Potential of ecological modelling and smart-drainage development for mitigating adverse effects of future global change-type droughts for the Estonian forest sector

Q4 Agricultural and Biological Sciences Forestry Studies Pub Date : 2020-12-01 DOI:10.2478/fsmu-2020-0017
Jan-Peter George, Mait Lang, M. Hordo, Sandra Metslaid, P. Post, T. Tamm
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引用次数: 1

Abstract

Abstract Global change-type droughts will become more frequent in the future and threaten forest ecosystems around the globe. A large proportion of the Estonian forest sector is currently subject to artificial drainage, which could probably lead to negative feedbacks when water supply falls short because of high temperatures and low precipitation during future drought periods. In this short article, we propose a novel research perspective that could make use of already gathered data resources, such as remote sensing, climate data, tree-ring research, soil information and hydrological modelling. We conclude that, when applied in concert, such an assembled dataset has the potential to contribute to mitigation of negative climate change consequences for the Estonian forest sector. In particular, smart-drainage systems are currently a rare phenomenon in forestry, although their implementation into existing drainage systems could help maintain the critical soil water content during periods of drought, while properly fulfilling their main task of removing excess water during wet phases. We discuss this new research perspective in light of the current frame conditions of the Estonian forest sector and resolve some current lacks in knowledge and data resources which could help improve the concept in the future.
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生态模拟和智能排水发展对减轻未来全球变化型干旱对爱沙尼亚森林部门的不利影响的潜力
未来,全球变化型干旱将更加频繁,并对全球森林生态系统构成威胁。爱沙尼亚森林部门的很大一部分目前采用人工排水,这可能会在未来干旱期间由于高温和低降水而导致供水不足时导致负面反馈。在这篇简短的文章中,我们提出了一个新的研究视角,可以利用已经收集的数据资源,如遥感、气候数据、树木年轮研究、土壤信息和水文模型。我们的结论是,在协调应用时,这样一个组装的数据集有可能有助于减轻气候变化对爱沙尼亚森林部门的负面影响。特别是,智能排水系统目前在林业中是一种罕见的现象,尽管在现有的排水系统中实施智能排水系统可以帮助在干旱期间保持关键的土壤含水量,同时适当地完成其在潮湿阶段排除多余水分的主要任务。我们根据爱沙尼亚森林部门目前的框架条件讨论这一新的研究观点,并解决目前在知识和数据资源方面的一些不足,这有助于在未来改进这一概念。
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来源期刊
Forestry Studies
Forestry Studies Agricultural and Biological Sciences-Forestry
CiteScore
0.70
自引率
0.00%
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0
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