An unsustainable level of take: on-farm storages and floodplain water harvesting in the northern Murray–Darling Basin, Australia
Bibliographic record
Abstract
Water resources for irrigation in the Murray–Darling Basin have been heavily over-allocated, with major detrimental effects on wetlands and rivers. The Murray–Darling Basin Plan is intended to return water from irrigated agriculture to the environment but requires comprehensive, accurate water accounting to achieve this objective. Floodplain harvesting – the diversion and storage of overland flows into on-farm dams – is widely practised by irrigators in the northern Basin. By reducing volumes of river flows, floodplain harvesting has negative effects on downstream water users and the environment. The volume of diversions is not known, creating a major source of uncertainty over water availability and use. We focussed on floodplain harvesting in northern New South Wales (NSW) catchments (Border Rivers, Gwydir, Namoi, Macquarie and Barwon-Darling) because the NSW government is attempting to licence and regulate the practice. We found in 2019–20 there were 1,833 storages in these catchments with a total surface area of 42,650 ha. Storage capacity has risen from 557 GL in 1993–94 to 1,067 in 1999–2000, 1,225 in 2008–09 to 1,393 GL in 2019–20, a 2.5-fold increase in 26 years. We estimated mean annual floodplain harvesting take (2004–2020) in northern NSW was 778 GL (range 632–926 GL). For context, this volume represents half of the mean volume of held environmental water released annually for the entire Basin between 2009–10 and 2018–19 (1,576 GL) and six times that for the northern NSW Basin (125 GL). The volume of take from floodplain harvesting is not sustainable and in breach of legislation on water use and management. We discuss the negative impacts of floodplain harvesting on downstream communities and flow-dependent ecosystems and their social justice implications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".