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Integrating Hydrograph Modeling with Real-Time Flow Monitoring To Journal of Hydrology 393 (2010) 331–340 Contents lists available at ScienceDirect Journal of Hydrology journal homepage: www.elsevier.com/locate/jhydrol Integrating hydrograph modeling with real-time flow monitoring to generate hydrograph-specific sampling schemes ⇑ Heather E. Gall a, Chad T. Jafvert a, , Byron Jenkinson b a School of Civil Engineering, Purdue University, 3145 Civil Engineering Building, West Lafayette, IN 47907, USA b Jenkinson Environmental, LLC, West Lafayette, IN 47906, USA article info abstract Article history: Automated sample collection for water quality research and evaluation generally is performed by simple Received 17 July 2009 time-paced or flow-weighted sampling protocols. However, samples collected on strict time-paced or Received in revised form 10 June 2010 flow-weighted schemes may not adequately capture all elements of storm event hydrographs (i.e., rise, Accepted 30 August 2010 peak, and recession). This can result in inadequate information for calculating chemical mass flux over storm events. In this research, an algorithm was developed to guide automated sampling of hydrographs This manuscript was handled by Philippe Baveye, Editor-in-Chief based on storm-specific information. A key element of the new ‘‘hydrograph-specific sampling scheme” is the use of a hydrograph recession model for predicting the hydrograph recession curve, during which flow-paced intervals are calculated for scheduling the remaining samples. The algorithm was tested at Keywords: Real-time monitoring a tile drained Midwest agricultural site where real-time flow data were processed by a programmable Automated sampling datalogger that in turn activated an automated sampler at the appropriate sampling times to collect a Tile drain total of twenty samples during each storm event independent of the number of sequential hydrographs Hydrograph modeling generated. The utility of the algorithm was successfully tested with hydrograph data collected at both a Recession analysis tile drain and agricultural ditch, suggesting the potential for general applicability of the method. This sampling methodology is flexible in that the logic can be adapted for use with any hydrograph recession model; however, in this case a power law equation proved to be the most practical model. Ó 2010 Elsevier B.V. All rights reserved. 1. Introduction also include chemical constituents that occur in natural waters in different forms, whose sum is the desired result, such as Total Kjel- Even though water is an essential resource, many of the ways dahl Nitrogen (TKN) or total phosphorus (TP), and whose determi- that it is affected by human activities are not well understood. nation requires sample digestion at high temperature. While such According to Montgomery et al. (2007), one of the reasons for this measurements are technologically feasible via automated sam- is that water is treated generally in a fragmented way that sepa- pling and other instrumentation installed in the field, it remains rates groundwater from surface water, and water quality from much more convenient and less expensive to collect water samples quantity. However, due to recent advances in affordable technolo- in the field with automated samplers and return these samples to gies (i.e., dataloggers, sensors, wireless communication devices), the lab for processing and analysis, especially if samples are col- our ability to simultaneously monitor watersheds for both water lected at multiple locations. quantity and quality has improved tremendously and will continue To implement an integrated monitoring plan for surface waters, to improve over time. Yet, there are many important water quality sample collection or in situ sensing of constituents of interest parameters for which in situ monitoring remains unfeasible due to should be performed in parallel with other in situ measurements, current instrument/sensor limitations or costs. This list of pollu- with discharge (i.e., volumetric flow rate) being of critical impor- tants includes many pesticides, antibiotics, hormones, and other tance. This is because changes in discharge due to precipitation organic contaminants that occur at trace levels in water for which events can result in rapid changes in water quality parameters. existing technologies may require samples to be concentrated, ex- Additionally, the calculation of mass fluxes requires corresponding tracted, or filtered prior to analysis by conventional chromatogra- information on discharge and constituent concentrations. When an phy, mass spectral, or photometric methods. These contaminants in situ sensor is deployed, the frequency at which the sensor col- lects the data is limited only by the sensor’s scanning interval or ⇑ Corresponding author. Address: 