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Hydrological Modelling and Remote Sensing: Selected Papers from the 2017 and 2018 SWAT International Conferences

Special Issue Information

Dear Colleagues,

Although considerable improvement has been made in hydrologic sciences, and the representation of the processes within the models, this has also led to increased data requirements for the spatial representation of a study area and model parameterization.  In the past decade, remote sensing data have become increasingly available to the hydrologic community for developing a representative spatially-distributed model for assessing the impacts of landuse change, and land and water management practices on water resources at the river basin scale. Further, data on hydrologic state variables, such as evapotranspiration from thermal sensors and soil moisture from microwave sensors, are also being explored for calibration and validation of hydrological models, either independently or within a data assimilation framework. Many research challenges are being actively explored, which aim toward calibration of hydrological models for watersheds and river basins taking into account the true spatial variability of observed processes as opposed to lumped calibration of the model parameters at the point of discharge measurements. This Special Issue of Water is envisioned to showcase the state of the art in the adaptation and use of remotely sensed data for hydrologic modelling using SWAT at different scales and climatic regions for model parameterization, calibration, validation and data assimilation. We sincerely hope that these research papers would address the lacuna that exists in the use of remote sensing data with the hydrologic models, methods to overcome them and identify issues that need further research.

Prof. Balaji Narasimhan
Dr. Paul Wagner
Prof. Claire Baffaut
Dr. Mou Leong Tan
Dr. Abeyou Wale Worqlul
Guest Editors

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Keywords

  • Model Parameterization
  • Calibration and Validation
  • Data Assimilation
  • Large-Scale Modelling
  • Climate Change Impact
  • SWAT Model
  • Remote Sensing

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Water - ISSN 2073-4441