404
A. Amani et al.
We use the latest version of the GLEAM dataset, V3.2a, which is a global dataset
spanning 1980–2017 at 0.5°, daily resolution. The dataset is based on reanalysis net
radiation and air temperature, satellite and gauged-based precipitation, VOD, soil
moisture and snow water equivalent. Components of evapotranspiration (transpiration, canopy interception, soil evaporation, open water evaporation and sublimation)
are calculated separately. The Priestley and Taylor equation is used to calculate
potential evaporation based on reanalysis surface net radiation and near-surface air
temperature. Estimates of potential evaporation for the land fractions of bare soil, tall
canopy and short canopy are converted into actual evaporation using a multiplicative evaporative stress factor based on observations of microwave Vegetation Optical
Depth (VOD) and estimates of root-zone soil moisture. The latter is calculated using
a multi-layer running-water balance. To try to correct for random forcing errors,
satellite retrievals of surface soil moisture are also assimilated into the soil profile.
Interception loss is calculated separately in using a Gash analytical model. Finally,
estimates of actual evaporation for water bodies and regions covered by ice and/or
snow are obtained using an adaptation of the Priestley and Taylor equation. We also
use the surface and root-zone soil moisture calculated by GLEAM.
d. Terrestrial Water Storage
GRACE—Gravity Recovery and Climate Experiment
The GRACE mission has been monitoring changes in the Earth’s gravity field since its
launch in 2002 by measuring the distance between two orbiting satellites. Variations
in these fields can be attributed to changes in terrestrial water storage after removal
of atmospheric and ocean bottom pressure changes. We use the JPL RL05 dataset of
approximately 30-day Total Water Storage (TWS) anomalies. We also use the JPL
RL05-based time series for the Lake Chad Basin using a 300 km Gaussian half-width
averaging kernel that is available from the University of Colorado.
References
Beck HE, van Dijk AIJM, Levizzani V, Schellekens J, Miralles DG, Martens B, de Roo BA (2017)
MSWEP: 3-hourly 0.25-global gridded precipitation (1979–2015) by merging gauge, satellite,
and reanalysis data. Hydrol Earth Syst Sci 21:589–615
Beck HE, Wood EF, Pan M, Fisher CK, Miralles DG, van Dijk AI, McVicar TR, Adler RF (2019)
MSWEP V2 global 3-hourly 0.1° precipitation: methodology and quantitative assessment. Bull
Amer Meteor Soc 100, 473–500. https://doi.org/10.1175/BAMS-D-17-0138.1
Chen J et al (2015) Global land cover mapping at 30 m resolution: A POK-based operational
approach. Isprs J Photogram Remote Sens 103:7–27
Hamman JJ, Nijssen B, Bohn TJ, Gergel DR, Mao Y (2018) The variable infiltration capacity model,
version 5 (VIC-5): infrastructure improvements for new applications and reproducibility. Geosci
Model Dev. https://doi.org/10.5194/gmd-11-3481-2018
Hengl T et al (2017) SoilGrids 250m: Global gridded soil information based on machine learning.
PLoS ONE 12(2):e0169748. https://doi.org/10.1371/journal.pone.0169748
A. Amani et al.
We use the latest version of the GLEAM dataset, V3.2a, which is a global dataset
spanning 1980–2017 at 0.5°, daily resolution. The dataset is based on reanalysis net
radiation and air temperature, satellite and gauged-based precipitation, VOD, soil
moisture and snow water equivalent. Components of evapotranspiration (transpiration, canopy interception, soil evaporation, open water evaporation and sublimation)
are calculated separately. The Priestley and Taylor equation is used to calculate
potential evaporation based on reanalysis surface net radiation and near-surface air
temperature. Estimates of potential evaporation for the land fractions of bare soil, tall
canopy and short canopy are converted into actual evaporation using a multiplicative evaporative stress factor based on observations of microwave Vegetation Optical
Depth (VOD) and estimates of root-zone soil moisture. The latter is calculated using
a multi-layer running-water balance. To try to correct for random forcing errors,
satellite retrievals of surface soil moisture are also assimilated into the soil profile.
Interception loss is calculated separately in using a Gash analytical model. Finally,
estimates of actual evaporation for water bodies and regions covered by ice and/or
snow are obtained using an adaptation of the Priestley and Taylor equation. We also
use the surface and root-zone soil moisture calculated by GLEAM.
d. Terrestrial Water Storage
GRACE—Gravity Recovery and Climate Experiment
The GRACE mission has been monitoring changes in the Earth’s gravity field since its
launch in 2002 by measuring the distance between two orbiting satellites. Variations
in these fields can be attributed to changes in terrestrial water storage after removal
of atmospheric and ocean bottom pressure changes. We use the JPL RL05 dataset of
approximately 30-day Total Water Storage (TWS) anomalies. We also use the JPL
RL05-based time series for the Lake Chad Basin using a 300 km Gaussian half-width
averaging kernel that is available from the University of Colorado.
References
Beck HE, van Dijk AIJM, Levizzani V, Schellekens J, Miralles DG, Martens B, de Roo BA (2017)
MSWEP: 3-hourly 0.25-global gridded precipitation (1979–2015) by merging gauge, satellite,
and reanalysis data. Hydrol Earth Syst Sci 21:589–615
Beck HE, Wood EF, Pan M, Fisher CK, Miralles DG, van Dijk AI, McVicar TR, Adler RF (2019)
MSWEP V2 global 3-hourly 0.1° precipitation: methodology and quantitative assessment. Bull
Amer Meteor Soc 100, 473–500. https://doi.org/10.1175/BAMS-D-17-0138.1
Chen J et al (2015) Global land cover mapping at 30 m resolution: A POK-based operational
approach. Isprs J Photogram Remote Sens 103:7–27
Hamman JJ, Nijssen B, Bohn TJ, Gergel DR, Mao Y (2018) The variable infiltration capacity model,
version 5 (VIC-5): infrastructure improvements for new applications and reproducibility. Geosci
Model Dev. https://doi.org/10.5194/gmd-11-3481-2018
Hengl T et al (2017) SoilGrids 250m: Global gridded soil information based on machine learning.
PLoS ONE 12(2):e0169748. https://doi.org/10.1371/journal.pone.0169748