Estimation of recharge and nutrient leaching at different scales to improve sustainable land and groundwater management

估算不同规模的补给和养分淋滤，以改善可持续的土地和地下水管理

• 批准号: RGPIN-2016-05966
• 负责人: Hollaender, Hartmut
• 金额: $ 1.75万
• 依托单位: University of Manitoba
• 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=679700
• 关键词: Estimation; recharge; nutrient; leaching; different

Ten million Canadians rely on groundwater as a drinking water source. This resource is under continuous threat of contamination. This is a growing problem; a study on environmental sustainability of Canadian agriculture reported a decline in water quality throughout Canada due to increased application of fertilizers (Eilers et al., 2010). As a result, Frost reported in 2006 that approximately 16% of Manitoban water supply wells had nitrate levels above the drinking water guidelines (10 mg/L NO3-N). Leaching of pollutants is controlled by fluxes in the unsaturated zone. Seasonal climatic variability produces extreme changes in both the spatial and temporal distribution of recharge. A recurring event-based recharge in the Canadian Prairies emerges during snowmelt in combination with soil thawing, which is characterized by fast water fluxes. Fast fluxes imply a high risk of contamination. This is in contrast to the average hydrologic conditions currently being used to identify the risk. Groundwater models are a valuable tool for the quantification, evaluation, and prediction of the risk. Present models that include the climatic variability cannot accurately predict the risk of groundwater contamination for sustainable land and groundwater management since they require vast amounts of temporal and spatial input data to predict future states reliably. The proposed work will pursue innovative research on sustainable land and groundwater management by consideration of the temporal and spatial variability of soil properties and the changing climate. The research will be evaluated in three sections, (i) recharge as the driving force of pollutant movement, (ii) pollutants leaching into the groundwater, and (iii) impact of recharge and leaching on groundwater quality.****We have previously shown that physically-based recharge modeling using data from low-cost observations has vast potential to predict recharge at the point scale. This work will be extended by focusing on fast fluxes in the unsaturated zone during soil thawing and snow melt situations and by using advanced geostatistical methods for spatial upscaling. Downscaled climate data will be used to identify recharge under changing climate situations. In the second part of the initial 5-year cycle, a robust measurement technique to estimate nitrate leaching rates with a focus on thawing will be established and applied in the field and in the laboratory. The observed pattern and kinetics of nitrate leaching will be used for numerical modeling. Recharge and leaching estimates will be integrated into groundwater modeling using split-operator techniques to identify the state of groundwater and future risk of nitrate contamination.*Thus, sophisticated sustainable land and groundwater management policies can be developed based on the knowledge of the temporal and spatial variability of soil and hydrological parameters.***

一千万加拿大人依靠地下水作为饮用水源。该资源受到污染的不断威胁。这是一个日益严重的问题；一项关于加拿大农业环境可持续性的研究报告说，由于肥料的施用增加，加拿大整个加拿大的水质下降（Eilers等，2010）。结果，弗罗斯特（Frost）在2006年报道说，大约16％的曼尼托邦供水井的硝酸盐水平高于饮用水指南（10 mg/l NO3-N）。污染物的浸出由不饱和区域的通量控制。季节性气候变异性会导致补给的空间和时间分布发生极大的变化。加拿大大草原的基于事件的经常性充电与土壤解冻结合结合在融雪过程中出现，其特征是快速水通量。快速通量意味着污染的高风险。这与目前用于识别风险的平均水文条件相反。地下水模型是对风险进行量化，评估和预测的宝贵工具。当前包括气​​候变异性的模型无法准确预测可持续土地和地下水管理的地下水污染风险，因为它们需要大量的时间和空间输入数据来可靠地预测未来的状态。拟议中的工作将通过考虑土壤特性的时间和空间变异性以及气候变化来对可持续土地和地下水管理进行创新研究。这项研究将在三个部分中进行评估，（i）充电为污染物运动的驱动力，（ii）浸出到地下水中的污染物，以及（iii）充值和浸出对地下水质量的影响。这项工作将通过在土壤解冻和雪融化的情况下关注不饱和区域的快速通量，并使用先进的地统计学方法进行空间升级，从而扩展这项工作。缩小的气候数据将用于识别在变化的气候情况下的充值。在最初的5年周期的第二部分中，将建立并在实验室和实验室中建立并应用的一种良好的测量技术，以估算硝酸盐浸出率。观察到的硝酸盐浸出的模式和动力学将用于数值建模。补给和浸出估计将使用分式操纵技术将其集成到地下水建模中，以识别地下水状态和未来的硝酸盐污染风险。