Comparative Analysis of Groundwater-Ecosystem Service Value of Protected Horticulture Complex and Paddy Fields

Jeong-woo, Son; Choi, Deuggyu; Lee, Siyoung; Kang, Dong-Hyun; Park, Min-Jung; Yun, Shan; Kim, Nam-Choon; Maria, Kong

doi:10.7851/ksrp.2018.24.2.047

Cited by 2 publications

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“…Groundwater recharge is a vital ecosystem service function [25,26], and agricultural regions have been found to have an excellent groundwater recharge function [27,28]. However, expanding the impermeable area makes it challenging to recharge groundwater [29][30][31][32]. The difficulty in cultivating groundwater depletes the groundwater [33][34][35][36][37], decreasing the amount of water humans can utilize [38][39][40][41][42].…”

Section: Introductionmentioning

confidence: 99%

Evaluation and Improvement Measures of the Runoff Coefficient of Urban Parks for Sustainable Water Balance

Son

Kwon

2022

Land

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As the impermeable sidewalk area increases in urban areas, diverse problems related to water occur. The purposes of this research were to increase the rainwater infiltration rate through water balance analysis and estimate the runoff coefficient according to land cover types in urban parks. The regression equations and runoff coefficients relative to the rainwater infiltration rate were estimated according to the land cover types and applied to eight urban parks. In the results of the experiment, the runoff coefficient was 0.245 for vegetation areas, 0.583 for permeable sidewalks, 0.963 for sidewalk blocks, and 1.000 for impervious sidewalks, which had 100% outflow. The results show that the vegetation area in urban parks is significantly related to rainfall–runoff, infiltration, and evapotranspiration. The average of eight urban parks was 126.52 mm, indicating that 11.80% of the rainfall was recharged into groundwater. Additionally, the average runoff rate was 498.56 mm, indicating that 46.52% was leaked externally. Therefore, it is suggested to decrease the impermeable sidewalk areas in urban parks. Additionally, extending the waterway, swamp, and gravel sidewalk areas is suggested. Urban parks should be developed in order to contribute to hydrological control through the water balance in urban land use.

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Section: Introductionmentioning

confidence: 99%

Evaluation and Improvement Measures of the Runoff Coefficient of Urban Parks for Sustainable Water Balance

Son

Kwon

2022

Land

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A machine learning-based approach to predict groundwater nitrate susceptibility using field measurements and hydrogeological variables in the Nonsan Stream Watershed, South Korea

Lee,

Ko,

Yoo

2023

Appl Water Sci

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Identifying and predicting the nitrate inflow and distribution characteristics of groundwater is critical for groundwater contamination control and management in rural mixed-land-use areas. Several groundwater nitrate prediction models have been developed; in particular, a nitrate concentration model that uses dissolved ions in groundwater as an input variable can produce accurate results. However, obtaining sufficient chemical data from a target area remains challenging. We tested whether machine learning models can effectively determine nitrate contamination using field-measured data (pH, electrical conductivity, water temperature, dissolved oxygen, and redox potential) and existing geographic information system (GIS) data (lithology, land cover, and hydrogeological properties) from the Nonsan Stream Watershed in South Korea, an area where nitrate contamination occurs owing to intensive agricultural activities. In total, 183 groundwater samples from different wells, mixed municipal sites, and agricultural activities were used. The results indicated that among the four machine learning models (artificial neural network (ANN), classification and regression tree (CART), random forest (RF), and support vector machine (SVM)), the RF (R2: 0.74; RMSE: 3.5) and SVM (R2: 0.80; RMSE: 2.8) achieved the highest prediction accuracy and smallest error in all groundwater parameter estimates. Land cover, aquifer type, and soil drainage were the primary RF and SVM model input variables, representing agricultural activity-related and hydrogeological infiltration effects. Our research found that in rural areas with limited hydro-chemical data, RF and SVM models could be used to identify areas at high risk of nitrate contamination using spatial variability, GIS-aided visualization, and easily accessible field-measured groundwater quality data.

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Comparative Analysis of Groundwater-Ecosystem Service Value of Protected Horticulture Complex and Paddy Fields

Cited by 2 publications

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Evaluation and Improvement Measures of the Runoff Coefficient of Urban Parks for Sustainable Water Balance

Evaluation and Improvement Measures of the Runoff Coefficient of Urban Parks for Sustainable Water Balance

A machine learning-based approach to predict groundwater nitrate susceptibility using field measurements and hydrogeological variables in the Nonsan Stream Watershed, South Korea

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