COVERAGE, APPROACH, AND ‘DELIVERABLES’ SUMMARY This project began with a chance observation of some interesting chemistry at a site on the Colorado River. One thing led to another and it has grown with time into a fairly comprehensive survey of Arizona stream chemistry. The approach used here is ‘integrated’ and presents a different way of looking at data in the ADEQ Water Quality Database. The hope is that the information gathered will lead to a better understanding of the dynamics of Arizona stream chemistry. The project developed in several stages. The first was creating the tools for an integrated approach. The second was to produce ‘profiles’ of site chemistry using average values for a number of Arizona streams. The third was to add the capabilities of USGS geochemical modelling programs. Fourth, the modelling programs were used to generate more detailed views of some of the same sites for which profiles had been made. Finally, tools were created that allow for the rapid characterization and depiction of stream chemistry. An integrated approach to stream chemistry involves trying to achieve a ‘complete’ picture of the system being considered. The mass and charge balance are the basic tools. The advantages of an integrated approach are that results can easily be checked and differences raise questions that lead to further investigation. The discovery process is, as it were, self-perpetuating. The mass balance, for example, can be checked against a physical measurement -- total dissolved solids results. The difference between the two is a measure of the completeness of the ‘picture’ of the system. The charge balance can use a number of tests (seven are used here). How many tests the charge balance passes or fails gives some indication of how well the numbers in the individual analyses ‘fit together.’ A poor charge balance indicates only that there is a problem somewhere in the ‘complete’ picture being produced and gives little or no indication where that may problem might be. Implicit in the approach is that all available data is used. There are some pitfalls as well as advantages to this aspect. The profiles produced are over the entire period of record but that may range from
Transcript
COVERAGE, APPROACH, AND ‘DELIVERABLES’ SUMMARY
This project began with a chance observation of some interesting chemistry at a site on the Colorado River. One thing led to another and it has grown with time into a fairly comprehensive survey of Arizona stream chemistry. The approach used here is ‘integrated’ and presents a different way of looking at data in the ADEQ Water Quality Database. The hope is that the information gathered will lead to a better understanding of the dynamics of Arizona stream chemistry.
The project developed in several stages. The first was creating the tools for an integrated approach. The second was to produce ‘profiles’ of site chemistry using average values for a number of Arizona streams. The third was to add the capabilities of USGS geochemical modelling programs. Fourth, the modelling programs were used to generate more detailed views of some of the same sites for which profiles had been made. Finally, tools were created that allow for the rapid characterization and depiction of stream chemistry.
An integrated approach to stream chemistry involves trying to achieve a ‘complete’ picture of the system being considered. The mass and charge balance are the basic tools. The advantages of an integrated approach are that results can easily be checked and differences raise questions that lead to further investigation. The discovery process is, as it were, self-perpetuating.
The mass balance, for example, can be checked against a physical measurement -- total dissolved solids results. The difference between the two is a measure of the completeness of the ‘picture’ of the system. The charge balance can use a number of tests (seven are used here). How many tests the charge balance passes or fails gives some indication of how well the numbers in the individual analyses ‘fit together.’ A poor charge balance indicates only that there is a problem somewhere in the ‘complete’ picture being produced and gives little or no indication where that may problem might be.
Implicit in the approach is that all available data is used. There are some pitfalls as well as advantages to this aspect. The profiles produced are over the entire period of record but that may range from hundreds of samples over 40 to 50 years to five to ten samples over a year or two. In general, sites with many samples over long periods of time were favored.
A few sites with lesser number of samples, however, were also used, typically those with a history of exceeding water quality standards and/or to fill gaps in long stretches along a stream. Obviously, some care has to be used in generalizing from results that were generated from only a few samples over a short span of time. With such sites, comparison with other more adequately covered sites either upstream or downstream, if available, can aid in evaluation.
Some sites have many samples but not all the samples were ‘complete’ analyzes. In this study, generally only samples with all the major ions (Na, Ca, Mg, Cl, SO4, HCO3) and at least some metals were used. These restrictions have to do both with the methods and with the purpose of the study. The idea is that there is an electronic structure created by the major ions (the ‘matrix’) and that minor constituents such as metals have to fit into this structure in certain ways. Some attempts were made to extrapolate major
ion concentrations from basic chemical measurements but these were not found satisfactory and were discontinued.
To date, about 100 ‘average’ value profiles have been completed. These profiles include mass and charge balance results and Piper Plot (software courtesy of Utah USGS) depictions of the major ions. As an aide to navigation, profiles are always saved at the same place (the Piper Plot on the ‘results’ sheet) and follow a set layout (described in ‘Intro-howto-metadata’ file)
Piper Plots are particularly good at depicting not only the relative position of the particular ‘mix’ of ions but also the variability. Some sites have most of the individual sample points clustered tightly into a small area, others have them in a wide swath across the diagram. The first represents what might be called in some senses a ‘stable’ matrix while the second is a more ‘diffuse’ or ‘more highly variable’ matrix. Some sites, like the Colorado at Lees Ferry, show both – a very diffuse matrix before 1964 and a very tight matrix afterwards. For the most part, however, the average value profiles are static, representing a ‘snapshot’ of the system over the entire period of record.
