Sensitivity analysis of criteria weights in geodiversity assessment of the Karkonosze National Park, Poland

The geodiversity assessment is particularly important in the case of areas belonging to critical zones, especially in the mountain regions. Recognizing the parts of a territory that are the most diversified and vulnerable to changes is a crucial issue for management and planning of protected and conserved areas (PCAs). Karkonosze National Park (KNP) located in south-western Poland in the border area between Poland and the Czech Republic was chosen as a research area. KNP covers the northern slopes of the Karkonosze Mountains, the largest range of the Sudetes.

The geodiversity assessment based on spatial multicriteria analysis (S-MCA) with crowdsourced data was conducted. The geodiversity of KPN was evaluated with weighted linear combination (WLC) technique basis on selected criteria: 1) lithology, 2) relief energy, 3) geomorphology, 4) land use/land cover, 5) soils, 6) mesoclimate, and 7) hydrography. The assessment input data comprised of seven environmental factor ratings and weights were obtained from 57 Earth science researchers worldwide. These data served as the bases for a joint assessment of geodiversity and then spatially explicit global sensitivity analysis (GSA). The Monte Carlo simulation was used to sweep through criteria weight space, where weights are expressed using probability distributions. Multiple output suitability maps were generated and summarized using: an average suitability map, a standard deviation uncertainty map, and a number of sensitivity maps. The results helped to identify highly geodiverse areas that are burdened by high uncertainty and then to investigate which specific abiotic component contribute to the uncertainty the most. This could be valuable in monitoring and management of PCAs and significantly contribute to improving the existing results of geodiversity assessments and some savings resulting from field work.

In the case of the mountainous area - KNP, the geodiversity value is the most sensitive to the lithological and the geomorphological criteria map. None of the weightings proved influential, suggesting a high consensus in weighting the factors among the geo-questionnaire respondents. In the future, it is worth conducting further simulations, considering another S-MCA technique, such as Ordered Weighted Averaging (OWA).