scholarly journals Mapping Interflow Potential and the Validation of Index-Overlay Weightings by Using Coupled Surface Water and Groundwater Flow Model

Water ◽  
2021 ◽  
Vol 13 (17) ◽  
pp. 2452
Author(s):  
Chuen-Fa Ni ◽  
Quoc-Dung Tran ◽  
I-Hsien Lee ◽  
Minh-Hoang Truong ◽  
Shaohua Marko Hsu
Keyword(s):  
Surface Water ◽  
Water Resource ◽  
Water Resource Management ◽  
River Flow ◽  
High Elevation ◽  
Coastal Zones ◽  
Near Surface ◽  
Practical Applications ◽  
Index Overlay ◽  
Overlay Model

Interflow is an important water source contributing to river flow. It directly influences the near-surface water cycles for water resource management. This study focuses on assessing the interflow potential and quantifying the interflow in the downstream area along the Kaoping River in southern Taiwan. The interflow potential is first determined based on the modified index-overlay model, which employs the analytical hierarchy process (AHP) to calculate the ratings and weightings of the selected factors. The groundwater and surface water flow (GSFLOW) numerical model is then used to link the index-overlay model to quantify the interflow potential for practical applications. This study uses the Monte Carlo simulations to assess the influence of rainfall-induced variations on the interflow uncertainty in the study area. Results show that the high potential interflow zones are located in the high to middle elevation regions along the Kaoping River. Numerical simulations of the GSFLOW model show an interflow variation pattern that is similar to the interflow potential results obtained from the index-overlay model. The average interflow rates are approximately 3.5 × 104 (m3/d) in the high elevation zones and 2.0 × 104 (m3/d) near the coastal zones. The rainfall uncertainty strongly influences interflow rates in the wet seasons, especially the peaks of the storms or heavy rainfall events. Interflow rates are relatively stable in the dry seasons, indicating that interflow is a reliable water resource in the study area.

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 An Integrative Approach for Environmental Assessment and Water Resources Management Using Direct Current Resistivity (DC), Geographic Information System (GIS), Remote Sensing, and Gain and Loss Method

2021 ◽  
Author(s):  
Dina Ragab Desouki Abdelmoneim
Keyword(s):  
Remote Sensing ◽  
Surface Water ◽  
Water Resource ◽  
Water Resource Management ◽  
Shallow Groundwater ◽  
Groundwater Table ◽  
Dc Resistivity ◽  
Seepage Rate ◽  
Loss Method ◽  
Shallow Groundwater Table

Sustainable water resource management is a crucial national and global issue (Currell et al., 2012). In arid areas, groundwater is often the major source of water or at least a crucial supplement to other freshwater resources for agriculture, industry and domestic consumption (Vrba and Renaud, 2016). The complexity associated with groundwater-surface water interactions creates uncertainty about water resource sustainability in semi-arid environments, especially with urbanization and population growth. Flood irrigation in the early 1900s increased the shallow groundwater table in the Treasure Valley (TV), but with increasing irrigation efficiencies, they have been declining since the 1960s with a mean decline rate of about 2.9-3.9x10^-9 (m/s) (Contor et al., 2011). Quantifying how much surface water is being exchanged with the shallow groundwater table through canals in the TV is necessary for gaining a better understanding of groundwater-surface water interactions in this heavily managed system. This knowledge would help evaluate alternative management options for achieving sustainable management of existing water resources. The key objectives of this project are to determine the seepage rate through some canal reaches in the TV, evaluate the integration of the gain and loss method, remote sensing, GIS, hydrogeophysical simulation, and direct current (DC) resistivity geophysical methods for water resource management. We hypothesize that the underlying lithology and size of canals affect the magnitude of the seepage rate. Flow measurements were collected weekly between July and August 2020 in canal reaches representing different sizes and lithological units to determine the seepage rate using the reach gain/loss method. Canal variability and measurement uncertainty were included in seepage estimation for the entire TV using 3 alternative scaling approaches. DC resistivity was used as a complementary method to monitor the seepage effect on the shallow GW aquifer over 2 months. This research evaluates to what extent canal size and its underlying lithology affects the seepage rate, and how the integration of methods may provide additional insight into groundwater exchange-surface water.

