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

Brief description

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.

Notes

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.

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).

Notes

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).