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Water 2015, 7(2), 599-614; doi:10.3390/w7020599

Short-Term Forecasting of Water Yield from Forested Catchments after Bushfire: A Case Study from Southeast Australia

1
Faculty of Agriculture and Environment, University of Sydney, 1 Central Avenue, Eveleigh, NSW 2015, Australia
2
Faculty of Engineering, Monash University, Clayton Campus, VIC 3800, Australia
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editor: Philipp Kraft
Received: 27 November 2014 / Accepted: 30 January 2015 / Published: 9 February 2015
(This article belongs to the Special Issue Hydro-Ecological Modeling)
View Full-Text   |   Download PDF [2295 KB, uploaded 9 June 2015]   |  

Abstract

Forested catchments in southeast Australia play an important role in supplying water to major cities. Over the past decades, vegetation cover in this area has been affected by major bushfires that in return influence water yield. This study tests methods for forecasting water yield after bushfire, in a forested catchment in southeast Australia. Precipitation and remotely sensed Normalized Difference Vegetation Index (NDVI) were selected as the main predictor variables. Cross-correlation results show that water yield with time lag equal to 1 can be used as an additional predictor variable. Input variables and water yield observations were set based on 16-day time series, from 20 January 2003 to 20 January 2012. Four data-driven models namely Non-Linear Multivariate Regression (NLMR), K-Nearest Neighbor (KNN), non-linear Autoregressive with External Input based Artificial Neural Networks (NARX-ANN), and Symbolic Regression (SR) were employed for this study. Results showed that NARX-ANN outperforms other models across all goodness-of-fit criteria. The Nash-Sutcliffe efficiency (NSE) of 0.90 and correlation coefficient of 0.96 at the training-validation stage, as well as NSE of 0.89 and correlation coefficient of 0.95 at the testing stage, are indicative of potentials of this model for capturing ecological dynamics in predicting catchment hydrology, at an operational level. View Full-Text
Keywords: multivariate regression; K-nearest neighbor; NARX-ANN; symbolic regression multivariate regression; K-nearest neighbor; NARX-ANN; symbolic regression
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Gharun, M.; Azmi, M.; Adams, M.A. Short-Term Forecasting of Water Yield from Forested Catchments after Bushfire: A Case Study from Southeast Australia. Water 2015, 7, 599-614.

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