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Offline Daily Evapotranspiration Datasets Based on Downscaled High-resolution CMIP6 Simulations in Australia 

Ver: 1.0
Status of Data: completed
Update Frequency: notPlanned
Security Classification: unclassified
Record Last Modified: 2025-12-02
Viewed 33 times
Accessed 15 times
Dataset Created: 2025-05-01
Dataset Published: 2025-07-21
Data can be accessed from the following links:
HTTPPoint-of-truth metadata URLHTTPro-crate-metadata.jsonOPeNDAPNetCDF - Offline Daily Evapotranspiration Data
How to cite this collection:
Zhang, H. & State Government of Queensland (2025). Offline Daily Evapotranspiration Datasets Based on Downscaled High-resolution CMIP6 Simulations in Australia. Version 1.0. Terrestrial Ecosystem Research Network. Dataset. https://dx.doi.org/10.25901/40kk-ek64 
Quantifying the impact of climate change on actual and potential evapotranspiration (AET and PET) is essential for water security, agriculture production and environmental management. AET and PET are strongly influenced by local factors such as topography, land cover and soil moisture, which limits the usability of global climate models for their projections. Here, we dynamically downscale Coupled Model Intercomparison Project Phase 6 (CMIP6) models using Conformal Cubic Atmospheric Model (CCAM) to a 10km resolution over Australia and derive AET and PET at a daily time step using the Morton method and project future changes under SSP126, 245 and 370. Three AET / PET datasets are provided by Queensland Government Climate Projection Service team, which include Areal AET, Wet Environment Areal PET and Point PET. These datasets are computed offline based on Morton’s Complementary Relationship Areal Evapotranspiration (CRAE) model. In addition, we also provide datasets for Pan Evaporation (linear regression model), Short and Tall Crop Reference Evapotranspiration (Penman–Monteith model) and Shallow Lake Evaporation (Morton’s Complementary Relationship Wet-surface Evaporation CRWE model). They have used dynamically downscaled CMIP6 models datasets as input. 
Credit
We at TERN acknowledge the Traditional Owners and Custodians throughout Australia, New Zealand and all nations. We honour their profound connections to land, water, biodiversity and culture and pay our respects to their Elders past, present and emerging. 
Purpose
Evaporation or Evapotranspiration plays a key role in the water cycle, crop irrigation and forestry. However, modelling evaporation or evapotranspiration is more challenging due to the complexity of the water cycle, its spatial and temporal variability and uncertainties in climate models. Actual evapotranspiration (AET) is often said to be the most difficult water balance component to directly measure due to the high costs of installation and maintenance (e.g., eddy-covariance flux towers – 26 OzFlux in Australia). Potential evaporation (PE) can be quantified from a spatial network of pan evaporation data dating back to 1975, but the station observation network of pan evaporation has been declining since 2010. Thus, models for the prediction of AET and PET are still nowadays preferred due to the relatively simplicity of computation and in the large availability of meteorological data required for the simulation. These datasets are required in various fields such as hydrology, irrigation management, water budgeting, trading and management. They are also applied to assess the water stress and compute drought indices (e.g., Standardized Precipitation Evapotranspiration Index -SPEI). Besides, ET data are essential to study compounding extremes (e.g., hydroclimate volatility indices, atmospheric thirstiness indices). 
Lineage
Using dynamically downscaled CMIP6 climate models datasets at a 10 km resolution as input, we assess AET and PET at a daily time step using the Morton method and project future changes to AET and PET for Australia. Morton’s Point PET, Wet Environment Areal PET, Areal AET are based on symmetric complementary relationship (Complementary Relationship Areal Evapotranspiration - CRAE model). The year range is 1981 – 2100. Three Shared Socioeconomic Pathways (SSPs) considered include SSP126, 245 and 370. We also provide evaporation and evapotranspiration datasets for the following four variables:
  • Pan Evaporation datasets (linear regression model);
  • Short Crop Reference Evapotranspiration (Penman–Monteith model);
  • Tall Crop Reference Evapotranspiration (Penman–Monteith model);
  • Shallow Lake Evaporation (Morton’s Complementary Relationship Wet-surface Evaporation CRWE model).
 
Method DocumentationData not provided.
Procedure StepsData not provided.
Temporal Coverage
From 1981-01-01 to 2100-12-31 
Spatial Resolution

Data not provided.

Vertical Extent

Data not provided.

Data Quality Assessment Scope
Data quality was evaluated against OzFlux AET measurements. QldFCP-2 AET datasets performs well and is ranked the second best. 
Projections of actual and potential evapotranspiration from downscaled high-resolution CMIP6 climate simulations in Australia
Data Quality Assessment Outcome
The performance of the various AET products was evaluated against 26 OzFlux towers in Australia. The percentage errors vary depending on the location of the flux tower.
  • CSIRO MODIS ReScaled EvapoTranspiration (CMRSET) AET estimates had the highest agreement with OzFlux and the smallest mean percent errors, averaging 15.7% across all 26 available flux towers.
  • The ensemble average of the unadjusted QldFCP-2 dataset also performed well, with the second lowest average percent errors (17%) from all flux tower sites.
  • In comparison, Scientific Information for Land Owners (SILO) had the highest mean percent error at 44%.
  • For other AET products, the mean percent error ranged from 21.5% to 27%.
This indicates that QldFCP-2 can provide valuable AET estimates, with similar or less bias to other remote sensing or reanalysis AET products. Within the QldFCP-2 dataset, the percentage errors vary from site to site and across the individual models, with the highest error (110.5%) obtained at the Samford OzFlux tower from the MPI-ESM1-2-LR model and the lowest error (0.1%) obtained at the Warra OzFlux tower from the CNRM-CM6-1-HR atmosphere only model. 
ANZSRC - FOR
Climate change science
Climatology
Hydrology
GCMD Sciences
ATMOSPHERE - EVAPORATION
ATMOSPHERE - EVAPOTRANSPIRATION
ATMOSPHERE - POTENTIAL EVAPOTRANSPIRATION
Horizontal Resolution
10 km - < 50 km or approximately .09 degree - < .5 degree
Parameters
atmospheric pressure
maximum temperature
minimum temperature
solar radiation
vapour pressure deficit
wind speed
Temporal Resolution
Daily Climatology
Topic
climatologyMeteorologyAtmosphere
Author
Zhang, Hong
State Government of Queensland
Contact Point
State Government of Queensland
Zhang, Hong
Publisher
Terrestrial Ecosystem Research Network
Supplemental Information
It is important to note several uncertainties associated with the datasets. Firstly, AET and PET are complex phenomena and may not be represented ideally using one model (e.g., Morton’s model). Thus, comparison with other nonlinear-CR models or water balance formulas may provide further insights in future studies. Second, the uncertainty surrounding emissions scenarios and the CMIP6 downscaled projections leads to subsequent uncertainty for future projections of AET in Australia. Lastly and most importantly, the uncertainty in AET projections is closely related to precipitation uncertainties of CMIP6 projections, which vary significantly across Australia. Despite these limitations, our study provides valuable outcomes, which will be useful for policymakers and scientists to establish climate change adaptation and mitigation strategies and to effectively manage water resources. 
Resource Specific Usage
Data not provided. 
Environment Description
Data not provided. 
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Terrestrial Ecosystem Research Network
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https://creativecommons.org/licenses/by/4.0/
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Please advise any work or publications that use this data via the online form at https://www.tern.org.au/research-publications/#reporting 
Please cite this dataset as {Author} ({PublicationYear}). {Title}. {Version, as appropriate}. Terrestrial Ecosystem Research Network. Dataset. {Identifier}. 

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Version:6.2.22