Impacts of land-use and land-cover change on stream hydrochemistry in the Cerrado and Amazon biomes

Studies on the impacts of land-use and land-cover change on stream hydrochemistry in active deforestation zones of the Amazon agricultural frontier are limited and have often used low-temporal-resolution datasets. Moreover, these impacts are not concurrently assessed in well-established agricultural areas and new deforestations hotspots. We aimed to identify these impacts using an experimental setup to collect high-temporal-resolution hydrological and hydrochemical data in two pairs of low-order streams in catchments under contrasting land use and land cover (native vegetation vs. pasture) in the Amazon and Cerrado biomes. Our results indicate that the conversion of natural landscapes to pastures increases carbon and nutrient fluxes via streamflow in both biomes. These changes were the greatest in total inorganic carbon in the Amazon and in potassium in the Cerrado, representing a 5.0- and 5.5-fold increase in the fluxes of each biome, respectively. We found that stormflow, which is often neglected in studies on stream hydrochemistry in the tropics, plays a substantial role in the carbon and nutrient fluxes, especially in the Amazon biome, as its contributions to hydrochemical fluxes are mostly greater than the volumetric contribution to the total streamflow. These findings demonstrate that assessments of the impacts of deforestation in the Amazon and Cerrado biomes should also take into account rapid hydrological pathways; however, this can only be achieved through collection of high-temporal-resolution data.

对于亚马逊农业前沿活跃砍伐区土地利用和土地覆盖变化对溪流水文化学的影响的研究有限，且往往采用低时间分辨率的观测数据集。此外，这些影响并未在已建立的农业区域和新出现的砍伐热点地区同时评估。本研究旨在通过实验装置收集亚马逊和巴西高原生物群落中两种低级别流域内，在对比土地利用和土地覆盖（原生植被与牧场）条件下的高时间分辨率水文和化学数据，以识别这些影响。研究结果表明，将自然景观转变为牧场在两个生物群落中均通过径流增加了碳和营养物质通量。这些变化在亚马逊地区表现为总无机碳的增加最为显著，而在巴西高原地区则表现为钾的增加，分别代表各自生物群落通量增加了5.0至5.5倍。研究发现，风暴径流，在热带地区溪流水文化学研究中常被忽视，在碳和营养物质通量中发挥着重要作用，尤其是在亚马逊生物群落中，其贡献于水文化学通量的体积比通常大于其对总径流体积的贡献。这些发现表明，对亚马逊和巴西高原生物群落砍伐影响的评估也应考虑快速水文途径；然而，这仅能通过收集高时间分辨率数据来实现。