Spatio-temporal effect of climate change on water balance and interactions between groundwater and surface water in plains

Ochoa, Cristian Guevara; Medina-Sierra, Agustín; Vives, Luis

doi:10.1016/j.scitotenv.2020.137886

Cited by 69 publications

(26 citation statements)

References 111 publications

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“…Therefore, estimating the water balance and its components is a necessary precondition for this challenge. Many of the water balance components (surface runoff and lake-groundwater exchanges) are difficult to estimate directly even at the local scale and are often assumed to be in the residuals of the water balance equation (Kampf and Burges, 2010;Faso et al, 2015;Huang et al, 2018;Guevara-Ochoa et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Water balance and influence mechanism analysis: a case study of Hongjiannao Lake, China

Yue

Liu

2021

Environ Monit Assess

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Hongjiannao groundwater exchange was the largest desert freshwater lake in China (57.25 km 2 in 1986). However, it shrank sharply over the past 34a (1986-2019), with the smallest lake area 31.41 km 2 in 2015. The objective of this study was to use the Landsat images, ASTER GDEM V2 data, and meteorology and statistics data, in combination with the water balance model to calculate the dynamics of water balance elements, quantify and characterize the interannual variations in lakegroundwater exchanges, and analyzed its influencing factors by using the geographical detector. The results showed that in the stable stage (1986-1997), the average rate of the lake area, water level, and lake volume change was-0.26 km 2 /a,-0.0483 m/a, and-0.0009 km 3 /a, respectively. Precipitation, river inflow, and groundwater were 0.0203 km 3 , 0.0485 km 3 , and 0.0098 km 3 , which accounts for the whole input were 25.83%, 61.70%, and 12.47%, respectively, evaporation was 0.0786 km 3. In the reduction stage (1998-2015), the average rate of the lake area, water level, and lake volume change was-1.21 km 2 /a,-0.2422 m/a, and-0.0101 km 3 /a, respectively. Before 2006, precipitation, river inflow, and groundwater were 0.0154 km 3 , 0.0475 km 3 , and-0.0025 km 3 , respectively; from 2006 to 2009, precipitation, river inflow, and groundwater were 0.0143 km 3 , 0.0334 km 3 , and 0.0058 km 3 , respectively; after 2009, precipitation, river inflow, and groundwater were 0.0139 km 3 , 0.0199 km 3 , and 0.0085 km 3 , respectively. Evaporation decreased from 0.0714 km 3 to 0.0480 km 3 from 1998 to 2015. In the growth stage (2016-2019), the average rate of the lake area, water level, and lake volume change were 1.38 km 2 /a, 0.27 m/a, and 0.0088 km 3 /a, respectively. Precipitation, river inflow, and groundwater were 0.0209 km 3 , 0.0005 km 3 , and 0.0373 km 3 , which accounts for the whole input were 46.63 %, 52.12 %, and 1.25 %, respectively, evaporation was 0.0187 km 3. Compared with the stable stage, groundwater in the growth stage reduced from 12.47 % (0.0098 km 3) to only 1.25 % (0.0005 km 3). From 1998 to 2004, Hongjiannao Lake experienced continuous losing conditions (discharge from the lake to groundwater), with a variable exchange volume of up to-0.01582 km 3 in 1999. Through geographical detector analysis, it was found that temperature was the dominant factor from 1988 to 1997, while human factors were the dominant factors from1998 to 2015.

