Characterisation and modelling of multi-compartment karst systems by integrated interpretation of spring signals - iKarst

岩溶含水层系统是具有明显各向异性的非均质水文系统。这种系统的特征是极具挑战性的，因为有几个过程还没有完全被理解。只要实地调查及其结果得不到适当的综合建模概念的补充，经典方法(即考虑具体方面的方法的组合)的适用性就有限。已经有各种工具来描述单独的岩溶区(表面区、渗流区和潜水区)，但通过物理方法考虑所有相关过程的综合模型仍然缺乏。我们的初步工作突出了基于物理的岩溶特征建模(输入信号生成、管道-基质相互作用和管道存储过程)方面的类似挑战。此外，高度参数化模型(模型结构、参数和结果)的非唯一解限制了复杂数值模型的应用。将整个岩溶系统分成单独的隔间，用于增强现有的分布参数模型，以进行基于物理和空间分布的表征。这将导致对流动和运输过程的更好理解。这些工具将结合成一种综合建模方法，该方法允许通过复杂的逆技术(例如数据加权、目标函数)来识别模型不确定性。理想化模型的正演模型允许根据多个弹簧信号(流动、热量和溶质)识别和量化不同的输入信号源。通过敏感度分析评价了具体工艺和参数的相关性。多个信号的联合反演被用于评估所需的模型复杂性，这取决于信息和数据的可用性。模型复杂性的逐步适应减少了高度参数化的复杂岩溶系统模型的计算需求。通过自动逆建模工具估计模型的不确定性。最后，这将有助于确定未来的勘探需求，从而促进岩溶系统管理。

Karst aquifer systems are heterogeneous hydrological systems with distinctive anisotropy. The characterisation of such systems is extremely challenging, since several processes are yet not fully understood. Classical approaches (i.e. combinations of methods considering specific aspects) are of limited applicability, as long as field investigations and their results are not complemented by appropriate comprehensive modelling concepts. Various tools for describing individual karst compartments (surface zone, vadose zone, and phreatic zone) already exist, however, integrated models considering all relevant processes by physically based approachesare still missing. Our preliminary work highlights similar challenges regarding a physically based modelling of karst features (input signal generation, conduit-matrix interaction, and conduit storage processes). Additionally, non-unique solutions of highly parameterisedmodels (model structure, parameters, and results) limit the application of complex numerical models. The separation of the entire karst system into individual compartments is used to enhance existing distributed-parameter models for a physically based and spatially distributed characterisation. This will lead to improved understanding of flow and transport processes. The tools are to be combined to an integrated modelling approach, which allows to identify model uncertainties through sophisticated inverse techniques (e.g. data weighting, objective functions). Forward modelling of idealised models allows to identify and quantify different input signal sources based on multiple spring signals (flow, heat, and solutes). The relevance of specific processes and parameters is evaluated by sensitivity analysis. Joint inversion of multiple signals is used to evaluate needed model complexity, depending on information and data availability. The stepwise adaption of model complexity reduces computational demands of highly parameterised complex karst system models. Model uncertainties are estimated via automatic inverse modelling tools. Concluding, this will allow to define future exploration demands and, therefore, foster karst system management.