Attributing variations of temporal and spatial groundwater recharge: A statistical analysis of climatic and non-climatic factors

Fu, Guobin; Crosbie, Russell; Barron, Olga; Charles, Stephen P.; Dawes, Warrick; Shi, Xiaogang; Niel, Tom Van; Li, Chris

doi:10.1016/j.jhydrol.2018.11.022

Cited by 74 publications

(63 citation statements)

References 46 publications

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“…The monthly quantities were used to capture the seasonal features of various predictors and their impacts on groundwater recharge. The study area in the paper was the same as that in Fu et al [18]. Fu et al analyzed extreme rainfall variables containing 99th percentile of rainfall, 95th percentile of rainfall and maximum daily rainfall.…”

Section: Groundwater Recharge and Potential Variable Datasetsmentioning

confidence: 99%

“…Previous studies on groundwater recharge have outlined numerous methods for estimating recharge, including chemical tracers [7][8][9][10][11], physical methods [12][13][14][15][16][17] and mathematical approaches [18,19]. The most widely used chemical tracer method is the chloride mass-balance method (CMB) [7][8][9][10], because it is conceptually simple and inexpensive to implement.…”

Section: Introductionmentioning

confidence: 99%

“…These methods provide different estimates of recharge, as they include or neglect factors such as runoff, evapotranspiration and changes in soil moisture, and represent either instantaneous estimates or historical averages. In mathematical simulation and statistical methods, linear regression is often adopted for estimating groundwater recharge [7,18,19]. Crosbie et al [7] used regression kriging to predict regional groundwater recharge across the Australian continent.…”

Section: Introductionmentioning

confidence: 99%

“…Global regression equations were applied to data-sparse areas while kriging of regression equation residuals was used for data-dense areas. Fu et al [18] also used multiple linear regression models together with 71 climate variables and 17 non-climate variables to analyze the groundwater recharge in South Australia. Mathematical simulation models can provide new insights into the factors that affect groundwater recharge.…”

Section: Introductionmentioning

confidence: 99%

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Groundwater Recharge Prediction Using Linear Regression, Multi-Layer Perception Network, and Deep Learning

Huang

Gao

Crosbie

et al. 2019

Water

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As the largest freshwater storage in the world, groundwater plays an important role in maintaining ecosystems and helping humans adapt to climate change. However, groundwater dynamics, such as groundwater recharge, cannot be measured directly and is influenced by spatially and temporally complex processes, models are therefore required to capture the dynamics and provide scientific advice for decision-making. This paper developed, estimated and compared the performance of linear regression, multi-layer perception (MLP) and Long Short-Term Memory (LSTM) models in predicting groundwater recharge. The experimental dataset consists of time series of annual recharge from the year 1970 to 2012, based on water table fluctuation estimates from 465 bores in the states of South Australia and Victoria, Australia. We identified the factors that influenced groundwater recharge and found that the correlation between rainfall and groundwater recharge was strongest. The linear regression model had the poorest fitting performance, with the root mean squared error (RMSE) being greater than 0.19 when various proportions of training data were considered. The MLP model outperformed the linear regression in the prediction capability, achieving RMSE = 0.11 when 80% of training data was considered. The LSTM model was found to have the best performance, whose root mean squared errors were less than 0.12 when various proportions of training data were applied. The relative importance of influential predictors was evaluated using the above three models.

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Section: Groundwater Recharge and Potential Variable Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Groundwater Recharge Prediction Using Linear Regression, Multi-Layer Perception Network, and Deep Learning

Huang

Gao

Crosbie

et al. 2019

Water

Self Cite

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“…It is difficult to quantity groundwater dynamics, due to these complex factors. Aside from temperature and rainfall, the underlying surface, permeability, etc., were the non-negligible factors affecting the groundwater recharge [49]. A higher temperature could accelerate the discharge of groundwater by increasing the evaporation or the crop stress.…”

Section: Influence Of Distinct Climate Phases On Groundwater Levelsmentioning

confidence: 99%

The Effect of Climatic and Non-climatic Factors on Groundwater Levels in the Jinghuiqu Irrigation District of the Shaanxi Province, China

Zhao

Jia

Guan

et al. 2019

Water

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A chronic decline of the groundwater levels has become one of the hot issues affecting groundwater resources management. The rising global temperature, the high frequency of extreme weather (higher temperature and stronger evaporation, heavy or less rainfall), and unreasonable management policies have become important driving factors, causing a dynamic change in groundwater levels, in many regions. This study aims to explore the impact of climate and non-climate factors on groundwater levels in the Jinghuiqu irrigation district. The climatic phases were defined by rainfall anomalies, and the Mann–Kendall trend test statistic (M–K test) and Sen’s slope method were used to statistically analyze the influence of temperature (1950–2017) and rainfall (1980–2017) on the groundwater level. The results showed that: (1) Dry, normal, and wet phases occurred alternately, including two normal, two wet, and one dry periods (Wet 1980–1984; Normal 1985–1996; Dry 1997–2002; Wet 2003–2011; Normal 2012–2017). (2) The groundwater levels in the dry phase, decreased significantly by 0.62 m/year (p < 0.05), and the groundwater levels in the wet phases did not have a complete recovery, due to the excessive extraction of groundwater. Meanwhile, extreme weather became an important signal to reflect the change of groundwater levels. (3) The groundwater levels decreased significantly in the west and northwest (p < 0.05), but not in the southeast, due to the regional difference of groundwater extraction, which is the primary factor resulting in a chronic decline of groundwater levels. (4) Besides human activities, temperature had a higher correlation with groundwater levels (p < 0.05), which indicated that the potential impact of climate change on groundwater levels should not be ignored while setting groundwater resource management policies for a sustainable cycle of atmosphere–land–water.

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Groundwater–surface water connectivity in a chain‐of‐ponds semiarid river

Lamontagne

Kirby

Johnston

2021

Hydrological Processes

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Semiarid rivers are often characterized by chains of small pools connected by riffles and wet meadows. The pools can be maintained by wet season surface runoff, groundwater discharge, or some combination thereof. Using synoptic surveys for several environmental tracers (δD and δ 18 O of H 2 O, specific electrical conductance at 25 C [EC], chloride and 222 Rn), we evaluated the groundwater-surface water connectivity of the Light River (South Australia) along an 8 km section in the vicinity of a proposed mining development. In all three surveys (representing spring, summer and winter conditions), the pools were maintained by regional groundwater discharge based on an elevated surface water EC (9-12 dS m −1 ) similar to regional groundwater, elevated radon-222 activities (0.09-3.0 Bq L −1 ) and low rainfall. Most pools were perennial, either because they directly received groundwater discharge or, indirectly, had an inflow originating from upstream groundwater-fed pools. The elevated salinity of regional groundwater is a key factor for the maintenance of perennial pools in the Light River because the potential for baseflow depletion by groundwater pumping is more limited.

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Attributing variations of temporal and spatial groundwater recharge: A statistical analysis of climatic and non-climatic factors

Abstract: Attributing variations of temporal and spatial groundwater recharge: a statistical analysis of climatic and non-climatic factors Guobin Fu,

Cited by 74 publications

References 46 publications

Groundwater Recharge Prediction Using Linear Regression, Multi-Layer Perception Network, and Deep Learning

Groundwater Recharge Prediction Using Linear Regression, Multi-Layer Perception Network, and Deep Learning

The Effect of Climatic and Non-climatic Factors on Groundwater Levels in the Jinghuiqu Irrigation District of the Shaanxi Province, China

Groundwater–surface water connectivity in a chain‐of‐ponds semiarid river

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