A Soil-water Balance and Continuous Streamflow Simulation Model

that Uses Spatial Data from a Geographic Information System (GIS)

Advisor: Dr. David Maidment

Research Sponsor: Hydrologic Engineering Center

Overview

• Hydrology review: Event-based vs. continuous simulation models

• Research objective

• Study area

• Spatial Data

radar precipitation, USDA soils

spatial analysis, parameter estimation

information transfer from GIS to an external model

Hydrologic process representation

Summary

Hydrologic Simulation

Well-known computer programs: HEC-1, HMS, TR-20

Sub-basin hydrograph methods:

• Loss rate

• Transform

• Baseflow recession

SHOW SLIDE WITH LOSS RATES ETC.!!

HMS Basin SchematicEVENT SIMULATION

Hydrologic Simulation(cont.)

CONTINUOUS SIMULATION

Well known computer programs:

• USGS PRMS/Stanford

• NWS/Sacramento

• HEC Continuous Simulation Model

interception and depression storage

evaporation

rainfall

surface runoff

subsurface runoff

soil root zone

soil transmission zone

groundwater storage zone(s)

leakage

Event vs. Continuous Simulation

Neither model provides mass closure for the entire hydrologic cycle.

Event

• simple

• infiltration losses are a sink

• difficult to initialize

Continuous

• more physical processes represented

• antecedent storm conditions are known

• complex -- many more parameters

PROS &CONS

hydrologic/hydraulic design flood forecasting

real time water control water resources planning climate change impacts on streamflow

Event Continuous

APPLICATIONS

Research Objectives

Develop and test a practical model that uses data describing spatial variability of soils and rainfall

- Develop GIS/hydrology procedures applicable anywhere in the U.S.

- Does added information improve runoff estimates? -- Particularly with regard to the validation stage.

- Make results reproducible by automating and working from standard databases.

- What spatial scales and modeling complexity are practical and useful?

Study Area

Little Washita River Watershed

• 600 km2

• Climate: moist and subhumid

Why choose Little Washita?

• NWS NEXRAD Stage III rainfall data

• Higher resolution soils data than is generally available.

• Site of numerous hydrologic and remote sensing studies -- data available for calibration and validation.

Problem Description

climate station streamflow gage

NEXRAD Precipitation Data

Stage III Product• 4 km x 4 km grid• hourly estimates• Composite of information from 17 radars and 500 rain gages

Density of Precipitation Gages

• 114 Oklahoma Climate Stations (Density ~ 1 gage/ 1600 km2)

• 100 NEXRAD Cells Per Gage

USDA STATSGO Soils Data

Map unit: grouping of map components.

Components: typically identify soils with similar properties.

Mapunit OK002

Spatial Variability in Soils

STATSGOCounty Level Data

1

2

Polygon 1: Mapunit OK151

89 % Sandy loam6% Loam2% Silty clay loam2% Clay1% Loamy sand

Polygon 2: Mapunit OK103

56 % Loam30% Silt Loam14% Sandy loam

Polygon 2 only comprises 10% of all polygons in mapunit OK103.

Main GIS Procedures

Assumed inputs:

• Coverage of Modeling Units (I.e. NEXRAD Cells, Thiessen polygons)

