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 U.S. Department of the Interior U.S. Geological Survey 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
Water-quality data: Why continuous, real-time water-quality monitoring? 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 Turbidity, FNUs Specific conductance, us/cm Frequency of exceedance, percent Dissolved oxygen, mg/l Frequency of exceedance, percent Rasmussen and others, USGS SIR2013-5221
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 Gage height/velocity Specific conductance Turbidity Parameter computed Streamflow Chloride, alkalinity, fluoride, dissolved solids, sodium, nitrate, atrazine 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). Sediment yield, tons/yr Sediment yield from construction runoff sites in northeast Kansas SSC, mg/l Turbidity, FNUs Road-salt runoff in 5 streams in northeast Kansas Chloride, mg/l SC, us/cm Sites in order of most to least upstream construction Lee and others, USGS SIR2010-5128 Rasmussen and others, USGS SIR2008-5014
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 conditionshydrologic, 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 mi 2 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 R 2 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 Blue River, Jackson County, Missouri Drainage area about 92 mi 2 Land use about 25% urban Graham and others, USGS SIR2010-5248
Nitrogen models improve even more when continuous nitrate monitors are used Example: Indian Creek, northeast Kansas Drainage area 63 mi 2 ; land use 82% urban; wastewater discharges Early model based on turbidity and season Newest model based on turbidity and instream NO3+NO2 2012-13 Early TN model (2003-05) not useful in subsequent years as relations changed because of changes related to upstream wastewater discharges logtn=-0.17logtby+0.12sin(2πd/365) +0.30cos(2 πd/365)+0.99, R 2 =0.61 TN=1.27logTBY+1.06(NO3NO2)-0.651, R 2 =0.96 Rasmussen and others, USGS SIR2008-5014 Stone and others, USGS OFR2014-XXXX
Continuous nitrate monitors produce valuable data Example: Little Arkansas River, south central Kansas Drainage area 1,200 mi 2 ; land use primarily agricultural Hach Nitratax
Continuous data provide a more complete dataset than discrete data for traditional data presentation Blue River, Johnson County, Kansas, TP=0.0754+0.0016TBY, R 2 =0.94 % contribution to downstream load Wastwater Contribution to Total Nutrient Load, in Percent 100 80 60 40 20 0 Total TN Nitrogen Total TP Phosphorus Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar 500 400 300 200 100 0 Mean monthly Q, cfs Mean Monthly Streamflow, in Cubic Feet Per Second Total phosphorus in milligrams per liter as P TP, mg/l 4 3 2 1 0 Site 119th St. 1 Site College 2 Site Marty3 Site Miss. Farms 4 Tomahawk Site 5 Ck. Site St. Line 6
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, R 2 =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). GPP Upstream Downstream CR Sites upstream and downstream from a disturbance Use to evaluate ecosystem function among sites and for different periods of time. After Young and others (2008) Graham and others, USGS SIR2010-5248
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://nrtwq.usgs.gov http://waterwatch.usgs.gov/wqwatch/ 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 rasmuss@usgs.gov http://ks.water.usgs.gov Kansas in the middle but on the leading edge