Simulating pan-Arctic runoff with a macro-scale terrestrial water balance model

A terrestrial hydrological model, developed to simulate the high-latitude water cycle, is described, along with comparisons with observed data across the pan-Arctic drainage basin. Gridded fields of plant rooting depth, soil characteristics (texture, organic content), vegetation, and daily time series of precipitation and air temperature provide the primary inputs used to derive simulated runoff at a grid resolution of 25 kin across the pan-Arctic. The pan-Arctic water balance model (P/WBM) includes a simple scheme for simulating daily changes in soil frozen and liquid water amounts, with the thaw-freeze model (TFM) driven by air temperature, modelled soil moisture content, and physiographic data. Climate time series (precipitation and air temperature) are from the National Centers for Environmental Prediction (NCEP) reanalysis project for the period 1980-2001. P/WBM-generated maximum summer active-layer thickness estimates differ from a set of observed data by an average of 12 cm at 27 sites in Alaska, with many of the differences within the variability (1sigma) seen in field samples. Simulated long-term annual runoffs are in the range 100 to 400 mm year(-1). The highest runoffs are found across northeastern Canada, southern Alaska, and Norway, and lower estimates are noted along the highest latitudes of the terrestrial Arctic in North America and Asia. Good agreement exists between simulated and observed long-term seasonal (winter, spring, summer-fall) runoff to the ten Arctic sea basins (r = 0.84). Model water budgets are most sensitive to changes in precipitation and air temperature, whereas less affect is noted when other model parameters are altered. Increasing daily precipitation by 25% amplifies annual runoff by 50 to 80% for the largest Arctic drainage basins. Ignoring soil ice by eliminating the TFM sub-model leads to runoffs that are 7 to 27% lower than the control run. The results of these model sensitivity experiments, along with other uncertainties in both observed validation data and model inputs, emphasize the need to develop improved spatial data sets of key geophysical quantities (particularly climate time series) to estimate terrestrial Arctic hydrological budgets better. Copyright (C) 2003 John Wiley Sons, Ltd.