The results of these profiles are depicted in a series of about 35 GIS maps and associated files. Thirteen of the maps are statewide depictions of the various water matrix compositions and groupings of interest. The main map, labelled AZwatermatrix, unfortunately had to be divided into 4 parts due to size limitations. The ‘composition’ maps show the matrix compositions at about 16 sites, representing 9 of the major streams around the state (1-3 samples along the Colorado, Bill Williams, Agua Fria, Verde, Gila, Salt, Santa Cruz, San Pedro and Little Colorado)
‘Composition1” uses a pie chart depiction of the charge percents of the major ions while ‘Composition2’ uses symbols proportional to size. ‘Confidence’ and ‘variability’ maps give chart depictions of the mass and charge balance results and some measurements of the variability shown in the Piper Plot. Ideally, confidence and variability information should be presented alongside composition.
Other maps in the statewide section depict additional, associated information. Three maps termed ‘AZhotspots’ show the locations of water quality exceedances at profiled sites in terms of number of parameters exceeding, magnitude of exceedances, and maximum values (regardless of exceedance status). There are also two maps showing areas of high or low solids production. One map and an Excel file categorize matrix compositions in terms of dominant anions and cations (alkalinity and hardness types).
Finally two files, one map and one Excel file, are the result of a largely abortive attempt to find evidence of ‘transition’ or ‘mixing’ zones at the various exceedance ‘hotspots’ around the state. Many ‘hotspots’ do occur at the junction of different water matrices but actually seeing the results of mixing demands that just the right flows and concentrations exist and last long enough to gather enough data to see them. ‘Mixing’ of some sort occurs, of course, every time a tributary flows into a stream but unless the flows are high and/or the concentrations very dissimilar there may be little or no concentration response.
The rest of the 35 maps are located in a subfolder called ‘Major Streams’. Here the 9 streams plus the Hassayampa are shown on individual maps with composition, confidence and variability all shown on the same map. Sites are labelled not only with the name but also with the number of samples and years covered included in parentheses. ‘Gila at Gillespie Dam(385/42)’ indicates the site has 385 (complete) analyses over a period of 42 years, while ‘Gila at Buckeye Canal(6/1)’, thrown in to help cover the long stretch between Gillespie and Kelvin, has only 6 complete analyses over 1 year (Fortunately the results are perfectly consistent with Kelvin and Gillespie.) Some of the tributaries to the Gila, Queen Creek and the San Carlos, however, have few samples but have widely different compositions from the Gila. Both show lower variance than the Gila but have poor charge balance results. How much they contribute to the Gila would depend on flows and relative concentrations.
As the profiles approached completion, work began investigating the use of USGS geochemical modelling programs. Two programs, WATEQ4F and PHREEQC, were used. WATEQ4F is an older program, somewhat limited in output and using different assumptions than PHREEQC. Both programs depend on an underlying database of thermodynamic data (standard enthalpies). What the programs offer is a quick way of doing difficult, iterative ‘best fit’ calculations to get speciation, activity, and solubility from pH and redox data. While handy, these programs come with assumptions and there is some risk in their use (see theoretical considerations below)
To date, twenty three profiles covering fourteen streams have been redone using the programs. The files are labelled with ‘grab’ at the end of the file name and the elements examined in detail in parentheses (usually Fe and Cu). The analysis in these profiles is limited to average values and placed next to the earlier values (Piper Plot) for comparison. In addition to the charge percent composition pie charts, however, are cation and anion speciation, activity and solubility charts as well as all metal s and predominant metals activities and solubilities. Results are lined up by program from left to right, WATEQ4F, PHREEQC with its own database, and PHREEQC with the more extensive Lawrence Livermore database. Results were generally fairly similar for all three programs and all were usually within 1-4% of earlier calculations.
Once some confidence had been built up with the average values, work began on getting a more detailed view of the various matrices. Tools were developed to generate graphs and correlation matrices from data on the ‘output2’ sheet of the ‘grab’ profiles. The ‘output2’ sheet has data grouped and organized in set locations making it very easy to pick out data by element, analysis type (speciation, concentration(activity) or solubility), and an independent (x) variable such as date, pH or conductivity (or another element). Virtually anything can be plotted or correlated with anything else (though the programs have not been checked for every possible combination so there are occasional hang ups and snafus)
The idea here is to give as many perspectives as possible on the system. Several graph templates were developed comparing matrix parameters against basic measurement and each other. The most used template has pH, flow and conductivity and ‘mass flux’ charts across the top of the sheet with different matrix parameters following below. The idea is to look at conductivity, flow, and pH ‘events’ and then
check the chemistry at the same points to see if there appears to be any response. Matrix parameters are also examined against each other to see if there are any patterns.
Because correlations can be chance, one-time events (coincidental) and patterns may need different time spans to become visible, maps and correlations can be generated using different time frames. Maps were usually generated with ‘all’ data, which are mostly useless for analysis due to too many points to see anything, but occasionally show patterns not seen in finer detail, and yearly increments. Correlations were usually done over the period of record, largely to verify if seeming correlations spotted on graphs had any general validity, but can also be done for any time frame of interest. One program runs correlations of one parameter against any number of other parameters on a yearly basis and it is interesting to see that correlations can come and go over time (though hard to judge what significance this may have)
To date, 14 ‘matrix’ studies have been completed on four streams (Gila, Salt, Colorado and Santa Cruz). When first begun, the studies took several days but refinements have reduced the time to 2-3 hours. One sheet of maps is produced using graphing programs, then the entire sheet is copied and another program used to change the dates on all the graphs of the new sheet. IN this way, annual graphs covering 20-30 years can be produced very quickly. Correlation matrices were developed and row and column headers copied to produce the next files correlations.
