Conceptual and numerical models for groundwater flow in an arid inland river basin

Yao, Yingying; Chen, Kewei; Liu, Jie; Cao, Guoliang; Hu, Xiao; Li, Haitao; Li, Wenpeng

doi:10.1002/hyp.10276

Cited by 78 publications

(46 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Carbonate rocks of the Tertiary and Cretaceous periods including dolomite, calcite, and aragonite were widely distributed in bedrocks and aquifers in both the upper and middle HRB basin [17,25]. Our interpretation of DIC sources in the Heihe River is consistent with previous findings based on water oxygen and hydrogen isotopes, which showed precipitation and snowmelt runoff from the Qilian Mountains were the primary water source for surface water of the entire HRB [16,26].…”

Section: Sources Of Dissolved Inorganic Carbonsupporting

confidence: 88%

“…The alluvial fan was characterized by deep groundwater (>200 m at the upstream section and about 50-200 m at the central section) with the aquifers recharged by the river. The alluvial plain, however, had a much shallower groundwater table (<10 m near the river and overall shallower than 50 m) with the river gaining water from groundwater [16,26,32]. This boundary thus represents a transition point for surface watergroundwater interactions and subsequently an increase in the input of nutrients from groundwater to the river.…”

Section: Groundwater Discharge Controls On River Water Dic and Nox −mentioning

confidence: 99%

See 1 more Smart Citation

Sources and Dynamics of Dissolved Inorganic Carbon, Nitrogen, and Phosphorus in a Large Agricultural River Basin in Arid Northwestern China

Liu

et al. 2017

Water

Self Cite

View full text Add to dashboard Cite

Abstract:The present study assessed the export of inorganic carbon, nitrogen, and phosphorus within a large agricultural basin in arid northwestern China. Groundwater of various depths and river water along a 160 km reach were sampled during contrasting flow conditions. Dissolved inorganic carbon (DIC) concentrations and δ 13 C-DIC values indicate that lithogenic carbonate weathering was the main source of DIC in the basin. Discharge played an important role in regulating the amount and flowpath of nutrients mobilized from soils to the river. Ammonium was mobilized mostly by storm flows whereas the other nutrients were exported through both storm and groundwater flows. Hydrological events, occurring on only about 10% of the days for a year, were responsible for more than 40% of annual nutrient exports. Shallow groundwater was an important source of DIC and nitrate in river water within the alluvial plain, where groundwater discharges regulated their longitudinal variability along the river. According to a mixing model using δ 13 C-DIC and chloride, groundwater comprised 9-34% and 39-60% of river water at high discharge and baseflow, respectively. Together, our data highlight the importance of reducing storm runoffs and monitoring nutrient pollution within this large basin.

show abstract

Section: Sources Of Dissolved Inorganic Carbonsupporting

confidence: 88%

Section: Groundwater Discharge Controls On River Water Dic and Nox −mentioning

confidence: 99%

Sources and Dynamics of Dissolved Inorganic Carbon, Nitrogen, and Phosphorus in a Large Agricultural River Basin in Arid Northwestern China

Liu

et al. 2017

Water

Self Cite

View full text Add to dashboard Cite

show abstract

“…All relevant features, such as topography, digital elevation model (DEM), borehole data, geological structure, drainage density, groundwater flow, and boundary conditions of the aquifer system are very important for setting up the model [31,32].…”

Section: Geological Modelmentioning

confidence: 99%

“…In this procedure, two types of data were used to calibrate the regional groundwater flow model for the JUA. First, observed groundwater level contour maps of the unconfined aquifer derived from the 190 borehole data points and 75 observation wells were used to match the computed water levels ( Figure 11) as a qualitative step before more comprehensive and quantitative calibration was performed [32]. Second, the variable heads of the 75 observation wells from 2001 to 2010 were used as calibration targets (well locations as shown in Figure 5.…”

Section: Model Calibration and Sensitivity Analysismentioning

confidence: 99%

Numerical Simulation of Groundwater Flow in a River Valley Basin in Jilin Urban Area, China

