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[1] Sea ice thicknesses derived from NASA's Ice, Cloud, and Land Elevation Satellite (ICESat) altimetry data are examined using two different approaches, buoyancy and empirical equations, and at two spatial scales-ICESat footprint size (70 m diameter spot) and Advanced Microwave Scanning Radiometer (AMSR-E) pixel size (12.5 km by 12.5 km) for the Bellingshausen and Amundsen Seas of west Antarctica. Ice thickness from the empirical equation shows reasonable spatial and temporal distribution of ice thickness from 2003 to 2009. Ice thickness from the buoyancy equation, however, additionally needing snow depth information derived from the AMSR-E, shows an overestimation in terms of maximum, mean (+63% to 75%), and standard deviation while underestimation in modal thickness (À20%) as compared with those from the empirical equation approach. When ICESat snow freeboard is used as the snow depth in the buoyancy equation, i.e., the zero ice freeboard assumption, the derived ice thicknesses match well with those from the empirical equation approach, within 5% overall. The AMSR-E, therefore, may underestimate snow depth and accounts for~95% of the ice thickness overestimation as compared with the buoyancy approach. Starting from autumn, a general picture of seasonal mean, modal, and median ice thickness increases progressively from autumn to spring and decreases from spring to the following autumn, when new thin ice dominates the ice thickness distribution. The asymmetric shape of the thickness distribution reflects the key role of ice deformation processes in the evolution of the thickness distribution. The statistical properties of the thickness distribution interannually (high range of mean thickness and standard deviation) indicate the variability of deformation processes. However, spring ice volume, the product of ice mean thickness and areal extent computed for the spring maximum, shows variability year to year but is primarily dominated by ice extent variability, with no increasing or decreasing trend over this record length. The dependence of the volume on the ice extent primarily suggests that ice thickness changes have also not covaried with the ice extent losses seen over the satellite record in this region, unlike the Arctic. These properties reflect the interactive processes of ice advection, thermodynamic growth and ice deformation that all substantially influence ice mass balance in the Bellingshausen-Amundsen Seas region.

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Snowmelt runoff in the mountainous eastern part of Turkey is of great importance as it constitutes 60-70% in volume of the total yearly runoff during spring and early summer months. Therefore, determining the amount and timing of snowmelt runoff especially in the Euphrates basin, where large dams are located, is an important task in order to use the water resources of the country in an optimal manner.The HBV model, being one of the well-known conceptual hydrological models used more than 45 countries over the world, is applied for the first time in Turkey to a small basin of 242 km 2 on the headwaters of Euphrates river for 2002-2004 water years. The input data are provided from the automatic snow-meteorological stations installed at various locations and altitudes in upper Euphrates basin operating in real-time. Since ground-based observations can only represent a small part of the region of interest, spatially and temporally distributed snow cover data are acquired through the use of Moderate Resolution Imaging Spectroradiometer (MODIS) optical satellite. In the first part of the study, an automatic model parameter estimation method, Shuffled Complex Evolution, University of Arizona (SCE-UA), is utilized to calibrate the HBV model parameters with a multi-variable criteria using runoff as well as snow-covered area (SCA) to ensure the internal validity of the model. Results show that calibrations against SCA in addition to discharge simulate discharge nearly as well as calibrations against discharge only, but further suggest that longer time periods and more study catchments should be included to achieve more comprehensible conclusions. In the second part of the study, the calibrated HBV model is applied to forecast runoff with a 1-day lead time using gridded input data from Mesoscale Model 5 (MM5) for the 2004 snowmelt period. Promising results indicate the possible operational use of runoff forecasting driven by numerical weather prediction data for flood mitigation, reservoir operation and dam safety.