KINEROS2and the AGWA

Modeling Framework

Darius J Semmens1, David C Goodrich2, Carl L Unkrich2,Roger E Smith3, David A Woolhiser3, and Scott N Miller4

1U.S. EPA Office of Research and Development, Las Vegas2USDA-ARS Southwest Watershed Research Center, Tucson, AZ

3USDA-ARS – Retired, Fort Collins, CO4Univ. of Wyoming, Department of Renewable Resources, Laramie

Outline

• Background• Overview• Conceptual Model• Numerical Model• Sensitivity• AGWA GIS Interface• Conclusions

Background • 1960’s - KINGEN• 1990 - KINEROS• 2002 – KINEROS2 • 2002 - AGWA – Coupling with

ArcView GIS• Still event based, but developing

continuous simulation w/ assimilated RS soil moisture

Overview • Physically based• Event model (continuous coming)

• Time step = minutes• 1-D overland and channel flow • Erosion and sediment transport• Dynamic infiltration• Distributed rainfall• Flexible, distributed model elements• GIS interface – AGWA

K2 Conceptual Model

• Overland flow• Urban overland• Channels • Detention structures (ponds)• Injection• Culverts

Model Elements:

Conceptual Model - Structure• Watershed approximated by cascade of overland-flow

planes, channels, impoundments

Conceptual Model – Overland Flow• Overland-flow planes can be split into multiple

components with different slopes, roughness, etc.• Adjacent planes need not have the same width

R a in fa l l

O ve rland F low

In f i l t ra t io nChannel Flow

Infiltration in Channe

Conceptual Model – Overland Flow• Small scale spatial variability of infiltration

represented in distribution sense and parameterized for numerical efficiency

• Microtopography can be represented

Conceptual Model – Urban Element• Urban element – various runoff – runon

combinations

Conceptual Model - Infiltration• Dynamic infiltration in K2 - interacts with both rainfall

and runoff

Conceptual Model - Infiltration• Infiltration with two-layer soil profile • Soil moisture re-distribution during storm hiatus

Conceptual Model - Sediment• Multiple particle class size sediment routing (non-

interactive) • Entrainment by raindrop impact and hydraulic shear

Conceptual Model - Channels• Compound channel routing with distinct main and

overbank channel infiltration

Ephemeral StreamflowEvent of Aug. 27, 1982

Conceptual Model - Culverts• Assumed to maintain free surface flow conditions at

all times

• No lateral inflow• Uses Darcy-Weisbach formula with kinematic

assumption so that

Numerical Model• Rainfall

– Uniform or distributed

– Time, depth/intensity

– Linear interpolation

– Uniform on individual elements

– Radar (AGWA)

• Interception– Rainfall rate is reduced by the cover fraction until the

amount retained reaches a specified interception depth.

Numerical Model - Infiltration• Infiltrability

– Parlange 3-Parameter model

– Green and Ampt (1911) and Smith and Parlange (1978) are included as the two limiting cases

– 3rd parameter, gamma, set to 0.85

– Infilitrability depth approximation – describes fc as a function of infiltrated depth, I, - eliminates the separate description of ponding time and the decay of f afterponding

f KI

G

c s

i

= +

−

11

γγexp∆θ

Numerical Model - Infiltration• Infiltration

– Small scale spatial variability accommodated by introducing a coefficient of variability in Ks, CVK

– Assumes that KS is distributed log-normally

– Closely describes ensemble infiltration behavior

– f e * and r e * are infiltrability and rain rate scaled on the ensemble effective asymptotic KS value.

