East Fork Watershed Water Quality Monitoring and Modeling Cooperative (EFWCoop): November 10th Meeting. Office of Research and Development1. National Risk Management Laboratory, Water Supply and Water Resources Division, Water Quality Management Branch November 10, 2011
Overarching R&D Objectives forestablishing the East Fork WatershedCooperative1. Integration of natural and built systems2. Coupled modeling and monitoring programs for decision support3. BMP/GI performance to effectiveness linkages4. Informational (data) architectures and required cooperation for sustainable total water management5. Consider scaling and extrapolation within and across systems.
R&D Projects Currently Supported by the EFWCoop Program• Linked models to support decisions across the natural/built system interface• Small stream ecology – monitoring/modeling• Small-scale modeling protocols for assessing BMP effectiveness• Conservation Innovation Grant – Innovative AgBMPs• Treatability translations• Evaluation of water quality trading market models• Development and testing of data management and exchange architectures
DWTP Sampling UpdateMike provide a review of hisDWTP intake samplingeffort. The next few slidessummarize
East Fork Lake Large Midwestern watershed draining to a National Scenic River and then the Ohio River Agriculture 2000 acre water surface890 km2 of upland drainage • 64% agriculture • 26% forest • 1.5 % imperviousness • 1.4% lawn • 1.3% impounded 20 MGD DWTP
20 MGD DWTP MnO4 pre-oxidation Coagulation Settling Filtration Cl2 • THM levels exceed 80 ug/L MCL during summer• increasing number of taste & odor episodes during summer • increasing period of (reduced) Mn(II) (necessitating more pre-oxidant usage) (spring-to- fall )• Increasing number of sulfide episodes (more pre-oxidant usage) during summer Adding deep-bed GAC to meet 2012 Stage-2 DBP Rule
Algal and Harmful-algal Derived Water Treatment Challenges Algal blooms Cl2 Reacti Stage-2 violations Advanced ve DOM DBPs Treatment $$$ ozonation CO Consumer complaints! AOP Taste & odor Consumer confidence! 2 HABs cmpds PAC/GAC btu Consumer confidence! algal Future regulations? toxin s
Algal Concerns• DBP precursors• Taste & Odors• Oxygen deficiencies• Toxins• Filter clogging• pH changes• Light limiting• Decreased recreational use• Decreased property values• Economy Content courtesy of Richard Lorenz City of Westerville
Real-time in-situ monitoring River and/or Reservoir Treatment Plant Processes chlorophyll a Ecology Processes coagulation, settling, filtration, chlorination, activated carbon, membranephycocyanin (cyanobact. pigment) biogeochemistry, hydrology, ecology filtration DO pH ORP turbidity Conductivity UV absorbance (DOM) In Plant Data Reservoir Modeling Source Modeling Data - Water Finished fate and Treatment Water various Data - transport Processes Data depths 1_depth Grab sampling chlorophyll a phycocyanin (cyano bact. pigment)algal taxonomy (species level counting) Grab sampling nutrients DBPs -THMs, HAAs pH UV absorbance (DOM) turbidity/sechi fluorescence EEMs (DOM) DOC/TOC, UV absorbance (DOM) Chlorine demand, etc. fluorescence EEMs (DOM) DBP (THMs) formation potential
UEFW SWAT Modeling Update and WQT Case Study – Large Scale ModelingOffice of Research and DevelopmentNational Risk Management Laboratory, Water Supply and Water Resources Division, Water Quality Management Branch
Overcoming model parameterization issues (UEFW Scale) 1. SSURGO vs. STATSGO Soils 2. SWAT Project Subbasin delineation
Summary of Working with the SSURGO data (11/9/2011 by SCK)•There are two commonly used soils databases: SSURGO and STATSGO. The scale of the SSURGO database generallyranges from 1:12,000 to 1:63,360. SSURGO is the most detailed level of soil mapping done by the Natural ResourcesConservation Service (NRCS) (http://soils.usda.gov/survey/geography/ssurgo/description.html). For the 48 conterminousstates, the STATSGO database is at the scale of 1:250,000 (http://www.il.nrcs.usda.gov/technical/soils/statsgo_inf.html).With regard to the information needed for modeling, the formats of SSURGO and STATSGO are the same. More informationon the format details can be found at http://soildatamart.nrcs.usda.gov/documents/SSURGO%20Metadata%20-%20Tables%20and%20Columns%20Report.pdf. To summarize, the databases include both tabular (text files) and spatial(shape files) data. Depending on the area to be covered, multiple files need to be combined into one dataset (comprised ofone tabular and one spatial). The East Fork Watershed (EFW) is covered by one STATSGO dataset. Six counties of SSURGOdata need to be aggregated into one dataset for use in SWAT.