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  • 1
    Monograph available for loan
    Monograph available for loan
    Berlin : Akad.-Verl.
    Call number: G 6654
    Type of Medium: Monograph available for loan
    Pages: 200 S.
    Uniform Title: Exercices de calcul informationnel avec leurs solutions
    Language: German
    Location: Upper compact magazine
    Branch Library: GFZ Library
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  • 2
    Call number: SR 90.1026(125)
    In: MPE-Report
    Type of Medium: Series available for loan
    Pages: I, 43 S.
    Series Statement: Max-Planck-Institut für Physik und Astrophysik, Institut für Extraterrestrische Physik 125
    Language: German
    Location: Magazine - must be ordered
    Branch Library: GFZ Library
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  • 3
    Publication Date: 2015-04-16
    Type: paper
    Format: application/pdf
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  • 4
    Publication Date: 2013-10-01
    Type: inreport
    Format: application/pdf
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  • 5
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    In:  Journal of Hydrology ; Year: 2008 ; Volume: 360 ; Issue: 1-4 ; Pages: 1-14
    Publication Date: 2014-12-16
    Description: The Process Modelling and Artificial Intelligence for Online Flood Forecasting (PAI-OFF) methodology combines the reliability of physically based, hydrologic/hydraulic modelling with the operational advantages of artificial intelligence. These operational advantages are extremely low computation times and straightforward operation. The basic principle of the methodology is to portray process models by means of ANN. We propose to train ANN flood forecasting models with synthetic data that reflects the possible range of storm events. To this end, establishing PAI-OFF requires first setting up a physically based hydrologic model of the considered catchment and – optionally, if backwater effects have a significant impact on the flow regime – a hydrodynamic flood routing model of the river reach in question. Both models are subsequently used for simulating all meaningful and flood relevant storm scenarios which are obtained from a catchment specific meteorological data analysis. This provides a database of corresponding input/output vectors which is then completed by generally available hydrological and meteorological data for characterizing the catchment state prior to each storm event. This database subsequently serves for training both a polynomial neural network (PoNN) – portraying the rainfall–runoff process – and a multilayer neural network (MLFN), which mirrors the hydrodynamic flood wave propagation in the river. These two ANN models replace the hydrological and hydrodynamic model in the operational mode. After presenting the theory, we apply PAI-OFF – essentially consisting of the coupled “hydrologic” PoNN and “hydrodynamic” MLFN – to the Freiberger Mulde catchment in the Erzgebirge (Ore-mountains) in East Germany (3000 km2). Both the demonstrated computational efficiency and the prediction reliability underline the potential of the new PAI-OFF methodology for online flood forecasting.
    Keywords: ddc:550
    Type: http://purl.org/escidoc/metadata/ves/publication-types/article
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  • 6
    facet.materialart.
    Unknown
    In:  Journal of Hydrology ; Year: 2008 ; Volume: 360 ; Issue: 1-4 ; Pages: 1-14
    Publication Date: 2014-12-16
    Description: The Process Modelling and Artificial Intelligence for Online Flood Forecasting (PAI-OFF) methodology combines the reliability of physically based, hydrologic/hydraulic modelling with the operational advantages of artificial intelligence. These operational advantages are extremely low computation times and straightforward operation. The basic principle of the methodology is to portray process models by means of ANN. We propose to train ANN flood forecasting models with synthetic data that reflects the possible range of storm events. To this end, establishing PAI-OFF requires first setting up a physically based hydrologic model of the considered catchment and – optionally, if backwater effects have a significant impact on the flow regime – a hydrodynamic flood routing model of the river reach in question. Both models are subsequently used for simulating all meaningful and flood relevant storm scenarios which are obtained from a catchment specific meteorological data analysis. This provides a database of corresponding input/output vectors which is then completed by generally available hydrological and meteorological data for characterizing the catchment state prior to each storm event. This database subsequently serves for training both a polynomial neural network (PoNN) – portraying the rainfall–runoff process – and a multilayer neural network (MLFN), which mirrors the hydrodynamic flood wave propagation in the river. These two ANN models replace the hydrological and hydrodynamic model in the operational mode. After presenting the theory, we apply PAI-OFF – essentially consisting of the coupled “hydrologic” PoNN and “hydrodynamic” MLFN – to the Freiberger Mulde catchment in the Erzgebirge (Ore-mountains) in East Germany (3000 km2). Both the demonstrated computational efficiency and the prediction reliability underline the potential of the new PAI-OFF methodology for online flood forecasting.
