Thehypothesis of this study is that effect of increasing (or decreasing)the proportion of human versus natural land covers in the watershedon eutrophication. The data provided supports this hypothesis as theyexplore the different variables under the study such as water qualityand nutrient levels.
Thewatersheds with the largest human activity include Lansing, devils,moon and margethe and Christiana watersheds. The common humanactivity in these watershed include urban and agriculture, andforest. The watersheds with least human activity include Indian, andIndian BW.
Itis important to consider multiple water quality metrics and multipleyears of water quality data when making an assessment of lake waterquality. This is mainly because the concentrations of the indicatorsin the lake vary within the specific seasons, from one year toanother. Hence it is advisable to collect multiple samples during theperiod of monitoring the quality of water. The variations from onelake to another arise due to different factors that affect theindicators such as temperature, amount and concentration of nutrientsavailable. In order to solve the problem, the samples of the variousindicators should be taken through the different seasons.
Thereis interesting relationship that is shown between land cover andwater quality. The level of human activity is high owing to thevarious land use activities in which they are involved. Theactivities include agriculture, urban and forests. In urban areas thewater seems to be disturbed by pollution as there is water run-off aswell as burst sewer lines. Agricultural activities are mainly foundin the wetlands and rangeland areas however, they affect these areasthrough the use of fertilizers, erosion and pesticide applications.The deforestation in the forested areas has major impact on thequality of water. The clearing of forests reduces nutrient retentionand interferes with carbon cycle this ultimately leads to erosionhence affecting water quality.
Increasingthe sample size increases the accuracy of the results presented.Increasing the number of lakes increases the strength of predictivepower of the relationships depicted in the graph. Increasing thesample size increases the relationship between agriculture versuswater quality variable. Further there is also an increase in therelationship of forest and urban versus water quality variable. Theincrease in sample size has an effect in that it increases theresults through error minimization and uncertainty. The results caneasily be generalized owing to the high confidence level hence easilyapplicable outside the sample space.
Thefirst category of high and low land use in urban development shouldbe represented in a pie chart. The representation is easy to make andcan easily be interpreted owing to the visual appeal that theypresent. The pie chart however oversimplifies information hence someinformation can easily be lost in the process. On the other hand, thesecond case is better represented in a line graph due to thecontinuity in gradient as well as the range of data, that is, dataspanning scale from 0% to 100%. The line graphs are precise andaccurate hence give information with high precision.
Thereis a strong direct positive correlation between nutrientconcentrations (total phosphorous) and algae abundance in the lake.It is shown that an increase in nutrient concentration leads to anincrease in algae in the lake this is due to increase in the generalbiological productivity of the system. The increase in the level ofnutrient concentration (phosphorous) translates to an increase inwater quality.