GIS-Based Hydrology and Water Quality Modeling

Dear Colleagues,

Hydrologic and water quality models have been developed across the world to understand, forecast, and manage water resources. These are valuable tools to fill the gaps in understanding water flow and solute transport processes. Accurate estimation of the temporal and spatial distribution characteristics of water resources are required to manage the optimum use of water resources in a sustainable manner. Recently, with advances in computer technology and computational capabilities, Geographic Information Systems (GIS) and remote sensing (RS) technologies have been widely used in hydrologic/water quality modeling areas, and, thus, in related decision-making contexts. GIS has been used to efficiently parameterize input data of various hydrologic and water quality models to represent spatial and temporal characteristic of factors affecting hydrologic components (surface, subsurface, groundwater, etc.) and pollutant generation (nonpoint pollution) and transport with water via surface or infiltration, thus flowing into streams. The use of satellites for the management of water resources can also play a significant role in providing valuable information and filling in the gaps between real conditions and hydrologic modeling. Various satellites have been developed and operated to provide necessary data that can make up for the lack of on-the-ground monitoring of water resources at various scales. The use of GIS and RS have increased in hydrological modeling and water resources system analysis (ET, soil moisture, runoff, groundwater, soil erosion, etc.) in the last decade. In this context, there is a growing need to improve the current GIS/RS technologies and obtain a better understanding of its use in hydrology. Furthermore, in recent years Machine-Learning/Deep-Learning applications have rapidly become the state-of-the-art, leading to enhanced performance in various hydrological modeling applications that can be integrated with GIS and hydrological modeling.

Therefore, the aim of this Special Issue is to foster advances in hydrologic and water quality modeling based on state-of-the-art GIS/RS technologies. In this Special Issue on “GIS/RS-Based Hydrology and Water Quality Modeling”, we welcome your contributions on GIS/RS, hydrology and non-point pollution modeling, integration of GIS and models, Machine-Learning/Deep-Learning application in GIS-based modeling, and decision support systems.

Prof. Kyoung Jae Lim
Prof. Younghun Jung
Dr. Jonggun Kim
Guest Editors

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