Spatiotemporal Variability Analysis of Rainfall and Water Quality: Insights from Trend Analysis and Wavelet Coherence Approach

An understanding of the trend and relationship between rainfall patterns and water quality dynamics can provide valuable guidelines for the effective management of water resources. The aim of this study was to reveal the synchronous trends in rainfall and water quality and to explore the potential connection between seasonal variation in rainfall volume and the water quality index. This study scrutinised the seasonal temporal trends of rainfall and water quality parameters of three water supply reservoirs in the Toowoomba region of Australia by applying the modified Mann–Kendall (MMK) test and innovative trend analysis (ITA) methods from data collected over 22 years (2002–2022). The models showed a significant increasing trend of rainfall in two rainfall stations during autumn season. The water quality parameters, such as PO43−, exhibited a significant decreasing trend in all seasons in three reservoirs. On the other hand, the water quality index (WQI) showed a decreasing trend in the Cooby and Cressbrook reservoirs, excepting the Perseverance reservoir, which exhibited an increasing trend. In addition to the detection of trends, this study investigated the potential correlation between seasonal variation of rainfall volume and the water quality index using the wavelet transform coherence (WTC) method. The data of twelve rainfall stations were brought into this analysis. The WTC analysis displayed an apparent correlation between the water quality index and rainfall pattern for 70% of the rainfall stations across 8–16 periods. The highest coherency was noticed in 8–16 periods from 2002–2022, as observed at both the Cooby Creek rainfall station and in the WQI of the Cooby reservoir. This evaluation revealed the intertwined dynamics of rainfall patterns and water quality, providing a deeper understanding of their interdependence and implications, which might be useful for environmental and hydrological management practices.

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Publication Details

Journal
Geosciences
Published
2024-08-21
DOI
https://doi.org/10.3390/geosciences14080225
Citations
6
Primary Topic
Hydrological Forecasting Using AI
Type
article
Field-Weighted Citation Impact
1.23

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article

Spatiotemporal Variability Analysis of Rainfall and Water Quality: Insights from Trend Analysis and Wavelet Coherence Approach

Jahangir Alam, Dev Raj Paudyal, Sreeni Chadalavada, Syeda Zehan Farzana
6 citations
Geosciences
Hydrological Forecasting Using AI
1.23
article

Spatiotemporal Variability Analysis of Rainfall and Water Quality: Insights from Trend Analysis and Wavelet Coherence Approach

Jahangir Alam, Dev Raj Paudyal, Sreeni Chadalavada, Syeda Zehan Farzana
article en
6 citations

Abstract

An understanding of the trend and relationship between rainfall patterns and water quality dynamics can provide valuable guidelines for the effective management of water resources. The aim of this study was to reveal the synchronous trends in rainfall and water quality and to explore the potential connection between seasonal variation in rainfall volume and the water quality index. This study scrutinised the seasonal temporal trends of rainfall and water quality parameters of three water supply reservoirs in the Toowoomba region of Australia by applying the modified Mann–Kendall (MMK) test and innovative trend analysis (ITA) methods from data collected over 22 years (2002–2022). The models showed a significant increasing trend of rainfall in two rainfall stations during autumn season. The water quality parameters, such as PO43−, exhibited a significant decreasing trend in all seasons in three reservoirs. On the other hand, the water quality index (WQI) showed a decreasing trend in the Cooby and Cressbrook reservoirs, excepting the Perseverance reservoir, which exhibited an increasing trend. In addition to the detection of trends, this study investigated the potential correlation between seasonal variation of rainfall volume and the water quality index using the wavelet transform coherence (WTC) method. The data of twelve rainfall stations were brought into this analysis. The WTC analysis displayed an apparent correlation between the water quality index and rainfall pattern for 70% of the rainfall stations across 8–16 periods. The highest coherency was noticed in 8–16 periods from 2002–2022, as observed at both the Cooby Creek rainfall station and in the WQI of the Cooby reservoir. This evaluation revealed the intertwined dynamics of rainfall patterns and water quality, providing a deeper understanding of their interdependence and implications, which might be useful for environmental and hydrological management practices.

GeosciencesVol. 14(8)
University of Southern Queensland (AU), Murray Darling Basin Authority (AU)
University of Southern Queensland
Clean water and sanitation
Openalex Percentile: Top 23%
Hydrological Forecasting Using AI
1.23
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