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Arctic Land Surface Hydrology: Moving Towards a Synthesis Global Datasets 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 Global Forcing Dataset Reanalysis Observations Bias-Corrected High temporal/low spatial resolution Generally low temporal/high spatial resolution High temporal/high spatial resolution CRU 1901-2000, Monthly, 0.5deg P, T, Tmin, Tmax, Cld GPCP 1997-, Daily, 1.0deg P NCEP/NCAR Reanalysis UW PGF50 1948-, 3hr, 6hr, daily, T62 P, T, Lw, Sw, q, p, w 1979-2000, Daily, 2.0deg P 1948-2000, 3hr, daily, 1.0deg P, T, Lw, Sw, q, p, w TRMM 2002-, 3hr, 0.25deg P SRB 1985-2000, 3hr, 1.0deg Lw, Sw Global Forcing Dataset: Correction of Daily Precipitation Statistics • High latitude anomaly in reanalysis rain days • Corrected to match observed wetwet, drydry 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 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 oC/km) • adjust q, p, Lw via water vapor state equations and Stefan-Boltzmann law Difference in elevation between reanalysis and 1.0deg grid • assumes that the relative humidity is constant to avoid the possibility of supersaturation Global Forcing Dataset: Disaggregation of Precipitation Disaggregation in Space p( I | A) p( A) p( I ) • A = sub-grid area of precipitation • I = daily precipitation amount • Bayes theorem used to derive the sub-grid areal coverage of precipitation for a given grid precipitation and season • weighted by neighboring cells 2.0 degree 1.0 degree • disaggregated from daily to 3-hr by resampling from TRMM p(3hr|daily) 0.4 Probability of precipitation p( A | I ) Disaggregation in Time 0.35 0.3 0.25 0.2 0.15 0.1 0.05 0 1 2 3 4 5 3-hr period 6 7 8 Global Forcing Dataset: Correction of Radiation Correction of Sw Trends • Spurious trend in reanalysis Sw • Form regression between reanalysis Sw and Cld • New Sw time series generated from Cru cld Monthly Bias Correction of Lw and Sw • Sw scaled to match SRB • Lw scaled to match SRB using probability matching. Global Forcing Dataset: P, T Monthly Bias Corrections Precipitation • P scaled to match observed monthly totals • Corrected for gauge undercatch • Orographic corrections can be added Temperature • T scaled to match observed monthly totals • Tmin, Tmax scaled to match observed DTR Global Retrospective Hydrology Simulations DJF MAM JJA SON Mean seasonal relative saturation Global Retrospective Hydrology Simulations DJF MAM JJA SON Mean seasonal evapotranspiration Global VIC Simulations Before Calibration After Calibration Global Runoff Data Center Gridded data at 30-min spatial resolution Monthly climatological mean runoff based on model output and adjusted to match observations Global River Discharge Database Data generally from 1969-1984 (http://www.rivdis.sr.unh.edu/) Adam et al. (2006) Precipitation Gridded monthly precipitation, 1979-99 Half degree resolution Applicable for regions with high-quality, longterm streamflow data with few anthropogenic effects. Basins must cover area with orographic effects (Adam et al., 2006)