Transitory storage of N R in watersheds occurs in both surface and subsurface reservoirs. The...
1
Transitory storage of N R in watersheds occurs in both surface and subsurface reservoirs. The factors pertaining to storage examined here are: precipitation, temperature, base flow index, soil depth, carbonate rock extent, and rate of terrestrial plant growth. We used Enhanced Vegetation Index (EVI) data from the Terra Satellite-borne MODIS sensor to account for transient storage of N R in terrestrial and aquatic vegetation. Calibration of the model was conducted with nonlinear regression. Calibrations All but four of the fourteen estimated coefficients (5 N sources, 6 land-to-water factors, 2 storage variables, 1 stream decay) were highly statistically significant (p<0.001; table 1). The coefficients for vegetation growth, was especially strong (t=9). The rate of stream decay was estimated to be 0.027 per day. The coefficient for carbonate rock extent showed slight significance and suggests storage of nitrogen is greater in carbonate areas. The coefficients for soil depth and base flow index were not significant. Overall R was 0.91. Dynamic Simulation Equation 1 is recursive and allows the SPARROW model to predict changes in export over time in response to changes in inputs and environmental conditions. We used the calibrated model to examine seasonal and year-to-year variations in SPARROW models (SPAtially-Referenced Regression on Watershed attributes; Smith, et al, 1997) are widely used to identify and quantify the sources of contaminants in watersheds and to predict their flux and concentration at specified locations downstream (e.g. Alexander et al, 2008. Conventional SPARROW models describe the average (“steady-state”) relationship between sources and stream conditions based on long-term water quality monitoring data and spatially-referenced explanatory information. But many watershed management issues stem from intra- and inter- annual changes in contaminant sources, hydrologic forcing, or other environmental conditions which cause a temporary imbalance between watershed inputs and export. Such imbalances result in transitory storage of contaminants. This study attempts to quantify residence times and relative importance of transient storage compared to more direct transport by calibrating a dynamic SPARROW model of reactive nitrogen (N R ) flux in the Potomac River Basin and interpreting the model behavior. Methods We used seasonal water quality and watershed attribute data for 80 monitoring stations over the period 2002 to 2008 (Figure 1). The spatial reference frame of the model is a 16,000-reach, 1:100,000-scale stream network, and the computational time step is seasonal. Precipitation and temperature data are from PRISM. Nonpoint nitrogen Calibration procedures for steady state SPARROW models are given in Schwarz et al, 2006. Our procedure for dynamic calibration required modifying the “source” and “land-to-water” terms for individual reach-level catchments as follows: E t = f t SS t + f 0 E 0 (1) where E t is export (m/t) of N R from the catchment during the current season; E 0 is export (m/t) of N R from the catchment Conclusions Use of MODIS Vegetation Data in Dynamic SPARROW Modeling of Reactive Nitrogen Flux By Richard Smith 1 , John Brakebill 1 , Gregory Schwarz 1 , Anne Nolin 2 , Jhih-Shyang Shih 3 , Joel Blomquist 2 , Richard Alexander 1 , and Molly Macauley 3 US Geological Survey (Reston, VA) 1 Oregon State University (Corvallis, OR) 2 Resources for the Future (Washington, DC) 3 The authors wish to thank the National Aeronautics and Space Administration for support of this research through the Research Opportunities in Space and Earth Sciences (ROSES) Program Introduction Results BRT = ( 1 – CTS) 45 + CTS (45 + 1 / ( -ln[f 0 ] ) The distribution of temporally-averaged SRT is given in Table 2. References Figure 1. Locations of 80 monitoring stations in the Potomac River Basin reactive nitrogen yield due to changes in source inputs, precipitation, temperature, and vegetation growth. Yields were found to vary greatly as a function of seasonal conditions, with high values in both