Stochastic Rainwater Harvesting System Modeling Under Random Rainfall Features and Variable Water Demands
Bibliographic record
Abstract
Abstract A stochastic rainwater harvesting system modeling (StRaHaS) method is developed to enhance both accurateness and applicability of hydrologic modeling in guiding extensive applications of individual rainwater harvesting (RWH) systems (especially over large data‐scarce regions). The reliability of a RWH system is characterized as the fraction of time water demands being satisfied by a rainwater storage unit (RWSU). The variations of water balances, the post‐rainfall RWSU full‐storage probability, and the system reliability with random rainfall features, variable water demands, and the other RWH system characteristics are derived as analytical, accurate, and easily applied models through stochastic integration. StRaHaS is applied to three domestic RWH systems in Toronto, Canada. Due to high accurateness and low complexity in modeling, designing, assessing, and analyzing the RWH systems, StRaHaS outperforms existing methods (e.g., inaccurate water‐balance estimation, complicated continuous simulation, and simplified stochastic simulation). Based on StRaHaS, we quantitatively expound the increases in the reliability of the systems with higher rainwater supplies (corresponding to higher rainfall depths, and lower rainfall losses and first‐flush depths), lower water demands (in shorter wet and dry periods), and higher RWSU capacities. Long dry periods are lengthened by climate change and play dominant roles in low, spatially heterogeneous reliability of the systems. Synchronic changes and extremely high values of rainfall features do not significantly affect the reliability. Overall, StRaHaS is a promising method in modeling RWH systems, revealing RWH mechanisms, scientizing RWH applications, and facilitating the Sustainable Development Goals for addressing global water issues.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".