1
Evaluating Status Network Cycle 2: Progress and Problems A Comparison of Cycle 2 Data to Cycle 1 Data: Trend Analysis Changes in Reporting Units The reporting units were changed from the 4 reporting units of the first cycle to the 7 reporting units of the second cycle in order to comply with TMDL efforts. As a result, The design of the Status Network was modified somewhat between cycle 1 and cycle 2. One of the design changes was that reporting unit areas were altered to the site selection strategy focused on different areas, and thus there are uneven numbers of sites in each reporting unit (Table 3). Such imbalance must have some effect on match with TMDL basins. As a result, reporting unit sizes and boundaries changed for SJB and SJC, although SJA and SJD remained the same. To compare sites network representation. For example, small lakes in SJ2 (Figure 2) look particularly poorly distributed between cycle 1 and cycle 2, especially in the sand hill lakes area. for trend analysis, cycle 1 sites were re-assigned to cycle 2 reporting units. Available water quality data from cycle 1 (2000 to 2003) and cycle 2 (2004-2007) were Also, there are fewer sites in SJ3 from cycle 1. How does changing reporting unit areas affect trend analysis? compared using a non-parametric Wilcoxon test (p<0.1). Cumulative frequency distributions for selected constituents give a visual reference of data comparison. Resource SJ1 SJ2 SJ3 SJ6 Table 1. Trend Results for Ocklawaha Basin (SJ1). Fig 2. Distribution of sites in SJ2 between cycles. Table 3. Number Type C1 C2 C1 C2 C1 C2 C1 C2 Figure 1. Cumulative Frequency Distributions for Selected Constituents in SJ1. of sites within Analyte Resource Type W_score Z_W_Score p_W_score Sig. Diff? SL 30 30 21 30 5 4 18 30 Air Temp SS 444.5 -3.3815 0.0007 Y each reporting unit 100 100 Apparent Color-Unfiltered SS 1024 2.3358 0.0195 Y 100 Chl-a_Corr SS 853 -0.2580 0.7964 LL 30 30 30 30 7 30 23 30 for each cycle. 90 90 90 Conductivity-Field SS 625.5 -3.6997 0.0002 Y cumulative percent of data values cumulative percent of data values cumulative percent of data values cumulative percent of data values cumulative percent of data values cumulative percent of data values Chl_a SS 30 29 30 30 14 30 6 30 SS LR 21 30 29 30 9 30 21 30 DO SS 869.5 0.0000 1.0000 Depth Collection-m SS 731.5 -2.1166 0.0343 Y Depth Stream-m SS 845 -0.3722 0.7098 Enterococci ME-MF SS 616 -2.3495 0.0188 Y Fecal Coliform-STORET 31616 SS 589.5 -2.7952 0.0052 Y NH4-T SS 610 0.0000 1.0000 NOx-T SS 764.5 -0.7530 0.4514 Pheophytin_Corr SS 855.5 -0.2205 0.8255 Salinity SS 540 -1.5943 0.1109 Secchi SS 218.5 -2.2963 0.0217 Y TKN-T SS 700 1.1661 0.2436 80 70 80 TKN TP 80 70 SS SS 60 60 70 60 50 40 30 20 50 40 30 20 Variation in Rainfall 50 40 TP-T SS 417 -0.2371 0.8126 10 10 c1 c1 c1 Turbidity SS 912.5 0.6373 0.5239 c2 c2 30 A possible confounding 2000 & 2004 - SJ1 2001 & 2005 - SJ2 Water Temp SS 595 -4.1625 0.0000 Y 0 c2 0 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 0 1 2 3 4 5 LARGE RIVERS LARGE RIVERS pH-Field SS 714.5 -2.3506 0.0187 Y SMALL LAKES SMALL LAKES factor in data interpretation SMALL STREAMS SMALL STREAMS LARGE LAKES LARGE LAKES Air Temp LR 431 -0.2033 0.8389 is that the rainfall varied Apparent Color-Unfiltered LR 637 1.7519 0.0798 Y 8 100 100 12 10.46 Rainfall Departure from Normal Rainfall (inches) Rainfall Departure from Normal Rainfall (inches) Rainfall Departure from Normal Rainfall (inches) Chl-a_Corr LR 569 0.4374 0.6618 100 10 8 6 4 between the index periods 6 4 90 90 Conductivity-Field LR 357 -3.6080 0.0003 Y DO LR 573.5 0.5168 0.6053 Depth Collection-m LR 621 1.6107 0.1072 Depth Stream-m LR 302.5 -4.6544 0.0000 Y Enterococci ME-MF LR 596 1.4249 0.1542 Fecal Coliform-STORET 31616 LR 574 0.9806 0.3268 NH4-T LR 598.5 0.9956 0.3195 