Sinkhole Flooding and Aquifer Recharge in Arid to Dry Sub-Humid Regions: A Systematic Review

Sinkhole flooding is an essential hydrological process to recharge karst aquifer in arid to dry sub-humid regions. On the other hand, the increase of rain extremes is one of the major consequences of the global warming, together with the expansion of drylands. Thus, appropriate runoff regulation in endorheic karst basins in order to reduce the risk of flooding and improve the quantity and quality of the water drained by sinkholes will be more and more crucial. With these premises, a systematic review was performed by using WoS engine to infer the best practices for the karst water management in regions actually or potentially affected by water scarcity. Hydrological models are essential to manage the consequences of climate change on karst water resource, however the review shows that providing the tools necessary for reliable modeling is still challenging. Finally, due to the intrinsic vulnerability of the karst aquifers, pollution reduction and wastewater recycling policy will play key role in the next decades.

Hydrological Modeling of Karst Watershed Containing Subterranean River Using a Modified SWAT Model: A Case Study of the Daotian River Basin, Southwest China

Karst watershed refers to the total range of surface and underground recharge areas of rivers (including subterranean rivers and surface rivers) in karst areas. Karst water resources, as the primary source of domestic water supply in southwest China, are vital for the social and economic development of these regions. These resources are greatly significant for guiding water resources management in karst areas to establish a high-precision hydrological model of karst watersheds. Choosing the Daotian river basin in the Wumeng Mountains of Southwest China as the study area, this paper proposed a method for simplifying karst subterranean rivers into surface rivers by modifying the digital elevation model (DEM) based on a field survey and tracer test. This method aims to solve the inconsistency between the topographical drainage divides and actual catchment boundaries in karst areas. The Soil and Water Assessment Tool (SWAT) model was modified by replacing the single-reservoir model in the groundwater module with a three-reservoir model to depict the constraints of multiple media on groundwater discharge in the karst system. The results show that the catchment areas beyond topographic watershed were effectively identified after simplifying subterranean rivers to surface rivers based on the modified DEM data, which ensured the accuracy of the basic model. For the calibration and two validation periods, the Nash–Sutcliffe efficiencies (NSE) of the modified SWAT model were 0.87, 0.83, and 0.85, respectively, and R2 were 0.88, 0.84, and 0.86, respectively. The NSE of the modified SWAT model was 0.09 higher than that of the original SWAT model in simulating baseflow, which effectively improved the simulation accuracy of daily runoff. In addition, the modified SWAT model had a lower uncertainty within the same parameter ranges than the original one. Therefore, the modified SWAT model is more applicable to karst watersheds.

Coupled nitrogen transformation and carbon sink in the karst aquatic system: a review

Carbonate bedrock regions represent that 14% of Earth's continental surface and carbon (C) sink in karst water plays an important role in the global C cycle due to the CO2 consumption during carbonate mineral weathering. Intensive agriculture and urbanization have led to the excessive input of nitrogen (N) into aquatic systems, while the high concentrations of inorganic C in the karst water might affect the N cycle. This paper summarized the characteristics of water in karst regions and discussed the N transformation coupled with the C cycle in the condition of high Ca2+ content, high pH, and high C/N ratios. Carbonates can consume more atmospheric and pedologic CO2 than non-carbonates because of their high solubility and high rate of dissolution, resulting in the higher average CO2 sink in karst basins worldwide than that in non-karst basins. Therefore, carbonate mineral weathering and aquatic photosynthesis are the two dominant ways of CO2 absorption, which are termed as coupled carbonate weathering. As the alkalinity and high C/N content of karst water inhibit the denitrification and mineralization processes, the karst aquatic environment is also served as the N sink.

Artificial Intelligence In Source Discrimination of Mine Water: A Deep Learning Algorithm For Water Source Discrimination

With increasing coal mining depth, the source of mine water inrush becomes increasingly complex. The problem of distinguishing the source of mine water in mines and tunnels has been addressed by studying the hydrochemical components of the Pingdingshan Coalfield and applying the artificial intelligence (AI) method to discriminate the source of the mine water. 496 data of mine water have been collected. Six ions of mine water are used as the input data set: Na++K+, Ca2+, Mg2+, Cl-, SO2- 4, and HCO- 3. The type of mine water in the Pingdingshan coalfield is classified into surface water, Quaternary pore water, Carboniderous limestone karst water, Permian sandstone water, and Cambrian limestone karst water. Each type of water is encoded with the number 0 to 4. The one-hot code method is used to encode the numbers, which is the output set. On the basis of hydrochemical data processing, a deep learning model was designed to train the hydrochemical data. Ten new samples of mine water were tested to determine the precision of the model. Nine samples of mine water were predicted correctly. The deep learning model presented here provides significant guidance for the discrimination of mine water.

Selection of Remote Sensing Images And Evapotranspiration Models On Complex Land Surfaces for a Humid Karst Catchment

Evapotranspiration (ET) is predominant variable for water management in various types of ecosystems, and ET processes in these ecosystems have been assessed through in-situ measuring and modelling methods. However, it is challenging to measure actual ET and upscale it to regional level. In addition, the accuracy of retrieved parameters from models is usually low for karst landscapes, where the underlying surface is more complex than non-karst landscapes. Due to various porosities and conduits, aquifers in karst landscapes typically show remarkable and rapid responses to precipitation events, leading to serious water stress. Therefore, there is an urgent need to quantify water fluxes to provide reliable evidence for the protection and sustainable management of karst water resources. In this study, five plots were built to observe actual ET based on Thermal Dissipation Probes (TDP), re-designed Ventilated-chamber and Micro-lysimeters in a karst catchment in southwest China. Then, three models (Penman-Monteith-Leurning, PML; Remote Sensing-Priestley and Taylor, RS-PT; and Hargreaves) were selected to upscale ET estimation to the regional level based on Landsant-8 and MODIS data. The results showed that: 1) The PML model performed better than other models (p < 0.01) with higher R2 values (0.72 for MODIS images and 0.87 for Landsat-8 images) and smaller RMSE values (1.4 mm·day-1 and 0.8 mm·day-1 for MODIS and Landsat-8 images, respectively); 2) Daily ET exhibited significant seasonal variability and different spatial distribution; 3) ET had a slightly positive correlation with DEM; however, ground temperature had a negative correlation with ET. By combining remote sensing data and upscaling it to the regional level, this study helps improve the accuracy of measured and estimated ET. It suggests that ET is strongly regulated by vegetation coverage and available energy in subtropical humid karst catchments.

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

Despite many existing approaches, modeling karst water resources remains challenging and often requires solid system knowledge. Artificial Neural Network approaches offer a convenient solution by establishing a simple input-output relationship on their own. However, in this context, temporal and especially spatial data availability is often an important constraint, as usually no or few climate stations within a karst spring catchment are available. Hence spatial coverage is often unsatisfying and can introduce severe uncertainties. To avoid these problems, we use 2D-Convolutional Neural Networks (CNN) to directly process gridded meteorological data followed by a 1D-CNN to perform karst spring discharge simulation. We investigate three karst spring catchments in the Alpine and Mediterranean region with different meteorologic-hydrological characteristics and hydrodynamic system properties. We compare our 2D-models both to existing modeling studies in these regions and to 1D-models, which use climate station data, as it is common practice. Our results show that our models are excellently suited to model karst spring discharge and rival the simulation results of existing approaches in the respective areas. The 2D-models learn relevant parts of the input data and by performing a spatial input sensitivity analysis we can further show their potential for karst catchment localization and delineation.