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、能源和地球化学循环中发挥着重要作用，并控制着陆地-大气的相互作用和反馈。由于森林潜在的大量用水和相关的冷却效应，森林可对当地气候产生重大影响，对水流既有积极影响，也有消极影响，减少干旱天气和洪水。此外，森林也受到气候的影响，影响森林状况、生长，从而影响水、二氧化碳和能源的交换。例如，据预测，气候变暖将导致更频繁的干旱，对森林功能和相关服务产生重大影响，并可能产生不利影响。英国林地扩张的一个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（尽管存在相互矛盾的估计，林地设计和管理可以产生很大的影响）。有必要对“林地扩张将如何影响未来气候下的水、能源和碳交换”进行可靠的评估。目前的森林建模工具存在严重缺陷，被认为对与森林水文、能源和碳循环过程有关的通量和状态变量提供了不可靠的估计。因此，迫切需要一个科学上可靠的森林基准模型-数据系统。这样一个系统将为不同的树木景观设计和布局以及气候情景提供更可靠的森林碳收益和水权衡之间的相互作用估计，以及潜在的协同作用。如上所述，在不断变化的气候条件下，在森林设计、布局和管理方面，迫切需要更好地了解森林碳固存与水利用之间复杂的相互作用。目前，我们对树木类型，森林结构的季节内和年际变化（受林分密度，物候和树木大小和年龄分布的影响）如何影响蒸发以及英国景观的能量和碳交换的知识相对有限。我们目前对森林生态系统功能的理解是基于一系列监测和评估工具，包括清单、现场测量、遥感数据和模型模拟。目前的森林模型仅限于物种适宜性模型、经验生长和产量模型以及预测CO2/H2O通量的林分尺度详细模型，通常高度调整到特定位置。(i)某些过程的固有简化或忽视（2）忽略了植被的三维结构以及林分内物种、年龄和高度的多样性;（3）忽略了森林功能的时空变化（例如，与树木和叶子年龄相关），例如，结构和生理参数是恒定的时间。学生将开发和测试一个新的立场水平的过程模型，这将解决上述缺点，并利用最新的原位和卫星信息，最终，该模型将为制定气候智能型森林战略、计划和指导提供信息，以最大限度地提高碳效益，同时保护未来的水资源。