Can one identify karst conduit networks geometry and properties from hydraulic and tracer test data?

Abstract

a b s t r a c tKarst aquifers are characterized by extreme heterogeneity due to the presence of karst conduits embedded in a fractured matrix having a much lower hydraulic conductivity. The resulting contrast in the physical properties of the system implies that the system reacts very rapidly to some changes in the boundary conditions and that numerical models are extremely sensitive to small modifications in properties or positions of the conduits. Furthermore, one major issue in all those models is that the location and size of the conduits is generally unknown. For all those reasons, estimating karst network geometry and their properties by solving an inverse problem is a particularly difficult problem.In this paper, two numerical experiments are described. In the first one, 18,0 0 0 flow and transport simulations have been computed and used in a systematic manner to assess statistically if one can retrieve the parameters of a model (geometry and radius of the conduits, hydraulic conductivity of the conduits) from head and tracer data. When two tracer test data sets are available, the solution of the inverse problems indicate with high certainty that there are indeed two conduits and not more. The radius of the conduits are usually well identified but not the properties of the matrix. If more conduits are present in the system, but only two tracer test data sets are available, the inverse problem is still able to identify the true solution as the most probable but it also indicates that the data are insufficient to conclude with high certainty.In the second experiment, a more complex model (including non linear flow equations in conduits) is considered. In this example, gradient-based optimization techniques are proved to be efficient for estimating the radius of the conduits and the hydraulic conductivity of the matrix in a promising and efficient manner.These results suggest that, despite the numerical difficulties, inverse methods should be used to constrain numerical models of karstic systems using flow and transport data. They also suggest that a pragmatic approach for these complex systems could be to generate a large set of karst conduit network realizations using a pseudo-genetic approach such as SKS, and for each karst realization, flow and transport parameters could be optimized using a gradient-based search such as the one implemented in PEST. (A. Borghi). tion, often resulting into karstic conduits which are organized in hierarchical networks.Annable [1] gives an exhaustive overview of the evolution of the conceptual models of speleogenesis over the last two centuries. The conceptual model that is considered in the present study [ [63] , e.g.] is the following: the karst aquifer is composed by 2 main hydrofacies: the matrix which represents more than 90% of the volume of the aquifer, and has an important storage role; and the conduits which represent a very small volume, but have a very high importance for flow, since they are considered to be responsible for more or less 90% of the total flow.P...

Impacts of Artificial Regulation on Karst Spring Hydrograph in Northern China: Laboratory Study and Numerical Simulations

“…Karst aquifers are characterized by extreme heterogeneity due to the presence of karst conduits with higher hydraulic conductivity and the surrounded matrix with a much lower hydraulic conductivity [8,11], which can lead to well-known permeability scale effects [47]. Some karst areas, such as Sete Lagoas aquifer in Brazil [47], Edwards Aquifer located in the United States [48], and Juras Mountain in Switzerland [49], have increased in permeability in scale effects from small-to regional-scale.…”

Karst landscapes of China: patterns, ecosystem processes and services

“…Karst aquifers are extremely heterogeneous due to the presence of hierarchical conduit networks in this fractured matrix (Borghi et al 2016). Karst hydrological models can facilitate our understanding of karst aquifer response to precipitation events at multiple scales and are critical for flood prediction, pollutant transport, nutrient loss and re-distribution and water resource management.…”

Coupling SKS and SWMM to Solve the Inverse Problem Based on Artificial Tracer Tests in Karstic Aquifers

“…The internal structure of karstic aquifers is the result of karstification processes: fracture and bedding planes are enlarged by dissolution, leading to a gradual establishment of a conduit network [2]. The location and the size of the conduits are generally unknown, leading to major issues for the potential application of physically distributed karst models [3]. Although the complete description of flow paths in karstic aquifers is not possible, several methods have been developed to characterize groundwater drainage structure in karstic aquifers, such as artificial tracer tests [4] and applied geophysics [5], among others.…”

“…Considering the above-mentioned information, it should be theoretically possible to infer conduit network geometry based on artificial tracer tests [3]. Nonetheless, it constitutes a complex task because one needs to solve several problems with numerical modeling of the conduit network and then about flow and solute transport in a synthetic karst model.…”