uilding, Suite 105
l Hill
4409
riangle Park, NC 27709
19) 485
) 485
k Cr
ll Schindler (MPCA)
Medicine River Detailed Model
scribes the
Medicine River, and other minor tributaries of the Minnesota River
020004
ough the center of this HUC8; however, it could readily be linked to
sota River.
ently labeled as
l Actions implemented to account for sediment transport through tile
esent tillage and bluff collapse)
d must be applied to a
at the
ration of less than 2 percent at all analyzed locations in the
erefore,
s adequate for most anal
sing a text editor (we suggest the shareware TextPad program) or
has advantages for different situations. The model may be run using
ote the following:
d be updated by copying the September 2009 version of hass
he Windows
i
Cape Fear
3200 Chap
P.O. Box
Research
Telephone: (
Telefax: (91
Cory Netland (Ha , 201
Hafiz Munir, Darr
Hawk Creek/Yello
This memorandum transmits and d t HUC
Hawk Creek, Yello Minnesota River
0 ulate the p
of the Minnesota River that runs th dily be linked to
scale model of the Minn
The model exists in two forms, cur ullSA
Speci port through tile
(in addition to those that rep ons
sediment transport are complex an out 15 minutes to
, a ations.
Experiments with the model show t y and
ge in average pollutant concen in the
T pecial Actions
for tile drain transport of sediment s.
The model uci files may be edited program) or
using the WinHSPF program. Eac may be run using
Please
The WinHSPF engine sho n of hass
provided) into greater number of
cannot be run rations.
2
dig
Hawk Creek, Yellow Medicine River, and other minor tributaries of th
The detailed model does not si
of the Minnesota River that runs through the center of this HUC8; however, it could re
Special Actions implemented to account for sediment tran
. These Special Act
, increasing run time from a
number of HRU combi
se Special Actions have no effect on hydrolo
ge in average pollutant concentration of less than 2 percent at all analyzed location
it is recommended that the version without
ysis purposes in these basi
The model uci files may be edited using a text editor (we suggest the shareware TextPa
using the WinHSPF program. Each has advantages for different situations. The model
The WinHSPF engine should be updated by copying the September 2009 versi
System32 directory. This allows for a
th the DOS version of HSPF due to number of op
TETRA TTECH, INC.
Cape Fear B Building, Suite 105
3200 Chapeel Hill-Nelson Hwy.
P.O. Box 1 14409
Research T Triangle Park, NC 27709
Telephone: (9 919) 485-8278
Telefax: (919 9) 485-8280
MEMORANDUM
To: Cory Netland (Haw wk Creek Watershed Project) Date: June 25 5, 2011
Hafiz Munir, Darre ell Schindler (MPCA)
From: Jonathan Butcher Project: Hawk Creek
Subject: Hawk Creek/Yellow w Medicine River Detailed Model
This memorandum transmits and de escribes the development and calibration of a 12-digi it HUC-scale
HSPF model of Hawk Creek, Yelloww Medicine River, and other minor tributaries of the e Minnesota River
contained within the 8-digit HUC 077020004 (Figure 1). The detailed model does not simmulate the portion
of the Minnesota River that runs thr rough the center of this HUC8; however, it could rea adily be linked to
the larger-scale model of the Minne esota River.
The model exists in two forms, curr rently labeled as HawkYM24.uci and HawkYM24-FFullSA.uci. The
latter version contains HSPF Specia al Actions implemented to account for sediment transsport through tile
drains (in addition to those that repr resent tillage and bluff collapse). These Special Actiions for tile
sediment transport are complex and d time-consuming to run, increasing run time from ab bout 15 minutes to
6 hours for a 10-year simulation, an nd must be applied to a large number of HRU combin nations.
Experiments with the model show thhat these Special Actions have no effect on hydrolog gy and contribute a
change in average pollutant concent tration of less than 2 percent at all analyzed locationss in the
Hawk/Yellow Medicine basins. Th herefore, it is recommended that the version without S Special Actions
for tile drain transport of sediment i is adequate for most analysis purposes in these basin ns.
The model uci files may be edited u using a text editor (we suggest the shareware TextPadd program) or
using the WinHSPF program. Each h has advantages for different situations. The model may be run using
WinHSPFLt or WinHSPF. Please n note the following:
• The WinHSPF engine shoululd be updated by copying the September 2009 versio on of hass_ent.dll
(previously provided) into t the Windows\System32 directory. This allows for a greater number of
operations.
• The uci files cannot be run wwith the DOS version of HSPF due to number of opeerations.
wq-ws4-13c
e River/Hawk Creek Basin
yonunty
The Yellow Medic
NAD_1983_StatePlane_Minnesota_Central_FIPS_2202_metersMil
Hawk - Yellow Medicine Watersheds
NAD_1983_StatePlane_Minnesota_Central_FIPS_2202_meters Map produced 06-15-2011 - P. Cada
SO
UT
H D
AK
OTA
MIN
NE
SO
TA
L Co
Lac Qui Parle County
Lincoln County
Yellow Medicine County
75
212
Legend
Highway
Major Waterway
Major Waterbody
Elevation
(meters)
High : 601
Low : 249
s
Lyon County
Redwood County
Chippewa County
Swift County
59
212
59
12
0 10 20 30 5 Mile
0 10 20 30 5 Kilometers
Yellow Medicine River
Hawk Creek
Renville County
Kandiyohi County
Brown County
71 12
212
es
Figure 1. The Yellow Medicin ine River/Hawk Creek Basin
2
response unit (HRU) basis, which takes into account land use,
ope
code
oup (HSG) combinations (although n
ed as (
precipitation station assignment.
ich is useful for parameter entry.
from multiple data sources.
spatial distribution of developed, agricultural, and undeveloped land
se NLCD lacks detailed information about types of crops and tillage
tatistical data were used to refine the agricultural land classification,
g Steps
ver in the Yellow Medicine/Hawk Creek Basin
ses representing degrees of development from rural to high intensity
ea grid was used to estimate representative percent impervious values
sses.
1)
The model is set up on a hydrologi t land use,
soil group (HSG), and s PERLND (and
IMPLND) has a three digit numeri del,
potential land use/hydrologic soil g . The three digit
code identifying an HRU is calcula e for the land
is th ERLNDs to be
ely by HRUs,
Land use/land cover was develope ased data
products were used to represent the ndeveloped land
Beca crops and tillage
level agricultural nd classification,
Processi
Land Use/Land C
NLCD provides four developed cla to high intensity
The HSPF model, on the other han impervious
The NLCD impervious a mpervious values
for each of the developed NLCD cl perv
Land Use and Land Cover Data (NLCD, 200
NAD_1983_StatePlane_Minnesota_Central_FIPS_2202_meter
Lac Qui Parl
s
The model is set up on a hydrologic response unit (HRU) basis, which takes into accou
along with weather station assignment. Each
. There are 14 weather stations used in the m
ot all are used
is the base co
This enables the
products were used to represent the spatial distribution of developed, agricultural, and
Because NLCD lacks detailed information about types of
level agricultural statistical data were used to refine the agricultural l
NLCD provides four developed classes representing degrees of development from rura
, was configured to separate developed pervious an
The NLCD impervious area grid was used to estimate representative percent
The analysis yielded the following percent i
Mil
1 Model Setup The model is set up on a hydrologic c response unit (HRU) basis, which takes into accoun nt land use,
hydrologic soil group (HSG), and sl lope – along with weather station assignment. Each PERLND (and
IMPLND) has a three digit numeric c code. There are 14 weather stations used in the mo odel, and 37
potential land use/hydrologic soil gr roup (HSG) combinations (although not all are used)). The three digit
code identifying an HRU is calculat ted as (hru – 1)*14 + wst. Where hru is the base cod de for the land
use/HSG combination and wst is the e precipitation station assignment. This enables the PPERLNDs to be
grouped consecutively by HRUs, whwhich is useful for parameter entry.
Land use/land cover was developed d from multiple data sources. NLCD 2001 GIS grid-bbased data
products were used to represent the spatial distribution of developed, agricultural, and u undeveloped land
in the watersheds (Figure 2). Becau use NLCD lacks detailed information about types of crops and tillage
practices, county-level agricultural s astatistical data were used to refine the agricultural la nd classification,
as discussed below under Processin ng Steps.
Land Use and Land Cover Data (NLCD, 200
NAD_1983_StatePlane_Minnesota_Central_FIPS_2202_meters Map produced 06-15-2011 - P. Cada
SO
UT
H D
AK
OTA
MIN
NE
SO
TA
Lac Qui Parle County
Lincoln County
Yellow Medicine County
Legend
Land Use/Land Cover (NLCD, 2001)
Open water
Developed, open space
Developed, low intensity
Developed, medium intensity
Developed, high intensity
Barren land
Deciduous forest
Evergreen forest
Mixed forest
Scrub/shrub
Grassland/herbaceous
Pasture/hay
Cultivated crops
Woody wetlands
Emergent herbaceous wetlands
1)
s
Lyon County
Redwood County
Chippewa County
e
Swift County
¯ 0 10 20 30 5 Mile
0 10 20 30 5 Kilometers
Yellow Medicine River
Hawk Creek
Renville County
Kandiyohi County
Brown County
es
Figure 2. Land Use/Land Co over in the Yellow Medicine/Hawk Creek Basin
NLCD provides four developed clas sses representing degrees of development from rural l to high intensity.
The HSPF model, on the other hand d, was configured to separate developed pervious and d impervious
surfaces. The NLCD impervious ar rea grid was used to estimate representative percent i impervious values
for each of the developed NLCD cla asses. The analysis yielded the following percent im mpervious values:
3
): 6.12%
7.65%
3): 58.65%
86.36%
ing county
soils were found to be rare in the watershed, and were combined
HRUs and simplify the HSPF model.
n
ons.
ed) designator was used for all other land uses.
oups
er DEM in ArcGIS.
CD grids, and smaller DEM cells would have yielded micro
consistent with the purpose of the slope classification.
ries
n areas t
inlets.
yonounty
Rural/open space (NLCD
Low intensity (NLCD 22):
Medium intensity (NLCD
High intensity (NLCD 24):
Soil HSG was developed by combi overage for the
HSG ere combined
with B soils to reduce the number ea also had a high
proportion of soils with a dual desi performance
under drained and undrained condit signator was used
for cropland and the second (undra
Hydrologic Soil G
Slope was calculated from a 30 me al for this project
because the cell size matched the N ed micro
ariations in slope that would not b on.
grid was reclassified into two categ than 1 percent).
