Geology topics

Carla P. Gomes

Publications and source records attributed to Carla P. Gomes.

7 recordsLinked to original sources

USGS2021

Climate change may impair electricity generation and economic viability of future Amazon hydropower

Numerous hydropower facilities are under construction or planned in tropical and subtropical rivers worldwide. While dams are typically designed considering historic river discharge regimes, climate change may induce large-scale alterations in river hydrology. Here we analyze how future climate change will affect river hydrology, electricity generation, and economic viability of > 350 potential hydropower dams across the Amazon, Earth’s largest river basin and a global hotspot for future hydropower development. Midcentury projections for the RCP 4.5 and 8.5 climate change scenarios show basin-wide reductions of river discharge (means, 13 and 16%, respectively) and hydropower generation (19 and 27%). Declines are sharper for dams in Brazil, which harbors 60% of the proposed projects. Climate change will cause more frequent low-discharge interruption of hydropower generation and less frequent full-capacity operation. Consequently, the minimum electricity sale price for projects to break even more than doubles at many proposed dams, rendering much of future Amazon hydropower less competitive than increasingly lower cost renewable sources such as wind and solar. Climate-smart power systems will be fundamental to support environmentally and financially sustainable energy development in hydropower-dependent regions.

Global Environmental Change↗

USGS2021

Warming sea surface temperatures fuel summer epidemics of eelgrass wasting disease

Seawater temperatures are increasing, with many unquantified impacts on marine diseases. While prolonged temperature stress can accelerate host-pathogen interactions, the outcomes in nature are poorly quantified. We monitored eelgrass wasting disease (EWD) from 2013-2017 and correlated mid-summer prevalence of EWD with remotely sensed seawater temperature metrics before, during, and after the 2015-2016 marine heatwave in the northeast Pacific, the longest marine heatwave in recent history. Eelgrass shoot density declined by 60% between 2013 and 2015 and did not recover. EWD prevalence ranged from 5-70% in 2013 and increased to 60-90% by 2017. EWD severity approximately doubled each year between 2015 and 2017. EWD prevalence was positively correlated with warmer temperature for the month prior to sampling while EWD severity was negatively correlated with warming prior to sampling. This complex result may be mediated by leaf growth; bigger leaves may be more likely to be diseased, but may also grow faster than lesions, resulting in lower severity. Regional stressors leading to population declines prior to or early in the heatwave may have exacerbated the effects of warming on eelgrass disease susceptibility and reduced the resilience of this critical species.

Marine Ecology Progress Series↗

USGS2019

Reducing greenhouse gas emissions of Amazon hydropower with strategic dam planning

Hundreds of dams have been proposed throughout the Amazon basin, one of the world’s largest untapped hydropower frontiers. While hydropower is a potentially clean source of renewable energy, some projects produce high greenhouse gas (GHG) emissions per unit electricity generated (carbon intensity). Here we show how carbon intensities of proposed Amazon upland dams (median = 39 kg CO 2 eq MWh −1 , 100-year horizon) are often comparable with solar and wind energy, whereas some lowland dams (median = 133 kg CO 2 eq MWh −1 ) may exceed carbon intensities of fossil-fuel power plants. Based on 158 existing and 351 proposed dams, we present a multi-objective optimization framework showing that low-carbon expansion of Amazon hydropower relies on strategic planning, which is generally linked to placing dams in higher elevations and smaller streams. Ultimately, basin-scale dam planning that considers GHG emissions along with social and ecological externalities will be decisive for sustainable energy development where new hydropower is contemplated.

Nature Communications↗

USGS2018

Efficiently optimizing for dendritic connectivity on tree-structured networks in a multi-objective framework

We provide an exact and approximation algorithm based on Dynamic Programming and an approximation algorithm based on Mixed Integer Programming for optimizing for the so-called dendritic connectivity on tree-structured networks in a multi-objective setting. Dendritic connectivity describes the degree of connectedness of a network. We consider different variants of dendritic connectivity to capture both network connectivity with respect to long and short-to-middle distances. Our work is motivated by a problem in computational sustainability concerning the evaluation of trade-offs in ecosystem services due to the proliferation of hydropower dams throughout the Amazon basin. In particular, we consider trade-offs between energy production and river connectivity. River fragmentation can dramatically affect fish migrations and other ecosystem services, such as navigation and transportation. In the context of river networks, different variants of dendritic connectivity are important to characterize the movements of different fish species and human populations. Our approaches are general and can be applied to optimizing for dendritic connectivity for a variety of multi-objective problems on tree-structured networks.