winter (January, February, March) and spring, but much lower summer yields due to low precipitation and temporary nitrogen retention in growing vegetation (EVI). Transient Storage The contribution of transient storage to export relative to “quick” runoff (CTS) can be estimated as f 0 E 0 / (f t SS t + f 0 E 0 ) , and varies in both time and space due to changes in the conditions and factors represented by the z i in equation 1 (precipitation, temperature, rock type, etc). Figures 2 present the apparent relationship between CTS and various factors. The map in Figure 3 shows the spatial pattern of temporally-averaged CTS in the Potomac Basin. Table 2 shows the spatial distribution of temporally-averaged CTS in quantiles. a) b) c) Figure 2. Relationship of the contribution of transient storage (CTS) to a) temperature; b) vegetation growth; c) precipitation. We estimated mean residence time in days as: 45 + 1 / ( -ln[f 0 ] ) based on the assumption that material spends, on average, half of the first seasonal time step in transit to the reservoir represented by the lag-1 term f 0 E 0 . The distribution of temporally-averaged SRT is given in Table 2. Table 1. Dynamic SPARROW Model Calibration Results PARAMETER ESTIMATE STD. T p ERROR g SOURCES Point sources 0.471 0.104 4.51 <0.0001 Urban area 26.700 7.780 3.43 0.0006 N-deposition 0.014 0.005 2.67 0.0075 Fertilizer use 0.036 0.013 2.70 0.0069 Manure production 0.077 0.012 6.06 <0.0001 TRANSPORT Transient storage 0.487 0.025 19.27 <0.0001 Delta log (runoff) 17.500 1.074 16.34 <0.0001 Precipitation (ln) 2.090 0.213 9.80 <0.0001 EVI (ln) -5.850 0.333 -17.55 <0.0001 Base flow index -0.001 0.004 -0.35 0.7199 Seasonal temperature 0.045 0.011 4.04 <0.0001 Water table depth 0.033 0.048 0.70 0.4837 Carbonate area -0.014 0.011 -1.27 0.2039 In-stream loss 0.063 0.027 2.28 0.0222 SUMMARY STATISTICS Number observations 2200 Number parameters 15 Root mean square error 0.6701 R-Square 0.9094 Adjusted R-Square 0.9088 Yield R-Square 0.7104 Table 2. The Spatial Distribution of Temporally-Averaged Variables Variable 5% 10% 25% 50% 75% 90% 95% 99% BRT (days) 123.1 132.7 146.4 161.7 185.4 235.6 329.2 910.1 SRT (days) 255.8 266.9 285.4 304.8 344.4 500.2 738.6 2470. 1 CTS (%) 50.8 54.2 60.4 64.9 70.1 74.9 77.6 82.7 sources, including fertilizer, manure, urban runoff, and atmospheric deposition, contribute to a common transient storage reservoir. Point sources bypass this reservoir. Calibration of a dynamic SPARROW model of N R based on seasonal time series of water quality and basin attribute data was successful. Precipitation, temperature, runoff, and terrestrial vegetation growth (EVI) were especially strong predictors. The strong EVI term appears to account for seasonal retention of nitrogen in basin vegetation. Model predictions for the 16,000 reach stream network show generally accurate (ln[flux] R 2 = .91; yield R 2 = .71) seasonal and year-to- year variations in yield. A source term based on previous season export was the strongest predictor and provides for gradual export decay under source reductions. The model can be used to estimate the contribution of transient storage (CTS) to catchment export. CTS decreases with contemporaneous runoff, and increases with previous runoff and vegetation growth. The estimate of the median storage contribution (64%) is in keeping with other estimates for the basin (Bachman et al, 1998). Estimates of storage residence time, and of the age of N R in Potomac stream water ( i.e. a blend of quick runoff and transient storage) are similar to, but generally shorter than previous estimates (Focazio et al, 1998). Alexander, R.B., Smith, R.A., Schwarz, G.E., Boyer, E.W., Nolan, J.V., and Brakebill, J.W., 2008, Differences in phosphorus and nitrogen delivery to the Gulf of Mexico from the Mississippi River Basin: Environmental Science & Technology, v. 42, no. 3, p. 822–830. Najjar, R, Patterson, L, Graham, S, 2009. Climate simulations of major estuarine watersheds in the Mid-Atlantic region of the US. Climatic Change 95:139–168. Smith, R.A., Schwarz, G.E., and Alexander, R.B., 1997, Regional interpretation of water-quality monitoring data: