Hydrochemical and isotopic characteristics of groundwater in the Jiuquan East Basin, China

Ren, Xiaohui; Gao, Zongjun; An, Ying; Liu, Jiutan; Wu, Xi; He, Minghao; Feng, Jianguo

doi:10.1007/s12517-020-05573-7

Cited by 18 publications

(10 citation statements)

References 42 publications

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“…However, the inconformity of the uncertainty analysis results among the three wells and the three models reveals that uncertainty is inevitable, even for those models with high accuracy. Typically, the uncertainty increases as the noise increases [65]. In this study, reasonable interpretations for this phenomenon may lie in the collective affect from every possible step of the methodological framework, the hydrogeological heterogeneity of the three distinguished GWL observation wells, the observed GWL data (the observation or the data entry processes) and the other sources.…”

Section: Model Interpretationmentioning

confidence: 66%

“…The tables show that the uncertainty analysis results of the CEEMDAN-GA-DBN, CEEMDAN-DBN and DBN models are not identical in terms of the MPI and PICP values either at the confidence level of 90% or the confidence level of 95%. It seems very difficult to derive balanced low MPI as well as high PICP values since other researchers also encountered this problem [65,66]. Nevertheless, the PICP values of the proposed CEEMDAN-GA-DBN model are higher than that of the CEEMDAN-DBN and DBN models in most circumstances.…”

Section: Uncertainty Analysismentioning

confidence: 94%

“…According to Wan et al (2017), the unconfined groundwater in this area belongs to a snow and ice melt water-groundwater system, while the confined groundwater was recharged in the late Pleistocene and middle Holocene and is non-renewable [64]. Ice-snow melted water and precipitation are the main sources of the shallow and deep phreatic groundwater [65].…”

Section: Materials 251 Study Areamentioning

confidence: 99%

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Deep Learning-Based Predictive Framework for Groundwater Level Forecast in Arid Irrigated Areas

Liu

Yang

et al. 2021

Water

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An accurate groundwater level (GWL) forecast at multi timescales is vital for agricultural management and water resource scheduling in arid irrigated areas such as the Hexi Corridor, China. However, the forecast of GWL in these areas remains a challenging task owing to the deficient hydrogeological data and the highly nonlinear, non-stationary and complex groundwater system. The development of reliable groundwater level simulation models is necessary and profound. In this study, a novel ensemble deep learning GWL predictive framework integrating data pro-processing, feature selection, deep learning and uncertainty analysis was constructed. Under this framework, a hybrid model equipped with currently the most effective algorithms, including the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) for data decomposition, the genetic algorithm (GA) for feature selection, the deep belief network (DBN) model, and the quantile regression (QR) for uncertainty evaluation, denoted as CEEMDAN-GA-DBN, was proposed for the 1-, 2-, and 3-month ahead GWL forecast at three GWL observation wells in the Jiuquan basin, northwest China. The capability of the CEEMDAN-GA-DBN model was compared with the hybrid CEEMDAN-DBN and the standalone DBN model in terms of the performance metrics including R, MAE, RMSE, NSE, RSR, AIC and the Legates and McCabe’s Index as well as the uncertainty criterion including MPI and PICP. The results demonstrated the higher degree of accuracy and better performance of the objective CEEMDAN-GA-DBN model than the CEEMDAN-DBN and DBN models at all lead times and all the wells. Overall, the CEEMDAN-GA-DBN reduced the RMSE of the CEEMDAN-DBN and DBN models in the testing period by about 9.16 and 17.63%, while it improved their NSE by about 6.38 and 15.32%, respectively. The uncertainty analysis results also affirmed the slightly better reliability of the CEEMDAN-GA-DBN method than the CEEMDAN-DBN and DBN models at the 1-, 2- and 3-month forecast horizons. The derived results proved the ability of the proposed ensemble deep learning model in multi time steps ahead of GWL forecasting, and thus, can be used as an effective tool for GWL forecasting in arid irrigated areas.

