Reliability and efficiency of rainwater harvesting systems under different climatic and operational scenarios
Bibliographic record
Abstract
Global demand for clean water supply is on the rise due to population growth, climate and land-use change.Resource limitation, climate, leading to increasing water scarcity, demographic and socioinstitutional shifts promote more integrated water management, such as: storm-water harvesting and re-use that may mitigate the risk of water restrictions for urban populations.There is a need to ascertain whether integrated strategies can achieve social and economic goals as well as good-quality ecosystem service and maintenance by long-term monitoring of existing reuse plant, design and feasibility analysis and probabilistic modelling of sustainable rainfall drainage, storage and re-use systems.Rainwater harvesting (RWH) systems may supply daily non-potable water for irrigation, toilette flushing, car washing and other uses.Optimal storage capacity size should correspond to that tank size for which further increases in size produced only a small increase in reliability.Design is usually performed with reference to tank efficiency, and previous studies evidenced how only in highdemanding scenarios there will be a marked dependence of the RWH system efficiency on the water demand.In this study, data taken from literature, and referred to many different place in the world, are re-examined in light of a new risk model.Efficiency, risk of overflow and risk of waterscarcity of RWH tank are examined under various climatic and operational scenarios (including system size and demand).Efficiency is compared with risk and their suitability as indicators of correct tank design is discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".