Parsimonious analytical modelling of rainwater harvesting systems’ performance under climate change in six Chinese cities
Bibliographic record
Abstract
Six climatically diverse cities in China (Beijing, Chongqing, Guangzhou, Jinan, Lanzhou and Xi'an). This study proposed a novel quantitative assessment for data-scarce regions by integrating daily rainfall event reconstruction with an analytical probabilistic model (APM) to evaluate climate change impacts on rainwater harvesting (RWH) systems’ performance. The proposed model aims to overcome the limitation of the conventional APMs which often rely on high temporal resolution rainfall data (i.e., hourly) as a basis to provide rainfall event characteristics for model inputs. The proposed methodology attains accuracy comparable to continuous simulations with hourly rainfall input when applied to daily rainfall data. Results of case studies using the proposed method in six Chinese cities reveal climate change poses impacts on RWH systems’ design, i.e., cities in humid regions such as Guangzhou experience the most significant increase in water yield but also a sharp rise in flood control pressure, whereas arid northwestern cities such as Lanzhou show modest variation. Under projected climate changes, RWH systems exhibit a significant trade-off: water supply reliability generally increases, while stormwater control efficacy decreases. This dual shift necessitates climate-adaptation strategies. Designing systems with larger storage capacities than currently required emerges as a key solution to simultaneously mitigate future urban flooding risk and optimize rainwater utilization potential across these diverse climates in China. • A rainfall event separation method based on daily rainfall data was proposed. • APM integrated with HTES closely resembles hourly-data simulations, with < 5.6 % MAE. • Simulation of future climate scenarios is applicable to the proposed APM-RWH. • The impact of climate change on RWH’s performance is analyzed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".