Variation of representative rainfall time series length for rainwater harvesting modelling in different climatic zones

Yue, Tongjia; Shou-hong, Zhang; Jian-jun, Zhang; Zhang, Bu; Li, Ruixian

doi:10.1016/j.jenvman.2020.110731

Cited by 22 publications

(2 citation statements)

References 34 publications

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“…Long-term rainfall data are recommended for assessing RWH, and a 30-year time series is commonly considered as representative (World Meteorological Organization, 1989) for this analysis. However, meeting this requirement can be unfeasible due to the lack of long-term historical data, and short-term time series data are often the only source for the RWH project (Geraldi & Ghisi, 2017;Zhang et al, 2020;Yue et al, 2020;Ali et al, 2020). Nevertheless, few studies (Ghisi et al, 2012; Influence of rainfall and design criteria on performance of rainwater harvesting systems placed in different Brazilian climatological conditions 2019) have researched its real impact on the performance of these facilities in Brazilian cities, especially by comparing the results in different climate conditions.…”

Section: Time Series Lengthmentioning

confidence: 99%

Influence of rainfall and design criteria on performance of rainwater harvesting systems placed in different Brazilian climatological conditions

Perius

Tassi

Lamberti

et al. 2021

RBRH

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Using rainwater harvesting (RWH) system is influenced by socioeconomic, environmental and technical factors. This work presents as analysis of the influence of the rainfall time series characteristics and design criteria on RWH performance of five Brazilian capitals with different climatic characteristic: Goiânia, João Pessoa, Manaus, Porto Alegre and São Paulo. The analysis combined different rooftop areas, storage volumes and the indoor and outdoor demands. Rainfall temporal discretization and the types of demands were the most important characteristics when assessing RWH reliability. Daily rainfall data were suitable for sizing the RWH, the time series length influenced the sizing of larger storage volumes, and the RWH efficiency was not significantly affected by the first-flush. Toilet flushing and the irrigation demands had the greatest impact on RWH performance. The greatest potentials for the implementation of RWH were observed for Porto Alegre, because of well distributed rainfall throughout the year, and for Manaus owing to higher annual volumes of precipitation. These results highlight relevant aspects that must be observed during the conception and design of RWH, complementing the guidelines provided in the Brazilian technical standards.

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Section: Time Series Lengthmentioning

confidence: 99%

Influence of rainfall and design criteria on performance of rainwater harvesting systems placed in different Brazilian climatological conditions

Perius

Tassi

Lamberti

et al. 2021

RBRH

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“…Temporary fluctuations in water quality are influenced by changes in weather conditions and seasonal factors (Eccles et al, 2020). This circumstance could result in daily fluctuations of water quality parameters in ponds during the cultivation period, as the weather fluctuates and is mitigated under the ongoing seasonal trend (Yue et al, 2020). Hence, this study aimed to investigate the dynamics of differences in water quality parameters in intensive L. vannamei cultivation ponds during dry and rainy seasons.…”

Section: Introductionmentioning

confidence: 99%

Water Quality Fluctuations in Shrimp Ponds During Dry and Rainy Seasons

Ariadi,

Azril,

Mujtahidah

2023

Croatian Journal of Fisheries

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Season and water quality are essential indicators in the life cycle of pond ecosystems. The season is a natural factor affecting the level of water quality dynamics in shrimp cultivation in ponds. This study aims to evaluate the dynamics of differences in water quality parameters in intensive Litopenaeus vannamei ponds during dry and rainy seasons. This current study applied an ex post facto causal design based on actual field conditions. During the rainy season, water quality parameters tended to fluctuate dynamically, with the parameters of phosphate (PO4), nitrite (NO2), and total organic matter (TOM) exceeding the threshold value of water quality standards for aquaculture, with a PO4 value of 0.734 mg/L, a NO2 of 0.180 mg/L, and a TOM of 101.29 mg/L. In the dry season, water quality parameters remained stable, with only the phosphate parameter showing a value above the water quality standard threshold of 0.633 mg/L. Based on the trend of fluctuations in the two seasons, a model of equation Y = 3.979 + 0.814x was made with a significance value <α (0.05), which means that the two seasons positively correlated with the impact on fluctuations of water quality in the ponds. The results of the dynamic modelling analysis showed contradictions in water quality and nutrients during rainy and dry seasons. Water quality parameters in intensive L. vannamei cultivation during dry and rainy seasons fluctuated dynamically and differed according to the type of weather conditions and the current season.

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Machine Learning Based Statistical Tools Estimation for Rainfall Forecasting for Smart Cites

Nuseir,

Akour,

Alzoubi

et al. 2024

Studies in Big Data

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Variation of representative rainfall time series length for rainwater harvesting modelling in different climatic zones

Cited by 22 publications

References 34 publications

Influence of rainfall and design criteria on performance of rainwater harvesting systems placed in different Brazilian climatological conditions

Influence of rainfall and design criteria on performance of rainwater harvesting systems placed in different Brazilian climatological conditions

Water Quality Fluctuations in Shrimp Ponds During Dry and Rainy Seasons

Machine Learning Based Statistical Tools Estimation for Rainfall Forecasting for Smart Cites

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