Jonah Lee Bas
@jonahleebas.bsky.social
PhD student at Monash University, Clayton, studying the link between hydrologic connectivity and nutrient dynamics in flat, agricultural catchments. #nutrients #tracers #hydrology #waterquality
Grateful to my supervisors and co-authors @annalintern.bsky.social, @perrancook.bsky.social, Andrew Western, Robert Sargent, and Wei Wen Wong, and everyone who supported this work!
April 24, 2025 at 7:14 AM
Grateful to my supervisors and co-authors @annalintern.bsky.social, @perrancook.bsky.social, Andrew Western, Robert Sargent, and Wei Wen Wong, and everyone who supported this work!
• The proximity of effluent ponds to the stream network was identified as an important predictor of stream nutrient concentrations during dry weather conditions.
• Deep learning was used to estimate the stocking rate from high-resolution aerial imagery.
• Deep learning was used to estimate the stocking rate from high-resolution aerial imagery.
April 24, 2025 at 7:14 AM
• The proximity of effluent ponds to the stream network was identified as an important predictor of stream nutrient concentrations during dry weather conditions.
• Deep learning was used to estimate the stocking rate from high-resolution aerial imagery.
• Deep learning was used to estimate the stocking rate from high-resolution aerial imagery.
• The study focused on 10 subcatchments in the Macalister Irrigation District (MID), Victoria, Australia.
• Effluent pond density, dairy shed density, and stocking rate were identified as key drivers of stream nutrient concentrations, particularly during wet periods.
• Effluent pond density, dairy shed density, and stocking rate were identified as key drivers of stream nutrient concentrations, particularly during wet periods.
April 24, 2025 at 7:14 AM
• The study focused on 10 subcatchments in the Macalister Irrigation District (MID), Victoria, Australia.
• Effluent pond density, dairy shed density, and stocking rate were identified as key drivers of stream nutrient concentrations, particularly during wet periods.
• Effluent pond density, dairy shed density, and stocking rate were identified as key drivers of stream nutrient concentrations, particularly during wet periods.
Summary and key takeaways:
• We investigated how dairy farm infrastructure and stocking rates – along with other catchment characteristics such as climate, hydrology, land use, land cover, etc. – influence nutrient concentrations (NH₄⁺, FRP, NOₓ, TP, TN) in streams.
• We investigated how dairy farm infrastructure and stocking rates – along with other catchment characteristics such as climate, hydrology, land use, land cover, etc. – influence nutrient concentrations (NH₄⁺, FRP, NOₓ, TP, TN) in streams.
April 24, 2025 at 7:14 AM
Summary and key takeaways:
• We investigated how dairy farm infrastructure and stocking rates – along with other catchment characteristics such as climate, hydrology, land use, land cover, etc. – influence nutrient concentrations (NH₄⁺, FRP, NOₓ, TP, TN) in streams.
• We investigated how dairy farm infrastructure and stocking rates – along with other catchment characteristics such as climate, hydrology, land use, land cover, etc. – influence nutrient concentrations (NH₄⁺, FRP, NOₓ, TP, TN) in streams.
Have fun at AGU!
November 29, 2024 at 2:44 AM
Have fun at AGU!