National scale conceptual modelling of hydrology coupled to groundwater processes to improve predictions of river flows

英国的河流，由于我们的气候每年的变化和相关的极端天气事件，很容易发生洪水和干旱和缺水的时期。对这些影响进行可靠的预测，对于现在和未来制定有效的规划和管理我们宝贵的水资源至关重要。预测河流流量，特别是极端高流量和低流量，涉及动态变化的复杂，相互作用和非线性过程的表面，近地下和更深的流动路径。在国家一级，现在可以使用一系列建模方法进行这种定性，这些方法在数学处理和这些组合集水过程的物理代表性水平方面有所不同。然而，这种更大规模的建模在如何单独地模拟每个河流流域方面存在许多挑战。因此，有必要确保主要的水文过程得到很好的代表，模型提供强大的预测河流流量的“正确的原因”在一系列的水文行为。这个博士项目将解决改善我们的河流集水区的概念化的一个关键方面，即地下水是水文循环的一个关键组成部分，以及它如何与近地表水文过程相互作用。在英国的背景下，更好地表示水文模型中的地下水动态在英格兰东南部将是特别重要的，这里的主要含水层提供高品质的水到公共供应数百万人，除了支持重要的水生生态系统。虽然已经开发了在复杂的分布式地下水模型中探索不确定性来源的策略（例如Refsgaard等人，2012），在概念水文模型中表示地下水时，很少有关于使用适当复杂程度的研究，尽管这被认为是一种限制（例如Rojas等人，2010年）。此外，该项目应利用新的国家规模不确定性分析建模框架，通过改进这些流动路径如何相互作用以及在空间和时间上耦合的概念化来探索近地表和地下水流路径之间的相互作用（Coxon等人，2014）。这将确保开发的概念在英国各地的河流流量数据和地下水监测可用的数百个集水区进行充分评估。此外，学生将在不确定性分析框架内量化我们对河流流量的预测能力的变化，该框架在评估建模方法的方式中重要地量化了河流流量和地下水数据的质量（Coxon等人2015）。

The UK's rivers, due to the variability of our climate from year to year and associated extreme weather events, are prone to flooding and periods of drought and water scarcity. Making robust predictions of these impacts is critical to developing effective planning and management of our precious water resources both for now and in the future. Predicting river flows, especially for extreme high and low flows, involve dynamically changing complex, interacting and non linear processes of surface, near subsurface and deeper flow pathways. At national scales, such characterisations are now possible using a range of modelling approaches that differ in their mathematical treatment and level of physically based representation of these combined catchment processes. However such larger scale modelling has many challenges in how to characterise each river catchment individually. Therefore it is necessary to ensure the dominant hydrological processes are well represented and that the models provide robust predictions of river flows for the 'right reasons' over a range of hydrological behaviour. This PhD project will address a critical aspect of improving our conceptualisation of river catchments, namely where groundwater is a critical component of the hydrological cycle and how it interacts with the near-surface hydrological processes. In the context of the UK, better representations of groundwater dynamics in hydrological models will be particularly important in south-east England; here major aquifers provide high quality water into public supply for millions of people, in addition to supporting important aquatic ecosystems. Whilst strategies for exploring sources of uncertainty in complex distributed groundwater models have been developed (e.g. Refsgaard et al., 2012), there has been little research on the appropriate degree of complexity to use when representing groundwater in conceptual hydrological models, though this is recognised as a limitation (e.g. Rojas et al., 2010). Furthermore the project shall utilise a new national scale uncertainty analysis modelling framework to explore these interactions between near surface and groundwater flow paths by improving the conceptualisation of how these flow paths interact and are coupled in space and time (Coxon et al., 2014). This will ensure the concepts developed are fully evaluated for hundreds of catchments across the UK where river flow data and groundwater monitoring are available. Furthermore the student will quantify the changes in our predictive capability of river flows within an uncertainty analyses framework that importantly quantifies the quality of both the river flow and the groundwater data in the way the modelling approaches are evaluated (Coxon et al. 2015).