•To be used in SWAT, each soil classification listed in the SSURGO/STATSGO for the area being modeled (the EFW) must havea match in the SWAT soils reference database. Using ArcGIS tools, the area of the SSURGO/STATSGO shape files were clippedclose (a buffer was applied) to the boundary of the EFW. A link was established between the clipped spatial data and thetabular data. Tabular data not related to the area associated to the buffered EFW was removed from further consideration.The EFW tabular databases (SSURGO and STATSGO) were each joined to the SWAT soils classification database. There wereno orphan records in the STATSGO database, but there were orphans in the SSURGO database. The following table showsthe orphan SSURGO soil classifications (COMPNAME) and also shows how the soil classification was renamed to match withthe SWAT classification table.•The substitutions were made based on an extensive review of the STATSGO data and the neighboring soils of the orphanSSURGO classifications. Information from the following site was also used http://soils.usda.gov/technical/classification/ whenassigning a SWAT classification to the SSURGO orphans.
UEFW Preliminary DiscretizationThe National Elevation Dataset 10 meter dataset was downloadedfor each of the six counties in the East Fork Watershed. The fileswere aggregated into one for use with SWAT. The layer wasreprojected from UTM 1983 17N to 16N. To reduce SWATprocessing time, the NED layer was clipped to just larger thanthe East Fork Watershed. The watershed delineation wasperformed in the SWAT model using an area of 500 to get thedefault outlet locations at this scale. Then the watersheddelineation was performed again using an area of 10. Bothdelineations used the NHD flowlines to burn in the streamnetwork. In the SWAT model that was delineated using an area of10, all the default outlet locations were removed, and the defaultoutlet locations from the area of 500 delineation were added.Then, the default outlets that were in the lake were removed, andseveral other outlets were added. This delineation still needs tobe fine tuned.
W ater Quality Trading Case Study: Determining feasibility and advancing the market modelAssumption: DWTP operator colludes with WWTPs to reduce Ag Loadings.Nutrient Trading program leads to:• Fewer algal blooms WWTPs• Shorter periods of eutrophicationand hence…. DWTPeasier/cheaper water treatment. Downstream Direction
WSC Proposal 2011 Submitted Oct 19th The central question is: can watersheds and drinking water plants be understood as a single human‐engineered‐natural system? system, and how can human decisions regarding water quality be improved by modeling the coupled
CIG Effort Update- Cover Crop Sign-up Soil Sampling Small-scale ModelingOffice of Research and DevelopmentNational Risk Management Laboratory, Water Supply and Water Resources Division, Water Quality Management Branch
GRT Headwatershed (2.5 km2) whereInnovative AgBMPs are being tested in HUC 12 Subwatershed (111 km2) with weekly monitoringcooperation with the CC SWCD, Farm points shown Services, and NRCS
Those areas were used by SWCD to target fields for cover crop Preliminary Modeling with placement. Land Owners wereAnnAGNPs and SWAT Models were approached. Gray areas are slated used to project high areas of for cover crops sediment yield (rust colored area)
Model Application Development: Rules/Criteria for 1) Delineating Subbasins at GRT Scale 2) Accounting for differences in spatial distributions of crop rotations 3) Modeling fertilizer and pesticide application rates. 4) Estimating channel widths and depth at subbasin outlets.
