As the amount of the attention payed to reservoir’s sedimentation increases, the need for its management grows even more. The objective of the thesis is to evaluate the Specific Sediment Yield (SSY) at the Italian dam’s reservoirs which can approximately provide a measure of the reservoir’s sedimentation at the Italian dams. By collecting data from 50 italian dams, it was possible to estimate the measure of the volume of sediments inside the reservoirs. This measure was then used to define the Specific Sediment Yield observed at the dam’s reservoirs. Objectively, the available data were just too few to implement any existing model as well as to compute the estimates for the observed values of SSY. Instead, it was the application of the Revised Universal Soil Loss Equation (RUSLE model) that made possible the computation of the average annual soil loss for the considered basins. In fact, this new information in addition to the observed SSY at the dam’s reservoirs, was used to define a dataset of observed values of Sediment Delivery Ratio (SDR). Consequently, a multiple linear regression analysis was applied by selecting and employing a set of 12 predictive variables suitable for profiling the basins. Specifically, the two samples used in the multiple linear regression analysis comprise respectively the basins geographically located in the Alps and those in the Appennines.
Tanto maggiore è la rilevanza di natura globale del fenomeno dell’interrimento degli invasi artificiali, quanto più necessaria sarà la sua gestione. L’obiettivo di questa tesi è quello di valutare l’apporto solido alle dighe italiane, che fornisce, sebbene attraverso approssimazioni, una misura del fenomeno della sedimentazione nelle dighe italiane. Dall’acquisizione di dati relativi a 50 dighe italiane, è stato possibile ricavare una misura del volume di interrimento, utile per definire la quantità di apporto di sedimenti medio annuo osservato in ingresso alle dighe. I dati raccolti, tuttavia, non erano sufficienti per operare una modellazione dell’apporto solido alle dighe analizzate tramite l’impiego di modelli già esistenti. Tramite l’applicazione quindi, della Revised Universal Soil Loss Equation (modello RUSLE), è stata ricavata una stima della perdita di suolo media annua sull’area dei bacini considerati. Dopo la definizione del dataset di osservazioni di SDR, si è applicata un’analisi di regressione multilineare partendo da un set di 12 variabili esplicative scelte per caratterizzare i bacini esaminati. Più specificamente, l’analisi è stata condotta in parallelo su due differenti campioni di Sediment Delivery Ratio (SDR), uno rappresentativo dei bacini collocati nell’area geografica caratterizzata dalla presenza delle Alpi e l’altro rappresentativo dei bacini collocati nell’area geografica caratterizzata dalla presenza degli Appennini.
Valutazione dell'apporto di sedimenti alle dighe italiane
GRANATA, GIANLUCA;BIAGINI, CHIARA
2019/2020
Abstract
As the amount of the attention payed to reservoir’s sedimentation increases, the need for its management grows even more. The objective of the thesis is to evaluate the Specific Sediment Yield (SSY) at the Italian dam’s reservoirs which can approximately provide a measure of the reservoir’s sedimentation at the Italian dams. By collecting data from 50 italian dams, it was possible to estimate the measure of the volume of sediments inside the reservoirs. This measure was then used to define the Specific Sediment Yield observed at the dam’s reservoirs. Objectively, the available data were just too few to implement any existing model as well as to compute the estimates for the observed values of SSY. Instead, it was the application of the Revised Universal Soil Loss Equation (RUSLE model) that made possible the computation of the average annual soil loss for the considered basins. In fact, this new information in addition to the observed SSY at the dam’s reservoirs, was used to define a dataset of observed values of Sediment Delivery Ratio (SDR). Consequently, a multiple linear regression analysis was applied by selecting and employing a set of 12 predictive variables suitable for profiling the basins. Specifically, the two samples used in the multiple linear regression analysis comprise respectively the basins geographically located in the Alps and those in the Appennines.File | Dimensione | Formato | |
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2020_04_Biagini_Granata.pdf
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https://hdl.handle.net/10589/164801