GIS and Coastal Nutrients Luke Cole

Transcription

1 GIS and Coastal Nutrients Luke Cole Human population density has been widely utilized as a valid predictor of terrestrial nitrogen loads into marine systems. As 50% of the world s population lives within 200 kilometers of the coastline, many studies have examined the effects of humans on the marine environment. Activities resulting in a release of materials (nutrients, metals, water) into a watershed can potentially affect the water quality in the receiving waters. Freshwater traveling through the watershed will carry the imprint of the processes affecting the watershed as a whole, and greatly influence the water quality in the receiving waters. Many coastal watersheds in the United States have been subject to a coupling of increasing human population and intensive agriculture growth in the last 25 years. With an increase in human population comes an increase in wastewater treatment plant (WWTP) discharge and septic system discharge. These point and non-point sources have been reported as significantly increasing nitrogen flux into marine systems (Nixon et al. 1995). With the continued input and growing magnitude of nutrient fluxes into marine systems, the need to understand the processes involving these fluxes has grown increasingly more important. Most methods of assessing nutrient inputs to a coastal watershed are based on area-weighed measurements, and until recently, these areas had to be estimated or measured using a planimeter. With the advent of GIS, however, these models now have the ability to a) be corrected based on the more accurate geographical measurements, and b) provide more accurate data one the model is run. Based on observed literature, remote sensing is often the framework of a GIS measuring nutrients in the coastal environment (Swaney et al. 1996; Chaubey et al. 2000; White et al. 2003). Orthophotographs and/or satellite images are often the basis of nutrient analyses, and are usually converted into raster (e.g. DEM) images. From the raster format, elevation models are able to be made which will allow the researcher to analyze flow patterns, slope, etc. These nutrient analyses also require sharp, discrete boundaries (e.g. watershed boundaries), and these are made via spatial analysis of rasterized images. A majority of nitrogen models require information regarding land use within the watershed of study. Land use models generate coefficients based on the type and percent of land use. Many of these models rely on Anderson Level II classifications, and from these, estimates of nitrogen export can be made (Maidment 1993; Poiani et al. 1996; Swaney et al. 1996; Chaubey et al. 2000). A combination of land use coefficients, water residence time and water quality datasets are used in correlation with the remotely sensed data to provide estimates of nitrogen loading rates. These raster datasets provide a more watershed-based overview of nitrogen loading rates. The watershed is viewed

2 as a whole unit, breaking down land use and other raster data types into percentages rather than individual units. Vectorized models of nitrogen loading deviate from the raster models by looking at the properties of a water body or land parcel as a whole rather than as a composite of pixels. Maidment (1993) explains the use of stream order and direction in the application of estimating stream flux which eventually drains into a coastal system. In an analysis of nutrient discharge in a New York watershed, White et al. (2003) utilize vector data in predicting nitrogen discharge from point source locations and stream topology. This object based approach to estimating nutrient loadings can often provide smoking gun data as particular point sources are often able to be singled out in the analysis of resulting fluxes. For a broader picture, however, a combined approach can yield large amounts of temporal and spatial data. In compiling historical and current geographic data, as well as through using written historical records, some researchers are able to tell a story beyond locations and amounts of coastally discharged nutrients. Paul (2001) writes of geographical signatures in the middle Atlantic. In compiling historical land use data, soils, parcels, and elevation data, the author was able to estimate and confirm past land use practices and the resulting nutrient loads within the middle Atlantic. In a separate study, (Kaitala et al. 2002) describe the production of a GIS database for the entirety of the White Sea ecosystem in northwest Russia. This study incorporated vector and raster data utilizing historic and present nutrient loading estimates and water quality measurements. The data analyzed by Paul and Kaitala et al. (2001, 2002 respectively) elucidate the importance and significance of GIS in analyzing large geographical areas. GIS has made the analysis of entire ecosystems possible by allowing for the compilation and analysis of previously incompatible datasets. Twenty years ago, estimating the impact of a chicken farm on an entire marine ecosystem was all but impossible and would have required days of running mathematical models and likely weeks and months of resulting data analysis. Models generated using GIS parameters are not only capable of explaining what has happened, but can serve as predictors of future nutrient loading. If forecasts of land use change or population flux are estimated and entered into nitrogen loading models, potential fluxes may be examined. Surely these models provide some merit to scientific and policy-based actions aimed to curb nutrient loading and are likely an excellent asset in preventing coastal eutrophication. Chaubey, I., P. Srivastava, et al. (2000). "Using GIS, remote sensing and water quality modeling to estimate animal waste pollution potential." Proc. Agricultural Water Quality and Quantity: Issues for the 21st Century: Kaitala, S., A. Shavykin, et al. (2002). Environmental GIS database for the White Sea. Open source GIS-GRASS users conference 2002, Trento, Italy.