Water Resources Research, v. 33, no. 12, p. 2781–2798. Schwarz, G.E., Hoos, A.B., Alexander, R.B., and Smith, R.A., 2006, The SPARROW surface water-quality model: theory, application and user documentation: U.S. Geological Survey Techniques and Methods Report, book 6, chap. B3. Bachman, L.J., Lindsey, B.D., Brakebill, J.W., and Powars, D.S., 1998, Groundwater discharge and base-flow nitrate loads of nontidal streams, and their relation to a hydrogeomorphic classification of the Chesapeake Bay Watershed, Middle Atlantic Coast: U.S. Geological Survey Water-Resources Investigations Report 98-4059, 71 p. Focazio, M.J., Plummer, L.N., Böhlke, J.K., Busenburg, E., Bachman, L.J., and Powars, D.S., 1998, Preliminary estimates of residence times and apparent ages of Groundwater in the Chesapeake Bay Watershed, and water quality data from a survey of springs: U.S. Storage Residence Time (SRT) To satisfy mass balance, f t and f 0 must be < 1.0 as a long-term average. When sources of N R are set to zero, catchment export falls exponentially according to the recursive relation: E t / E 0 = f 0 . Assuming the export rate of material from transient storage at a given point in time is proportional to the quantity in storage, the exponential decay rate can be used to estimate the mean residence time of stored material. Figure 3. spatial pattern of temporally-averaged CTS in the Potomac Basin 30 35 40 45 50 55 60 65 70 75 80 0 20 40 60 80 Temperature (F) CTS Source Share (%) 0.1 0.2 0.3 0.4 0.5 0.6 0 20 40 60 80 Enhanced Vegetation Index (EVI) CTS Source Share (%) 100 200 300 400 500 600 0 20 40 60 80 Precipitation (mm) CTS Source Share (%) “t”
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Transcript of Transitory storage of N R in watersheds occurs in both surface and subsurface reservoirs. The...
Transitory storage of NR in watersheds occurs in both surface and subsurface reservoirs. The factors pertaining to storage examined here are: precipitation, temperature, base flow index, soil depth, carbonate rock extent, and rate of terrestrial plant growth. We used Enhanced Vegetation Index (EVI) data from the Terra Satellite-borne MODIS sensor to account for transient storage of NR in terrestrial and aquatic vegetation. Calibration of the model was conducted with nonlinear regression.
Calibrations All but four of the fourteen estimated coefficients (5 N sources, 6 land-to-water factors, 2 storage variables, 1 stream decay) were highly statistically significant (p<0.001; table 1). The coefficients for vegetation growth, was especially strong (t=9). The rate of stream decay was estimated to be 0.027 per day. The coefficient for carbonate rock extent showed slight significance and suggests storage of nitrogen is greater in carbonate areas. The coefficients for soil depth and base flow index were not significant. Overall R2 was 0.91.
Dynamic SimulationEquation 1 is recursive and allows the SPARROW model to predict changes in export over time in response to changes in inputs and environmental conditions. We used the calibrated model to examine seasonal and year-to-year variations in
SPARROW models (SPAtially-Referenced Regression on Watershed attributes; Smith, et al, 1997) are widely used to identify and quantify the sources of contaminants in watersheds and to predict their flux and concentration at specified locations downstream (e.g. Alexander et al, 2008. Conventional SPARROW models describe the average (“steady-state”) relationship between sources and stream conditions based on long-term water quality monitoring data and spatially-referenced explanatory information. But many watershed management issues stem from intra- and inter-annual changes in contaminant sources, hydrologic forcing, or other environmental conditions which cause a temporary imbalance between watershed inputs and export. Such imbalances result in transitory storage of contaminants. This study attempts to quantify residence times and relative importance of transient storage compared to more direct transport by calibrating a dynamic SPARROW model of reactive nitrogen (NR) flux in the Potomac River Basin and interpreting the model behavior.