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Section: Model Interpretationmentioning

confidence: 66%

Section: Uncertainty Analysismentioning

confidence: 94%

Section: Materials 251 Study Areamentioning

confidence: 99%

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Deep Learning-Based Predictive Framework for Groundwater Level Forecast in Arid Irrigated Areas

Liu

Yang

et al. 2021

Water

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“…Analysing the hydrochemistry and water quality cannot only identify the mechanisms of interaction between water and the surrounding environmental factors, but also reflect the status of regional water quality, thus, allowing for more rational protection and sustainable utilisation of water resources (Ghezzi et al, 2019;Xiao et al, 2021;Wang and Li, 2022). Under natural conditions, weathering of rocks due to water can lead to the dissolution of various minerals, which results in the gradual accumulation of chemical ions in water (Zhou et al, 2016;Ren et al, 2020). Contrastingly, pollutants originating from anthropogenic activities may occur in water within a relatively short period (Yan et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…Contrastingly, pollutants originating from anthropogenic activities may occur in water within a relatively short period (Yan et al, 2021). The hydrochemistry of water bodies, particularly source and aggregation mechanisms of ions in water, can be assessed through methods, such as multivariate statistical analysis (Yidana et al, 2008;Mao et al, 2021), Piper diagram (Piper, 1944), Gibbs diagram (Gibbs, 1970), and ion ratios (Xiao et al, 2016;Ren et al, 2020). Additionally, water quality index (WQI) can effectively combine multiple physical and chemical parameters into a comprehensive value, and the method can reflect the overall status of water quality and is widely used in the water quality evaluation (Abbasnia et al, 2018;Rabeiy, 2018;Yan et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Hydrochemical evaluation of water quality and its influencing factors in a closed inland lake basin of Northern China

Ren

Kang

et al. 2022

Front. Ecol. Evol.

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Understanding the water quality and its influencing factors of different water bodies is essential for managing water resources in closed inland lake basins in semi-arid regions. However, generally, groundwater or surface water is assessed separately, and the differences among different water bodies are neglected. This study assessed the water quality and its influencing factors of different water bodies in the Daihai Lake Basin (a closed inland lake basin in a semi-arid region) by analysing the hydrochemical data of groundwater, and spring, river, and lake waters in the dry and wet seasons. The dominant hydrochemical type of groundwater (81.48%), spring water (80%), and river water (83.33%) was HCO3–Ca•Mg, while that of lake water was Cl-Na (100%). Groundwater, spring water, and river water were suitable for drinking and agricultural irrigation; however, the groundwater quality was worse in the wet season than in the dry season. Na+ and Cl– majorly affected the lake water quality. The mean NO3– concentration in groundwater was 28.39 mg/L, and its non-carcinogenic hazard quotient indicated that high risk areas were mainly distributed in Tiancheng and northern Maihutu. The hydrochemical compositions of groundwater, spring water, and river water were mainly influenced by rock (silicate and carbonate) weathering and cation exchange, and agricultural activities were the main sources of groundwater NO3–. Moreover, the lake hydrochemical composition was mainly affected by evaporation and halite dissolution. Thus, groundwater NO3– pollution and lake water salinisation should be prioritised. These findings provide a more thorough understanding of water quality and its influencing factors in the closed inland lake basin in the semi-arid region, and can be used to develop the protection of ecosystems and water resources management strategies in the Daihai Lake Basin.

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Hydrochemical characterization and comprehensive water quality assessment of groundwater within the main stream area of Yishu River

Zhao,

Gao,

Liu

et al. 2024

Environ Monit Assess

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Hydrochemical and isotopic characteristics of groundwater in the Jiuquan East Basin, China

Cited by 18 publications

References 42 publications

Deep Learning-Based Predictive Framework for Groundwater Level Forecast in Arid Irrigated Areas

Deep Learning-Based Predictive Framework for Groundwater Level Forecast in Arid Irrigated Areas

Hydrochemical evaluation of water quality and its influencing factors in a closed inland lake basin of Northern China

Hydrochemical characterization and comprehensive water quality assessment of groundwater within the main stream area of Yishu River

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