Refining knowledge of biogeochemical cycling and ecological interactions within the Lake Erie watershed

完善伊利湖流域内生物地球化学循环和生态相互作用的知识

• 批准号: RGPIN-2018-03996
• 负责人: Crossman, Jill
• 金额: $ 2.19万
• 依托单位: University of Windsor
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=683601
• 关键词: Refining; knowledge; biogeochemical; cycling; ecological

Harmful algal blooms (HABs) and low dissolved oxygen (DO) levels are major concerns in large waterbodies, and have been linked with high phosphorus inputs and intensified algal growth and decay. While watershed management plans have helped to reduce nutrient loads received by lakes, HAB frequency has recently increased and DO concentration targets have not consistently been met, particularly in the Laurentian Great Lakes. This failure of management strategies has been linked to increasing availability of certain fractions of phosphorus, despite declines in the total loads, and attributed to complex local interactions between land management approaches, climate change and long-term soil storage. Although there are many theories as to the nutrient sources, a solution to their availability and that of the recurring HABs has not yet been found.*******Recent management failures result from our incomplete knowledge of nutrient transport mechanisms, complex process interactions and associated lake biogeochemical responses. Until these knowledge gaps are filled, the potential effects of new management strategies cannot be predicted. This research program will improve our knowledge of threshold drivers of HABs and lake health through deploying mobile monitoring units around the watershed of Lake Erie, monitoring physical, chemical and biological interactions across land, river, shoreline and deep lake interfaces at hourly intervals.*******Models remain the best available tools to simulate efficacy of management strategies, however they are only as accurate as our process understanding. Historically observational uncertainty and a lack of biogeochemical knowledge has obstructed models from generating helpful management solutions to eutrophication issues. Using the new monitoring data a catchment-lake model will be implemented, with reduced observational uncertainty, to simulate sediment, chemical, hydrological and biological fluxes through the terrestrial, riverine and lake corridor. Land management strategies for reducing frequency of occurrence of HABs and increasing lake DO concentrations will be simulated, and their long term resilience assessed under potential future changes in climate and land-use.*******Improvement of our understanding of interactions between land management and aquatic ecosystems benefits policy makers, residents and users of the lake by generating new information on areas particularly vulnerable to change (hotspots), highlighting areas for management priority and generating novel, more resilient management strategies enabling a healthier lake to be sustained in the face of future change. The large database of high resolution monitoring information generated will be beneficial to a wide range of users, including government (MOECC and EC), local landowners, industry (water treatment plants), and other researchers.***

有害藻华 (HAB) 和低溶解氧 (DO) 水平是大型水体中的主要问题，并且与高磷输入和加剧的藻类生长和腐烂有关。虽然流域管理计划有助于减少湖泊接收的养分负荷，但最近 HAB 频率有所增加，并且溶解氧浓度目标并未始终如一地达到，特别是在劳伦森五大湖。尽管总负荷下降，但管理策略的失败与某些磷的可用性增加有关，并归因于土地管理方法、气候变化和长期土壤储存之间复杂的局部相互作用。尽管关于养分来源有很多理论，但尚未找到其可用性和反复出现的有害细菌问题的解决方案。********最近的管理失败是由于我们对养分运输机制的不完全了解、复杂的过程造成的相互作用和相关的湖泊生物地球化学反应。在填补这些知识空白之前，无法预测新管理策略的潜在影响。该研究计划将通过在伊利湖流域周围部署移动监测装置，每小时监测陆地、河流、海岸线和深湖交界处的物理、化学和生物相互作用，提高我们对有害藻华和湖泊健康阈值驱动因素的了解。** *****模型仍然是模拟管理策略功效的最佳可用工具，但它们的准确性取决于我们对流程的理解。历史上观测的不确定性和生物地球化学知识的缺乏阻碍了模型为富营养化问题生成有用的管理解决方案。利用新的监测数据，将实施集水湖泊模型，以减少观测不确定性，以模拟通过陆地、河流和湖泊廊道的沉积物、化学、水文和生物通量。将模拟减少赤潮发生频率和增加湖泊溶解氧浓度的土地管理策略，并根据未来潜在的气候和土地利用变化评估其长期恢复能力。********提高我们对之间相互作用的理解土地管理和水生生态系统通过生成有关特别容易发生变化的区域（热点）的新信息、突出管理优先领域并制定新颖、更具弹性的管理战略，使湖泊更加健康，使决策者、居民和湖泊使用者受益。面对未来的变化。生成的高分辨率监测信息的大型数据库将有利于广泛的用户，包括政府（MOECC和EC）、当地土地所有者、工业（水处理厂）和其他研究人员。***