Hydrological Modeling

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Hydrological Modeling

• FISH 513
• April 10, 2002

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Overview

• What is wrong with simple statistical regressions
  of hydrologic response on impervious area?
• Toward a more complete understanding of normal
  flows.
• Distributed Hydrological Modeling
• Example from applications of Distributed
  Hydrological Modeling at UW
• Changes in impervious area.
• Changes in forest cover.
• Global Climate Change.

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Typical Representation of Effect of Impervious
Area on Runoff Coefficient
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Runoff Coefficient
0
0
100
Percent Impervious Area
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What we really want to know is What is the
change from normal? Previous graph is 100
correct for dry initial conditions. What if it
has just rained nonstop for five days . . . Well
that never happens around here?
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Representation of Effect of Impervious Area on
Runoff Coefficient for extremely wet initial
conditions
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Runoff Coefficient
0
0
1
Percent Impervious Area
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Therefore, normal response depends Static
Variables Land Cover Impervious Area,
etc Dynamic Variables Soil
Moisture Precipitation Intensity Storm
Duration, etc. Numerous Studies have shown
decreased effects of land use Change as
antecedent conditions become wetter. Our task is
to build a predictive model of what is
normal And that cant be done without
considering interaction of meteorology with land
cover changes
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Hydrological Modeling to the Rescue
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DHSVM Snow Accumulation and Melt Model
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Land surface characterization required by DHSVM

• Terrain - 150 m. aggregated from 10 m.
  resolution DEM
• Land Cover - 19 classes aggregated from over 200
  GAP classes
• Soils - 3 layers aggregated from 13 layers (31
  different classes) variable soil depth from 1-3
  meters
• Stream Network - based on 0.25 km2 source area

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• Calibration to two USGS sites
• Split sample validation at over 60 sites
• Parameters transfer extremely well to other
  watersheds without recalibration

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Effects of Impervious Area
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Application of DHSVM to lower Cedar River
Watershed to assess impacts of changes in
impervious area on basin hydrology
Taylor Creek (14 km2) 5 imperv.
Madsen Creek (5.4 km2) 20 imperv.
Fraction Impervious Area (1998)
100 75 50 25 0
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DHSVM Calibration to determine baseline
parameters. Taylor Creek (5 impervious area)
CFS
Feb 1991 Mar 1991
Apr 1991 May
1991
Test of Impervious Area Representation (no
re-calibration) Madsen Creek (20 impervious area)
CFS
0 20 40 60 80 100 120
4/1/91 4/4/91
4/7/91 4/10/91
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Observed (1991) 120 cfs peak, 3.6 inches total
runoff
1991 Land Cover (20 imperv.) 115 cfs peak,
3.2 inches total runoff
Old Growth Forest 58 cfs peak, 2.3 inches total
runoff 100 increase in peak
cfs
4/5/1991 4/15/1991
4/20/1991
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100 80 60 40 20
5 4 3 2 1
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Effect of Climate Change
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Predicted Change in Mean Monthly Temperature due
to Increased Carbon Dioxide Levels (Mean of 4
GCMs)
2020s (w.r.t mid 20th century climate) 2040s
(w.r.t mid 20th century climate)
Temperature Increase (degrees C)
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Methodology for Assessing Impacts of Climate
Change on Watershed hydrology
Observed Meteorology At Stations in and
near Target Watershed
Synthetic Observed Record TaltTobs Delta
T Palt Pobs(Delta P)
DHSVM
SWE
Reservoir Inflow
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Cedar River Watershed Retrospective Analysis of
Average Snow Water Equivalent Under Current and
Altered Climates
Current 2025 2045
Current Low Year Becomes . . . 2025/2045 Best Case
Snow Water Equivalent (mm)
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Effect of Climate Change on Mean Monthly Inflow
(1988 to 1996) to Cedar Reservoir
126,000 acre-ft 90,000 acre-ft 78,000
acre-ft
Monthly Inflow (meters)
Month
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Effect of Forest Harvest
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GAP, 1991
Basins for which streamflow was simulated for
each vegetation scenario. GAP, 1991 is based on a
1991 LandSat image. Band Harvest has a total
clear-cut area identical to GAP, 1991 but
concentrated in the transient snow zone (700-900
m). The control simulation is the historic
vegetation coverage (based on GAP with all
clear-cuts regrown).
Band harvest
Historic Vegetation
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Effect of forest canopy removal, Snoqualmie River
at Snoqualmie Falls, February 1996 event
Hourly Precipitation (mm)
Low elevation(lt300 m) snow (mm SWE)
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Questions?