Datta, Bithin, Roy, Dilip Kumar, Kelley, Jonathan J., and Bradley, Stevens (2021) Prediction of impending drought scenarios based on surface and subsurface parameters in a selected region of tropical Queensland, Australia. Journal of Water Resource and Protection, 13 (8). pp. 605-631.

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Abstract

Droughts occur in all climatic regions around the world costing a large expense to global economies. Reasonably accurate prediction of drought events helps water managers proper planning for utilization of limited water resources and distribution of available waters to different sectors and avoid catastrophic consequences. Therefore, a means to create a simplistic approach for forecasting drought conditions with easily accessible parameters is highly desirable. This study proposes and evaluates newly developed accurate prediction models utilizing various hydrologic, meteorological, and geohydrology parameters along with the use of Artificial Neural Network (ANN) models with various forecast lead times. The present study develops a multitude of forecasting models to predict drought indices such as the Standard Precipitation Index with a lead-time of up to 6 months, and the Soil Moisture Index with a lead-time of 3 months. Furthermore, prediction models with the capability of approximating surface and groundwater storage levels including the Ross River Dam level have been developed with relatively high accuracy with a lead-time of 3 months. The results obtained from these models were compared to current values, revealing that ANN based approach can be used as a simple and effective predictive model that can be utilized for prediction of different aspects of drought scenarios in a typical study area like Townsville, North Queensland, Australia which had suffered severe recent drought conditions for almost six recent years (2014 to early 2019).

Item ID:	72657
Item Type:	Article (Research - C1)
ISSN:	1945-3108
Copyright Information:	Copyright © 2021 by author(s) and Scientific Research Publishing Inc. This work is licensed under the Creative Commons Attribution International License (CC BY 4.0)
Date Deposited:	27 Feb 2022 23:54
FoR Codes:	40 ENGINEERING > 4005 Civil engineering > 400513 Water resources engineering @ 100%
SEO Codes:	18 ENVIRONMENTAL MANAGEMENT > 1803 Fresh, ground and surface water systems and management > 180399 Fresh, ground and surface water systems and management not elsewhere classified @ 100%
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