NOx-T LR 432.5 -0.1710 0.8642 Pheophytin_Corr LR 706.5 3.2115 0.0013 Y Salinity LR 122 -3.8059 0.0001 Y Secchi LR 42.5 -2.2369 0.0253 Y TKN-T LR 523 0.2478 0.8043 90 80 80 70 Chl_a for each year (Figure 3). For 2.77 2 1.33 1.20 1.19 0.66 0.20 0.04 0 -0.11 TKN TP 70 LR example, the trend analysis 80 LR 1.56 2 LR 0.81 60 60 -0.79 -0.88 0 shows that total phosphorus -2 -4 -0.18 70 -0.57 -2.00 -0.71 -0.85 -0.67 -0.99 -1.22 50 40 30 20 -2 50 40 30 20 -1.65 -1.94 -2.29 -2.56 -2.95 increased in large lakes in -4 -3.37 60 -6 -6 Nov May June July Aug Sep Oct Dec May Sep Oct Nov July Aug June Dec SJ1 from cycle 1 to cycle 2. 50 Month Month Cycle 1 (2001) Cycle 2 (2005) Cycle 1 (2000) Cycle 2 (2004) The chart below indicates 40 TP-T LR 640 1.7923 0.0731 Y 10 10 2002 & 2006 - SJ3 2001 & 2005 - SJ6 c1 c1 LARGE RIVERS that 2004 was a much c2 c1 Turbidity LR 491 -1.0438 0.2966 LARGE RIVERS c2 0 c2 30 SMALL LAKES SMALL LAKES 0 Water Temp LR 519.5 -0.4976 0.6187 pH-Field LR 498 -0.9093 0.3632 SMALL STREAMS SMALL STREAMS 0 1 2 3 4 5 0 10 20 30 40 50 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 LARGE LAKES LARGE LAKES wetter year than 2000 6 8 Air Temp SL 785.5 0.7243 0.4689 during the index period. 6.68 6.66 Rainfall Departure from Normal Rainfall (inches) 4.87 4 Algal Growth Potential SL 64 1.0457 0.2957 6 4 100 4.62 100 100 This complicates the Apparent Color-Unfiltered SL 786 -1.9117 0.0559 Y Chl-a_Corr SL 750 -2.4606 0.0139 Y 1.79 90 2 0 90 2 0 analysis, because without 1.08 0.54 0.27 Conductivity-Field SL 909.5 -0.0739 0.9411 0.16 0.10 cumulative percent of data values cumulative percent of data values 0.27 90 TKN 80 DO SL 827 -1.2937 0.1958 Depth Collection-m SL 630 -4.6309 0.0000 Y Depth Stream-m SL 1116.5 2.9732 0.0029 Y Enterococci ME-MF SL 940.5 0.3751 0.7076 Fecal Coliform-STORET 31616 SL 884 -0.4514 0.6517 NH4-T SL 707.5 -3.0763 0.0021 Y NOx-T SL 910.5 1.6981 0.0895 Y Pheophytin_Corr SL 777 0.6206 0.5349 Phytoplankton Identification SL 7 -2.6266 0.0086 Y Salinity SL 486 -4.0677 0.0000 Y Secchi SL 257.5 -1.0325 0.3019 TKN-T SL 880.5 0.8408 0.4004 cumulative percent of data values cumulative percent of data values cumulative percent of data values Chl_a 80 70 -0.22 -0.19 adjusting for rainfall, are -0.44 TP -0.24 -0.50 -1.08 -1.09 -2 -1.67 -1.92 -0.97 70 SL 60 50 40 30 20 -2.33 -2 -2.60 SL -1.85 changes real or simply due SL -4 -3.65 80 -4 -6 60 50 40 30 20 May June July Aug Oct Nov Sep Dec May June July Aug Sep Oct Nov Dec to hydrological differences Month Month 70 Cycle 1 (2001) Cycle 2 (2005) Cycle 1 (2002) Cycle 2 (2006) between the time periods? Figure 3. Rainfall Departures from 30-year Averages in the Cycle 2 Reporting Units. 60 Access Denied! 50 TP-T SL 852.5 0.4048 0.6856 10 10 Lake Vegetation Index c1 Turbidity SL 848 -0.9837 0.3253 c1 c1 c2 One of the problems often encountered in the status network c2 c2 Water Temp SL 813.5 -1.4937 0.1353 0 0 40 0 1 2 3 4 5 6 7 8 9 pH-Field SL 900.5 -0.2070 0.8360 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0 100 200 300 400 500 sampling is a denial of land access by landowners. This issue Air Temp LL 654 -2.0551 0.0399 Y Apparent Color-Unfiltered LL 1077.5 2.4193 0.0156 Y Chl-a_Corr LL 813.5 -1.4945 0.1350 100 100 100 may be especially important when a landowner has several Field staff spend a Conductivity-Field LL 750.5 -2.4253 0.0153 Y 90 thousand acres and thus can effectively exclude large areas of SJ1 Small Lakes SJ6 Small Lakes 90 90 DO LL 889.5 -0.3696 0.7117 Depth Collection-m LL 525 -6.3965 0.0000 Y Depth Stream-m LL 1086.5 2.5306 0.0114 Y Enterococci ME-MF LL 1026.5 2.3813 0.0173 Y Fecal Coliform-STORET 31616 LL 906 0.5678 0.5702 NH4-T LL 1012.5 1.4370 0.1507 NOx-T LL 635.5 -3.9455 0.0001 Y Pheophytin_Corr LL 852 1.8418 0.0655 Y Phytoplankton Identification LL 90 -2.9914 