Carbon gain vs water loss - using state-of-the-art simulation models and remote sensing to examine the potential impacts of woodland expansion

碳增益与水损失 - 使用最先进的模拟模型和遥感来检查林地扩张的潜在影响

• 批准号: 2600395
• 金额: --
• 依托单位: University of Reading
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2600395
• 关键词: Carbon; gain; vs; water; loss

Forests play an important role in the Earth's hydrological, energy and biogeochemical cycles, and control land-atmosphere interactions and feedbacks. Due to their potential considerable water use and related cooling effects, forests can significantly affect local climate and have both positive and negative impacts on water flows, reducing both dry weather and flood flows. Moreover, in turn forests are impacted by climate, affecting forest condition, growth and thus water, carbon dioxide and energy exchange. For example, climate warming is predicted to lead to more frequent droughts, with significant implications for forest functioning and related services and potential disbenefits. A major driver for the expansion of UK woodland is carbon sequestration but this poses a well-known threat to water resources (although there are conflicting estimates, and woodland design and management can exert a strong influence). There is a need for a reliable assessment of: "How woodland expansion will affect water-, energy- and carbon exchange under a future climate". Current forest modelling tools have severe shortcomings and are deemed to deliver unreliable estimates of fluxes and state variables related to forest hydrological, energy and carbon cycle processes. Hence there is an urgent need for a scientifically robust Forest Benchmark Model-Data system. Such a system will generate more reliable estimates of the interplay between forest carbon gains and water trade-offs, plus potential synergies, for different treescape designs and placements, and climate scenarios. As mentioned above, there is an urgent need to better understand the complex interactions between forest carbon sequestration and water use, in the context of forest design, placement and management under changing climatic conditions. Currently, we have relatively limited knowledge of how tree type, intra-seasonal and inter-annual changes in forest structure (as affected by stand density, phenology and the distribution of tree size and age) affect evaporation, as well as energy and carbon exchange across the UK landscape. Our current understanding of forest ecosystem functioning is based on a mix of monitoring and assessment tools ranging from inventories, in-situ measurements, remote sensing data and model simulations. Current forest models are limited to species suitability models, empirical growth and yield models, and stand-scale detailed models that predict CO2/H2O fluxes, typically highly tuned to the specific site.Unfortunately, there are a number of major draw-backs to these kinds of models, i.e., (i) the inherent simplification or neglect of certain processes (e.g. interception), (ii) ignoring of the vegetation 3-D structure and within-stand species-, age-, and height diversity (iii) neglecting of the spatio-temporal variation in forest functioning (e.g. in relation to tree and leaf age), by using, for example, structural and physiological parameters that are constant in time.The student will develop and test a novel stand-level process model, that will address the shortcomings outlined above and utilise the latest in-situ and satellite information, to better characterise forest form and function.Ultimately this model will inform the development of climate-smart forest strategies, plans and guidance to maximise carbon benefits while protecting future water resources.

森林在地球的水文，能量和生物地球化学周期中起着重要作用，并控制着土地大气的相互作用和反馈。由于其潜在的大量用水和相关的冷却效应，森林会严重影响当地气候，并对水流产生正面和负面影响，从而减少天气干燥和洪水流量。此外，森林又受到气候的影响，影响森林状况，生长，从而影响水，二氧化碳和能量交换。例如，预计气候变暖会导致更频繁的干旱，这对森林功能和相关服务以及潜在的无限效果产生了重大影响。扩大英国林地的主要驱动力是碳固存，但这对水资源构成了众所周知的威胁（尽管估计有矛盾，林地的设计和管理可以产生强大的影响力）。需要对：“林地扩张将如何影响未来气候下的水，能量和碳交换”。当前的森林建模工具具有严重的缺点，并被认为是对与森林水文，能源和碳循环过程有关的通量和状态变量的不可靠估计。因此，迫切需要科学强大的森林基准模型数据系统。这样的系统将对森林碳获取和水位折衷的相互作用，以及潜在的协同作用，以及针对不同的树木景观设计和位置以及气候场景之间的相互作用。如上所述，在森林设计，安置和管理不断变化的情况下，迫切需要更好地了解森林碳固存与用水之间的复杂相互作用。目前，我们对森林结构的树木类型，季节内和青年时期变化的了解相对有限（受林分密度，物候学和树大小和年龄的分布的影响）影响蒸发，以及整个英国景观的能量和碳交换。我们目前对森林生态系统功能的理解是基于监测和评估工具的组合，包括库存，原位测量，遥感数据和模型模拟。当前的森林模型仅限于物种适合性模型，经验增长和产量模型以及预测CO2/H2O通量的详细模型，通常高度调用特定地点。年龄和身高多样性（III）忽略了森林功能的时空变化（例如，与树木和叶时代有关），例如使用，例如，学生将不断发展和测试新颖的独立过程模型，以解决上面的不断效果，并在最新的境内构建了森林，并将其用于详细范围的森林和actell sigition interiity和satell section interiity and syte interiation and syitue sigity and sytue sigity and syitue sigity interiity and sytue sigity sigun intern的效果，并测试了森林的功能。模型将为气候智能森林策略的制定，计划和指导，以最大程度地利用碳利益，同时保护未来的水资源。