Terrestrial Hydrology Research Group
Data
Global Meteorological Forcing Dataset for land surface modeling
A global 50-yr (1948-2000) dataset of meteorological forcings derived by combining reanalysis with observations. Available at 1.0-degree spatial resolution and 3-hourly, daily and monthly temporal resolution.
Sheffield, J., G. Goteti, and E. F. Wood, 2006: Development of a 50-yr high-resolution global dataset of meteorological forcings for land surface modeling, J. Climate, 19 (13), 3088-3111
Global PDSI
Global datasets of PDSI and Potential Evaporation
Sheffield, J., E. F. Wood, and M. L. Roderick, 2012: Little change in global drought over the past 60 years. Nature, 491, 435–438. doi:10.1038/nature11575
Global Land Surface Model Simulations
Global datasets of surface hydrology from multiple land surface model simulations
Sheffield, J., and E. F. Wood (2007), Characteristics of global and regional drought, 1950-2000: Analysis of soil moisture data from off-line simulation of the terrestrial hydrologic cycle, J. Geophys. Res., 112, D17115, doi:10.1029/2006JD008288
Sheffield, J., G. Goteti, and E. F. Wood, 2006: Development of a 50-yr high-resolution global dataset of meteorological forcings for land surface modeling, J. Climate, 19 (13), 3088-3111
Bias-Corrected and Downscaled Future Climate Global Meterological Forcing Data: 1948-2099
A global 150-yr (1948-2099) dataset of meteorological forcings derived by bias correcting and downscaling 20th century (scenario 20C3M) and 21st century future climate projections (scenario SRES A2) from the NCAR PCM climate model. The dataset is bias-corrected and downscaled using the newly developed equidistant quantile matching method (Li et al., 2010) which better represents changes in the full distribution (not just the mean change). In addition to precipitation and temperature, radiation, humidity, pressure and windspeed are also downscaled. The downsclaing is based on the observational based global forcing dataset of Sheffield et al. (2006) listed above. Available at 1.0-degree spatial resolution and 3-hourly temporal resolution.
Li, H., J. Shefffield, and E. F. Wood, 2010: Bias correction of monthly precipitation and temperature fields from Intergovernmental Panel on Climate Change AR4 models using equidistant quantile matching, J. Geophys. Res., 115, D10101, DOI:10.1029/2009JD012882.
High resolution daily meteorological data for northern/west/east Africa
A 10km, daily meteorological dataset for west/east Africa has been developed for 1979‐2008. The dataset is based on the NCEP‐NCAR reanalysis (NNR) merged with the University of East Anglia Climate Research Unit (CRU) monthly gridded temperature product and the NASA Langley Surface Radiation Budget (SRB) product. Downscaling in space is carried out with account for changes in elevation. The CRU product has been evaluated for temporal inconsistencies that are attributable to changes in contributing gauges and simple adjustments are carried out to remove step changes. Adjustments are made to ensure consistency among variables, such as between downward longwave and humidity/temperature, and between temperature and humidity to maintain relative humidity. Empirical adjustments to downward short and longwave surface fluxes are made to achieve consistency with precipitation. The dataset has been evaluated against observations from the US National Climatic Data Center (NCDC) Global Summary of the Day (GSOD) station database, for a range of statistics at annual to daily time scale, including extremes. A method for assimilating station data into the gridded dataset was developed and tested, and was used to merge available station data into the full 1979‐2008 gridded dataset. Value‐added products such as annual, monthly and daily statistics, extreme values and potential evaporation were calculated from the dataset.
Chaney, N., and J. Sheffield, 2013: Spatial Analysis of Trends in Climatic Extremes with a High Resolution Gridded Daily Meteorological Data Set over Central Africa, to be submitted
Sheffield, J., G. Goteti, and E. F. Wood, 2006: Development of a 50-yr high-resolution global dataset of meteorological forcings for land surface modeling, J. Climate, 19 (13), 3088-3111
High resolution evapotranspiration data from remote sensing for Mexico
Evapotranspiration (ET) data have been generated for Mexico using a remote-sensing based Penman-Monteith approach. Inputs are derived from the ISCCP dataset which is downscaled from 2.5deg to 0.125deg using statistical relationships with the NARR. Vegetation distribution and LAI are taken from AVHRR databases. Canopy resistance is based on minimum values for each vegetation type and is modified by LAI and near surface humidity and minimum temperatures. Soil evaporation is also calculated.
Sheffield, J., E. F. Wood, and F. Munoz-Arriola, 2010: Long-term regional estimates of evapotranspiration for Mexico based on downscaled ISCCP data. J. Hydrometeor., 11(2), 253-275.
Soil Moisture retrievals from TMI
A surface soil moisture dataset for the southern United States using measurements from TMI and a land surface microwave emission model at 1/8th degree resolution for 1998-2004.
Gao, H., Wood, E. F., Jackson, T. J., Drusch, M., and Bindlish, R., 2006: Using TRMM/TMI to retrieve surface soil moisture over the southern United States from 1998 to 2002. J. Hydrometeorology, 7(1), 23-38, DOI: 10.1175/JHM473.1
Ensemble Meteorological Forcing Dataset for the Southeast U.S.
A daily atmospheric forcing dataset over the Southeast U.S. generated from the seasonal hydrologic ensemble prediction system. The forcing variables are: precipitation, daily maximum temperature and daily minimum temperature, average wind speed. This dataset contains 6-month forcing starting from the beginning of each month from 1981 to 1999. Two types of forcing are included: the CFS-based and the ESP-based. Both of them are used for seasonal hydrological predictions, but their forecast skills are different.
Luo, L., E. F. Wood, and M. Pan (2007), Bayesian merging of multiple climate model forecasts for seasonal hydrological predictions,J. Geophys. Res.,112, D10102, doi:10.1029/2006JD007655.
Luo, L., and E. F. Wood, Seasonal Hydrologic Prediction with the VIC Hydrologic Model for the Ohio River Basin. in preparation for J. Hydromet.
Luo, L., and E. F. Wood, Monitoring and Prediction of the 2007 U.S Drought. Geophys. Res. Lett., 34, L22702, doi:10.1029/2007GL031673
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Dept. Civil and Environmental Engineering
Princeton University
Princeton, NJ 08544