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## Runoff generation and its representation in land surface models

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Title: Runoff generation and its representation in land surface models

1
Runoff generation and its representation in land
surface models
• Dennis P. Lettenmaier
• Department of Civil and Environmental Engineering
• University of Washington
• for presentation at
• GSSP Seminar Series
• NASA/GSFC
• June 14, 2002

2
OUTLINE OF THIS TALK
• 1. Runoff generation processes
• 2. Spatially distributed modeling
• 3. Macroscale modeling
• a) Strategy
• b) Testing and evaluation
• c) Implementation
• Example 1 Puget Sound flood forecast system
• Example 2 Seasonal ensemble forecasting
• Example 3 Climate change assessment

3
1. Runoff generation processes
4

Darcys Equation (fundamental equation of motion
in subsurface, applies to both saturated and
unsaturated zones)  where  q flow per
unit cross-sectional area (units L/T)  K
hydraulic conductivity (L/T)    Definitions
? volume of water/total volume ? porosity
(volume of voids/total volume ? suction head
(height to which moisture is drawn above free
surface
5
let
diffusivity
From continuity
Combining,
(Richards equation)
6
Complications in the application of Richards
Equation
• Applies at point scale, well behaved porous
medium
• K is highly nonlinear spatially varying function
• K varies over orders of magnitude due to
variations in soil properties at meter scales
(much less than typical scale of application)
• Direct estimation of K difficult even at small
scale (and scale complications in interpretation
of measurements)
• Methods of estimating K from e.g. mapable soil
properties are highly approximate, and subject to
scale complications

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Runoff generation mechanisms
• 1) Infiltration excess precipitation rate
exceeds local (vertical) hydraulic conductivity
-- typically occurs over low permeability
surfaces, e.g., arid areas with soil crusting,
frozen soils
• 2) Saturation excess fast runoff response
over saturated areas, which are dynamic during
storms and seasonally (defined by interception of
the water table with the surface)

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12
Infiltration excess flow (source Dunne and
Leopold)
13
Runoff generation mechanisms on a hillslope
(source Dunne and Leopold)
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16
Saturated area (source Dunne and Leopold)
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18
Seasonal contraction of saturated area at
Sleepers River, VT following snowmelt (source
Dunne and Leopold)
19
Expansion of saturated area during a storm
(source Dunne and Leopold)
20
Seasonal contraction of pre-storm saturated
areas, Sleepers River VT (source Dunne and
Leopold)
21
2. Spatially distributed modeling
Distributed Hydrology Soil Vegetation Model
(DHSVM)
22
Explicit Representation of Downslope Moisture
Redistribution
Lumped Conceptual (Processes parameterized)
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DHSVM Snow Accumulation and Melt Model
26
Distributed vs Spatially Lumped Hyrologic Models
Lumped Conceptual
Fully Distributed Physically-based
Suitable for flood forecasting and a wide range
of water resource related issues
Suitable for flood forecasting
27
Macroscale modeling a strategy
28
approach (subbasin by subbasin)
29
Macroscale modeling approach (top down)
1 Northwest 5 Rio Grande 10 Upper Mississippi 2
California 6 Missouri 11 Lower Mississippi 3
Great Basin 7 Arkansas-Red 12 Ohio 4 Colorado 8
Gulf 13 East Coast 9 Great Lakes
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3. Macroscale hydrologic models, b Testing
and evaluation
33
Investigation of forest canopy effects on snow
accumulation and melt
Measurement of Canopy Processes via two 25 m2
weighing lysimeters (shown here) and additional
Direct measurement of snow interception
34
Calibration of an energy balance model of canopy
effects on snow accumulation and melt to the
weighing lysimeter data. (Model was tested
against two additional years of data)
35
Summer 1994 - Mean Diurnal Cycle
Point Evaluation of a Surface Hydrology Model for
BOREAS
SSA Mature Black Spruce
SSA Mature Jack Pine
NSA Mature Black Spruce
Flux (W/m2)
Local time (hours)
36
Range in Snow Cover Extent
Observed and Simulated
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Mean Normalized Observed and Simulated Soil
Moisture
Central Eurasia, 1980-1985
39
Cold Season Parameterization -- Frozen Soils
Key Observed Simulated 5-100 cm layer 0-5
cm layer
40
3. Macroscale hydrologic models, c
Implementation
41
Shasta Reservoir inflows
42
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43
5. Example 1 Puget Sound flood forecasting
44
• 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

Data Requirements for applying DHSVM.
45
Calibration-Validation with all available
meteorological observations (50 sites)
Validation 1991-1996
• Calibration (Snohomish River)
• From 1987-1991
• (USGS gauges at
• Gold Bar and Carnation only )

46
DHSVM Calibration (Snoqualmie at Carnation)
Flood of record
• Principal calibration locations were the
Skykomish at Gold Bar and the Snoqualmie at
Carnation

