A Method for Quantifying the Role of Carbonate Acid, Sulfuric Acid and Nitric Acid in Carbonate Weathering After Modifying the Effect of Evaporite in Qingjiang Karst Catchment

Qingjiang river is the second largest tributary of the Yangtze River in Hubei province, it’s also a typical karst catchment. Eighty-two important groundwater samples were collected during high and low water period of 2019. The results show that: (1) The major hydrochemistry types are Ca+Mg-HCO3 and Ca-HCO3, indicate that carbonate weathering is the main source of groundwater chemistry; (2) The results of inverse hydrochemical modeling show that there are two kinds of groundwater-carbonate rock interactions. One is co-dissolution of calcite and dolomite, the other is dedolomitization, and thereinto, dedolomitization is widespread in dolomite aquifers. Furthermore, gypsum has a tendency to dissolve in each aquifer, and the common ion effect of Ca2+ caused by gypsum dissolution promotes dedolomitization. The modeling results suggest that major elements have a good traceability effect on the material source of groundwater. (3) The chemical weathering of carbonate rock is mainly affected by carbonic acid, sulfuric acid and nitric acid. After modifying the impact of evaporite and atmospheric input, the calculations show that the contribution of carbonic acid involved in carbonate weathering is 70.9% (high water period) and 70.0% (low water period). Through statistics of karst springs discharge and contribution of acid involved in carbonate weathering, the two are in a positive relationship. The result can reflect the laws of sulfuric acid and nitric acid under the hydrodynamic condition in different seasons. Therefore, the carbonate weathering should be carefully evaluated in karst areas which have abundant groundwater and the role of groundwater in carbonate weathering is worthy of further study.

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.

Understanding storage and discharge behavior of a high-elevated and geologically complex karst catchment using numerical approach, Dolomites (Alps)

&lt;p&gt;Water resources from high-alpine karst aquifers are used for drinking, hydropower generation and artificial snowmaking. Therefore, understanding of their storage and flow dynamics is crucial for a sustainable water management. However, high-alpine karst areas are characterized by a great geological complexity due to the presence of mountain block fractured and karst aquifers interdigitating with the heterogeneous valley floor porous aquifers. For that reason, hydrogeological characterization and model prediction remains a big challenge. In this work, we investigated a geologically complex alpine catchment in the Dolomites (Italian Alps) by using experimental data and a reservoir numerical model to simulate three years of stream discharge. The structure of the model is based on experimental knowledge of the catchment and on previous studies and investigations. It (1) includes snow dynamics and accounts for hydrogeological heterogeneities, (2) separately considers karstic conduit and matrix flow in a dolomitic aquifer and flow through the porous deposits accumulating on the slopes and at the valley floor in an unconsolidated aquifer (non-karst), and (3) takes into account the groundwater transfer between the two aquifers. In the frame of a multi-step model evaluation, we used a Regional Sensitivity Analysis with three performance measures including observations of catchment discharge, karst spring discharge and unconsolidated aquifer spring discharge to assess the realism of model simulations. We show that the newly developed model reliably reproduces the hydrogeological variability of the catchment, even during strongly different hydroclimatic conditions. Analyzing its simulated storage dynamics, we can show that despite its moderate storage, the porous aquifer contributes most to catchment discharge, while the largest storage of the system is the matrix of the dolomite aquifer that recharges the unconsolidated aquifer together with discharge from the karstic conduits. A clear seasonality of groundwater storage in the karst matrix and of unconsolidated aquifer discharge indicates a strong sensitivity of this complex aquifer system to climatic variability.&lt;/p&gt;