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。这种系统的特性是非常具有挑战性的，因为一些过程尚未完全理解。只要实地调查及其结果没有得到适当的综合建模概念的补充，传统方法（即考虑到具体方面的各种方法的组合）的适用性就有限。已经存在用于描述单个岩溶隔间（表层区、渗流区和潜水区）的各种工具，然而，通过基于物理的方法考虑所有相关过程的综合模型仍然缺失。我们的初步工作突出了类似的挑战，基于物理的建模岩溶功能（输入信号生成，矩阵的相互作用，管道存储过程）。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。将整个岩溶系统分离成单独的隔间，以增强现有的分布参数模型的物理基础和空间分布特性。这将导致更好地理解流动和运输过程。这些工具将与综合建模方法相结合，通过复杂的逆技术（例如数据加权、目标函数）查明模型的不确定性。理想模型的正向建模允许基于多个弹簧信号（流量、热量和溶质）识别和量化不同的输入信号源。通过敏感性分析评估特定工艺和参数的相关性。多个信号的联合反演用于评估所需的模型复杂度，这取决于信息和数据的可用性。逐步适应模型的复杂性降低了计算需求的高度参数化的复杂岩溶系统模型。模型的不确定性是通过自动逆向建模工具估计的。最后，这将允许确定未来的勘探需求，因此，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.