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U.S. Department of the Interior U.S. Geological Survey
Continuous, Real-time Nutrient Data and Regression Models – Valuable Information for
Monitoring Aquatic Ecosystem Restoration
Teresa Rasmussen, Jennifer Graham, Mandy Stone, US Geological Survey, Kansas Water Science Center
Conference on Ecological and Ecosystem Restoration New Orleans, Louisiana, July 28-August 1, 2014
Overview of presentation
Why continuous, real-time water-quality monitoring? Why regression models? Why nutrients? Examples of regression models and the information
that can be gained from them Applications in ecosystem restoration Perceived barriers to using this approach The challenge
Why continuous, real-time water-quality monitoring?
Water-quality data: Measure progress toward defined restoration goals Explain changes in other measured variables such as biological
communities
Continuous data: Time-dense data describes variability by capturing diel, seasonal,
event-driven, and unexpected fluctuations Improve understanding of aquatic systems Improve concentration and load estimates with defined uncertainty
Real-time data: Provide immediate information about changes Allow optimized timing for collection of other samples Indicate when monitor maintenance is needed
Examples of time-dense data measured directly in streams to describe water-quality variability
Rasmussen and others, USGS SIR2013-5221
Turb
idity
, FN
Us
Spec
ific
cond
ucta
nce,
us/
cm
Frequency of exceedance, percent
Dis
solv
ed o
xyge
n, m
g/L
Frequency of exceedance, percent
Why regression models? Use water-quality parameters that can be continuously measured directly in the stream to compute continuous concentrations for constituents of particular interest that cannot easily be measured directly.
Parameter directly measured Parameter computed
Gage height/velocity Streamflow
Specific conductance Chloride, alkalinity, fluoride, dissolved solids, sodium, nitrate, atrazine
Turbidity Suspended sediment, total suspended solids, E. coli bacteria, particulate nitrogen, total nitrogen, total phosphorus, total organic carbon
Regression model approach • Install water-quality monitors at
streamflow gages and transmit data in real time
• Collect water samples over the range of hydrologic and chemical conditions
• Develop site-specific regression models using samples and monitor data
• Display computed concentrations, loads, uncertainty, and probability of exceedance on the Web
• Continue sampling to verify models
Regression model approach Highly successful for computing water-quality constituents such as suspended sediment concentration (using turbidity) and major ions (using specific conductance).
Rasmussen and others, USGS SIR2008-5014
Road-salt runoff in 5 streams in northeast
Kansas
Chl
orid
e, m
g/L
SC, us/cm
SSC
, mg/
L
Turbidity, FNUs
Sediment yield from construction
runoff sites in northeast Kansas
Lee and others, USGS SIR2010-5128
Sedi
men
t yie
ld, t
ons/
yr
Sites in order of most to least upstream construction
Nutrient models can be more challenging Because nutrients occur in particulate and dissolved phases, and have many and varied sources.
Find nutrient diagram
http://cnx.org - modification of work by John M. Evans and Howard Perlman, USGS
Why monitor nutrients (nitrogen and phosphorus)?
A leading cause of stream impairments across the U.S. Stimulate nuisance and harmful algal blooms Pose public health risks A persistent problem - in spite of federal, state, and local efforts to
control nonpoint source nutrients in streams, little progress has been made
Characterization of nutrient occurrence, variability, and transport leads to better understanding of other aquatic health factors.
Dubrovsky and Hamilton, 2010; Sprague and others, 2011
Important considerations for developing nutrient models
Collect samples over the range of conditions-hydrologic, rising, falling, chemical, seasonal.
Analyze both particulate and dissolved forms.
Collect enough samples to adequately describe relations (15-?).
Preliminary exploration of available data will help identify possible explanatory variables.
Models are site specific.
Rasmussen and others, 2009, USGS T&M3C4
Turbidity can be a useful surrogate for TN and TP when particulate forms are dominant
Kansas River Drainage area about 60,000 mi2
Land use about 75% agricultural (cropland, grassland)
Rasmussen and others, USGS SIR2005-5165
In more complex systems, nutrient models may improve when separated into particulate and dissolved forms.
