H178-13
Assessing potential evaporation for drought modelling in southeast Australia

Tuesday, 15 December 2020: 09:06
Virtual
Esther Zhu1, Anthony John Clark1, Jennifer B Wurtzel2, Kimberly Broadfoot3, Scott Wallace3 and Kimberly Broadfoot, (1)Orange Agricultural Institute, New South Wales Department of Primary Industries, Australia, Sydney, Australia, (2)NSW Department of Primary Industries, Orange, NSW, Australia, (3)Orange Agricultural Institute, New South Wales Department of Primary Industries, Australia, Orange, Australia
Abstract:
Southeast Australia is a region prone to extreme weather and high climate variability. Since 1996, multiple large-scale, severe droughts have impacted agriculturein this region. Due to the combined influence of large-scale climate modes and projected rising temperatures on inter-annual and long-term climate variability, it is particularly important to use high-resolution spatio-temporal data to monitor and forecast droughts at a farm-scale. A crucial variable in evaluation of drought is potential evapotranspiration (PET), which has linkages to soil water availability.

This study aims to evaluate different methods for determining PET (e.g., Penman-Monteith, Hargreaves-Samani), then apply the estimations to calculate soil water and plant growth drought indicators. These indicators are based on the Enhanced Drought Information System (EDIS) developed by New South Wales Department of Primary Industries (DPI).

In this study, high spatiotemporal resolution (daily, 1 km x 1 km) climate datasets (ANUClimate) were used to estimate PET and further to calculate drought indices. Near-surface wind speed data interpolated by ANUSPIN was acquired from CSIRO. Seasonal anomalies of PET have been computed to evaluate different PET formulas. Observation sites (e.g., BoM pan evaporation and automatic weather sensor data) across NSW have been selected to calibrate and verify the modelled gridded surface evapotranspiration results.

The Penman-Monteith results over the recent 2016-2019 drought period are highly affected by limited reliable wind data in Australia, which will be similarly problematic in climate change projection data. In contrast, the Hargreaves-Samani method (based on temperature and radiation only) performed well against the observational data and might provide a suitable alternative for modelling drought under climate change. This work supports an improvement of understanding and reliability of drought monitoring under current and future climate, while significantly improve the early detection and forecasting of drought at a regional and farm level in New South Wales. Future work will include evaluating the sensitivity of different PET methods to monitor droughts under global warming scenarios.

<< Previous Abstract | Next Abstract >>