Assessing water quality from farms how much detail is required to - - PowerPoint PPT Presentation

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Oct 17, 2023 207 likes •341 views

Assessing water quality from farms how much detail is required to model?

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Assessing water quality from farms

– how much detail is required to model?

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Context and Overview

Assess impact of management on water quality from range

f land uses at the paddock scale (sediment, nutrients, pesticides)

– Capture key datasets and insights from WQ studies

(n= 112, 12 with detail)

– Add value: best bet model parameters, and – “Publish” on a web site ( www.Howleaky.net )

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A common question in synthesis and modelling of experimental studies: How much effort/accuracy is needed in describing a system (soils, vegetation, agronomy) to adequately simulate the real world?

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Levels of experimental description vary

Of 112 studies, 12 had detailed data

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Vegetation/crop Grazing at Mt Mort Management practice Exclosed treatment

20 40 60 80

Cover (%)

Averaged green cover Actual green cover

How much detail is needed to describe vegetation?

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Are generic models able to predict runoff and soil loss? Runoff

Optimised model Semi-optimised model Generic model

Mt Mort Pasture study – Mark Silburn

3 soils 3 veg series 1 soils 3 veg series 1 soils 3 veg annual

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Are generic models able to predict runoff and soil loss? Sediment loss

Optimised model Semi-optimised model Generic model

Mt Mort Pasture study – Mark Silburn

3 soils 3 veg series 1 soils 3 veg series 1 soils simple annual veg

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For the statistically minded!

Mt Mort Pasture study – Mark Silburn

Bare soil loss Bare runoff Grazed runoff Exclosed runoff Grazed, exclosed soil loss Three levels

f vegetation

description

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System descriptions predict runoff and soil loss

Level 4 data for Goomboorian Pineapple study per Cyril Ciesolka

Runoff Sediment loss

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In summary

We can live with less detail in system description and still use conservative (mass balance) models with reasonable confidence This opens up access to a wider set of experimental datasets Empirical evidence remains king, and with pragmatic application of models we can stretch our data sets a long way

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