CAEP Study Site: Cannonsville Reservoir Watershed

Tammo Steenhuis, Zach Easton, Eric White, C. Shoemaker Joshua Woodbury and many others of the Soil and Water Laboratory, Farming Community and the Watershed Agriculture Program in Walton This project is supported by National Research Initiative Competitive Grant from the USDA Cooperative State Research, Education, and Extension Service

Best Management The Catskills (or the New York City Source Practices Watersheds) is were one of the done few watersheds right! where the (dissolved) phosphorus concentration is significantly decreasing after BMP installation Why is that?

This is why! In the temperate northeast hydrology is, to a great extent, driven by topology Near stream areas, and areas with shallow, soils over a slowly permeable glacial till or bedrock produce the majority of runoff

Biological and Environmental Engineering Runoff location Variable Source Areas concept included in BMP installation from early on Soil & Water Research Group

BMP s BMP effectiveness is determined by the three L s Location Location Location

The Science of Reducing P Keep the manure out of the runoff water Requires knowledge of saturated areas Reduce P loading Reducing P in feed import To optimize P reductions, models are needed simulating hydrology of the region and are in agreement with farmer s perceptions

The Case of SWAT

Biological and Environmental Engineering Soil Water Assessment Tool (SWAT) Basin scale hydrological and water quality.6 ha continental Runs on easily available data Process based chemistry vs export coefficient type models Does not require much calibration for hydrology More for chemistry and sediment Soil & Water Research Group

Soils Landuse SWAT defines HRUs as the coincidence of soil type and landuse Hydrological/chemical properties are defined at the HRU HRUs So runoff/p loss is the same here (lowland pasture) As here (upland pasture) We know this is not the case

hydrology model approach For each hydrologic response unit rainfall ET If rainfall > ET, soil wets up Q P e P 2 e S

Biological and Environmental Engineering USDA-NRCS Curve Number Model Runoff =P e2 /(Pe+S) That is a big problem S=254/CN-254 Tables link CN to land use and soil infiltration capacity Infiltration capacity in excess of rainfall intensity! Soil & Water Research Group

hydrology model approach Total runoff and the saturated protion of the watershed is a function of amount of precipitation rainfall ET If rainfall > ET, soil wets up If rainfall > soil storage, excess overland flow Q P e P 2 e S

Why not use a distributed storage directly in the water balance? If done wisely, it should give same overall S and runoff distribution

Available Storage Defining the distribution of available storage or water deficit The difference between saturation and field capacity is the availability storage or water deficit σ S( 1/(1 As ) 1) It is a function of the fractional area, A s and the total storage, S in the watershed Area (fraction)

Rainfall Effective Total

SWAT-VSA or SWAT-WB Thus by assigning field capacities according to FldCap Saturon ` S( 1/(1 As ) 1) and not according to soil type, runoff can be determined as saturation excess. Expectsame hydrograph at outlet

terrain analysis Distribute runoff areas in the landscape DEM Upstrean area, A Slope β soil depth, D soil hydraulic conductivity K The location of the saturated areas is mainly determined by the topography ln tan A K s D Soil topographic wetness index

STI SSURGO Landuse SWAT-VSA defines HRUs as the coincidence of soil topographic index (and soil) and landuse Weighted average of soil properties nested with in an area weighted index class HRUs So runoff/p loss is now not the same here (lowland pasture) As here (upland pasture)

Soil Topographic Index 1=f(S) =f(s) =f(s) =f(s) Soil Topographic Index 33.1 High : 33.135 3.52 Low : 3.5211 =f(s) =f(s) =f(s) =f(s) =f(s) f(s) Lyon et al. 24. Hydrol. Proc. 18(15): 2757-2771. Schneiderman et al. 26. Hydrol. Proc. (in press)

Application of two SWATs to the Cannonsville watershed

Cannonsville Watershed - Modeling Options Modeling Option SWAT VSA ICRK: crack flow Turned off Turned off IRTE: water routing method Variable Variable IDEG: channel degradation code Turned off Turned on IWQ: in-stream water quality Turned off Turned off

Output at watershed outlet 1 2 3 4 5 6 7 8 Flow (cms) Flow Measured SWAT VSA 1 2 3 4 5 6 7 Jan-94 Mar-94 May-94 Jul-94 Sep-94 Nov-94 Jan-95 Mar-95 May-95 Jul-95 Sep-95 Nov-95 Jan-96 Mar-96 May-96 Jul-96 Sep-96 Nov-96 Jan-97 Mar-97 May-97 Jul-97 Sep-97 Nov-97 Jan-98 Mar-98 May-98 Jul-98 Sep-98 Nov-98 Jan-99 Mar-99 May-99 Jul-99 Sep-99 Nov-99 TDP (kg) TDP 2 4 6 8 1 12 Sediment (tons) Sediment 2 4 6 8 1 12 PP (kg) PP

