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Both statistical and dynamical downscaling methods are well established techniques to bridge the gap between the coarse information produced by global circulation models and the regional-to-local scales required by the climate change Impacts, Adaptation, and Vulnerability (IAV) communities. A number of studies have analyzed the relative merits of each technique by inter-comparing their performance in reproducing the observed climate, as given by a number of climatic indices (e.g. mean values, percentiles, spells). However, in this paper we stress that fair comparisons should be based on indices that are not affected by the calibration towards the observed climate used for some of the methods. We focus on precipitation (over continental Spain) and consider the output of eight Regional Climate Models (RCMs) from the EURO-CORDEX initiative at 0.44° resolution and five Statistical Downscaling Methods (SDMs) —analog resampling, weather typing and generalized linear models— trained using the Spain044 observational gridded dataset on exactly the same RCM grid. The performance of these models is inter-compared in terms of several standard indices —mean precipitation, 90th percentile on wet days, maximum precipitation amount and maximum number of consecutive dry days— taking into account the parameters involved in the SDM training phase. It is shown, that not only the directly affected indices should be carefully analyzed, but also those indirectly influenced (e.g. percentile-based indices for precipitation) which are more difficult to identify. We also analyze how simple transformations (e.g. linear scaling) could be applied to the outputs of the uncalibrated methods in order to put SDMs and RCMs on equal footing, and thus perform a fairer comparison. 相似文献
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Ralf Seidler Kateryna Padalkina H. Martin Bücker Anozie Ebigbo Michael Herty Gabriele Marquart Jan Niederau 《Computational Geosciences》2016,20(2):375-383
During geothermal reservoir development, drilling deep boreholes turns out to be extremely expensive and risky. Thus, it is of great importance to work out the details of suitable borehole locations in advance. Here, given a set of existing boreholes, we demonstrate how a sophisticated numerical technique called optimal experimental design helps to find a location of an additional exploratory borehole that reduces risk and, ultimately, saves cost. More precisely, the approach minimizes the uncertainty when deducing the effective permeability of a buried reservoir layer from a temperature profile measured in this exploratory borehole. In this paper, we (1) outline the mathematical formulation in terms of an optimization problem, (2) describe the numerical implementation involving various software components, and (3) apply the method to a 3D numerical simulation model representing a real geothermal reservoir in northern Italy. Our results show that optimal experimental design is conceptually and computationally feasible for industrial-scale applications. For the particular reservoir and the estimation of permeability from temperature, the optimal location of the additional borehole coincides with regions of high flow rates and large deviations from the mean temperature of the reservoir layer in question. Finally, the presentation shows that, methodologically, the optimization method can be generalized from estimating permeability to finding any other reservoir properties. 相似文献
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