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Variabiliteti klimatik, trendet meteorologjike dhe zhvendosjet e klasifikimit klimatik në pellgun ujëmbledhës të Lumit Mat

Authors: hasimi, albana; Papajani, Blerina;

Variabiliteti klimatik, trendet meteorologjike dhe zhvendosjet e klasifikimit klimatik në pellgun ujëmbledhës të Lumit Mat

Abstract

The Mati River flows in the centre of the northern geographical region of the Republic of Albania. From a climatic perspective, this region exhibits significant variability in temperature and precipitation, influenced by its diverse topography, with altitudes ranging from 1,800 meters above sea level to sea level at the estuary. The operation of multiple hydroelectric power plants along the Mati River underscores the significance of this study, as precipitation variability directly influences hydropower generation and, consequently, Albania's energy sector. On the other hand, the occurrence of extreme temperatures in this region directly impacts the quality of life, agricultural productivity, and the agro-industrial sector. The presence of old mining dumps, along with the region's geological formation, which is known to be mineral-rich, explains the occurrence of heavy metals in the river’s water. Climatic conditions directly influence the concentration of these pollutants, making this a key reason for undertaking this study. The comparative analysis of climatic data across seven meteorological stations within the Mat River Basin indicates a consistent and measurable warming trend, accompanied by spatially variable precipitation changes over the past three decades. The evolution of the Köppen Climate Classification (KCC) and Köppen -Trewartha Climate Classification (KTC) further supports an ongoing transition toward warmer and, in some cases, drier climatic regimes across the basin. The PCA results indicate that recent climatic changes in the study area are predominantly driven by increasing temperatures, whereas changes in precipitation regimes exhibit a more heterogeneous pattern with pronounced local variability. The first three principal components account for nearly all of the observed variance, confirming the robustness of the multivariate structure and supporting the application of PCA as an effective tool for the synthesis and interpretation of complex climatic datasets

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