Improving hydropower choices via an online and open access tool

<div><p>This paper describes and validates the HydroCalculator Tool developed by Conservation Strategy Fund. The HydroCalculator Tool allows researchers, policy-makers and citizens to easily assess hydropower feasibility, by calculating traditional financial indicators, such as the levelized cost of energy, as well as greenhouse gas emissions and the economic net present value including emissions costs. Currently, people other than project developers have limited or no access to such information, which stifles informed public debate on electric energy options. Within this context, the use of the HydroCalculator Tool may contribute to the debate, by facilitating access to information. To validate the tool’s greenhouse gas calculations, we replicate two peer-reviewed articles that estimate greenhouse gas emissions from different hydropower plants in the Amazon basin. The estimates calculated by the HydroCalculator Tool are similar to the ones found in both peer-reviewed articles. The results show that hydropower plants can lead to greenhouse gas emissions and that, in some cases, these emissions can be larger than those of alternative energy sources producing the same amount of electricity.</p></div

Estimates on carbon emission from Abril et al. (2005) [3] and HCT.

<p>Estimates on carbon emission from Abril et al. (2005) [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0179393#pone.0179393.ref003" target="_blank">3</a>] and HCT.</p

Economic evaluation for hydropower plants (50-year time horizon).

<p>Economic evaluation for hydropower plants (50-year time horizon).</p

Major factors influencing reservoir GHG emissions.

<p>Major factors influencing reservoir GHG emissions.</p

Project evaluation for some hydropower plants in the Amazon basin (50-year time horizon).

<p>Project evaluation for some hydropower plants in the Amazon basin (50-year time horizon).</p

Illustration of heterogeneous cluster structures in two contexts (datasets).

<p>Each context corresponds to different data source (gene expression, ribosome profiling, proteomics etc.) describing the same set of biological samples. In the first context, there are two distinct clusters on the local level. In the second context, there is only a single local cluster. From the overall perspective, there are two global clusters defined by the combined behaviour across the two contexts.</p

Survival curves for global clusters in the breast cancer dataset from TCGA, using the model with 3 context-specific clusters and up to 18 global clusters.

<p>The differences between the survival curves are significant with <i>p</i> = 0.0382 using the log-rank test.</p

Consistency between local clustering results for different number of global clusters with 3 context-specific clusters, as measured by the ARI.

<p>The ARI values show several local optima. (a) Gene expression context. (b) DNA methylation context. (c) miRNA context. (d) RPPA context.</p

Consistency between global clustering results for different number of global clusters with 3 context-specific clusters, as measured by the ARI.

<p>Consistency between global clustering results for different number of global clusters with 3 context-specific clusters, as measured by the ARI.</p

ARI comparing global clustering of simulated datasets for varying values of <i>p</i> (see Fig 2).

<p>Each point corresponds to the corresponding algorithm applied to one dataset, the plot shows also the loess curve for each method. Higher values correspond to better agreement between the estimated cluster assignments and the true cluster membership.</p