Bicarbonate Daily Variations in a Karst River:the Carbon Sink Effect of Subaquatic Vegetation Photosynthesis

Using the Guancun River, an underground stream-fed river, in Rong＇an County of Guangxi, China as a case study, the daily biochemical cycle was examined in this paper based on the data collected a weeklong via high resolution data logger monitoring and high-frequency sampling. Furthermore, the loss of inorganic carbon along its flow path was estimated. Results show that chemical components of the groundwater input are quite stable, showing little change extent; while all of the chemical parameters from two downstream monitoring stations show diel variation over the monitoring period, suggesting that plant activity in the river has a strong influence on water chemistry of the river. The comparison of the input fluxes from the groundwater with the output fluxes of HCO~ estimated at the downstream monitoring station during the high-frequency sampling period shows a strong decrease of HCO~, indicating that the river is losing inorganic carbon along its flow path. The loss is estimated to be about 1,152 mmol/day/m of HCO~ which represent about 94.9 kg/day of inorganic carbon along the 1,350 m section of the Guancun River. It means that HCO~ entering the river from karst underground stream was either consumed by plants or trapped in the authigenic calcite and thus constitutes a natural sink of carbon for the Guancun karst system.

Effects of acid mine drainage on photochemical and biological degradation of dissolved organic matter in karst river water

Dissolved organic matter(DOM)can be removed or transformed by photochemical and biological processes,producing the negative effect of transforming organic carbon into inorganic carbon,which plays a vital role in the karst carbon cycle.However,acid mine drainage(AMD)will affect this process,so the degradation of DOM in karst river water(KRW)needs to be studied in this context.In this study,to reveal the evolution processes of DOM under photochemical and biological conditions in AMD-impacted KRW,AMD and KRW were mixed in different ratios under conditions of visible light irradiation(VL),biodegradation(BD),ultraviolet irradiation(UV)and ultraviolet irradiation+biodegradation(UV+BD).The average DOC concentrations in samples after mixing AMD and KRW in different proportions decreased significantly(by 23%)in UV+BD,which was 1.2–1.4 times higher than under the other conditions and would lead to a significant release of inorganic carbon.Further analysis of the fluorescence parameters via parallel factor analysis(PARAFAC)revealed that the DOM fluorescence components in AMD comprised mainly protein-like substances derived from autochthonous components,while the DOM fluorescence components in KRW were mainly humic-like substances with both autochthonous and allochthonous sources.Therefore,AMD could promote both the photochemical and biological degradation of DOM in karst receiving streams,resulting in the conversion of DOC to inorganic carbon.The results showed that the synergistic effects of UV+BD and AMD accelerated the degradation of DOM and the release of inorganic carbon in KRW,thus affecting the stability of the karst carbon cycle.

Comparing the performances of WRF QPF and PERSIANN-CCS QPEs in karst flood simulation and forecasting by coupling the Karst-Liuxihe model

Long-term rainfall data are crucial for flood simulations and forecasting in karst regions.However,in karst areas,there is often a lack of suitable precipitation data available to build distributed hydrological models to forecast karst floods.Quantitative precipitation forecasts(QPFs)and estimates(QPEs)could provide rational methods to acquire the available precipitation data for karst areas.Furthermore,coupling a physically based hydrological model with QPFs and QPEs could greatly enhance the performance and extend the lead time of flood forecasting in karst areas.This study served two main purposes.One purpose was to compare the performance of the Weather Research and Forecasting(WRF)QPFs with that of the Precipitation Estimations through Remotely Sensed Information based on the Artificial Neural Network-Cloud Classification System(PERSIANN-CCS)QPEs in rainfall forecasting in karst river basins.The other purpose was to test the feasibility and effective application of karst flood simulation and forecasting by coupling the WRF and PERSIANN models with the Karst-Liuxihe model.The rainfall forecasting results showed that the precipitation distributions of the 2 weather models were very similar to the observed rainfall results.However,the precipitation amounts forecasted by WRF QPF were larger than those measured by the rain gauges,while the quantities forecasted by the PERSIANN-CCS QPEs were smaller.A postprocessing algorithm was proposed in this paper to correct the rainfall estimates produced by the two weather models.The flood simulations achieved based on the postprocessed WRF QPF and PERSIANN-CCS QPEs coupled with the Karst-Liuxihe model were much improved over previous results.In particular,coupling the postprocessed WRF QPF with the Karst-Liuxihe model could greatly extend the lead time of flood forecasting,and a maximum lead time of 96 h is adequate for flood warnings and emergency responses,which is extremely important in flood simulations and forecasting.