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评估阿拉斯加北极苔原植物群落中动态植被模型参数的不确定性。

Assessing dynamic vegetation model parameter uncertainty across Alaskan arctic tundra plant communities.

机构信息

Institute of Arctic Biology, University of Alaska Fairbanks, Fairbanks, Alaska, 99775, USA.

Terrestrial Ecosystem Science & Technology Group, Environmental Sciences Department, Brookhaven National Laboratory, Upton, New York, 11973, USA.

出版信息

Ecol Appl. 2022 Mar;32(2):e2499. doi: 10.1002/eap.2499. Epub 2021 Dec 13.

Abstract

As the Arctic region moves into uncharted territory under a warming climate, it is important to refine the terrestrial biosphere models (TBMs) that help us understand and predict change. One fundamental uncertainty in TBMs relates to model parameters, configuration variables internal to the model whose value can be estimated from data. We incorporate a version of the Terrestrial Ecosystem Model (TEM) developed for arctic ecosystems into the Predictive Ecosystem Analyzer (PEcAn) framework. PEcAn treats model parameters as probability distributions, estimates parameters based on a synthesis of available field data, and then quantifies both model sensitivity and uncertainty to a given parameter or suite of parameters. We examined how variation in 21 parameters in the equation for gross primary production influenced model sensitivity and uncertainty in terms of two carbon fluxes (net primary productivity and heterotrophic respiration) and two carbon (C) pools (vegetation C and soil C). We set up different parameterizations of TEM across a range of tundra types (tussock tundra, heath tundra, wet sedge tundra, and shrub tundra) in northern Alaska, along a latitudinal transect extending from the coastal plain near Utqiaġvik to the southern foothills of the Brooks Range, to the Seward Peninsula. TEM was most sensitive to parameters related to the temperature regulation of photosynthesis. Model uncertainty was mostly due to parameters related to leaf area, temperature regulation of photosynthesis, and the stomatal responses to ambient light conditions. Our analysis also showed that sensitivity and uncertainty to a given parameter varied spatially. At some sites, model sensitivity and uncertainty tended to be connected to a wider range of parameters, underlining the importance of assessing tundra community processes across environmental gradients or geographic locations. Generally, across sites, the flux of net primary productivity (NPP) and pool of vegetation C had about equal uncertainty, while heterotrophic respiration had higher uncertainty than the pool of soil C. Our study illustrates the complexity inherent in evaluating parameter uncertainty across highly heterogeneous arctic tundra plant communities. It also provides a framework for iteratively testing how newly collected field data related to key parameters may result in more effective forecasting of Arctic change.

摘要

随着北极地区在气候变暖的影响下进入未知领域,改进有助于我们理解和预测变化的陆地生物圈模型 (TBM) 非常重要。TBM 中的一个基本不确定性与模型参数有关,模型参数是模型内部的配置变量,其值可以从数据中估计。我们将一种针对北极生态系统开发的陆地生态系统模型 (TEM) 版本纳入到预测生态系统分析器 (PEcAn) 框架中。PEcAn 将模型参数视为概率分布,根据现有实地数据的综合情况估算参数,然后量化给定参数或参数集对模型的敏感性和不确定性。我们研究了总初级生产力方程中的 21 个参数变化如何影响两个碳通量(净初级生产力和异养呼吸)和两个碳库(植被碳和土壤碳)的模型敏感性和不确定性。我们在阿拉斯加北部沿从乌特恰维克附近的沿海平原延伸到布鲁克斯山脉南麓,再到苏厄德半岛的纬度梯度,针对不同的苔原类型(丛生苔原、石南苔原、湿地莎草苔原和灌木苔原)设置了 TEM 的不同参数化。TEM 对与光合作用温度调节相关的参数最为敏感。模型不确定性主要归因于与叶面积、光合作用温度调节以及气孔对环境光条件的响应相关的参数。我们的分析还表明,给定参数的敏感性和不确定性在空间上存在差异。在某些地点,模型的敏感性和不确定性往往与更广泛的参数有关,这突显了在环境梯度或地理位置上评估苔原生态系统过程的重要性。一般来说,在各个地点,净初级生产力 (NPP) 的通量和植被碳库的不确定性大致相同,而异养呼吸的不确定性高于土壤碳库。我们的研究说明了在高度异质的北极苔原生态系统中评估参数不确定性所固有的复杂性。它还为迭代测试与关键参数相关的新收集的实地数据如何可能更有效地预测北极变化提供了一个框架。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84be/9285828/3adbce90051e/EAP-32-0-g002.jpg

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