This was done to distinguish betwe er slopes) versus
those that are not likely to have suc ulated only for
NAD_1983_StatePlane_Minnesota_Central_FIPS_2202_meters
level SSURGO GIS data into a unified
HSG A soils were found to be rare in the watershed, and
The study a
The two designators represen
During HRU processing, the first (drained) d
Thirty meter grid cells were id
because the cell size matched the NLCD grids, and smaller DEM cells would have yiel
ariations in slope that would not be consistent with the purpose of the slope classificat
Low (less than 1 percent), and High (greater
hat are likely to have surface tile inlets (lo
Sediment transport through tile drains is si
Mil
u212
• Rural/open space (NLCD 2211): 6.12%
• Low intensity (NLCD 22): 227.65%
• Medium intensity (NLCD 2 23): 58.65%
• High intensity (NLCD 24): 86.36%
Soil HSG was developed by combin ning county-level SSURGO GIS data into a unified c coverage for the
entire study area (Figure 3). HSG A A soils were found to be rare in the watershed, and w were combined
with B soils to reduce the number ooff HRUs and simplify the HSPF model. The study ar rea also had a high
proportion of soils with a dual desig gnation (e.g., “B/D”). The two designators represent t performance
under drained and undrained conditiions. During HRU processing, the first (drained) de esignator was used
for cropland and the second (undrainined) designator was used for all other land uses.
Hydrologic Soil Group (HSG, SSURGO)
NAD_1983_StatePlane_Minnesota_Central_FIPS_2202_meters Map produced 06-15-2011 - P. Cada
SO
UT
H D
AK
OTA
MIN
NE
SO
TA
L C
Lac Qui Parle County
Lincoln County
Yellow Medicine County
tu75
t
Legend
Hydrologic Soil Group
(SSURGO)
Water/Rock
A
A/D
B
B/D
C
C/D
D
s
Yellow Medicine River
Hawk Creek
Lyon County
Redwood County
Chippewa County
Swift County
tu59
tu212
tu59
tu12
¯ 0 10 20 30 5 Mile
0 10 20 30 5 Kilometers
Renville County
Kandiyohi County
Brown County
tu71
tu12
tu212
es
Figure 3. Hydrologic Soil Gr roups
Slope was calculated from a 30 met ter DEM in ArcGIS. Thirty meter grid cells were ide eal for this project
because the cell size matched the NLLCD grids, and smaller DEM cells would have yield ded micro-
variations in slope that would not be e consistent with the purpose of the slope classificati ion. The slope
grid was reclassified into two catego ories – Low (less than 1 percent), and High (greater than 1 percent).
This was done to distinguish betwee en areas that are likely to have surface tile inlets (low wer slopes) versus
those that are not likely to have suchh inlets. Sediment transport through tile drains is sim mulated only for
4
h slope categories were applied only to tilled
ow/High slope grid were combined in ArcGIS to produce a grid with
of NLCD land cover, HSG, and slope.
fol
uous, evergreen, and mixed) and shrubland were lumped into one
.
parately
lands were lumped into a single wetland category.
were lumped into a single grass/pasture category.
soils.
re found to be
relatively rare, and were lumped with C soils.
es were assumed to behave like D soils with poor infiltration, so all
e two catego
ion of the watershed (about 0.1 percent), so its HSG classes were
ry.
ed for all land except tilled land and grass/pasture.
BASINS4
us land retained the HSG subcategories.
use/land cover table with each HRU and weather station assignmen
re imported into the Land Use Processing spreadsheet, and the
ped in the
rocessed to further categorize by tillage practice and man
e and manured tillage land use classes were assigned based on
nd information on CRP/CREP/RIM acres as shown in the Land Use
ere is no explici
proximate proportions within each subbasin.
D water land use area was corrected for the surface area of lakes that
in the Land Use Processing spreadsheet
marized in
et.
Low and Hi d
NLCD land use, soil HSG, and the oduce a grid with
unique values for each combinatio
The resulting grid was simplified a
The three forest types (deci mped into one
category representing fores area in the study
s
Woody and herbaceous we
Grassland and Pasture/Hay
A soils were lumped with
For untilled land, C soils w category.
For tilled land, D soils wer
Water and wetland categor filtration, so all
HSGs were lumped for the
Barren land was a tiny frac classes were
lumped into a single categ
Slope classes were elimina
The simplified grid was processed nto pervious and
Developed pervi l result of the
BASINS4 operation provided a lan ation assignmen
Data w et, and the
processed developed lands were lu
man
The conservation tilla based on
level agricultural statistics in the Land Use
As such, t icultural land use.
Rather, they should be viewed as a
To avoid double counting, the NL rea of lakes that
y simulated as reache
The HRU numbering scheme is su d by the HRU
spreadsh
land a
NLCD land use, soil HSG, and the Low/High slope grid were combined in ArcGIS to p
The three forest types (deciduous, evergreen, and mixed) and shrubland were l
Shrubland made up a fraction of a percent of lan
B
Water and wetland categories were assumed to behave like D soils with poor i
Barren land was a tiny fraction of the watershed (about 0.1 percent), so its HS
, splitting the developed NLCD classes
The overa
BASINS4 operation provided a land use/land cover table with each HRU and weather s
Data were imported into the Land Use Processing spreadsh
processed to further categorize by tillage practice an
The conservation tillage and manured tillage land use classes were assigne
level agricultural statistics and information on CRP/CREP/RIM acres as shown
t spatial location for the types of ag
To avoid double counting, the NLCD water land use area was corrected for the surface
. Setup of the HRUs is controll
the lower slope areas. Low and Hig gh slope categories were applied only to tilled land annd
grassland/pasture.
Processing Steps
NLCD land use, soil HSG, and the L rLow/High slope grid were combined in ArcGIS to produce a grid with
unique values for each combination n of NLCD land cover, HSG, and slope.
The resulting grid was simplified as s follows, according to NLCD land cover classes:
• The three forest types (decidduous, evergreen, and mixed) and shrubland were lu umped into one
category representing forestt. Shrubland made up a fraction of a percent of land d area in the study
area and is not simulated se eparately.
• Woody and herbaceous wet tlands were lumped into a single wetland category.
• Grassland and Pasture/Hay were lumped into a single grass/pasture category.
• HSG classes
• A soils were lumped with B B soils.
• For untilled land, C soils we ere found to be rare, and were lumped with the A-B category.
• For tilled land, D soils were e relatively rare, and were lumped with C soils.
• Water and wetland categori ies were assumed to behave like D soils with poor in nfiltration, so all
HSGs were lumped for thes se two categories.
• Barren land was a tiny fract tion of the watershed (about 0.1 percent), so its HSG G classes were
lumped into a single catego ory.
• Slope classes were eliminat ted for all land except tilled land and grass/pasture.
The simplified grid was processed iinn BASINS4, splitting the developed NLCD classes iinto pervious and
impervious area. Developed pervio ous land retained the HSG subcategories. The overal ll result of the
BASINS4 operation provided a land d use/land cover table with each HRU and weather station assignment t,
as well as model subbasin. Data we ere imported into the Land Use Processing spreadshe eet, and the
processed developed lands were lum mped in the RawLU worksheet.
The tabulated tilled land was post-pprocessed to further categorize by tillage practice and d manure
application. The conservation tillag ge and manured tillage land use classes were assignedd based on
county-level agricultural statistics a and information on CRP/CREP/RIM acres as shown in the Land Use
Processing spreadsheet. As such, th here is no explicit spatial location for the types of agrricultural land use.
Rather, they should be viewed as ap pproximate proportions within each subbasin.
To avoid double counting, the NLC CD water land use area was corrected for the surface aarea of lakes that
were explicitly simulated as reaches s in the Land Use Processing spreadsheet
The HRU numbering scheme is sum mmarized in Table 1. Setup of the HRUs is controlle ed by the HRU and
Land Use Processing.xlsx spreadshe eet.
5
cheme
ed to model subbasins based on proximity.
e NLCD stations, and the model uses disaggregated hourly time
ech for the Minnesota River Turbidity TMDL project
HRU Numbering
The precipitation stations are assig ions are identified
stations hourly time
series previously created by Tetra t
. stations obtained
The sta
stations are NLCD stations, and the model uses disaggregate
series previously created by Tetra Tech for the Minnesota River Turbidity TMDL proje
, “SWCD” stations are additional SO
Table 1. HRU Numbering S Scheme
Land Use HSG Slope Base Code
Water all all 01
Barren all all 02
Wetland all all 03
Forest A,B,C all 04
Forest D all 06
Grass/Pasture A,B,C L 10
Grass/Pasture A,B,C H 11
Grass/Pasture D L 14
Grass/Pasture D H 15
Conventional Tillage A,B L 16
Conventional Tillage A,B H 17
Conventional Tillage C,D L 18
Conventional Tillage C,D H 19
Conservation Tillage A,B L 22
Conservation Tillage A,B H 23
Conservation Tillage C,D L 24
Conservation Tillage C,D H 25
Manured Tillage A,B L 28
Manured Tillage A,B H 29
Manured Tillage C,D L 30
Manured Tillage C,D H 31
Developed Pervious A,B,C all 34
Developed Pervious D all 36
Impervious all all 37
The precipitation stations are assign ned to model subbasins based on proximity. The stat tions are identified
in Table 2. The last eight stations aarre NLCD stations, and the model uses disaggregated d hourly time
series previously created by Tetra T Tech for the Minnesota River Turbidity TMDL projecct (through 2006),
with augmented data through 2009. The first six, “SWCD” stations are additional SOD D stations obtained
and processed by MPCA.
6
ons
d
outh
e numbering. The reaches and associated precipitation station
hile the spatial distribution of subbasins is shown in
ns, and Weather Station Assignments
edicine River mouth
dicine River at Gage
Trib 3 to Yellow Medicine River
Trib 4 to Yellow Medicine River
dicine River
Trib 2 to Yellow Medicine River
nch Yellow Medicine River
nch Yellow Medicine River
Trib 1 to South Branch Yellow Medicine River
Precipitation Stat
subbasins a llow Medicine
River from the USGS gage to the t drainage). The
hes have the sa on station
,
Reaches, Subbas
Yellow
Yellow M
Unname
Unname
ellow M
Unname
South Br
South Br
Unname
Y
dire
hes have the same numbering. The reaches and associated precipitat
, while the spatial distribution of subbasins is shown i
Table 2. Precipitation Stati ions
Met Station WST# Name
SWCD1 1 253879-4947441
SWCD2 2 270561-4962989
SWCD3 3 297650-4952225
SWCD4 4 302088-4940775
SWCD5 5 329702-4946407
SWCD6 6 341200-4980208
MN213311 7 Granite Falls
MN215204 8 Marshall
MN215482 9 Minneota
MN215563 10 Montevideo 1 SW
MN216152 11 Olivia 1 SE
MN218429 12 Tyler
MN219004 13 Willmar RTC
SD390422 14 Astoria, SD 4S
The model contains 75 subbasins an nd 76 stream reaches (RCHRES 100, representing Yeellow Medicine
River from the USGS gage to the m mouth, is a routing reach only, with no assigned direc ct drainage). The
subbasins and reaches have the sam me numbering. The reaches and associated precipitatiion station
assignments are shown in Table 3, w while the spatial distribution of subbasins is shown inn Figure 4.
