Long-range forecasts of River Po discharges based on predictable solar activity and a fuzzy neural network model / Prévisions à long terme des débits du Fleuve Pô basées sur l’activité solaire prévisible et sur un modèle de réseau de neurones flou

Tomasino, Mario; Zanchettin, Davide; Traverso, Pietro

doi:10.1623/hysj.49.4.673.54431

Cited by 20 publications

(3 citation statements)

References 25 publications

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“…In fact, the two major historical drought periods recorded in the Po discharge time series, in the early 1940s and early 2000s ( Figure 1b), relate differently to interannual-to-decadal NAO variations [5,15]. Tomasino et al [16] used a statistical model based on the NAO index and other climatic indices as predictors in an attempt to seasonally forecast Po River discharges. It was specifically designed for winter, when anomalies in Euro-Atlantic modes of large-scale atmospheric variability are most persistent and predictable.…”

Section: Introductionmentioning

confidence: 99%

Robust Decadal Hydroclimate Predictions for Northern Italy Based on a Twofold Statistical Approach

et al. 2020

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The Mediterranean area belongs to the regions most exposed to hydroclimatic changes, with a likely increase in frequency and duration of droughts in the last decades. However, many climate records like, e.g., North Italian precipitation and river discharge records, indicate that significant decadal variability is often superposed or even dominates long-term hydrological trends. The capability to accurately predict such decadal changes is, therefore, of utmost environmental and social importance. Here, we present a twofold decadal forecast of Po River (Northern Italy) discharge obtained with a statistical approach consisting of the separate application and cross-validation of autoregressive models and neural networks. Both methods are applied to each significant variability component extracted from the raw discharge time series using Singular Spectrum Analysis, and the final forecast is obtained by merging the predictions of the individual components. The obtained 25-year forecasts robustly indicate a prominent dry period in the late 2020s/early 2030s. Our prediction provides information of great value for hydrological management, and a target for current and future near-term numerical hydrological predictions.

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Section: Introductionmentioning

confidence: 99%

Robust Decadal Hydroclimate Predictions for Northern Italy Based on a Twofold Statistical Approach

et al. 2020

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“…Climate sensitivity to solar variations (magnetic activity of sunspots and the Sun's orbital dynamics), which follows a frequency-dependent solar energy transfer function (Scafetta and West, 2006), can cause regional changes in the moisture-holding capacity of the atmosphere and alter the hydrological cycle and precipitation patterns (North, 2004). In river basins in particular, the integrated nature and the inertia of river and sediment discharges may reveal solarinduced pulses that amplify precipitation signals, while the links between solar activity and precipitation may be comparably weak (e.g., Milly and Wetherald, 2002;Tomasino et al, 2004).…”

Section: Influence Of Solar and Teleconnection Cyclesmentioning

confidence: 99%

Climate Patterns in the World’s Longest History of Storm-Erosivity: The Arno River Basin, Italy, 1000–2019 CE

Diodato¹,

Ljungqvist

Bellocchi

2021

Front. Earth Sci.

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Rainfall erosivity causes considerable environmental damage by driving soil loss. However, the long-term evolution of erosive forcing (over centennial to millennial time-scales) remains essentially unknown. Using a rainfall erosivity model (REMARB), this study simulates the variability of rainfall erosivity in Arno River Basin (ARB), Italy, a Mediterranean fluvial basin, for the period 1000–2019 CE resulting in the world’s longest time-series of erosivity. The annual estimates show a noticeable and increasing variability of rainfall erosivity during the Little Ice Age (∼1250–1849), especially after c. 1490, until the end of 18th century. During this cold period, erosive forcing reached ∼1600 MJ mm hm−2 h−1 yr−1 once every four years, and ∼3000 MJ mm hm−2 h−1 yr−1 once every 20 years. The extremes of rainfall erosivity (the 98th percentile) followed a similar increasing trend, with an acceleration of the hydrological hazard (erosivity per unit of rainfall) during the 20th century. The comparison of REMARB output with the sediment yield of the basin (1951–2010) confirmed the model’s ability to predict geomorphological effects in the ARB. Thus, our methodology could be applied to simulate erosivity in environmentally similar basins. A relationship has been identified between the Atlantic Multidecadal Variation and erosivity patterns, suggesting a role of North Atlantic circulation dynamics on the hydrology of central Italy’s fluvial basins.

