minnesota water resource conference, 2015 · gssha modelling background ... hydrology chapter 5. 2....
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Minnesota Water Resource Conference, 2015
Outline Discovery Farms Overview Blue Earth Discovery Farm
Location Monitoring
GSSHA Modelling Background Modeling procedure
Calibration Applications Conclusion and further steps
Discovery Farms Objectives and Goals Farmer leadership and
engagement in water quality issues
Collection of real world, field-scale water quality data, from working Minnesota farms
Information on farm management practices and sediment and nutrient losses
Educational forum for farmers, researchers, agency, policy makers, and the general public
Farmer leadership Credible research design and implementation Effective communication and outreach
Data Collected 365 Days a Year Weather Edge-of-field monitoring
Surface runoff subsurface tile drainage
Water Quantity Water Quality
Total Suspended Solids Total Phosphorus Soluble Ortho
Phosphorus Nitrate + Nitrite Ammonia Nitrogen Total Kjeldahl Nitrogen Chloride
Locations10 Core Farms:
Surface & Tile: 5 stations
Surface only: 3 stations
Tile only: 3 stations 2 Special Projects
In-season N application
Irrigation farm
Importance of Discovery Farms Data Representative farm enterprises and landscapes by region
Baseline data collection – assessing current water quality conditions Compiled data – range of farming practices, landscapes, and weather conditions
Range of actual losses Timing of losses Surface or tile MN and/or data from other Discovery Farms (WI/ND/IL/AR) or similar research projects
Individual farm data Baseline data collection to identify areas for improvement, recommend changes if needed Monitor effects of recommended changes
What can this type of data be used for? Establish baseline levels Identify water quality strengths and weaknesses of current agricultural
management practices Identify management practices to reduce losses Assess current water quality models, calibrate future models Assess potential BMPs – will X BMP effect X constituent at X time period
Outline Discovery Farms Overview Blue Earth Discovery Farm
Location Monitoring
GSSHA Modelling Background Modeling procedure
Calibration Applications Conclusion and further steps
Site locationN
CobbMaple
Monitoring Data Provided2011 thru 2014
Hydrological Water Quality: (flume and tile)
Climate Data
- Flume discharge
- Tile discharge
- Total suspended solids
- Total phosphorous
- Dissolved phosphates
- Nitrites and Nitrates
- Ammonia
- Total Kjeldahl Nitrogen, TKN
- Chloride
- Precipitation
- Humidity
- Air temperature
- Soil moisture
- Soil temperature at 5 cm, 30 cm and 60 cm.
GSSHA Gridded Surface Subsurface Hydrological Analysis
“Physically-based, distributed-parameter, structured grid, hydrologic model that simulates the hydrological response of a watershed subject to given hydrometeorological inputs”
- GSSHA Manual
Slide showing grid diagram
Grid size = 5 x 5 metersTotal area = 14.3 acres
HEC-DSSVuerid
rid
Modeling sequence
Build Grid
DataCollection
ridDataProcessing
ridBuild & RunModel
ridReviewResults
ridSensitivityAnalysis &Calibration
ridOutput
ridApplications
HEC-DSSVue
ridObjectives
Model Calibration and Results
Continuous Simulation for soybean (2012 Growing Season)
Flume Flow Continuous Simulation: soybean (2012 Growing Season)
44.14.24.34.44.54.64.74.84.9
0.1 0.11 0.12 0.13 0.14 0.15 0.16 0.17 0.18
flum
e_cf
s
Roughness
Tile Flow Continuous Simulation: soybean (2012 Growing Season)
Flume Flow Continuous Simulation: corn (2013 Growing Season)
Re-calibrate after 7/4
Tile Flow Continuous Simulation: corn (2013 Growing Season)
Re-calibration
Tile Flow Continuous Simulation: corn (2013 Growing Season)
Scenarios and Applications
Drainage Control (turning tiles on and off)
Short term predictive tool
Apply crop cover (WEPP scenarios)
Different crop rotation
Monitoring results at middle of field
Comparing GW of the tile and no tile flow at middle of field
Ground elevation
no tiles
with tiles
tile elevation
Soybean Root Growth
304.4000
304.6000
304.8000
305.0000
