Multi-Objective Surface Water Resource Management Considering Conflict Resolution and Utility Function Optimization

2018 ◽  
Vol 32 (14) ◽  
pp. 4487-4509 ◽  
Author(s):  
Hadi Tarebari ◽  
Amir Hossein Javid ◽  
Seyyed Ahmad Mirbagheri ◽  
Hedayat Fahmi
Keyword(s):  
Conflict Resolution ◽  
Resource Management ◽  
Surface Water ◽  
Utility Function ◽  
Water Resource ◽  
Water Resource Management ◽  
Function Optimization ◽  
Multi Objective ◽  
Surface Water Resource

scholarly journals How is Baseflow Index (BFI) impacted by water resource management practices?

2021 ◽  
Author(s):  
John P. Bloomfield ◽  
Mengyi Gong ◽  
Benjamin P. Marchant ◽  
Gemma Coxon ◽  
Nans Addor
Keyword(s):  
Resource Management ◽  
Surface Water ◽  
Water Resource ◽  
Water Resource Management ◽  
Management Practices ◽  
Explanatory Power ◽  
Minor Importance ◽  
Groundwater Abstraction ◽  
Large Sample ◽  
Water Abstraction

Abstract. Water resource management (WRM) practices, such as abstractions and discharges, may impact baseflow. Here the CAMELS-GB large-sample hydrology dataset is used to assess the impacts of such practices on baseflow index (BFI) using statistical models of 429 catchments from Great Britain. Two complementary modelling schemes, multiple linear regression (LR) and machine learning (random forests, RF), are used to investigate the relationship between BFI and two sets of covariates (natural covariates only and a combined set of natural and WRM covariates). The LR and RF models show good agreement between explanatory covariates. In all models, the extent of fractured aquifers, clay soils, non-aquifers, and crop cover in catchments, catchment topography and aridity are significant or important natural covariates in explaining BFI. When WRM terms are included, groundwater abstraction is significant or the most important WRM covariate in both modelling schemes and discharge to rivers is also identified as significant or influential, although natural covariates still provide the main explanatory power of the models. Surface water abstraction is a significant covariate in the LR model but of only minor importance in the RF model. Reservoir storage covariates are not significant or are unimportant in both the LR and RF models for this large-sample analysis. Inclusion of WRM terms improves the performance of some models in specific catchments. The LR models of high BFI catchments with relatively high levels of groundwater abstraction show the greatest improvements, and there is some evidence of improvement in LR models of catchments with moderate to high discharges. However, there is no evidence that the inclusion of the WRM covariates improves the performance of LR models for catchments with high surface water abstraction or that they improve the performance of the RF models. These observations are used to formulate a conceptual framework for baseflow generation that incorporates WRM practices. It is recommended that information on WRM, particularly groundwater abstraction, should be included where possible in future large-sample hydrological data sets and in the analysis and prediction of BFI and other measures of baseflow.

scholarly journals How is Baseflow Index (BFI) impacted by water resource management practices?

2021 ◽  
Vol 25 (10) ◽  
pp. 5355-5379
Author(s):  
John P. Bloomfield ◽  
Mengyi Gong ◽  
Benjamin P. Marchant ◽  
Gemma Coxon ◽  
Nans Addor
Keyword(s):  
Resource Management ◽  
Surface Water ◽  
Water Resource ◽  
Water Resource Management ◽  
Low Flow ◽  
Minor Importance ◽  
Groundwater Abstraction ◽  
Large Sample ◽  
Water Abstraction ◽  
Wide Range

Abstract. Water resource management (WRM) practices, such as groundwater and surface water abstractions and effluent discharges, may impact baseflow. Here the CAMELS-GB large-sample hydrology dataset is used to assess the impacts of such practices on Baseflow Index (BFI) using statistical models of 429 catchments from Great Britain. Two complementary modelling schemes, multiple linear regression (LR) and machine learning (random forests, RF), are used to investigate the relationship between BFI and two sets of covariates (natural covariates only and a combined set of natural and WRM covariates). The LR and RF models show good agreement between explanatory covariates. In all models, the extent of fractured aquifers, clay soils, non-aquifers, and crop cover in catchments, catchment topography, and aridity are significant or important natural covariates in explaining BFI. When WRM terms are included, groundwater abstraction is significant or the most important WRM covariate in both modelling schemes, and effluent discharge to rivers is also identified as significant or influential, although natural covariates still provide the main explanatory power of the models. Surface water abstraction is a significant covariate in the LR model but of only minor importance in the RF model. Reservoir storage covariates are not significant or are unimportant in both the LR and RF models for this large-sample analysis. Inclusion of WRM terms improves the performance of some models in specific catchments. The LR models of high BFI catchments with relatively high levels of groundwater abstraction show the greatest improvements, and there is some evidence of improvement in LR models of catchments with moderate to high effluent discharges. However, there is no evidence that the inclusion of the WRM covariates improves the performance of LR models for catchments with high surface water abstraction or that they improve the performance of the RF models. These observations are discussed within a conceptual framework for baseflow generation that incorporates WRM practices. A wide range of schemes and measures are used to manage water resources in the UK. These include conjunctive-use and low-flow alleviation schemes and hands-off flow measures. Systematic information on such schemes is currently unavailable in CAMELS-GB, and their specific effects on BFI cannot be constrained by the current study. Given the significance or importance of WRM terms in the models, it is recommended that information on WRM, particularly groundwater abstraction, should be included where possible in future large-sample hydrological datasets and in the analysis and prediction of BFI and other measures of baseflow.

