Trends in Water Temperature: A Case Study for New South Wales, Australia

River water temperature (Tw) plays an important role for balancing the ecosystem and biodiversity of the aquatic species. For this, it is very important to investigate the relationship between Tw and factors influencing it. This study used 24 years of data from five stations from different river basins within New South Wales (NSW) that were accessed from WaterNSW and Australian Bureau of Meteorology (BOM). This paper investigates the factors influencing Tw by the application of statistical methods like multiple linear regression (MLR) analysis and Man-Kendall (MK) test. MLR analysis was used to find the relationship between Tw and other independent variables like water level (WL), discharge (Q), electrical conductivity (EC) and air temperature (Ta), and MK test was used to find the trends of the variables over the time. It was found that for Stations 419003, 210055 and 410073, the group of predictors significantly explained the variability in Tw based on regression statistics. For annual minimum data series, none of the stations showed significance at the 0.10 level. Station 210055 showed highest co-efficient of determination (R2 ) value indicating better explanatory power. The MK test results revealed that for both the annual maximum and minimum temperature series, Station 409061 had significant trend for most of the selected variables and Stations 418058, 210055 and 410073 had significant trend for one variable only. Further study is being conducted covering a greater number of stations across NSW.

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Publication Details

Published
2024-11-23
DOI
https://doi.org/10.71427/icaeed2025/22
Primary Topic
Hydrological Forecasting Using AI
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article
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article

Trends in Water Temperature: A Case Study for New South Wales, Australia

Kishor Chandra Joshi, Xiao Pan, Atta Rahman
Hydrological Forecasting Using AI
article

Trends in Water Temperature: A Case Study for New South Wales, Australia

Kishor Chandra Joshi, Xiao Pan, Atta Rahman
article en

Abstract

River water temperature (Tw) plays an important role for balancing the ecosystem and biodiversity of the aquatic species. For this, it is very important to investigate the relationship between Tw and factors influencing it. This study used 24 years of data from five stations from different river basins within New South Wales (NSW) that were accessed from WaterNSW and Australian Bureau of Meteorology (BOM). This paper investigates the factors influencing Tw by the application of statistical methods like multiple linear regression (MLR) analysis and Man-Kendall (MK) test. MLR analysis was used to find the relationship between Tw and other independent variables like water level (WL), discharge (Q), electrical conductivity (EC) and air temperature (Ta), and MK test was used to find the trends of the variables over the time. It was found that for Stations 419003, 210055 and 410073, the group of predictors significantly explained the variability in Tw based on regression statistics. For annual minimum data series, none of the stations showed significance at the 0.10 level. Station 210055 showed highest co-efficient of determination (R2 ) value indicating better explanatory power. The MK test results revealed that for both the annual maximum and minimum temperature series, Station 409061 had significant trend for most of the selected variables and Stations 418058, 210055 and 410073 had significant trend for one variable only. Further study is being conducted covering a greater number of stations across NSW.

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Hydrological Forecasting Using AI
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Trends in Water Temperature: A Case Study for New South Wales, Australia — Kishor Chandra Joshi, Xiao Pan, et al. · (2024) | TGRS Research Map | TGRS