minnesota water resource conference, 2015 · gssha modelling background ... hydrology chapter 5. 2....

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

Questions Tim Radatz

608-443-6587 radatz@mawrc.org

Salam Murtada: 651-259-5688 Salam.Murtada@state.mn.us

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