3 Maidment, D. R. (1993). GIS and Hydrologic Modeling. Environmental Modeling with GIS. M. F. Goodchild, B. O. Parks and L. T. Steyaert. New York, Oxford University Press: Nixon, S. W., S. L. Granger, et al. (1995). "An assessment of the annual mass balance of carbon, nitrogen, and phosphorus in Narragansett Bay." Biogeochemistry 31: Paul, R. W. (2001). "Geographical Signatures of Middle Atlantic Estuaries: Historical Layers." Estuaries 24(2): Poiani, K. A., B. L. Bedford, et al. (1996). "A GIS-based index for relating landscape characteristics to potential nitrogen leaching to wetlands." Landscape Ecology 11(4): Swaney, D. P., D. Sherman, et al. (1996). "Modeling Water, Sediment and Organic Carbon Discharges in the Hudson-Mohawk Basin: Coupling to Terrestrial Sources." Estuaries 19(4): White, D. L., D. E. Porter, et al. (2003). "Spatial and temporal analyses of water quality and phytoplankton biomass in an urbanized versus a relatively pristine salt marsh estuary." Journal of Experimental Marine Biology and Ecology in press. Bib Chaubey, I., P. Srivastava, et al. (2000). "Using GIS, remote sensing and water quality modeling to estimate animal waste pollution potential." Proc. Agricultural Water Quality and Quantity: Issues for the 21st Century: As is the case in many coastal rural areas, agricultural non-point sources are often the largest contributors of nutrients to the coastal zone. The authors of this paper have utilized GIS technology to locate and approximate the impact of chicken farms on aquatic systems. This paper looks at the combination of GIS, modeling, and database development within the coastal and riverine zones. An enjoyable paper to read and provides a large amount of useful information. Kaitala, S., A. Shavykin, et al. (2002). Environmental GIS database for the White Sea. Open source GIS-GRASS users conference 2002, Trento, Italy. This review by Kaitala et al. was generated from conference proceedings where linking of ecological, environmental, and socio-economic databases were discussed. In this particular review, the authors give a brief, but well written article discussing the purpose, implementation, and linking of GIS databases for the White Sea ecosystem. Using the Digital Chart of the World and established nutrient databases, the authors compile the information into an ecosystem based

4 analysis. For the size of the ecosystem studied, this brief review provides major results with minor details. A fair assessment of a small-scale system. Maidment, D. R. (1993). GIS and Hydrologic Modeling. Environmental Modeling with GIS. M. F. Goodchild, B. O. Parks and L. T. Steyaert. New York, Oxford University Press: A very detailed and well written analysis of the connections and possible interactions between the worlds of GIS and hydrologic modeling. In describing the most effective means of translating field data into GIS cartography, the author highlights the necessity of GIS in analyzing physical, chemical and geological processes governing the inputs of water and nutrients into freshwater and marine systems. A hefty read, but very clear and detailed. Despite being written in 1993, many of the same GIS processes remain true and this paper is a clear description of the processes surrounding a well-made GIS project. Paul, R. W. (2001). "Geographical Signatures of Middle Atlantic Estuaries: Historical Layers." Estuaries 24(2): In analyzing the effects of humans on a coastal habitat, the author was able to produce a layered GIS database supported by historic records of land use and telltale field-measured values. In analyzing broad land use changes in the middle Atlantic, nutrient fluxes into the coastal zone were able to be approximated. The title of this paper as well as the abstract lead the reader to think the publication will be largely GIS-based. While verbose and dense, the paper tells a fascinating story despite an apparent lack of GIS methodology. Poiani, K. A., B. L. Bedford, et al. (1996). "A GIS-based index for relating landscape characteristics to potential nitrogen leaching to wetlands." Landscape Ecology 11(4): This publication by Poiani et al. shows the importance of a complete GIS database with which to perform analyses. By utilizing core datasets (soils, wetlands, land use) predictive models of nitrogen and phosphorus inputs were able to be produced. This paper goes through the processes of obtaining, modifying and presenting GIS datasets necessary for modeling nutrient fluxes and illustrates the importance of a GIS in formulating the framework of a model. This paper took a sharp turn towards groundwater modelling after the first few pages, but remained a useful and informative paper. Swaney, D. P., D. Sherman, et al. (1996). "Modeling Water, Sediment and Organic Carbon Discharges in the Hudson-Mohawk Basin: Coupling to Terrestrial Sources." Estuaries 19(4): Swaney et al. used this publication as a forum to improve upon an area and coefficient based sediment and nutrient loading model. GIS was used here to properly assess areal values for watersheds, stream lengths, etc. using nationally available data. The impact of this study elucidated the fact that established water quality-predicting evaluations, if areally based, can be greatly

5 improved using GIS technologies. This was a very interesting paper, and showed an approximate 25% improvement in the ability to predict discharge and nutrient loading to the aquatic system. White, D. L., D. E. Porter, et al. (2003). "Spatial and temporal analyses of water quality and phytoplankton biomass in an urbanized versus a relatively pristine salt marsh estuary." Journal of Experimental Marine Biology and Ecology in press. In a very large-scale study, White et al. analyze the effects of land use on an urban and pristine estuary in the Southeast US. The authors incorporate land use changes as well as water quality measurements in formulating a hypothesis relating nutrients and land use. This paper is well-written, concise and contains useful methods and figures. While heavy on statistical analyses, this publication is an excellent resource fur understanding the usefulness of a GIS in evaluating water quality parameters and nutrient loading.