Qiu

Liang

Xiao

et al. 2015

Water

View full text Add to dashboard Cite

Abstract:In order to evaluate the groundwater resources and aquifer system of the Jilin urban area (JUA), a groundwater numerical flow model was established by using the groundwater modeling system based on data from 190 boreholes. River stages were interpolated to control the groundwater flow field. The input parameters such as hydraulic conductivity and specific yield were based on data from 260 pumping test data. The model was calibrated by trial and error, simulated results were compared to the observed head and contour maps, which were generally in good agreement, and the root mean squared error was 0.66 m. Sensitivity analysis was carried out and recharge proved to be the most sensitive factor in this model. The water budget showed that the input was 2.07× 10

show abstract

“…(e.g. Matej et al, 2007;Pool et al, 2011;Yao et al, 2015). Numerical models solve the deterministic equations to simulate the groundwater systems based on the knowledge of the system characteristics, initial conditions, system forcings, etc.…”

Section: Introductionmentioning

confidence: 99%

Technical note: Application of artificial neural networks in groundwater table forecasting – a case study in a Singapore swamp forest

Sun

Wendi

Kim

et al. 2016

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. Accurate prediction of groundwater table is important for the efficient management of groundwater resources. Despite being the most widely used tools for depicting the hydrological regime, numerical models suffer from formidable constraints, such as extensive data demanding, high computational cost, and inevitable parameter uncertainty. Artificial neural networks (ANNs), in contrast, can make predictions on the basis of more easily accessible variables, rather than requiring explicit characterization of the physical systems and prior knowledge of the physical parameters. This study applies ANN to predict the groundwater table in a freshwater swamp forest of Singapore. The inputs to the network are solely the surrounding reservoir levels and rainfall. The results reveal that ANN is able to produce an accurate forecast with a leading time of 1 day, whereas the performance decreases when leading time increases to 3 and 7 days. IntroductionPhysical-based numerical models are widely used in groundwater table simulation. Different numerical models have been developed for different regions with different objectives, such as to describe regional groundwater flow patterns, and to understand local hydrological processes. (e.g. Matej et al., 2007;Pool et al., 2011;Yao et al., 2015). Numerical models solve the deterministic equations to simulate the groundwater systems based on the knowledge of the system characteristics, initial conditions, system forcings, etc. To develop a groundwater numerical model, essential data include: topography, geological coverage, soil properties, land use map, vegetation distribution, evapotranspiration information, hydrologic and climatic data, etc. Extensive data demanding makes numerical models highly data dependent and data sensitive. Fitting a physical model is not possible when data are not sufficient; the accuracy of the numerical model to a great extent depends on how accurate the model inputs are. Numerical models are also less competent in forecasting as most of the system forcings (e.g. evapotranspiration, rainfall) are less predictable. As a result of aforementioned constraints, numerical models tend to produce imperfect results in spite of the perfect knowledge of the governing laws (Sun et al., 2010).To combat the deficiencies of the numerical models, artificial neural networks (ANNs) have emerged as an alternative modelling and forecasting approach with a variety of applications in hydrology research (e.g. French et al., 1992;Maier and Dandy, 2000). Unlike the traditional physicalbased models, the ANN-based approach does not require explicit characterization of the physical properties, or accurate representation of the physical parameters, but rather simply determines the system patterns based on the relationships between inputs and outputs mapped in the training process. ANNs typically use input variables that are more accessible to make predictions, and therefore circumvent the data reliance inherent to the numerical models. As compared to classical regression techni...

show abstract

Conceptual and numerical models for groundwater flow in an arid inland river basin

Cited by 78 publications

References 25 publications

Sources and Dynamics of Dissolved Inorganic Carbon, Nitrogen, and Phosphorus in a Large Agricultural River Basin in Arid Northwestern China

Sources and Dynamics of Dissolved Inorganic Carbon, Nitrogen, and Phosphorus in a Large Agricultural River Basin in Arid Northwestern China

Numerical Simulation of Groundwater Flow in a River Valley Basin in Jilin Urban Area, China

Technical note: Application of artificial neural networks in groundwater table forecasting – a case study in a Singapore swamp forest

Contact Info

Product

Resources

About