550 Stadium Mall Drive West Lafayette, IN by power limitations during extended deployment. When water 47907, USA. Tel.: +1 765 494 2196; fax: +1 765 496 1107. samples are collected, the number of discrete or composite E-mail address: [email protected] (C.T. Jafvert). samples collected for analysis may be limited: (i) by the number 0022-1694/$ - see front matter Ó 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.jhydrol.2010.08.028 332 H.E. Gall et al. / Journal of Hydrology 393 (2010) 331–340 Nomenclature a recession sample number ti time of peak flow rate c model parameter TS total number of sample bottles (20) EN–S coefficient of efficiency tsample time when a sample is triggered during the hydro- n model parameter graph’s recession Oi observed flow rate V cumulative flow volume before the recession curve Oavg average observed flow rate Vr predicted cumulative flow volume during the recession Pi predicted flow rate curve from time ti to tend Pavg average predicted flow rate Vro observed cumulative flow volume during the recession Q(t) flow rate curve from time ti to tend Q(tend) 5% of the peak flow rate Vs observed volume of flow under the recession curve di- Qo peak flow rate vided by the number of remaining samples (out of 20) R2 coefficient of determination at the beginning of the recession curve RS number of remaining samples (out of 20) at the begin- Vsample equal cumulative flow volume that each recession sam- ning of the recession curve ple is predicted to represent S(t) storage s storage coefficient tend time at which the flow rate is 5% of the peak flow rate of bottles that the automated sampler holds, especially when the et al., 2008; Green and Wang, 2008; Manzoni and Porporato, sampling location is remote or (ii) by the cost of processing and 2009; Royer et al., 2006; Vanni et al., 2001). With the increasing analyzing the samples (Harmel et al., 2006). The sampling method- interest in studying emerging contaminants (e.g., hormones, phar- ology therefore is critical to obtain results that are representative maceuticals, etc.) whose transport mechanisms are not yet well of the hydrological characteristics of the study site, while at the understood, there is a need to develop a sampling methodology same time minimizing the costs associated with remote sample that collects discrete samples over hydrographs in their entirety. collection and laboratory analysis. Although a variety of simple and complex sampling methodol- Because the use of traditional time-paced sample collection is ogies have been reported, to our knowledge no reported sampling often ineffective in capturing events of interest, many researchers methodology uses volumetric flow data measured in real-time as have implemented some form of a flow-based sampling strategy, input to a hydrograph model to generate hydrograph-specific sam- ultimately leading to samples that more accurately represent con- pling schemes. The main objective of this study was to develop stituent loads during these events and during periods of high flow such a strategy. The main advantage of this approach for any dis- (Abtew and Powell, 2004; King and Harmel, 2003; King et al., 2005; crete sampling location is that the flow recession portion of both Rekolainen et al., 1991; Richards and Holloway, 1987). This is par- small and large hydrographs can be sampled at different flow-pro- ticularly true for smaller watersheds that have hydrographs of rel- portional frequencies, such that sufficient sampling occurs over atively short durations. For example, Kjær et al. (2007) used a real- smaller hydrographs, and less frequent sampling occurs over larger time monitoring system to study water quality at an agricultural hydrographs, avoiding filling all sample bottles before the hydro- field treated with manure. A datalogger (Campbell Scientific, Inc. graph recession is complete. We have implemented this sampling CR10X) was programmed to calculate the volumetric flow rate at methodology in an agricultural tile drain and ditch to collect sam- the discharge point of a tile drain based on the water level within ples for analysis of hormones and nutrients. the tile drain measured with a pressure transducer. The datalogger Tile drains are commonly used in Midwestern US states to drain triggered an automated sampler (ISCO 6700) to collect 200 mL excess water from agricultural landscapes. They typically are ori- subsamples based on volumetric flow after every 1500 or 3000 L, ented parallel to each other, spaced 12–24 m apart, and installed depending on the season, with the lower trigger of 1500 L used approximately 1 m below ground surface, lowering the water table during the summer months. Results of this work showed that hor- more quickly to this depth. They range in diameter from 10 to mones, 17b-estradiol and estrone, leached
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