English
Sažetak
Високоалпски регион северне Италије карактеришу јединствени екосистеми, сложено хидрогеолошко окружење, стрми топографски градијенти, разноврсност типова вегетације и пејсажних делова, и разноврсност климатских и метеоролошких фактора. Алпски екосистем је још сложенији када у саставу вегетације доминирају четинари, јер услови и правци подземних токова имају непредвидиве количине воде. Моделирање таквих екосистема захтева напредне алате програмирања и рачунарских приступа, као што је Питхон. Овај чланак је фокусиран на моделирање дистрибуираног водног биланса у алпским сливовима. Подручјем доминирају четинарске шуме (смрека, бор) са дрвећем различите старости (старо >200 година и младо, <30 година). Одабрано дрвеће прекривено је епихитима (лишајевима). За ефикасно планирање и управљање коришћењем водних ресурса, Питхон подржане процене и статистичко моделирање су неопходни приступи за праћење шума у животној средини. Конкретно, највиша погодна просторна резолуција која се може постићи у проценама водног биланса процењује се у компликованом топографском окружењу Јужнотиролских Алпа са ограниченим познавањем физиографских фактора шума и метеоролошких варијабли (падавине, температура, влажност ваздуха).
Reference
Ausserladscheider, V. (2024). Decoupling climate change: winter tourism and the maintenance of regional growth. New Political Economy, 29(5), 693–708. https://doi.org/10.1080/13563467.2024.2330486
Bilina, R., & Lawford, S. (2012). Python for Unified Research in Econometrics and Statistics. Econometric Reviews, 31(5), 558–591. https://doi.org/10.1080/07474938.2011.553573
Bilish, S. P., Callow, J. N., & McGowan, H. A. (2020). Streamflow variability and the role of snowmelt in a marginal snow environment. Arctic, Antarctic, and Alpine Research, 52(1), 161–176. https://doi.org/10.1080/15230430.2020.1746517
De Niel, J., Van Uytven, E., & Willems, P. (2019). On the correlation between precipitation and potential evapotranspiration climate change signals for hydrological impact analyses. Hydrological Sciences Journal, 64(4), 420–433. https://doi.org/10.1080/02626667.2019.1587615
du Toit, W. H. O., Purchase, J. L., & Hensley, M. (1997). Evaluation of CERES-wheat v2.10: Soil water content under rainfed conditions. South African Journal of Plant and Soil, 14(4), 139–145. https://doi.org/10.1080/02571862.1997.10635097
Kadereit, J. W., Licht, W., & Uhink, C. H. (2008). Asian relationships of the flora of the European Alps. Plant Ecology & Diversity, 1(2), 171–179. https://doi.org/10.1080/17550870802328751
Karssenberg, D., de Jong, K., & van der Kwast, J. (2007). Modelling landscape dynamics with Python. International Journal of Geographical Information Science, 21(5), 483–495. https://doi.org/10.1080/13658810601063936
Keiler, M., Kellerer-Pirklbauer, A., & Otto, J. (2012). Preface: concepts and implications of environmental change and human impact: studies from austrian geomorphological research. Geografiska Annaler: Series A, Physical Geography, 94(1), 1–5. https://doi.org/10.1111/j.1468-0459.2012.00457.x
Klaučo, M., Gregorová, B., Stankov, U., Marković, V. & Lemenkova, P. (2013). Determination of ecological significance based on geostatistical assessment: a case study from the Slovak Natura 2000 protected area. Central European Journal of Geography, 5, 28–42. https://doi.org/10.2478/s13533-012-0120-0
Klaučo, M., Gregorová, B., Koleda, P., Stankov, U., Marković, V. & Lemenkova, P. (2017). Land Planning as a Support for Sustainable Development Based on Tourism: A Case Study of Slovak Rural Region. Environmental Engineering and Management Journal, 16(2), 449–458. https://doi.org/10.30638/eemj.2017.045
Kruse, S., & Pütz, M. (2014). Adaptive Capacities of Spatial Planning in the Context of Climate Change in the European Alps. European Planning Studies, 22(12), 2620–2638. https://doi.org/10.1080/09654313.2013.860516
Joly, K., Jandt, R. R., & Klein, D. R. (2009). Decrease of lichens in Arctic ecosystems: the role of wildfire, caribou, reindeer, competition and climate in north-western Alaska. Polar Research, 28(3), 433–442. https://doi.org/10.1111/j.1751-8369.2009.00113.x
Laghari, A. N., Vanham, D., & Rauch, W. (2012). To what extent does climate change result in a shift in Alpine hydrology? A case study in the Austrian Alps. Hydrological Sciences Journal, 57(1), 103–117. https://doi.org/10.1080/02626667.2011.637040