The results of these studies are examined below but first a few considerations of some of the risks involved in this type of analysis are necessary.
THEORETICAL CONSIDERATIONS
The USGS programs take the ‘total’ analyses of the database and work out the various compounds that are most likely to exist at a given pH and redox potential. The major ions, particularly Na, Cl and SO4, are predominantly in their ionic form. Na as Na and Cl as Cl, for example, are almost always nearly 100% (Na/Na, Cl/Cl ~ 100%, SO4/SO4 is typically 60-80%) Ca, Mg, and HCO3 often exist in compounds such as CaHCO3, CaSO4 etc.
PO4 exists in small amounts as PO4, is most commonly found as HPO4 and forms compounds primarily with other major ions and iron. What this means is that, if PO4 has any effect on free metal concentrations, it has to be an indirect one, possibly through a lower level competition with OH and CO3, and to a lesser extent S and SO4, for major cations.
Ultimately, the whole analysis depends on LaChateliers principle (a system under a stress will move to relieve that stress) and its particular form appropriate to geochemical systems, the law of mass action. These principles posit that if two reactants come into contact in a closed system there is a tendency for them to form a product (time not specified). Each set of reactants form product up to a set amount specific to the system, at which time the ‘stress’ on the system reverses direction toward dissolution of the product back into reactants. At this point the system has reached what is termed ‘equilibrium’ which is defined as the reaction toward creation of product being equal to that toward dissolution. The
equilibrium is termed dynamic in that the concentrations, while always fluctuating slightly, appear unchanging because there is no overall movement in either direction.
Equilibrium situations are most easily analyzed in lab beakers. Say small amounts of soluble Ca and SO4 compounds (CaNO3 and NaSO4 would probably do) are placed in a beaker of DI water on a lab bench (that is, with no analyzable inputs of mass or heat). The compounds will dissolve almost instantly and very quickly CaSO4 will begin to form. First there would be a tendency for Ca and SO4 ions to associate, then some pairs would begin to form molecular bonds, finally if conditions are right, CaSO4 would begin to precipitate out of solution.
Ca + SO4 [CaSO4] (CaSO4 aq
Here a couple of potential problems crop up. What the programs find, as far as I know, are the associated ions or so called ‘ion pairs’ not actual molecular species. Ion pairs are described as groupings of ions that are held together by very weak forces (coulombic interactions) as opposed to the stronger bonds of actual molecules. Whether they are, in general, strong enough to withstand the forces of filtration is not known (at least by me) so whether they would be in the dissolved or the total analysis portion is not clear. In these analysis dissolved data was used whenever possible unless total was specified (WATEQ4F specifies total Fe). The rationale here is that the suspended solid portion, which includes largely uncharged particles, does not figure directly into the electronic structure.
Even more significant, however, is that equilibrium is, I believe, generally considered as between reactants and (molecular) products. There may also be an equilibrium between reactants and ion pairs but it might be very difficult to analyze. IF there is no molecular product there is no equilibrium and results may be “misleading,’ according to one authority (Hems).
Of course, the whole concept of equilibrium is not quite appropriate for real world systems either. Natural systems are usually not ‘closed’ to inputs of mass and/or heat. The CaSO4 reaction that occurs in seconds in a beaker, apparently lasted over a period of eight months on Boulder Creek (at least if increased ion pair formation is any indication and predicted precipitation). The term ‘steady state’ is used to describe open systems in which inputs of mass and heat are assimilated in a fashion that mimics equilibrium.
But while most of us are happy enough to set aside the whole notion of ‘equilibrium’ as ‘theoretical’ and use the resulting information based on its assumption to solve problems, there are other difficulties. Even in the case of Ca and SO4 in a beaker, the results might be very different if a competing ion were present. We rely on the program to sort out these competing relationships but the programs can only use the information we give them and the information in the underlying database. Typically ‘modelling’ means that a given system is analyzed to include all the parameters that are involved and the underlying database is checked for both internal consistency and relevance to the system. Where appropriate, the information in the database may need to be changed or added to. None of that was done here.
Instead the programs were used with very little ‘tweaking’ to investigate the water systems not because this is the best way to do it but because of lack of knowledge on the users part (i.e. me!). At first, the
inputs were limited to average values and compared to the profiles generated using simply methods. Usually the activities derived from the programs were within 1-4 percent of the concentrations (even though the two are not the same). The redox potential was set to that of the H20/O2 pair – that is for full saturation. This ‘dominant’ pair assumption is hotly debated in groundwater studies but accepted (Fraser et al.) and probably o.k. for the typically more homogeneous surface water sample.