Rules to Delineate subbasins at GRT scale1. A threshold area (critical source area) of 2 hectares was selected to define the stream network.2.The stream network generated by ArcSWAT did not match the actual stream in the field (based on the aerial photos). So this generated stream network was edited in ArcGIS to conform to the actual stream. This edited stream network was then burned in to the DEM. The flow direction and accumulation was recalculated after the burn in. The same threshold area of 2 hectares was selected and the stream network was generated again.3.The GRT watershed outlet point was selected as the whole watershed outlet and the watershed delineation was done keeping all the default subbasin outlets generated by ArcSWAT.4.The areas of the subbasins generated were analyzed to make sure that the individual subbasin areas were greater than 10% and less than 190% of the mean subbasin area. Any subbasin smaller than the threshold was removed by deleting the corresponding subbasin outlet and those subbasins which are higher than the threshold were divided by adding outlets.5.Adding or deleting an outlet had to be done by redefining the stream network. After the changes are made, the whole watershed delineation is done again.6.This process may take a few tries till you obtain a final watershed delineation.
Rules to account for differences in spatial distributions of crop rotations at GRT Scale1.National Agricultural Statistics Service (NASS) provides the Cropland Data Layer (CDL), which contains crop specific digital data layers. CDL is available for the Ohio from 2005 to 2010.2.The CDL for the GRT watershed (Clermont County) was downloaded and for all the years available (2005- 2010). The land use layer for the GRT watershed was super-imposed on the CDL to determine the actual crop rotations for the watershed. The crop rotations for GRT are shown in Figure 1. The green cells represent soybean and the yellow cells represent corn.3.The agricultural land within this watershed was further subdivided into separate land use classes based on the unique crop rotation pattern they follow. Figure 2 shows the polygon numbers of the agricultural parcels created within GRT. The crops currently (2011) planted in the GRT watershed was obtained from NRCS and Clermont County. Having the crop rotation data from 2006 to 2011, the same crop rotation was assumed to be present in those areas for the years starting at 1989. Table 1 was created based on this assumption and it shows what crop is grown in which parcel during the years 1989 to 2011. 1989 is the starting year for available climate data and the starting year of SWAT simulation. The different colors in the table represent unique crop rotation patterns and the parcels having the unique crop rotation were grouped together to form a unique land use. These land uses were further subdivided into parcels which would get a cover crop (in winter) and those that will not. The “NC” at the end of the land use title represents parcels with no cover crops. The land use/land cover data obtained from EPA for the Little Miami watershed for the year 2002 is shown in Figure 3 and it shows that only soybean was planted in the GRT watershed during that year. Based on the Table 1, for year 2002 all the parcels would have had only soybean planted and this further validates our assumption.
Figure 1 – Crop rotations for GRT from CDL data. 2006 2007 2008 2009 2010 Crop rotations in recent years do not follow the Corn, Bean, Bean rotation rule of thumbGreen represents soybean and yellow is corn
Figure 2 - GRT Parcel NumbersHad to re-characterize LandUsefor SWAT Modelparameterization based on parceland specific crop rotationschedule. Otherwise all fieldwould be corn or bean in a givenyear. This matters when it comesto differences in fertilizer andpesticide application pending thecrop type.
