Guest post by Aaron Whittemore
Hi everyone! I am Aaron. I am a collaborator with FIRE Lab and am currently working as a science communications specialist after receiving my Master’s at Virginia Tech in the US. Today, I’m sharing newly published research that I led with a team of people from around the world to map our world’s instream infrastructure.
We are living in an increasingly data-rich world. It seems that everything is constantly being tracked and made easily available for our reference. In many ways this is fantastic.
For example, I can quickly find out what type of soil is lying beneath my feet without having to dig into the ground. I can even reference the daily low temperature in Honolulu, Hawaii from 50 years ago, or follow along the migration paths of tagged great white sharks. The ability to access such information and data allows for endless possibilities to advance science and decision making, but first, that data must exist.
Given all the other copious amounts of available data, one area that has been surprisingly lacking is the global cataloguing of instream infrastructure and river obstructions like dams, locks, and jetties. Several existing global datasets of instream infrastructure capture the largest of dams that create reservoirs behind them.
Overlooked are the thousands of other small structures that cumulatively have as much, if not more, impact on global river systems. Regardless of size, instream infrastructure obstruct water flow, altering riverscapes and preventing the travel of many aquatic species up or downstream. Having a global dataset of instream infrastructure could be extremely useful for scientists, engineers, conservationists, and policymakers for use in further study or decision making related to the world’s river systems.
From this need came the idea to create a new instream infrastructure database, the Global River Obstruction Database (GROD), housing locations of different types of river obstructions around the globe. After several years of hard work, we have released initial results for GROD detailed in a paper published in Earth’s Future. This initial paper overviews and validates our approach and releases results for the US.
Within the US, GROD initially catalogued 4,197 instream infrastructure which is more than 15 times that recorded in the Global Reservoir and Dam Database (GRanD), perhaps the most well known global dam database. These initial findings showcase the potential of GROD to expand the cataloguing of instream infrastructure worldwide. Further, GROD captures many of the smaller instream infrastructure that were missing in other available global data sets, such as low-head dams, locks, and partial dams (often jetties or structures extending partially across a waterway).
Not only is GROD a valuable stand alone database, but our approach for creating GROD has the potential to make further positive impacts in the science world. To identify global instream infrastructure, we created an application within Google Earth Engine (GEE) that allows people to follow river lines obtained from the Global River Widths from Landsat (GRWL) Database across global satellite imagery and mark any instream infrastructure that they see.
The GEE application was extremely simple to use, allowing GROD to take the form of a participatory science project. Participants from several different countries and many different backgrounds were recruited to both validate our methods and contribute to the collection of data for GROD. This allowed members of the public to become part of the scientific process and help us to better understand global rivers.
Michiel Jorissen, a GROD participant and author on our paper, was recruited via Twitter, and relished the opportunity to participate. “The GROD project offered an excellent front row view of the science behind the scenes,” he said.
The GROD approach also has potential to be expanded to address other existing knowledge and data gaps. For example, the GROD GEE application could be modified to catalogue other similar environmental variables of interest such as beetle-kill, fire, hurricane windfall, and more. Finally, the GROD data set could be used as training data for machine learning methods in the future, enabling even more of the smaller, overlooked instream infrastructure to be catalogued.
We hope the GROD project is representative of a direction that spatial science is taking and will continue to take in the future, which improves global data on environmentally important variables, creates new opportunities for scientists and decision-makers, and promotes participatory science.
FIRE LAB 