For example, urban areas with multiple nutrient sources and altered pathways
Regression model R2
MSPE, lower
MSPE, upper
TN=0.0023TBY+2.32 0.48 30 88 TON=0.331logTBY-0.471 0.63 44 78 log(NO3+NO2)=-0.238logQ+0.547 0.71 28 38 TP=0.0009TBY+0.484 0.56 39 52 Ppart=0.0013TBY+0.0586 0.78 45 49 log(Pdis)=-0.307logQ+0.0866 0.42 54 117
Graham and others, USGS SIR2010-5248
Blue River, Jackson County, Missouri Drainage area about 92 mi2
Land use about 25% urban
Nitrogen models improve even more when continuous nitrate
monitors are used Example: Indian Creek, northeast Kansas Drainage area 63 mi2; land use 82% urban; wastewater discharges
logTN=-0.17logTBY+0.12sin(2πD/365) +0.30cos(2 πD/365)+0.99, R2=0.61
Early TN model (2003-05) not useful in subsequent years as relations changed because of changes related to upstream wastewater discharges
Rasmussen and others, USGS SIR2008-5014 Stone and others, USGS OFR2014-XXXX
TN=1.27logTBY+1.06(NO3NO2)-0.651, R2=0.96
2012-13 Early model based on turbidity and season
Newest model based on turbidity and instream NO3+NO2
Continuous nitrate monitors produce valuable data
Example: Little Arkansas River, south central Kansas Drainage area 1,200 mi2; land use primarily agricultural
Hach Nitratax
Blue River, Johnson County, Kansas, TP=0.0754+0.0016TBY, R2=0.94
Continuous data provide a more complete dataset than discrete data for traditional data presentation
Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar
Was
twat
er C
ontr
ibut
ion
to T
otal
N
utri
ent L
oad,
in P
erce
nt
0
20
40
60
80
100
Mea
n M
onth
ly S
trea
mflo
w,
in C
ubic
Fee
t Per
Sec
ond
0
100
200
300
400
500
Total NitrogenTotal Phosphorus
Mea
n m
onth
ly Q
, cfs
119th St. College Marty Miss. Farms Tomahawk Ck. St. Line
Tota
l pho
spho
rus i
n m
illigr
ams p
er lit
er a
s P
0
1
2
3
4
Site 1 Site 2 Site 3 Site 4 Site 5 Site 6
TP, m
g/L
TN TP
% c
ontr
ibut
ion
to d
owns
trea
m lo
ad
…and for less traditional data presentation...
Streamflow vs turbidity Little Arkansas R, 1999-2009
Probability of exceeding criterion
…and more less-traditional data presentation Total phosphorus computed from model TP=1.31logTBY+0.26(NOx)-2.49, R2=0.89
Floating sensor platform
Foster and others, NWQMC, 2014
Bonus information: stream metabolism, a measure of ecosystem function
Streamflow, dissolved oxygen, water temperature, and specific conductance can be used to calculate daily estimates of gross primary production (GPP), community respiration (CR), and net ecosystem production (NEP).
Graham and others, USGS SIR2010-5248
After Young and others (2008)
GPP
CR
Use to evaluate ecosystem function among sites and for different periods of time.
Sites upstream and downstream from a disturbance
Upstream Downstream
Continuous total and dissolved organic carbon
Text box
Little Arkansas River, Kansas, March 2012-Sept 2013 DOC comprised approximately 65% of TOC
Stone and others, USGS OFR2014-XXXX
Real-time data available on the Web
http://waterwatch.usgs.gov/wqwatch/
http://nrtwq.usgs.gov
Data available in real-time to all partners involved in restoration allow for timely data sharing, user defined notifications of conditions, and adaptive management.
http://waterdata.usgs.gov/nwis/
Applications in ecosystem restoration
Define variability in concentrations of different forms of nitrogen and phosphorus.