Table 3. Reaches, Subbasi ins, and Weather Station Assignments
Reach Number Name WST #
100 Yellow M Medicine River mouth 3
101 Yellow Me edicine River at Gage 3
102 Unnamed d Trib 3 to Yellow Medicine River 3
103 Unnamed d Trib 4 to Yellow Medicine River 8
104 Yellow Me edicine River 8
105 Unnamed d Trib 2 to Yellow Medicine River 9
106 South Bra anch Yellow Medicine River 9
107 South Bra anch Yellow Medicine River 9
108 Unnamed d Trib 1 to South Branch Yellow Medicine River 12
7
nch Yellow Medicine River
Trib 2 to South Branch Yellow Medicine River
dicine River
Trib 1 to Yellow Medicine River
dicine River
dicine River
Lake
nch Yellow Medicine Ri
nch Yellow Medicine River
k
ek
Trib 3 to Spring Creek
ek
Trib 2 to Spring Creek
ek
Trib 1 to Spring Creek
ring Creek
e Creek
Trib 1 to Wood Lake Creek
Trib 2 to Wood Lake Creek
e
k
k
Trib 2 to Minnesota River
South Br
Unname
Yellow M
Unname
Yellow M
Yellow M
Shaokota
North Br
North Br
Mud Cre
Spring Cr
Unname
Spring Cr
Unname
Spring Cr
Unname
Rice Cree
Boiling S
Wood La
Unname
Unname
Wood La
Hazel Cre
Hazel Cre
Unname
Reach Number Name WST #
109 South Bra anch Yellow Medicine River 12
110 Unnamed d Trib 2 to South Branch Yellow Medicine River 9
111 Yellow Me edicine River 1
112 Unnamed d Trib 1 to Yellow Medicine River 9
113 Yellow Me edicine River 1
114 Yellow Me edicine River 14
115 Shaokotan n Lake 14
116 North Bra anch Yellow Medicine River 1
117 North Bra anch Yellow Medicine River 14
118 Mud Cree ek 1
119 Spring Cre eek 2
120 Unnamed d Trib 3 to Spring Creek 2
121 Spring Cre eek 2
122 Unnamed d Trib 2 to Spring Creek 1
123 Spring Cre eek 1
124 Unnamed d Trib 1 to Spring Creek 1
130 Rice Creekk 5
140 Boiling Sp pring Creek 4
150 Wood Lak ke Creek 3
151 Unnamed d Trib 1 to Wood Lake Creek 4
152 Unnamed d Trib 2 to Wood Lake Creek 4
153 Wood Lak ke 3
160 Hazel Creeek 3
161 Hazel Creeek 2
170 Unnamed d Trib 2 to Minnesota River 7
8
Creek
Trib 1 to Minnesota River
k
k
tch 11
Trib 2 to Hawk Creek
k
k
Trib 1 to Hawk Creek
s Lake
mon Lake
k
k
k
tch 2
Creek
tch 1
Creek
Creek
tch 8
eek
Beaver Creek
Stony Ru
Unname
Hawk Cre
Hawk Cre
County D
Unname
Hawk Cre
Hawk Cre
Unname
Saint Joh
West Sol
Long Lak
Hawk Cre
Hawk Cre
Hawk Cre
Foot Lak
Eagle Lak
Judicial D
Chetomb
Judicial D
Chetomb
Chetomb
County D
Beaver C
West For
Reach Number Name WST #
180 Stony Run n Creek 10
190 Unnamed d Trib 1 to Minnesota River 10
201 Hawk Creeek 7
202 Hawk Creeek 7
203 County Di itch 11 7
204 Unnamed d Trib 2 to Hawk Creek 7
205 Hawk Creeek 7
206 Hawk Creeek 7
207 Unnamed d Trib 1 to Hawk Creek 13
208 Saint John ns Lake 13
209 West Solo omon Lake 13
210 Long Lake e 13
211 Hawk Creeek 6
212 Hawk Creeek 13
213 Hawk Creeek 13
214 Foot Lake e 13
215 Eagle Lake e 13
216 Judicial Diitch 2 6
217 Chetomba a Creek 7
218 Judicial Diitch 1 7
219 Chetomba a Creek 6
220 Chetomba a Creek 6
221 County Di itch 8 6
230 Beaver Cr reek 5
231 West Fork k Beaver Creek 5
9
Beaver Creek
tch 59
Beaver Creek
Beaver Creek
ek
art Creek, East Branch
art Creek
Trib 3 to Minnesota River
eek
West For
County D
East Fork
East Fork
Timms Cr
Sacred H
Sacred H
Palmer C
esot
esot
esot
esot
esot
esot
esot
esot
Reach Number Name WST #
232 West Fork k Beaver Creek 6
233 County Di itch 59 11
234 East Fork Beaver Creek 11
235 East Fork Beaver Creek 11
240 Timms Cre eek 5
250 Sacred He eart Creek, East Branch 5
260 Sacred He eart Creek 5
270 Unnamedd Trib 3 to Minnesota River 7
280 Palmer Cr reek 7
301 Minnesotaa River direct drainage 301 5
302 Minnesotaa River direct drainage 302 5
303 Minnesotaa River direct drainage 303 5
304 Minnesotaa River direct drainage 304 5
305 Minnesotaa River direct drainage 305 7
306 Minnesotaa River direct drainage 306 7
307 Minnesotaa River direct drainage 307 10
308 Minnesotaa River direct drainage 308 10
10
on
ere developed from
, 104, and 111)
was not able to provide these.
es generated by BASINS4.
akes are explicitly represented in the model
ns, Eagle, Swan, Willmar, Foot, Shaokotan
ted for these
arge characteristics.
ssing spreadsheet reassigns agricultura
nd categories, and also removes the water area represented by
adsheet is also set up with functionality to represent a variety of
me procedures as for the Minnesota River model. This is set up on
ustments are implemented at this time.
sition of nitrogen are included in the model based on information
odel
ns are shown in
n the model using the generic, aggregated approach employed in the
e are summarized in
Model Segmentat
Hydraulic FTables for the reaches s where available
(Yellow Medicine reaches 100, 10 for Beaver Creek
and lower Hawk Creek, but MDN covered by HEC
default FTab portant in the
Long, Solomon,
West Solomon, Lindgren, Saint Jo nd Mud Lakes
and separate FTables have been cre d on available
information about storage and disc
As noted above, the Land Use Proc entional tillage,
conservation tillage, and manured l sented by
explicitly simulated lakes. This spr t a variety of
using the s his is set up on
the Scenario tab, but no scenario a
Point sources and atmospheric dep information
developed for the Minnesota River ct
simulated major point source locati rges from
ted employed in the
Minnesota River basin model. The
NAD_1983_StatePlane_Minnesota_Central_FIPS_2202_meter
1
21
10
s
RAS mode
RAS models apparently exis
Flowing reaches not
Lakes are particularly i
Ringo
, Wood,
bas
l land in con
conservation tillage, and manured land categories, and also removes the water area repr
explicitly simulated lakes. This spreadsheet is also set up with functionality to represe
using the same procedures as for the Minnesota River model.
Point sources and atmospheric deposition of nitrogen are included in the model based o
and supplemented through 2009 for this proj
. In addition, minor disch
ted in the model using the generic, aggregated approach
2
22
Mil
Model Segmentation
NAD_1983_StatePlane_Minnesota_Central_FIPS_2202_meters Map produced 06-15-2011 - P. Cada
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Lac Qui Parle County
Lincoln County
Yellow Medicine County
118
113 117
109
112
123
105
108
116
107 110
111
124
122
115
114
106
Legend
Major Waterway
Major Waterbody
Model Subwatershed
Yellow Medicine River
s
Lyon County
Redwood County
Chippewa County
Swift County
180
219
104
161
305
232
307
23
308
103
211
2
140
204
160
151
280
207
306
101
220
130
119
5
303
201
301
212
120
216
302
217
240 304
260
203
170
190
202
250
102
205
152
206
270
213
218
209
121
153
150
208
¯ 0 10 20 30 5 Mile
0 10 20 30 5 Kilometers
Hawk Creek
Renville County
Kandiyohi County
Brown County
234 31
221
0
230
235
215
233
214
210
es
Figure 4. Model Segmentati ion
Hydraulic FTables for the reaches w were developed from Lyon County HEC-RAS modells where available
(Yellow Medicine reaches 100, 101 1, 104, and 111). HEC-RAS models apparently exist t for Beaver Creek
and lower Hawk Creek, but MDNR R was not able to provide these. Flowing reaches not covered by HEC
RAS models have the default FTabl les generated by BASINS4. Lakes are particularly im mportant in the
Hawk Creek headwaters. Thirteen llakes are explicitly represented in the model (Ringo, , Long, Solomon,
West Solomon, Lindgren, Saint Joh hns, Eagle, Swan, Willmar, Foot, Shaokotan, Wood, aand Mud Lakes)
and separate FTables have been creaated for these lakes (or sets of connected lakes) base ed on available
information about storage and disch harge characteristics.
As noted above, the Land Use Proce essing spreadsheet reassigns agricultural land in convventional tillage,
conservation tillage, and manured la and categories, and also removes the water area repreesented by
explicitly simulated lakes. This spreeadsheet is also set up with functionality to represen nt a variety of
management measures, using the sa ame procedures as for the Minnesota River model. TThis is set up on
the Scenario tab, but no scenario adjdjustments are implemented at this time.
Point sources and atmospheric depo osition of nitrogen are included in the model based onn information
developed for the Minnesota River mmodel and supplemented through 2009 for this proje ect. Explicitly
simulated major point source locatio ons are shown in Figure 5. In addition, minor discha arges from
stabilization ponds are represented i in the model using the generic, aggregated approach employed in the
Minnesota River basin model. Thes se are summarized in Table 4.
11
d Point SourcesExplicitly Simulat
NAD_1983_StatePlane_Minnesota_Central_FIPS_2202_metersMil
Point Sources Included in Model
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Lac Qui Parle County
Lincoln County
Yellow Medicine County
Legend
#0 Point Source
Major Waterway
Major Waterbody
Model Subwatershed
Yellow Medicine River
s
#0
#0
#0
#0
#0
WILLMAR WWTP
Lyon County
Redwood County
Chippewa County
Swift County
OLIVIA WWTP
MAYNARD WWTP
RENVILLE WWTP
CLARA CITY WWTP
SOUTHERN MINN. SUGAR COOP.
¯ 0 10 20 305 Mile
0 10 20 305 Kilometers
Hawk Creek
#0
#0
Renville County
Kandiyohi County
Brown County
es
Figure 5. Explicitly Simulateed Point Sources
12
s in the Model
lls WWTP
od WWTP
WWTP
WTP
WTP
TP
TP
WTP
TP
e WWTP
WWTP
WWTP
WWTP
WTP
d WWTP
t pipe d
communities”) are explicitly represented on a population basis
k Creek basin, Hazel Run and Delhi in the Yellow Medicine basin).
ed to stabilization ponds prior to 2007, but this is not yet represented
eas the model assumes a regional rate of direct
sing the m
on and Validation Report
should be reviewed before using the model for bacteria simulati
ich larger
ek, Bunde).
