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

由于气候年复一年的变化和相关的极端天气事件，英国的河流容易发生洪水，并出现干旱和缺水的时期。对这些影响作出强有力的预测，对于制定目前和未来对我们宝贵的水资源的有效规划和管理至关重要。预报河流流量，特别是极高流量和极低流量时，涉及到地表、近地表和深层水流的复杂、相互作用和非线性过程的动态变化。在国家尺度上，现在可以使用一系列不同的建模方法来描述这种特征，这些方法在数学处理和这些综合集水过程的物理表示水平上有所不同。然而，这种更大规模的建模在如何单独描述每个河流集水区方面存在许多挑战。因此，有必要确保主要的水文过程得到很好的描述，并确保模型为一系列水文行为提供可靠的河流流量预测。这个博士项目将解决改善我们对河流集水区概念化的一个关键方面，即地下水在哪里是水文循环的关键组成部分，以及它如何与近地表水文过程相互作用。就英国而言，在水文模型中更好地表示地下水动态在英格兰东南部将特别重要；在这里，主要含水层除了支持重要的水生生态系统外，还为数百万人提供高质量的公共供水。虽然已经制定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年)，但很少有关于在概念性水文模型中表示地下水时使用的适当复杂程度的研究，尽管这被认为是一个限制(例如Rojas等人，2010年)。此外，该项目将利用一个新的国家规模的不确定性分析建模框架，通过改进这些流动路径如何在空间和时间上相互作用和耦合的概念化，来探索这些近地表和地下水流动路径之间的相互作用(Coxon等人，2014年)。这将确保所开发的概念在英国数百个有河流流量数据和地下水监测的流域得到充分评估。此外，学生将在不确定性分析框架内量化我们的河流流量预测能力的变化，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年)。

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).