {english},
	file = {Gudmundsson et al. - 2012 - Technical Note Downscaling RCM precipitation to t.pdf:C\:\\Users\\david.dorchies\\Zotero\\storage\\6QZDBHE6\\Gudmundsson et al. - 2012 - Technical Note Downscaling RCM precipitation to t.pdf:application/pdf}
}

@article{boe_statistical_2007,
	title = {Statistical and dynamical downscaling of the Seine basin climate for hydro-meteorological studies},
	volume = {27},
	rights = {Copyright © 2007 Royal Meteorological Society},
	issn = {1097-0088},
	url = {https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/joc.1602},
	doi = {https://doi.org/10.1002/joc.1602},
	abstract = {Two downscaling methods designed for the study of the hydrological impact of climate change on the Seine basin in France are tested for present climate. First, a multivariate statistical downscaling ({SD}) methodology based on weather typing and conditional resampling is described. Then, a bias correction technique for dynamical downscaling based on quantile–quantile mapping is introduced. To evaluate the end-to-end {SD} methodology, the atmospheric forcing derived from the large-scale circulation ({LSC}) of the {ERA}40 reanalysis by {SD} is used to force a hydrological model. Simulated discharges reproduce historical values reasonably well. Next, the dynamical and statistical approaches are compared using the Météo–France {ARPEGE} general circulation model in a variable resolution configuration (resolution around 60 km over France). The {ARPEGE} simulation is downscaled using the two methodologies, and hydrological simulations are performed. Regarding downscaled temperature and precipitation, the statistical approach is more efficient in reproducing the temporal and spatial autocorrelation properties. The simulated river discharges from the two approaches are nevertheless very similar: the two methods reproduce well the seasonal cycle and the daily distribution of streamflows. Finally, the results of the study are discussed from a practical impact study perspective. Copyright © 2007 Royal Meteorological Society},
	pages = {1643--1655},
	number = {12},
	journaltitle = {International Journal of Climatology},
	author = {Boé, J. and Terray, L. and Habets, F. and Martin, E.},
	urldate = {2020-12-02},
	date = {2007},
	langid = {english},
	note = {\_eprint: https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/joc.1602},
	keywords = {bias correction, climate change, dynamical downscaling, hydrological impacts, regional climate, statistical downscaling},
	file = {Snapshot:C\:\\Users\\david.dorchies\\Zotero\\storage\\XZV3AGYE\\joc.html:text/html}
}