305.2000
305.4000
305.6000
305.8000
306.0000
120 140 160 180 200 220 240
Elev
Days
root depth
gw_wtiles
ground
tile_elev
gw_noTiles
Corn Root Growth
304.4000
304.6000
304.8000
305.0000
305.2000
305.4000
305.6000
305.8000
120 140 160 180 200 220 240 260 280 300
Elev
Days
root depth
gw_wtiles
ground
tile_elev
gw_noTiles
Comparing tile and no tile flow (2012)
Tiles No-Tiles Reduction
Runoff Peak Discharge
4.604 CFS 5.47 CFS 84.17%
RunoffVolume
26,736.7 ft3 41,078 ft3 65.1%
Tributary flow 11.3 CFS 12.71 CFS 89%
Scenarios and Applications
Drainage Control (turning tiles on and off)
Short term predictive tool
Apply crop cover (WEPP scenarios)
Different crop rotation
Effects of adding a 10-YR Storm event on tile flow (2013 growing cycle)
10-YR Storm Event
0
20
40
60
0 20 40
Hou
rs
Precipitation_mm
Actual
10_yr
Effects of 10-storm on water table fluctuations
0
20
40
60
0 20 40
Hou
rs
Precipitation_mm
Actual
10_yr
Removing a storm (2012, Soybean growing cycle)
Scenarios and Applications
Drainage Control (turning tiles on and off)
Short term predictive tool
Apply crop cover (WEPP scenarios)
Different crop rotation
Effects of applying crop cover on tile and surface flowfor soybean growing season
According to the model, cover crop decreases total surface runoff volume by up to 78%!
According to the model, cover crop increases tile flow by up to 16% of total tile flow volume!
2012 growing season
Effects of applying crop cover on tile and surface flow for corn growing season
According to the model, cover crop decreases total surface runoff volume by up to 91%!
2013 growing season
According to the model, cover crop increases tile flow by up to 26% of total tile flow volume!
Scenarios and Applications
Drainage Control (turning tiles on and off)
Short term predictive tool
Apply crop cover (WEPP scenarios)
Different crop rotation
2012 2013
Calibrated for Soybean Soybean
Calibrated for CornCorn
corn
soybean
Tile Flow
corn
soybean
Runoff
2012
2013 corn
soybean
corn
soybean
Comparing effects of soybean and corn on tile flow According to the model, corn contributed less tile flow
than soybean In 2012, reduction = 17% In 2013, reduction = 35%
According to the model, corn causes sharper peaks in tile flow.
However, soybean contributed less surface runoff than corn In 2012, reduction= 50% In 2013, reduction = 68.6%
Water Depth Animation (26May2012, 07:00 AM to 28May2012, 11:45 PM)
Conclusion GSSHA calibrated model can be used to address and
manage drainage control and various tillage practices.
As a predictive tool, GSSHA can embed various storms to forecast changes in surface runoff and tile flow.
By using crop specific calibrated models, crop rotation can be simulated and results used to compare the effects of specific crops on soil hydrology.
Further studies
Sediment transport
Nutrient loading
Use GSSHA for other Discovery Farms
Soil Parameterization For
GSSHA ModelDaniel Reinartz, PE, MNDNR
Minnesota Water Resource Conference14 October 2015
GSSHA
• Gridded Surface subsurface Hydrologic Analysis• Hydrologic Model• Distributed• Physics based• Gridded• US Army ERDC
– Maintained– Supported – Distributed
Objective
• Simulate land use effects on:– Runoff– Sediment loading– Water Quality
• Nutrient loading– Ag tile effects
• Different landscapes– ½ of state is farmland– Land use– Land cover– Crop management practice
• Model BMPs– Clean water– Riparian buffers– Quality fish & wildlife habitat– Strong conservation practices
Objective & Challenge• Simulate existing and alternative landscapes• Adopt Soil Parameters as most representative as
possible using a standard method• Calibrate model with only slight modification of values• Adopt alternative landscape soil parameters using
standard method • Adjust scenario parameters by same degree adjustment
as in the calibration• Comparison based only on difference between
landscapes with any adjustments made due to calibration - minor
Soil Parameterization For GSSHA Model
1) Infiltration
2) Water Erosion & Prediction Project a) G & A k value to account for:
i. Soil Typeii. Land Use, Land Cover, Crop Management Practice.