The underlying databases were not examined for consistency, completeness or relevance though they probably should have been. Instead, the results were evaluated against generally accepted findings. For example, PHREEQC has two databases that can be plugged in, one comes with the program and the other is a compilation from the Lawrence Livermore Laboratories. The latter is a very complete set of data but some values may have been derived in very specific circumstances. Using the Lawrence Livermore dataset on Colorado River water yielded the finding that CuCO3 was the most common form of copper, while WATEQ4F and PHREEQC datasets agree with the more generally accepted finding that Cu(OH)2, is more common. The Lawrence Livermore dataset was used but more for a ‘what if’ comparison.
In some cases, however, the problems resulting from not tweaking the underlying dataset to match the system being analyzed may have and probably did make the analyses meaningless. The Santa Cruz in particular seems almost ‘unanalyzable’, showing very little correlation or patterns among the major ions, but that may be because, historically, there has been a significant concentration of ammonia and the programs have ammonia ‘uncoupled’ from other reactions. This is an area where further work is definitely needed.
FINDINGS.
This project began with the chance notice of some interesting chemistry in the ADEQ surface water database for the Colorado River at Morelos. Sulfate typically runs about a 100 mg/L higher than bicarbonate at this site but in 1992, sulfate concentrations dipped below bicarbonate. The situation persisted for almost a year, from Dec. 1992 to Dec 1993. Such a change in one of the fundamental constituents over such a long period of time seemed significant.
The next step occurred sometime later when metals at the same site were being examined. Plotting a number of metals, scaled to appear in the same part of the chart, vs time, reveals that metals concentrations go up and down in a regular pattern with a period of about 5-7 years. But there are also several points along the graph (nodes) where something different seems to be going on. One of these points turns out to be Dec 1992- Dec 1993.
The metals in this period seem to be moving in unison in a pattern somewhat different from the overall pattern. Removing any metals that do not have at least two points within the time frame of interest, yields the following picture.
SULFATE, TOTAL (MG/L AS SO4)CALCIU x 4.48 + -99.12MAGNES x 12.49 + -86.67
SODIUM x 1.96 + 8.37POTASS x 75.57 + -86.88
BORON, x 1182.90 + 63.40ARSENI x 41684.85 + 185.38MANGAN x 4930.23 + 233.11
COPPER x 44421.93 + 218.25BARIUM x 4337.11 + -98.62
12/92 to 12/933-6/92
12/94-8/95
Obviously this period would be either a very ‘good’ or a very ‘bad’ time to do metals sampling depending on ones’ purpose. One might be tempted to label the period as ‘upset’ in a general sense and leave it at that. But such a correlated movement raises the question of what might be the cause.
The reason for the above phenomenon remained a mystery for some time. Then an examination of flows revealed that the period 1992-1993 was one of those times when major flows on the Gila caused it to flow all the way to the Colorado. The movement in the metals is, apparently, the result of mixing of two very different waters. The Gila is high in sodium and chloride whereas the predominant cation and anion in the Colorado charge structure are sodium and sulfate. What we seem to be seeing are the metals adjusting from their position in the Colorado matrix to that in the Gila matrix and then slowly returning as Gila flow tapers off.
A search was made for similar patterns of mixing at other ‘transition’ zones between different matrices around the state. Very few examples were found probably because, while some mixing occurs every time a tributary enters a stream, flow, concentration and time span need to be just right to actually see a concentration response (most ‘grab’ samples are taken a month apart – for that reason the mixing needs to go on for almost a year as here)
Having reached something of a dead end, focus changed to examining the two matrices involved. The Gila, like the rest of the south or west flowing streams examined (Colorado, Gila, Salt, Verde) gains in sulfate as it progresses. (North flowing streams (Santa Cruz, San Pedro and Little Colorado) gain in Cl.)
More significantly, relationships between the major ions change with elevation as the Gila proceeds. The Gila at Safford shows the original matrix most clearly while Gillespie and Dome show the same pattern but with additional things going on.
At Safford the major ion concentrations (activities) are highly correlated with each other with the exception of bicarbonate. In the following ‘correlation matrix’ that covers the period of record, high correlation (>.90) is in light magenta while some correlation (>.75,<.89) is in light blue.
Saffordconcentration Ca Mg Na Cl SO4 HCO3
Ca 10.9539
730.8816
170.8790
230.8330
190.2983
55
Mg0.9539
73 10.9342
120.9301
910.9199
090.1637
43
Na0.8816
170.9342
12 10.9966
870.9599
780.2156
96
Cl0.8790
230.9301
910.9966
87 10.9516
30.1822
05
SO40.8330
190.9199
090.9599
780.9516
3 10.1642
68
HCO30.2983
550.1637
430.2156
960.1822
050.1642
68 1
The high correlation is immediately apparent in graphs of major ion concentrations.
Comparing the up and down of the concentrations with flow and conductivity reveals that the response is to two different types of events which are dubbed ‘dilution’ and ‘concentration’ events (though concentration tends to be less (singular) events than extended periods). In a dilution event flow goes up while conductivity (standing in for concentration) goes down, while in a concentration event flow goes down and conductivity goes up.
Not all flow/concentration events can be labelled dilution or concentration. Instances of flow and conductivity both rising are dubbed ‘influx’ while both dropping are called ‘outflux.’ (Note this is a point to point evaluation with points typically a month apart) Of the 160 samples at Safford, 79 were concentration and 61 were dilution (49 and 38% respectively) Only 9 were labelled ‘influx’ and 12 ‘outflux’ (6 & 8%).