Lemenkova P. (2019a), Processing oceanographic data by Python libraries NumPy, SciPy and Pandas. Aquatic Research, 2(2), 73–91. https://doi.org/10.3153/AR19009
Lemenkova P. (2019b), Statistical Analysis of the Mariana Trench Geomorphology Using R Programming Language. Geodesy and Cartography, 45(2), 57–84. https://doi.org/10.3846/gac.2019.3785
Lemenkova P. (2019c), AWK and GNU Octave Programming Languages Integrated with Generic Mapping Tools for Geomorphological Analysis. GeoScience Engineering, 65(4), 1–22. https://doi.org/10.35180/gse-2019-0020
Lemenkova P. (2022a), Mapping Ghana by GMT and R scripting: advanced cartographic approaches to visualize correlations between the topography, climate and environmental setting. Advances in Geodesy and Geoinformation, 71(1), e16. https://doi.org/10.24425/gac.2022.141169
Lemenkova, P. (2022b). Evapotranspiration, vapour pressure and climatic water deficit in Ethiopia mapped using GMT and TerraClimate dataset. Journal of Water and Land Development, 54(7-9), 201-209. https://doi.org/10.24425/jwld.2022.141573
Lemenkova, P. (2024a). Artificial Intelligence for Computational Remote Sensing: Quantifying Patterns of Land Cover Types around Cheetham Wetlands, Port Phillip Bay, Australia. Journal of Marine Science and Engineering, 12(8), 1279. https://doi.org/10.3390/jmse12081279
Lemenkova, P. (2024b). Deep Learning Methods of Satellite Image Processing for Monitoring of Flood Dynamics in the Ganges Delta, Bangladesh. Water 16(8), 1141. https://doi.org/10.3390/w16081141
Lemenkova, P. (2025a). Improving Bimonthly Landscape Monitoring in Morocco, North Africa, by Integrating Machine Learning with GRASS GIS. Geomatics, 5(1), 1-29. https://doi.org/10.3390/geomatics5010005
Lemenkova, P. (2025b). Automation of image processing through ML algorithms of GRASS GIS using embedded Scikit-Learn library of Python. Examples and Counterexamples, 7, 100180. https://doi.org/10.1016/j.exco.2025.100180
Lindh, P., & Lemenkova, P. (2022). Shear bond and compressive strength of clay stabilised with lime/cement jet grouting and deep mixing: A case of Norvik, Nynäshamn. Nonlinear Engineering, 11(1), 693–710. https://doi.org/10.1515/nleng-2022-0269
Lindh, P., & Lemenkova, P. (2023a). Optimized Workflow Framework in Construction Projects to Control the Environmental Properties of Soil. Algorithms, 16(6), 303. https://doi.org/10.3390/a16060303
Lindh, P., & Lemenkova, P. (2023b). Utilising Pareto efficiency and RSM to adjust binder content in clay stabilisation for Yttre Ringvägen, Malmö. Acta Polytechnica, 63(2), 140-157. https://doi.org/10.14311/AP.2023.63.0140
Lindh, P., & Lemenkova, P. (2023c). Effects of Water—Binder Ratio on Strength and Seismic Behavior of Stabilized Soil from Kongshavn, Port of Oslo. Sustainability, 15(15), 12016. https://doi.org/10.3390/su151512016
Lindh, P., & Lemenkova, P. (2023d). Behaviour of Moraine Soils Stabilised with OPC, GGBFS and Hydrated Lime. Archives of Mining Sciences, 68(2), 319–334. https://doi.org/10.24425/ams.2023.146182
Mehlhase, A. (2013). A Python framework to create and simulate models with variable structure in common simulation environments. Mathematical and Computer Modelling of Dynamical Systems, 20(6), 566–583. https://doi.org/10.1080/13873954.2013.861854
Memmott, T., Koçanaoğulları, A., Lawhead, M., Klee, D., Dudy, S., Fried-Oken, M., & Oken, B. (2021). BciPy: brain–computer interface software in Python. Brain-Computer Interfaces, 8(4), 137–153. https://doi.org/10.1080/2326263X.2021.1878727
Miftari, B., Berger, M., Derval, G., Louveaux, Q., & Ernst, D. (2023). GBOML: a structure-exploiting optimization modelling language in Python. Optimization Methods and Software, 39(1), 227–256. https://doi.org/10.1080/10556788.2023.2246169
Persson, I., & Khojasteh, J. (2021). Python Packages for Exploratory Factor Analysis. Structural Equation Modeling: A Multidisciplinary Journal, 28(6), 983–988. https://doi.org/10.1080/10705511.2021.1910037
Remias, D., Procházková, L., Holzinger, A., & Nedbalová, L. (2018). Ecology, cytology and phylogeny of the snow alga Scotiella cryophila K-1 (Chlamydomonadales, Chlorophyta) from the Austrian Alps. Phycologia, 57(5), 581–592. https://doi.org/10.2216/18-45.1
Zeng, C., Shao, M., Wang, Q., & Zhang, J. (2011). Effects of land use on temporal-spatial variability of soil water and soil-water conservation. Acta Agriculturae Scandinavica, Section B — Soil & Plant Science, 61(1), 1–13. https://doi.org/10.1080/09064710903352589
