Advances in Macroscale Hydrology Modeling for the Arctic Drainage Basin Dennis P. Lettenmaier Department of Civil and Environmental Engineering University of Washington 53 rd Arctic Science Conference University of Alaska Fairbanks September 19, 2002

Thermohaline Circulation G. Holloway, Institute of Ocean Sciences, Sidney, BC

Arctic drainage basin Ob Mackenzie Lena Yenesei 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 km 2 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

Saturated extent 1999 and = 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 ( ) Toolik Lake, Alaska

Lake ice formation and break-up Torne River, Sweden ice formationice break-up = area > 20 km 2 = area < 20 km 2

Wetland Algorithm soil saturated land surface runoff enters lake evaporation depletes soil moisture lake recharges soil moisture

Simulated saturated extent Putuligayuk River, Alaska

Simulated mean annual evaporation with lake algorithmwithout lake algorithm Simulated annual evaporation increases by 60%

Blowing Snow Günter Eisenhardt , Iceland

Distribution of terrain slopes Trail Valley Creek, NWTImnavait 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 = 0transport = Q t (x= f) snow

Estimating average fetch vegetation type terrain slopeterrain st. dev

Simulated annual sublimation from blowing snow Sensitivity to fetch

SWE and active layer depth