Upscaling the spatial distribution of enchytraeids and humus forms in a high mountain environment on the basis of GIS and fuzzy logic

Abstract

The aim of this study was to map the spatial distribution of enchytraeids and humus forms in a study area in the Italian Alps by means of a knowledge-based modeling approach. The modeled area is located around Val di Sole and Val di Rabbi (Trentino, Italy) and includes the forested parts in the range between 1100 m and 1800 m a.s.l. Elevation and slope exposure are considered as environmental covariates. Models were implemented regarding the spatial distribution of three variables at the landscape scale: 1) enchytraeids indicating mull humus forms, 2) enchytraeids indicating moder/mor humus forms, 3) humus forms showing an OH horizon. All three models reveal a consistent trend of an increasing accumulation of plant residues and humus in organic layers from low to high elevations and from south-facing to north-facing slopes. Validation and uncertainty analysis of input data confirm these trends, although some deviations are to be expected (RMSE values from validation sites range from 26.3 to 36.2 percentage points). Effects of additional potentially influencing variables may lead to uncertainties of the model predictions especially at positions with particular landforms (e.g. gullies and ridges). In the high mountains environmental conditions are often quite heterogeneous due to a highly variable topography, which also affects the species composition of the decomposer community and the occurrence of different humus forms.

Humus Forms and Soil Microbiological Parameters in a Mountain Forest: Upscaling to the Slope Scale

“…Consequently, the analogous distributions of humus forms and pH values generally turn out as expected [6]: the presence of mull-like humus forms characterized by a high biological activity in the mineral soil coincides with higher pH values as compared to moder-like humus forms. Altogether, these findings at the slope scale confirm and amplify the trends from modeling at broader scales [9,57].…”

“…When integrated with those of previous studies [8,9,28], our findings show that the humus form proves to be a comprehensive indicator for soil ecological processes in a high mountain environment of the Central Alps, including soil macro-and mesofaunal as well as microbiological properties.…”

“…This approach was specially designed for highly heterogeneous areas in the high mountains, where only a low amount of sample data is available due to poor accessibility. Recent implementations of this model addressed the presence of OH horizons [9,57]. The humus form model presented in this study also involves the biological activity in the mineral soil, thus accounting for the properties of five different humus form classes (Figure 2).…”

“…In order to understand spatial decomposition patterns in a high mountain environment, it would be useful if humus forms could be applied as indicators not only of the distribution of enchytraeid species [9,28], but also of microbiological parameters [29]. Hence, the aims of this study were (i) to map humus forms and topsoil pH values in a mountain forest area in the Italian Alps as a basis for upscaling, (ii) to examine correlations between humus forms, pH values and microbiological parameters of the topsoil, (iii) to rank the examined parameters in terms of their usability for upscaling by proxy of humus forms and topsoil pH values, (iv) to upscale microbiological parameters utilizing humus forms and topsoil pH values if feasible according to (iii).…”

Qualitative zoning of groundwater to assessment suitable drinking water using fuzzy logic spatial modelling via GIS

“…Fuzzy modelling allows flexible standardization and aggregation by partial membership. Numerous studies attempted to use fuzzy modelling in development of water quality index and water quality assessment (Dahiya et al 2007;Icaga 2007;S¸en 2009;Mofarrah & Husain 2011;Sahu et al 2011;Gharibi et al 2012;Liu & Zou 2012;Scannapieco et al 2012;Kord & Asghari Moghaddam 2014) or in the other proposes that are near to aim of our study (Chen & Paydar 2012;Lewis et al 2014;Akumu et al 2015;Caniani et al 2016;Araya-Muñoz et al 2017;Hellwig et al 2017;Zhang et al 2017). All of these researches are emphasized on the very high accuracy of the results due to use of the fuzzy logic method.…”