Characterize responses to storm runoff events and other factors. Evaluate differences between sites upstream and downstream from
restoration projects. Evaluate relations among nutrients, biological communities, and
ecosystem function. Evaluate conditions and trends relative to established criteria and
goals. Keep track of conditions in real-time and optimize timing of additional
data collection to coincide with desired conditions. Use data as one component of a comprehensive evaluation of project
success.
Perceived barriers to using regression model approach and possible solutions
1. Cost of equipment - Consider rental - Target conditions - Considering volume of data collected, cost per sample is low 2. Cost and time required for monitor operation and data review - Efficiency increases with experience - Data review tools 3. Lack of training/experience in monitor operation - Resources available-USGS, manufacturer, webinars
Perceived barriers to using regression model approach and possible solutions-continued
4. Lack of training/experience in regression model development - online resources, keep it simple 5. Unable to transmit data in real time - USGS-webpages available; others-work with manufacturers 6. Lack of explanatory variable for computing water-quality
constituent of interest - Explore available discrete data to identify other possible variables
Summary Continuous monitoring is necessary to fully describe variability
in water-quality conditions Continuous, real-time computations of nutrient concentrations
would probably enhance the monitoring approach for your restoration project
Resources are available to get you started The challenge: Look for opportunities to incorporate continuous
data and the regression model approach into your project to improve understanding of dynamic aquatic ecosystems
References Dubrovsky, N.M. and Hamilton, P.A., 2010, Nutrients in the Nation’s streams and groundwater: National Findings and Implications: U.S. Geological Survey Fact Sheet 2010-3078, 6 p. Graham, J.L., Stone, M.L., Rasmussen, T.J., Poulton, B.C, 2010, Effects of wastewater effluent discharge and treatment facility upgrades on environmental and biological conditions of the upper Blue River, Johnson County, Kansas and Jackson County, Missouri, January 2003 through March 2009: U.S. Geological Survey Scientific Investigations Report 2010-5248, 85 p. Lee, C.J., Ziegler, A.C., 2010, Effects of urbanization, construction activity, management practices, and impoundments on suspended-sediment transport in Johnson County, northeast Kansas, February 2006 through November 2008: U.S. Geological Survey Scientific Investigations Report 2010-5128, 54 p. Rasmussen, P.P., Gray, J.R., Glysson, G.D., Ziegler, A.C., 2009, Guidelines and procedures for computing time-series suspended-sediment concentrations and loads from in-stream turbidity-sensor and streamflow data: U.S. Geological Survey Techniques and Methods book 3, chap. C4, 53 p. Rasmussen, T.J., Ziegler, A.C., Rasmussen, P.P., 2005, Estimation of constituent concentrations, densities, loads, and yields in lower Kansas River, northeast Kansas, using regression models and continuous water-quality monitoring, January 2000 through December 2003: U.S. Geological Survey Scientific Investigations Report 2005-5165, 117 p. Rasmussen, T.J., Lee, C.J., Ziegler, A.C., 2008, Estimation of constituent concentrations, loads, and yields in streams of Johnson County, northeast Kansas, using continuous water-quality monitoring and regression models, October 2002 through December 2006: U.S. Geological Survey Scientific Investigations Report 2008-5014, 103 p. Rasmussen, T.J., Gatotho, J.W., 2014, Water-quality variability and constituent transport and processes in streams of Johnson County, Kansas, using continuous monitoring and regression models: U.S. Geological Survey Scientific Investigations Report 2013–5221, p. 53. Sprague, L.A., Hirsch, R.M., and Aulenbach, B.T., 2011, Nitrate in the Mississippi River and Its Tributaries, 1980 to 2008: Are We Making Progress?, Environmental Science and Technology, vol., 45, pp. 7209-7216.
Teresa Rasmussen USGS Kansas Water Science Center Lawrence, Kansas [email protected]
http://ks.water.usgs.gov
Kansas – in the middle but on the leading edge