Population growth, water scarcity and climate change are three major factors making the understanding of variations in water availability increasingly important. Therefore, reliable medium-to-long range forecasts of streamflows are essential to the development of water management policies. Recent statistical modeling efforts have been focusing on seasonal to inter-annual flow forecasts based on the teleconnection between "at-site" hydrology and large-scale low frequency fluctuations such as El Niño Southern Oscillation (ENSO). In order to develop reliable statistical prediction models, the ENSO influence on physical processes at the basin scale has to be first verified, and inputs to the forecast model (predictors) have to be appropriately identified. This thesis proposes a novel procedure for detecting the impact of ENSO on hydro-meteorological dynamics at the basin scale, and assessing the potential of building medium-to-long range statistical streamflow prediction models incorporating ENSO indicators. The procedure includes not only traditional graphical and statistical data analyses, but also Input Variable Selection (IVS) techniques that are employed in order to find the most relevant forcings of streamflow variability and to derive a predictive model based on the inputs selected. The procedure is first tested on the Columbia River (USA) and the Williams River (Australia), where ENSO influence is well-documented in literature, and then is adopted on the unexplored Red River basin in Vietnam. Results show that Input Variable Selection outcomes are consistent with those from other established methods and provide an additional analysis in terms of streamflow forecast potential. Tests conducted over the two benchmarking cases confirm that ENSO seems to be a promising factor for obtaining streamflow forecasts months in advance, while experiments on the Red River basin show that the ENSO influence is modest, inducing little effects on the hydro-meteorological processes of the basin. ENSO indicators result weak at predicting streamflows in the short-term, but they are associated to slightly better results when considering longer lead-times.

ENSO teleconnection patterns on large scale water resources systems

BELTRAME, LUDOVICA;CARBONIN, DANIELE
2012/2013

Abstract

Population growth, water scarcity and climate change are three major factors making the understanding of variations in water availability increasingly important. Therefore, reliable medium-to-long range forecasts of streamflows are essential to the development of water management policies. Recent statistical modeling efforts have been focusing on seasonal to inter-annual flow forecasts based on the teleconnection between "at-site" hydrology and large-scale low frequency fluctuations such as El Niño Southern Oscillation (ENSO). In order to develop reliable statistical prediction models, the ENSO influence on physical processes at the basin scale has to be first verified, and inputs to the forecast model (predictors) have to be appropriately identified. This thesis proposes a novel procedure for detecting the impact of ENSO on hydro-meteorological dynamics at the basin scale, and assessing the potential of building medium-to-long range statistical streamflow prediction models incorporating ENSO indicators. The procedure includes not only traditional graphical and statistical data analyses, but also Input Variable Selection (IVS) techniques that are employed in order to find the most relevant forcings of streamflow variability and to derive a predictive model based on the inputs selected. The procedure is first tested on the Columbia River (USA) and the Williams River (Australia), where ENSO influence is well-documented in literature, and then is adopted on the unexplored Red River basin in Vietnam. Results show that Input Variable Selection outcomes are consistent with those from other established methods and provide an additional analysis in terms of streamflow forecast potential. Tests conducted over the two benchmarking cases confirm that ENSO seems to be a promising factor for obtaining streamflow forecasts months in advance, while experiments on the Red River basin show that the ENSO influence is modest, inducing little effects on the hydro-meteorological processes of the basin. ENSO indicators result weak at predicting streamflows in the short-term, but they are associated to slightly better results when considering longer lead-times.
GALELLI, STEFANO
ING I - Scuola di Ingegneria Civile, Ambientale e Territoriale
18-dic-2013
2012/2013
Tesi di laurea Magistrale
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10589/88205