1. Arctic Land Surface Hydrology: Moving Towards a Synthesis Global Datasets

2. Available Datasets  ERA-40 Reanalysis  NCEP-NCAR Reanalysis  Remote sensing data  Global Runoff Data Center (GRDC, UNH)  Global River Discharge Database (RivDis, UNH)  Adam et al. (2006) Precipitation Dataset  Sheffield et al. (2006) 50-yr Meteorological Forcings

3. PGF50 1948-2000, 3hr, daily, 1.0deg P, T, Lw, Sw, q, p, w CRU 1901-2000, Monthly, 0.5deg P, T, Tmin, Tmax, Cld GPCP 1997-, Daily, 1.0deg P UW 1979-2000, Daily, 2.0deg P TRMM 2002-, 3hr, 0.25deg P SRB 1985-2000, 3hr, 1.0deg Lw, Sw NCEP/NCAR Reanalysis 1948-, 3hr, 6hr, daily, T62 P, T, Lw, Sw, q, p, w Reanalysis High temporal/low spatial resolution Observations Generally low temporal/high spatial resolution Bias-Corrected High temporal/high spatial resolution Global Forcing Dataset

4. Global Forcing Dataset: Correction of Daily Precipitation Statistics High latitude anomaly in reanalysis rain days Corrected to match observed wet  wet, dry  dry statistics By resampling wet and dry days from reanalysis record Other variables resampled for the same days for consistency Monthly P totals scaled to match observations

5. Global Forcing Dataset: Interpolation and Elevation Corrections disaggregated from 2.0 to 1.0 degree using bilinear interpolation but with adjustments for differences in elevation between the two grids air temperature adjusted using the environmental lapse rate (6.5 o C/km) adjust q, p, Lw via water vapor state equations and Stefan-Boltzmann law assumes that the relative humidity is constant to avoid the possibility of super- saturation Difference in elevation between reanalysis and 1.0deg grid

6. Global Forcing Dataset: Disaggregation of Precipitation A = sub-grid area of precipitation A = sub-grid area of precipitation I = daily precipitation amount I = daily precipitation amount Bayes theorem used to derive the sub-grid areal coverage of precipitation for a given grid precipitation and season Bayes theorem used to derive the sub-grid areal coverage of precipitation for a given grid precipitation and season weighted by neighboring cells weighted by neighboring cells Disaggregation in Space disaggregated from daily to 3-hr by resampling from TRMM p(3hr|daily) disaggregated from daily to 3-hr by resampling from TRMM p(3hr|daily) Disaggregation in Time 2.0 degree 1.0 degree

7. Global Forcing Dataset: Correction of Radiation Sw scaled to match SRB Sw scaled to match SRB Lw scaled to match SRB using probability matching. Lw scaled to match SRB using probability matching. Spurious trend in reanalysis Sw Spurious trend in reanalysis Sw Form regression between reanalysis Sw and Cld Form regression between reanalysis Sw and Cld New Sw time series generated from Cru cld New Sw time series generated from Cru cld Correction of Sw Trends Monthly Bias Correction of Lw and Sw

8. Global Forcing Dataset: P, T Monthly Bias Corrections Precipitation P scaled to match observed monthly totals P scaled to match observed monthly totals Corrected for gauge undercatch Corrected for gauge undercatch Orographic corrections can be added Orographic corrections can be added Temperature T scaled to match observed monthly totals T scaled to match observed monthly totals Tmin, Tmax scaled to match observed DTR Tmin, Tmax scaled to match observed DTR

9. Global Retrospective Hydrology Simulations Mean seasonal relative saturation DJF JJA MAM SON

10. Global Retrospective Hydrology Simulations Mean seasonal evapotranspiration DJF JJA MAM SON

11. Global VIC Simulations Before Calibration After Calibration

12. Global Runoff Data Center  Gridded data at 30-min spatial resolution  Monthly climatological mean runoff based on model output and adjusted to match observations

13. Global River Discharge Database  Data generally from 1969-1984 (http://www.rivdis.sr.unh.edu/) 1969-1984 (http://www.rivdis.sr.unh.edu/)

14. Adam et al. (2006) Precipitation  Gridded monthly precipitation, 1979-99  Half degree resolution  Applicable for regions with high-quality, long- term streamflow data with few anthropogenic effects. Basins must cover area with orographic effects (Adam et al., 2006)