• Watershed boundaries

• Flowlength grid

• STATSGO/SSURGO coverage w/ component and layer tables

1. Calculate soil component properties using attribute tables and lookup table

Component properties dBase file

NEXRAD cell/watershedshape file

2. Intersect the precipitation cells with the watershed boundary

3. Determine the component names and component percentages in each NEXRAD cell.

4. Determine the average flowlength from each NEXRAD cell the watershed outlet.

719 STATSGO texture names

12 standardUSDA classes

USDATexture Class

Texture ClassAbreviation

porosity r , residual

water content,cm3/cm3

hb, bubbling

pressure (cm), pore-size index

Sand S 0.43 0.045 6.0 1.68

Loamy sand LS 0.41 0.057 8.1 1.28

Sandy loam SL 0.41 0.065 13.3 0.89

Sandy clayloam

SCL 0.39 0.1 17.0 0.48

Loam L 0.43 0.078 27.8 0.56

Silt loam SIL 0.45 0.067 50.0 0.41

Clay loam CL 0.41 0.095 52.6 0.31

Silt SI 0.46 0.034 62.5 0.37

Clay C 0.38 0.068 125.0 0.09

Sandy clay SC 0.38 0.1 37 0.23

Silty clay loam SICL 0.43 0.089 100 0.23

Silty clay SIC 0.36 0.07 200 0.09

Texture Name to Soil Parameters

Soil parameters

Tabulation of Component Names and Percentages

External Model for Hydrologic Calculations

GIS as a Pre-processor for Hydrologic Models

+ Add spatial information

+ Automate/Create a Reproducible Product

- Increase computational burden

Accounting for spatial variability in a simple way increases the computational burden in the Little Washita by a factor of :

55 cells * 10 components/cell = 550

LESSON: KEEP MODEL SIMPLE

Soil-water Balance Model

transmissionzone

root zone

percolation

infiltration evaporation

direct runoff

GW Reservoir(s)subsurface runoff

Green-Ampt Infiltration Model

r

soil

dept

hActual profile

r

L

soil

dept

h

Idealized profile

Infiltration Rate as a Function of InitialMoisture Content

Infiltration and Precipitation Rates vs. Time for a Loam Soil

0

1

2

3

4

5

6

7

1 2 3 4 5 6 7 8 9 10 11

time (hours)

Rat

e (c

m/h

ou

r)

Initial Eff. Saturation = 0.2Direct Runoff = 3.5 cm

Initial Eff. Saturation = 0.7Direct Runoff = 5.1 cm

Percolation/Redistribution

soil

dep

th

t = 1

t = 2

t = 3

t = 1

t = 2

t = 3

Layer 1

Layer 2

Evaporation

Evaporation depends on many factors including :

• energy available at the surface• water content of the soils• soil type• vegetation characteristics• atmospheric conditions

Evaporationm

oist

ure

extr

actio

n

func

tion,

f(q)

wp

c

fc

1

0

soil moisture fraction

E = f()*PE• Seasonal effects?

• PE changes as soil dries out.

• Penman-Monteith is widely cited alternative but how do you determine surface resistance, especially when the soil begins to dry out?

Questions and Data Related to Evaporation

• EBBR : Energy Budget Bowen Ratio

• SWATS : Soil Water and Temperature Systems

• SMOS : Surface Meteorological Observation Stations

ARM Data Streams

How to account for the following factors using a simple(daily) model?

• How to quantify influence of the moisture stateon the evaporation rate.

• To what depth(s) does surface drying influence soilmoisture ?

• Is it possible to account for seasonal effects?

Bowen Ratio Method

Rn + H + E + G = 0

Lo

SiSi

Li

Rn = Si(1-) + Li-Lo

E

H

ee

TT

dzdqK

dzdTKc

wa

hpa

)(

)(

12

12

)1(

)(

GR

E n

Energy Fluxes at the Surface

-800

-600

-400

-200

0

200

400

600

800

0 10 20 30 40

time (1/2 hr)

En

erg

y F

lux

(W/m

2)

Rn + G Latent Sensible

June 14, 1997

SWATS Data

Suction Head vs. Depth

0

50

100

150

200

-1000-800-600-400-2000

Suction Head (cm of water)

Dep

th (

cm)

- heat dissipation sensors calibrated against matric potential

- water retention curve usedto estimate soil water

- measurements at eight depths

Summary

• Utilize spatial data describing soils and rainfall in a hydrology model.

• GIS programs are used to automate parameter estimation.

• Evaluate soil-water balance model using both observed soil moisture and runoff data.

•Data availability determines model complexity.