Utilization of GIS in Analysis of Climate Change Predictions for Santa Cruz County

Kelsea Ranks

GEOG 350   Fall 2021  Veisze

Abstract

Comparison of average maximum temperatures from the current decade and predicted average maximum temperatures for the middle and end of the century.

Introduction

Climate change is a present and persistent threat to California’s diverse ecosystems. Ecosystems such as the coastal redwood forests in Santa Cruz County are particularly at risk. As the impacts of climate change are observed across the state, new methods of predicting and tracking these changes are emerging (Cowell & Zeng, 2003). GIS has become a staple in documenting the effects of climate change. Comparison of GIS data allocated at different times display the changes happening in the state each year. From decreased precipitation days and higher precipitation amounts to higher average temperatures to increasing instances and sizes of natural disasters, climate change is altering California’s ecosystems in adverse ways (Langridge, 2018). The utilization of GIS is key in predicting and preparing for the future impacts of climate change.

Background

I lived in Santa Cruz from 2015 to 2019 when I attended university there. I came to love the redwood forests around the campus and often wondered what the changing climate would mean for the area. I experienced the typical mild weather that the region is known for, but I also experienced weather anomalies. I remember a summer day in 2018 where the temperature in downtown Santa Cruz was over 100 degrees, a rarity, but a situation that could become more common in the future as temperatures are predicted to rise. Historically, the average maximum temperature in Santa Cruz County is around 67.5 degrees, but climate models predict that the average maximum temperature by the end of the century could be 71.9 to 74.5 degrees (Langridge, 2018).

Methods

To detail the use that GIS has in modelling climate change data, the following maps were created in ArcGIS Pro using data from the California Heat Assessment Tool (CHAT) (See figures 1-3). Figure 1 displays average maximum temperature data from 2011 combined with data predicted until 2030. And figures 2 and 3 display predicted maximum temperatures for 2051-2070 and 2080-2099, respectively. These future time periods were chosen because the represented the middle of the century and the end of the century. The county of Santa Cruz is divided into its census tracts to better reflect the temperature changes throughout the county.

 

 

Results

Figure 1

Map

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Figure 2

Map

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Figure 3

Map

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Analysis

The available data already showed that temperatures were expected to increase over time, but the utilization of GIS allows for the data to be visualized in a way that makes the temperature changes more obvious. I was surprised to see that some tracts had a decrease in temperature over the course of the century. Opposed to the tracts in the county that had lower average maximum temperatures, which showed an increase in temperature over the century. For instance, in the area of downtown Santa Cruz, the average maximum temperature is 79.9 currently, but is predicted to rise to 83 degrees at the end of the century. The gap between the coolest and hottest areas of the county is predicted to decrease from a difference of almost 20 degrees to around 16 degrees at the end of the century.

Conclusions

GIS software like ArcGIS Pro is incredibly helpful in creating visuals out of data that is hard to visualize. The maps created for this project provide a useful way to visualize changing climate data side-by-side. By creating GIS climate data, counties in California are better able to predict where in their jurisdictions action is needed to mitigate or prepare for the effects of a changing climate.

References

(n.d.). Retrieved from California Heat Assessment Tool: https://www.cal-heat.org/explore

Chornesky, E. A., Ackerly, D. D., Beier, P., Davis, F. W., Flint, L. E., Lawler, J. J., . . . Weiss, S. B. (2015, March). Adapting California's Ecosystems to a Changing Climate. BioScience, 65(3), 247-262. Retrieved 10 2, 2021, from https://academic.oup.com/bioscience/article/65/3/247/237354

Cowell, P. J., & Zeng, T. Q. (2003). Integrating Uncertainty Theories with GIS for Modeling Coastal Hazards of Climate Change. Marine Geodesy, 5-18.

Langridge, Ruth. (University of California, Santa Cruz). 2018. Central Coast Summary Report.
California’s Fourth Climate Change Assessment. Publication number: SUM-CCCA4-2018-006.