Design and Application of the Tank Simulation Model (TSM): Assessing the Ability of Rainwater Harvesting to Meet Domestic Water Demand
Bibliographic record
Abstract
Rainwater harvesting (RWH) is a necessary technology to supplement and/or replace insufficient ground and surface water resources for domestic water supplies, especially under changing climate conditions. An accessible and flexible Excel-based RWH simulation tool is developed and applied to investigate the utility of RWH in two regional case studies, under both present conditions and future climate scenarios, through examination of relationships between tank volumes, roof areas, rainfall patterns, and yield. The conversion of complex mathematical formula into a tool with a simple data entry form for infinite combinations of the critical variables enables non-experts to manipulate and optimize designs at the level of RWH implementation. The results clearly show that RWH can augment problematic or insufficient water supplies. Roof area and rainfall distribution have the greatest impact on the ability of a RWH system to meet demand; tank size has a minimal effect, providing a buffer during short dry periods within any given month. Demand met improves in both geographies under future scenarios. Thus, while RWH is insufficient as the sole source of domestic water now and in the future, it is a low-cost supply augmentation solution even in cold climates. RWH solutions are made more accessible through planning tools such as the Tank Simulation Model presented here, which is sufficiently flexible to incorporate climate change scenario planning.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".