Output at Subbasin 15.2.4.6.8 1 1.2 1.4 1.6 Flow (cms) Flow SWAT VSA 5 1 15 2 25 3 35 4 45 5 Ma Ma Ma Ma Ma Ma Ma Ma Ma Ma Ma Ma TDP (kg) TDP 1 2 3 4 5 6 7 Sediment (tons) Sediment 2 4 6 8 1 12 PP (kg) PP

Output at Subbasin 4 1 2 3 4 5 6 7 8 Flow (cms) Flow S W 5 1 15 2 25 3 TDP (kg) TDP 1 2 3 4 5 6 Sediment (tons) Sediment 1 2 3 4 5 6 PP (kg) PP

Average Values - Values at subbasin 15 Flow PP TDP Sediment SWAT.431462 7.597742 8.775495 11.92149 VSA.44872 16.99449 7.576776 19.34477 - Values at subbasin 4 Flow PP TDP Sediment SWAT 1.389932 66.3428 52.83943 7.62377 VSA.273268 8.373887 1.23619 13.51877

Application to Townbrook.

Streamflow (cm) SWAT Streamflow (cm) SWAT 16 14 12 1 a r 2 =.76 E =.82 Measured SWAT-VSA 15 8 6 Measured 15 4 2 16 Oct-98 Mar-99 Aug-99 Jan- Jun- Nov- Apr-1 Sep-1 15 Feb-2 Jul-2 Dec-2 May-3 Oct-3 Mar-4 Aug-4 14 b Measured 12 1 8 6 4 r 2 =.74 E =.83 SWAT Measured 15 2 Oct-98 Mar-99 Aug-99 Jan- Jun- Nov- Apr-1 Sep-1 Feb-2 Jul-2 Dec-2 May-3 Oct-3 Mar-4 Aug-4

Runoff SWAT-VSA swat_vsa_hrus SWAT_VSA SURQ Runoff [mm] (mm) -.939.939-2.21 2.21-4.532 4.532-8.868 8.868-1.213 swat_hrus SWAT_Standard SURQ Runoff [mm] (mm) SWAT-Standard 1.978-4.14 4.14-4.44 4.44-4.783 4.783-5.214 5.214-5.75

Soil Water SWAT-VSA SWAT_VSA SW_END Soil Water [mm] (mm) 93-163 163-242 242-277 277-298 298-317 SWAT-Standard SWAT_Standard Soil SW_END Water [mm] (mm) 65-99 99-139 139-196 196-245 245-34

Cumulative Runoff (mm) SWAT-VSA Runoff 35 3 25 2 15 1 Index1 Index9 Index8 Index7 Index6 Index5 Index4 Index3 Index2 Index1 Pasture 5 Jan-96 Jan-97 Jan-98 Jan-99 Jan- Jan-1 Jan-2 Jan-3 Jan-4 Jan-5

Cumulative Runoff (mm) SWAT-Standard Runoff 25 2 Pastures 15 1 5 Jan-96 Jan-97 Jan-98 Jan-99 Jan- Jan-1 Jan-2 Jan-3 Jan-4 Jan-5

Phosphorus SWAT-VSA swat_vsa_hrus SWAT_VSA ORGP Dissolved KG/HA P (kg ha -1 ) -.44.44 -.88.88 -.223.223 -.694.694 -.99 SWAT-Standard swat_hrus SWAT_Standard ORGP Dissolved KG/HA P (kg ha -1 ) -.76.76 -.154.154 -.189.189 -.225.225 -.287

Dissolved P (kg) Dissolved P (kg) SWAT SWAT 8, 6, 3 25 2 15 1 a Measured SWAT-VSA r 2 =.76 E =.71 3 Measured 3 4, 2, 1998 1999 2 21 22 Base Calendar Year Cum dis nut[p] : ld_townbrook_measured Cum dis nut[p] : ld_townbrook_swat_standard Cum dis nut[p] : ld_townbrook_swat_vsa 5 25 Oct-98 Apr-99 Oct-99 Apr- Oct- Apr-1 Oct-13Apr-2 Oct-2 Apr-3 Oct-3 Apr-4 b Measured 2 SWAT 15 1 5 r 2 =.68 E =.47 Measured 3 Oct-98 Apr-99 Oct-99 Apr- Oct- Apr-1 Oct-1 Apr-2 Oct-2 Apr-3 Oct-3 Apr-4

Conclusions Both SWAT models give similar watershed outlet predictions For effective BMP implementation, hotspots should be identified correctly Validating distributed output is difficult