Stabilization Pon
odel Subbasin
Hanley F
Cottonw
Minneot
Taunton
Ivanhoe
Porter W
St Leo W
Belview
Echo W
Wood La
Clarkfiel
Pennock
Raymon
Danube
Bird Isla
The model also accounts for straig communities
with direct discharges (“unsewered ation basis
(Blomkest and Prinsburg in the Ha edicine basin).
Both Prinsburg and Hazel Run shif t yet represented
in the model. For unincorporated a le individual
sewage treatment systems (ISTS), 2
River Basin Model, Model Calibrat are estimated on a
per capita basis. These assumption eria simulati
in w s
(e.g., Svea, Roseland, Gl
ischarges to tile drain systems. Incorporate
with direct discharges (“unsewered communities”) are explicitly represented on a popu
(Blomkest and Prinsburg in the Hawk Creek basin, Hazel Run and Delhi in the Yellow
Both Prinsburg and Hazel Run shifted to stabilization ponds prior to 2007, but this is n
t
ethodology described in Tetra Tech’s 20
, in which pollutant loads
per capita basis. These assumptions should be reviewed before using the model for bac
concentrations of direct discharging syste
Table 4. Stabilization Pond ds in the Model
Permit No. Name Receiving Water MModel Subbasin
MNG580122 Hanley Fa alls WWTP Yellow Medicine 101
MNG580010 Cottonwo ood WWTP JD #10 103
MNG580033 Minneota a WWTP SB Yellow Medicine 106
MNG580090 Taunton W WWTP unnamed to YMR 112
MNG580103 Ivanhoe W WWTP Yellow Medicine 113
MNG580128 Porter WW WTP Mud Cr 118
MN0024775 St Leo WWWTP unnamed to YMR 123
MNG580003 Belview W WWTP CD #12 130
MNG580059 Echo WW WTP Boiling Springs Ck 140
MNG580107 Wood Lakke WWTP JD #10 152
MNG580093 Clarkfield d WWTP CD #9 161
MNG580104 Pennock WWTP trib to Hawk Cr, ds St. Johns lake 207
MN0045446 Raymond d WWTP Hawk Cr 211
MNG580057 Danube W WWTP WF Beaver Ck 231
MN0022829 Bird Islan nd WWTP CD #66 234
The model also accounts for straigh ht pipe discharges to tile drain systems. Incorporated d communities
with direct discharges (“unsewered communities”) are explicitly represented on a popul lation basis
(Blomkest and Prinsburg in the Haw wk Creek basin, Hazel Run and Delhi in the Yellow MMedicine basin).
Both Prinsburg and Hazel Run shift ted to stabilization ponds prior to 2007, but this is no ot yet represented
in the model. For unincorporated ar reas the model assumes a regional rate of direct-to-ti ile individual
sewage treatment systems (ISTS), u using the methodology described in Tetra Tech’s 200 02 Minnesota
River Basin Model, Model Calibratiion and Validation Report, in which pollutant loads are estimated on a
per capita basis. These assumptions s should be reviewed before using the model for bactteria simulations,
particularly in regard to areas in wh hich larger concentrations of direct discharging system ms have been
identified (e.g., Svea, Roseland, Glu uek, Bunde).
13
(This page left intentionally blank.)
14
ibrationlong period of record in
ith the Minnesota River basin model revealed this as a difficult gage
somewhat difference rainfall
to mid
Beaver Creek watersheds by the Hawk Creek Watershed Project
pera
the spring runoff may be missed, complicating efforts to fit an overall
ve been concerns with rating curves at some of
esults for 2000 yielded flows that are significantly higher than those at
th gage, later determined to be a result of deficiencies in the rating
ating curves have been field measured and adjusted is apparently less
d Water Quality Monitoring Locations
calibration was to foc
age were then extended to the various Hawk Creek and Beaver Creek
alidation test
Hydrologic CaThere is only one USGS gage with ne River near
and previous work s a difficult gage
to calibrate, which apparently has a he last decade
seen in the 1980s and earl en conducted at
multiple locations in the Hawk and shed Project
. These gages, however, ugh September),
which means that a large portion of ts to fit an overall
water balance. In addition, there h gage locations
(for instance, the Chetomba Creek igher than those at
the downstream Hawk Creek at mo es in the rating
ncy at which is apparently less
Stream Gaging a
The general strategy for hydrologic e gage for 2001
2009. Parameters derived for that nd Beaver Creek
gages as a spatial corroboration or unified
NAD_1983_StatePlane_Minnesota_Central_FIPS_2202_meter
-341
03-867
s
Yellow Medic
and previous work with the Minnesota River basin model revealed this
runoff response over
1990s. Since 1999 additional gaging has b
multiple locations in the Hawk and Beaver Creek watersheds by the Hawk Creek Wate
te only on a seasonal basis (generally April thr
which means that a large portion of the spring runoff may be missed, complicating effo
thes
(for instance, the Chetomba Creek results for 2000 yielded flows that are significantly
the downstream Hawk Creek at mouth gage, later determined to be a result of deficienc
ncy at which rating curves have been field measured and adjuste
us first on the Yellow Medici
2009. Parameters derived for that gage were then extended to the various Hawk Creek
, with some iterative adjustments to th
S00
S
Mil
2 Hydrologic Callibration There is only one USGS gage with aa long period of record in the basin – Yellow Mediciine River near
Granite Falls – and previous work w with the Minnesota River basin model revealed this a as a difficult gage
to calibrate, which apparently has a somewhat difference rainfall-runoff response over t the last decade
than was seen in the 1980s and early y to mid-1990s. Since 1999 additional gaging has beeen conducted at
multiple locations in the Hawk and Beaver Creek watersheds by the Hawk Creek Water rshed Project
(Figure 6). These gages, however, o operate only on a seasonal basis (generally April throough September),
which means that a large portion of the spring runoff may be missed, complicating effor rts to fit an overall
water balance. In addition, there ha ave been concerns with rating curves at some of thesee gage locations
(for instance, the Chetomba Creek r results for 2000 yielded flows that are significantly h higher than those at
the downstream Hawk Creek at mou uth gage, later determined to be a result of deficienciies in the rating
curve), and the frequency at which r rating curves have been field measured and adjusted d is apparently less
than the USGS standard.
Flow Monitoring Stations
NAD_1983_StatePlane_Minnesota_Central_FIPS_2202_meters Map produced 06-15-2011 - P. Cada
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Lac Qui Parle County
Lincoln County
Yellow Medicine County
Legend
#* Flow Monitoring Station
Major Waterway
Major Waterbody
Model Subwatershed
Yellow Medicine River
s
#* #*
#* #* #*
#*#*#*
#*
Lyon County
Redwood County
Chippewa County
Swift County
S002-140
S002-148
S002-152
S002-136
S002-012
S000-159
S002-316 S001
S0
¯ 0 10 20 30 5 Mile
0 10 20 30 5 Kilometers
Hawk Creek
#*
#* Renville County
Kandiyohi County
Brown County
S000-405
1-341
003-867
S000-666
es
Figure 6. Stream Gaging an nd Water Quality Monitoring Locations
The general strategy for hydrologic calibration was to focus first on the Yellow Medicin ne gage for 2001
2009. Parameters derived for that g gage were then extended to the various Hawk Creek aand Beaver Creek
gages as a spatial corroboration or v validation test, with some iterative adjustments to the e unified
15
rmance on the earlier gage records for Yellow Medicine was
tion test.
rameters was provided by the Hawk/Yellow Medicine section of the
el and refined to reflect the more detailed representation of HRUs.
were to the lower zone nominal soil moisture storage (LZSN, varied
meter (INFILT, assigned according
adjusted to (1) reflect lessons learned in the development of the
2) to better conform
r model was that snowmelt was shifted from an energy balance
OP=1), depending only on air temperature. This was done because
eld better results for the simulation of s
into account solar radiation, wind, relative humidity, cloud cover)
be very sensitive to uncertainty in the meteorological inputs. In
implemented in HSPF provides little user control over the time
ajor factor in the uptake of heat by the snowpack.
uired to achieve acceptable fit. This was in the exer
required for portions of the Hawk Creek (exclusive of Chetomba
d number
libration are provided in A
ine gage for 2001
s good, except that there is considerable uncertainty regarding the
daily Nash
round 0
s been disaggregated, not by true hourly precipitation.
ations, flows at the Yellow Medicine gage for 1994 to 2000 tend to
all seasons and for both high and low flows, although the NSE values
epancy are not fully understood.
r set. Finally, model perf cine was
examined as an additional corrobor
The starting point for hydrologic p e section of the
er Basin mo ation of HRUs.
The primary calibration adjustment ge (LZSN, varied
by land use) and the infiltration par ic group).
Various other parameters were also ment of the
LeSueur River detailed model, and in BASINS
change from the pri gy balance
method to a degree day method (S s done because
the degree day method appears to y elt. In theory, the
energy balance method (also taking , cloud cover)
should be superior; however, it can l inputs. In
gy balance metho ver the time
course of snow albedo, which is a
Only one spatial adjustment was re
which a somewhat lower factor wa of Chetomba
Creek) and East Fork Beaver Cree f Penman Pan
Evaporation estimates from a limit surements.
Detailed results of the hydrologic c s obtained to all
aspects of flow at the Yellow Medi e various
and Beaver Creek gages also appea regarding the
For these stations, th fair, while the
excellent ( el is driven
mostly by daily precipitation that h n.
As was noted in earlier model appli to 2000 tend to
in h the NSE values
are good. The reasons for this disc
r set. Finally, model performance on the earlier gage records for Yellow Med
The starting point for hydrologic parameters was provided by the Hawk/Yellow Medici
er Basin model and refined to reflect the more detailed represen
The primary calibration adjustments were to the lower zone nominal soil moisture stor
to soil hydrolo
Various other parameters were also adjusted to (1) reflect lessons learned in the develo
recommendations containe
change from the prior model was that snowmelt was shifted from an ene
method to a degree day method (SNOP=1), depending only on air temperature. This w
pring snow
energy balance method (also taking into account solar radiation, wind, relative humidit
should be superior; however, it can be very sensitive to uncertainty in the meteorologic
gy balance method implemented in HSPF provides little user control
Only one spatial adjustment was required to achieve acceptable fit. This was in the exe
which a somewhat lower factor was required for portions of the Hawk Creek (exclusiv
This likely reflects uncertainties in extrapolation
of stations with wind and temperature me
. A good fit
2009. Fit to observed flows at t
and Beaver Creek gages also appears good, except that there is considerable uncertaint
Sutcliffe coefficients (NSE) tend to be
.9). This likely reflects the fact that the mo
mostly by daily precipitation that has been disaggregated, not by true hourly precipitati
As was noted in earlier model applications, flows at the Yellow Medicine gage for 199
in all seasons and for both high and low flows, althou
parameter set. Finally, model perfo ormance on the earlier gage records for Yellow Medi icine was
examined as an additional corrobora ation test.