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Section: Introductionmentioning

confidence: 99%

Water balance and influence mechanism analysis: a case study of Hongjiannao Lake, China

Yue

Liu

2021

Environ Monit Assess

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“…Climatic factors (precipitation and evaporation) are effective in recharging the groundwater and also water table drawdown (Guevara-Ochoa et al 2018). To estimate the spatial distribution of precipitation and evaporation in the plain, statistics of annual precipitation (climatology stations) and annual evaporation (evaporation stations) for 30 years in the GIS environment were used.…”

Section: Model Inputs In Predicting Groundwater Depth Drawdownmentioning

confidence: 99%

Spatial modeling of groundwater depth fluctuations using co-active neuro-fuzzy inference system (CANFIS) and geographic information system (GIS)

Gholami

2022

Appl Water Sci

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Groundwater drawdown and reduction of groundwater resources are serious problems in the water supply. Unfortunately, sufficient and accurate data on the groundwater depth fluctuations values are not available in all plains of the world. In the present study, a co-active neuro-fuzzy inference system (CANFIS) network and geographic information system (GIS) were used to simulate the annual groundwater drawdown values on the southern coasts of the Caspian Sea (Iran). For this purpose, the annual drawdown values in 250 piezometric wells as the output variable were evaluated. Further, the effective factors in groundwater depth fluctuations were evaluated as inputs of the CANFIS network, including water table depth, annual precipitation, annual evaporation, the transmissivity of aquifer formations, topography, distance from the sea, distance from water sources, population density, and withdrawal values in the influence radius of the study wells. The modeling process was performed in three stages: training, cross-validation, and test. According to the results, the CANFIS network has a high performance in modeling groundwater drawdown (training R-sqr = 0.97; test R-sqr = 0.82). Then, the inputs of the tested network were prepared in the GIS in raster format for the study plain. The tested network was used to simulate the annual fluctuations of groundwater depth in the plain and the simulation results were presented in the GIS as an annual groundwater drawdown map. Overlap and comparison of the observed values of groundwater drawdown and the simulated values in the map indicate the high performance of the CANFIS network (R-sqr = 0.81). Therefore, the used methodology can be applied to simulate the annual drawdown of groundwater depth in plains without groundwater depth data.

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“…scenarios to analyze the impact of climate change on water resources by quantifying the spatio-temporal dynamics of water balance and groundwater-surface water interactions 9 . However, several issues of the traditional hydrological models should be addressed which include the relatively low accuracy, the high computation costs and the necessary requirements of expert knowledge.…”

Section: Guevara-ochoa Et Al Coupled Hydrological-hydrogeological Momentioning

confidence: 99%

A Novel Deep Learning Algorithm for Groundwater Level Prediction based on Spatiotemporal Attention Mechanism

Chen¹,

Zhou²,

Hui³

et al. 2020

Preprint

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Groundwater resources play a vital role in production, human life and economic development. Effective prediction of groundwater levels would support better water resources management. Although machine learning algorithms have been studied and applied in many domains with good enough results, the researches in hydrologic domains are not adequate. This paper proposes a novel deep learning algorithm for groundwater level prediction based on spatiotemporal attention mechanism. Short-term (one month ahead) and long-term (twelve months ahead) prediction of groundwater level are conducted with observed groundwater levels collected from several boreholes in the middle reaches of the Heihe River Basin in northwestern China. Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) are used to evaluate the performance of the proposed algorithm and several baseline models (i.e., SVR, Support Vector Regression; FNN, Feedforward Neural Networks; LSTM, Long Short-Term Memory neural network). The results show that the proposed model can effectively improve the prediction accuracy compared to the baseline models with MAE of 0.0754, RMSE of 0.0952 for short-term prediction and MAE of 0.0983, RMSE of 0.1215 for long-term prediction. This study provides a feasible and accurate approach for groundwater prediction which may facilitate decision making for water management.

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Spatio-temporal effect of climate change on water balance and interactions between groundwater and surface water in plains

Cited by 69 publications

References 111 publications

Water balance and influence mechanism analysis: a case study of Hongjiannao Lake, China

Water balance and influence mechanism analysis: a case study of Hongjiannao Lake, China

Spatial modeling of groundwater depth fluctuations using co-active neuro-fuzzy inference system (CANFIS) and geographic information system (GIS)

A Novel Deep Learning Algorithm for Groundwater Level Prediction based on Spatiotemporal Attention Mechanism

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