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

Cinkus, Guillaume; Mazzilli, Naomi; Jourde, Hervé

doi:10.1007/s12665-023-10830-5

Cited by 5 publications

(2 citation statements)

References 46 publications

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“…However, current disaster studies of karst mainly focus on single hazard aspects, such as the classification, formation, and evolution mechanisms of rocky desertification [27,28], drought-related soil and vegetation research [29,30], or the development of karst floods [31,32]. The phenomena of compound multiple disasters, e.g., drought and waterlogging (DW), and their temporal and spatial distribution has not been analyzed in-depth [33][34][35].…”

Section: Introductionmentioning

confidence: 99%

Spatial and Temporal Dynamics of Drought and Waterlogging in Karst Mountains in Southwest China

et al. 2023

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Under the synergetic effect of land use and climate change, natural disasters occur frequently in the karst region of southwest China. This study used the daily precipitation data from 33 meteorological stations across 61 years (1960–2020), utilized the MK test and the Z index to calculate the levels of drought and waterlogging (DW) at multiple times (month and year) and spatial (province, sub-divisions, station) scales, and investigated the spatiotemporal patterns and their associated factors in DW in the karst mountains of Guizhou, southwest China. The results showed that: (1) DW occurred frequently and increasingly during the study period in Guizhou, with seven mutations of annual DW. (2) There were more droughts (especially heavy droughts) based on annual data, but more waterlogging (especially heavy waterlogging) based on monthly data. Drought occurred most frequently in summer, while waterlogging was most frequent in spring, followed by winter. (3) The normalized difference drought and waterlogging index (NDDWI) was created in this study to exhibit combined DW phenomena, which could be improved in the future to better present the compound hazards. The spatiotemporal patterns of DW in Guizhou were complicated and associated with terrain, geology, climate change, vegetation, land use, etc.

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Section: Introductionmentioning

confidence: 99%

Spatial and Temporal Dynamics of Drought and Waterlogging in Karst Mountains in Southwest China

et al. 2023

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“…Some of these methods can be applied using a spreadsheet, a numerical code or a statistical analysis software package, but there has been a lack of easy-to-use tools that gather all these methods with the aim to standardize their application. The recent work of Cinkus et al (2023) can however be mentioned in this regard with an application of some methods limited to the analysis of the discharge time series, while the developed XLKarst tool is also used to interpret the rainfall/discharge relationship (or any input/output relationship of a black-box system that can be considered as a linear and time-invariant system).…”

Section: Introductionmentioning

confidence: 99%

XLKarst, an Excel tool for time series analysis, spring recession curve analysis and classification of karst aquifers

Bailly-Comte,

Ladouche,

Charlier

et al. 2023

Hydrogeol J

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Karst aquifers are complex hydrogeological systems that require numerous in-situ measurements of hydrological and physico-chemical parameters to characterize transfer processes from the recharge area to the karst spring. Numerous graphical, statistical or signal processing methods have been developed for decades to interpret these measurements, but there is no simple and standardized tool that can be used for this purpose, which is necessary for a rigorous comparison of results between case studies. This Technical Note presents XLKarst, which has been developed to provide a simple and easy-to-use tool to process a selection of proven methods that characterize the functioning of karst systems. This tool allows (i) time series analysis based on correlation and spectral analysis and, for flow measurements, the use of other statistics and base flow separation, (ii) calculation of the cumulative distribution function to build a spring flow probability plot, and (iii) analysis of spring flow recession and expression of the results in a karst system classification scheme. These methods are first described by providing the key elements of their use and interpretation in the scientific literature. Then, an application to the Fontaine de Nîmes karst system (southern France) is used to highlight the complementarity of the methods proposed by XLKarst to describe the hydrodynamic behavior of a karst system based on daily data of rainfall and discharge over 22 years.

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Rational gaze: Presenting the open-source HYPEtools R package for analysis, visualization, and interpretation of hydrological models and datasets

Brendel,

Capell,

Bartosova

2024

Environmental Modelling & Software

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

Cited by 5 publications

References 46 publications

Spatial and Temporal Dynamics of Drought and Waterlogging in Karst Mountains in Southwest China

Spatial and Temporal Dynamics of Drought and Waterlogging in Karst Mountains in Southwest China

XLKarst, an Excel tool for time series analysis, spring recession curve analysis and classification of karst aquifers

Rational gaze: Presenting the open-source HYPEtools R package for analysis, visualization, and interpretation of hydrological models and datasets

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