‘Influx’ is hypothesized to be an inflow of higher concentration water such as might occur with an inflow of groundwater or ag returns. ‘Outflux’ might be an event similar to infiltration, water percolating through soil and losing some of its solids content. These events are harder to visualize as physical phenomena (particularly ‘outflux’) and may in some cases actually be errors in designation. As a check, conductivity may be compared to TDS and ADEQ flows (grabs) to USGS flows (means). If the two do not agree in direction, then the designation may be erroneous due to a bad conductivity or flow read. In general, the simple dilution/concentration model seems appropriate about 90-95% of the time. Of the influx and outflux designations most were found to be problematic, so 5-10% is either error or actual influx or outlflux.
The concentration response at Safford to dilution and concentration events in the same time frames as the graphs above is clear.
All the concentrations, with the possible exception of bicarbonate, go down at a dilution event and up at a concentration event. The drop in sodium and chloride is proportionally greater than that of calcium (or bicarbonate when it drops). The result is that charge%, a function of concentration but as a percent sensitive to relative change, goes down for sodium and chloride goes while calcium and bicarbonate go up. This means that the major charge carriers can change from sodium and chloride to calcium and bicarbonate. This is termed a ‘matrix inversion and is clearly seen in the graph of charge on the left (dilution event- same year as above dilution graph). The significance of the matrix inversion is not known but one suspects it might be a ‘trigger’ for biological activity.
Note here that a different type of charge% calculation is used: moles e (moles of electrons). Charge%, as used in the USGS programs, divides moles of plus or minus species by the total plus or minus moles, moles e multiplies moles by charge, ionicity multiplies moles by charge squared. Charge% emphasizes the effect of relative concentrations, moles e is closest to the contribution to conductivity, while iconicity emphasizes the charge. Graphs of all three show only slight, expected variations – which is used depends on focus and interest.
Several more graphs show variations in how charge responds to dilution events.
Charge response depends on the extent of flow/concentration drop or rise and the number of data points over which the event is spread. Single point events appear sharply defined while multiple point events are flattened out regardless of relative drop/rise magnitudes.
The dynamic between sodium-chloride and calcium-bicarbonate charge is strikingly revealed in the following matrix. Sodium and chloride are strongly correlated to each other and negatively correlated to calcium and bicarbonate.
Saffordmols e Ca Mg Na Cl SO4 HCO3
Ca 10.86985
4-
0.93593 -0.86880.24148
30.85265
4
Mg0.86985
4 1-
0.89957-
0.837210.43779
90.81354
1
Na-
0.93593-
0.89957 10.93065
6-
0.29567-
0.90326
Cl -0.8688-
0.837210.93065
6 1-
0.35323-
0.97398
SO40.24148
30.43779
9-
0.29567-
0.35323 10.28334
8
HCO30.85265
40.81354
1-
0.90326-
0.973980.28334
8 1
The hypothesis here is that precipitation brings dilute water, definitely lower in sodium and chloride (associated more with the base flow), and more variable in bicarbonate. Hems and other others have suggested that higher bicarbonate content in surface runoff is due to increased contact with air (CO2) and vegetation. While bicarbonate content may or may not be higher, calcium and bicarbonate changes in concentration are invariably relatively lower than those of sodium and chloride.
Note that in the area of charge, sulfate is not correlated with the other ions, as in concentration, bicarbonate is not. The ion not correlated may be suspected to be the one that ‘tips the balance’ and
determines what is ‘going on.’ To see how sulfate may be involved in the response to dilution/ concentration events requires a digression on sulfate chemistry.
Sulfate, like bicarbonate but unlike chloride, has a tendency to form ion pairs with cations such as calcium, magnesium and sodium. Formation of sulfate ion pairs can be seen on speciation graphs such as that for Boulder Creek. The’ SO4 as SO4’, ‘Ca as Ca’. and ‘Mg as Mg’ percent go down as CaSO4, MgSO4 and NaSO4 concentrations go up. Formation apparently increased steadily for about 8 months.
Growth is generally exponential, though appearing linear or logarithmic at times, and the order of magnitudes is usually CaSO4, MgSO4, NaSO4. CaSO4 and MgSO4 are uncharged while NaSO4 has a minus 1 charge so formation involves not only competition for Ca, Mg and, to a lesser extent, Na but also removes charge from the system.
Though the percentage of sulfate as sulfate is decreasing, sulfate concentrations actually have to be rising because that’s what is pushing formation of the ion pairs. Ion pair formation is self-regulating in accord with the law of mass action (sulfate input on open system with steady state approximation).
The above graph shows sulfate concentration in blue with ‘sulfate backcalc’ (sulfate plus sulfate ion pairs as sulfate) in red. The divergence of red and blue lines shows the accelerating growth of ion pair
concentrations. Ion pair formation continues as long as sulfate concentration are rising and, in itself, serves to lower sulfate concentration. This is termed a ‘deceleration’, the ion pair formation conceived as acting as a break on the rise of SO4 as SO4. Similar decelerations occur for Ca and Mg but very little if at all for Na. HCO3 ion pairs seem to function similarly but with more variability and at lower magnitudes in most waters. The blue square shows where CaSO4 precipitation is expected.
