Seawater intrusion and brine water/freshwater interaction have significantly affected agriculture, industry and public water supply at Laizhou Bay, Shandong Province, China. In this study, a two-dimensional SEAWAT model is developed to simulate the seawater intrusion to coastal aquifers and brine water/fresh water interaction in the south of Laizhou Bay. This model is applied to predict the seawater intrusion and brine water/freshwater interface development in the coming years. The model profile is perpendicular to the coastal line with two interfaces, freshwater-saline water interface near the shore and inland brine water-saline water-seawater interface. The hydrogeological parameters in the SEAWAT-2000 model are calibrated by the head and salinity measurements. The precipitation infiltration coefficient, boundary conditions and thicknesses of aquifers are studied in a sensitivity analysis. The predicted results indicate that equivalent freshwater head in shallow freshwater-saline water area will decline 2.0 m by the end of the forecasting period, caused by groundwater over-pumping for farmland irrigation. The groundwater head in the brine-saline water area will also decrease about 1.8 m by the end of forecasting period, caused by excessive brine mining. Salinity finally decreases below 105 g/L in the brine area, but increases in other areas and contaminates fresh groundwater resources.

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A survey of the hydrochemistry and isotopes of the Quaternary aquifer on the southern coast of Laizhou Bay provides new insights into the hydrodynamic and geochemical relationships between freshwater, seawater, and brine at different depths in coastal sediments. This study used a combination of groundwater level analysis, hydrochemistry, and isotopic methods to study the chemical characteristics of groundwater and the origin of groundwater recharge and salinity. Because the sedimentary structure of the area and the formation background of saltwater were important factors controlling the distribution of groundwater, we analyzed the distribution of groundwater in Holocene and Late Pleistocene sediments. The variation of groundwater levels in the Holocene and Late Pleistocene sediments in the saline–freshwater transition zone over time showed that the Holocene and Late Pleistocene groundwater flow directions differed in the saltwater–freshwater transition zone. From south to north in the study area, the hydrochemical types of groundwater in the Holocene and Late Pleistocene sediments were as follows: HCO3-Ca (freshwater), SO4-Mg and HCO3-Ca (brackish water), Cl-Na·Mg (saltwater), and Cl-Na (brine). The results of the hydrochemical and isotopic studies indicated that the saltwater in the Holocene and Late Pleistocene sediments and the brine in the Late Pleistocene sediments were the result of evaporation. The salinity of freshwater in the Holocene sediments was produced by rock weathering, while the salinity of freshwater in the Late Pleistocene sediments was not only derived from rock weathering, but was also affected by evaporation and precipitation. The salinity of brackish water in the Holocene and Late Pleistocene sediments was derived from evaporation and precipitation. Ultimately, the origin of groundwater recharge in the Holocene and Late Pleistocene sediments was atmospheric precipitation.

In this study, a two-dimensional SEAWAT 2000 model is developed to simulate the seawater intrusion to coastal aquifers and brine water/fresh water interaction in the south of Laizhou Bay, Shandong Province, China and forecast the seawater intrusion and brine water/freshwater interface development in the coming years. The model profile is perpendicular to the coastal line, about 40 km long and 110 m in depth, and consists of two interfaces, freshwater-saline water interface and brine water-saline water-seawater interface. The parameters of aquifers in the SEAWAT-2000 model are calibrated by trial-error method repeatedly to fit the head and salinity measurements. Based on the historical groundwater and brine water exploration and natural precipitation condition, the prediction results indicate that equivalent freshwater head in shallow freshwater-saline water area will decrease year by year and decline 2.0 m in the forecasting period, caused by groundwater over-pumping for irrigating farmlands. The groundwater head in the brine-saline water area will also decrease about 1.8 m in forecasting period. A larger depression cone appears in the brine area, with smaller funnels in other areas. The salinity in the brine area finally drops below 105g/l. In the meanwhile, the salinity increases in other areas, damage fresh groundwater resources.

<p>Water quality prediction is an important technical means for preventing and controlling water pollution and is crucial in the formulation of reasonable water pollution prevention and control measures. The time series structure of natural water quality is complex and heteroscedastic, so it is difficult for the traditional prediction model to reflect the actual situation well. Hence, Markov-switching (MS) theory is applied to a water quality autoregression (AR) prediction model (MSAR) in this paper. Further, MSAR is improved by introducing the crow search algorithm to obtain model parameters (CSA-MSAR). Then existing water quality time series for COD<sub>Mn</sub> was selected as the data for the CSA-MSAR model after a normality test and the Box–Cox normality transformation. The results show that the CSA-MSAR model for COD<sub>Mn</sub> with (s, p) values of (3, 5) has the best performance. The improvement degree for selection criteria compared with AR model is as follows: Akaike information criterion for MSAR is 32.020% and 31.611% for CSA-MSAR; Bayesian information criterion for MSAR is 10.632% and 13.464% for CSA-MSAR; likelihood value for MSAR is 40.016% and 40.801% for CSA-MSAR; C for MSAR is 63.559% and 64.968% for CSA-MSAR. Moreover, the results show that the average prediction precision of the first- to fifth-order prediction is raised by 89.016% for MSAR and 89.340% for CSA-MSAR compared with AR, indicating that the introduction of MS makes the CSA-MSAR and MSAR models conform to the smoothness of the mean and variance in each state. The results also indicate that the introduction of CSA into the maximum likelihood estimation to obtain the parameters raise the model prediction precision (the average prediction precision of CSA-MSAR is higher than MSAR by 5.231% excluding the fifth-order prediction) and the CSA-MSAR model is scientifically valid and reasonable for water quality prediction.</p>