Soil Methanotrophy Model (MeMo v1.0): a process-based model to quantify global uptake of atmospheric methane by soil

Abstract

Abstract. Soil bacteria known as methanotrophs are the sole biological sink for atmospheric methane (CH 4 ), a potent greenhouse gas that is responsible for ∼ 20 % of the humandriven increase in radiative forcing since pre-industrial times. Soil methanotrophy is controlled by a plethora of factors, including temperature, soil texture, moisture and nitrogen content, resulting in spatially and temporally heterogeneous rates of soil methanotrophy. As a consequence, the exact magnitude of the global soil sink, as well as its temporal and spatial variability, remains poorly constrained. We developed a process-based model (Methanotrophy Model; MeMo v1.0) to simulate and quantify the uptake of atmospheric CH 4 by soils at the global scale. MeMo builds on previous models by Ridgwell et al. (1999) and Curry (2007) by introducing several advances, including (1) a general analytical solution of the one-dimensional diffusion-reaction equation in porous media, (2) a refined representation of nitrogen inhibition on soil methanotrophy, (3) updated factors governing the influence of soil moisture and temperature on CH 4 oxidation rates and (4) the ability to evaluate the impact of autochthonous soil CH 4 sources on uptake of atmospheric CH 4 . We show that the improved structural and parametric representation of key drivers of soil methanotrophy in MeMo results in a better fit to observational data. A global simulation of soil methanotrophy for the period 1990-2009 using MeMo yielded an average annual sink of 33.5 ± 0.6 Tg CH 4 yr −1 . Warm and semi-arid regions (tropical deciduous forest and open shrubland) had the highest CH 4 uptake rates of 602 and 518 mg CH 4 m −2 yr −1 , respectively. In these regions, favourable annual soil moisture content (∼ 20 % saturation) and low seasonal temperature variations (variations < ∼ 6 • C) provided optimal conditions for soil methanotrophy and soil-atmosphere gas exchange. In contrast to previous model analyses, but in agreement with recent observational data, MeMo predicted low fluxes in wet tropical regions because of refinements in formulation of the influence of excess soil moisture on methanotrophy. Tundra and mixed forest had the lowest simulated CH 4 uptake rates of 176 and 182 mg CH 4 m −2 yr −1 , respectively, due to their marked seasonality driven by temperature. Global soil uptake of atmospheric CH 4 was decreased by 4 % by the effect of nitrogen inputs to the system; however, the direct addition of fertilizers attenuated the flux by 72 % in regions with high agricultural intensity (i.e. China, India and Europe) and by 4-10 % in agriculture areas receiving low rates of N input (e.g. South America). Globally, nitrogen inputs reduced soil uptake of atmospheric CH 4 by 1.38 Tg yr −1 , which is 2-5 times smaller than reported previously. In addition to improved characterization of the contemporary soil sink for atmospheric CH 4 , MeMo provides an opportunity to quantify more accurately the relative importance of soil methanotrophy in the global CH 4 cycle in the past and its capaci...

Effects of nitrogen deposition on growing-season soil methane sink across global forest biomes

“…Our results also have important implications for improving the performance of process-based models to simulate and quantify the effect of global N deposition on soil CH 4 sinks (e.g., Ridgwell et al, 1999;Curry, 2007;Murguia-Flores et al, 2018). Currently, the existing models simply account for a negative effect of N inputs on soil CH 4 uptake by involving an inhibition factor (Ridgwell et al, 1999;Curry et al, 2007;Murguia-Flores et al, 2018).…”

“…Based on the mean rate of growing-season soil CH 4 uptake (-0.038±0.029 mg CH 4 m -2 h -1 ) for tropical forest (Dutaur and Verchot, 2007), biomescale soil CH 4 sink in tropical forest was estimated to be -5.99±4.57 Tg CH 4 . Overall, global forest soils contributed to a CH 4 sink of 18.5 Tg CH 4 during growing season, accounting for more than half of the annual sum soil CH 4 sink (~30 Tg CH 4 yr -1 ) in global terrestrial biomes (Kirschke et al, 2013;Murguia-Flores et al, 2018). If the amount of soil CH 4 sink in nongrowing season is accounted, forest soils can contribute an even higher proportion of global soil CH 4 sinks.…”

“…Currently, the existing models simply account for a negative effect of N inputs on soil CH 4 uptake by involving an inhibition factor (Ridgwell et al, 1999;Curry et al, 2007;Murguia-Flores et al, 2018). Using a process-based model, Murguia-Flores et al (2018) global soil CH 4 sinks. This indicates the necessity of models to separately consider the effect of low-level N addition from that of high-level N addition and distinguish the effects across forest biomes.…”

Identification of soil methane oxidation activity by inverse modelling

“…где V cons,0 -базовая константа потребления при некоторых условиях среды (час -1 ), обычно этомаксимальная константа, достигаемая при оптимальных условиях; f i -переменные по глубине безразмерные эмпирические функции параметров среды (если V cons,0 имеет смысл максимальной константы, то 0 ≤ f i ≤ 1). Они определяются либо на основе лабораторных экспериментов с отобранными образцами почвы [5], либо на основе полевых данных путём решения задачи оптимизации, когда подбираются такие их значения, которые позволят получить модельные значения удельных потоков СН 4 , наиболее близкие к экспериментально измеренным [3]. Такое феноменологическое описание позволяет отразить только влияние тех факторов среды, которые учтены в (2).…”

Downscaling Satellite Soil Moisture using Geomorphometry and Machine Learning

“…458 Other errors are derived from input data imperfections and difficulties meeting modeling assumptions. 459 These errors in soil moisture modeling inputs increase the risk of bias and uncertainty propagation to 460 subsequent soil moisture modeling outputs and soil mapping applications [72][73][74]. For example, 461 elevation data surfaces derived from remote sensing data (such as the global DEM used here) could 462 show artifacts (i.e., false pikes or spurious sinks) due to data saturation or signal noise that can be 463 propagated to final soil moisture predictions [75].…”