Chesapeake Bay Challenge - Essay Example

First Prof. Chesapeake Bay Challenge Excess nutrients are among the ultimate water quality challenges that haunt the Chesapeake Bay. The excessive amount of nitrogen and phosphorus in bay waters are pollutants affecting the bay’s aquatic life and precious resources. To rectify the situation governmental units in the Bay region – Maryland, Pennsylvania, etc. as well as Chesapeake Bay Commission signed the Chesapeake Bay Agreement in 1983. The agreement marks the cooperative approach towards resolving the bay’s problems. To understand this problem in great detail, a hypothesis has been formulated that the amount of agricultural land in a county proportionately contributes to the depletion of oxygen levels in Chesapeake Bay. To prove this, data from Anne Arundel, Talbot, Dorchester, Calvert County, and their relative Chesapeake Bay areas, will be collected and compared. Excessive amounts of nutrients can cause the growth of microscopic plants algae. When its population explodes or thrives, the water quality depletes. The water itself appears cloudy and blocks sunlight to the aquatic plants (SAV) that need the precious sunlight for their survival. When they dont get this energy, they die. These aquatic plants provide food and shelter for Bays inhabitants. Fish and crabs are among the prime animals. Overall, the whole Bays habitat is damaged. Algae grows due to excessive nutrients. And preventing sunlight from reaching the aquatic plants is not the only problem they cause. By blocking the sunlight, they also reduce the oxygen levels in the water during certain months. This is especially true through the summertime as decaying algae starts to sink in the water. The microscopic animals and bacteria feed on these plants. This process consumes the limited oxygen present in the water. As a result of the dying algae and hot summers the oxygen levels in the Bays water start to decline, killing different organisms in the process. It is very difficult to find data on this topic. The Chesapeake Bay area is enormous and finding this complicated, and huge volume of data is a time-consuming process. The primary sources used to obtain data for this study were; • County Boundaries and Chesapeake Bay Boundary GIS files. • Agriculture Lands from Maryland Dept. of Planning • Maryland Land Use Land Cover from MD iMAP • Chesapeake Bay Dead Zones from Dept. of Natural Resources • Chesapeake Bay Oxygen levels from Dept. of Natural Resources First the research area was selected. Then a focused area was selected using ArcMap. This process revealed the permanently preserved agricultural lands. Talbot, Dorchester, Calvert and an Anne Arundel are the four primary agricultural areas. Later the priority preserved agricultural areas were identified such as Annapolis in Anne Arundel. The data revealed oxygen levels less than 0.2 mg/l in anoxic conditions. The common area between Calvert and Dorchester has this oxygen level (see map 1 below). Map 1 Similarly the oxygen levels of 0.2-2.0 mg/l were found in hypoxic conditions, as shown in the following map. Map 2 Map 3 The oxygen levels of 2-3 mg/l were found to be borderline lethal as shown in Map 4. Map 4 Oxygen levels of 3-5 mg/l stressful to some organisms can be located in the Map 5. Map 5 Acceptable levels of oxygen, which is greater than 5 mg/l can be located in Maps 6 and 7. Map 6 Map 8 shows the complete data. Methodology Geoprocessing is the fundamental aspect of Spatial Data Infrastructure (SDI). As more functions of this system are becoming available as web-based services, the quality is becoming more efficient in a distributed environment. The methodology used for this paper mainly comprised of kriging. It is a concept from statistics based in geostatistics. This technique depends on the method of interpolation of data. This interpolated data is modeled by Gaussian process. Covariances that are set in advance monitor this process. This is contrary to the piecewise polynomial spline that makes statistical interpretation easier. The methodology was carried out in five basic steps. First the data was clipped to match the extent of each test area. The areas were separated into four counties. Next the normalization of the distribution was performed by running the Geoprocessing. In this regards, Kriging was performed. Kriging is a complex process that demands greater knowledge of spatial statistics. The data needs to be appropriate to model this technique (Graaf 162). The best linear unbiased projection is used in spatial statistics through universal kriging. The basic idea behind kriging is to predict the value of a function at a particular time by computing the weighted average of known values of a function. The method is similar to regression analysis. Next the percentage of lethal levels (2 mg/l and below) were determined. Then the areas with the worst conditions were identified. The last step consisted of comparing with the land use and agricultural land area to see if there is a proportional pattern. Several image processing programs allow advanced tools for Geoprocessing (Jiang & Yao 232). Geostatistical models use an approach where the sample data gets interpreted as a consequence of random processing. There is uncertainty in these models, but it does not imply that phenomenon occurs as a random process. It gradually allows the researcher to formulate the methodological basis for spatial inference. To gauge other problems such as landslide susceptibility the researcher would probably have to collect slope data of the terrain as well as slope aspect, land cover and the vegetation by using Normalized Difference Vegetation Index (NDVI) data (Yue 4). The Geoprocessing algorithm obtained through geospatial services might handle only a small portion of the overall Geoprocessing, or it might expand on a large aggregate. In both cases the services should be precise, have clear input and output and should be independently executable (Peisheng 318). Such a service can be used again for constructing different geoprocessing workflows. Through the World Wide Web, there are several independent data providers. A complex geoprocessing system may be scattered through several service providers. Hence, the standards for publishing, finding, binding and executing services are required (Peisheng 318). By following the guidelines for interfaces, the interoperation of software is accomplished. Neural networks have been widely recognized for becoming a part of a wide variety of nonlinear statistical techniques, as well as decision trees and kernel methods (Williams 599). The US geological survey and the US Department of Interior issued a report identifying new methods for studying the problems at Chesapeake Bay. The methodology introduced is an upgrade because the old system needed flexible descriptions of change, needed an estimation concentration influx, needed estimates of the actual history and the requirement for being able to use the data for diagnostic purposes. It has been termed as “The Weighted Regression on Time Discharge and Season (WRTDS) with an application to Chesapeake Bay River inputs” (Hirsch, Moyer, 2013). There are some complexities with this system because the results it gives can differ substantially from estimations. WRTDS does not offer significance levels for trends, it implies that a change is a given. Just like economic indicators new information trigger some provision of recently results top Due to time constraints and limited resources this research is by no means a comprehensive study on the problem of Chesapeake Bay. For further study, it is recommended that the scope should be expanded to the entire Chesapeake Bay. In this regards a comparison between the Marilyn shorelines counties and Delaware would be helpful. In addition, identifying different runoffs from agricultural land to observe if a pattern emerges can diagnose the problem with area-wise precision. However limited this research is an eye-opener regarding the dangerously low levels of oxygen in the Chesapeake Bay area. References De Graaf, Gertjan. Geographic information systems in fisheries management and planning: technical manual. No. 449. Food & Agriculture Org., 2003. Bob Hirsch and Doug Moyer. Update on a New Statistical Tool for Chesapeake Bay Nontidal Network Data: Weighted Regressions on Time, Discharge, and Season WRTDS 2013. USGS. 2013. Jiang, Bin, and Xiaobai Yao, eds. Geospatial analysis and modelling of urban structure and dynamics. Vol. 99. Springer Science & Business Media, 2010. Williams, C. K. I. "Prediction with Gaussian Processes: From Linear Regression to Linear Prediction and Beyond". Learning in Graphical Models. 1998. pp. 599–621 Yue, Peng. Semantic web-based intelligent geospatial web services. Springer, 2013. Zhao, P. (Ed.). (2010). Geospatial Web Services: Advances in Information Interoperability: Advances in Information Interoperability. IGI Global. Read More