3) Proposed Method for Parameterizationa) Automated by spreadsheet
4) Future Effortsa) Automatic MU selection b) Porosity adjustments c) WEPP time variant Ks
INFILTRATIONG & A multi-layer
• Physically based• 3 Layers
– Top Layer: land use, land cover, crop management practice combined with soil texture
– 2nd & 3rd layer: Soil Type • Soil Texture • Map Unit
G&A Approaches
1. Rawls & Brakensiek Method (RB) as presented in Maidment Handbook of Hydrology chapter 5.
2. Water Erosion & Prediction Project (WEPP)
Green & Ampt• The G & A method was first presented in 1911 (reference 1). It was
formulated based on the application of Darcy’s Law which is:
• 𝒒𝒒 = −𝑲𝑲 � ∆𝑯𝑯∆𝑳𝑳
• Where: • q = flow flux; volume per sq. area per unit time; [L/T]. (Positive
upward, negative downward)• K = hydraulic conductivity [L/T].• ∆𝑯𝑯
∆𝑳𝑳= 𝐡𝐡𝐡𝐡𝐡𝐡𝐡𝐡𝐡𝐡𝐡𝐡𝐡𝐡𝐡𝐡𝐡𝐡 𝐡𝐡𝐡𝐡𝐡𝐡𝐡𝐡 𝐠𝐠𝐡𝐡𝐡𝐡𝐡𝐡𝐡𝐡𝐡𝐡𝐠𝐠𝐠𝐠 𝑳𝑳
𝑳𝑳.
• Or:
• 𝒒𝒒 = −𝑲𝑲(𝝋𝝋) 𝝏𝝏𝝏𝝏𝝏𝝏
(𝝋𝝋 + 𝝏𝝏)• Where: • Ψ = soil capillary pressure or head• K (ψ) = hydraulic conductivity @ matric pressure ψ; [L/T]
Piston Model of Infiltration
Wet soilϴ = ϴs
Wetting front
Dry soilϴ = ϴi
Soil Parameters
• (Ks) cm/h Hydraulic conductivity• λ = (1/b) Pore distribution index• (ψF) cm Capillary head (wetting front suction head)• φ (cm3/cm3) porosity• fc (cm3/cm3) field capacity• pwp (cm3/cm3) permanent wilting point• θr (cm3/cm3) residual saturation• θi (cm3/cm3) initial moisture content• Layer Thickness Increments (cm)
Land Use CategoriesNLCD
Land ID #11, Open WaterLand ID #21, Developed, Open SpaceDeveloped, Low IntensityLand ID #41, Deciduous ForestLand ID #42, Evergreen ForestLand ID #43, Mixed ForestShrub/ScrubLand ID #71, Grassland/HerbaceousLand ID #81, Pasture/HayLand ID #82, Cultivated Crops
Fallowcorn: conventionalcorn: conservation
soybeans: conventionalsoybeans: conservation
small grainalfalfa
Land ID #90, Woody WetlandsLand ID #95, Emergent Herbaceous Wetlands
Crop Data Layer
• Corn• Soybeans• Spring Wheat• Alfalfa• Pasture/Grass
Table 5.3.2 Maidment/Rawls 1983
Soil Water Characteristics
K: Hydraulic Conductivity• Soil characteristics that determine K
– Particle size– Porosity– Bulk density
• Function of pressure or moisture content– Low matric potential = high moisture content = high k
• Want to know unsaturated hydraulic conductivity, K– As opposed to Ks
• K = Ks/2 (account for air entrainment)– Denominator can vary widely from 2
• Defer to WEPP
Laboratory Estimate of Ks(e.g. falling head)
WEPP
• Water Erosion Prediction Project• Initiated in 1985 • Physically based erosion prediction
technology• Use in soil & water conservation & environ
planning and assessment.• Field measurements of G & A, K
WEPP
• K EC Constant Effective Hydraulic Conductivity– Time Invariant– Must be representative of:
• Soil• Land cover• Land use• Crop management practice
WEPP: Kef Time-invariantStep 1
WEPP: Kef Time-invariantStep 1
WEPP: Kec Time-invariantStep 2
Proposed Parametrization Method
SSURGOMU
Huse Creek Basin3.8 sq. mi.