– Has the effect of creating a gradual evolution of runoff rather than it commencing at the time of ponding

( ) ( )f rr

e re ee I

c c

ee

* **

/

*( ) ,*= + − +−

−

>

−

1 1 11

1 1

1

γγ

Numerical Model – Overland Flow

• One-dimensional flow process in which a simple power relation relates flux to the unit-area storage

• Used in conjunction with the equation of continuity:

Numerical Model – Overland Flow• Boundary conditions

– Flow = 0 if upper boundary is a flow divide

– Equivalence of discharge between the upstream and downstream elements otherwise

• Roughness relationships– Manning

– Chezy

Numerical Model – Channel Flow• Kinematic approximation again

• Continuity equation for a channel with lateral inflow is

• Compound channels– Flows routed separately

– Explicit assumption of no energy transfer

– Implicit assumption of uniform water-surface elevation

Numerical Model – Channel Flow• Base flow – user-specified constant base flow

added at a fractional rate at each computational node along the channel

• Channel infiltration – Optional

– At low flow an empirical expression is used to estimate an "effective wetted perimeter" for use in infiltration calculations, until a threshold depth reached

Numerical Model – Sediment• Optional rain-splash and hydraulic erosion

computed for computed for upland, channel, and pond elements

• General mass-balance equation

• Net erosion, e, is a sum of splash-erosion rate ases and hydraulic-erosion rate as eh,

• Splash erosion computed by Meyer and Wischmeier (1969) only when flow > 0

Numerical Model – Sediment• Hydraulic erosion/deposition modeled as a kinetic-

transfer process

• Transport capacity, Cm, computed by modified version of Engelund and Hansen (1967)

• Transfer-rate coefficient, cg, – Deposition – set by particle settling velocity (Fair and

Geyer, 1954) divided by the hydraulic depth, h

– Erosion – limited for cohesive soils by vs/h

Numerical Solution

• Four-point implicit finite difference method

• Solutions obtained by Newton's method (Newton-Raphson technique)

• Unconditionally stable, but accuracy for routing is highly dependent on the size of x and t values used– New version will set x and t for optimal solution

K2 SENSITIVITY • Relative ranking of most sensitive

inputs and parameters

– Rainfall Inputs (particularly in arid and semi-arid areas)

– Saturated Hydraulic Conductivity– Hydraulic Roughness

• All a function of watershed geometric complexity

CSA: 2.5% (6.9 km2)44 watershed elements29 channel elements

CSA: 20% (55 km2)8 watershed elements5 channel elements

CSA: 5% (13.8 km2)23 watershed elements15 channel elements

CSA: 10% (27.5 km2)11 watershed elements7 channel elements

0 5 10 km

N

the influence of CSA on watershed complexitySensitivity to Geometric Complexity

Darius Semmens, Bill KepnerDarius Semmens, Bill KepnerUS US –– EPA EPA

Landscape Ecology BranchLandscape Ecology BranchLas Vegas, NVLas Vegas, NV

David Goodrich, Mariano Hernandez, David Goodrich, Mariano Hernandez, Ian Burns, Averill Ian Burns, Averill CateCate, , Soren Soren ScottScott

USDA USDA –– Agricultural Research ServiceAgricultural Research ServiceSouthwest Watershed Research CenterSouthwest Watershed Research Center

Tucson, AZTucson, AZ

GISGIS--BASED HYDROLOGIC MODELING:BASED HYDROLOGIC MODELING:THE AUTOMATED GEOSPATIAL THE AUTOMATED GEOSPATIAL

WATERSHED ASSESSMENT TOOLWATERSHED ASSESSMENT TOOL

Project Background & Acknowledgements• Long-Term Research Project

– Landscape Ecology Branch – 6 years

• Interdisciplinary– Watershed management– Landscape ecology– Atmospheric modeling– Remote sensing– GIS

• Multi-Agency– USDA – ARS– US – EPA– University of Arizona– University of Wyoming– USGS

• Student Support– 2 Post-Doc– 2 PhD– 2 Masters

USDA-ARSDavid GoodrichMariano HernandezAverill CateIan BurnsCasey TifftSoren Scott

US-EPABill KepnerDarius SemmensDan HeggemBruce JonesDon Ebert

University of ArizonaPhil Guertin

University of WyomingScott Miller

• PC-based GIS tool for watershed modeling– KINEROS & SWAT (modular)

• Investigate the impacts of land-use/cover change on runoff, erosion, and water quality at multiple scales

• Compare and visualize results• Targeted for use by research

scientists and management specialists

• Analyses can be integrated with those from other GIS-based tools & data

• Widely applicable

Automated Geospatial Watershed Assessment (AGWA) Tool

• Used with US-EPA Analytical Tool Interface for Landscape Assessment (ATtILA)

• Simple, direct method for model parameterization

• Provide accurate, repeatable results

• Require basic, attainable GIS data– DEM– Soil data (FAO soil map)– Land-use/cover data (easily adapted to local data)

• Useful for scenario development, alternative futures simulation work.

