1,721,756 research outputs found
Estimation of the design rainfall in ungauged sites using novel regionalization approaches: an application over Thessaly region, Greece
Full text linkIn this work we apply two different rainfall regionalization techniques over Thessaly region (Greece), an area of about 13,700 km2 with different topographic and climatological characteristics, to estimate design rainfall in ungauged sites. The first methodology is the Patched Kriging technique (Libertino et al., 2018), a year-by-year application of the ordinary kriging, followed by a bias-correction procedure developed to restore the variance of the sample distribution reduced during the spatial interpolation step. The hypothesis of stationarity of the second order must be satisfied to apply the ordinary kriging: it is therefore necessary to remove, from the measured values, any dependencies on the elevation via a detrending operation. The second methodology is instead based on a bilinear surface smoothing method (Malamos and Koutsoyiannis, 2016). Elevation is incorporated into the model as an additional explanatory variable, being available with a denser sampling compared to that of the rainfall one. In both cases, rainfall quantiles are estimated using the method of K-moments (Koutsoyiannis, 2019), an advanced estimation framework that allows reliable high-order moment estimation considering space dependence. Both methodologies differ from the classical rainfall regional frequency analysis being developed to take advantage of all the information available for the area under investigation, even those included in short and fragmented time series
Statistical analysis of hydroclimatic time series: Uncertainty and insights
No full textToday, hydrologic research and modeling depends largely on climatological inputs, whose physical and statistical behavior are the subject of many debates in the scientific community. A relevant ongoing discussion is focused on long-term persistence (LTP), a natural behavior identified in several studies of instrumental and proxy hydroclimatic time series, which, nevertheless, is neglected in some climatological studies. LTP may reflect a long-term variability of several factors and thus can support a more complete physical understanding and uncertainty characterization of climate. The implications of LTP in hydroclimatic research, especially in statistical questions and problems, may be substantial but appear to be not fully understood or recognized. To offer insights on these implications, we demonstrate by using analytical methods that the characteristics of temperature series, which appear to be compatible with the LTP hypothesis, imply a dramatic increase of uncertainty in statistical estimation and reduction of significance in statistical testing, in comparison with classical statistics. Therefore we maintain that statistical analysis in hydroclimatic research should be revisited in order not to derive misleading results and simultaneously that merely statistical arguments do not suffice to verify or falsify the LTP ( or another) climatic hypothesis
Climate Extrapolations in Hydrology: The Expanded Bluecat Methodology
Full text linkBluecat is a recently proposed methodology to upgrade a deterministic model (D-model) into a stochastic one (S-model), based on the hypothesis that the information contained in a time series of observations and the concurrent predictions made by the D-model is sufficient to support this upgrade. The prominent characteristics of the methodology are its simplicity and transparency, which allow its easy use in practical applications, without sophisticated computational means. In this paper, we utilize the Bluecat methodology and expand it in order to be combined with climate model outputs, which often require extrapolation out of the range of values covered by observations. We apply the expanded methodology to the precipitation and temperature processes in a large area, namely the entire territory of Italy. The results showcase the appropriateness of the method for hydroclimatic studies, as regards the assessment of the performance of the climate projections, as well as their stochastic conversion with simultaneous bias correction and uncertainty quantification
A blueprint for process-based modeling of uncertain hydrological systems
No full textWe present a probability based theoretical scheme for building process-based models of uncertain hydrological systems, thereby unifying hydrological modeling and uncertainty assessment. Uncertainty for the model output is assessed by estimating the related probability distribution via simulation, thus shifting from one to many applications of the selected hydrological model. Each simulation is performed after stochastically perturbing input data, parameters and model output, this latter by adding random outcomes from the population of the model error, whose probability distribution is conditioned on input data and model parameters. Within this view randomness, and therefore uncertainty, is treated as an inherent property of hydrological systems. We discuss the related assumptions as well as the open research questions. The theoretical framework is illustrated by presenting real-world and synthetic applications. The relevant contribution of this study is related to proposing a statistically consistent simulation framework for uncertainty estimation which does not require model likelihood computation and simplification of the model structure. The results show that uncertainty is satisfactorily estimated although the impact of the assumptions could be significant in conditions of data scarcity
Bluecat: A Local Uncertainty Estimator for Deterministic Simulations and Predictions
Full text linkWe present a new method for simulating and predicting hydrologic variables with uncertainty assessment and provide example applications to river flows. The method is identified with the acronym “Bluecat” and is based on the use of a deterministic model which is subsequently converted to a stochastic formulation. The latter provides an adjustment on statistical basis of the deterministic prediction along with its confidence limits. The distinguishing features of the proposed approach are the ability to infer the probability distribution of the prediction without requiring strong hypotheses on the statistical characterization of the prediction error (e.g., normality, homoscedasticity), and its transparent and intuitive use of the observations. Bluecat makes use of a rigorous theory to estimate the probability distribution of the predictand conditioned by the deterministic model output, by inferring the conditional statistics of observations. Therefore Bluecat bridges the gaps between deterministic (possibly physically based, or deep learning-based) and stochastic models, as well as between rigorous theory and transparent use of data with an innovative and user oriented approach. We present two examples of application to the case studies of the Arno river at Subbiano and Sieve river at Fornacina. The results confirm the distinguishing features of the method along with its technical soundness. We provide an open software working in the R environment, along with help facilities and detailed instructions to reproduce the case studies presented here
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