scholarly journals Response of Surface Water Quality Characteristics to Socio-economic Factors in Eastern-Central China

2021 ◽  
Author(s):  
Maoqing Duan ◽  
Shilu Zhang ◽  
Junyu He ◽  
MIngxia Xu ◽  
Yuanyuan Gao ◽  
...  
Keyword(s):  
Water Quality ◽  
Resource Management ◽  
Surface Water ◽  
Influencing Factors ◽  
Water Resource ◽  
Quality Index ◽  
Water Resource Management ◽  
Surface Water Quality ◽  
Ccme Wqi

Following the implementation of the strictest water resource management system in China, it has become increasingly important to understand and improve the surface water quality and the rate at which water function zones reach the water quality standard. Based on the monthly monitoring data from 450 monitoring sites at the provincial borders of 27 provinces in China in 2019, the overall surface water quality at provincial boundaries in China was as follows: 61.7% of the water was classified under Class I–III; and 5%, 8.6%, and 12.2% of the water was classified under Class IV, V, and inferior V, respectively. The main standard items are DO, CODMn, COD, BOD5, NH3-N, and TP. The Canadian Council of Ministers of the Environment-water quality index (CCME-WQI) showed that the provincial boundary water quality exceeded the fair level, and F1 was the most influential factor. Then, 27 factors that directly or indirectly affect the water quality of surface water at the provincial boundaries of 27 provinces were identified, and the indirect influencing factors were integrated into the ecological environmental quality index and human activities quantitative index. Finally, the 27 factors were integrated into six factors, and the relationship between these indicators and CCME-WQI as well as the concentration of influencing elements with respect to regulatory standard limits were analyzed. The proportion of building land was the most significant factor affecting the quality of the aquatic environment in provincial boundaries. In addition, the economic development level, proportion of farmland, and degree of social development were identified as significant influencing factors. The six factors have different degrees of impact on the concentrations of major elements with respect to standard limits. This study basically explores water resource management and offers significant reference and guidelines for the improvement of the quality of surface water at provincial boundaries in China

scholarly journals Groundwater monitoring system and groundwater policy in relation to unified water resource management in Korea

Water Policy ◽  
2020 ◽  
Vol 22 (2) ◽  
pp. 211-222
Author(s):  
Lae-Soo Kang ◽  
Se-Yeong Hamm ◽  
Jae-Yeol Cheong ◽  
Hang-Tak Jeon ◽  
Jae Hyun Park
Keyword(s):  
Climate Change ◽  
Resource Management ◽  
Surface Water ◽  
Water Resources ◽  
Water Resource ◽  
Groundwater Monitoring ◽  
Water Resource Management ◽  
Social Issues ◽  
Groundwater Resources ◽  
Monitoring Stations

Abstract The demand for water resources is consistently increasing due to industrialization and urbanization. Water resource management can become difficult because of climate change and social issues. Due to the difficulty in securing stable water resources, reasonable utilization and management of water is crucial for the sustainable development of groundwater resources that are an efficient alternative to surface water. For groundwater management, the National Groundwater Information Management Service (GIMS) Center for K-Water measures groundwater data hourly (groundwater level, water temperature, and electrical conductivity) at national groundwater monitoring stations and analyzes the long-term variation of groundwater with regard to climate change. According to the Groundwater Act (1993), auxiliary groundwater monitoring stations for groundwater use and water quality are activated by local governments. The observed data after the calibration process are provided for utilization by citizens, industries, schools, institutes, and government policies through annual reports on groundwater monitoring by the GIMS Center. In 2018, the Korean government merged water resources affairs that were once divided between the Ministry of Environment and the Ministry of Land, Infrastructure, and Transport. The change will be favorable for effective management of the surface water and groundwater resources as well as ensuring both quality and quantity.

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