LTLS Freshwater Ecosystems ("LTLS-FE"): Analysis and future scenarios of Long-Term and Large-Scale freshwater quality and impacts
LTLS 淡水生态系统(“LTLS-FE”):长期和大规模淡水质量和影响的分析和未来情景
基本信息
- 批准号:NE/X015688/1
- 负责人:
- 金额:$ 33.79万
- 依托单位:
- 依托单位国家:英国
- 项目类别:Research Grant
- 财政年份:2022
- 资助国家:英国
- 起止时间:2022 至 无数据
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
Long Term Large Scale - Freshwater Ecosystems (LTLS-FE):UK freshwaters have historically and are currently subject to many chemical pressures on their quality and consequent biological condition. While some pressures (e.g. metals, acidification, oxygen-consuming substances) appear to have eased over recent decades, others (e.g. industrial and personal organic micropollutants, nitrogen and phosphorus) remain and may be increasing. Whether these contemporary improvements in UK freshwater quality and biodiversity will continue into the future is of great interest to the public who rely on freshwaters for recreation, to water companies for drinking water supply, to industry and to the statutory regulators - and there is recent evidence that some recent improvements to biodiversity may have stalled. The impact of projected future climate on UK freshwater quantity has been recently quantified, but few studies have comprehensively explored how UK freshwater quality processes, pollutant interactions and biodiversity, might be impacted by a warmer climate and catchment changes. Exploring how water quality at the UK scale may change in the future requires an understanding of its numerous, spatially variable drivers and how they interact over time, and a model that can put all this information together. In this project, called "Long Term Large Scale - Freshwater Ecosystems" or "LTLS-FE", our objective is to use a dynamic model of the UK landscape to simulate the health and quality of our freshwaters from the present day into the future. However, the future is highly uncertain, so we have to consider a number of possibilities, or "scenarios". These scenarios will be used as input to our model (LTLS-Freshwater Model) and will allow us to quantify the responses of rivers and lakes to projected future climates and associated scenarios of polluting chemicals and pathogens (collectively referred to as "pollutants") and their likely impacts on freshwater biodiversity.To link the national-scale water quality model with a freshwater biodiversity model we will first use available UK freshwater data for diatoms, macrophytes, macroinvertebrates and fish to develop an understanding of how riverine ecosystems are affected by multiple stressors such as nutrients, hydrology, morphology, temperature and toxins. A second approach using statistical models will allow us to investigate the extent to which ecological traits account for stressor responses. Models will be used to extrapolate ecological responses to stressors in space and under the different scenarios up to 2080.The hydrological and ecological modelling of multiple scenarios up to 2080 will result in future scenarios of pollutants and ecological responses for every UK river (on a 5km grid) across the UK. These scenarios will be discussed with stakeholders and the project team to identify the best way to present them. Options include analyses showing how particular regions or rivers can mitigate against freshwater risks, and/or analyses to help identify which of the many changes, or combination of changes in each catchment has the greatest impact on downstream freshwater quality and biota. To maximise the uptake of the outputs from the project we will engage with stakeholders to better understand the models and datasets that are of extended use to them. Following on from this, all model code developed in the project will be open source and available in a public repository (github), and datasets will be made available in the publicly available NERC Environmental Information Data Centre (EIDC). For those stakeholders who may not wish to download model code and datasets, a more-visual alternative will be provided in the form of a map-based web-tool to show how water quality and biodiversity in UK rivers might change in the coming decades.
长期大规模-淡水生态系统(LTLS-FE):英国淡水在历史上和目前都受到许多化学压力对其质量和随之而来的生物条件的影响。虽然近几十年来某些压力(例如金属、酸化、耗氧物质)似乎有所缓解,但其他压力(例如工业和个人有机微污染物、氮和磷)仍然存在,而且可能正在增加。英国淡水质量和生物多样性的这些当代改善是否会在未来持续下去,对于依赖淡水娱乐的公众、饮用水供应的自来水公司、工业界和法定监管机构来说都非常感兴趣-最近有证据表明,最近对生物多样性的一些改善可能已经停滞。预测未来气候对英国淡水量的影响最近已经量化,但很少有研究全面探讨英国淡水质量过程,污染物相互作用和生物多样性,可能会受到气候变暖和集水区变化的影响。探索英国规模的水质在未来可能如何变化,需要了解其众多的,空间可变的驱动因素,以及它们如何随着时间的推移相互作用,以及一个模型,可以把所有这些信息放在一起。在这个名为“长期大规模-淡水生态系统”或“LTLS-FE”的项目中,我们的目标是使用英国景观的动态模型来模拟从现在到未来的淡水健康和质量。然而,未来是高度不确定的,所以我们必须考虑一些可能性,或“情景”。这些场景将被用作我们模型的输入(LTLS-淡水模型),并将使我们能够量化河流和湖泊对预测的未来气候以及污染化学品和病原体的相关情景的响应(统称为“污染物”)及其对淡水生物多样性可能产生的影响。规模水质模型与淡水生物多样性模型,我们将首先使用现有的英国淡水数据的硅藻,水生植物,大型无脊椎动物和鱼类的研究,以了解河流生态系统如何受到多种压力因素的影响,如营养物质,水文,形态,温度和毒素。使用统计模型的第二种方法将使我们能够调查生态特征在多大程度上解释压力反应。模型将被用来推断生态的压力源在空间和不同的情况下,到2080年的水文和生态模拟的多个方案,到2080年将导致未来的情景污染物和生态响应的每一个英国河流(在一个5公里的网格)在英国。将与利益相关者和项目团队讨论这些情景,以确定呈现这些情景的最佳方式。备选方案包括分析特定区域或河流如何能够减轻淡水风险,和/或分析以帮助确定每个集水区的许多变化或变化组合中的哪一个对下游淡水质量和生物群影响最大。为了最大限度地吸收项目的产出,我们将与利益相关者合作,以更好地了解对他们有广泛用途的模型和数据集。在此之后,该项目中开发的所有模型代码都将是开源的,并可在公共存储库(github)中获得,数据集将在公开的NERC环境信息数据中心(EIDC)中提供。对于那些可能不希望下载模型代码和数据集的利益相关者,将以基于地图的网络工具的形式提供一个更直观的替代方案,以显示英国河流的水质和生物多样性在未来几十年可能发生的变化。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Daniel Lapworth其他文献
Daniel Lapworth的其他文献
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{{ truncateString('Daniel Lapworth', 18)}}的其他基金
Land Ocean Carbon Transfer (1-year extension)
陆地海洋碳转移(延长1年)
- 批准号:
NE/V013211/1 - 财政年份:2021
- 资助金额:
$ 33.79万 - 项目类别:
Research Grant
HyCRISTAL: Integrating Hydro-Climate Science into Policy Decisions for Climate-Resilient Infrastructure and Livelihoods in East Africa
HyCRISTAL:将水文气候科学纳入东非气候适应性基础设施和生计的政策决策中
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NE/M020452/1 - 财政年份:2015
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$ 33.79万 - 项目类别:
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TryGGER:Application of tryptophan Florescence Sensors for improved raw water quality monitoring of Faecal Contamination in Groundwater sources
TryGGER:色氨酸荧光传感器在改善地下水源粪便污染原水质量监测中的应用
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NE/M021939/1 - 财政年份:2015
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$ 33.79万 - 项目类别:
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Mapping groundwater quality degradation beneath growing rural towns in sub-Saharan Africa
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NE/L002078/1 - 财政年份:2013
- 资助金额:
$ 33.79万 - 项目类别:
Research Grant
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