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Ambient Water Quality: Update April 19, 2017 Point Statics for - PowerPoint PPT Presentation

TDS/Nitrogen Management Plan for the Santa Ana River Basin Groundwater Monitoring Requirements Ambient Water Quality: Update April 19, 2017 Point Statics for 20-Year Moving Average 1. Annualized Averages 2. At least 3 years of water quality (TDS


  1. TDS/Nitrogen Management Plan for the Santa Ana River Basin Groundwater Monitoring Requirements Ambient Water Quality: Update April 19, 2017

  2. Point Statics for 20-Year Moving Average 1. Annualized Averages 2. At least 3 years of water quality (TDS or NO3-N) in 20 year period 3. Shapiro Wilk test for normality 4. Point Statistics – mean plus t*standard error of the mean 2

  3. Method Shapiro-Wilk Test for Normality  Annualized Averages  Assumes “normal” data  Shapiro-Wilk test for  Bias towards low values normality to remove “Most  No apparent limit on the Discordant Value” (MDV) number of iterations iteratively until data is  Rejects data that may normalized correspond to event changes 3

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  6. Potential Bias on Bimodal data 1003134 1003134 2.0 2.0 1.5 1.5 1.0 1.0 0.5 0.5 Normal Quantile Normal Quantile 0.0 0.0 -0.5 -0.5 -1.0 -1.0 -1.5 -1.5 Mean = 8.5 Mean = 6.8 -2.0 -2.0 0 5 10 15 20 Remove 1 MDV 25 0 5 10 15 20 25 NO3-N NO3-N 6

  7. Potential Bias on Bimodal data 1003134 1003134 2.0 2.5 2.0 1.5 1.5 1.0 1.0 0.5 Normal Quantile Normal Quantile 0.5 0.0 0.0 -0.5 -0.5 -1.0 -1.0 -1.5 -1.5 Mean = 8.5 Mean = 5.6 -2.0 -2.0 0 5 10 15 20 Remove 2 MDV 25 0 5 10 15 20 25 NO3-N NO3-N 7

  8. Potential Bias on Bimodal data 1003134 1003134 2.0 2.0 1.5 1.5 1.0 1.0 0.5 0.5 Normal Quantile Normal Quantile 0.0 0.0 -0.5 -0.5 -1.0 -1.0 -1.5 -1.5 Mean = 8.5 Mean = 4.2 -2.0 -2.0 0 5 10 15 20 Remove 3 MDV 25 0 2 4 6 8 10 12 14 16 NO3-N NO3-N 8

  9. Potential Bias on Bimodal data 1003134 1003134 2.0 2.5 2.0 1.5 1.5 1.0 1.0 0.5 Normal Quantile Normal Quantile 0.5 0.0 0.0 -0.5 -0.5 -1.0 -1.0 -1.5 -1.5 Mean = 8.5 Mean = 3.1 -2.0 -2.0 0 5 10 15 20 Remove 4 MDV 25 0 2 4 6 8 10 12 NO3-N NO3-N 9

  10. Potential Bias on Bimodal data 1003134 1003134 2.0 2.0 1.5 1.5 1.0 1.0 0.5 0.5 Normal Quantile Normal Quantile 0.0 0.0 -0.5 -0.5 -1.0 -1.0 -1.5 -1.5 Mean = 8.5 Mean = 2.1 -2.0 -2.0 0 5 10 15 20 Remove 5 MDV 25 0 0.5 1 1.5 2 2.5 3 3.5 NO3-N NO3-N 10

  11. Updated Shapiro Wilk 1003134 1003134 2.0 2.0 1.5 1.5 1.0 1.0 0.5 0.5 Normal Quantile Normal Quantile 0.0 0.0 -0.5 -0.5 -1.0 -1.0 -1.5 -1.5 Mean = 4.8 Calculated Statistic = 6.8 -2.0 -2.0 0 5 10 15 20 No MDV Removed 25 0 5 10 15 20 25 NO3-N NO3-N 11

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  13. Questions?

  14. Criteria for Determining Point Statistics: Identifying Outliers: Outcome: 1. Limit S-W iterations to no 1. Fewer data points more than 2 times removed 2. Median absolute difference: Nitrate-N > 5x; TDS > 10x 2. Accounts for bimodal and non-linear trends Checking for Normality: 1. Linear Normality: Pass = Point 3. Does not significantly Statistic change contouring 2. Log Transform Normality: Pass = Point Statistic 3. Fail Both = ‘Average’ 14

  15. 1996 – 2015 AWQ Data Flow Diagram Yes n < 3 or Mean Detects = 0 No Shapiro- Pass Mean +SE Remove MDV Wilks UCL84 Test Yes Fail MDV Remove Yes 5x (NO3-N) MTV <2 & Max C > 1 /10x (TDS) No No Pass GM + GSE S-W Log Transform UCL84 Fail Median

  16. Annualized Nitrate Samples and Outliers Annualized TDS Samples and Outliers Removed Removed [CELLRANGE] 40000 40000 [CELLRANGE] [CELLRANGE] 35000 [CELLRANGE] 35000 [CELLRANGE] [CELLRANGE] 30000 30000 25000 25000 20000 20000 [CELLRANGE] [CELLRANGE] [CELLRANGE] [CELLRANGE] 15000 15000 [CELLRANGE] 10000 10000 [CELLRANGE] 5000 5000 0 0 Period 1 2000 2003 2006 2009 2012 Period 1 2000 2003 2006 2009 2012 Nitrate Nitrate Outliers TDS TDS Outliers 16

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