Tracing the Source

Using unique data and sophisticated models, one science team seeks to inform policy development to address harmful algal blooms in northern Spain by answering the question: where did that shit* come from? (*Literally. Human and animal waste is a huge contributor of excess nutrients to the environment… so is synthetic fertilizer, but it’s harder to make a catchy joke about that.)

 

Paper Citation: Alvarez, et al., (2017) “Anthropogenic nutrients and eutrophication in multiple land use watersheds: Best management practices and policies for the protection of water resources.” Land Use Policy, 69:1-11.

 

Excessive nutrient loading, and the resulting impacts to both fresh- and salt-water bodies including dead zones and harmful algal blooms (HABs), is a growing problem around the globe and one that is a projected to get worse as the climate continues to change (see last post here). Addressing the problem is complex, and while the challenge is global, solutions will invariably cross scales and ultimately be local, responding to the unique mix of water and nutrient contaminants and sources within particular watersheds. Thus, much effort is being put toward not only the big picture science and the scientific data and tools needed to inform solutions, but also the specific conditions and possible solutions given the local context.

 

The study by Alvarez et al. addresses both of these points: the local conditions, but also the tools and data needed to drive comprehensive solution development. The study area focuses on the A Baxe reservoir and Umia catchment in Northern Spain. Utilizing a series of models that capture the spatial distribution of land use and land cover for the watershed, the rainfall and runoff dynamics, and the resulting nutrients loads, the present study predicts the “critical source areas” or hotspots of nutrient loading within the watershed.

 

Not surprisingly, the study found that “diffuse” sources were the overwhelming contributor of problem nutrients to the system. Diffuse sources include agricultural systems and practices like crop and livestock production or soil loss. Small overall nutrient contributions were also made by sewage discharge from human settlements from what are known as “point source” locations, typically literal pipes flowing into the water way.

 

Perhaps most interesting about this study is the hot spot analysis, that is, the spatial distribution of nutrient sources that the researchers were able to assign to particular parts of the watershed. This was made possible by the data and models available to the research team and the local context of their work.

 

Specifically, the European Union (EU) requires the monitoring of HABs and also lays out a number of frameworks for addressing them. Within the local context of the study this has led to the installation of a distributed network of sensors to monitor the concentration of nitrogen and phosphorus (the two nutrients most commonly responsible for HABs) throughout the basin. The authors use this data to both calibrate and validate the output of their models giving confidence in the results. The information can now be used to address the problem. 

 

Given the modeled findings, the authors assert that strategies for addressing the problem must be holistic, taking into account factors across the watershed and will benefit from the jurisdiction provided by broader EU regulatory frameworks. In this case, as in many others, identifying the source is just the beginning of the process. Making sure that farmers, urban dwellers, and others across the landscape benefit and move forward together toward solutions that work is equally if not more challenging. 

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E.M.B. Doran

E.M.B. Doran

Dr. Doran is a Postdoctoral Associate with the VT EPSCoR Basin Resilience to Extreme Events (BREE) project where she is conducting research at the interface of land use and land cover (LULC) change, water quality, and human decision making and policy. Her other research interests include urban climate, energy use and using systems science and modeling techniques to inform decision making under uncertainty.

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