Hydroclimatology of the U.S. Gulf Coast Under Global Climate Change Scenarios

2011 ◽  
Vol 32 (6) ◽  
pp. 561-582 ◽  
Author(s):  
Barry D. Keim ◽  
Royce Fontenot ◽  
Claudia Tebaldi ◽  
David Shankman
Author(s):  
Aaiysha Khursheed ◽  
George Simons ◽  
Brad Souza ◽  
Jennifer Barnes

Over the past few decades, interest in the effects of greenhouse gas (GHG) emissions on global climate change has peaked. Increasing temperatures worldwide have been blamed for numerous negative impacts on agriculture, weather, forestry, marine ecosystems, and human health. The U.S. Environmental Protection Agency reports that the primary GHG emitted in the U.S. is carbon dioxide (CO2), most of which stems from fossil fuel combustion [1]. In fact, CO2 represents approximately 85% of all GHG emissions nationwide. The other primary GHGs include nitrous oxide (N2O), methane (CH4), ozone (O3), and fluorinated gases. Since the energy sector is responsible for a majority of the GHGs released into the atmosphere, policies that address their mitigation through the production of electricity using renewable fuels and distributed generation are of significant interest. Use of renewable fuels and clean technologies to meet energy demand instead of relying on traditional electrical grid systems is expected to result in fewer CO2 and CH4 emissions, hence reducing global climate change impacts. Technologies considered cleaner include photovoltaics, wind turbines, and combined heat and power (CHP) devices using microturbines or internal combustion engines. The Self-Generation Incentive Program (SGIP) in California [2] provides incentives for the installation of these technologies under certain circumstances. This paper assesses the GHG emission impacts from California’s SGIP during the 2005 program year by estimating the reductions in CO2 and CH4 released when SGIP projects are in operation. Our analysis focuses on these emissions since these are the two GHGs characteristic of SGIP projects. Results of this analysis show that emissions of GHGs are reduced due to the SGIP. This is because projects operating under this program reduce reliance on electricity generated by conventional power plants and encourage the use of renewable fuels, such as captured waste heat and methane.


Nature ◽  
2002 ◽  
Vol 416 (6881) ◽  
pp. 626-629 ◽  
Author(s):  
A. Townsend Peterson ◽  
Miguel A. Ortega-Huerta ◽  
Jeremy Bartley ◽  
Victor Sánchez-Cordero ◽  
Jorge Soberón ◽  
...  

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6731 ◽  
Author(s):  
Lili Tang ◽  
Runxi Wang ◽  
Kate S. He ◽  
Cong Shi ◽  
Tong Yang ◽  
...  

Background As global climate change accelerates, ecologists and conservationists are increasingly investigating changes in biodiversity and predicting species distribution based on species observed at sites, but rarely consider those plant species that could potentially inhabit but are absent from these areas (i.e., the dark diversity and its distribution). Here, we estimated the dark diversity of vascular plants in China and picked up threatened dark species from the result, and applied maximum entropy (MaxEnt) model to project current and future distributions of those dark species in their potential regions (those regions that have these dark species). Methods We used the Beals probability index to estimate dark diversity in China based on available species distribution information and explored which environmental variables had significant impacts on dark diversity by incorporating bioclimatic data into the random forest (RF) model. We collected occurrence data of threatened dark species (Eucommia ulmoides, Liriodendron chinense, Phoebe bournei, Fagus longipetiolata, Amentotaxus argotaenia, and Cathaya argyrophylla) and related bioclimatic information that can be used to predict their distributions. In addition, we used MaxEnt modeling to project their distributions in suitable areas under future (2050 and 2070) climate change scenarios. Results We found that every study region’s dark diversity was lower than its observed species richness. In these areas, their numbers of dark species are ranging from 0 to 215, with a generally increasing trend from western regions to the east. RF results showed that temperature variables had a more significant effect on dark diversity than those associated with precipitation. The results of MaxEnt modeling showed that most threatened dark species were climatically suitable in their potential regions from current to 2070. Discussions The results of this study provide the first ever dark diversity patterns concentrated in China, even though it was estimated at the provincial scale. A combination of dark diversity and MaxEnt modeling is an effective way to shed light on the species that make up the dark diversity, such as projecting the distribution of specific dark species under global climate change. Besides, the combination of dark diversity and species distribution models (SDMs) may also be of value for ex situ conservation, ecological restoration, and species invasion prevention in the future.


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