Conference Paper↗

USGS2018

Efficiently approximating the Pareto frontier: Hydropower dam placement in the Amazon basin

Real–world problems are often not fully characterized by a single optimal solution, as they frequently involve multiple competing objectives; it is therefore important to identify the so-called Pareto frontier, which captures solution trade-offs. We propose a fully polynomial-time approximation scheme based on Dynamic Programming (DP) for computing a polynomially succinct curve that approximates the Pareto frontier to within an arbitrarily small > 0 on treestructured networks. Given a set of objectives, our approximation scheme runs in time polynomial in the size of the instance and 1/. We also propose a Mixed Integer Programming (MIP) scheme to approximate the Pareto frontier. The DP and MIP Pareto frontier approaches have complementary strengths and are surprisingly effective. We provide empirical results showing that our methods outperform other approaches in efficiency and accuracy. Our work is motivated by a problem in computational sustainability concerning the proliferation of hydropower dams throughout the Amazon basin. Our goal is to support decision-makers in evaluating impacted ecosystem services on the full scale of the Amazon basin. Our work is general and can be applied to approximate the Pareto frontier of a variety of multiobjective problems on tree-structured networks.

Conference Paper↗

USGS2017

Dynamic optimization of landscape connectivity embedding spatial-capture-recapture information

Maintaining landscape connectivity is increasingly important in wildlife conservation, especially for species experiencing the effects of habitat loss and fragmentation. We propose a novel approach to dynamically optimize landscape connectivity. Our approach is based on a mixed integer program formulation, embedding a spatial capture-recapture model that estimates the density, space usage, and landscape connectivity for a given species. Our method takes into account the fact that local animal density and connectivity change dynamically and non-linearly with different habitat protection plans. In order to scale up our encoding, we propose a sampling scheme via random partitioning of the search space using parity functions. We show that our method scales to realworld size problems and dramatically outperforms the solution quality of an expectation maximization approach and a sample average approximation approach.

Conference Paper↗

USGS2016

Trade-offs and efficiencies in optimal budget-constrained multispecies corridor networks

Conservation biologists recognize that a system of isolated protected areas will be necessary but insufficient to meet biodiversity objectives. Current approaches to connecting core conservation areas through corridors consider optimal corridor placement based on a single optimization goal: commonly, maximizing the movement for a target species across a network of protected areas. We show that designing corridors for single species based on purely ecological criteria leads to extremely expensive linkages that are suboptimal for multispecies connectivity objectives. Similarly, acquiring the least-expensive linkages leads to ecologically poor solutions. We developed algorithms for optimizing corridors for multispecies use given a specific budget. We applied our approach in western Montana to demonstrate how the solutions may be used to evaluate trade-offs in connectivity for 2 species with different habitat requirements, different core areas, and different conservation values under different budgets. We evaluated corridors that were optimal for each species individually and for both species jointly. Incorporating a budget constraint and jointly optimizing for both species resulted in corridors that were close to the individual species movement-potential optima but with substantial cost savings. Our approach produced corridors that were within 14% and 11% of the best possible corridor connectivity for grizzly bears (Ursus arctos) and wolverines (Gulo gulo) , respectively, and saved 75% of the cost. Similarly, joint optimization under a combined budget resulted in improved connectivity for both species relative to splitting the budget in 2 to optimize for each species individually. Our results demonstrate economies of scale and complementarities conservation planners can achieve by optimizing corridor designs for financial costs and for multiple species connectivity jointly. We believe that our approach will facilitate corridor conservation by reducing acquisition costs and by allowing derived corridors to more closely reflect conservation priorities.

Conservation Biology↗