Lithological and Tectonic Control on Groundwater Contribution to Stream Discharge During Low-Flow Conditions

Subglacial Meltwater Recharge in the Dongkemadi River Basin, Yangtze River Source Region

Glaciers on the Tibetan Plateau play an important role in the local hydrological cycle. However, there are only few studies on groundwater in the alpine basins in the Tibetan Plateau which considered the effects of glaciers. Glaciers are extensively distributed in the Dongkemadi River Basin, which is a representative alpine basin in the Yangtze River source region. This study focuses on building a numerical groundwater flow model with glaciations using HydroGeoSphere (HGS) to simulate subglacial meltwater recharge to groundwater in the Dongkemadi River Basin in response to future climate changes. Effects of hydraulic conductivity, precipitation, and temperature on subglacial meltwater recharge to groundwater were discussed. Glacier changes in the future 50 years were predicted under different climate change scenarios. Results show that: (1) the average thickness of the glacier will change significantly; (2) the simulated rate of annual mean subglacial meltwater recharge to groundwater is 4.58 mm, which accounts for 6.33% of total groundwater recharge; and (3) hydraulic conductivity has the largest influence on subglacial meltwater recharge to groundwater, followed by temperature and precipitation. Results of this study are also important to sustainable water resource usage in the Yangtze River source region.

Identification of Groundwater Potential Zones Using GIS and Multi-Criteria Decision-Making Techniques: A Case Study Upper Coruh River Basin (NE Turkey)

In this study, geographic information system (GIS)-based, analytic hierarchy process (AHP) techniques were used to identify groundwater potential zones to provide insight to decisionmakers and local authorities for present and future planning. Ten different geo-environmental factors, such as slope, topographic wetness index, geomorphology, drainage density, lithology, lineament density, rainfall, soil type, soil thickness, and land-use classes were selected as the decision criteria, and related GIS tools were used for creating, analysing and standardising the layers. The final groundwater potential zones map was delineated, using the weighted linear combination (WLC) aggregation method. The map was spatially classified into very high potential, high potential, moderate potential, low potential, and very low potential. The results showed that 21.5% of the basin area is characterised by high to very high groundwater potential. In comparison, the very low to low groundwater potential occupies 57.15%, and the moderate groundwater potential covers 21.4% of the basin area. Finally, the GWPZs map was investigated to validate the model, using discharges and depth to groundwater data related to 22 wells scattered over the basin. The validation results showed that GWPZs classes strongly overlap with the well discharges and groundwater depth located in the given area.

Application of deep neural network to capture groundwater potential zone in mountainous terrain, Nepal Himalaya

This study aims to capture groundwater potential zones integrating deep neural network and groundwater influencing factors. The present work was carried out for Gopi khola watershed, mountainous terrain in Nepal Himalaya as the watershed mainly relies upon the groundwater assets; it is a need to explore groundwater potential for better management of the aquifer framework. Ten groundwater influencing factors were collected such as elevation, slope, curvature, topographic positioning index, topographic roughness index, drainage density, topographic wetness index, geology, lineament density, and land use thematic layers. Among those influencing factors, topographic roughness index was removed because of multicollinearity issue to reduce the dimension of the dataset. A spring inventory map of 145 spring locations was prepared using field survey method and an equal number of spring absence points were randomly generated. The 70% of spring and spring absence pixels were used as training dataset and remaining as test dataset. The final map was created based on predicted probabilities ranging from 0 to 1. The validation was done using the receiver operating characteristic curve, which shows that the area under the curve is 76.1% for the training dataset and 82.1% for the test dataset. The sensitivity analysis was performed using Jackknife test which shows that the lineament density is the most important factor. The experimental results demonstrated that deep neural network is highly capable to capture groundwater potential zone in mountainous terrain. The present study might be useful and preliminary work to exploit the groundwater. The consequences of the current study may be valuable to water administrators to settle on appropriate choices on the ideal utilization of groundwater assets for future arranging in the basic investigation zone.