MODELING NITROGEN DYNAMICS IN THE TIOUGHNIOGA RIVER, NEW YORK Li Jin1, Kristina Gutchess2 and Zunli Lu2
1 Geology Department, SUNY Cortland, Cortland, NY 13045
[email protected]; 2 Department of Earth Sciences, Syracuse University, 204 Heroy Geology Laboratory, Syracuse, NY 13244
Abstract
Integrated Catchment Model (INCA)
Elevated nutrient concentrations such as nitrogen have led to serious problems of surface water eutrophication and groundwater contamination in many places around the world. Chesapeake Bay has been the subject of intensive research on eutrophication and nutrient input reductions. Tioughnioga River, the headwater to the Susquehanna River and Chesapeake Bay, plays an important role in controlling the transport of nutrients downstream. In this study, an integrated catchment nitrogen model (INCA-N) is used to simulate in-stream nitrate concentrations in the headwater of Tioughnioga River which begins as two branches with contrasting land use characteristics. The model is calibrated using the weekly nitrate concentrations at the mouths of two branches and monthly nitrate concentrations from multiple locations along the two branches from 2012 to 2014. Preliminary modeling results suggest the main drives of the in-stream nitrate concentrations are nitrogen levels in atmospheric deposition and groundwater. The model is sensitive to nitrogen process-reparameters e.g. denitrification rates and plant uptake rates. Projected climate change effects in the Tioughnioga River using the INCA-N model will be explored in future studies.
Sub-catchment comprising 1 to 6 land use types
Reach 1
Sub-catchment boundary Sub-catchment river network
( !
! ( .! ( . # *# ( ! . *
# *
TRE3 ( !
TR1
2
( !
8 Kilometers
# *
( !
TR2
¯
! (
WWTP
# *
PPT
. !
Flow Stations
( !
Chemistry
! ( .! ( . # *# ( ! . *
# *
WWTP
# *
PPT
. !
Flow Stations
( !
Chemistry
landuse_clip
merge_7677_Clip1 Elevation Value
! (
81
( !
# *
LUCODE
High : 645
Urban
Low : 272
# *
0
# *
2
4
8 Kilometers
# *
¯
Agriculture
# * # *
Forest
Tioughnioga River Catchment Basic Characteristics: Land use:
Size: ~900 km
primarily agricultural and forest with urban concentrated at the bottom of the watershed
Geology: River - unconfined glacial-outwash comprised of sand and gravel with till-covered bedrock hills. Aquifer - Upper to Middle Devonian bedrock consisting predominantly of shale interbedded with siltstone, sandstone, and limestone.
Reach No. Name TRE1 1 TRE2 2 TRE3 3 TRE4 4 TRE5 5 TRW1 6 TRW2 7 TRW3 8 TRW4 9 TR1 10 TR2 11 East Branch West Branch
Data Collection:
Land Use Class (%) Subcatchment Area (km2) 219.22 93.66 147.84 10.74 24.32 65.36 25.83 100.93 73.40 3.17 130.17 495.77 265.53
Reach Length (m) 26634 10578 13847 2847 7190 12166 3293 6274 4918 947 4956
Urban 1.21 1.51 0.31 0.00 3.16 2.61 1.07 2.79 17.45 25.96 3.72 1.24 5.98
Highway Agriculture Wetland 0.00 42.87 2.01 0.00 24.65 2.22 0.00 30.51 1.45 0.00 47.99 0.41 0.00 52.77 0.00 3.33 42.27 5.98 4.42 42.41 6.09 1.79 42.02 1.56 1.58 52.11 0.00 6.71 2.02 0.00 0.87 37.44 0.28 0.00 39.76 1.22 2.78 44.70 3.41
Nitrate: weekly sampling at TRW4 and TRE5 from 2012-2014; Monthly-bimonthly sampling at all sites in 2014 Flow: daily flow data from USGS flow station Precipitation: Daymet data and individual stations from neighboring area
Observations: flow water chemistry (nitrate)
Data input for PERSiST Hydrological inputs: Daily precipitation Daily temperature Observation: Flow
Wetland
DEM map and land use map for Tioughnioga River Watershed with flow station, water quality station, precipitation stations.
