A Worrying Future for River Flows in the Brazilian Cerrado Provoked by Land Use and Climate Changes

Abstract

In this study, we assessed the impacts of land use and climate changes on the river flows of 81 watersheds within the Cerrado biome, Brazil, based on a comprehensive analysis of field and secondary data acquired between 1985 and 2018. Complementarily, we projected a future deforestation and climate scenario up to 2050 and predicted their impacts on surface water in the study area. We observed that direct impacts by large-scale deforestation oriented to the production of irrigated agricultural commodities have more significantly impacted river flows than climate changes. We estimated an average decrease of 8.7% and 6.7% in the streamflow due to deforestation and climate changes, respectively. Most of the observed changes (56.7%) were due to land use and land cover changes and occurred in recent decades. Climate and land use and land cover changes combined were responsible for a total surface water reduction of −19,718 m³/s within the Cerrado watersheds. By assuming the current deforestation rates, we predicted a total water reduction of 23,653 m³/s by 2050, equivalent to a decrease of 33.9% of the river flows in the study region. It will cause severe streamflow discontinuity in many rivers and strongly affect agricultural, electric power production, biodiversity, and water supply, especially during dry seasons in that region.

Dinâmica espaço-temporal da expansão de pivôs centrais e da diversificação agrícola no município de Casa Branca (SP)

“…Dessa forma, informações acerca da dinâmica espaço-temporal dos pivôs são fundamentais do ponto de vista econômico, social e ambiental (Albuquerque et al, 2020). Essas informações são indispensáveis para a funcionalidade dos instrumentos de controle e gestão da água e que, por sua vez, são fundamentais para uma agricultura mais sustentável, principalmente no Cerrado, considerando-se os estudos que mostram que, até 2050, o bioma pode perder um terço de suas águas (Salmona et al, 2023).…”

Land Use Change Modelling Using Logistic Regression, Random Forest and Additive Logistic Regression in Kubu Raya Regency, West Kalimantan

“…The process is repeated 10 times to obtain 10 goodness-of-fit values. In addition to the sample data, all of the above steps are also applied to the entire observation dataset (Salmona et al, 2023).…”

Reference Evapotranspiration for Sparse Data and Data Gap Filling in Python: A Study Case at Urucuia River Basin, Brazil

“…For instance, Machado et al (2019) identified a 9% gap in data over a 13-year time series from 564 INMET weather stations distributed across the entire Brazilian territory. Similarly, Salmona et al (2023) noted a 7% gap in precipitation data for various pluviometric stations of the National Water Agency (ANA) throughout the Cerrado biome. These instances underscore the importance of addressing data gaps when conducting ET calculations, especially when relying on physical weather station data.…”