Land Surface Hydrology Research Group
Long-Term Evapotranspiration Product for Mexico from Remote Sensing
>Overview
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.
The data are calculated daily on a 0.125deg grid for Mexico. The data are available in GraDS format which is a simple flat 4-byte binary (.bin) with a data descriptor file (.ctl) which contains details of the domain and dimensions.
Four variables are given:
Data Access
The dataset is freely available but we ask that you leave a few details about yourself and how you intend to use the dataset. Also, please cite the reference below if you use the data.
Updates
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.
Abstract
The development and evaluation of a long-term high-resolution dataset of potential and actual evapotranspiration for Mexico based on remote sensing data are described. Evapotranspiration is calculated using a modified version of the Penman–Monteith algorithm, with input radiation and meteorological data from the International Satellite Cloud Climatology Project (ISCCP) and vegetation distribution derived from Advanced Very High Resolution Radiometer (AVHRR) products. The ISCCP data are downscaled to ⅛° resolution using statistical relationships with data from the North American Regional Reanalysis (NARR). The final product is available at ⅛°, daily, for 1984–2006 for all Mexico. Comparisons are made with the NARR offline land surface model and measurements from approximately 1800 pan stations. The remote sensing estimate follows well the seasonal cycle and spatial pattern of the comparison datasets, with a peak in late summer at the height of the North American monsoon and highest values in low-lying and coastal regions. The spatial average over Mexico is biased low by about 0.3 mm day−1, with a monthly rmse of about 0.5 mm day−1. The underestimation may be related to the lack of a model for canopy evaporation, which is estimated to be up to 30% of total evapotranspiration. Uncertainties in both the remote sensing–based estimates (because of input data uncertainties) and the true value of evapotranspiration (represented by the spread in the comparison datasets) are up to 0.5 and 1.2 mm day−1, respectively. This study is a first step in quantifying the long-term variation in global land evapotranspiration from remote sensing data.
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Dept. Civil and Environmental Engineering
Princeton University
Princeton, NJ 08544