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Assessing the impacts of climatic changes on forests requires the analysis of actual climatology within the forested area. In mountainous areas, climatological indices vary markedly with the micro-relief, i.e., with altitude, slope, and aspect. Consequently, when modelling potential shifts of altitudinal belts in mountainous areas due to climatic c...
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... 2 shows the average annual relative air humidity at 1:30 p.m. in Switzerland. The resulting maps are listed and described in Table 4. ...Similar publications
IntisariTelah dilakukan analisis kondisi atmosfer di berbagai lapisan ketinggian untuk melihat keterkaitannya dengan kejadian hujan ekstrim di wilayah perbatasan Jawa Barat-Jawa Tengah pada tanggal 15 Februari 2017. Area penelitian difokuskan di area 108.00-109.50 BT dan 6.50-7.50 LS yang merupakan area hujan ekstrim (AHE). Analisis sebaran dan wak...
Citations
... Global climate datasets often neither capture local-scale physical features nor satisfy requirements for climate impact studies [62,64,94]. Furthermore, they have been found to be inferior to local fine-scale climate datasets [69,95,96], whereas climate data with higher resolution (<1 km), capturing microclimates and spatial heterogeneity, shows increased prediction accuracy when modeling species in mountainous regions [97][98][99][100]. Moreover, local, fine-grained climate datasets (250 m) intended for ecological applications have been shown to improve model performance by incorporating climate variability and average extremes [101]. ...
Comparing and evaluating global climate datasets and their effect on model performance in regions with limited data availability has received little attention in ecological modeling studies so far. In this study, we aim at comparing the interpolated climate dataset Worldclim 1.4, which is the most widely used in ecological modeling studies, and the quasi-mechanistical downscaled climate dataset Chelsa, as well as their latest versions Worldclim 2.1 and Chelsa 1.2, with regard to their suitability for modeling studies. To evaluate the effect of these global climate datasets at the meso-scale, the ecological niche of Betula utilis in Nepal is modeled under current and future climate conditions. We underline differences regarding methodology and bias correction between Chelsa and Worldclim versions and highlight potential drawbacks for ecological models in remote high mountain regions. Regarding model performance and prediction plausibility under current climatic conditions, Chelsa-based models significantly outperformed Worldclim-based models, however, the latest version of Chelsa contains partially inherent distorted precipitation amounts. This study emphasizes that unmindful usage of climate data may have severe consequences for modeling treeline species in high-altitude regions as well as for future projections, if based on flawed current model predictions. The results illustrate the inevitable need for interdisciplinary investigations and collaboration between climate scientists and ecologists to enhance climate-based ecological model quality at meso- to local-scales by accounting for local-scale physical features at high temporal and spatial resolution.
... High resolution maps of climatological data are essential for modeling the potential impacts of climate change on forests. For the Swiss Alps, Zischg et al. [15] presented such maps of temperature, relative humidity, radiation, and "föhn" winds. Apart from altitude, these maps also take into account micro-relief, slope, and aspect. ...
Concerns have been raised with respect to the state of high-altitude and high-latitude treelines, as they are anticipated to undergo considerable modifications due to global change, especially dueto climate warming [...]
... In order to predict the species' range with regression models (see paragraphs 3.3 and 3.4), I considered a set of external indicators on climate and soil. The baseline was provided by the climate geodata offered by Zischg et al. (2019). I then selected the best accessible geodata compatible with my baseline. ...
... An extensive subset of high-resolution climate rasters was shared by Zischg et al. (2019) on the online platform Zenodo. Since these rasters were developed for the Adapted Ecograms Project, also the corresponding climate projections for the periods 2071-2090 were obtainable (e.g. ...
... The climate indicators used in this thesis had been distributed for research purposes requiring a high-resolution climate geodata (Zischg et al. 2019). These indicators were developed for the project Adapted ecograms (e.g. ...
Introduction
In this master thesis, I aim to identify the main processes and factors shaping the Norway spruce’s range in study area of the Southern Swiss Alps. I combined an analytic and modeling approach based on the most recent forest data from the Swiss National Forest Inventory (NFI) with a set of high-resolution indicators.
Methods
Firstly, I calculated the changes in the Norway spruce’s stands between NFI2 and NFI4 in terms of stem density and basal area of living and dead trees, as well as the change in the number of seedlings and saplings.
