Global Lake Evaporation Volume (GLEV) dataset
Data available at: Zenodo
Data description: This global dataset contains the monthly surface area and evaporation volumes for 1,427,687 lakes and reservoirs from Jan 1985 to Dec 2018.
Related publication: Zhao, G., Y. Li, L. Zhou, H. Gao (2022), Evaporative water loss of 1.42 million global lakes, Nature Communications, 13, 3686. doi.org/10.1038/s41467-022-31125-6
Interactive map: Earth Engine App
Note: Newer version from 1984 to 2020 is under development.
Lake temperature and evaporation model (LTEM)
Model info: please contact me for more information
Model description: LTEM uses satellite water surface temperature (WST) as the boundary condition to accurately simulate temperature profile and evaporation rate for lakes and reservoirs.
Related publication: Zhao, G., H. Gao, and X. Cai (2020), Estimating lake temperature profiles and evaporation losses by leveraging MODIS LST data, Remote Sensing of Environment, doi.org/10.1016/j.rse.2020.112104.
Global reservoir surface area dataset (GRSAD)
Data available at: Texas Data Repository (v2), Google Drive (v3)
Data description: This dataset contains the time series of area values for 7246 global reservoirs (with an integrated capacity of 6810 km3) from 1984 to 2020 (updated). It was based on the dataset by Pekel et al. (2016), with the contaminations from clouds, cloud shadows, and terrain shadows corrected automatically.
Related publication: Zhao, G. and H. Gao (2018), Automatic correction of contaminated images for assessment of reservoir surface area dynamics, Geophysical Research Letters, doi.org/10.1029/2018GL078343
Interactive map: Earth Engine App
CONUS reservoir evaporation dataset (CRED)
Data available at: Texas Data Repository
Data description: This dataset contains the monthly evaporation volumes for 721 reservoirs from March 1984 to October 2015 in the Contiguous United States.
Related publication: Zhao, G. and H. Gao (2019), Remote Sensing of Environment, Estimating reservoir evaporation losses for the United States: Fusing remote sensing and modeling approaches, doi.org/10.1016/j.rse.2019.03.015
Interactive map: Earth Engine App
Distributed Hydrology Soil Vegetation Model with Reservoir (DHSVM-Res)
Model available at: please contact me for code
Model Description: A multi-purpose reservoir module was integrated into the Distributed Hydrology Soil Vegetation Model (DHSVM; Wigmosta et al., 1994). Conditional operating rules, which are designed to reduce flood risk and enhance water supply reliability, were adopted in this module.
Conceptual representation of DHSVM-Res includes: (a) topographically based basin discretization in DHSVM; (b) water movement for each grid cell; and (c) the newly integrated, multi-purpose reservoir with a flood control pool, a conservation pool, and an inactive pool. Blue points in (a) represent the point reservoirs that can be simulated in the integrated model.
Related publication: Zhao, G., Gao, H., Naz, B. S., Kao, S. C., & Voisin, N. (2016). Integrating a reservoir regulation scheme into a spatially distributed hydrological model. Advances in water resources, 98, 16-31, doi.org/10.1016/j.advwatres.2016.10.014.
Zhao, G., H. Gao, S.-C. Kao, N. Voisin, & B. S. Naz (2018), A modeling framework for evaluating the drought resilience of a surface water supply system under non-stationarity. Journal of Hydrology, 563, 22-32, doi: 10.1016/j.jhydrol.2018.05.037