Identifying watershed-scale spatiotemporal groundwater and surface water mixing function in the Yiluo River, Middle of China

Wang, Xihua; Liu, Changli; Hongbing, Hou; Wang, Xiuyan

doi:10.1007/s11356-020-11285-y

Cited by 6 publications

(4 citation statements)

References 29 publications

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“…The basin belongs to warm temperate continental monsoon climate, the average annual temperature is 7.8 ~ 13.9 °C, the average annual rainfall is 710 ~ 930 mm, and the rainfall is concentrated in June to September. The lower reaches of the Yiluo River are one of the largest wheat production bases in China, with concentrated urban distribution and frequent industrial and agricultural activities (Wang et al, 2021;Wang et al, 2023).…”

Section: Study Area and Datamentioning

confidence: 99%

A new data-driven model to predict monthly runoff at watershed scale: insights from deep learning method applied in data-driven model

Jia,

Wang,

Liu

et al. 2023

Preprint

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Accurate forecasting of mid to long-term runoff is essential for water resources management and planning. However, the traditional model can’t predict well and the precision of runoff forecast needs to be further improved. Here, we proposed a noval data-driven model called RLMD -SMA-GRU for mid to long-term runoff prediction in three hydrographic stations (Heishiguan, Baimasi and Longmenzhen) of Yiluo River Watershed (middle of China) using monthly runoff data from 2007 to 2022. The results showed that (1) the new data-driven model (RLMD -SMA-GRU) had the highest monthly runoff prediction accuracy. Both RLMD and SMA can improve the prediction accuracy of the model (NSE=0.9466). (2) The accuracy of Models in wet season outperformed in dry season. (3) The hydrological stations with large discharge and stable runoff sequence have better forecasting effect. The RLMD-SMA-GRU model has good applicability and can be applied to the monthly runoff forecast at watershed scale.

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Section: Study Area and Datamentioning

confidence: 99%

A new data-driven model to predict monthly runoff at watershed scale: insights from deep learning method applied in data-driven model

Jia,

Wang,

Liu

et al. 2023

Preprint

Self Cite

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“…At present, many scholars use Piper three-line diagrams to analyze the ion composition and variation characteristics of surface water and groundwater [38,39]. D. Gamvroula and D et al [40] used this method to explain in detail the distribution and characteristics of macronutrient ions in the groundwater of Megara Basin.…”

Section: Analysis Of Hydrochemical Types and Characteristicsmentioning

confidence: 99%

Hydrochemical Characteristics and Hydrogeochemical Simulation Research of Groundwater in the Guohe River Basin (Henan Section)

Zhou

et al. 2022

Water

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With the implementation of the policy of ecological protection and high-quality development of the Yellow River Basin, the Guohe River Basin, which is close to the middle reaches of the Yellow River Basin, is also an important part of future development. Mathematical statistics, the Piper diagram, ion proportion coefficient method, Gibbs diagram and reverse hydrogeochemical simulation are used to analyze the chemical characteristics and evolution of groundwater in the Guohe River Basin (Henan Section). The dominant ions in the study area are HCO3− and Na+, and the three-layer aquifer has obvious zoning characteristics. The results show that the chemical types of shallow groundwater in this area are HCO3−Na • Mg • Ca, intermediate HCO3-Na and deep HCO3−Na. Using the ion proportion coefficient method, it is found that Na+, Ca2+, and Mg2+ in the groundwater aquifer undergo cation exchange in the aquifer. According to the reverse hydrogeochemical simulation, gypsum in the three aquifers is in a dissolved state, carbonate and sulfide ores in the shallow layer are dissolved, dolomite and halite in the intermediate layer are dissolved, calcite and sulfide ores are precipitated and carbonate, halite and sulfide ores in the deep layer are precipitated; the hydrogeochemical evolution model is established to find that Ca2+ in groundwater displaces Na+ in the aqueous medium. This research can provide a scientific basis for the rational development and utilization of groundwater and ecological protection in the Yellow River Basin.

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“…Compared with the results obtained from a temperature tracer and analytical method, Qiao et al [13] confirmed that the numerical simulation method is a better choice for studying the interaction between lake water and GW in semiarid areas with detailed data. Wang et al [14] used hydrochemistry, radioactive isotopes ( 14 C), and stable isotopes ( 13 C, 18 O, and 15 N) to indicate that the Yiluo River and shallow groundwater had frequent interactions.…”

Section: Introductionmentioning

confidence: 99%

Hydrochemical and Isotopic Explanations of the Interaction between Surface Water and Groundwater in a Typical-Desertified Steppe of Northern China

et al. 2023

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The Tabu catchment, a typical-desertified steppe in China, was selected as the study area to qualitatively analyze the interaction between surface water (SW) and groundwater (GW), and an integration of hydrochemical analysis and isotopic techniques was applied. The results show that the ion contents in SW and GW increased from upstream to downstream, and the hydrochemical evolutions were both controlled by rock weathering and influenced by evaporation. The δD–δ18O lines of SW and GW were δD = 5.14δ18O − 24.68 and δD = 6.89δ18O − 5.81, respectively. Along the I–I′ profile, the contents of most indices, δD and δ18O in SW and GW both showed increasing tendencies. All of the similarities in the hydrochemical characteristics and isotopic techniques indicated that SW was recharged by GW. The δD–δ18O inconsistency in SW and GW samples from midstream and downstream areas indicated that SW did not recharge to GW in these areas and was consumed by evaporation or replenished the moisture in the vadose zones. The runoff decreased, which was mainly caused by excessive exploitation of GW and a decline in the GW level. This study deepens the understanding of the hydrological cycle and provides guidance for the optimal combined utilization of SW and GW.

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Identifying watershed-scale spatiotemporal groundwater and surface water mixing function in the Yiluo River, Middle of China

Cited by 6 publications

References 29 publications

A new data-driven model to predict monthly runoff at watershed scale: insights from deep learning method applied in data-driven model

A new data-driven model to predict monthly runoff at watershed scale: insights from deep learning method applied in data-driven model

Hydrochemical Characteristics and Hydrogeochemical Simulation Research of Groundwater in the Guohe River Basin (Henan Section)

Hydrochemical and Isotopic Explanations of the Interaction between Surface Water and Groundwater in a Typical-Desertified Steppe of Northern China

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