Secondly, I assessed the ecologic connection between the Norway spruce and the alpenroses (Rhododendron spp.), which are vectors of the needle’s bladder rust fungus (Chrysomyxa rhododendri). I used a Likelihood-Ratio Test applied to a multi-linear regression through a broad spectrum of topographic and pedo-climatic control variables.
Thirdly, I modeled the Norway spruce’s range either referring to the established rejuvenation and to the adult trees. The method applied was a Tobit multi-regression. I produced four different models’ versions based on high resolution climate data. I extended the models accounting for the occurrence of the rusty-leaved alpenrose (Rhododendron ferrugineum), recursively predicted with an additional model. Finally, all models’ versions were projected in the second half of the 21st Century by integrating the corresponding future climate indicators based on the CLM scenario.
Results and discussion
The first part denotes an increase in the species’ stocks but also an increase in the tree mortality, an overall uplift of the species’ range and a strong reduction in the frequency of the species’ rejuvenation.
The second part shows that the alpenroses’ cover degree is depressing the Norway spruce’s mixture and cover degree, both of the young and in the adult trees. This relationship is also true in the opposite direction. This evidence highlights the ecologic meaning of the allelopathic association between these species.
The third part displays that models based on the current climate allow a reliable reconstruction of the actual Norway spruce’s range in the study area. The projections based on the future climate data present a dramatic retreat of the Norway spruce’s range towards the inner alpine valleys. The shift of the Norway spruce’s range in the upper forest’s belts is hindered by the the rusty-leaved alpenrose's occurrence.
Conclusion
This work gives an insight in the processes and factors shaping the Norway spruce stands in the Southern Swiss Alps. The results contrast with conventional models based on the whole of Switzerland. The Norway spruce's decline could be much greater than assumed in other scenarios. Nevertheless, any extrapolation drawn from modelling approach must be taken very cautiously and, under no circumstances, should be considered as quantitative forecasts.
Aims
Climate change is expected to markedly change site factors, tree species composition and finally ecosystem services provided by forests. Here, we describe the development of a framework for modelling how these changes may transform forest site types. Site types capture information on site conditions like climate, topography and soil, all factors with strong influence on tree species occurrence.
Location
Switzerland.
Methods
We elicited expert knowledge and followed a participatory modelling approach for quantifying upward shifts of altitudinal vegetation belts and the changes in the zonal distribution of main tree species, as a basis for assessing transformation pathways of forest site types for three climate projections.
Results
The model results show marked range shifts of altitudinal vegetation belts. The change in the vegetation belt and a rule base for forest site type transformations allow for assessing the location-specific and long-term transformation pathway from the current to a future forest site type.
Conclusions
The resulting maps enable forest managers to take climate change into account when selecting tree species. The presented method complements statistical distribution models as it considers more site information, integrates expert knowledge and is based on a forest site type classification which is already widely used by forest practitioners.
Modelling species across vast distributions in remote, high mountain regions like the Himalayas remains a challenging task. Challenges include, first and foremost, large-scale sampling of species occurrences and acquisition of sufficient high quality, fine-scale environmental parameters. We compiled a review of 157 Himalayan studies published between 2010 and 2021, aiming at identifying their main modelling objective in relation to the conceptualization of their methodological framework, evaluating origin of species occurrence data, taxonomic groups, spatial and temporal scale, selection of predictor variables and applied modelling algorithms. The majority of the analysed studies (40%) attempted to answer questions about potential range changes under future or past climatic conditions. The most studied organisms were trees (27%), followed by mammals (22%), herbaceous plants (20%), and birds (9%).
For almost all studies we noted that a critical investigation and evaluation of input parameters and their ability to account for the species ecological requirements were neglected. Over 87% of all studies used Worldclim climate data as predictor variables, while around 50% of these studies solely relied on Worldclim climate data. Climate data from other sources were incorporated in only 7% and an additional 6% solely used remotely sensed predictors. Only around 2% of all studies attempted to compare the influence of different climate data sources on model performance. By far, Maxent was the most used modelling algorithm with 66%, followed by ensemble approaches (16%), whereas statistical modelling techniques lagged far behind (9%). Surprisingly, we found in 37% of the studies no interpretation on the relationship between the species and the predictor variables, while 27% of all studies included brief information, and 36% provided an elaborate, detailed interpretation on species ecological needs reflected in the final model.
With this review we highlight the necessity to identify and reduce biases and uncertainty associated with species’ occurrence records and environmental data a priori. Since flawed input parameters produce misleading models without ecological causality, their implementation may have detrimental consequences when the best possible adaptation to future climatic conditions is at stake.
