Runoff generation and its representation in land surface models

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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

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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

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1. Runoff generation processes
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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
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let
diffusivity
From continuity
Combining,
(Richards equation)
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Complications in the application of Richards
Equation

• Applies at point scale, well behaved porous
  medium
• K is highly nonlinear spatially varying function
  of suction head, moisture
• 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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Infiltration excess flow (source Dunne and
Leopold)
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Runoff generation mechanisms on a hillslope
(source Dunne and Leopold)
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Saturated area (source Dunne and Leopold)
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Seasonal contraction of saturated area at
Sleepers River, VT following snowmelt (source
Dunne and Leopold)
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Expansion of saturated area during a storm
(source Dunne and Leopold)
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Seasonal contraction of pre-storm saturated
areas, Sleepers River VT (source Dunne and
Leopold)
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2. Spatially distributed modeling
Distributed Hydrology Soil Vegetation Model
(DHSVM)
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Explicit Representation of Downslope Moisture
Redistribution
Lumped Conceptual (Processes parameterized)
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DHSVM Snow Accumulation and Melt Model
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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
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Macroscale modeling a strategy
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Traditional bottom up hydrologic modeling
approach (subbasin by subbasin)
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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
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Investigation of forest canopy effects on snow
accumulation and melt
Measurement of Canopy Processes via two 25 m2
weighing lysimeters (shown here) and additional
lysimeters in an adjacent clear-cut.
Direct measurement of snow interception
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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)
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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)
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Range in Snow Cover Extent
Observed and Simulated
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Mean Normalized Observed and Simulated Soil
Moisture
Central Eurasia, 1980-1985
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Cold Season Parameterization -- Frozen Soils
Key Observed Simulated 5-100 cm layer 0-5
cm layer
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3. Macroscale hydrologic models, c
Implementation
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Shasta Reservoir inflows
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5. Example 1 Puget Sound flood forecasting
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• 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.
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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 )

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DHSVM Calibration (Snoqualmie at Carnation)
Flood of record

• Principal calibration locations were the
  Skykomish at Gold Bar and the Snoqualmie at
  Carnation

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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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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
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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
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5. Example 2 Seasonal ensemble streamflow
forecasting
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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

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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

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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
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GSM Regional Bias a spatial example
Bias is removed at the monthly GSM-scale from the
meteorological forecasts (so 3rd column 1st
column)
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Downscaling Test

• Start with GSM-scale monthly observed met data
  for 21 years
• Downscale into a daily VIC-scale timeseries
• Force hydrology model to produce streamflow
• Is observed streamflow reproduced?

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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
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Simulations
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CRBInitial Conditionslate-May SWE water
balance
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CRBInitial Conditions(percentile)
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CRB May forecast
forecast
observed
forecast medians
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CRB May forecast
hindcast observed
forecast
forecast medians
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CRB May forecast
forecast
hindcast observed
forecast medians
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CRB May forecastbasin avg. soil moisture
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CRB May Forecast Streamflow
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CRB sequential streamflow forecasts
climatologies
forecasts
hindcast
ensemble medians
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CRBMay Forecastcumulative flow averages
forecast medians
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6. Example 3 Climate change assessment
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Accelerated Climate Prediction Initiative (ACPI)
NCAR/DOE Parallel Climate Model (PCM) grid over
western U.S.
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Regional Climate Model (RCM) grid and hydrologic
model domains
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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
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ACPI PCM-climate change scenarios, historic
simulation v air temperature observations
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ACPI PCM-climate change scenarios, historic
simulation v precipitation observations
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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

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Bias Correction
Note future scenario temperature trend (relative
to control run) removed before, and replaced
after, bias-correction step.
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Downscaling
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BAU 3-run average
historical (1950-99)
control (2000-2048)
PCM Business-as-Usual scenarios Columbia River
Basin (Basin Averages)
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RCM Business-as-Usual scenarios Columbia
River Basin (Basin Averages)
PCM BAU B06.44
RCM BAU B06.44
control (2000-2048)
historical (1950-99)
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PCM Business-as-Usual scenarios California
(Basin Average)
BAU 3-run average
historical (1950-99)
control (2000-2048)
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PCM Business-as-Usual scenarios Colorado (B
asin Average)
BAU 3-run average
historical (1950-99)
control (2000-2048)
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PCM Business-as-Usual Scenarios Snowpack
Changes Columbia River Basin April 1 SWE
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PCM Business-as-Usual Scenarios Snowpack
Changes California April 1 SWE
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PCM Business-As-Usual Mean Monthly Hydrographs
Columbia River Basin _at_ The Dalles, OR
1 month 12
1 month 12
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PCM Business-As-Usual Mean Monthly Hydrographs
Shasta Reservoir Inflows
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CRB Operation Alternative 1 (early refill)
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CRB Operation Alternative 2 (reduce flood storage
by 20)
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