Reclassification of land use. Table 1 Land use Classification Parcel No. 65 71 73 246 101 103 74 240 43 243 239 102 98 75 77 80 81 241 57 44 248 244 31 32 30 85 86 247 216 105 59 2011 C C C C C C B B B B B B B B B B B B B B B B B B B B B B B B B 2010 B B B B B B C C C C C C C C C C C C C C C C C C C C C B B B B 2009 C C C C C C B B B B B B B B B B B B B B B B B B B B B B B B C 2008 B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B 2007 C C C C C C C C C C C C C C C C C C C C C B B B B B B B B B B 2006 B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B C 2005 C C C C C C B B B B B B B B B B B B B B B C C C C C C B B B B 2004 B B B B B B C C C C C C C C C C C C C C C B B B B B B B B B B 2003 C C C C C C B B B B B B B B B B B B B B B B B B B B B B B B C 2002 B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B 2001 C C C C C C C C C C C C C C C C C C C C C B B B B B B B B B B 2000 B B B B B B B B B B B B B B B B B B B B B C C C C C C B B B C 1999 C C C C C C B B B B B B B B B B B B B B B B B B B B B B B B B 1998 B B B B B B C C C C C C C C C C C C C C C B B B B B B B B B B 1997 C C C C C C B B B B B B B B B B B B B B B B B B B B B B B B C 1996 B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B 1995 C C C C C C C C C C C C C C C C C C C C C C C C C C C B B B B 1994 B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B C 1993 C C C C C C B B B B B B B B B B B B B B B B B B B B B B B B B 1992 B B B B B B C C C C C C C C C C C C C C C B B B B B B B B B B 1991 C C C C C C B B B B B B B B B B B B B B B B B B B B B B B B C 1990 B B B B B B B B B B B B B B B B B B B B B C C C C C C B B B B 1989 C C C C C C C C C C C C C C C C C C C C C B B B B B B B B B BLanduse title CBCB CBCBNC CBB CBBNC BCBBB BCBBBNC BBBB BBBBNC BBCLanduse code 21 22 23 24 25 26 27 28 29SWAT Landuse COR1 COR2 COR3 COR4 SOY1 SOY2 SOY3 SOY4 SOY5“C” represents Corn and “B” represents Soybean
Figure 3 – EPA Land use/ Land cover data for 2002.Dark Green represents soybean and yellow is corn. Light green is forest.
Rules to model fertilizer and pesticide application ratesFertilizer•Steve had provided the following application rates:Corn:N – 200 lbs per acre per planting seasonP2O5 – 37 lbs per acre per yearK – 37 lbs per acre per yearSoybean:P2O5 – 24 lbs per acre per yearK – 52 lbs per acre per yearHe had mentioned that 20-30 lbs./acre of N and P would be applied before planting and the remaining amount would be applied 30-40 daysafter planting. Also due to the non-availability of phosphate, MAP (Mono-ammonium phosphate 11-52-0) and DAP (Di-ammonium phosphate18-46-0) are being used. UAN (28%) is used as the source for nitrogen.•Lori had provided the following fertilizer recommendation:Corn:N – 1 lbs per acre per bushel yieldP2O5 – 20-40 lbs per acre per year (for soil test level “H”) and 40-80 lbs per acre per year (for soil test level “M”)K – 20-40 lbs per acre per year (for soil test level “H”) and 40-80 lbs per acre per year (for soil test level “M”)Soybean:P2O5 – 40-80 lbs per acre per year (for soil test level “H”) and 80-160 lbs per acre per year (for soil test level “M”)K – 40-80 lbs per acre per year (for soil test level “H”) and 80-160 lbs per acre per year (for soil test level “M”)•NASS survey for fertilizer application in Ohio for the year 2010:Corn:N – 141 lbs per acre per year (Average)P2O5 – 64 lbs per acre per year (Average)K – 91 lbs per acre per year (Average)Soybean: - No data was available•Based on these recommendations, it was decided on the following application rates:(Since SWAT does not track Potash, only Nitrogen and Phosphorus were considered)P2O5 – 40 lbs per acre per year (for both corn and soybean). Out of which 20 lbs/acre will be applied before planting and 20 lbs/acre will beapplied 35 days after planting.N – 200 lbs per acre per year (for corn). Out of which 30 lbs/acre will be applied before planting and 170 lbs/acre will be applied 35 days afterplanting.MAP was assumed to be source for P2O5. Since MAP contains 0.11kg N/kg, this would be deducted from the required N so that the total appliedN would sum up to a total of 200 lbs/acre.
