When best is the enemy of good – critical evaluation of performance criteria in hydrological models

Performance criteria play a key role in the calibration and evaluation of hydrological models and have been extensively developed and studied, but some of the most used criteria still have unknown pitfalls. This study set out to examine counterbalancing errors, which are inherent to the Kling–Gupta efficiency (KGE) and its variants. A total of nine performance criteria – including the KGE and its variants, as well as the Nash–Sutcliffe efficiency (NSE) and the modified index of agreement (d1) – were analysed using synthetic time series and a real case study. Results showed that, when assessing a simulation, the score of the KGE and some of its variants can be increased by concurrent overestimation and underestimation of discharge. These counterbalancing errors may favour bias and variability parameters, therefore preserving an overall high score of the performance criteria. As bias and variability parameters generally account for two-thirds of the weight in the equation of performance criteria such as the KGE, this can lead to an overall higher criterion score without being associated with an increase in model relevance. We recommend using (i) performance criteria that are not or less prone to counterbalancing errors (d1, modified KGE, non-parametric KGE, diagnostic efficiency) and/or (ii) scaling factors in the equation to reduce the influence of relative parameters

Karst spring discharge modeling based on deep learning using spatially distributed input data

Despite many existing approaches, modeling karst water resources remains challenging as conventional approaches usually heavily rely on distinct system knowledge. Artificial neural networks (ANNs), however, require only little prior knowledge to automatically establish an input–output relationship. For ANN modeling in karst, the temporal and spatial data availability is often an important constraint, as usually no or few climate stations are located within or near karst spring catchments. Hence, spatial coverage is often not satisfactory and can result in substantial uncertainties about the true conditions in the catchment, leading to lower model performance. To overcome these problems, we apply convolutional neural networks (CNNs) to simulate karst spring discharge and to directly learn from spatially distributed climate input data (combined 2D–1D CNNs). We investigate three karst spring catchments in the Alpine and Mediterranean region with different meteorological–hydrological characteristics and hydrodynamic system properties. We compare the proposed approach both to existing modeling studies in these regions and to our own 1D CNN models that are conventionally trained with climate station input data. Our results show that all the models are excellently suited to modeling karst spring discharge (NSE: 0.73–0.87, KGE: 0.63–0.86) and can compete with the simulation results of existing approaches in the respective areas. The 2D models show a better fit than the 1D models in two of three cases and automatically learn to focus on the relevant areas of the input domain. By performing a spatial input sensitivity analysis, we can further show their usefulness in localizing the position of karst catchments

Identification of relevant indicators for the assessment of karst systems hydrological functioning: proposal of a new classification

International audienceClassification is a first-line tool for understanding the main characteristics of a natural system's response. We propose a new classification of karst systems hydrological functioning that is based on spring discharge time series and takes profit of spring discharge databases to encompass the high diversity of karst hydrological functioning. It discriminates six different classes based on three relevant indicators of karst hydrological functioning. A core dataset made of 10 karst systems was first considered for the setup of the classification. The spring discharge time series were investigated according to recession curves, statistical and signal analyses to identify relevant indicators of hydrological functioning. The selection of the most relevant indicators and the proposal of the classification were based on multivariate analyses. The classification was then tested on spring discharge time series of 78 karst systems located worldwide. All the systems homogeneously spread among the six proposed classes, which highlights the relevance of the approach and the representativeness of the various classes of hydrological functioning. Results from the proposed methodology were finally discussed to explore its limitations and define guidelines for its application

Proposal of a typology of karst systems functioning based on relevant indicators of karst springs hydrodynamics

International audience10% of the world’s population is dependent on karst water resources for drinking water. Understanding the functioning of these complex and heterogeneous systems is therefore a major challenge for long term water resource management. Over the past century, different methods have been developed to analyse hydrological series, and subsequently characterize the functioning of karst systems. These methods can be considered as a preliminary step in the development and design of hydrological models of karst functioning for sustainable water resource management. Recent progress in analytical tools, as well as the emergence of data bases of discharge time series (e.g. the French SNO KARST database and the WoKaS database at global scale) allow reconsidering former typology of karst system hydrodynamic responses. Ten karst systems and associated spring discharge time series were considered for developing the typology. The systems are well-known with a high-quality monitoring and they cover a wide range of hydrological functioning, which ensure the relevance of the analyses. The methodology for the assessment and the development of the typology consisted in (i) the analysis of springs discharge time series according to four different methods, (ii) the selection or proposal of the most relevant indicators of karst systems hydrodynamics, and (iii) the interpretation of the information from these indicators based on principal component analysis and clustering techniques. A typology of karst systems accounting for 6 different classes is finally proposed, based on 3 aspects of functioning: the capacity of dynamic storage, the draining dynamic of the capacitive function and the variability of the hydrological functioning. The typology was applied to a wider dataset composed of spring discharge of 78 karst systems. The results show a relevant distribution of the systems among the different classes

KarstID: an R Shiny application for the analysis of karst spring discharge time series and the classification of karst system hydrological functioning

International audienceKarst spring discharge time series analyses are often used to gain preliminary insights into the hydrological functioning of a karst system. KarstID is an R Shiny application that facilitates the completion of such analyses and allows the identification of karst system hydrological functioning. The application permits (i) to perform statistical, recession curves, classified discharges and signal (simple correlational and spectral) analyses; (ii) to calculate relevant indicators representative of distinct hydrological characteristics of karst systems, (iii) to classify karst systems hydrological functioning; and (iv) to compare the results to a database of 78 karst systems. The KarstID software is free, open source, and actively developed on a developer community platform. The user-friendly installation and launch make it especially accessible even for non-programmers; therefore, KarstID can be used for both research and educational purposes. The application and its user manual are both available on the French SNO KARST website (https://sokarst.org/en/softwares-en/karstid-en/)

KarstID: An interactive R application for karst spring hydrograph analysis that also provides a classification of karst hydrological functioning

International audienc

KarstID: An interactive R application for karst spring hydrograph analysis that also provides a classification of karst hydrological functioning

International audienc