Brendan Foley-Marsello 12/1/09 NRS 509 


GIS and Climate Change 


Global climate change is one of the most important issues facing the world today. The effects will be felt in almost all facets of society but especially in the natural world. Climate change is particularly frightening because of the uncertainty that surrounds it. Global climate temperatures have varied since the earth’s creation, but now our climate is warming faster than ever due to human means. Various problems are popping up all over the world that many say are results of this human induced climate warming. Figuring out if these problems are climate change related and predicting future problems is going to be increasingly more important as climate change continues. Geographic Information System (GIS) and remote sensing are starting to play a large role in making these predictions. Creating predictions that are as accurate as possible is a very important tool in predicting the effects of climate change and, if done properly, could give us a head start on solving these problems and possibly curbing them before they start. 


The number of already existing datasets for GIS makes it an ideal way to create climate change predictions. Many of these GIS datasets relate to issues affected by climate change like coastline, ecosystem and groundwater mapping. Most of these datasets contain current data but are great baselines for creating climate change maps. Datasets for climate change and temperatures have to be based on current trends and future predictions based on those trends. The most in-depth studies use multiple climate datasets that represent different predictions of the changing climate. This is important because climate change is hard to predict and predictions can vary greatly. By choosing multiple climate change models you can represent both the high and low ends of your final prediction. Though the results don’t seem as finite, at least you are giving a relatively accurate range of prediction. Just giving one result for an issue that is so complex doesn’t provide a very accurate representation. For example X. Zhang used both low and high climate change prediction in his study of Luohe River basin. He used existing climate predictions from the Special Report on Emissions Scenarios (SRES) and chose both scenarios A2 and B2 because he felt they best represented the high and low end of possible future climate change. This gave him varying results but at least he has confidence the true future result will fall in the range he established. (Zhang: 2007) This is far more believable then giving one finite final answer.

Using existing datasets to help make climate change predictions is effective and time saving because of the overlaying abilities of GIS. For instance Catherine Burns created a model that predicts shifts in mammalian species ranges as a result of a warming climate. This was done through a series of GIS overlays that helped simplify the project. First Burns and her coauthors overlaid an existing polygon dataset of species locations in a national park with a vegetation polygon dataset to see what attracted the species to the habitat. Next GIS was used to overlay the vegetation data that now contains species locations with a current climate map. This was able to show what vegetation polygons corresponded with what climate levels. With that figured out they also overlaid future climate predictions over the previous datasets and were able to hypothesize where the mammalian habitats will shift based on the changing climate. (Burns: 2003) This is an effective way to create climate change predictions and using preexisting datasets will keep the cost of the project down. Though some new dataset generally have to be made, utilizing existing sets will streamline the project and allow more money to be used in other areas of the analysis. Combined with GIS, remote sensing is a great way to create climate change datasets that can be used for many types of analysis. The ability of remote sensing to detect changes in vegetation though electromagnetic radiation (EMC) allows us to see the beginning of the effects of human induced climate change. EMC allows us to see where some types of vegetation are flourishing while others are dying out. Using GIS to overlay these new EMC vegetation datasets against historical datasets will show where vegetation and ecosystems are shifting and allow us to make predictions about animals and other organisms that rely on these habitats. This will be particularly helpful for organisms like trees that have a hard time shifting their range and ecosystem due to their poor dispersal abilities. Remote sensing is become more and more useful in climate change applications due to improving technology. As spatial and spectral resolutions of sensors become more advanced, the datasets derived from them will be more in-depth and provide more detailed results. In the future remote sensing may be able to use EMC to detect actual individual organisms in a given area, allowing for a more direct connection. But for now the indirect data remote sensing provides can be very helpful. Line datasets can also be a very helpful tool in predicting the consequences of global climate change. Polygon datasets work well for overlaying various habitats and vegetation zones, but line datasets work well for predicting climatic events like sea level rise and flooding. Datasets that show current sea levels can be overlaid against historic datasets and from that we can see the magnitude of sea level rise to this point. The current and historic data provide a great starting point and baseline for predicting future sea level rise. Climate change data can be overlaid against these datasets to make future predictions based on past sea level rise and the climactic conditions that