Pesticide•Steve had provided the following application rates :Spring application:2,4-D – 0.5 to 1.0 lb per acreRoundup – 0.56 to 1.12 lbs per acre for Corn and 0.56 to 1.5 lbs per acre for SoybeanAtrazine – 1.4 to 2 lbs per acreSpring application:2,4-D – 1.0 qt. per acre (that would be 1 lb/Acre)Roundup – 0.75 to 1.5 lbs per acreCanopy – 2.25 oz per acre (Since Canopy is not in SWAT database and since we do notmonitor for Canopy, it is not applied).•For the Spring application, it was decided to apply all three pesticides at the rate of 1/3rd therecommended rates for all agricultural land. For the Fall application, it was decided to applyboth 2,4-D and Roundup at half the recommended rate for all agricultural land.
Criteria for estimation of channel widths and depths at subbasins (GRT)•The actual stream bank full width and depth was measured at the GRT watershed outlet and near the EPA monitoringpoint near Cornwell farm.•SWAT assumes the channel sides have a 2:1 run to rise ratio. Based on this assumption, the channel cross-section area atthe watershed outlet is 60 sq.ft. and the cross-section area near Cornwell farm is 9.63 sq. ft.•The total area of the GRT watershed is 623 acres and the area of the watershed draining at the monitoring point nearCornwell farm is 277 acres.•The width to depth ratios at the two cross-sections were almost the same(0.2). So it was decided to keep this ratio aconstant throughout the entire stream reach of the watershed and linearly interpolate the widths and depths between theCornwell site and the watershed outlet. For the channel reaches upstream of the Cornwell site, the same width-depth ratioswill be maintained. The cross-sections at the different reaches will also be cross checked from the lidar DEM.•The slopes of the different reaches will be calculated based on the DEM elevations at the start and the end of the streamreaches.
Lake Sampling in Oct 2011Office of Research and DevelopmentNational Risk Management Laboratory, Water Supply and Water Resources Division, Water Quality Management Branch
October Sediment Sampling Funded by USACE OM contents came up in the Lake Sediment discussion for comparison I’ve provided %OM contents from Site ID LOI (%) lake, crop fields, and stream Bethel-Surface 8.37 beds. All from east fork areas. *Note, these need to be EFLMR-Surface 14.49 corrected for bulk density to be EFL-Surface 2EFRWT 19.90 directly comparable, but BD DAM-Surface 2EFR20001 21.17 would probably be highest n lake sediment, so…. GRT Fields Site ID LOI (%) Accumulated Field 6 3.42 Stream Beds Field 7 3.17 10.23 Field 8 3.21Office of Research and Development Field 9 3.63National Risk Management Laboratory, Water Supply and Water Resources Division, Water Quality Management Branch
GHG Sampling and closing the lake C and N budgetFirst event of new project looking at Green House Gas productionin the Lake along with providing help to close the N and C budget Nutrient Data I collected below suggest that lake anoxia is limitingfor the lake. Leads are colleagues Jake Beaulieu from EPA and Amy nitrification (Ammonia increasing over LG profile of lake).Townsand-Small from UC. The work will be the master’s thesis ofBecky Smolenski DATE TIME SITE ID DEPTH UNIT TN TDN TNH4 DNH4 TNO23 DNO23 TUREA DUREA TP TDP TRP DRP20111025 01:30:00 PM EUS 0 ug N(P)/L 914 814 81.3 97.7 344 324 32.8 19.5 76 52.7 60 51.520111025 01:30:00 PM EUS 2M ug N(P)/L 959 840 94.7 101 355 320 42.5 23.9 85.4 54.8 65.4 51.520111025 02:30:00 PM EEN 0 ug N(P)/L 894 826 123 130 312 303 51.7 26.4 73.3 50.7 56.5 48.420111025 02:30:00 PM EEN 7.5M ug N(P)/L 873 814 109 122 342 312 24.2 26 72 48.4 55.6 46.920111025 03:30:00 PM EWN 0 ug N(P)/L 840 810 129 138 307 295 23.8 16.1 70.8 51.6 53.9 5120111025 03:30:00 PM EWN 8M ug N(P)/L 853 793 146 144 302 290 30.5 31.9 69.5 55.4 57.8 5320111025 04:30:00 PM EDW 0 ug N(P)/L 831 824 126 132 306 301 31.4 22.9 67.7 55.4 55.9 53.220111025 04:30:00 PM EDW 14M ug N(P)/L 839 827 123 132 308 301 30.8 17.5 67.8 54.8 55.2 57.720111025 05:30:00 PM EOF 0 ug N(P)/L 873 849 201 206 263 256 27 27.3 73.9 58.1 57.7 56.720111025 05:30:00 PM EOF 27M ug N(P)/L 1280 1160 211 205 392 383 59 53 259 211 237 21620111024 10:59:00 AM ELI (inflow) 0 ug N(P)/L 1720 56.3 617 65.2 453 39120111024 12:38:00 PM DAM (outflow) 0 ug N(P)/L 915 230 245 23.5 83.4 51.4
