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: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=648328
• 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（HABs）和低溶解氧（DO）水平是大型水体中的主要问题，并与高磷输入和藻类生长和腐烂加剧有关。虽然流域管理计划有助于减少湖泊所接受的营养负荷，但最近赤潮发生频率增加，溶解氧浓度目标未能始终达到，特别是在劳伦特五大湖。这种失败的管理策略已被链接到增加可用性的某些部分的磷，尽管在总负荷下降，并归因于复杂的土地管理方法，气候变化和长期土壤储存之间的相互作用。虽然关于营养来源有许多理论，但尚未找到解决其可获得性和反复出现的有害藻华的办法。最近的管理失败是由于我们对营养盐传输机制、复杂过程相互作用和相关湖泊生态地球化学响应的不完全了解。在填补这些知识空白之前，无法预测新管理战略的潜在影响。这项研究计划将通过在伊利湖流域周围部署移动的监测装置，每小时监测一次陆地、河流、海岸线和深湖界面的物理、化学和生物相互作用，提高我们对有害生物和湖泊健康的阈值驱动因素的认识。模型仍然是模拟管理策略有效性的最佳工具，但它们的准确性取决于我们对流程的理解。从历史上看，观测的不确定性和缺乏生物地球化学知识阻碍了模型产生有益的管理解决方案，以富营养化问题。将利用新的监测数据建立一个集水区-湖泊模型，减少观测不确定性，以模拟陆地、河流和湖泊走廊的沉积物、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战略，并根据未来气候和土地利用的潜在变化评估其长期恢复力。提高我们对土地管理和水生生态系统之间相互作用的理解，有利于决策者、居民和湖泊使用者，因为它产生了关于特别易受变化影响的地区（热点）的新信息，突出了管理优先领域，并产生了新的、更具复原力的管理战略，使一个更健康的湖泊在面对未来变化时能够持续下去。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。