Karst water resources are valuable freshwater sources for around 10 % of the world population. Nonetheless, anthropogenic factors and global changes have been seriously deteriorating the karst water quality and dependent ecosystems. Solute transport models are powerful tools to monitor, control, and manage the water quality and dependent ecosystem functioning. By representing and predicting the spatiotemporal behavior of solute migration in karst systems, the transport models enhance our understanding about the karst transport processes, thus enabling us to explore contamination risks and potential outcomes. This paper reviews the current state of knowledge on the modelling of solute transport processes in karst aquifers, thereby unveiling the fundamental challenges underlying a successful karst transport modelling. We discuss to what extent and in what ways we can handle these challenges and derive the key challenges and directions for reliable modelling of transport processes in karst systems in the present context of global changes.

Salt dilution is a well-established streamflow measurement method in creeks, which works particularly well downstream of turbulent flow sections as mixing of the salt tracer is enhanced. Usually salt dilution measurements are performed manually, which considerably limits the observations of rare peak flow events. However, these events are particularly important for constructing robust rating curves and avoiding large uncertainties in the extrapolation of river discharge values. An additional challenge is the variability of the river cross-section, especially after larger discharge events, leading to non-stationary rating curves. Therefore, discharge measurements well distributed over time are needed to both construct a reliable streamflow-water level relationship and to detect changes caused by erosion and deposition processes. To overcome these two issues, we used an automated streamflow measuring systems at three different sites in the Alps for event-based discharge measurements. This system allowed us to measure close to the highest peak flows at all three sites in the observation period (2020-2021) and to detect abrupt changes in the rating curve. Based on a very large data set of almost 300 measurements, we were able to evaluate the reliability of the system and to identify the main sources of uncertainty in the experimental setup. One key aspect was the site selection for the downstream electrical conductivity sensors as measurement location strongly controls the signal-to-noise ratio (SNR) in the recorded breakthrough curves.

Global sensitivity analysis of model parameters is an important step in the development of a hydrological model. If available, time series of different variables are used to increase the number of sensitive model parameters and better constrain the model output. However, this is often not possible. To overcome this problem, we coupled the active subspace method with the discrete wavelet transform. The Haar mother wavelet is the most appropriate for this purpose in case of homoschedastic measurement error, since it avoids any loss of information through the discrete wavelet transform of the signal. With this methodology, we study how the temporal scale dependency of hydrological processes affects the structure and dimension of the active subspaces. We apply the methodology to the LuKARS model of the Kerschbaum spring discharge in Waidhofen a.d. Ybbs (Austria). Our results reveal that the dimensionality of an active subspace increases with increasing hydrologic processes which are affecting a temporal scale. As a consequence, different parameters are sensitive on different temporal scales. Finally, we show that the total number of sensitive parameters identified at different temporal scales is larger than the number of sensitive parameters obtained using the complete spring discharge signal. Hence, instead of using multiple data time series to identify more sensitive parameters, we can also obtain more information about parameter sensitivities from one single, decomposed time series.