accompanied them. This is a helpful tool in areas including coastal development. Land use polygon data sets can also be overlaid what effect rising sea levels will have on various residential, commercial and ecological land zones. (Thumerer: 2000) Overall GIS and remote sensing are playing a very important role in climate change predictions, but the potential these technologies show could allow for much more accurate future predictions. I feel that as technology gets better, more specific results will be able to be derived. Since we are now only starting to accept that current climate change is accelerated by human means, issues related to climate change are still a very new concept. As GIS and remote sensing technology continues to increase, new ways to analyze climate change data will emerge. I feel that as predicting issues related to climate change become more important to the public, more time and money will be put into tailoring GIS and remote sensing technology to better assist climate change modeling. The government also needs to play a larger role in creating these predictions. Privately funded studies can only do so much due to financial restraints, but that would not be as big a deal if the government was more involved. GIS and remote sensing technology has come so far in the last fifty years, it will be amazing to see what levels it will reach during the next fifty. Though there is still room to improve, without GIS and remote sensing we would not be anywhere near where we are today with regards to climate change modeling. 
 


Annotated Bibliography Burns, Catherine E., Kevin M. Johnston and Oswald J. Schmitz. “Global Climate Change and Mammalian Species Diversity in U.S. National Parks.” Proceedings of the National Academy of Science of the United States of America. September 30, 2003. Vol. 100, No. 20. Pg. 11474-11477.

This study uses GIS technology to map various species in the United States national parks and applies climate change models to the mammalian reaction to a warming climate. The authors feel that mapping these range shifts will show what species habitat will no longer fall within the national park where it now resides. This is a major ecological problem because the land outside of many national parks is developed and is no longer a suitable habitat for wild mammals. This study is also helpful because it gives us an idea of how range shifts will affect species outside the parks. This study

was done by taking an existing climate change model developed by the Vegetation/Ecosystem Modeling and Analysis Project (VEMAP) and used GIS to overlay various animal habitat polygons over the climate change zones. The author’s compiled data for the current distributions of 213 mammalian species that best represent the taxonomic orders within the United States. They also compiled a database of ecosystem locations within national parks and overlaid these against maps of different levels or predicted warming based on atmospheric carbon dioxide levels. So the model based specie gains and losses on how the vegetation associated with their known ecosystem shifted in times of an increasingly warming climate. Through various GIS overlays, the study found that in a time of doubled atmospheric carbon dioxide levels, all United States national parks stand to lose between 0% and 20% of their current mammalian species diversity. The average species diversity loss for all parks is 8.3%. It’s a scary thought that we could lose twenty percent of the species diversity in some national parks, but this study is very helpful because it could help predict what might happen and allow for preventative action.

Iverson, Louis R. and Anantha M. Prasad. “Prediction Abundance of 80 Tree Species Following Climate Change in the Eastern United States.” Ecological Monographs. (1998) Vol. 68, No. 4. Pg. 465-485.

In this study the authors look at what would happen to 80 different tree species in the Eastern United States as a result of projected climate change. To establish a base line, current tree species information and information about other factors that influence species ranges were entered into a GIS database. They used GIS as well as regression tree analysis to develop a working database that predicts tree range movements based on various climatic data variables. Historical data was gathered and entered from the Eastern United States as well as other regains that share a similar climate. Data was used from more than 2100 counties east of the 100th meridian and from more than 100,000 forested plots in the Eastern United States that show tree species ranges. The model was then run under two scenarios of climate change with a two-fold increase in atmospheric carbon dioxide levels. The model showed that 30 or so tree species could expand their range by at least 10%. It also showed 30 or so species could decrease their range by at last 10%. The model also showed that 4-9 species could leave the United States to the north completely and 36 species could shift their range at least 100km to the north. If nothing else, this study showed that tree species ranges in the Eastern United States will shift and for the most part they will shift northward with warming temperatures. Exactly what will happen is only a predictions but this study is a great guide for what could happen and what we should prepare for.

Kienast, Felix., et al. “Long-Term Adaptation Potential of Central European Mountain Forests to Climate Change: A GIS-assisted Sensitivity Assessment.” Forest Ecology and Management. Vol. 80, Issues 1-3, January 1996. Pg. 133-153.