Tipping PointResearch -initiated Office of Research and Development National Risk Management Laboratory, Water Supply and Water Resources Division, Water Quality Management Branch
COLLABORATIVE RESEARCH: ROLE OF ORGANIC MATTER SOURCE ON THE PHOTOCHEMICAL FATE OF PHARMACEUTICAL COMPOUNDS – LEAD PI: ALLISON MACKAY, UCONN - FUNDED PROLOGUE An earlier version of this proposal was reviewed by a CBET panel and recommended for funding. The panel noted that we had “identified an important problem … that deserves attention … [because] there are knowledge gaps regarding their [pharmaceutical compounds] fate and the contribution of different degradation mechanisms in actual aquatic systems.” The panel was “impressed by the … collaboration with [the] Pomperaug River Watershed Coalition, which will serve as a way to disseminate results and offer basic community training.” “However, the panel felt that a more unified experimental plan would have strengthened the proposal.” In response to our panel comments, we have developed a new proposal that articulates in more detail how the experimental tasks are integrated to meet our project objectives. We have also changed our second field site from Boulder Creek, CO to the East Fork of the Little Miami River, OH to collaborate with the USEPA (Collaborator Nietch) in this networked experimental watershed. This proposal targets the CBET emphasis area of “emerging contaminants.” Figure 2. Fate processes for pharmaceutical Merged Data Interpretation compounds in aquatic systems. Arrow width is • NOM/EfOM physiochem contrasts • Photochem / physiochem relationships proportional to the relative importance. • Photochem / spectral relationships • OM contribution to PO influence in kfield • Variability in NOM/EfOM contribution to PO influence in kfield vs season • Variability in NOM/EfOM contribution to PO influence in kfield vs siteEngineer (PI MacKay), a geochemist (PI Chin), a photochemist (PI Sharpless) and a systemsecologist (Collaborator Nietch)
Monitoring Program Issues: Flow Gauge in the UEFW! We talked about where to best install and how to obtain the funding. Seemed most logical to partner with USACE who already has flow monitoring contracts with USGS. Need to get Erich’s input on this. In the meantime we (EPA) will install a sonteck depth integrated velocity and level gauge at a location in a stretch above the Williamsburg Treatment Plant.Office of Research and DevelopmentNational Risk Management Laboratory, Water Supply and Water Resources Division, Water Quality Management Branch
A. Aerial and Streams Data Stormwater BMP Files Retrofit Project? B. Subcatchment John discussed the potential for a discretization stormwater BMP retrofit demonstration project. We turned to some of the C. Land Cover and Properties Delineation headwatershed locations that EPA has studied in the past and are currently part of the weekly monitoring program. The D. SWMM Project-Existing headwatershed at left could be an Conditions appropriate one, and it is already modeled E. Alternative Scenarios13.
Nex t m eeting scheduled for Decem ber 10 th ; 9:00am *The ideas and opinions expressed herein are those of the primary author and do not reflect official EPA position or policy.Office of Research and DevelopmentNational Risk Management Laboratory, Water Supply and Water Resources Division, Water Quality Management Branch November 10, 2011