Objectives of the AGWA tool

(SWAT)• Daily time step• Distributed: empirical and physically-based model• Hydrology, sediment, nutrient, and pesticide yields• Larger watersheds (> 1,000 km2)• Similar effort used by BASINS

71

7373

Soil and Water Assessment Tool

7173

pseudo-channel 71

channel 73

Abstract Routing Representation

to next channel

Watershed Discretization(model elements) ++

LandCover

Soils

Rain (Observed or

Design Storm)

Results

Run model and import results

Intersect model elements with

Watershed Delineation using Digital Elevation

Model (DEM)

Sediment yield (t/ha)Sediment discharge (kg/s)

Water yield (mm)Channel Scour (mm)

Transmission loss (mm)Peak flow (m3/s or mm/hr)

Channel Disch. (m3/day)Sediment yield (kg)

Percolation (mm)Runoff (mm or m3)

ET (mm)Plane Infiltration (mm)

Precipitation (mm)Channel Infiltration (m3/km)

SWAT OutputsKINEROS Outputs

AGWA Conceptual Design: Inputs and Outputs

Output results that can be displayed in AGWA

AGWA ArcView Interface

PROCESS

runoff, sediment hydrographtime

runo

ff

STATSGONALC, MRLCUSGS 7.5' DEM

Conceptual Design of AGWA

Build Model Input Files

Derive Secondary Parameterslook-up tables

Characterize Model Elementsf (land cover, topography, soils)

Discretize Watershedf (topography)

View Model Resultslink model to GIS

Build GIS Database

PRODUCTS

ContributingSource Area

Gravelly loam SoilKs = 9.8 mm/hrG = 127 mmPor. = 0.453

inte

nsity

time

10-year, 30-minute event

Texture Ksat Suction Porosity Smax CV Sand Silt Clay Dist KffClay 0.6 407.0 0.475 0.81 0.50 27 23 50 0.16 0.34Fractured Bedrock 0.6 407.0 0.475 0.81 0.50 27 23 50 0.16 0.05Clay Loam 2.3 259.0 0.464 0.84 0.94 32 34 34 0.24 0.39Sandy Clay Loam 4.3 263.0 0.398 0.83 0.60 59 11 30 0.40 0.36Silt 6.8 203.0 0.501 0.97 0.50 23 61 16 0.23 0.49Loam 13.0 108.0 0.463 0.94 0.40 42 39 19 0.25 0.42Sandy Loam 26.0 127.0 0.453 0.91 1.90 65 23 12 0.38 0.32Gravel 210.0 46.0 0.437 0.95 0.69 27 23 50 0.16 0.15

KINEROS2 Parameter EstimationParameters based on soil texture (STATSGO, SSURGO, FAO)

Parameters based on land-cover classification (e.g. NLCD)

Land Cover Type Interception (mm/hr) Canopy (%) Manning's nForest 1.15 30 0.070Oak Woodland 1.15 20 0.040Mesquite Woodland 1.15 20 0.040Grassland 2.0 25 0.050Desertscrub 3.0 10 0.055Riparian 1.15 70 0.060Agriculture 0.75 50 0.040Urba n 0.0 0.0 0.010

AZ061

Component 1

20%

Component 2

45% Component 3

35%

9 inches

Layer 1

Layer 2

Layer 3

2

2

5

Layers for component 3

Components for MUID AZ061

Intersection of model element with soils map

AGWA Soil Weighting (KINEROS2)• Area and depth weighting of soil parameters

• Area weighting of averaged MUID values for each watershed element

AZ076

AZ067

Parameter Manipulation (optional)

