Record Details

Ensemble forecasting of short-term system scale irrigation demands using real-time flow data and numerical weather predictions

MELSpace

View Archive Info
 
 
Field Value
 
Title Ensemble forecasting of short-term system scale irrigation demands using real-time flow data and numerical weather predictions
 
Creator Perera, Kushan C.
 
Contributor Western, Andrew W.
Robertson, David
George, Biju Alummoottil
Nawarathna, Bandara
 
Description Irrigation demands fluctuate in response to weather variations and a range of irrigation management decisions, which creates challenges for water supply system operators. This paper develops a method for real-time ensemble forecasting of irrigation demand and applies it to irrigation command areas of various sizes for lead times of 1 to 5 days. The ensemble forecasts are based on a deterministic time series model coupled with ensemble representations of the various inputs to that model. Forecast inputs include past flow, precipitation, and potential evapotranspiration. These inputs are variously derived from flow observations from a modernized irrigation delivery system; short-term weather forecasts derived from numerical weather prediction models and observed weather data available from automatic weather stations. The predictive performance for the ensemble spread of irrigation demand was quantified using rank histograms, the mean continuous rank probability score (CRPS), the mean CRPS reliability and the temporal mean of the ensemble root mean squared error (MRMSE). The mean forecast was evaluated using root mean squared error (RMSE), Nash–Sutcliffe model efficiency (NSE) and bias. The NSE values for evaluation periods ranged between 0.96 (1 day lead time, whole study area) and 0.42 (5 days lead time, smallest command area). Rank histograms and comparison of MRMSE, mean CRPS, mean CRPS reliability and RMSE indicated that the ensemble spread is generally a reliable representation of the forecast uncertainty for short lead times but underestimates the uncertainty for long lead times.
 
Date 2016-06-24
2017-02-23T13:09:27Z
2017-02-23T13:09:27Z
 
Type Journal Article
 
Identifier https://mel.cgiar.org/reporting/download/hash/8JLLQV1l
Kushan C. Perera, Andrew W. Western, David Robertson, Biju Alummoottil George, Bandara Nawarathna. (24/6/2016). Ensemble forecasting of short-term system scale irrigation demands using real-time flow data and numerical weather predictions. Water Resource Research, 52 (6), pp. 4801-4822.
https://hdl.handle.net/20.500.11766/5906
Limited access
 
Language en
 
Rights CC-BY-NC-4.0
 
Format PDF
 
Publisher American Geophysical Union (AGU)
 
Source Water Resource Research;52,(2016) Pagination 4801-4822