Catchment properties as predictors of greenhouse gas concentrations across a gradient of boreal lakes
Catchment properties as predictors of greenhouse gas concentrations across a gradient of boreal lakes
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DOI：
10.3389/fenvs.2022.880619
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发表时间：
2022-09
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通讯作者：
N. Valiente;A. Eiler;Lina Allesson;T. Andersen;F. Clayer;C. Crapart;P. Dörsch;L. Fontaine;Jan Heuschele;R. Vogt;Jing Wei;H. D. de Wit;D. Hessen
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作者：
N. Valiente;A. Eiler;Lina Allesson;T. Andersen;F. Clayer;C. Crapart;P. Dörsch;L. Fontaine;Jan Heuschele;R. Vogt;Jing Wei;H. D. de Wit;D. Hessen
北方湖泊是地球上最丰富的湖泊。酸雨沉积、气候和集水区土地利用的变化增加了陆地溶解有机物（DOM）的横向通量，导致北方淡水广泛的布朗宁。这种布朗宁会影响水生群落和生态系统过程，并增加温室气体（GHG）CH 4、CO2和N2 O的排放。在这项研究中，我们预测生物饱和度的温室气体在北方湖泊使用一套化学，水文，气候和土地利用参数。为此，浓度的温室气体和营养物质（有机C，-P，和-N）的地表水样品中确定从73个湖泊在挪威东南部覆盖范围广泛的DOM和营养物质的浓度，以及集水特性和土地利用。每种温室气体饱和度的空间变化与解释变量有关。集水特征（水文和气候参数），如湖泊的大小和夏季降水，以及植被指数，是关键的决定因素时，拟合GAM模型的CH 4和CO2饱和度（解释71%和54%，分别），而夏季降水和土地利用数据是最好的预测N2 O饱和度，解释近50%的偏差。我们的研究结果表明，湖泊的大小，降水量和陆地初级生产的流域控制温室气体在北方湖泊的饱和度。这些预测的基础上的73个湖泊的数据集进行了验证，对一个独立的数据集，从46个湖泊在同一地区。总之，这提供了一个更好的了解驱动程序和空间变化的温室气体饱和度在北方湖泊跨越宽梯度的湖泊和集水区的属性。评估强调，需要在温室气体预测模型中纳入多个解释性参数，以便在整个北方生物群落中进行外推。
Boreal lakes are the most abundant lakes on Earth. Changes in acid rain deposition, climate, and catchment land use have increased lateral fluxes of terrestrial dissolved organic matter (DOM), resulting in a widespread browning of boreal freshwaters. This browning affects the aqueous communities and ecosystem processes, and boost emissions of the greenhouse gases (GHG) CH4, CO2, and N2O. In this study, we predicted biotic saturation of GHGs in boreal lakes by using a set of chemical, hydrological, climate, and land use parameters. For this purpose, concentrations of GHGs and nutrients (organic C, -P, and -N) were determined in surface water samples from 73 lakes in south-eastern Norway covering wide ranges in DOM and nutrient concentrations, as well as catchment properties and land use. The spatial variation in saturation of each GHG is related to explanatory variables. Catchment characteristics (hydrological and climate parameters) such as lake size and summer precipitation, as well as NDVI, were key determinants when fitting GAM models for CH4 and CO2 saturation (explaining 71 and 54%, respectively), while summer precipitation and land use data were the best predictors for the N2O saturation, explaining almost 50% of deviance. Our results suggest that lake size, precipitation, and terrestrial primary production in the watershed control the saturation of GHG in boreal lakes. These predictions based on the 73-lake dataset was validated against an independent dataset from 46 lakes in the same region. Together, this provides an improved understanding of drivers and spatial variation in GHG saturation in boreal lakes across wide gradients of lake and catchment properties. The assessment highlights the need to incorporate multiple explanatory parameters in prediction models of GHGs for extrapolation across the boreal biome.