scholarly journals Nutrient Pollutants in Surface Water—Assessing Trends in Drinking Water Resource Quality for a Regional City in Central Europe

2019 ◽  
Vol 11 (7) ◽  
pp. 1988 ◽  
Author(s):  
Włodzimierz Kanownik ◽  
Agnieszka Policht-Latawiec ◽  
Wioletta Fudała
Keyword(s):  
Surface Water ◽  
River Water ◽  
Water Resource ◽  
Water Resource Management ◽  
Control Point ◽  
Ammonium Nitrogen ◽  
Nutrient Concentrations ◽  
The European Union ◽  
Treatment Processes ◽  
Kjeldahl Nitrogen

This paper presents the changes in concentration of seven biogenic indices in the Wisłok River water and determines the water treatment processes required in order to obtain water fit for consumption. The investigations were conducted during 2004–2013, and water samples were collected at a measuring-control point was situated at 67.9 km on the river at the surface water intake for the water supply to the Rzeszów city dwellers. Analysis of the research results allows for the forecasting of technological and organizational changes in the treatment processes of the abstracted water. It was found that only the mean concentration of Kjeldahl nitrogen exceeded the value admissible for class I, which allowed the Wisłok River water to be classified as class II with good potential and determined the water quality category as A2, which indicates the necessity for typical performance physical and chemical treatment. Downward trends in the contents of the tested nutrients occurred during the period of investigation, except for nitrite nitrogen. Statistically significant downward trends were registered for ammonium nitrogen, Kjeldahl nitrogen, total nitrogen and phosphates. The decline in nutrient concentrations in the water of Wisłok is a tangible result of the introduction of new standards of water resource management in the catchment, compliant with the European Union legislation.

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 How seasonal forecast could help a decision maker: an example of climate service for water resource management

2016 ◽  
Vol 13 ◽  
pp. 51-55 ◽  
Author(s):  
Christian Viel ◽  
Anne-Lise Beaulant ◽  
Jean-Michel Soubeyroux ◽  
Jean-Pierre Céron
Keyword(s):  
Water Resource ◽  
Water Resource Management ◽  
River Flow ◽  
Forecast Model ◽  
Seasonal Forecast ◽  
Blind Test ◽  
Climate Services ◽  
Great Opportunity ◽  
Skill Scores ◽  
The Impact

Abstract. The FP7 project EUPORIAS was a great opportunity for the climate community to co-design with stakeholders some original and innovative climate services at seasonal time scales. In this framework, Météo-France proposed a prototype that aimed to provide to water resource managers some tailored information to better anticipate the coming season. It is based on a forecasting system, built on a refined hydrological suite, forced by a coupled seasonal forecast model. It particularly delivers probabilistic river flow prediction on river basins all over the French territory. This paper presents the work we have done with "EPTB Seine Grands Lacs" (EPTB SGL), an institutional stakeholder in charge of the management of 4 great reservoirs on the upper Seine Basin. First, we present the co-design phase, which means the translation of classical climate outputs into several indices, relevant to influence the stakeholder's decision making process (DMP). And second, we detail the evaluation of the impact of the forecast on the DMP. This evaluation is based on an experiment realised in collaboration with the stakeholder. Concretely EPTB SGL has replayed some past decisions, in three different contexts: without any forecast, with a forecast A and with a forecast B. One of forecast A and B really contained seasonal forecast, the other only contained random forecasts taken from past climate. This placebo experiment, realised in a blind test, allowed us to calculate promising skill scores of the DMP based on seasonal forecast in comparison to a classical approach based on climatology, and to EPTG SGL current practice.

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