2
TRW4
Rainfall-runoff Model-PERSiST
Legend
( !
( !
4
Modified from Whitehead et al., 1998; 2011
( !
# *
0
( !
Observed Simulated
TRE5
Forest 53.90 71.62 67.72 51.60 44.07 45.80 46.01 51.83 28.86 65.31 57.70 57.78 43.13
Left: Generic bucket structure and relative evapotranspiration rate as a function of water depth Right: Hydrologic response units with each subcatchment Modified from Futter et al., 2014.
2.0
TRE5
Observed
1.5
Modeled
1.0 0.5 0.0 2.5 1
2
3
4
5
6
7
8
TRW4
2.0
9
10
Observed
11
12
Modeled
1.5 1.0 0.5 0.0
2
3
4
5
6
7
8
9
10
11
Monthly Nitrate-N Concentration Comparison
12
TRE5
80000
TRW4
70000
Linear (all)
60000
y = 1.0765x R² = 0.9635
50000 40000 30000 20000 10000 0
1
Monthly Nitrate-N loads Comparison
90000
Observed Nitrate Load (kg N/month)
TRW4
( !
( !
Legend
INCA Nitrate Results
Nitrate Concentrations (mg N/L)
( !
TRE4 ( ! ( TRE5 !
Multi-branch River Network
N inputs: atmospheric N inputs to the whole catchment N inputs as fertilizer and manure to agriculture land N inputs to the stream (point source-STW)
TRW1
TRW3
Reach 4
Nitrate Concentrations (mg N/L)
TRE1
Simulated
Hydrological inputs (SMD and HER from PERSiST): daily precipitation and temperature soil moisture deficit (SMD) hydrological effective rainfall (HER)
# *
# *
r2=0.72 N-S=0.68
Land use data: up to 6 different land use types
# *
# *
( !
Reach 3
Observed
Reach and sub-catchment characteristics: reach length, area, etc (derived from DEM using GIS)
# *
# *
TRW2
Reach 2
TR1
Sub-catchment reach structure
Land Use
# *
TRE2
Diffuse STW
List of basic data need for INCA
DEM
( !
Link between diffuse and point sources and in-stream components at the sub-catchment level
Cell Model
Site Description
# *
INCA Flow Results
0
10000 20000 30000 40000 50000 60000 70000 80000 90000
Modeled Nitrate Load (kg N/month)
Conclusions Nitrate concentrations are generally low at both West Branch and East Branch of Tioughnioga River. West Branch has slightly higher nitrate concentrations comparing to the East Branch. Nitrate concentrations follow a seasonal pattern reaching the lowest values in the summer and early fall due to high denitrification rates and plant uptake. PERSiST and INCA-N models catch flow dynamics at both high flow and low flow conditions. Timing and magnitude of snowmelt peaks are well simulated. INCA-N model simulates nitrogen reasonably well in this snow-dominated catchment. Model is sensitive to atmospheric deposition, groundwater concentrations and nitrogen-process related parameters such as denitrification, mineralization and plant uptake. Ackowledgements Thanks to SUNY Cortland Faculty Research Program and Syracuse University Water Fellowship NRT: Education Model Program on Water-Energy Research (EMPOWER) at Syracuse University (DGE-1449617).
References
Whitehead, P. G., Wilson, E. J. and Butterfield, D., Sci. Total Environ., 1998, 210/211, 547-558. Whitehead, P.G., L. Jin, H. M. Baulch, D. A. Butterfield, S. K. Oni, P. J. Dillon, M. N. Futter, A. J. Wade, R. North, E. M. O'Connor and H. Jarvie, Sci. Total Environ., 2011, 412–413, 315–323. M. N. Futter, M. A. Erlandsson, D. ButterfieldP. G. Whitehead, S. K. Oni and A. J. Wade, Hydrol. Earth Syst. Sci., 2014, 18, 855–873.
