The Spatial-Temporal Variability of Karst Water Chemistry in Houzhai Karst Basin, Guizhou, China

Abstract

The spatial-temporal variability of karst water chemistry is affected by lithological information, precipitation and agricultural activities. Based on the data of 10 hydro-chemical monitoring stations in Houzhai karst basin from 1989 to 2001, the spatial-temporal variability of karst water chemistry and the relationships between karst water chemistry dynamic and their main control factors were interpreted and analyzed by using the integrating methods of the stochastic theory and geostatistics. The results indicate that the karst water chemistry dynamic of Houzhai karst basin show obvious spatial-temporal distribution characteristics. The temporal variations of main ions are closely related to precipitation. The main ions vary regularly in space because of lithological and karst morphology, but the spatial variations of K+, Na+ are controlled by agricultural activities.

Validating the KAGIS black‐box GIS‐based model in a Mediterranean karst aquifer: Case of study of Mela aquifer (SE Spain)

“…KAGIS does not consider temporal variability in the karst system. Temporal variability in karst aquifers, both in flow dynamics (Li, Wang, & Liu, ; Pacheco Castro et al, ) and/or in water quality (Delbart et al, ; Hartmann, Lange, Weiler, Arbel, & Greenbaum, ; Ravbar, ), occurs in response to different hydrologic and climatic conditions, which may result in changes in flow directions and velocities and may have an impact on karst springs. In the present study case, KAGIS simulated fairly well the aquifer dynamics.…”

“…KAGIS does not consider temporal variability in the karst system. Temporal variability in karst aquifers, both in flow dynamics FIGURE 5 Significant scatter plots of the weighting parameters according to Nash-Sutcliffe efficiency index adapted for arid environments (Li, Wang, & Liu, 2014;Pacheco Castro et al, 2018) and/or in water quality (Delbart et al, 2014;Hartmann, Lange, Weiler, Arbel, & Greenbaum, 2012;Ravbar, 2013) The multiresolution cross-correlation performed with precipitation (as input signal) and discharged volume (as output signal) helps FIGURE 6 Annual precipitation (mm), average water level (masl), and accumulated annual discharged volume (hm 3 ) and deviated values from the mean FIGURE 7 Cross-correlation function between overall precipitation (input signal) and discharged water at different multiresolution levels (output signal)…”