Skip to main content

UDiscoverIt Downloads — Reducing Adverse Impacts of Dams Using Multiobjective Optimization

All projects

Featured Publications
  • Alexander S. Flecker, Qinru Shi, Rafael M. Almeida, Héctor Angarita, Jonathan M. Gomes-Selman, Roosevelt García-Villacorta, Suresh A. Sethi, Steven A. Thomas, N. LeRoy Poff, Bruce R. Forsberg, Sebastian A. Heilpern, Stephen K. Hamilton, Jorge D. Abad, Elizabeth P. Anderson, Nathan Barros, Isabel Carolina Bernal, Richard Bernstein, Carlos M. Cañas, Olivier Dangles, Andrea C. Encalada, Ayan S. Fleischmann, Michael Goulding, Jonathan Higgins, Céline Jezequel, Erin I. Larson, Peter B. McIntyre, John M. Melack, Mariana Montoya, Thierry Oberdorff, Rodrigo Paiva, Guillaume Perez, Brendan H. Rappazzo, Scott Steinschneider, Sandra Torres, Mariana Varese, M. Todd Walter, Xiaojian Wu, Yexiang Xue, Xavier E. Zapata-Ríos, Carla P. Gomes. Reducing adverse impacts of Amazon hydropower expansion. Science (2022). [free access]
Reducing Adverse Impacts of Dams Using Multiobjective Optimization
Migratory dourada catfish

Migratory dourada catfish (Brachyplatystoma rousseauxii) captured at the Teotônio Rapids in the state of Rondônia while moving upstream to reach spawning headwaters in Bolivia and Peru. A downstream dam now blocks these important migrations.   Photo by Michael Goulding

The Amazon is in the midst of a hydropower boom. More than 350 new dams are proposed across four Amazonian countries (Bolivia, Brazil, Ecuador, and Peru), with more already under construction. Environmental impacts are assessed for individual dams—but what are the combined costs of the hydropower explosion for biodiversity, sediment and nutrient transport, fisheries, navigation, and other benefits provided by intact rivers? Our multidisciplinary team has developed a framework for evaluating cumulative impacts in areas of rapid hydropower growth. The new models can help guide design of more sustainable dam networks that meet hydropower targets while reducing damage to key ecosystem services.

Computing the exact and approximate Pareto frontier on tree-structured networks

Multi-objective optimization plays a key role in the study of real-world problems, as they often involve multiple criteria. In multi-objective optimization, it is important to identify the so-called Pareto frontier, which characterizes the trade-offs between the objectives of different solutions. We provide a C++ implementation of exact and approximate dynamic programming (DP) algorithms for computing the Pareto frontier on tree-structured networks. The code uses a specialized divide-and-conquer approach for the pruning of dominated solutions. This optimization outperforms the previous approaches, leading to speed-ups of two to three orders of magnitude in practice. We apply a rounding technique to the exact dynamic programming algorithm that provides a fully polynomial-time approximation scheme (FPTAS). The FPTAS finds a solution set of polynomial-size, which approximates the Pareto frontier within an arbitrary small e factor and runs in time that is polynomial in the size of the instance and 1/ e. We illustrate the code by evaluating trade-offs in ecosystem services due to the proliferation of hydropower dams throughout the Amazon basin. In particular, we apply our algorithms to identify portfolios of hydropower dam sites that simultaneously minimize impacts on river flow, river connectivity, sediment transport, fish diversity, and greenhouse gas emissions while achieving energy production goals, at different scales, including the entire Amazon basin. The code can be easily adapted to compute the Pareto frontier of various multi-objective problems for other river basins or other tree-structured networks.

Files

Demo Links

Related Publications

  • Alexander S. Flecker, Qinru Shi, Rafael M. Almeida, Héctor Angarita, Jonathan M. Gomes-Selman, Roosevelt García-Villacorta, Suresh A. Sethi, Steven A. Thomas, N. LeRoy Poff, Bruce R. Forsberg, Sebastian A. Heilpern, Stephen K. Hamilton, Jorge D. Abad, Elizabeth P. Anderson, Nathan Barros, Isabel Carolina Bernal, Richard Bernstein, Carlos M. Cañas, Olivier Dangles, Andrea C. Encalada, Ayan S. Fleischmann, Michael Goulding, Jonathan Higgins, Céline Jezequel, Erin I. Larson, Peter B. McIntyre, John M. Melack, Mariana Montoya, Thierry Oberdorff, Rodrigo Paiva, Guillaume Perez, Brendan H. Rappazzo, Scott Steinschneider, Sandra Torres, Mariana Varese, M. Todd Walter, Xiaojian Wu, Yexiang Xue, Xavier E. Zapata-Ríos, Carla P. Gomes. Reducing adverse impacts of Amazon hydropower expansion. Science (2022). [free access]
Pareto Frontier Tree Structured Networks

This is a C++ source code package implementing a dynamic programming algorithm for computing the Pareto frontier (for two criteria) on tree structured networks. A dataset for hydroelectric dams, using the energy and GHG emissions criteria, are provided.

Files

Related Publications

Publications — Reducing Adverse Impacts of Dams Using Multiobjective Optimization

2022

  • Alexander S. Flecker, Qinru Shi, Rafael M. Almeida, Héctor Angarita, Jonathan M. Gomes-Selman, Roosevelt García-Villacorta, Suresh A. Sethi, Steven A. Thomas, N. LeRoy Poff, Bruce R. Forsberg, Sebastian A. Heilpern, Stephen K. Hamilton, Jorge D. Abad, Elizabeth P. Anderson, Nathan Barros, Isabel Carolina Bernal, Richard Bernstein, Carlos M. Cañas, Olivier Dangles, Andrea C. Encalada, Ayan S. Fleischmann, Michael Goulding, Jonathan Higgins, Céline Jezequel, Erin I. Larson, Peter B. McIntyre, John M. Melack, Mariana Montoya, Thierry Oberdorff, Rodrigo Paiva, Guillaume Perez, Brendan H. Rappazzo, Scott Steinschneider, Sandra Torres, Mariana Varese, M. Todd Walter, Xiaojian Wu, Yexiang Xue, Xavier E. Zapata-Ríos, Carla P. Gomes. Reducing adverse impacts of Amazon hydropower expansion. Science (2022). [free access]

2021

2019

2018