advances in macroscale hydrology modeling for the arctic drainage basin dennis p. lettenmaier...
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Advances in Macroscale Hydrology Modeling for the Arctic Drainage Basin
Dennis P. LettenmaierDepartment of Civil and Environmental Engineering
University of Washington
53rd Arctic Science ConferenceUniversity of Alaska Fairbanks
September 19, 2002
Thermohaline Circulation
G. Holloway, Institute of Ocean Sciences, Sidney, BC
Arctic drainage basin
Ob
Mackenzie
Lena
Yenesei
0
20
40
60
80
100
45 55 65 75
Latitude (degrees)
Ba
sin
Are
a (
%)
gauged area
ungauged area
Mackenzie River basin early version VIC snow season length results
RS
T Index
Energy Balance
Ob River basin early version VIC snow season length results
RS
T Index
Energy Balance
• 21 participating land surface models (typically land surface representations in coupled land-atmosphere models, representing surface energy and water balances
• Study site: Torne-Kalix River basin (Sweden and Finland), ~58,000 km2
• Each model provided ~10 years of gridded (1/4 degree) surface radiative and meteorological forcings
• Streamflow, snow extent, and surface water balance observed or inferred from observations
PILPS (Project for Intercomparison of Land surface Parameterization Schemes) Experiment 2e
Figure 1. Location of the Torne and Kalix Rivers (red) within the BALTEX domain (white)
Mean annual snowfall apportionment to melt and sublimation
Predicted annual average latent heat flux (1989 – 1998) and estimate from basin water balance
Predicted average last day of snow cover (1989 – 1998) and satellite estimate
PILPS-2e Conclusions• Inter-model variations in mean annual runoff were primarily related to
winter snow sublimation, even though summer ET was much higher.
• Storage of snowmelt runoff in the soil column primarily influenced the timing of peak runoff, rather than volume.
• Models with high sublimation generally lost their snow pack too early and underpredicted annual runoff. Differences in snow sublimation were largely a result of differences in snow surface roughness.
• The greatest among-model differences in energy and moisture fluxes occurred during the spring snowmelt period.
• Differences in net radiation were governed by differences in the surface temperature during winter, and by differences in surface albedo during snowmelt, but were minor when snow was absent
• The formulation of aerodynamic resistance and stability corrections in areas of no overstory were at least as important as the sensitivity to representation of canopy interception in explaining intermodel differences in winter evaporation.
Lakes and wetlands
Source: San Diego State University Global Change Research Group
Landcover from Landsat MSS images (Muller et al. 1999).
Putuligayuk River
Snowmelt water balance
Snow Water Equivalent +87 +124 +89Surface Runoff -56 -87 -56
Evaporation/Condensation
-6 -7 +4
Change in SurfaceStorage
+25 +30 +37
1999 2000 2001
Saturated extent 1999 and 2000
0
100
200
300
400
6/10 6/30 7/20 8/9 8/29Inu
nd
ate
d a
rea
(km
2 )
19992000
2000
= wet = dry
a.
b. c. d. e.
Predicting the effects of lakes and wetlands
• Lake energy balance based on:– Hostetler and Bartlein
(1990)
– Hostetler (1991)
• Assumptions:– One “effective” lake for
each grid cell;
– Laterally-averaged temperatures; and
Lake energy balance
Lake surface energy balance
Mean daily values, June-August 2000
Mean diurnal values, June-August 2000‘Lake 1’, Arctic
Coastal Plain, Alaska
Observed
Simulated
Mean temperature profile (1993-1997)Toolik Lake, Alaska
Lake ice formation and break-upTorne River, Sweden
ice formationice break-up
= area > 20 km2 = area < 20 km2
Wetland Algorithm
soilsaturated
land surface runoff enters
lake
evaporation depletes soil
moisture
lake recharges
soil moisture
Simulated saturated extentPutuligayuk River, Alaska
Simulated mean annual evaporation
with lake algorithm without lake algorithm
• Simulated annual evaporation increases by 60%
Blowing Snow
Günter Eisenhardt 3.31.2002, Iceland
Distribution of terrain slopes
Trail Valley Creek, NWT Imnavait Creek, Alaska
Sub-grid variability in wind speed
• Wind speeds assumed to follow a Laplace (double exponential) distribution
• Requires the standard deviation of wind speed, proportional to: – grid cell mean wind speed – standard deviation of terrain slope– autocorrelation of terrain slope
• Total sublimation flux found by summing sublimation for the average wind speed of ten equally-probable intervals
Non-equilibrium Transport
average fetch, f
transport = 0
transport = Qt(x= f)
snow
Estimating average fetch
vegetation type terrain slope terrain st. dev
Simulated annual sublimation from blowing snow
Sensitivity to fetch
SWE and active layer depth