U.S. Department of the Interior

U.S. Geological Survey

USGS Open-File Report 2006–1043

Chlorophyll a and Inorganic Suspended Solids in 

Backwaters of the Upper Mississippi River System: 

Backwater Lake Effects and Their Associations With 

Selected Environmental Predictors

By James T. Rogala and Brian R. Gray

The Long Term Resource Monitoring Program (LTRMP) 

uses a stratified random sampling design to obtain water quality 

statistics within selected study reaches of the Upper Mississippi 

River System (UMRS). LTRMP sampling strata are based on 

aquatic area types generally found in large rivers (e.g., main 

channel, side channel, backwater, and impounded areas). For 

hydrologically well-mixed strata (i.e., main channel), variance 

associated with spatial scales smaller than the strata scale is 

a relatively minor issue for many water quality parameters. 

However, analysis of LTRMP water quality data has shown that 

within-strata variability at the strata scale is high in off-channel 

areas (i.e., backwaters). A portion of that variability may be 

associated with differences among individual backwater lakes 

(i.e., small and large backwater regions separated by channels) 

that cumulatively make up the backwater stratum. The objective 

of the statistical modeling presented here is to determine if 

differences among backwater lakes account for a large portion 

of the variance observed in the backwater stratum for selected 

parameters. If variance associated with backwater lakes is high, 

then inclusion of backwater lake effects within statistical models 

is warranted. Further, lakes themselves may represent natural 

experimental units where associations of interest to management 

may be estimated.

Patterns of chlorophyll a and inorganic suspended solids 

concentrations are an integral part of our understanding of the 

water quality of the UMRS. For this reason, predictive models 

for these parameters are the initial focus of a multiyear effort 

to model the LTRMP water quality data. However, the high 

variability in both of these parameters within the backwater 

stratum suggests that we should consider backwater lake effects 

in these models. This is justified in that differences in means, 

variance, and relations with predictors all potentially vary among 

backwater lakes. This is significant because managers typically 

plan projects at the backwater lake scale (e.g., alter flows within 

a selected lake) more frequently than at the across-strata scale 

(e.g., water level drawdown). Correspondingly, models that 

incorporate backwater lake effects on water quality parameters 

should assist backwater restoration planning through an enhanced 

understanding of how the Mississippi River functions.

We began investigations into substrata level effects by 

determining whether a substantial portion of the observed 

variance was explainable by differences among backwater lakes. 

We did this using hierarchical linear mixed statistical models. 

These models allow variance terms associated with different 

spatial scales to be estimated using a single model. The Akaike 

Information Criteria (AIC) statistic (Akaike 1973), a measure of 

the fit of a model to a dataset, was used to compare models with 

backwater lake effects to models without. Models with smaller 

AIC values are considered better estimating models. All models 

allowed for year effects. Summer data from sampling years 

1993–2002 were modeled for each of Pools 4, 8, and 13.

Chlorophyll a was measured fluorometrically. Inorganic 

suspended solids concentration was calculated as the difference 

between measured total suspended solids and organic suspended 

solids. Backwater lakes were identified in a geographical 

information system (GIS) by using channel types to determine 

isolated regions within the backwater stratum. These lakes 

include very small and highly connected (e.g., submerged islands) 

portions of the backwater stratum. Selected morphometric 

characteristics used as predictors of the backwater effects were 

measured in a GIS.

Models that included backwater lake effects were observed 

to fit the data substantially better for both chlorophyll a and 

inorganic suspended solids (compare models C1 with C2 and 

I1 with I2, respectively, in Table). The variance estimates from 

models C2 and I2, expressed as percentages of the sum of the 

variance estimates, illustrate that a large portion (20–50%) 

of the variance in these data were associated with backwater 

lakes, either across all years or differing effects among years 

Chlorophyll a Inorganic suspended solids

0%

20%

40%

60%

80%

100%

Pool 4 Pool 8 Pool 13 Pool 4 Pool 8 Pool 13

P

erc

ent of v

arianc

e

Bwlake Bwlake (year) year residual

Figure. Percent of variance associated with selected variance components in 

models for backwater chlorophyll a and inorganic suspended solids for summer 

data 1993–2002 in Pools 4, 8, and 13.
(Figure). Of the variance associated with backwaters themselves, 

a majority was associated with effects that did not vary across 

years (excepting the chlorophyll a model for Pool 8). This 

result suggests that differences in chlorophyll a and inorganic 

suspended solids among backwater lakes are partially driven by 

processes that are consistent across at least the 10 years modeled.

