This article presents a statistical and visual comparison of water quality changes caused by a large river restoration project. Since water quality data are often shown as a non-normal distribution with high seasonal variations, appropriate statistical methods should be selected according to data characteristics for accurate scientific decision-making. In this study, a normality test was first performed using the Shapiro–Wilk test and two statistical comparison tests were then performed, including the paired T-test and the sign-test. Seasonality was considered by comparing monthly data pairs. In addition, a diagonal pair comparison plot was proposed as a visual comparison method. This plot is a graphic data display, where monthly paired water quality is represented by X–Y coordinates. From this study, it was concluded that the series of statistical and visual methods would be suggested for comparison of non-normally distributed water quality with high seasonal variations.
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References
1.
AhnJ.M., LeeS., and KangT.S. (2014). Evaluation of dams and weirs operating for water resource management of the Geum River. Sci. Total Environ. 478, 103.
2.
BainbridgeZ.T., WolanskiE., Alvarez-RomeroJ.G., LewisS.E., and BrodieJ.E. (2012). Fine sediment and nutrient dynamics related to particle size and floc formation in a Burdekin River flood plume, Australia. Mar. Pollut. Bull. 65, 236.
3.
BoyerJ.N., FourqureanJ.W., and JonesR.D. (1999). Seasonal and long-term trends in the water quality of Florida bay (1989–1997). Estuaries, 22, 417.
4.
ChaY.K., ParkS.S., LeeH.W., and StowC.A. (2016). A Bayesian hierarchical approach to model seasonal algal variability along an upstream to downstream river gradient. Water Resour. Res. 52, 348.
5.
CharlesJ.C., and TerryL.Z. (1992). The specification and power of the Sign Test in event study hypothesis tests using daily stock returns. J. Financ. Quant. Anal. 27, 465.
6.
DufourJ.M., FarthatA., GardiolL., and KhalafL. (1998). Simulation-based finite sample normality test in linear regression. Economet. J. 1, 154.
7.
HamedK.H. (2008). Trend detection in hydrologic data: The Mann-Kendall trend test under the scaling hypothesis. J. Hydrol. 349, 350.
8.
HelselD.R., and HirschR.M. (1992). Statistical methods in water resources. In Studies in Environmental Science. New York: Elsevier, p. 49.
9.
HickmanR.E., and HirschR.M. (2017). Trends in the Quality of Water in New Jersey Streams, Water Years 1971–2011: U.S. Geological Survey Scientific Investigations Report 2016-5176, p. 58. https://doi.org/10.3133/sir20165176.
10.
HirschR.M., ArchfieldS.A., and De CiccoL.A. (2015). A bootstrap method for estimating uncertainty of water quality trends. Environ. Modell. Softw. 73, 148.
11.
HirschR.M., MoyerD.L., and ArchfieldS.A. (2010). Weighted regression on time, discharge, and season (WRTDS), with an application to Chesapeake Bay River inputs. J. Am. Water Resour. Assoc. 46, 857.
12.
HomokyW.B., SevermannS., McManusJ., BerelsonW.M., RiedelT.E., StathamP.J., and MillsR.A. (2012). Dissolved oxygen and suspended particles regulate the benthic flux of iron from continental margins. Mar. Chem. 134–135, 59.
13.
Intergovernmental Panel on Climate Change (IPCC). (2013). Climate Change 2013: The Physical Science Basis. Cambridge: Cambridge University Press.
14.
JungB.J., JeanneauL., AlewellC., KimB., and ParkJ.H. (2015). Downstream alteration of the composition and biodegradability of particulate organic carbon in a mountainous, mixed land-use watershed. Biogeochemistry, 122, 79.
15.
KimS.H. (2018). Drought and weir construction impact stationarity assumption in watershed water quality modeling in South Korea. Ecol. Inform. 45, 38.
16.
KroonF.J., KuhnertP.M., HendersonB.L., WilkinsonS.N., Kinsey-HendersonA., AbbottB., BrodieJ.E., and TurnerR.D.R. (2012). River loads of suspended solids, nitrogen, phosphorus and herbicides delivered to the Great Barrier Reef lagoon. Mar. Pollut. Bull. 65, 167.
