1,721,487 research outputs found
Large sample behaviors of the generalized likelihood uncertainty estimation (GLUE) in assessing the uncertainty of rainfall-runoff simulations
No full textSeveral methods have been recently proposed for quantifying the uncertainty of hydrological models. These techniques are based upon different hypotheses, are diverse in nature, and produce outputs that can significantly differ in some cases. One of the favored methods for uncertainty assessment in rainfall-runoff modeling is the generalized likelihood uncertainty estimation (GLUE). However, some fundamental questions related to its application remain unresolved. One such question is that GLUE relies on some explicit and implicit assumptions, and it is not fully clear how these may affect the uncertainty estimation when referring to large samples of data. The purpose of this study is to address this issue by assessing how GLUE performs in detecting uncertainty in the simulation of long series of synthetic river flows. The study aims to (1) discuss the hypotheses underlying GLUE and derive indications about their effects on the uncertainty estimation, and (2) compare the GLUE prediction limits with a large sample of data that is to be simulated in the presence of known sources of uncertainty. The analysis shows that the prediction limits provided by GLUE do not necessarily include a percentage close to their confidence level of the observed data. In fact, in all the experiments, GLUE underestimates the total uncertainty of the simulation provided by the hydrological model
Generazione di scenari tecnici di clima futuro per lo studio di eventi di magra in presenza di cambiamento climatico
No full textNo abstrac
La spada di damocle della piena impossibile
No full textExtremely large floods are often considered virtually ‘impossible’ because they exceed our expectation based on historical experience. However, in reality, events often occur that are a priori considered impossible, causing enormous damage also as a result of their supposed improbability. Four reasons for the low importance attached to these floods are analysed here, including physical (e.g. climate change), psychological, socioeconomic and combined reasons. It is argued that the risk of an ‘impossible’ flood may be greater than one thinks, and potential solutions are proposed
BLUECAT. Un metodo innovativo per stimare l’incertezza di previsioni di deflussi fluviali
No full textWe present a new method for simulating and predicting hydrologic variables and in particular river flows,
which is rooted in the probability theory and conceived in order to provide a reliable quantification of its
uncertainty for operational applications. Our approach, which we term with the acronym "Bluecat", results
from a theoretical and numerical development, and is conceived to make a transparent and intuitive use of
the observations. Therefore, Bluecat makes use of a rigorous theory while at the same time proofing the
concept that environmental resources should be managed by making the best use of empirical evidence and
experience. We provide an open and user friendly software to apply the method to the simulation and
prediction of river flows and test Bluecat's reliability for operational applications
Negligent killing of scientific concepts: the stationarity case
No full textIn scientific vocabulary, the term process is used to denote change in time. Even a stationary process describes a system changing in time, rather than a static one that keeps a constant state all the time. However, this is often missed, which has led to misuse of the term nonstationarity as a synonym of change. A simple rule to avoid such misuse is to answer the question: can the change be predicted in deterministic terms? Only if the answer is positive is it legitimate to invoke nonstationarity. In addition, we should have in mind that models are made to simulate the future rather than to describe the past; the past is characterized by observations (data). Usually future changes are not deterministically predictable and thus the models should, on the one hand, be stationary and, on the other hand, describe in stochastic terms the full variability, originating from all agents of change. Even if the past evolution of the process of interest contains changes explainable in deterministic terms (e.g. urbanization), it is better to describe the future conditions in stationary terms, after stationarizing the past observations, i.e. adapting them to represent the future conditions
Uncertainty estimation for environmental multimodel predictions: The BLUECAT approach and software
Full text linkAn extension of the BLUECAT approach and software for uncertainty assessment of environmental predictions is presented, allowing the application to multimodel outputs. BLUECAT operates by transforming a point prediction provided by deterministic models to a corresponding stochastic formulation, thereby allowing the estimation of a bias corrected expected value along with confidence limits. In this paper we also propose to use BLUECAT for model selection in the context of multimodel predictions, by using a measure of uncertainty as selection criterion. We emphasise here the value of BLUECAT for gaining an improved understanding of the underlying environmental systems and multimodel combination. Two examples of applications are presented, highlighting the benefits attainable through uncertainty driven integration of several prediction models. These case studies can be reproduced through the BLUECAT software, that is available in the public domain along with help facilities and instructions
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