    Type: http://purl.org/escidoc/metadata/ves/publication-types/article
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  • 7
    Publication Date: 2015-12-01
    Description: We present for the first time a study on alternative forest management at the European scale to account for climate change impacts. We combine insights into detailed studies at high resolution with the actual status of the forest and a realistic estimate of the current management practices at large scale. Results show that the European forest system is very inert and that it takes a long time to influence the species distribution by replacing species after final felling. By 2070, on average about 36 % of the area expected to have decreased species suitability will have changed species following business as usual management. Alternative management, consisting of shorter rotations for those species and species planting based on expected trends, will have increased this species transition to 40 %. The simulated forward-looking alternative management leads to some reduction in increment, but does not influence the amount of wood removed from the forest. Northern Europe is projected to show the highest production increases under climate change and can also adapt faster to the new (proposed) species distribution. Southwest Europe is expected to face the greatest challenge by a combination of a predicted loss of production and a slow rate of management alteration under climate change. ©2015 The Author(s)〈br /〉〈br /〉〈a href="http://doi.org/10.1007/s10113-015-0788-z" target="_blank"〉〈img src="http://bib.telegrafenberg.de/typo3temp/pics/f2f773b55e.png" border="0"〉〈/a〉
    Print ISSN: 1436-3798
    Electronic ISSN: 1436-378X
    Topics: Biology , Energy, Environment Protection, Nuclear Power Engineering
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  • 8
    Publication Date: 2013-02-27
    Description: European forests are threatened by climate change with impacts on the distribution of tree species. Previous discussions on the consequences of biome shifts have concentrated only on ecological issues; however, research now shows that under forecasted changes in temperature and precipitation there could be a decline of economically valuable species, which would lead to a loss in the value of European forest land. Nature Climate Change 3 203 doi: 10.1038/nclimate1687
    Print ISSN: 1758-678X
    Electronic ISSN: 1758-6798
    Topics: Geosciences
    Published by Springer Nature
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  • 9
    Publication Date: 2014-09-23
    Description: Shifts in tree species distributions caused by climatic change are expected to cause severe losses in the economic value of European forestland. However, this projection disregards potential adaptation options such as tree species conversion, shorter production periods, or establishment of mixed species forests. The effect of tree species mixture has, as yet, not been quantitatively investigated for its potential to mitigate future increases in production risks. For the first time we use survival time analysis to assess the effects of climate, species mixture and soil condition on survival probabilities for Norway spruce and European beech. Accelerated Failure Time (AFT) models based on an extensive dataset of almost 30,000 trees from the European Forest Damage Survey (FDS) – part of the European-wide Level I monitoring network – predicted a 24% decrease in survival probability for Norway spruce in pure stands at age 120 when unfavorable changes in climate conditions were assumed. Increasing species admixture greatly reduced the negative effects of unfavorable climate conditions, resulting in a decline in survival probabilities of only 7%. We conclude that future studies of forest management under climate change as well as forest policy measures need to take this, as yet unconsidered, strongly advantageous effect of tree species mixture into account. This article is protected by copyright. All rights reserved
    Print ISSN: 1354-1013
    Electronic ISSN: 1365-2486
    Topics: Biology , Energy, Environment Protection, Nuclear Power Engineering , Geography
    Published by Wiley
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  • 10
    Publication Date: 2016-07-20
    Description: ABSTRACT Regional climate models (RCMs) include both terrestrial and atmospheric compartments and thereby allow studying land–atmosphere feedback, in particular, the impact of land-use land cover driven by biogeophysical processes on regional climate. In this study, a method is developed to separate the signals from the noise in RCM simulations of the effects of changes in land use, using perturbed initial boundary conditions (PICs). We want to know how many ensemble members are required to identify robust and statistically significant land-use land cover change (LULCC) effects from RCM LULCC studies. The method is applied to a case study of urbanization and deforestation, for which LULCC scenarios are implemented in the RCM Weather Research and Forecasting (WRF). Based on WRF ensemble simulations with PICs for 2010, the signal-to-noise ratio (SNR) is used to identify areas with pronounced effect of an LULCC or, rather, the parametrization of the land-use classes. While in the urbanization scenarios clear and statistically significant signals are found for air temperature and for both latent- and sensible heat (SNR values up to 24), the effects are less pronounced for precipitation, and for deforestation in general (SNR values 〈 1). For the case study of urbanization and precipitation, the impact of the ensemble size is studied in order to derive robust conclusions about the effects of LULCC on precipitation. We conclude that single RCM realizations of different land-use representations are not sufficient to derive LULCCinduced signals, particularly not for precipitation. Small ensemble sizes led to concluding there were significant LULCC-induced precipitation signals, but these disappeared when the ensemble size was increased. Our regional analysis suggests the need for ensemble sizes well above 10 for precipitation.
    Print ISSN: 0899-8418
    Electronic ISSN: 1097-0088
    Topics: Geosciences , Physics
    Published by Wiley
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