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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.