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

森林在地球的水文、能源和生物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。由于潜在的大量用水和相关的降温作用，森林可以显著影响当地气候，对水流有积极和消极的影响，从而减少干旱天气和洪水流量。此外，森林又会受到气候的影响，从而影响森林状况、生长，进而影响水、二氧化碳和能量交换。例如，气候变暖预计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，并可能带来不利影响。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(尽管有相互矛盾的估计，而且林地设计和管理可能会产生强大的影响)。有必要对以下问题进行可靠的评估：“在未来气候条件下，林地扩展将如何影响水、能源和碳交换”。目前的森林建模工具存在严重缺陷，被认为不能可靠地估计与森林水文、能源和碳循环过程有关的通量和状态变量。因此，迫切需要一个科学可靠的森林基准模型--数据系统。这样的系统将对森林碳收益和水的权衡之间的相互作用产生更可靠的估计，并针对不同的树篱设计和放置以及气候情景产生潜在的协同效应。如上所述，迫切需要在不断变化的气候条件下的森林设计、安置和管理的背景下，更好地了解森林碳汇和水利用之间的复杂相互作用。目前，我们对森林结构的树木类型、季节内和年际变化(受林分密度、物候、树木大小和年龄分布的影响)如何影响蒸发以及整个英国景观的能量和碳交换的了解相对有限。我们目前对森林生态系统功能的理解是基于各种监测和评估工具的组合，包括清查、现场测量、遥感数据和模型模拟。当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地点。不幸的是，这些模型有许多主要的缺点，即(I)固有的简化或忽略某些过程(例如，拦截)，(Ii)忽略植被三维结构和林内物种、年龄和高度的多样性(Iii)忽略森林功能的时空变化(例如，与树龄和树叶年龄有关)，例如，通过使用时间恒定的结构和生理参数。学生将开发和测试一种新的标准水平的过程模型，该模型将解决上述缺陷，并利用最新的现场和卫星信息，更好地描述森林的形态和功能。最终，该模型将为开发气候智能森林战略、计划和指导提供信息，以在保护未来水资源的同时最大限度地提高碳效益。

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.