Hydrogeochemical evolution of groundwater in a Quaternary sediment and Cretaceous sandstone unconfined aquifer in Northwestern China

A better understanding of the hydrogeochemical evolution of groundwater in vulnerable aquifers is important for the protection of water resources. To assess groundwater chemistry, groundwater sampling was performed from different representative aquifers in 2012–2013. A Piper trilinear diagram showed that the groundwater types can be classified into Na–SO4 and Na–Cl types. Only one groundwater sample was Na–HCO3 type. The dominant cations for all samples were Na+. However, the dominant anions varied from HCO3− to SO42−, and as well Cl−. The mean total dissolved solid (TDS) content of groundwater in the region was 1889 mg/L. Thus, only 20% of groundwater samples meet Chinese drinking water standards (< 1000 mg/L). Principal component analysis (PCA) combined with hierarchical cluster analysis (HCA) and self-organizing maps (SOM) were applied for the classification of the groundwater geochemistry. The three first principal components explained 58, 20, and 16% of the variance, respectively. The first component reflects sulfate minerals (gypsum, anhydrite) and halite dissolution, and/or evaporation in the shallow aquifer. The second and third components are interpreted as carbonate rock dissolution. The reason for two factors is that the different aquifers give rise to different degree of hydrogeochemical evolution (different travel distances and travel times). Identified clusters for evolution characteristic and influencing factors were confirmed by the PCA–HCA methods. Using information from eight ion components and SOM, formation mechanisms and influencing factors for the present groundwater quality were determined

Using Self-Organizing Maps to infill missing data in hydro-meteorological time series from the Logone catchment, Lake Chad basin

Hydro-meteorological data is an important asset that can enhance management of water resources. But existing data often contains gaps, leading to uncertainties and so compromising their use. Although many methods exist for infilling data gaps in hydro-meteorological time series, many of these methods require inputs from neighbouring stations, which are often not available, while other methods are computationally demanding. Computing techniques such Artificial Intelligence can be used to address this challenge. Self-Organizing Maps (SOMs), which are a type of Artificial Neural Network, was used for infilling gaps in a hydro-meteorological time series in a Sudano-Sahel catchment. The coefficients of determination obtained were all above 0.75 and 0.65 while the average topographic error was 0.008 and 0.02 for rainfall and river discharge time series respectively. These results further indicate that SOMs are a robust and efficient method for infilling missing gaps in hydro-meteorological time series