The starting point for hydrologic pa arameters was provided by the Hawk/Yellow Medicinne section of the
existing Minnesota River Basin moddel and refined to reflect the more detailed represent tation of HRUs.
The primary calibration adjustments s were to the lower zone nominal soil moisture stora age (LZSN, varied
by land use) and the infiltration para ameter (INFILT, assigned according to soil hydrolog gic group).
Various other parameters were also adjusted to (1) reflect lessons learned in the develop pment of the
LeSueur River detailed model, and ((2) to better conform to recommendations contained d in BASINS
Technical Note 6.
One important change from the prio or model was that snowmelt was shifted from an enerrgy balance
method to a degree day method (SN NOP=1), depending only on air temperature. This wa as done because
the degree day method appears to yiield better results for the simulation of spring snowm melt. In theory, the
energy balance method (also taking into account solar radiation, wind, relative humidity y, cloud cover)
should be superior; however, it can be very sensitive to uncertainty in the meteorologica al inputs. In
addition, the energy balance method d implemented in HSPF provides little user control o over the time
course of snow albedo, which is a m major factor in the uptake of heat by the snowpack.
Only one spatial adjustment was req quired to achieve acceptable fit. This was in the exerrted PET, for
which a somewhat lower factor was s required for portions of the Hawk Creek (exclusive e of Chetomba
Creek) and East Fork Beaver Creekk.. This likely reflects uncertainties in extrapolation o of Penman Pan
Evaporation estimates from a limite ed number of stations with wind and temperature meaasurements.
Detailed results of the hydrologic ca alibration are provided in Attachment 1. A good fit iis obtained to all
aspects of flow at the Yellow Medic cine gage for 2001-2009. Fit to observed flows at th he various Hawk
and Beaver Creek gages also appear rs good, except that there is considerable uncertainty y regarding the
lowest flows. For these stations, the e daily Nash-Sutcliffe coefficients (NSE) tend to be fair, while the
monthly NSE values are excellent (aaround 0.9). This likely reflects the fact that the moddel is driven
mostly by daily precipitation that ha as been disaggregated, not by true hourly precipitatio on.
As was noted in earlier model appliccations, flows at the Yellow Medicine gage for 1994 4 to 2000 tend to
be consistently underestimated – in all seasons and for both high and low flows, althoug gh the NSE values
are good. The reasons for this discr repancy are not fully understood.
16
alibrationthe Minnesota Rive
nt, temperature, BOD/DO, nutrients, and bacteria. However, full
r sediment, inorganic N, total N, inorganic P, and total P at this time.
e
Note 8.
the Minnesota River Basin model, cri
t h
the 15
e
tress (Tau) Distribution for Yellow Mnite Falls)
t with the Minnesota River model using Special Actions to replenish
hese loads consisted of 1.
he lowermost reach of Hawk Creek (201). A reach
t was conducted to ensure reasonable sediment accumulation/loss
ed
7
nd in the lake reaches (
is a temporary phenomenon, reflecting accumulation during 2008
bed storages in preceding years.
clusive of bluff loads) had a median of
segments, consistent with observations that a significant portion of the
annel
Water Quality The model is set up consistent with ange of water
quality simulation, including sedim However, full
calibration has been pursued only f tal P at this time.
Sediment calibration is the most ti onducted
consistent with BASINS Technical ased on
As wit esses for
cohesive sedime ated distribution.
shear stress,
was set to deposit below mple of the shear
Figu
Simulated Shear ch 101 (Yellow Medicine at USGS Gage near Gr
Bluff loads were assigned consiste ions to replenish
the sediment available in the bed. reach of Yellow
in
balance analysis of channel sedime mulation/loss
behavior and approximately stable tes of channel
degradation over the course of the et accumulation
gain represented in the bluff reache n during 2008
and 2009 after large events reduce ency (depth/scour
a fraction of influent sediment, e with positive
trapping (> 80 percent) for the lake cant portion of the
total load in this basin arises from
r Basin model to provide a full
quality simulation, including sediment, temperature, BOD/DO, nutrients, and bacteria.
calibration has been pursued only for sediment, inorganic N, total N, inorganic P, and t
consuming part of model development, and was
Upland sediment erodibility rates were set
tical shear st
are set equal to percentiles of the simu
percentile dail
percentile. An ex
edicine Re
Bluff loads were assigned consistent with the Minnesota River model using Special Ac
mos
balance analysis of channel sediment was conducted to ensure reasonable sediment acc
The final simulation shows slow r
, with the exception of
210, 21
gain represented in the bluff reaches is a temporary phenomenon, reflecting accumulati
Net trapping effic
11 percent
trapping (> 80 percent) for the lake segments, consistent with observations that a signif
3 Water Quality C CalibrationT
au
(lb
/ft
2)
The model is set up consistent with the Minnesota River Basin model to provide a full r range of water
quality simulation, including sedime ent, temperature, BOD/DO, nutrients, and bacteria. However, full
calibration has been pursued only fo or sediment, inorganic N, total N, inorganic P, and to otal P at this time.
Sediment calibration is the most tim me-consuming part of model development, and was cconducted
consistent with BASINS Technical Note 8. Upland sediment erodibility rates were set b based on
SSURGO USLE K factors. As with h the Minnesota River Basin model, critical shear str resses for scour
and deposition of cohesive sedimen nt in channels are set equal to percentiles of the simullated distribution.
Silt was set to deposit below the 20tth percentile and scour above the 95
thpercentile dailyy shear stress,
while clay was set to deposit below the 15th and scour above the 90
thpercentile. An exa ample of the shear
stress distribution is shown in Figur re 7.
0.3
0.25
0.2
0.15
0.1
0.05
0
0.1 1 10 100 1000 10000
Flow (cfs)
Figure 7. Simulated Shear S Stress (Tau) Distribution for Yellow Medicine Rea ach 101 (Yellow Medicine at USGS Gage near Gra anite Falls)
Bluff loads were assigned consisten nt with the Minnesota River model using Special Act tions to replenish
the sediment available in the bed. T These loads consisted of 1.2 tons/hr in the lowermost t reach of Yellow
Medicine (100) and 0.97 tons/hr in tthe lowermost reach of Hawk Creek (201). A reach--by-reach mass
balance analysis of channel sedimen nt was conducted to ensure reasonable sediment accu umulation/loss
behavior and approximately stable b bed composition. The final simulation shows slow rates of channel a
degradation over the course of the 1 17-year simulation (Figure 8), with the exception of n net accumulation
in the bluff reaches (100 and 201), aand in the lake reaches (111, 115, 153, 208-210, 214 4-215). The large
gain represented in the bluff reaches s is a temporary phenomenon, reflecting accumulatio on during 2008
and 2009 after large events reduced d bed storages in preceding years. Net trapping efficiiency (depth/scour
as a fraction of influent sediment, ex xclusive of bluff loads) had a median of -11 percent,, with positive
trapping (> 80 percent) for the lake segments, consistent with observations that a signifi icant portion of the
total load in this basin arises from chchannel processes (Figure 9).
17
in Sediment Bed Depth over 1996
ping Efficiency by Reach
same approach used in the Minnesota River Basin models. Ammonia,
d generalized organic matter are simulated on the land surface, with
ildup
e Mass
ganic phosphorus, and BOD at entry to the stream.
has c
this effort, the calibration focused on six stations with long periods of
hetomba Creek (S002
est Fork Beaver Creek (S000
iver at Mouth (S000
esota
etomba Creek and Yellow Medicine River at Mouth as these are not
in Prinsburg, Svea, Blomkest, and Roseland; Yellow Medicine River
ystems but no major discharges
Simulated Chang on
Net Sediment Tra
llows the odels. Ammonia,
nitrate nitrogen, orthophosphate, a d surface, with
the first two being represented by b ted as sediment
T rganic matter to
organic carbon, organic nitrogen, o
The Hawk Creek Watershed Projec ity calibration.
Given the limits of the schedule for th long periods of
record and a variety of conditions: rd (S002
012), ine River at Gage
316), and Yellow Medicine ations was used
for calibration in the previous Min
The initial calibration focused on C h as these are not
strongly impacted by point sources k
system w Medicine River
has a number of stabilization pond ing for the 2006
11
81
20
2009 Simulat
llows the same approach used in the Minnesota River Basin
nitrate nitrogen, orthophosphate, and generalized organic matter are simulated on the la
two simul
Link table is used to apportion generalized
ollected a wealth of data useful for water qua
Given the limits of the schedule for this effort, the calibration focused on six stations w
n
405), Yellow Medi
159). Only the last of these s
The initial calibration focused on Chetomba Creek and Yellow Medicine River at Mou
point sources on Chetomba Cre
systems in Prinsburg, Svea, Blomkest, and Roseland; Yell
). Intensive monito
0.9
0.8
0.7
30
7
Figure 8. Simulated Change e in Sediment Bed Depth over 1996-2009 Simulatiion
120%
100%�
80%�
60%�
40%�
20%�
0%�
-20%�
-40%�
-60%�
-80%�
Ch
an
ge
in
De
pth
(ft
)
10
0
10
21
00
10
4
10
6
10
8
11
0
11
2
11
4
11
6
11
8
12
0
12
2
12
4
14
0
15
1
15
3
16
1
18
0
20
1
20
3
20
5
20
7
20
9
21
1
21
3
21
5
21
7
21
9
22
1
23
1
23
3
23
5
23
5
25
0
10
3
10
6
10
9
11
2
11
5
11
8
12
1
12
4
15
0
15
3
17
0
20
1
20
4
20
7
21
0
21
3
21
6
21
9
23
0
23
3
24
02
40
27
02
70
30
23
01
30
33
05
3
05
0.6
0.5
0.4
0.3
0.2
0.1
0
-0.1
-0.2
Figure 9. Net Sediment Trap pping Efficiency by Reach
The nutrient simulation follows the same approach used in the Minnesota River Basin m models. Ammonia,
nitrate nitrogen, orthophosphate, an nd generalized organic matter are simulated on the lannd surface, with
the first two being represented by buuildup-washoff processes and the second two simula ated as sediment-
associated with potency factors. Th he Mass-Link table is used to apportion generalized o organic matter to
organic carbon, organic nitrogen, or rganic phosphorus, and BOD at entry to the stream.
The Hawk Creek Watershed Projectt has collected a wealth of data useful for water quallity calibration.
Given the limits of the schedule for this effort, the calibration focused on six stations wi ith long periods of
record and a variety of conditions: C Chetomba Creek (S002-152), Hawk Creek at Mayna ard (S002-148),
Hawk Creek at Mouth (S002-012), WWest Fork Beaver Creek (S000-405), Yellow Medic cine River at Gage
(S002-316), and Yellow Medicine R River at Mouth (S000-159). Only the last of these st tations was used
for calibration in the previous Minn nesota River modeling effort.
