1) Terrestrial Evapotrasnpiration Dataset for China, 1982-2017
By using the Complementary Relationship (CR) method, we have developed a terrestrial evapotrasnpiration dataset across China. This dataset covers the period of 1982-2017 with spatial and temporal resolutions of 0.1-degree and monthly, respectively. (Future update for a longer temporal length will be implemented once the forcing is available). The dataset has been fully validated against 13 plot-scale eddy-covariance flux towers and basin-scale water balance results of Chinese 10 major river basins, showing a very well accurancy.
The data is freely available here (in English) or here (in Chinese). The details on how this dataset was derived could be found in below reference. By using this ET data you agree to cite below paper:
Ma, N., Szilagyi, J., Zhang, Y., Liu, W. 2019. Complementary-relationship-based modeling of terrestrial evapotranspiration across China during 1982-2012: Validations and spatiotemporal analyses. Journal of Geophysical Research: Atmospheres, 124(8), 4326-4351. doi: 10.1029/2018JD029850
2) Terrestrial Evapotrasnpiration Dataset for the Continental United States, 1979-2015
This dataset involves the CR-based ET estimation over the continental United States (CONUS) for 1979-2015 with a spatial and spatial and temporal resolutions of 4-km and monthly, respectively. In addtion, this dataset also provides the water-balance-based ET (ETwb) for 18 HUC2 basins and 327 HUC6 basins, the latter relies on the precipitation data from PRISM, the runoff data from USGS, and the changes in terrestrial water storage from GRACE.
This dataset is available here. By using this dataset you agree to cite below paper:
Ma, N., Szilagyi, J. 2019. The CR of evaporation: A calibration-free diagnostic and benchmarking tool for large-scale terrestrial evapotranspiration modeling. Water Resources Research, 55, 7246–7274. doi: 10.1029/2019WR024867
3) Global Terrestrial Evapotrasnpiration Dataset (1982-2016 & 2000-2022)
The global monthly ET products were developed by same Complementary Relationship (CR) method mentioned above but with different forcing. Specifically, this dataset includes two versions, one is driven by primarily ERA5 forcing, as described in Ma et al. (2021), for the period of 1982-2016 (Version 1.0). The details on how this product was developed could be found in below reference. Another is driven by net radiation from GLDAS 2.1 and air temperature, dew temperature, and wind speed still from ERA5 for the period of 2000-2022 (Version 1.3). The latter one starts from 2000 because of the availablity of GLDAS 2.1. In each dataset, there is a "readme.txt" file to tell more details. Both datasets are at 0.25 degree resolution for global land. Note that the two datasets cannot be combined for a longer temporal coverage because they are driven by different forcings. The links for downloading them are as follows:
a. ET during 1982-2016 driven mainly by ERA5: https://doi.org/10.6084/m9.figshare.13634552
b. ET during 2000-2022 driven by ERA5 and GLDAS 2.1: https://www.doi.org/10.12072/ncdc.et.db4024.2023
By using above datasets you agree to cite below paper:
Ma, N., Szilagyi, J., Zhang, Y. 2021. Calibration-free complementary relationship estimates terrestrial evapotranspiration globally. Water Resources Research, 57, e2021WR029691, doi: 10.1029/2021WR029691
4) Terrestrial Evapotrasnpiration Dataset across the Tibetan Plateau, 1982-2016
A monthly, 0.05° terrestrial evapotranspiration dataset covering 1982-2016 across the Tibetan Plateau is produced by using the water-carbon coupled model, PML_V2. The PML_V2 model was calibrated against observed latent heat flux and gross primary productivity data from 14 eddy-covariance flux towers in Tibetan Plateau. Validations agaisnt EC and basin-wide water balance demonstrate this ET dataset has a great accurancy in Tibetan Plateau. This ET dataset is available here. Please read below paper for the details of this ET product:
Ma, N., Zhang, Y. 2022. Increasing Tibetan Plateau terrestrial evapotranspiration primarily driven by precipitation. Agricultural and Forest Meteorology, 317, 108887, doi: 10.1016/j.agrformet.2022.108887
5) Water-balance-based evapotranspiration for 56 large river basins across the world, 1983-2016
A water-balance-based evapotranspiration (ETwb) dataset for 56 large (> 100,000 km2) river basins of the world over the 1983–2016 period is produced. ETwb is estimated by basin-averaged annual precipitation (Prec) less runoff less the change in terrestrial water storage (dS). For each basin, the observed runoff at the hydrological station, four different Prec (GPCC, CRU, MSWEP, GPCP), and three types of dS (JPL, GSFC, CSR) estimates are used, yielding altogether 12 annual ETwb time-series. An optimally merged ETwb time-series is eventually produced using the Bayesian-based three-cornered hat method for each basin, which is probably the most appropriate one for benchmarking ET models. The dataset is available here. Please read below paper for the details of this basin-scale ETwb dataset:
Ma, N., Zhang, Y., Szilagyi, J. 2024. Water-balance-based evapotranspiration for 56 large river basins: A benchmarking dataset for global terrestrial evapotranspiration modeling. Journal of Hydrology, 630, 130607, doi: 10.1016/j.jhydrol.2024.130607
6) Daily Snow Depth dataset across the Tibetan Plateau, 2000-2018
A daily, 0.05° Snow Depth product covering 2000-2018 is produced by combining the existing high temporal resolution daily SD data and the high spatial resolution 8-day cloud-free MODIS-based snow cover probability data. This dataset is available here (In Chinese) and here (In English). Please read below paper for the details of this snow depth product:
Yan, D., Ma, N., Zhang, Y. 2022. Development of a fine-resolution snow depth product based on the snow cover probability for the Tibetan Plateau: Validation and spatial-temporal analyses. Journal of Hydrology, 604, 127027, doi: 10.1016/j.jhydrol.2021.127027
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Should you have any questions/comments in these datasets, please feel free to contact Ning Ma (ningma@igsnrr.ac.cn) for help
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