Learning Hydrology by an Australian High School Student

This paper presents the learning experience of a high school student (first author) who was working with an expert hydrologist (third author) for his year 10 industry experience. A student-centred approach was adopted to teach hydrology to the student. In this case study, it was assumed that a bridge was to be designed at the Allyn River, at Halton, New South Wales, Australia (station ID 210022). The task involved abstraction of annual maximum flood (AMF) data from the WaterNSW website and several candidate probability distributions to the AMF data using EasyFit and R software. The ranking of the candidate probability distributions was based on three goodness of fit tests (Kolmogorov Smirnov, Anderson Darling and Chi Squared), which enabled selection of the top five distributions for each test. It was concluded that the Generalised Extreme Value distribution was the best fitting probability distribution for the AMF data at the site. It was found that a student-centred approach can aid in learning hydrology, which is regarded as a difficult subject due to its empirical nature.

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Publication Details

Published
2024-11-23
DOI
https://doi.org/10.71427/icaeed2025/19
Primary Topic
Water Quality Monitoring Technologies
Type
article
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article

Learning Hydrology by an Australian High School Student

Sumya Rahman, Shaista Khan Shaista Khan, Atta Rahman
Water Quality Monitoring Technologies
article

Learning Hydrology by an Australian High School Student

Sumya Rahman, Shaista Khan Shaista Khan, Atta Rahman
article en

Abstract

This paper presents the learning experience of a high school student (first author) who was working with an expert hydrologist (third author) for his year 10 industry experience. A student-centred approach was adopted to teach hydrology to the student. In this case study, it was assumed that a bridge was to be designed at the Allyn River, at Halton, New South Wales, Australia (station ID 210022). The task involved abstraction of annual maximum flood (AMF) data from the WaterNSW website and several candidate probability distributions to the AMF data using EasyFit and R software. The ranking of the candidate probability distributions was based on three goodness of fit tests (Kolmogorov Smirnov, Anderson Darling and Chi Squared), which enabled selection of the top five distributions for each test. It was concluded that the Generalised Extreme Value distribution was the best fitting probability distribution for the AMF data at the site. It was found that a student-centred approach can aid in learning hydrology, which is regarded as a difficult subject due to its empirical nature.

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Water Quality Monitoring Technologies
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Learning Hydrology by an Australian High School Student — Sumya Rahman, Shaista Khan Shaista Khan, et al. · (2024) | TGRS Research Map | TGRS