Assessing the soil moisture-precipitation feedback in Australia: CYGNSS observations

Previous modelling and case studies highlight the impacts of antecedent soil moisture on precipitation, showing the connection between the anomalous land surface and atmospheric conditions. However, observational evidence is lacking, especially on daily timescales, primarily due to the difficulty in...

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Veröffentlicht in:Environmental research letters 2024-01, Vol.19 (1), p.14055
Hauptverfasser: Bui, Hien X, Li, Yi-Xian, Sherwood, Steven C, Reid, Kimberley J, Dommenget, Dietmar
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description Previous modelling and case studies highlight the impacts of antecedent soil moisture on precipitation, showing the connection between the anomalous land surface and atmospheric conditions. However, observational evidence is lacking, especially on daily timescales, primarily due to the difficulty in assessing the interaction between soil moisture and atmospheric variability and dataset quality. Using satellite retrievals, this study investigates the relationship between soil moisture and next-day precipitation in Australia. Analysing the 5 year soil moisture data from the Cyclone Global Navigation Satellite System, we find that soil moisture anomalies influence next-day precipitation probability where higher soil moisture is associated with a higher probability of precipitation, even allowing for precipitation persistence. We also find that this feedback is generally positive in northern Australia but slightly negative in the southern regions, suggesting regional dependence. Linkages between the persistence of dry/wet soil moisture days and the possibility of wildfires and floods are also discussed. These findings have direct implications for the management and predictions of extreme conditions.
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subjects Anomalies
Atmospheric conditions
extreme events
Feedback
Flood management
Global navigation satellite system
land-atmosphere interaction
Precipitation
Soil moisture
Wildfires
title Assessing the soil moisture-precipitation feedback in Australia: CYGNSS observations
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