Ksat

Can manually change parameters for each channel and plane element

Stream channel attributes

Upland plane attributes Ksat

Automated tracking of simulation inputs

Calculate and view differences between

model runs

Multiple simulation runs for a given watershed

Color-ramping of results for each element to show spatial variability

Visualization of Results

Spatial and Temporal Scaling of Results

High urban growth1973-1997

Upper San PedroRiver Basin

#

#

ARIZONA

SONORA

Phoenix

Tucson

<<WY >>WY

Water yield change between 1973 and 1997

SWAT Results

Sierra Vista Subwatershed

KINEROS Results

N

ForestOak WoodlandMesquite DesertscrubGrasslandUrban1997 Land Cover

Concentrated urbanization

Using SWAT and KINEROS for integrated watershed assessmentLand cover change analysis and impact on hydrologic response

Urbanization Effects (KINEROS2)

Pre-urbanization

1973 Land cover

Post-urbanization

1997 Land cover

• Results from pre- and post-urbanization simulations using the 10-year, 1-hour design storm event

0 4 8 12 16 200

4

8

12

16

20

1:1 line

observed runoff depth (mm)

sim

ulat

ed ru

noff

dept

h (m

m)

KINEROS - Simulated runoff depth(distributed and uniform rainfall)

Rainfall Input

uniform PPT

distributed PPT

Land-Cover Modification ToolAllows users to build management scenariosLocation of land-cover alterations specified by either drawing a polygon on

the display, or specifying a polygon map

Types of Land-Cover Changes:• Change entire user-defined area to new land cover • Change one land-cover type to another in user-defined area • Change land-cover type within user-supplied polygon map • Create a random land-cover pattern

• e.g. to simulate burn pattern, change to 64% barren, 31% desert scrub, and 5% mesquite woodland

Alternative Futures: Base-Change Scenarios1. CONSTRAINED – Assumes population increase less than 2020 forecast

(78,500). Development in existing areas, e.g. 90% urban.

2. PLANS – Assumes population increase as forecast for 2020 (95,000). Development in mostly existing areas, e.g. 80% urban and 15% suburban.

3. OPEN – Assumes population increase more than 2020 forecast (111,500).Most constraints on land development removed. Development occurs mostly into rural areas (60%) and less in existing urban areas (15%).

Percent Change in Runoff under Future Scenarios

• Relative change for each scenario can be mapped for each model output

• Highlights spatial variability in response to different scenarios

• One component of planning efforts

(Derived from using future land covers

and AGWA)

Plans

Limitations of GIS - Model Linkage• Model Parameters are based on look-up tables

- need for local calibration for accuracy- FIELD WORK!

• Subdivision of the watershed is based on topography- prefer it be based on intersection of soil, lc, topography

• No sub-pixel variability in source (GIS) data- condition, temporal (seasonal, annual) variability- MRLC created over multi-year data capture

• No model-element variability in model input- averaging due to upscaling

Most useful for relative assessment unless calibrated

AGWA Support & Distribution• Fact Sheets, Product Announcement, Brochures• Documentation and User Manual• Quality Assurance Report

Research PlanCode Structure (Avenue Scripts, Dialogs, System Calls)EPA and USDA/ARS companion WebsitesJournal Publications (Hernandez et al. 2000, Miller et al. 2002a, Miller et al. 2002b, Kepner et al., 2004)Training: Las Vegas (2001); Reston (2002); Tucson (2003); San Diego (2004)

• AGWA Web Siteshttp://www.epa.gov/nerlesd1/land-sci/agwa/index.htmhttp://www.tucson.ars.ag.gov/agwa

CONCLUSIONS• KINEROS2 – Evolved from a research model to

one gaining wider applicability

• Physically based, distributed, event model

• Most sensitive to rainfall input and Ks, but model geometric complexity also significant

• GIS interface, AGWA, is key to developing complex spatial organization and parameter inputs

• AGWA facilitates calibration, change analyses, and planning work through scenario development