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

Monitoring of groundwater redistribution in a karst aquifer using a superconducting gravimeter

Geodetic tools monitor the earth’s deformation and gravity field. They are presently sensitive enough to record subtle changes triggered by hydrological processes, thus providing complementary data to standard hydrological measurements. Among these tools, superconducting gravimeter (SG) have proven useful to unravel groundwater redistribution, which significantly alter the gravity field. In the frame of the EquipEx MIGA (Matter wave-laser based Interferometer Gravitation Antenna) project, one SG (iOSG-24) was set up in July 2015 in the Low-noise Underground Laboratory (LSBB) at Rustrel, France, in a gallery located 500 m beneath the surface. In this work, we analyse the underground iOSG-24 gravity time series together with hydro-meteorological data and basic gravity modelling. We find that the gravimeter recorded the redistribution of water in the ground and that most of this redistribution occurs in the unsaturated zone located above the gravimeter. Nevertheless, residuals between our model and the gravity data suggest the occurrence of large lateral fluxes and rapid runoff not considered in our model. We discuss how the setting of a second SG, planned in July 2018, at the surface of the LSBB could help unravelling such hydrological processes

SNO KARST: a French network of observatories for the multidisciplinary study of critical zone processes in karst watersheds and aquifers

Karst aquifers and watersheds represent a major source of drinking water around the world. They are also known as complex and often highly vulnerable hydrosystems due to strong surface groundwater interactions. Improving the understanding of karst functioning is thus a major issue for an efficient management of karst groundwater resources. A comprehensive understanding of the various processes can be achieved only by studying karst systems over a wide range of spatio-temporal scales under different geological, geomorphological, climatic and soil cover settings. The objective of the French Karst National Observatory Service (SNO Karst) is to supply the international scientific community with appropriate data and tools, with the ambition of i) facilitating the collection of long-term observations of hydro-geo-chemical variables in karst, and ii) promoting knowledge-sharing and developing cross-disciplinary research on karst. The present paper provides an overview of the monitoring sites and of collective achievements such as the KarstMod modular modelling platform and the PaPRIKa toolbox. It also presents the research questions addressed within the framework of SNO Karst, along with major research results regarding i) the hydrological response of karst to climate and anthropogenic changes, ii) the influence of karst on geochemical balance of watersheds in the critical zone, and iii) the relationships between the structure and hydrological functioning of karst aquifers and watersheds

Production of He-4 and (4) in Pb-Pb collisions at root(NN)-N-S=2.76 TeV at the LHC

Results on the production of He-4 and (4) nuclei in Pb-Pb collisions at root(NN)-N-S = 2.76 TeV in the rapidity range vertical bar y vertical bar <1, using the ALICE detector, are presented in this paper. The rapidity densities corresponding to 0-10% central events are found to be dN/dy4(He) = (0.8 +/- 0.4 (stat) +/- 0.3 (syst)) x 10(-6) and dN/dy4 = (1.1 +/- 0.4 (stat) +/- 0.2 (syst)) x 10(-6), respectively. This is in agreement with the statistical thermal model expectation assuming the same chemical freeze-out temperature (T-chem = 156 MeV) as for light hadrons. The measured ratio of (4)/He-4 is 1.4 +/- 0.8 (stat) +/- 0.5 (syst). (C) 2018 Published by Elsevier B.V.Peer reviewe

Sensibilité et incertitude de modélisation sur les bassins versants à forte composante karstique

The present thesis aims to work out the general characteristics of the sensitivity of numerical models of flow within karst systems. A special attention is devoted to the study of the influence of karst specificities (high heterogeneity, duality of flow, highly non-linear behaviour) on the sensitivity propagation, with the final purpose of answering the following questions: (i) is it possible to calibrate the model ? (ii) is the calibration robust ? (iii) is it possible to reduce equifinality, through multi-objective calibration or through multi-variable calibration ? The analysis is performed for global reservoir models and distributed, hybrid flow models. This contribution stresses the potentialities of local sensitivity analyses. Despite their inherent limitations (local approximation), local analyses have proved to bring valuable insights into the general behaviour of complex, non-linear flow models, at little computational cost. Besides, this contribution also stresses the interest of multi-variable calibration as compared to multi-objective calibration, as regards equifinality reduction.L'objectif de cette thèse est de déterminer des caractéristiques générales du comportement de la sensibilité dans la modélisation hydrodynamique des écoulements en milieu karstique. Nous étudions l'influence des spécificités du milieu karstique (forte hétérogénéité de structure, dualité de l'écoulement, forte non-linéarité de fonctionnement) sur la propagation de la sensibilité en vue de déterminer des règles générales pour la calibration. En particulier, nous essayons de répondre aux questions suivantes: (i) la calibration est-elle possible ? (ii) la calibration est-elle robuste ? (iii) est-il possible de réduire l'équifinalité via une calibration multi-objectif ou multi-variable ? L'analyse est menée pour le cas d'une modélisation conceptuelle globale et pour celui d'une modélisation hybride distribuée. Cette contribution met en évidence le potentiel des méthodes locales d'analyse de sensibilité. En dépit des limitations inhérentes à cette approche (approximation locale), l'analyse locale permet une compréhension fine du fonctionnement du modèle, pour un coût de calcul réduit. Par ailleurs, cet travail souligne l'intérêt d'une calibration multi-variable par rapport à une calibration multi-objectif, dans une optique de réduction de l'équifinalité