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

1000万加拿大人依赖地下水作为饮用水源。这一资源不断受到污染的威胁。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了由于肥料施用量的增加而导致整个加拿大的水质下降（Eilers等人，2010年）。因此，弗罗斯特在2006年报告说，马尼托巴省大约16%的供水威尔斯井的硝酸盐含量高于饮用水指南（10毫克/升NO3-N）。污染物的淋溶受包气带通量的控制。季节性气候变率在补给的空间和时间分布上都产生极端的变化。一个经常性的事件为基础的补给在加拿大大草原出现在融雪结合土壤解冻，其特点是快速的水通量。快速通量意味着高污染风险。这与目前用于确定风险的平均水文条件形成对照。地下水模型是量化、评估和预测风险的重要工具。目前的模型，包括气候变化不能准确地预测地下水污染的风险，可持续的土地和地下水管理，因为他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。拟议的工作将通过考虑土壤特性的时空变异性和气候变化，对可持续土地和地下水管理进行创新性研究。研究将分三个部分进行评价：（一）作为污染物移动驱动力的补给;（二）污染物渗入地下水;（三）补给和渗入对地下水质量的影响。 我们以前已经表明，基于物理的充电建模使用低成本的观测数据具有巨大的潜力，预测在点尺度的充电。这项工作将得到扩展，重点是土壤融化和积雪融化情况下非饱和区的快速通量，并使用先进的地质统计方法进行空间放大。将使用缩小尺度的气候数据来确定气候变化情况下的补给。在最初的5年周期的第二部分，将建立一个可靠的测量技术，以估计硝酸盐的浸出率，重点是解冻，并在实地和实验室应用。所观察到的硝酸盐淋溶模式和动力学将用于数值模拟。补给和浸出估计将被纳入地下水建模使用分裂算子技术，以确定地下水的状态和未来的硝酸盐污染的风险。 因此，可以根据对土壤和水文参数的时间和空间变异性的了解，制定复杂的可持续土地和地下水管理政策。

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.