0.0028 Y Salinity LL 777 -0.0890 0.9291 Secchi LL 660 0.2069 0.8361 TKN-T LL 947 0.4661 0.6411 lot of time getting cumulative percent of data values 80 70 TP the District from being sampled. In the map below, Rayonier in 80 70 60 80 lake vegetation the north and Deseret Ranch in the south have both effectively TKN 70 Chl_a index (LVI) 60 50 LL reduced the area covered by the status network. How is this LL 60 LL samples. However, it is not clear what 50 40 30 20 50 problem addressed in the network design, given that overflow 40 30 20 40 sites in denied access areas will also not be sampled? the data will be used 30 for. Here, some TP-T LL 650 -3.3307 0.0009 Y 20 10 10 Turbidity Water Temp pH-Field LL LL LL 921.5 1151.5 926.5 0.0887 3.4893 0.1626 0.9293 0.0005 0.8708 Y 0 0.00 0.05 0.10 0.15 0.20 0.25 0.30 c1 c2 0.35 0 0 1 2 3 c1 c2 4 5 6 c1 c2 10 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 regressions of LVI against typical trophic state total phosphorus (mg/L) total phosphorus (mg/L) A Comparison of Cycle 2 Data to Ambient and Temporal Variability Monitoring Data indicator constituents are presented to examine how well they correlated. The status network was primarily designed to indicate the status of resource types such as small and large rivers, and small and large lakes within drainage basins. Every 5 years, the basins are re- sampled, allowing for the possibility of trend measurements over that period of time. For example, the Ocklawaha basin was first sampled in 2000, and then again in 2005. Cycle 1 and cycle 2 data were compared, and results can be found in Table 1. In addition, cycle 2 sites from SJ1, SJ2, SJ3, and SJ6 were compared to long term temporal variability and ambient monitoring sites within the SJRWMD to see if there were significant differences (D) or not (S) between the two networks (Table 2). Long term data were filtered for the same index periods as status network data, and were located within the same basins. A non-parametric Wilcoxon test was used (p<0.1) to compare the datasets. It appears that there are significant differences between the networks, indicating that the status network is possibly reporting on a different population of water quality from the ambient and temporal variability network for many constituents. Thus, both networks enable characterization of Florida’s water resources. Constituent lakes rivers lakes rivers lakes rivers lakes rivers Water Temp S D S S S S D Air Temp D D S S S S D Stage D D S Secchi D S S S D D S Apparent Color-Unfiltered D S D D S S D Conductivity-Field D S D D S D D Depth Collection-m D D D D D D D DO D D S D S D D pH-Field D S D S D D S NH4-T S S S S S S TKN-T D D D S D D D NOx-T S D S D S S TP-T D D S D S D S Fecal Coliform-STORET 31616 D S D S Enterococci ME-MF S S S S Chl-a_Corr D D D D S S D Pheophytin_Corr D S S D S S S Turbidity D D D D S D S SJ1 SJ2 SJ3 SJ6 Table 2. Comparison of Cycle 2 Data with TV and Ambient Monitoring Data. S= similar; D= significantly different (p<0.1) Figure 4. Cycle 2 sites with denied access. Apparently, LVI doesn’t correlate very well with TKN, TP, Secchi, or chlorophyll. With the exception of Secchi, these constituents make up the Trophic State Index, or TSI. Good correlation with any of these constituents would suggest good correlation with TSI. So, the question is how is the lake vegetation index data providing any useful information for water quality determination in the status network? Figure 5. Lake Vegetation Index regressed against selected constituents in small lakes in reporting units SJ1 and SJ6 total kjeldahl nitrogen (mg/L) chlorophyll _a (uncorr. ug/L) Secchi depth (m) Secchi depth (m) chlorophyll _a (uncorr. ug/L) total kjeldahl nitrogen (mg/L) 5.80 -3.70 -1.23 -0.25 0.24 3.55 -2.56 0.33 -1.85 3.45 -0.11 -0.27