17.
LeeH.W., BhangK.J., and ParkS.S. (2010). Effective visualization for the spatiotemporal trend analysis of the water quality in the Nakdong River of Korea. Ecol. Inform. 5, 281.
18.
LeeH.W., KimE.J., ParkS.S., and ChoiJ.H. (2012). Effects of climate change on the thermal structure of lakes in the Asian Monsoon Area. Clim. Change, 112, 859.
19.
LeeH.W., KimE.J., ParkS.S., and ChoiJ.H. (2015). Effects of climate change on the movement of turbidity flow in a stratified reservoir. Water Resour. Manag. 29, 4095.
20.
LeeH.W., and ParkS.S. (2013). A hydrodynamic modeling study to estimate the flushing rate in a large coastal embayment. J. Environ. Manage. 115, 278.
21.
LeeR., QianM., and ShaoY. (2014). On rotational robustness of Shapiro-Wilk type tests for multivariate normality. Open J. Stat. 4, 964.
22.
NaddeoV., ScannapiecoD., ZarraT., and BelgiomoV. (2013). River water quality assessment: Implementation of non-parametric tests for sampling frequency optimization. Land Use Policy, 30, 197.
23.
RazaliN.M., and WahY.B. (2011). Power comparisons of Shapiro-Wilk, Kolmogorov-Smirov, Lilliefors and Anderson-Darling tests. J. Stat. Mod. Anal. 2, 21.
24.
RoystonJ.P. (1982a). An extension of Shapiro and Wilk's W tests for normality to large samples. Appl. Stat. 31, 115.
25.
RoystonJ.P. (1982b). Algorithm AS 177: Expected normal order statistics (exact and approximate). Appl. Stat. 31, 161.
26.
RoystonJ.P. (1995). Remark AS R94: A remark on algorithm AS181: The W-test for normality. J. R. Stat. Soc. 44, 547.
27.
Ruiz-JeanM.C., and Mitchell AideT. (2005). Restoration success: How is it being measured?. Restor. Ecol. 13, 570.
28.
SchaffelkeB., CarletonJ., SkuzaM., ZagorskisI., and FurnasM.J. (2012). Water quality in the inshore Great Barrier Reef lagoon: Implications for long-term monitoring and management. Mar. Pollut. 65, 249.
29.
ShapiroS.S., and WilkM.B. (1965). An analysis of variance test for normality (complete sample). Biometrika, 52, 591.
30.
SpragueL.A., HirschR.M., and AulenbachB.T. (2011). Nitrate in the Mississippi River and its tributaries, 1980 to 2008: Are we making progress?. Environ. Sci. Technol. 45, 7209.
31.
WanR., CaiS., LiH., YangG., LiZ., and NieX. (2014). Inferring land use and land cover impact on stream water quality using a Bayesian hierarchical modeling approach in the Xitiaoxi River Watershed, China. J. Environ. Manage. 133, 1.
32.
WoolseyS., CapelliF., GonserT., and HoehnE. (2007). A strategy to assess river restoration success. Freshwater Biol. 52, 752.
33.
YueS., and PilonP. (2004). A comparison of the power of the T-test, Mann-Kendall and bootstrap tests for trend detection. Hydrolog. Sci. J. 49, 21.
34.
ZhangR., JiangD., ZhangL., CuiY., and LiM. (2014a). Distribution of nutrients, heavy metals, and PAHs affected by sediment dredging in the Wujin'gang River basin flowing into Meiliang Bay of Lake Taihu. Environ. Sci. Pollut. Res. Int. 21, 2141.
35.
ZhangR., ZengF.X., LiuW.J., ZengR.J., and JiangH. (2014b). Precise and economical dredging model of sediments and its field application: Case study of a river heavily polluted by organic matter, nitrogen, and phosphorus. Environ. Manage. 53, 1119.
36.
ZipperC.E., HoltzmanG.I., DarkenP.F., GildeaJ.J., and StewartR.E. (2002). Virginia USA water-quality, 1978 to 1995: Regional interpretation. J. Am. Water Resour. Assoc. 38, 789.
