Terrestrial Hydrology Research Group

Princeton University

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

Dataset Details and Access

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

Dataset Details and Access

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

Dataset Details and Access

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.

Dataset Details and Access

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

Dataset Details and Access

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.

Dataset Details and Access

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

Dataset Details and Access

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

Dataset Details and Access

Iceberg.

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