MethodsWe used seasonal water quality and watershed attribute data for 80 monitoring stations over the period 2002 to 2008 (Figure 1). The spatial reference frame of the model is a 16,000-reach, 1:100,000-scale stream network, and the computational time step is seasonal. Precipitation and temperature data are from PRISM. Nonpoint nitrogen
Calibration procedures for steady state SPARROW models are given in Schwarz et al, 2006. Our procedure for dynamic calibration required modifying the “source” and “land-to-water” terms for individual reach-level catchments as follows: Et = ft SSt + f0 E0 (1)where Et is export (m/t) of NR from the catchment during the current season; E0 is export (m/t) of NR from the catchment during the previous season;SSt is the sum of sources of new NR to the catchment during the current season;ft and f0 are functions of catchment characteristics.
The form of the functions ft and f0 is: exp( a0 + a1z1 + a2z2 +…. + anzn) and the zi are catchment characteristics which determine storage and transport of NR.
Conclusions
Use of MODIS Vegetation Data in Dynamic SPARROW Modeling of Reactive Nitrogen FluxBy
Richard Smith1, John Brakebill1, Gregory Schwarz1, Anne Nolin2, Jhih-Shyang Shih3, Joel Blomquist2, Richard Alexander1, and Molly Macauley3
US Geological Survey (Reston, VA)1 Oregon State University (Corvallis, OR)2 Resources for the Future (Washington, DC)3
The authors wish to thank the National Aeronautics and Space Administration for support of this research through the Research Opportunities in Space and Earth Sciences (ROSES) Program
Introduction
Results
BRT = ( 1 – CTS) 45 + CTS (45 + 1 / ( -ln[f0] )
The distribution of temporally-averaged SRT is given in Table 2.
References
Figure 1. Locations of 80 monitoring stations in the Potomac River Basin
reactive nitrogen yield due to changes in source inputs, precipitation, temperature, and vegetation growth. Yields were found to vary greatly as a function of seasonal conditions, with high values in both winter (January, February, March) and spring, but much lower summer yields due to low precipitation and temporary nitrogen retention in growing vegetation (EVI).
Transient StorageThe contribution of transient storage to export relative to “quick” runoff (CTS) can be estimated as f0 E0 / (ft SSt + f0 E0) , and varies in both time and space due to changes in the conditions and factors represented by the zi in equation 1 (precipitation, temperature, rock type, etc).
Figures 2 present the apparent relationship between CTS and various factors. The map in Figure 3 shows the spatial pattern of temporally-averaged CTS in the Potomac Basin. Table 2 shows the spatial distribution of temporally-averaged CTS in quantiles. a) b) c)
Figure 2. Relationship of the contribution of transient storage (CTS) to a) temperature; b) vegetation growth; c) precipitation.
We estimated mean residence time in days as: 45 + 1 / ( -ln[f0] ) based on the assumption that material spends, on average, half of the first seasonal time step in transit to the reservoir represented by the lag-1 term f0 E0 . The distribution of temporally-averaged SRT is given in Table 2.
According to Equation 1, however, NR in stream water is a blend of material from contemporaneous sources transient storage. The “age” of the blend is dependent on the factors which determine CTS and its compliment. We estimated the blended residence time (BRT) in days as:
Table 1. Dynamic SPARROW Model Calibration ResultsPARAMETER ESTIMATE STD. T p
ERROR gSOURCES Point sources 0.471 0.104 4.51 <0.0001 Urban area 26.700 7.780 3.43 0.0006 N-deposition 0.014 0.005 2.67 0.0075 Fertilizer use 0.036 0.013 2.70 0.0069 Manure production 0.077 0.012 6.06 <0.0001TRANSPORT Transient storage 0.487 0.025 19.27 <0.0001 Delta log (runoff) 17.500 1.074 16.34 <0.0001 Precipitation (ln) 2.090 0.213 9.80 <0.0001 EVI (ln) -5.850 0.333 -17.55 <0.0001 Base flow index -0.001 0.004 -0.35 0.7199 Seasonal temperature 0.045 0.011 4.04 <0.0001 Water table depth 0.033 0.048 0.70 0.4837 Carbonate area -0.014 0.011 -1.27 0.2039 In-stream loss 0.063 0.027 2.28 0.0222SUMMARY STATISTICS Number observations 2200 Number parameters 15 Root mean square error 0.6701 R-Square 0.9094 Adjusted R-Square 0.9088 Yield R-Square 0.7104
Table 2. The Spatial Distribution of Temporally-Averaged Variables
Variable 5% 10% 25% 50% 75% 90% 95% 99%
BRT (days) 123.1 132.7 146.4 161.7 185.4 235.6 329.2 910.1
SRT (days) 255.8 266.9 285.4 304.8 344.4 500.2 738.6 2470.1
CTS (%) 50.8 54.2 60.4 64.9 70.1 74.9 77.6 82.7
sources, including fertilizer, manure, urban runoff, and atmospheric deposition, contribute to a common transient storage reservoir. Point sources bypass this reservoir.
