Hydrological characteristics of Australia: national catchment classification and regional relationships

Jaffrés, Jasmine B.D.; Cuff, Ben; Cuff, Chris; Knott, Matthew; Rasmussen, Cecily

doi:10.1016/j.jhydrol.2022.127969

Cited by 7 publications

(4 citation statements)

References 28 publications

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“…This finding is significant because C5 cyclones can produce high rainfall over some of Australia's largest catchments. Compared to the smaller coastal catchments, those located west of the Great Dividing Range can produce significant floods with lower rainfall totals (due to the size of the drainage basin and topography; Jaffrés et al, 2021Jaffrés et al, , 2022. These regions (e.g.…”

Section: Discussionmentioning

confidence: 99%

Characterizing Australia's east coast cyclones (1950–2019)

Gray

Verdon‐Kidd

Jaffrés

et al. 2023

Intl Journal of Climatology

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East coast cyclones (ECCs) provide an essential reprieve from dry periods across eastern Australia. They also deliver flood‐producing rains with significant economic, social and environmental impacts. Assessing and comparing the influence of different types of cyclones is hindered by an incomplete understanding of ECC typology, given their widely variable spatial and temporal characteristics. This study employs a track‐clustering method (probabilistic, curve‐aligned regression model) to identify key cyclonic pathways for ECCs from 1950 to 2019. Six spatially independent clusters were successfully distinguished and further sub‐classified (coastal, continental and tropical) based on their genesis location. The seasonality and long‐term variability, intensity (maximum Laplacian value ± 2 days) and event‐based rainfall were then evaluated for each cluster to quantify the impact of these lows on Australia. The highest quantity of land‐based rainfall per event is associated with the tropical cluster (Cluster 6), whereas widespread rainfall was also found to occur in the two continental clusters (clusters 4 and 5). Cyclone tracks orientated close to the coast (clusters 1, 2 and 3) were determined to be the least impactful in terms of rainfall and intensity, despite being the most common cyclone type. In terms of interannual variability, sea surface temperature anomalies suggest an increased cyclone frequency for clusters 1 (austral winter) and 4 (austral spring) during a central Pacific El Niño. Furthermore, cyclone incidence during IOD‐negative conditions was more pronounced in winter for clusters 1, 2, 3— and clusters 4 and 5 in spring. All cyclones also predominantly occurred in SAM‐positive conditions. However, winter ECCs for clusters 1 and 3 had a higher frequency in SAM‐negative. This new typology of ECCs via spatial clustering provides crucial insights into the systems that produce extreme rainfall across eastern Australia and should be used to inform future hazard management of cyclone events.

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Section: Discussionmentioning

confidence: 99%

Characterizing Australia's east coast cyclones (1950–2019)

Gray

Verdon‐Kidd

Jaffrés

et al. 2023

Intl Journal of Climatology

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“…Large-sample hydrology (LSH) yields reliable insights into hydrological processes and models by leveraging comprehensive basin datasets. Recent review studies 1 have underscored the fundamental role of these datasets in a wide range of hydrological investigations, including catchment classification 2 , assessments of terrestrial water storage and extreme events 3 , evaluations of hydrological models 4 , benchmarking 5 , parameter estimation 6 , regionalisation through machine learning algorithms 7 , analyses of human impacts on hydrology 8 , streamflow forecasting 9 , exploration of climate change impacts 10 , and assessments of data and model uncertainties 11 .…”

Section: Background and Summarymentioning

confidence: 99%

BULL Database – Spanish Basin attributes for Unravelling Learning in Large-sample hydrology

Senent-Aparicio,

Castellanos-Osorio,

Segura-Méndez

et al. 2024

Sci Data

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We present a novel basin dataset for large-sample hydrological studies in Spain. BULL comprises data for 484 basins, combining hydrometeorological time series with several attributes related to geology, soil, topography, land cover, anthropogenic influence and hydroclimatology. Thus, we followed recommendations in the CARAVAN initiative for generating a truly open global hydrological dataset to collect these attributes. Several climatological data sources were used, and their data were validated by hydrological modelling. One of the main novelties of BULL compared to other national-scale datasets is the analysis of the hydrological alteration of the basins included in this dataset. This aspect is critical in countries such as Spain, which are characterised by rivers suffering from the highest levels of anthropisation. The BULL dataset is freely available at https://zenodo.org/records/10605646.

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“…Catchment classification plays an important role in many hydrologic, environmental, and ecologic applications. Such applications include the following: (1) hydrologic regionalization for extrapolation of information [ 1 , 2 , 3 , 4 , 5 , 6 ]; (2) identification of model complexity [ 7 , 8 , 9 ]; (3) prediction and model parameterization in ungauged catchments [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ]; (4) predictions under changed flow conditions [ 6 , 11 , 19 , 20 , 21 , 22 ]; (5) assessment of environmental flows [ 23 , 24 , 25 , 26 , 27 ]; and (6) eco-hydrologic classification [ 28 , 29 ].…”

Section: Introductionmentioning

confidence: 99%

“…A review of the literature suggests that catchments can be classified based on river morphology [ 30 ], river/flow regimes [ 1 , 25 ], hydrologic similarity indexes [ 3 , 5 , 6 , 14 , 16 , 18 , 31 , 32 , 33 , 34 , 35 ], hydroclimatic factors [ 11 , 36 ], ecohydrologic factors [ 28 , 33 , 37 , 38 , 39 ], and other factors. Many methods have been employed to use these bases for catchment classification, including regression-based methods [ 3 , 8 , 33 , 40 ], clustering [ 5 , 6 , 8 , 16 , 17 , 19 , 21 , 33 , 34 , 35 , 36 ], flow duration curve analysis [ 12 , 13 , 41 ], principal component analysis [ 3 , 6 , 18 , 29 ], and process-based modeling [ 11 ], among others. Applications of the concepts of community structure within the context of complex networks for catchment classification are also starting to emerge [ 42 , 43 ].…”

Section: Introductionmentioning

confidence: 99%

A Nonlinear Local Approximation Approach for Catchment Classification

Khan,

Sivakumar

2024

Entropy

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Catchment classification plays an important role in many applications associated with water resources and environment. In recent years, several studies have applied the concepts of nonlinear dynamics and chaos for catchment classification, mainly using dimensionality measures. The present study explores prediction as a measure for catchment classification, through application of a nonlinear local approximation prediction method. The method uses the concept of phase-space reconstruction of a time series to represent the underlying system dynamics and identifies nearest neighbors in the phase space for system evolution and prediction. The prediction accuracy measures, as well as the optimum values of the parameters involved in the method (e.g., phase space or embedding dimension, number of neighbors), are used for classification. For implementation, the method is applied to daily streamflow data from 218 catchments in Australia, and predictions are made for different embedding dimensions and number of neighbors. The prediction results suggest that phase-space reconstruction using streamflow alone can provide good predictions. The results also indicate that better predictions are achieved for lower embedding dimensions and smaller numbers of neighbors, suggesting possible low dimensionality of the streamflow dynamics. The classification results based on prediction accuracy are found to be useful for identification of regions/stations with higher predictability, which has important implications for interpolation or extrapolation of streamflow data.

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Hydrological characteristics of Australia: national catchment classification and regional relationships

Cited by 7 publications

References 28 publications

Characterizing Australia's east coast cyclones (1950–2019)

Characterizing Australia's east coast cyclones (1950–2019)

BULL Database – Spanish Basin attributes for Unravelling Learning in Large-sample hydrology

A Nonlinear Local Approximation Approach for Catchment Classification

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