Returning to Safford we can see that, in the original Gila matrix, sulfate and bicarbonate ion pair formation drop in dilution events and grow to a peak during concentration events. The graphs below (same years as earlier graphs (pp.9-11)) show % moles e with concentration in mg/L of ion pairs (SO4 IP = SO4 backcalc mg/L– SO4 as SO4 mg/L)
The supposition here is that the ion pair formation competing for calcium and magnesium combines with decreasing (dilute) surface runoff and re-increasing base flow to allow sodium and chloride to once again become dominant. The end result is most clearly seen by plotting the major ion concentrations and charge against conductivity. Here the ‘mediating effect’ of bicarbonate is clearly seen, that of sulfate is more indirect, through ion pair formation.
This picture is the Gila matrix at its most clear and the patterns noted above show the ‘self-regulating’ mechanisms involved. This level of order among the major ions raises the question ‘how far down in the structure is order apparent?’
Unfortunately, minor constituent metal and nutrient concentrations are not highly correlated with the major ion patterns at Safford. Metal concentrations are in fact related to each other – that is the whole
point of the USGS programs. But high correlations tend to be between different compounds of the same metal. Whether there are any correlations or patterns with major ions depends on the picture we can construct. The further we go down in the ‘structure,’ the less complete our picture is, the more the ‘local’ environment is determined by everything above it.
Comparing metals concentrations with the dominant anion charge % in different matrices yields some suggestive plots. Here HCO3 and SO4 charge percents at a number of different sites are plotted against arsenic concentrations. Higher arsenic concentrations seem to cluster in certain portions of the graphs.
If the appropriate charge% for the Verde and Colorado are labelled, it is clear that the Verde is in a high concentration cluster while the Colorado is not. Does this indicate a higher ‘carrying capacity’ for arsenic in the Verde as opposed to Colorado?
Probably not. It could just be coincidental. The Verde is higher in arsenic due to geological formations and there is no obvious causal connection with the matrix. A stream does not have a choice in accepting or rejecting materials in its path, instead it has to adjust to them. If there is anything to the ‘carrying capacity’ idea it probably lies in speciation. This argument does not do much for arsenic, which invariably exists as AsO4. But it could salvage the theory by positing that certain species might migrate more readily into the suspended sediment portion in some matrices.
The major problem finding patterns and correlations with minor constituent metals is that they have a variable ‘presence.’ (Imagine the charge % graphs above with every second or third point missing.) There is a large element of chance in whether minor metals will be present and in what concentrations. Base-flow concentrations can be compared to high flow but this tells us nothing about relations.
Returning to the Gila matrix at Safford, however, one can see that metals do seem to respond to major ion patterns in a very general way. If all bicarbonate and sulfate species are plotted together on separate charts and their movement related with flow and pH, trace metal bicarbonate and sulfate compounds seem to largely move in opposite direction to those of major ions.
V V
The major ion compounds, rather flattened by the log scale, at the top of the graph dip down at the dilution event, while underneath many of trace metal compounds are trending up, though not all in unison. Hydroxide compounds, which are primarily metals compounds, follow the upward motion of bicarbonates and sulfates while phosphates, which are mostly major cation compounds (exception, iron), seem to be variable in response (indicating a possible bridge between the two systems).
There are a couple more interesting points about the above graphs. The first is that the large dilution flow peak and conductivity drop of 8/16 is accompanied by a dropping pH. The second is that the response of several trace metals occurs on a small side peak (7/19) to the main peak (the upswing side peak). Eleven dilution events showed upside peaks. (Only 22% of all events but that is heavily dependent on (chance) spacing of samples). Ten had matrix inversions, nine showed some metals response, and four showed a correlated response.
In cases with response at the side peak, a drop in pH and a switch of OH and CO3 species were usually also observed. Different metals respond at the side peak or at the main peak (presumably by chance). Free metal speciation and charge percent (in black below)often go up with a drop in pH as expected.
These pictures suggest that ‘first flush’ may be a more extended phenomenon than commonly thought. Meteorologists do sometimes say that the early monsoon season may present with spotty precipitation. As isolated tributaries begin to run, if they chance to pick up higher concentrations of metals along the way, they ‘hit’ the main stream with the full force. In other words, the contrast between incoming and receiving water concentrations is likely to be greater than later in the season when more tributaries are running and their concentrations tend to cancel each other out.
That this hypothetical scenario is most appropriate for higher elevations is brought out by changes in the Gila as it flows. How different the Gila is at Gillespie than it is at Safford can be seen by comparing the concentration and charge vs conductivity for Gillespie with those for Safford above.
The plot up to about 1500 uS/cm is exactly the same as Safford. At higher conductivity, sulfate becomes an increasing factor while bicarbonate has less of a ‘mediating’ role than it has at Safford. In fact, sulfate increases and bicarbonate decreases particularly in the spring as the Gila progresses. (Safford left, Gillespie right, bicarbonate axis lower right side of diamond increasing going down, spring- yellow)
While the same dilution/concentration response evident at Safford is still seen at Gillespie, there are differences. Concentrations are even more correlated than at Safford but the correlation of charge between Na and Cl has weakened and the opposition with calcium and bicarbonate is less clear.
There appears to be less contrast between incoming and receiving waters at Gillespie than at Safford. High TDS groundwater or ag returns flowing into a generally higher TDS water rather than a dilute meeting a more concentrated receiving water (particularly in sodium and chloride). The lack of contrast makes response harder to gauge.