Calibration of a dynamic SPARROW model of NR based on seasonal time series of water quality and basin attribute data was successful. Precipitation, temperature, runoff, and terrestrial vegetation growth (EVI) were especially strong predictors. The strong EVI term appears to account for seasonal retention of nitrogen in basin vegetation. Model predictions for the 16,000 reach stream network show generally accurate (ln[flux] R2 = .91; yield R2 = .71) seasonal and year-to-year variations in yield. A source term based on previous season export was the strongest predictor and provides for gradual export decay under source reductions. The model can be used to estimate the contribution of transient storage (CTS) to catchment export. CTS decreases with contemporaneous runoff, and increases with previous runoff and vegetation growth. The estimate of the median storage contribution (64%) is in keeping with other estimates for the basin (Bachman et al, 1998). Estimates of storage residence time, and of the age of NR in Potomac stream water ( i.e. a blend of quick runoff and transient storage) are similar to, but generally shorter than previous estimates (Focazio et al, 1998).
Alexander, R.B., Smith, R.A., Schwarz, G.E., Boyer, E.W., Nolan, J.V., and Brakebill, J.W., 2008, Differences in phosphorus and nitrogen delivery to the Gulf of Mexico from the Mississippi River Basin: Environmental Science & Technology, v. 42, no. 3, p. 822–830.
Najjar, R, Patterson, L, Graham, S, 2009. Climate simulations of major estuarine watersheds in the Mid-Atlantic region of the US. Climatic Change 95:139–168.
Smith, R.A., Schwarz, G.E., and Alexander, R.B., 1997, Regional interpretation of water-quality monitoring data: Water Resources Research, v. 33, no. 12, p. 2781–2798.
Schwarz, G.E., Hoos, A.B., Alexander, R.B., and Smith, R.A., 2006, The SPARROW surface water-quality model: theory, application and user documentation: U.S. Geological Survey Techniques and Methods Report, book 6, chap. B3.
Bachman, L.J., Lindsey, B.D., Brakebill, J.W., and Powars, D.S., 1998, Groundwater discharge and base-flow nitrate loads of nontidal streams, and their relation to a hydrogeomorphic classification of the Chesapeake Bay Watershed, Middle Atlantic Coast: U.S. Geological Survey Water-Resources Investigations Report 98-4059, 71 p.
Focazio, M.J., Plummer, L.N., Böhlke, J.K., Busenburg, E., Bachman, L.J., and Powars, D.S., 1998, Preliminary estimates of residence times and apparent ages of Groundwater in the Chesapeake Bay Watershed, and water quality data from a survey of springs: U.S. Geological Survey Water-Resources Investigations Report 97-4225, 75 p.
Storage Residence Time (SRT)To satisfy mass balance, ft and f0 must be < 1.0 as a long-term average. When sources of NR are set to zero, catchment export falls exponentially according to the recursive relation: Et / E0 = f0 . Assuming the export rate of material from transient storage at a given point in time is proportional to the quantity in storage, the exponential decay rate can be used to estimate the mean residence time of stored material.
Figure 3. spatial pattern of temporally-averaged CTS in the Potomac Basin
30 35 40 45 50 55 60 65 70 75 800
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Temperature (F)
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0.1 0.2 0.3 0.4 0.5 0.60
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Enhanced Vegetation Index (EVI)
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(%)
1001502002503003504004505005500
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Precipitation (mm)
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“t”
