New optimization and variants of quantile-based methods are developed for bias corrections of monthly and daily general circulation model (GCM)-based statistically downscaled precipitation data. These methods use opti...
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New optimization and variants of quantile-based methods are developed for bias corrections of monthly and daily general circulation model (GCM)-based statistically downscaled precipitation data. These methods use optimization formulations involving several linear and nonlinear corrections with single and multiple objectives and integrate artificial neural networks (ANNs) with quantile matching (QM) methods. The proposed methods were evaluated at 18 rain gauge sites in Florida using several error and performance measures. Downscaled monthly precipitation data are derived from two statistical downscaling models, including a support vector machine (SVM)-based method developed in this study. Downscaled daily precipitation data from two different climatic zones are also used for the evaluation of bias-correction methods. The methods are assessed based on several performance and error measures, along with their ability to replicate all the moments of the distribution. The selection of the best method among several others for a specific site was found to be dependent on specific performance and error measures adopted for evaluation. The proposed methods not only replicated the observed precipitation data distributions but also minimized the quantitative errors between observed and downscaled precipitation data sets, which could not be accomplished using existing methods. ANN-based methods performed better than QM-based ones in replicating extreme precipitation indices at a daily temporal scale. The multiobjective optimization methods require careful selection of objectives and assignment of weights, with the latter heavily influencing the performance of methods. Variation in performances of methods is observed when methods are calibrated with varying baseline periods with a constant length of test data.
Regional hydrological modelling, or hydrological macro-modelling implies the repeated use of a model everywhere within a region using a global set of parameters. A majority of parameters of the macroscale hydrological...
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Regional hydrological modelling, or hydrological macro-modelling implies the repeated use of a model everywhere within a region using a global set of parameters. A majority of parameters of the macroscale hydrological model must be estimated, a priori, using existing climate, soil and vegetation data. Observations for calibration and validation of the model are only available at a, subset of sites where the model is applied. For all other sites without observations the model application needs to be based on global parameters. Ecomag is a distributed, physically-based model, adapted for application to a regular grid and is used as a platform for model development at the University of Oslo (UiO). Valuable insights into hydrological processes and incitements for model development may be gained by comparing high-quality data sets and model calculations. The inter-European multidisciplinary NOPEX (NOrthern hemisphere climate Process land surface EXperiment) project is one of a few prioritized full-scale land surface experiments that provides high quality data sets for a boreal environment that are utilized herein. These are complemented with data from a new experimental area in the mountains in mid-eastern Norway around Lake Aursunden. The UiO model platform facilitates the use of different parameterizations of sub-grid variability. The present work focuses on the identification of process scales for the two study areas and related process parameterization as evaluated from the available data sets. The establishment of a regional set of parameters and data: requirements are two other important issues discussed.
A deterministic approach which avoids extreme failures in the management of a multipurpose reservoir is presented and discussed in the paper. The main feature of the method is to suggest a whole range of possible deci...
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A deterministic approach which avoids extreme failures in the management of a multipurpose reservoir is presented and discussed in the paper. The main feature of the method is to suggest a whole range of possible decisions which guarantee the efficient performance of the system. This allows the manager to choose the release which better fits with the additional informations or forecasts he might have, as well as to accomodate for secondary objectives which were not considered in the formulation of the problem. The results of the application of this approach to the management of Lake Como (Northern Italy) favourably compare with those obtained by a more traditional stochastic optimal control formulation and with the historical data.
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