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  • Other Sources  (2)
  • ddc:551.35  (1)
  • ddc:551.577  (1)
  • Chichester, UK  (2)
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  • 2020-2023  (2)
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  • 2020-2023  (2)
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  • 1
    Publication Date: 2022-06-28
    Description: Braided reaches were common along near‐natural Alpine rivers, and the associated habitat dynamics supported plant and animal species specialized on early‐successional stages. The extensive riparian zones could mitigate climate change by absorbing floods and by retaining water during droughts. Human impacts largely reduced active river corridors through altered discharge and construction of dykes, while recent restoration projects aim at increasing river dynamics. The causes and consequences of Alpine river degradation are well understood, but there are only few quantitative studies on floodplain degradation and restoration. Thus, we have reconstructed historical changes of gravel bars along five Alpine rivers (Iller, Inn, Isar, Lech, and Wertach) in Southern Germany in the period 1808–2009, based on historical maps and aerial images. We found losses of 〉90% in gravel bar area along these rivers since the mid‐19th century. The decline was caused by a reduction of the active river corridor and by ongoing succession of the remaining open habitats. Within the past 30 years, at the Isar River, restoration measures were realized with the aim to widen the active river corridor and to recreate gravel bars. In four restored reaches, we found that 5% of the historical gravel bar area recovered, and that the proportion of restored gravel bar area was highest after intermediate flooding. We conclude that the active river corridors of German Alpine rivers are almost completely lost, and that more extensive restoration needs to be done to preserve the habitat dynamics and biodiversity of these systems, and to adapt Alpine rivers to climate change.
    Description: Deutsche Bundesstiftung Umwelt http://dx.doi.org/10.13039/100007636
    Keywords: ddc:551.35
    Language: English
    Type: doc-type:article
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  • 2
    Publication Date: 2022-03-28
    Description: We use symbolic regression to estimate daily precipitation amounts at six stations in the Alpine region from a global reanalysis. Symbolic regression only prescribes the set of mathematical expressions allowed in the regression model, but not its structure. The regression models are generated by genetic programming (GP) in analogy to biological evolution. The two conflicting objectives of a low root‐mean‐square error (RMSE) and consistency in the distribution between model and observations are treated as a multi‐objective optimization problem. This allows us to derive a set of downscaling models that represents different achievable trade‐offs between the two conflicting objectives, a so‐called Pareto set. Our GP setup limits the size of the regression models and uses an analytical quotient instead of a standard or protected division operator. With this setup we obtain models that have a generalization performance comparable with generalized linear regression models (GLMs), which are used as a benchmark. We generate deterministic and stochastic downscaling models with GP. The deterministic downscaling models with low RMSE outperform the respective stochastic models. The stochastic models with low IQD, however, perform slightly better than the respective deterministic models for the majority of cases. No approach is uniquely superior. The stochastic models with optimal IQD provide useful distribution estimates that capture the stochastic uncertainty similar to or slightly better than the GLM‐based downscaling.
    Description: We have fitted deterministic and stochastic empirical‐statistical downscaling models that represent different possible compromises between two conflicting objectives: (a) a low RMSE and (b) consistency in the distribution between downscaled series and reference observations. The graphic shows the skill of our downscaling models w.r.t. the two objectives (larger is better) for the station Sonnblick.
    Description: CRC/TR32: Patterns in Soil‐Vegetation‐Atmosphere Systems: Monitoring, Modelling and Data Assimilation; funded by the German Research Foundation (Deutsche Forschungsgemeinschaft DFG) http://dx.doi.org/10.13039/501100001659
    Keywords: ddc:551.577 ; ddc:550.2
    Language: English
    Type: doc-type:article
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