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James McPhee

Researcher at University of Chile

Publications -  78
Citations -  2848

James McPhee is an academic researcher from University of Chile. The author has contributed to research in topics: Snow & Streamflow. The author has an hindex of 23, co-authored 77 publications receiving 2237 citations. Previous affiliations of James McPhee include University of California, Los Angeles & Institute for Technology Assessment and Systems Analysis (ITAS).

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The 2010–2015 megadrought in central Chile: impacts on regional hydroclimate and vegetation

TL;DR: In this article, the authors investigated the nature and biophysical impacts of the megadrought in central Chile and reported some of the measures taken by the central government to relieve the effects and the public perception of this event.
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Climate change impacts on the hydrology of a snowmelt driven basin in semiarid Chile

TL;DR: In this article, the authors present an analysis of the direct impacts of climate change on the hydrology of the upper watersheds of the snowmelt-driven Limari river basin, located in north-central Chile.
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Assessment of ecosystem services as an opportunity for the conservation and management of native forests in Chile

TL;DR: In this paper, the effect of forest management on streamflow in two watersheds in the Valdivian Andes (600-650 m of elevation; 39837 0S), and fish abundance as a function of forest cover in 17 watersheds located in the Coastal Range and the Central Depression (39850 0 -42830 0 S).
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Revealing the impact of forest exotic plantations on water yield in large scale watersheds in South-Central Chile

TL;DR: In this paper, the authors examined the temporal variation of the residuals between best-fit precipitation-runoff relationships and instrumental streamflow records for two large watersheds (Purapel en Nirivilo, PNN and Cauquenes en el Arrayan, CQA) located in the Mediterranean-climate coastal range of South-Central Chile.
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A diversified multiobjective GA for optimizing reservoir rule curves

TL;DR: Results of this work indicate that the proposed macro-evolutionary multiobjective genetic algorithm (MMGA) is highly competitive and provides a viable alternative to solve multiobjectives optimization problems for water resources planning and management.