One corollary of this may be that so called ‘influx’ and ‘outflux’ situations are more common at lower elevations. Gillespie certainly has ag and municipal returns which may be of generally high TDS water and the Gila flows underground in certain spots which might (somehow) make inflltration a possibility. While some ‘influx’ and ‘outflux’ designation may be erroneous, as at Safford, the ratios of the different types of events changes dramatically at Gillespie and Dome where influx and outflux are 16 and 19-20% for a combined total of about 35-36% of all events (as opposed to 14% at Safford).
Under similar circumstances as Safford, one is more likely to see ‘partial’ than ‘full’ matrix inversions at Gillespie. The major ions merely take a slight move toward or away from each other. These are not, strictly speaking, matrix ‘inversions’ but they do bear the same relation to dilution/concentration events and point toward the same mechanism as at Safford.
Note that bicarbonate is still uncorrelated for concentration and sulfate is still uncorrelated for charge. It may be that, as the dynamic between na/cl and ca/hco3 weakens, the roles of bicarbonate and sulfate ion pairs in maintaining the high sodium chloride matrix may change but whether more or less important is not clear.
With major ion dynamics less clear, it is not surprising that minor constituent response to dilution/concentration are muted and/or confused. Even with fairly large dips in conductivity/peaks in flow one is more likely to see flat lines or a confused jumble.
But while response to dilution/concentration events is less clear there are a number of new relationships emerging at Gillespie. pH changes unaccompanied by change in flow or conductivity, seem to be apparent causes of concentration changes more often than at Safford. There may also be different responses involving bicarbonate, iron, and silica but these have not been fully worked out.
Some responses seen at Gillespie are particularly suggestive. In certain years, the phosphates oscillate in a sine pattern. The regularity and tightness of the response suggests some sort of fine-tuning is going on but no relation to flow/concentration or other metal trends have been found. If fairly high concentrations weren’t represented, one might suspect an analytical artifact of some sort.
This kind of tight movement is reminiscent of iron speciation changes except that the latter is easily explained. Every time the pH crosses the pH = 8 line there is a shift in speciation from Fe(OH)4 to Fe(OH)2 or vice versa depending on the direction of change.
The significance of these changes is hard to show but it seems that iron may have a role in fine-tuning charge relationships. Fe(OH)4 is minus charged, while Fe(OH)2 is plus charged and Fe(OH)3 is uncharged. Which species predominate may not be directly related to flow/concentration events but more to the total charge structure of the incoming flow.
Iron has several strong correlations that are very interesting as well. At most sites examined there is a strong correlation between Fe(OH)4- speciation and H3SiO4 concentration (>0.9). H3SiO4, with a minus charge and often existing at intermediate concentrations, can be a major charge carrier. Fe(OH)4- speciation also has a pretty fair correlation with HCO3 speciation which may be relevant to what one sees at Lees Ferry (below)
In fact, the Colorado at Lees Ferry looks very much like the Gila but only before 1964. Colorado River chemistry changes in 1964-5, presumably due to the construction of dams. The major ion concentrations become less variable and higher bicarbonate concentrations in the spring are no longer evident on the
Piper Plot. (left: 1926-1965, right: 1966-2008, spring – green, bicarbonate axis is the lower right side of the diamond, going from 0 (high) to 100(low))
After 1964 concentrations go down and high seasonal variability is replaced by a sine curve with amplitude of about 5-7 years. The graphs below show major ion concentrations before (left) and after1964 (right).
A closer examination of the post-dam sulfate concentrations reveals that seasonal fluctuations remain but as an inner, tighter curve inside the larger sine curve.
In the earlier period, the relationship between flow and concentration is clear, with high flows correlating with lower conductivity. The matrix response to this scenario is similar to that of the Gila, complete with matrix inversion, in spite of the fact that the Colorado is not a high sodium chloride matrix.
Post 1964, the 5-7 year sine curve correlates very well with the release data from Lake Powell. Lees Ferry may indeed have been used as a gauging station for dam releases, since quoted release flows from Glen Canyon Dam are identical with flows at Lees Ferry until about 1979, afterwards they differ slightly.
Whether dam releases are having a concentrating or a diluting effect is not entirely clear. It may depend on how releases are made, ‘over the top’ having a presumably ‘decanting’ effect, while releases that stir up the bottom might provide more concentrated waters. In general, TDS along the Colorado is rather steady at the 5 sites examined at about 550-570 mg/L until one reaches Morelos where it jumps to about 800 mg/L.
Over the long span, it would appear that higher flows are accompanied by lower TDS. Looking at a smaller time span, though, reveals that the relationship is not always that clear. Similarly with what is seen at Gillespie, there appear to be more ‘influx’ and ‘outflux’ scenarios. In fact, before 1964 influx and outfluxs are 13 and 17% for a total of 30% of total events, after 1964 they comprise more than 50% of event designations (21 and 30%)
Given these considerations, it is hardly surprising that Lees Ferry after 1964 looks a lot more like the Gila at Gillespie than the Gila at Safford. Whatever the exact flow/concentration relation may be, the lessening contrast between inflowing and receiving waters, seems to operate similarly whether caused by change in elevation or dam construction.
