Next Article in Journal
Extreme Drought Events over the Amazon Basin: The Perspective from the Reconstruction of South American Hydroclimate
Previous Article in Journal
Analysis and Optimization of Open Circulating Cooling Water System
Article Menu
Issue 11 (November) cover image

Export Article

Open AccessArticle
Water 2018, 10(11), 1593; https://doi.org/10.3390/w10111593

Improving Seasonal Forecasts for Basin Scale Hydrological Applications

School of Environmental Engineering, Technical University of Crete, 73100 Chania, Greece
*
Author to whom correspondence should be addressed.
Received: 17 October 2018 / Revised: 2 November 2018 / Accepted: 4 November 2018 / Published: 7 November 2018
(This article belongs to the Section Water Resources Management and Governance)
Full-Text   |   PDF [5269 KB, uploaded 7 November 2018]   |  

Abstract

Seasonal forecasting is a fast-growing climate prediction application that puts into practice the latest improvements in the climate modeling research. Skillful seasonal forecasts can drastically aid practical applications and productive sectors by reducing weather-related risks such as water availability. In this study two operational seasonal forecasting systems are tested in a water resource important watershed on the island of Crete. Hindcast precipitation and temperature data from the European Centre for Medium-Range Weather Forecasts (ECMWF) System 4 and Met Office GloSea5 systems are tested for their forecast skill up to seven months ahead. Data of both systems are downscaled and corrected for biases towards the observations. Different correction methods are applied and evaluated. Post-processed data from these methods are used as an input to the hydrological model HYPE, to provide streamflow forecasts. Results show that a prior adjustment of the two systems’ precipitation and temperature may improve their forecast skill. Adjusted GloSea5 forecasts are slightly better estimates than the corresponding forecasts based on System 4. The results show that both systems provide a skillful ensemble streamflow prediction for one month ahead, with the skill decreasing rapidly beyond that. Update of the initial state of HYPE results in the reduction of the variability of the ensemble flow predictions and improves the skill but only as far as two months of forecast. Finally, the two systems were tested for their ability to capture a limited number of historical streamflow drought events, with indications that GloSea5 has a slightly better skill. View Full-Text
Keywords: seasonal ensemble streamflow prediction; Met Office GloSea5; ECMWF System 4; bias correction; initial conditions effect; basin scale seasonal ensemble streamflow prediction; Met Office GloSea5; ECMWF System 4; bias correction; initial conditions effect; basin scale
Figures

Figure 1

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).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Grillakis, M.; Koutroulis, A.; Tsanis, I. Improving Seasonal Forecasts for Basin Scale Hydrological Applications. Water 2018, 10, 1593.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Water EISSN 2073-4441 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top