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“…The relatively recent methods of investigation bring more information about the solar impact, as well as the response of the most important climatic factors that have an influence on the evolution of the discharge of the world's large rivers [20,21,24,27,32,39,[55][56][57][58][59][60][61][62][63]. These investigations are based on the application of robust techniques for systems with non-linear and non-stationary evolution, as well as the detection of the appropriate signal with the spatio-temporal scale of interest.…”

Section: Introductionmentioning

confidence: 99%

The Combined Effect of Atmospheric and Solar Activity Forcings on the Hydroclimate in Southeastern Europe

Mares,

Dobrica,

Demetrescu

et al. 2023

Atmosphere

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The purpose of this study was to analyze the influence of solar activity described by the sunspot number (SSN) on certain terrestrial variables that might impact the Southeastern European climate at different spatio-temporal scales (the North Atlantic Oscillation Index, NAOI, and the Greenland–Balkan Oscillation Index, GBOI—on a large scale; the Palmer Hydrological Drought Index, PHDI—on a regional scale; the Danube discharge at the Orsova (lower basin), Q, representative of the Southeastern European climate—on a local scale). The investigations were carried out for the 20th century using the annual and seasonal averages. To find the connections between terrestrial (atmospheric and hydrological) parameters and SSN, the wavelet coherence were used both globally and in the time–frequency domain. The analyses were carried out for the time series and considered simultaneously (in the same year or season), as well as with lags from 1 to 5 years between the analyzed variables. For the annual values, the type of correlation (linear/non-linear) was also tested using elements from information theory. The results clearly revealed non-linear links between the SSN and the terrestrial variables, even for the annual average values. By applying the wavelet transform to test the solar influence on the terrestrial variables, it was shown that the connections depend on both the terrestrial variable, as well as on the considered lags. Since, in the present study, they were analyzed using wavelet coherence, but only the cases in which the coherence was significant for almost the entire analyzed time interval (1901–2000) and the terrestrial variables were in phase or antiphase with the SSN were considered. Relatively few results had a high level of significance. The analysis of seasonal averages revealed significant information, in addition to the analysis of annual averages. Thus, for the climatic indices, the GBOI and NAOI, a significant coherence (>95%) with the solar activity, associated with the 22-year (Hale) solar cycle, was found for the autumn season for lag = 0 and 1 year. The Hale solar cycle, in the case of the PHDI, was present in the annual and summer season averages, more clearly at lag = 0. For the Danube discharge at Orsova, the most significant SSN signature (~95%) was observed at periods of 33 years (Brüuckner cycle) in the autumn season for lags from 0 to 3 years. An analysis of the redundancy–synergy index was also carried out on the combination of the terrestrial variables with the solar variable in order to find the best synergistic combination for estimating the Danube discharge in the lower basin. The results differed depending on the timescale and the solar activity. For the average annual values, the most significant synergistic index was obtained for the combination of the GBOI, PHDI, and SSN, considered 3 years before Q.

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Long-range forecasts of River Po discharges based on predictable solar activity and a fuzzy neural network model / Prévisions à long terme des débits du Fleuve Pô basées sur l’activité solaire prévisible et sur un modèle de réseau de neurones flou

Cited by 20 publications

References 25 publications

Robust Decadal Hydroclimate Predictions for Northern Italy Based on a Twofold Statistical Approach

Robust Decadal Hydroclimate Predictions for Northern Italy Based on a Twofold Statistical Approach

Climate Patterns in the World’s Longest History of Storm-Erosivity: The Arno River Basin, Italy, 1000–2019 CE

The Combined Effect of Atmospheric and Solar Activity Forcings on the Hydroclimate in Southeastern Europe

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