The initial calibration focused on Chhetomba Creek and Yellow Medicine River at Moutth as these are not
strongly impacted by point sources ((there are no major point sources on Chetomba Cree ek, although there
are some direct discharging systems s in Prinsburg, Svea, Blomkest, and Roseland; Yello ow Medicine River
has a number of stabilization pond s systems but no major discharges). Intensive monitor ring for the 2006
18
sis for calibration, with earlier monitori
ted by a number of factors, including the presence of large point
awk Creek) and Southern Minnesota Beet Sugar (discharged to
er Creek through August 2004, subsequently to County Ditch
entrations are generally reported only monthly, leading to
ading time series. In addition, ammonia is the
s, requiring use of approximate assumptions to specify concentrations
surprisingly, this leads to considerable uncertainty in the prediction
m of these sources. In Hawk Creek it appears that the total N loading
ng the period around 2005, leading to over
presented in more recent dat
rient simulation is evident strong interactions between nutrients and
ts planktonic algae and periphyton, but does not have a routine to
se of nutri
ty of nutrient and algal processes when stream depth falls below 3
y). In addition to preventing algal uptake of nutrients, thi
il depth again increases above the threshold. These factors tend to
utrient concentrations at very low flows, particularly for stations on
nally predicts very high concentration values (but low net loads)
e stranding phenomenon. This biases statistics based on average
edian error statistics.
nt fit generally required only small modifications to the previous
. One particular area of improvement was in the specification of
d with tile drainage. Model fit was great
oncentrations associated with the areas nearer the Minnesota River
ns 3
be characterized by more wetlands and ponds (areas associated with
and confirm
ion results is provided in Attachment 2. The quality of model fit
ing to parameter and location, and is consistent with other
ota River Basi
urce nutrient loads and the simulation of nutrients under very low
is appropriately calibrated to support scenario evaluation for
ed above, the model has not been fully calibrated for dissolved
2009 period was used as the main g a corroboration
Calibration for nutrients is complic f large point
sources at Willmar (discharging to ischarged to
t Fork Bea nty Ditch
Sacred Heart Creek). Pollutant con ing to
considerable uncertainty in actual l form of nitrogen
regularly monitored in the discharg ify concentrations
of nitrate and organic nitrogen. No n the prediction
rations downstre the total N loading
estimated dur on of instream N
concentrations, but is much better r
Another factor complicating the nu en nutrients and
algae/macrophytes. HSPF represe e a routine to
explicitly represent uptake and rele PF model code
shuts down the simulation of a vari falls below 3
(to prevent numeric instabili ts, thi
in “stranding” of pollutant mass un factors tend to
prediction of y for stations on
casi w net loads)
during low flow conditions due to t d on average
concentration, but does not affect
rove nutri the previous
setup of the Minnesota River mode cification of
interflow N concentrations associat d by assigning
two seasonal patterns, with higher innesota River
(areas associated with weather stati ociated with the
at appear t associated with
9, and 1 is apparent
difference need further investigatio
The full set of water quality simula of model fit
ranges from fair to excellent, accor other
applications of HSPF in the Minne model and data
appear to be associated with point s nder very low
Overall, the mode ation for
sediment and nutrients. As mentio r dissolved
ng providi
Calibration for nutrients is complicated by a number of factors, including the presence
sources at Willmar (discharging to Hawk Creek) and Southern Minnesota Beet Sugar (
t Fork Beaver Creek through August 2004, subsequently to Co
Sacred Heart Creek). Pollutant concentrations are generally reported only monthly, lea
only
regularly monitored in the discharges, requiring use of approximate assumptions to spe
of nitrate and organic nitrogen. Not surprisingly, this leads to considerable uncertainty
rations downstream of these sources. In Hawk Creek it appears that
estimat
Another factor complicating the nutrient simulation is evident strong interactions betw
algae/macrophytes. HSPF represents planktonic algae and periphyton, but does not ha
ents by macrophytes. In addition, the H
shuts down the simulation of a variety of nutrient and algal processes when stream dept
(to prevent numeric instability). In addition to preventing algal uptake of nutrie
in “stranding” of pollutant mass until depth again increases above the threshold. These
prediction of nutrient concentrations at very low flows, particular
casionally predicts very high concentration values (but l
during low flow conditions due to the stranding phenomenon. This biases statistics bas
rove nutrient fit generally required only small modifications t
setup of the Minnesota River model. One particular area of improvement was in the sp
ly improv
two seasonal patterns, with higher concentrations associated with the areas nearer the
11) and lower concentrations as
at appear to be characterized by more wetlands and ponds (area
). The geochemical reasons for t
The full set of water quality simulation results is provided in Attachment 2. The qualit
ranges from fair to excellent, according to parameter and location, and is consistent wit
n. The largest discrepancies between
appear to be associated with point source nutrient loads and the simulation of nutrients
Overall, the model is appropriately calibrated to support scenario eval
sediment and nutrients. As mentioned above, the model has not been fully calibrated f
2009 period was used as the main bbaasis for calibration, with earlier monitoring providin ng a corroboration
test.
Calibration for nutrients is complica ated by a number of factors, including the presence o of large point
sources at Willmar (discharging to H Hawk Creek) and Southern Minnesota Beet Sugar (d discharged to
County Ditch 37 to West Fork Beav ver Creek through August 2004, subsequently to Cou unty Ditch 45 to
Sacred Heart Creek). Pollutant conccentrations are generally reported only monthly, leadding to
considerable uncertainty in actual lo oading time series. In addition, ammonia is the only form of nitrogen
regularly monitored in the discharge es, requiring use of approximate assumptions to spec cify concentrations
of nitrate and organic nitrogen. Not t surprisingly, this leads to considerable uncertainty in the prediction i
of nutrient concentrations downstreaam of these sources. In Hawk Creek it appears that the total N loading
from Willmar is over-estimated duriing the period around 2005, leading to over-estimati ion of instream N
concentrations, but is much better re epresented in more recent data.
Another factor complicating the nut trient simulation is evident strong interactions betwe een nutrients and
algae/macrophytes. HSPF represen nts planktonic algae and periphyton, but does not hav ve a routine to
explicitly represent uptake and relea ase of nutrients by macrophytes. In addition, the HS SPF model code
shuts down the simulation of a varie ety of nutrient and algal processes when stream depthh falls below 3
inches (to prevent numeric instabilitty). In addition to preventing algal uptake of nutrien nts, this can result
in “stranding” of pollutant mass unt til depth again increases above the threshold. These factors tend to
cause consistent over-prediction of nnutrient concentrations at very low flows, particularlly for stations on
smaller streams. The model occasio onally predicts very high concentration values (but lo ow net loads)
during low flow conditions due to thhe stranding phenomenon. This biases statistics baseed on average
concentration, but does not affect m median error statistics.
Parameter updates to improve nutrie ent fit generally required only small modifications to o the previous
setup of the Minnesota River model l. One particular area of improvement was in the spe ecification of
interflow N concentrations associate ed with tile drainage. Model fit was greatly improve ed by assigning
two seasonal patterns, with higher c concentrations associated with the areas nearer the M Minnesota River
(areas associated with weather statio ons 3 – 7, and 10 – 11) and lower concentrations ass sociated with the
upland moraine areas that appear to o be characterized by more wetlands and ponds (areass associated with
weather stations 1 – 2, 8 – 9, and 12 2 – 14; see Table 3). The geochemical reasons for th his apparent
difference need further investigation n and confirmation.
The full set of water quality simulat tion results is provided in Attachment 2. The quality y of model fit
ranges from fair to excellent, accord ding to parameter and location, and is consistent with h other
applications of HSPF in the Minnes sota River Basin. The largest discrepancies between model and data
appear to be associated with point soource nutrient loads and the simulation of nutrients u under very low
flow conditions. Overall, the modell is appropriately calibrated to support scenario evalu uation for
sediment and nutrients. As mention ned above, the model has not been fully calibrated fo or dissolved
oxygen and bacteria at this time.
19
(This page left intentionally blank.)
20
sount of budget and calendar t
the Hawk Creek Watershed Project. Scenario 1 evaluates the
des to the Willmar wastewater treatment plant, which was a major
eek. Scenario 2 investigates the potential impact of widespread
cultural lands.
Rook a major upgrade to their wastewater tr
potential water quality benefits of the plant upgrade.
moval and nitrification, resulting in order of magnitude decreases in
gen l
s also been a significant decrease in biochemical oxygen demand
moved its discharge point. However, the new disch
t (213).
r quality in the Willmar effluent were provided by the Hawk Creek
with the wastewater treatment plant superintendent. T
61 mg/L
0 mg/L
mg/L
mg/L
57 mg/L
#/100 ml
g/L
ew plant is 4.73 MGD, whereas t
ffluent flow is correlated to weather conditions the scenario was
ather and effluent flow time series, scaling up the effluent flow by a
n added based on the concentration assumptions provided above.
LTURAL, designed to investigate the
the Hawk Creek watershed. Partial implementation would
n current conditions and full implementation.
ed to all NHD streamlines in the watershed. Further, the average
entire watershed are assumed to apply to croplands within each
ed. A GIS analysis indicates that
t of the
y, but not the entirety of public drainage ditches within the watershed.
Model ScenariThis task order included a limited a io analysis. Two
specific scenarios were requested b aluates the
potential impacts of the recent upgr ch was a major
t load to Hawk C widespread
acceptance of stream buffers in agr
ILLMThe City of Willmar recently under lant.
scenario is designed to evaluate the .
included phosphorus r ude decreases in
total phosphorus and ammonia nitr it is mostly
There h gen demand
As part of the upgrade, Willmar als h
falls within the same model segme
upgrade wa he Hawk Creek
Watershed Project after consultatio . T
0.
0.
17
1.
5.
59
2
The projected average flow for the the model for
2009 is 3.55 MGD. Because scenario was
constructed by using the historic w fluent flow by a
llutant loads are th vided above.