The above estimates were not corrected for spatial 

correlation among backwater lake effects. Such correlation 

appears important and, if so, adjusting for it will lead to revising 

upward the proportion of variance in chlorophyll a and inorganic 

suspended solids levels associated with backwaters (i.e., the 

study's backwater variance estimates provided are expected to be 

conservative). This issue was not conclusively addressed because 

spatial correlation among backwater lakes is indistinguishable 

from spatial correlation among observations when the number 

of samples in a lake in a given year is only one. The models we 

present did, however, address spatial correlation within lakes.

We next examined whether lake effects on chlorophyll a

and inorganic suspended solids levels might be explained by 

morphometric predictors that themselves vary at the backwater 

scale. These predictors included backwater area, amount of 

connection to adjacent channels (a surrogate for water retention 

time), shoreline development index (a surrogate for water 

retention time) and backwater mean depth. All backwater 

metrics were assumed constant over the study years and based on 

conditions present in 1989. 

None of these morphometric predictors explained more 

than trivial proportions (<5%) of among lake variability in 

chlorophyll a or inorganic suspended solids levels. The hope 

that the selected backwater metrics would explain variability in 

water quality parameters derived from some success in earlier 

modeling of water quality-based habitat using backwater metrics. 

In those models, backwater metrics were found to explain 

habitat suitability when considering thresholds, as opposed to 

linear correlation, for several selected backwater metrics. It 

could be that similar correlations with these backwater metrics 

are present in the chlorophyll a and inorganic suspended solids 

data investigated in this study, but need to be determined using 

nonlinear assumptions.

Although the emphasis in these initial models was to 

examine backwater lake effects, we also considered whether 

variation in chlorophyll a at the sampling scale might be 

associated with variation in inorganic suspended solids at the 

same scale. The resulting models, which included backwater lake 

effects, suggested that 10–30% of the variation in chlorophyll a

at the sampling scale was associated with inorganic suspended 

solids (a positive correlation between chlorophyll a and inorganic 

suspended solids). In some of the models, up to 30% of the 

variance associated with backwater lakes was associated with 

inorganic suspended solids, a result that is not surprising given 

that we found substantial backwater lake effects when modeling 

inorganic suspended solids data. These associations between 

chlorophyll a and inorganic suspended solids likely reflect, in 

part, common effects, such as water exchange rates that influence 

both chlorophyll a and inorganic suspended solids. We also 

considered vegetation cover index (four categories of cover in 

proximity to sample site) effects on chlorophyll a, but found 

little (<5%) variance in chlorophyll a to be associated with the 

vegetation index.

These results demonstrate substantial variation among 

backwater lakes for at least two water quality variables. These 

findings suggest that contiguous backwater lakes exhibit unique 

characteristics and act partially independently, and that these 

features may be used to clarify relationships among physical and 

biotic variables of interest to managers and scientists.

This report is a product of 

the Long Term Resource 

Monitoring Program.

For more information, please contact:

Jim Rogala1 or Brian Gray2

U.S. Geological Survey

Upper Midwest Environmental Sciences Center

2630 Fanta Reed Road

La Crosse, Wisconsin 54603

Telephone: 1(608) 781-6373 E-mail: 1jrogala@usgs.gov

 2(608) 781-6234 2brgray@usgs.gov

http://www.umesc.usgs.gov

AIC values

Model Chlorophyll a (Chl) Pool 4 Pool 8 Pool 13

C1 (naïve) Chl = year effects 1,195.4 1,107.2 1,369.0

C2 Chl = year effects + backwater lake + backwater lake by year 992.0 1,057.9 1,191.2

Inorganic Suspended Solids (ISS)

I1 (naïve) ISS = year effects 1,420.1 1,460.4 1,655.9

I2 ISS = year effects + backwater lake + backwater lake by year 1,168.0 1,394.1 1,416.0

Table. Akaike Information Criteria (AIC) values for chlorophyll a and inorganic suspended solids models with and without backwater lake effects 

for data from Pools 4, 8 and 13. Decreases in AIC values of >2 units or more indicate a better approximating model.