Soil Texture Land Use
+
6 soil textures
10 LU
Combined Soil Texture NLCD 2006 Land Use
50 combinations
Mapping Table Categories
Option 1• Dominate MU: 6• LU: 10• Combined
– Dominate MU - LU: 50
Option 2• MU: 24• LU: 10• Combined MU – LU: 143
Combined Soil Texture as dominate MU with NLCD 2006 Land Use
50 combinations
NLCD 2006 – Soils - Index Map Table
Dominate MU – from GIS
Specific Dominate MUSoil Physical Characteristics
Soil Physical Characteristics DATA SHEET INPUT
Soil Physical Characteristics DATA SHEET INPUT
Soil Physical Characteristics DATA SHEET INPUT
Soil Physical Characteristics DATA SHEET INPUT
Soil Physical Characteristics DATA SHEET INPUT
Layer Tab with Roll-up
Future Efforts
• Automate MU selections from MU database based on watershed delineation.
• Automate Extraction of Soil Physical Characteristics from those MUs that were compiled. e.g.– % sand– % clay– % OM– Bulk density– Porosity– Ks
• Submit to computations as per Excel spreadsheet• Pause for QC• Roll-up results in GSSHA Map Table format
Future Efforts
• Soil Porosity Predictions for tillage systems– (plow tillage increases & growing season decreases)
• Moldboard Plow• Chisel• Plow-disk-harrow• Rotary• Plow & Pack
– Source: W.J. Rawls, D. L. Brakensiek, B. Soni; Agricultural Management Effects on Soil Water Processes: Part I: Soil Water Retention and Green and Ampt Infiltration Parameters, in ASAE 1983
Tillage effects on soil characteristics
Scie
ntifi
c Am
eric
an: N
o-Ti
ll: th
e Q
uiet
Rev
olut
ion
Tillage Implements
Photos by Ardell Halvorson, USDA
Tillage Practices
Photos by Ardell Halvorson, USDA
Future Efforts using WEPP
• Infiltration Parameters for the WEPP Green-Ampt Model– Cropland Temporally-varying Case: ‘Baseline’ Effective Conductivity– Cropland Temporally-varying Case: Fallow Soil Adjustments to Effective Conductivity– Cropland Temporally-Varying Case: Cropping Adjustments to Effective Conductivity
• Temporal Adjustment for Row Crops• Temporal Adjustment for Perennial Crops
– Cropland Time-Invariant Effective Hydraulic Conductivity Values– Cropland Bio-pore Adjustments to Effective Conductivity– Rangeland Effective Hydraulic Conductivity Estimation– Freeze and Thaw Adjustments to Conductivity Values
Future Efforts WEPP: Time - variant
• Kb Kbare Ke
• Kb Baseline Effective Conductivity– idealized
• Kbare Bare Earth Effective Conductivity– adjusted for soil crusting
• Ke Effective Conductivity– adjusted for vegetation & residue
SUMMARY - Soil Parameterization For GSSHA Model
1) Infiltration
2) Water Erosion & Prediction Project a) G & A k value to account for:
i. Soil Typeii. Land Use, Land Cover, Crop Management Practice.
3) Proposed Method for Parameterizationa) Automated by spreadsheet
4) Future Effortsa) Automatic MU selection b) Porosity adjustments c) WEPP time variant Ks