GRICThis scenario is a bounding scenari plying 50
opland withi would
approximate a linear scaling betwe
The buffers are assumed to be appl , the average
am density characteristics of th within each
subbasin of the Hawk Creek waters 0 feet of NHD
streamlines accounts for 1.57 perce ination suggests
that the NHD represents the majori in the watershed.
ime to support scena
specific scenarios were requested by the Hawk Creek Watershed Project. Scenario 1 e
potential impacts of the recent upgrades to the Willmar wastewater treatment plant, wh
t load to Hawk Creek. Scenario 2 investigates the potential impact of
eatment
scenario is designed to evaluate the potential water quality benefits of the plant upgrad
included phosphorus removal and nitrification, resulting in order of magni
oad. Nitrogen is not removed, however; rathe
There has also been a significant decrease in biochemical ox
As part of the upgrade, Willmar also moved its discharge point. However, the new dis
upgrade water quality in the Willmar effluent were provided by
Watershed Project after consultation with the wastewater treatment plant superintenden
he average flow i
2009 is 3.55 MGD. Because effluent flow is correlated to weather conditions the
constructed by using the historic weather and effluent flow time series, scaling up the e
llutant loads are then added based on the concentration assumptions pr
impact of a
opland within the Hawk Creek watershed. Partial implementatio
The buffers are assumed to be applied to all NHD streamlines in the watershed. Furthe
am density characteristics of the entire watershed are assumed to apply to croplands
area within
(A cursory exa
that the NHD represents the majority, but not the entirety of public drainage ditches wit
4 Model Scenarioos This task order included a limited ammount of budget and calendar time to support scenar rio analysis. Two
specific scenarios were requested by y the Hawk Creek Watershed Project. Scenario 1 ev valuates the
potential impacts of the recent upgraades to the Willmar wastewater treatment plant, whi ich was a major
source of pollutant load to Hawk Cr reek. Scenario 2 investigates the potential impact of widespread
acceptance of stream buffers in agri icultural lands.
4.1 SCENARIO 1: WILLMAAR TREATMENT PLANT UPGRADES The City of Willmar recently underttook a major upgrade to their wastewater treatment p plant. This
scenario is designed to evaluate the potential water quality benefits of the plant upgrade e.
The upgrade included phosphorus reemoval and nitrification, resulting in order of magnittude decreases in
total phosphorus and ammonia nitro ogen load. Nitrogen is not removed, however; rather r it is mostly
converted to nitrate form. There ha as also been a significant decrease in biochemical oxy ygen demand
(BOD).
As part of the upgrade, Willmar also o moved its discharge point. However, the new disc charge location still
falls within the same model segmen nt (213).
Specifications for post-upgrade watteer quality in the Willmar effluent were provided by tthe Hawk Creek
Watershed Project after consultation n with the wastewater treatment plant superintendentt. The following
assumptions are used:
Total Phosphorus 0.6 661 mg/L
Ammonia-N 0.2 20 mg/L
Nitrate-N 17 mg/L
Organic N 1.6 6 mg/L
TSS 5.8 857 mg/L
Fecal Coliform 59 #/100 ml
BOD5 2 m mg/L
The projected average flow for the n new plant is 4.73 MGD, whereas the average flow in n the model for
1996-2009 is 3.55 MGD. Because eeffluent flow is correlated to weather conditions the scenario was
constructed by using the historic we eather and effluent flow time series, scaling up the ef ffluent flow by a
factor 1.214. Pollutant loads are the en added based on the concentration assumptions pro ovided above.
4.2 SCENARIO 2: AGRICU ULTURAL BUFFER IMPLEMENTATION This scenario is a bounding scenario o, designed to investigate the maximum impact of ap pplying 50-foot
stream buffers to all cropland within n the Hawk Creek watershed. Partial implementationn would
approximate a linear scaling betwee en current conditions and full implementation.
The buffers are assumed to be appli ied to all NHD streamlines in the watershed. Further r, the average
stream density characteristics of the e entire watershed are assumed to apply to croplands within each
subbasin of the Hawk Creek watershhed. A GIS analysis indicates that land area within 550 feet of NHD
streamlines accounts for 1.57 percen nt of the total area in the watershed. (A cursory exam mination suggests
that the NHD represents the majorit ty, but not the entirety of public drainage ditches within the watershed. h
21
rs based on the NHD may be slightly less than could be obtain
age ditches.)
d as vegetative filter strips (VFSs) with perennial grass cover. Thus,
ift in land use with 1.57% of cropland converting
val by buffers in HSPF presents challenges because HSPF is a
as within a subbasin do not have a specific position relative to
idded model
are present in SWAT, also a lumped model. In the recent release of
ped to address this issue, and the same approach is adopted for
.
ite and J.G. Arnold, 2009, Development of a simplistic vegetative
utrient retention at the field scale,
mpirical analyses of field studies and application of the vegetative
ate an approximation of treatment by buffers and filter strips that can
. The approach contain
n BMP efficiency in an agricultural setting, and regression equations
conditional on flow path. The approach also replaces the tradition
rnate measure, the ratio of contributing area to buffer area, which is
ncertain geometry of lumped models.
rformance is obtained when all flow i
he length of the buffer. In contrast, flow that becomes fully
the buffer with little or no pollutant removal. White and Arnold’s
orld applications of buffers occur in situations where a majority of
vely small portion of the buffer. It thus divides the flow from the
eneral loading to the b
that is directed to the most heavily loaded 10 percent of the filter
t is subject to minimal pollutant removal.
nd on the magnitude of flow and the magnitude of sediment loading
by White and Arnold. This is inconvenient to implement in HSPF
ORTRAN code; however, at the ranges of sur
n the Hawk Creek watershed, the impacts on removal rates are
in the range of uncertainty in the regression models. It is thus
tation of treatment efficiency for incorporation into HSPF.
nd Arnold approach is the determination of the different flow
surface runoff that is expected to be fully channelized.
in dispersed sheet flow over a distance of more than 300 feet from the
of 50 feet, would result in a ratio of contributing area to buffer area of
atio of contributing area to buffer area is 62.6. This suggests that 90
fer will be concentrated; however, this may fall within the 10 percent
hannelized. We assu
d, and that the remaining 50% receives some treatment (although less
uffer that receives sheet flow).
ffic
d in the buffer, stranding any particulate pollutants. However, these
zed and transported to the streams during subseq
ften saturated during wet weather events,
ble to settle on fixed (rather than flow
Thus, the impact of instituting buff be obtain
instituting buffers on all public drai
Buffers are assumed to be maintain ass cover. Thus,
the first effect of the scenario is a s grass.
The representation of pollutant rem HSPF is a
lumped model. That is, land use ar relative to
streams, as would be the case in a affect total
pollutant loading. Similar problem recent release of
SWAT2009 an approach was devel adopted for
HSPF representation of this scenari
The SWAT2009 approach (M.J. W stic vegetative
filter strip model for sediment and cesses
lops a method based on the vegetative
filter strip model (VFSMOD) to cr er strips that can
be incorporated into lumped model onceptualization
of the flow paths and their impacts ression equations
that estimate pollutant removal rate es the tradition
reliance on buffer width with an alt r area, which is
more appropriate to the varied and
The best buffer pollutant removal p he buffer as sheet
flow and evenly distributed across es fully
channelized is able to punch throug te and Arnold’s
re a majority of
the field runoff is directed to a relat flow from the
upland area into three categories: w, the fraction of
channelized) concentrated flo nt of the filter
strip, and fully channelized flow th
tionships dep ediment loading
in the regression models developed ment in HSPF
without changes to the underlying noff flow and
surface sediment loading expected l rates are
and also well wit . It is thus
HSPF.
A key factor in applying the White erent flow
especially the fraction o ed.
believed that it is difficult to maint 300 feet from the
which, with a buffer width a to buffer area of
is indicates that suggests that 90
percent of the flow reaching the bu in the 10 percent
focus area and may not all be fully ed to the
concentrated area is fully channeliz ent (although less
effective than in the portion of the
As mentioned above, the treatment or small flows,
be infiltrat However, these
may be remobil t events. Further,
soils in streamside buffer areas are or
infiltration. Therefore, it is reason moval rates.
Thus, the impact of instituting buffers based on the NHD may be slightly less than coul
Buffers are assumed to be maintained as vegetative filter strips (VFSs) with perennial g
t
The representation of pollutant removal by buffers in HSPF presents challenges becaus
lumped model. That is, land use areas within a subbasin do not have a specific positio
, so it is difficult to assess how buffers
pollutant loading. Similar problems are present in SWAT, also a lumped model. In the
SWAT2009 an approach was developed to address this issue, and the same approach is
The SWAT2009 approach (M.J. White and J.G. Arnold, 2009, Development of a simpl
Hydrological Pr
lops a method based on empirical analyses of field studies and application o
filter strip model (VFSMOD) to create an approximation of treatment by buffers and fil
s two major components: a
of the flow paths and their impacts on BMP efficiency in an agricultural setting, and re
that estimate pollutant removal rates conditional on flow path. The approach also repla
reliance on buffer width with an alternate measure, the ratio of contributing area to buff
s directed to
flow and evenly distributed across the length of the buffer. In contrast, flow that beco
channelized is able to punch through the buffer with little or no pollutant removal. Wh
world applications of buffers occur in situations wh
the field runoff is directed to a relatively small portion of the buffer. It thus divides the
uffer without concentrated fl
channelized) concentrated flow that is directed to the most heavily loaded 10 perc
tionships depend on the magnitude of flow and the magnitude of
in the regression models developed by White and Arnold. This is inconvenient to impl
face r
surface sediment loading expected in the Hawk Creek watershed, the impacts on remov
and also well within the range of uncertainty in the regression models
entation of treatment efficiency for incorporation int
A key factor in applying the White and Arnold approach is the determination of the dif
especially the fraction of surface runoff that is expected to be fully channeli
believed that it is difficult to maintain dispersed sheet flow over a distance of more tha
which, with a buffer width of 50 feet, would result in a ratio of contributing ar
is indicates that ratio of contributing area to buffer area is 62.6. This
percent of the flow reaching the buffer will be concentrated; however, this may fall wit
me that 50% of the flow direc
concentrated area is fully channelized, and that the remaining 50% receives some treat
iency of buffers varies with flow loading rate.
be infiltrated in the buffer, stranding any particulate pollutants
ue
reducing
dependent) r
Thus, the impact of instituting buffe ers based on the NHD may be slightly less than couldd be obtained by
instituting buffers on all public drainnage ditches.)
Buffers are assumed to be maintaine ed as vegetative filter strips (VFSs) with perennial grrass cover. Thus,
the first effect of the scenario is a sh hift in land use with 1.57% of cropland converting to o grass.
The representation of pollutant remo oval by buffers in HSPF presents challenges because e HSPF is a
lumped model. That is, land use are eas within a subbasin do not have a specific position n relative to
streams, as would be the case in a ggrridded model, so it is difficult to assess how buffers affect total
pollutant loading. Similar problems s are present in SWAT, also a lumped model. In the recent release of
SWAT2009 an approach was develooped to address this issue, and the same approach is adopted for the
HSPF representation of this scenarioo.
The SWAT2009 approach (M.J. Wh hite and J.G. Arnold, 2009, Development of a simpli istic vegetative
filter strip model for sediment and n nutrient retention at the field scale, Hydrological Pro ocesses, 23: 1602
1616) develops a method based on e empirical analyses of field studies and application of f the vegetative
filter strip model (VFSMOD) to cre e tate an approximation of treatment by buffers and filter strips that can
be incorporated into lumped models s. The approach contains two major components: a c conceptualization
of the flow paths and their impacts o on BMP efficiency in an agricultural setting, and reg gression equations
that estimate pollutant removal ratess conditional on flow path. The approach also replacces the traditional
reliance on buffer width with an alte ernate measure, the ratio of contributing area to buffeer area, which is
more appropriate to the varied and u uncertain geometry of lumped models.