47
• Calibration to two USGS sites
• Split sample validation at over 60 sites
• Parameters transfer extremely well to other
watersheds without recalibration

48
2000/2001 Real-time Streamflow Forecast
System 26 basins 48,896 km2 2,173,155
pixels _at_ 150 m resolution
http//hydromet.atmos.washington.edu
49
The average relative absolute error in peak
runoff forecast for six events during water year
1999 (Westrick et al 2002).
Obs-based MM5 MM5 no bias RFC
Sauk Skykomish N.F. Snoq M.F. Snoq Snoq
Cedar
50
5. Example 2 Seasonal ensemble streamflow
forecasting
51
General Approach
• climate model forecast
• meteorological outputs
• 1.9 degree resolution (T62)
• monthly total P, avg T
• Use 3 step approach 1) statistical bias
correction
• 2) downscaling
• 3) hydrologic simulation

? hydrologic (VIC) model inputs
• streamflow, soil moisture,
• snowpack,
• runoff
• 1/8-1/4 degree resolution
• daily P, Tmin, Tmax

52
Models Global Spectral Model (GSM) ensemble
forecasts from NCEP/EMC
• forecast ensembles available near beginning of
each month, extend 6 months beginning in
following month
• each month
• 210 ensemble members define GSM climatology for
monthly Ptot Tavg
• 20 ensemble members define GSM forecast

53
One Way Coupling of GSM and VIC models
a) bias correction climate model
climatology ? observed climatology b) spatial
interpolation GSM (1.8-1.9 deg.) ? VIC (1/8
deg) c) temporal disaggregation (via resampling
of observed patterns) monthly ? daily
54
GSM Regional Bias a spatial example
Bias is removed at the monthly GSM-scale from the
meteorological forecasts (so 3rd column 1st
column)
55
Downscaling Test
for 21 years
• Downscale into a daily VIC-scale timeseries
• Force hydrology model to produce streamflow
• Is observed streamflow reproduced?

56
GSM forecast and climatology ensembles
(21 sets)
10 member climatology ensembles
from 1979 SSTs
from 1980 SSTs
from 1981 SSTs
from 1999 SSTs
20 member forecast ensemble
from current SSTs
57
Simulations
58
CRBInitial Conditionslate-May SWE water
balance
59
CRBInitial Conditions(percentile)
60
CRB May forecast
forecast
observed
forecast medians
61
CRB May forecast
hindcast observed
forecast
forecast medians
62
CRB May forecast
forecast
hindcast observed
forecast medians
63
CRB May forecastbasin avg. soil moisture
64
CRB May Forecast Streamflow
65
CRB sequential streamflow forecasts
climatologies
forecasts
hindcast
ensemble medians
66
CRBMay Forecastcumulative flow averages
forecast medians
67
6. Example 3 Climate change assessment
68
Accelerated Climate Prediction Initiative (ACPI)
NCAR/DOE Parallel Climate Model (PCM) grid over
western U.S.
69
Regional Climate Model (RCM) grid and hydrologic
model domains
70
Climate Change Scenarios
PCM Simulations
Historical B06.22 (greenhouse CO2aerosols
forcing) 1870-2000 Climate Control
B06.45 (CO2aerosols at 1995 levels) 1995-2048
Climate Change B06.44 (BAU6, future
scenario forcing) 1995-2099 Climate Change
B06.46 (BAU6, future scenario forcing)
1995-2099 Climate Change B06.47 (BAU6,
future scenario forcing) 1995-2099
PNNL Regional Climate Model (RCM) Simulations
Climate Control B06.45 derived-subset
1995-2015 Climate Change B06.44
derived-subset 2040-2060
71
ACPI PCM-climate change scenarios, historic
simulation v air temperature observations
72
ACPI PCM-climate change scenarios, historic
simulation v precipitation observations
73
Bias Correction and Downscaling Approach
• climate model scenario
• meteorological outputs

? hydrologic model inputs
• snowpack
• runoff
• streamflow
• 2.8 (T42)/0.5 degree resolution
• monthly total P, avg. T
• 1/8-1/4 degree resolution
• daily P, Tmin, Tmax

74
Bias Correction
Note future scenario temperature trend (relative
to control run) removed before, and replaced
after, bias-correction step.
75
Downscaling
76
BAU 3-run average
historical (1950-99)
control (2000-2048)
Basin (Basin Averages)
77
River Basin (Basin Averages)
PCM BAU B06.44
RCM BAU B06.44
control (2000-2048)
historical (1950-99)
78
(Basin Average)
BAU 3-run average
historical (1950-99)
control (2000-2048)
79
asin Average)
BAU 3-run average
historical (1950-99)
control (2000-2048)
80
Changes Columbia River Basin April 1 SWE
81
Changes California April 1 SWE
82
Columbia River Basin _at_ The Dalles, OR
1 month 12
1 month 12
83