English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The current study aims to assess the potential of statistical multiple linear regression (MLR) techniques to develop long-term streamflow forecast models for New South Wales (NSW). While most of the past studies were concentrated on revealing the relationship between streamflow and single concurrent or lagged climate indices, this study intends to explore the combined impact of large-scale climate drivers. Considering their influences on the streamflow of NSW, several major climate drivers – IPO (Inter Decadal Pacific Oscillation)/PDO (Pacific Decadal Oscillation), IOD (Indian Ocean Dipole) and ENSO (El Nino-Southern Oscillation) are selected. Single correlation analysis is exploited as the basis for selecting different combinations of input variables for developing MLR models to examine the extent of the combined impacts of the selected climate drivers on forecasting spring streamflow several months ahead. The developed models with all the possible combinations show significantly good results for all selected 12 stations in terms of Pearson correlation (r), root mean square error (RMSE), mean absolute error (MAE) and Willmott index of agreement (d). For each region, the best model with lower errors provides statistically significant maximum correlation which ranges from 0.51 to 0.65.

Assessing the predictability of MLR models for long-term streamflow using lagged climate indices as predictors: a case study of NSW (Australia)