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

1000万加拿大人依靠地下水作为饮用水来源。这种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称，由于增加了化肥的使用，整个加拿大的水质都在下降(Eilers等人，2010年)。因此，Frost在2006年报告称，大约16%的马尼托巴省供水水井的硝酸盐含量超过饮用水指导标准(10毫克/L NO3-N)。污染物的淋溶受非饱和带中的通量控制。季节气候变化导致补给量的空间和时间分布发生极端变化。在积雪融化和土壤融化的过程中，加拿大草原出现了一种反复出现的基于事件的补给，其特点是水的快速流动。快速的通量意味着很高的污染风险。这与目前用于确定风险的平均水文条件形成了鲜明对比。地下水模型是定量、评价和预测地下水风险的有价值的工具。目前包括气候变异性的模型不能准确地预测地下水污染的风险，以进行可持续的土地和地下水管理，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。拟议的工作将考虑到土壤性质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研究。这项研究将分三个部分进行评估，(I)作为污染物移动的驱动力的补给，(Ii)污染物渗入地下水，以及(Iii)补给和淋滤对地下水水质的影响。 我们之前已经证明，使用来自低成本观测数据的基于物理的充电建模在预测点尺度的充电方面具有巨大的潜力。这项工作将扩大，重点放在土壤融化和积雪融化情况下非饱和地带的快速通量，并利用先进的地质统计学方法进行空间提升。缩小尺度的气候数据将被用来确定在不断变化的气候情况下的补给。在最初5年周期的第二部分，将建立一种可靠的测量技术来估计硝酸盐淋失率，重点是解冻，并在田间和实验室中应用。观察到的硝酸盐淋失模式和动力学将被用于数值模拟。补给和淋失估计将被整合到地下水模型中，使用分裂算子技术来确定地下水的状态和未来硝酸盐污染的风险。 因此，可以在了解土壤和水文参数的时间和空间变异性的基础上，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.