Current and future land use and land cover scenarios in the Arroio Marrecas watershed

• Autor(a) principal: Tornquist,Carlos G.
• Data de Publicação: 2019
• Outros Autores: Silva,Diego S. da
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Revista Brasileira de Engenharia Agrícola e Ambiental (Online)
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-43662019000300215
• topic: geospatial analysis; environmental modeling; CLUE-S; grasslands

ABSTRACT This study evaluated historic land use and land cover changes in the Arroio Marrecas watershed (Caxias do Sul, Rio Grande do Sul state of Brazil) and simulated future land use scenarios until 2034. Spatial and temporal simulations were conducted with the Conversion of Land Use and its Effects - Small Regional Extent (CLUE-S) model. Three land use scenarios were developed to include public policies and agricultural trends in the study region for 20 years (2015-2034). Geospatial analysis of different land uses showed that areas that were originally covered grasslands and forests decreased, which can be attributed to the expansion of intensive agricultural uses, such as fruit farming/forestry and urbanization. The reallocation dynamics of land use with CLUE-S was primarily driven by the soil class (especially Typic and Humic Dystrudepts and Lithic Undorthents), with limited effects of altitude and slope. Analysis of the land use maps of the Arroio Marrecas watershed until 2015 identified major human-induced changes that were driven by expanding agricultural production and urbanization. The allocation of land use derived from the proposed future scenarios with CLUE-S showed that in this regional context, Humic Dystrudepts and Rhodic Kanhapludults were the key drivers of the allocation of agricultural expansion. Conversely, natural resource conservation was indicated to most likely occur in Typic Dystrudepts. The main limitation of this approach is the recognition of driving factors that have a high correlation with each land use as effective predictor variables.