These conclusions are borne out by correlations. Before 1964 the dynamic between NaCl & CaHCO3 is clear, afterwards, not so much
LEES FERRY1947 1964
mols e/mols e Ca Mg Na Cl SO4 HCO3
Ca 1-
0.40573-
0.94925-
0.89028-
0.643470.85036
3
Mg-
0.40573 10.10064
10.18991
7-
0.04384-
0.04691
Na-
0.949250.10064
1 10.90544
8 0.71796-
0.91212
Cl-
0.890280.18991
70.90544
8 10.48847
7-
0.79756
SO4-
0.64347-
0.04384 0.717960.48847
7 1-
0.91546
HCO30.85036
3-
0.04691-
0.91212-
0.79756-
0.91546 1
1965 2006mols e/mols e Ca Mg Na Cl SO4 HCO3
Ca 1-
0.14401-
0.71956-
0.52467-
0.410350.58947
2
Mg-
0.14401 1 -0.542-
0.37188-
0.282830.40029
7
Na-
0.71956 -0.542 1 0.668970.61396
3-
0.80739
Cl-
0.52467-
0.37188 0.66897 10.26729
6-
0.74255
SO4-
0.41035-
0.282830.61396
30.26729
6 1-
0.83879
HCO30.58947
20.40029
7-
0.80739-
0.74255-
0.83879 1
With regulated flows, the chemistry becomes very dull(!)
Metals were not analyzed at Lees Ferry before 1964, so it is not possible to compare before and after metals. Iron, however, was analyzed and shows a marked change in speciation at Lees Ferry around 1964 as well.
The connection between bicarbonate and iron has been studied in groundwater but whether the same connection exists in surface water and what it might mean are not known Suspecting that the change in iron speciation might be linked with decreased bicarbonate composition in the spring, iron speciation graphs at the Gila at Safford and Gillespie were compared but no change found analogous to that at Lees Ferry. The correlation between Fe(OH)4- speciation and HCO3/CO3 speciation, however, is -.54 before 1964, but jumps to -.94 after at Lees Ferry. On the Gila, the correlation is -.5 at Safford and moves up to -.79 at Gillespie. What the significance is of such a speciation-speciation connection is not clear at this point.
One would suspect that at Morelos, where lower elevation and dam construction are both operative, metals would be pretty stable and that is indeed the case. One sees the same change in correlations as at Lees Ferry but exacerbated.
MORELOS1961 1963mols e/mols e Ca Mg Na Cl SO4 HCO3
Ca 10.18137
2-
0.89389 -0.76650.70859
90.83626
9
Mg0.18137
2 1-
0.60292-
0.19981 0.166660.26391
2
Na-
0.89389-
0.60292 10.71066
5-
0.64784-
0.79814
Cl -0.7665-
0.199810.71066
5 1-
0.98879-
0.94914
SO40.70859
9 0.16666-
0.64784-
0.98879 10.89165
1
HCO30.83626
90.26391
2-
0.79814-
0.949140.89165
1 1
1964 2006Ca Mg Na Cl SO4 HCO3
Ca 1-
0.417980.13444
2
-0.2495
70.58640
2 0.71817
Mg-
0.41798 1-
0.95317
-0.5920
90.30190
70.17661
8
Na0.13444
2-
0.95317 10.7399
2-
0.63879-
0.71494
Cl-
0.24957-
0.59209 0.73992 1-
0.95912-
0.87269SO4 0.58640
20.30190
7-
0.63879-
0.95911 0.69925
7
2
HCO3 0.718170.17661
8-
0.71494
-0.8726
90.69925
7 1
The following graphs show the flow, conductivity and mass flux relations and major ion response at Morelos in 1993 to increased flow from the Gila.
The massive dilution event was not accompanied by a very large drop in conductivity or TDS. There was however a large negative mass flux (a point to point concentration times volume calculation). As the next graphs show, there was a concentration inversion (sulfate lower than bicarbonate which is not usual for the Colorado) but no charge inversion.
As might be expected, while the carbonate and sulfate major ion compounds do dip, there is not s very convincing correlated upward movement among trace metal compounds..
Individual metals were graphed and the response found to be quite variable both in terms of magnitude and timing with many showing no response at all. A few examples of individual metal concentration responses over 1993:
Given these results, it is hard to see how the initial graph (p.8), made from a random group of metals, was even produced. Attempts were made to reconstruct that graph from the original concentration data (that is, not run through USGS software) and the difficulties run into are interesting. At first, to make the results as close to the modelling results, only dissolved species were used. Then only total species were used (there was no suspended data for 1993, the analyses having been suspended earlier).
The difference between dissolved and total results may help to explain why modelled results show little correlated metals response and also suggests that, in this case, leaving solids forms out results in an incomplete picture of what is going on.
But the above graphs still don’t look like the original. What was needed was scaling the responses to sulfate as was done in the original graph. (Correlation is, after all, in the eye of the beholder! The 5-7 sine curve seen in the original does appear in the below graph as it is being constructed but disappears when (too) many points are added) Here are total and dissolved species scaled to sulfate, parameters with no points between 1/93 and 6/93 removed, and the same time frame as the original. And having arrived back where we started, having raised far more questions than provided answers . . . this seems like a good place to end.
