Phosphorus Risk Assessment Using the Variable Source Loading Function Model Zachary Easton 1, Elliot Schneiderman 2, M. Todd Walter 1, and Tammo Steenhuis 1 1 Dept. Biological and Environmental Engineering, Cornell University, Ithaca, NY 2 New York City Dept. Environmental Protection, Kingston, NY * zme2@cornell.edu
We know that in many areas with steep slopes and shallow soils hydrology is, to a great extent, driven by topology. Variable Source Areas (VSA) Near stream areas, areas with shallow, slowly permeable soil or a large contributing areas produce the majority of runoff
Why is P Modeling Difficult in VSA Watersheds Spatially and temporally heterogeneous Watershed properties (soil, landuse, topology, STP, management) can display great variability Thus another level of complexity is added to predicting P loss How can we as scientists, planners, and watershed managers identify areas of the landscape prone to high runoff and thus potentially high P losses?
Many Current Water Quality Models Cannot Capture this Complexity, e.g., General Watershed Loading Function (GWLF) Soil Water Assessment Tool (SWAT) Agricultural Nonpoint Source Pollution Model (AGNPS)
First Then we must be delineate able to P correctly source areas predict where runoff source areas are Now we can visualize and begin to manage Only critical distributed source areas models can do this
Plant tissue Disturbed un-vegetated sites Impervious surfaces Fertilized areas P Sources Manured Areas
Modeling P Loss Since different P sources contribute P differently, model processes separately Fertilizer/manure Impervious areas Baseflow Soils Hydrologic input were taken from VSLF and used in the P model
Dissolved P (kg ha -1 ) Fertilizer/manure P Fertilizer/manure P declines with successive runoff (R) events following application: D F,t is the 0.5 0.45 0.4 0.35 0.3 0.25 0.2 0.15 0.1 0.05 0 dd F, t dr k M D F F, t F, t Apr-03 Aug-03 Dec-03 Apr-04 Aug-04 Dec-04 Apr-05 2 cumulative P loss, k F is the reaction constant, M F,t is the amount of P left at time t
P Load (kg ha -1 ) Impervious Surfaces P accumulates on impervious surfaces Modeled with an exponential relationship 0.6 0.5 0.4 0.3 0.2 0.1 0 Apr-03 May-03 Jun-03 Wash off of accumulated P is modeled with a first order relationship
Baseflow Represents integrated influence of surface and subsurface factors. An export coefficient approach was used L B B, t B t where L B,t is baseflow P load, μ B is the mean expected P concentration in baseflow, and B t is the baseflow volume
P Runoff (mg L -1 ) Soil P P losses from soil can be approximated using soil test P values: 1.5 1.2 0.9 0.6 D M R S, t S S t where D S,t is the runoff P load μ s is the soil specific coefficient determined from sampled runoff M S is the soil test P R t is the runoff 0.3 y = 0.0612x + 0.1279 r 2 = 0.74 0 0 5 10 15 20 25 Morgan's STP (mg kg -1 )
SRP Load (kg/d) Discharge (cm/d) Agricultural 15 10 5 0
TDP Load (kg d -1 ) DP Load (kg d -1 ) 1.4 Developed 1.2 1.0 0.8 Measured Modeled 0.6 0.4 0.2 2.5 2.0 Measured Modeled 0.0 Nov-03 Dec-03 Jan-04 Feb-04 Mar-04 1.5 1.0 0.5 0.0 Apr-03 Jul-03 Oct-03 Jan-04 May-04 Aug-04 Nov-04 Mar-05
SRP (mg/l) Dissolved P (ug L -1 ) How Does Temp Affect P Availability? Suburban Watershed Ithaca, NY Forested Watershed (Catskills, NY) 140 120 0.05 100 Scott et al. 2001 Biogeochemistry 0.04 80 0.03 60 0.02 40 20 0.01 0 0 Apr-03 Feb-96Jul-03 Jun-96 Oct-03 Jan-04 Oct-96 May-04 Aug-04 Feb-97 Nov-04 Jun-97
Phosphate (mg/l) 0.8 Possible Link to DOC 0.6 0.4 0.2 0 R 2 = 0.3 50 150 250 350 450 DOC (mg/l)
DOC in Streamflow (mg/l) Simulated Linking Biogeochemical Systems DOC P 15 30.97376 15 13 11 Chile Study 12 0 y = 1.00x - 0.03 R 2 = 0.81 0 Observed 12 Van t Hoff (1898) C Q 10 T 10 T o 9 7 5 3 1 Valdivia, Walter, Hedin. 2006. <in preparation> 12/01/1996-1 06/19/1997 01/05/1998 07/24/1998 02/09/1999 Observed DOC Simulated DOC
Temp (C) How Does Temp Affect P Availability? 25 20 15 10 5 0-5 -10-15 Q S T, S S T TR 10 Soil Surface Baseflow (50cm) -20 Apr-03 Jul-03 Oct-03 Jan-04 Apr-04 Jul-04 Oct-04 Jan-05 Apr-05
SRP Load (kg/d) Discharge (cm/d) 15 10 5 0 Improved Corroboration Hively et al. 2005. HESS
DP Load (kg d -1 ) TDP Load (kg d -1 ) DP Load (kg d-1) Streamflow (mm d -1 ) 16 14 12 10 8 6 4 2 Measured Modeled Urban Measured 18.3 Modeled 19.5 Measured 23.9 Modeled 23.7 0 2.5 Apr-03 Jun-03 Sep-03 1.4 Dec-03 Mar-04 Jun-04 Sep-04 Dec-04 Mar-05 2.5 2.0 2.0 1.5 1.5 Measured Modeled r 2 = 0.77 E = 0.75 Measured1 Modeled 1.2 0.8 0.6 0.4 0.2 0 r 2 = 0.77 E = 0.75 Measured Modeled Nov-03 Dec-03 Jan-04 Feb-04 Mar-04 Measured 3.2 Modeled 3.1 1.0 1.0 0.5 0.0 Apr-03 Jul-03 Oct-03 Jan-04 May-04 Aug-04 Nov-04 Mar-05 Apr-03 Jul-03 Oct-03 Jan-04 May-04 Aug-04 Nov-04 Mar-05
Where are the High Risk Areas? High Runoff Low P High Runoff High P Low Runoff High P Low Runoff Low P Weti P Loss LU Dissolved (kg/ha) P 25-Jan-1999 Sept 20 2000 0-0.007 0.007-0.019 0.019-0.053 0.053-0.110 0.110-0.325
P Loss (kg/ha) 0-0.0246 0.0246-0.0391 0.0391-0.0569 0.0569-0.1414 0.1414-0.4941
VSAs Discussed Spatial and temporal components to P loss Areas with high runoff losses can be P source areas, but not all of them! Interaction between runoff source areas and P source areas
Conclusions Landscape P loss originates from definable areas of the watershed VSAs Landscapes contribute differing P loads at different times.temp dependency Winter vs. summer Soils vs. fertilized/manured vs. baseflow To reduce P loads in surface waters we need to consider all these factors