Multi-Decadal Assessment of Soil Loss in a Mediterranean Region Characterized by Contrasting Local Climates
Caterina Samela,
Vito Imbrenda,
Rosa Coluzzi,
Letizia Pace,
Tiziana Simoniello and
Maria Lanfredi
Additional contact information
Caterina Samela: Institute of Methodologies for Environmental Analysis, National Research Council of Italy (IMAA-CNR), 85050 Tito Scalo, Italy
Vito Imbrenda: Institute of Methodologies for Environmental Analysis, National Research Council of Italy (IMAA-CNR), 85050 Tito Scalo, Italy
Rosa Coluzzi: Institute of Methodologies for Environmental Analysis, National Research Council of Italy (IMAA-CNR), 85050 Tito Scalo, Italy
Letizia Pace: Institute of Methodologies for Environmental Analysis, National Research Council of Italy (IMAA-CNR), 85050 Tito Scalo, Italy
Tiziana Simoniello: Institute of Methodologies for Environmental Analysis, National Research Council of Italy (IMAA-CNR), 85050 Tito Scalo, Italy
Maria Lanfredi: Institute of Methodologies for Environmental Analysis, National Research Council of Italy (IMAA-CNR), 85050 Tito Scalo, Italy
Land, 2022, vol. 11, issue 7, 1-25
Abstract:
Soil erosion is one of the most widespread soil degradation phenomena worldwide. Mediterranean landscapes, due to some peculiar characteristics, such as fragility of soils, steep slopes, and rainfall distribution during the year, are particularly subject to this phenomenon, with severe and complex issues for agricultural production and biodiversity protection. In this paper, we present a diachronic approach to the analysis of soil loss, which aims to account for climate variability and land cover dynamics by using remote data about rainfall and land cover to guarantee sufficient observational continuity. The study area (Basilicata, Southern Italy) is characterized by different local climates and ecosystems (temperate, Csa and Csb; arid steppic, Bsk; and cold, Dsb and Dsc), and is particularly suited to represent the biogeographical complexity of the Mediterranean Italy. The well-known Revised Universal Soil Loss Equation (RUSLE) was applied by integrating information from remote sensing to carry out decadal assessments (1994, 2004, 2014, and 2021) of the annual soil loss. Changes in the rainfall regime and vegetation cover activity were derived from CHIRPS and Landsat data, respectively, to obtain updated information useful for dynamical studies. For the analyzed region, soil loss shows a slight reduction (albeit always remarkable) over the whole period, and distinct spatial patterns between lowland Bsk and Mediterranean mountain Dsb and Dsc climate areas. The most alarming fact is that most of the study area showed soil erosion rates in 2021 greater than 11 t/ha*y, which is considered by the OECD (Organization for Economic Cooperation and Development) the threshold for identifying severe erosion phenomena. A final comparison with local studies shows, on average, differences of about 5 t ha −1 y −1 (minimum 2.5 and maximum 7) with respect to the local estimates obtained with the RUSLE model. The assessment at a regional scale provided an average 9.5% of soil loss difference for the arable lands and about 10% for all cultivated areas. The spatial-temporal patterns enhance the relevance of using the cover management factor C derived from satellite data rather than land cover maps, as remote observations are able to highlight the heterogeneity in vegetation density within the same vegetation cover class, which is particularly relevant for agricultural areas. For mountain areas, the adoption of a satellite-gridded rainfall dataset allowed the detection of erosion rate fluctuations due to rainfall variability, also in the case of sparse or absent ground pluviometric stations. The use of remote data represents a precious added value to obtain a dynamic picture of the spatial-temporal variability of soil loss and new insights into the sustainability of soil use in a region whose economy is mostly based on agriculture and the exploitation of natural resources.
Keywords: water erosion; soil degradation; RUSLE; Basilicata; agricultural areas; land management; climate variability; remote sensing; CHIRPS; biogeographical complexity (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
https://www.mdpi.com/2073-445X/11/7/1010/pdf (application/pdf)
https://www.mdpi.com/2073-445X/11/7/1010/ (text/html)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:11:y:2022:i:7:p:1010-:d:854532
Access Statistics for this article
Land is currently edited by Ms. Carol Ma
More articles in Land from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().