What is it about?
Understanding long-term seasonal or annual or inter-annual rainfall variability and its relationship with large-scale atmospheric variables (LSAVs) is important for water resource planning and management. In this study, rainfall forecasting models using the artificial neural network technique were developed to forecast seasonal rainfall and determine the effects of future climate on seasonal rainfall.
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Why is it important?
We found that Sea Level Pressure, Air Temperature and Wind are the most significant predictors for seasonal rainfall forecasting over Ping River Basin, Thailand. Future variability (i.e. intensity and phase) in seasonal rainfall was identified. Proposed ANN model is able to forecast the rainfall with different leads time for four seasons considered.
Perspectives
I learnt that its always a challenge to convey the message to audience. Future is always uncertain, but forecasting gives information up to some extent with reliable accuracy. This article will be benefited for relevant key sectors in water resources planning and management.
Ms Jeewanthi G. Sirisena
UNESCO-IHE, Delft Netherlands
Read the Original
This page is a summary of: Incorporating large-scale atmospheric variables in long-term seasonal rainfall forecasting using artificial neural networks: an application to the Ping Basin in Thailand, Hydrology Research, July 2016, IWA Publishing,
DOI: 10.2166/nh.2016.212.
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