This article documents a study of the impacts of a warming climate on forests in Switzerland. The authors took 11,000 Swiss Forest Inventory Points and used a GIS database to ally different climate change scenarios. What was created was a spatially explicit forest community model that shows current vegetation levels and what would happen if various climate change models were applied. The Bayes formula was used to construct this model. The model was run with both “moderate climate change” and “strong climate change” variables. Also both of these categories were run with the mean overall annual temperature increase and July temperature increase. The study showed that considerable variance was shown in 15 potential forest types under both moderate and strong climate change models. It shows that adaptation to climate change is going to be very important in the future. It was found that 25-30% of the forest will be poorly adapted to change and will suffer considerably from climate change. This was a very interesting study to get insight into what could or will happen in the future.

Thomson, Allison M., et al. “Climate Change Impacts for the Conterminous USA: An Integrated Assesment. Part 4: Water Resources.” Climate Change. (2005) Vol. 69. Pg. 67-88.

This study looks at how the United States hydrological cycle will be impacted by global climate change. Changing climatic conditions and temperatures will impact the ability of the atmosphere to retain moisture. This will then affect precipitation patters in the United States. The hypothesis was that increased evaporation would occur at low latitudes while precipitation would increase at middle and higher latitudes. Twelve different climate change scenarios were applied to the Hydrologic Unit Model of the United States (HUMUS) to simulate different levels of water supply. HUMUS is a GIS based tool that provides input data that drives the Soil and Water Assessment Tool (SWAT) hydrology model. Hydraulic cycles were simulated at the scale of the 8-digit USGS hydrologic unit areas (HUA). Various input data sets that are affected by precipitation levels were assembled for the United States at the scale of 1:250,000 and imputed into the HUMUS system. The results varied by different parts of the county and

provided different results. In some locations the hypothesis was confirmed and in others it was not. Overall it showed that climate change will affect the United States hydrological cycle but to what extent is hard to tell. This study, like many that relate to climate change, shows the unpredictability of climate change modeling and how these results are best used as a tool for future planning.

Thumerer, T., A.P. Jones and D. Brown. “A GIS Based Coastal Management System for Climate Change Associated Flood Risk Assessment on the East Coast of England.” International Journal of Geographical Information Science. Volume 14, Issue 3. January 2000. Pg. 265-281.

This study looked at potential sea level rise that could occur along the east coast of England due to climate change. It looked primarily into the flooding potential associated with climate change and rising sea levels. GIS data was used to map current sea levels as well as coastal land use to see the potential impact of flooding. The model looked at potential future flooding between the years 2050-2100. They used the “house equivalent” concept as a way to assess coastal damage from flooding in monetary terms. The hope of the authors of this study was to someday create a Decision Support System to help made decisions about future coastal development. This would give guidance to someone looking to build near the coast and let them know what they might see in the future as far as sea level rise and flooding. The model they created did predict significant sea level rise by the year 2100 and should be a sign that more coast line mapping should be done in the future to aid in coastal development.

Zhang, X., R. Srinivasan and F. Hao. “Predicting Hydrologic Response to Climate Change in the Luohe River Basin Using the SWAT Model.” American Society of Agriculture and Biological Engineers. (2007) Vol. 50, No. 3. Pg. 901-910.

This article looks at the effect of future climate change on stream flow in the Luohe River Basin. The study used the Soil and Water Assessment Tool (SWAT) to model stream flow. SWAT is a physically based distributed hydrological model. Historic stream flows from 1992-1996 were used to calibrate the model. The model was then checked against stream flows from 1997-2000 and it showed that they calibration worked well enough to model daily stream flows. For the climate change variables, two Special Report on Emissions Scenarios (SRES) were used. Scenarios A2 and B2 were

chosen because they represented two ends of the spectrum. Also two general circulation models, HadCM3 and CGCM2, where used in the stream flow model. The model predicted little change in the stream flow up until 2020. It did show an increase of 10% by the year 2050, though there is a 20% variance associated with these figures. So this model shows that the stream flow increase in the Luohe River basin will be relatively small. The authors do stress that this is only a prediction and due to the uncertainty of the climate change models, these results should be considered as a tool and not used as an absolute fact.