The best buffer pollutant removal peerformance is obtained when all flow is directed to t the buffer as sheet
flow and evenly distributed across t the length of the buffer. In contrast, flow that becom mes fully
channelized is able to punch throughh the buffer with little or no pollutant removal. Whi ite and Arnold’s
approach recognizes that most real-wworld applications of buffers occur in situations whe ere a majority of
the field runoff is directed to a relatiively small portion of the buffer. It thus divides the flow from the
upland area into three categories: ggeneral loading to the buffer without concentrated flo ow, the fraction of
(non-channelized) concentrated flow w that is directed to the most heavily loaded 10 perce ent of the filter
strip, and fully channelized flow tha at is subject to minimal pollutant removal.
Pollutant removal relationships depeend on the magnitude of flow and the magnitude of ssediment loading
in the regression models developed by White and Arnold. This is inconvenient to imple ement in HSPF
without changes to the underlying F FORTRAN code; however, at the ranges of surface ruunoff flow and
surface sediment loading expected i in the Hawk Creek watershed, the impacts on removaal rates are
relatively small – and also well with hin the range of uncertainty in the regression models . It is thus
appropriate to adopt a static represeenntation of treatment efficiency for incorporation into o HSPF.
A key factor in applying the White aand Arnold approach is the determination of the diff ferent flow
fractions – especially the fraction of f surface runoff that is expected to be fully channeliz zed. It is generally
believed that it is difficult to mainta ain dispersed sheet flow over a distance of more than n 300 feet from the
buffer – which, with a buffer width of 50 feet, would result in a ratio of contributing are ea to buffer area of
6. The GIS analysis indicates that r ratio of contributing area to buffer area is 62.6. This suggests that 90
percent of the flow reaching the buf ffer will be concentrated; however, this may fall with hin the 10 percent
focus area and may not all be fully c channelized. We assume that 50% of the flow directted to the
concentrated area is fully channelizeed, and that the remaining 50% receives some treatm ment (although less
effective than in the portion of the b buffer that receives sheet flow).
As mentioned above, the treatment eefficiency of buffers varies with flow loading rate. FFor small flows,
most of the volume may be infiltrateed in the buffer, stranding any particulate pollutants. . However, these
stranded pollutants may be remobili ized and transported to the streams during subsequen nt events. Further,
soils in streamside buffer areas are o often saturated during wet weather events, reducing or eliminating
infiltration. Therefore, it is reasona able to settle on fixed (rather than flow-dependent) re emoval rates.
22
Hawk Creek watershed rates of generation of overland flow and
e low, with much of the flow proceeding through ground water and
ent being generated from scour associated w
ents. We undertook a modeling analysis of the predicted pollutant
White and Arnold under a variety of flows ranging from the median
centile overland surface flow. While removal rates are predicted to
range is generally small (due in part to the assumptions regarding
urther, the majority of pollutant
s calculated at the 95
ulting removal rates relative to the total upland field load generation,
ach employed in SWAT2009, are shown in
Rates for Agricultural Buffer Scenario
al Rates (50erl
heet and
.
ing this scenario is as follows:
the grass land use category (in the corresponding slope a
)
ble to incorporate the reduction rates for pollutant loads associated
ng ravine sediment load) as shown in the table above.
loads. It should be noted, however, that the analysis will not account
t accrue from increasing streambank stability throu
by comparing pollutant loads and concentrations at
also shown at the mouth of Beaver Creek (Scenario 1 does not affect
compared on the basis of medians, as the averages are potentially
imulating concentrations at very low flows.
icted to result in large decreases in both median concentrations and
h the median concentration of nitrate
and permeable nd flow and
overland sediment transport are qui ound water and
tile drainage, and much of the sedi drain outlets and
channel erosion during high flow e icted pollutant
removal rates using the equations o from the median
pe are predicted to
be greater at lower flow depths, the ons regarding
fully channelized or bypass flow). a few large
events. Therefore, the removal rat ow appear
appropriate for the analysis. The r load generation,
nsistent with the appr
Pollutant Remova
Range of Net Remopercentile o
Note that these rates apply only to /gully erosion are
assumed to not be treated by buffer
In sum, the approach for implemen
Shift 1.57 % of cropland to pe a
hydrologic soil group class
LINK t ads associated
with surface runoff (exclud e.
streamside
buffers in reducing upland pollutan s will not account
for any additional benefits that mig h the use of
Results of the scenarios are analyz t
Hawk Creek. Scenario 2 results ar 1 does not affect
ek). Concentrations are re potentially
biased by the model difficulties in
Scenario results are summarized in Willmar
Wastewater Treatment Plant is pre centrations and
althou cted to change.
and permeable Hawk Creek watershed rates of generation of overl
overland sediment transport are quite low, with much of the flow proceeding through g
ith tile
channel erosion during high flow events. We undertook a modeling analysis of the pre
removal rates using the equations of White and Arnold under a variety of flows rangin
percentile overland surface flow. While removal rates
be greater at lower flow depths, the range is generally small (due in part to the assumpt
loads move durin
percentile overland surface f
appropriate for the analysis. The resulting removal rates relative to the total upland fiel
rill erosion; loads generated through ravin
Shift 1.57 % of cropland to the grass land use category (in the corresponding sl
LINK table to incorporate the reduction rates for pollutant l
with surface runoff (excluding ravine sediment load) as shown in the table abo
imate of the potential benefits associated wit
buffers in reducing upland pollutant loads. It should be noted, however, that the analys
Results of the scenarios are analyzed by comparing pollutant loads and concentrations
Hawk Creek. Scenario 2 results are also shown at the mouth of Beaver Creek (Scenari
ek). Concentrations are compared on the basis of medians, as the averages
. In general, the upgrade of the
Wastewater Treatment Plant is predicted to result in large decreases in both median co
In the relatively flat and permeable Hawk Creek watershed rates of generation of overla and flow and
overland sediment transport are quitte low, with much of the flow proceeding through gr round water and
tile drainage, and much of the sedim ment being generated from scour associated with tile drain outlets and
channel erosion during high flow ev vents. We undertook a modeling analysis of the pred dicted pollutant
removal rates using the equations off White and Arnold under a variety of flows ranging g from the median
overland surface flow to the 99th
per rcentile overland surface flow. While removal rates are predicted to
be greater at lower flow depths, the range is generally small (due in part to the assumpti ions regarding
fully channelized or bypass flow). FFurther, the majority of pollutant loads move during g a few large
events. Therefore, the removal rate es calculated at the 95th
percentile overland surface fl low appear
appropriate for the analysis. The reessulting removal rates relative to the total upland fieldd load generation,
calculated consistent with the appro oach employed in SWAT2009, are shown in Table 5..
Table 5. Pollutant Removall Rates for Agricultural Buffer Scenario
Constituent Range of Net Remov 99
th percentile ov
val Rates (50th
to verland flow)
Selected Removal Rate (95th
percentile overland flow)
Sediment 48 – 55 % 51 %
Organic N 38 – 47 % 42 %
Inorganic N 34 – 54 % 43 %
Sorbed and Organic P
43 – 50 % 46 %
Dissolved P 27 – 44 % 35 %
Note that these rates apply only to s sheet and rill erosion; loads generated through ravine e/gully erosion are
assumed to not be treated by buffers s.
In sum, the approach for implement ting this scenario is as follows:
1. Shift 1.57 % of cropland to the grass land use category (in the corresponding slo ope and
hydrologic soil group class. .)
2. Modify the MASS-LINK ta able to incorporate the reduction rates for pollutant lo oads associated
with surface runoff (excludiing ravine sediment load) as shown in the table abov ve.
This should provide a realistic first--cut estimate of the potential benefits associated with h streamside
buffers in reducing upland pollutant t loads. It should be noted, however, that the analysiis will not account
for any additional benefits that migh ht accrue from increasing streambank stability througgh the use of
buffers.
4.3 SCENARIO RESULTS Results of the scenarios are analyzeedd by comparing pollutant loads and concentrations a at the mouth of
Hawk Creek. Scenario 2 results are e also shown at the mouth of Beaver Creek (Scenario o 1 does not affect
Beaver Creek). Concentrations are compared on the basis of medians, as the averages a are potentially
biased by the model difficulties in s simulating concentrations at very low flows.
Scenario results are summarized in Table 6 and Table 7. In general, the upgrade of the Willmar
Wastewater Treatment Plant is pred dicted to result in large decreases in both median con ncentrations and
loads of total P and total N – althouggh the median concentration of nitrate-N is not prediicted to change.
23
icted 11 percent decrease in TSS load in Hawk Creek and a 15
ith smaller fractional losses of N and P. The net effect of the buffers
e to the assumptions regarding the fraction of flow that can be
ed form, as well as the fact that subsurface loads
uffers. The buffers have little impact on media
ce loading during high flow events.
Hawk Creek Mouth (1996
Beaver Creek Mouth (1996
The buffer scenario results in a pre k and a 15
percent decrease in Beaver Creek, ect of the buffers
e d hat can be
concentr cluding tile
are not mitigated by the centrations
because they primarily address surf
Scenario Results,
Scenario Results,
The buffer scenario results in a predicted 11 percent decrease in TSS load in Hawk Cre
percent decrease in Beaver Creek, with smaller fractional losses of N and P. The net ef
e due to the assumptions regarding the fraction of flow
(i
n co
The buffer scenario results in a pred dicted 11 percent decrease in TSS load in Hawk Cree ek and a 15
percent decrease in Beaver Creek, w with smaller fractional losses of N and P. The net efffect of the buffers
is reduced at the watershed scale du ue to the assumptions regarding the fraction of flow t that can be
effectively treated in non-concentraatted form, as well as the fact that subsurface loads (in ncluding tile
drainage) are not mitigated by the b buffers. The buffers have little impact on median con ncentrations
because they primarily address surfaace loading during high flow events.
Table 6. Scenario Results, Hawk Creek Mouth (1996-2009 Simulation)
Baseline Scenario 1 Scenario 2
TSS median concentration (mg/L) 10.80 10.10 10.10
TSS mass (tons/yr) 8927 8921 7901
NOx median concentration (mg/L) 6.30 6.30 6.20
Total N median concentration (mg/L) 13.40 7.20 13.10
Total N mass (tons/yr) 2025 1123 1965
Total P concentration (mg/L) 1.00 0.49 0.96
Total P Mass (tons/yr) 59.9 36.3 58.8
Table 7. Scenario Results, Beaver Creek Mouth (1996-2009 Simulation)
Baseline Scenario 2
TSS median concentration (mg/L) 9.40 8.70
TSS mass (tons/yr) 4203 3571
NOx median concentration (mg/L) 3.40 3.30
Total N median concentration (mg/L) 6.20 6.10
Total N mass (tons/yr) 413 385
Total P concentration (mg/L) 0.45 0.45
Total P Mass (tons/yr) 14.2 13.5
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