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Hydrology Research Special Issue on

Data Fusion in Hydrological Modeling



Hydrological modelling plays a crucial role in understanding and predicting the behaviour of water systems, which is important for water resource management, flood forecasting, and environmental planning. However, the accuracy of these models heavily relies on accurate input data, which can be challenging to obtain, especially in regions with limited ground-based observations. This is where remote sensing technology comes into play. By harnessing data from remote sensing platforms, researchers can provide spatially and temporally comprehensive information on precipitation and soil moisture, filling critical gaps in traditional observation networks.

Data fusion in hydrological modelling involves combining remote sensing-derived data with ground-based measurements to create a more complete picture of the hydrological cycle. This integration is achieved through a synergy of advanced techniques such as data assimilation, machine learning algorithms and statistical methods.

This Special Issue is linked to the EU Waterline project (CHIST-ERA-19-CES-006) and will accept submissions from selected papers presented in the EGU 2024 Session ITS1.7/HS12.1 "Data fusion in hydrological modeling: improving hydrological forecasts incorporating remote sensing precipitation and soil moisture estimates", which will take place in April 2024. It will also be open for submissions outside of this event, that are in line with the topics.

We are pleased to invite you to submit a manuscript to Hydrology Research for peer review and possible publication in a Special Issue entitled “Data Fusion in Hydrological Modeling”.


Relevant topics include:

This Special Issue welcomes, but is not limited to, contributions on:

  • Novel data assimilation methods that effectively incorporate remote sensing precipitation and soil moisture estimates into hydrological models
  • Applications of machine learning algorithms for fusing remote sensing data with ground-based observations to improve hydrological predictions
  • Methodologies for quantifying and propagating uncertainties associated with remote sensing precipitation and soil moisture estimates through hydrological models
  • Methodologies for downscaling remote sensing data to finer spatial and temporal resolutions, making them compatible with hydrological models that require higher detail for accurate predictions
  • Techniques for validating and verifying hydrological models that incorporate remote sensing data
  • Emerging trends in data fusion in hydrological modeling


Key dates:

Deadline for manuscript submission: July 31st 2024

Expected publication: Articles will be published online as soon as possible after acceptance.


Guest Editors:

Alexandra Gemitzi, Professor, Democritus University of Thrace, Greece

Venkat Lakshmi, Professor, University of Virginia, USA

Ali Torabi Haghighi, Associate Professor, University of Oulu, Finland

Przemysław Wachniew, AGH University of Science and Technology, Poland


How to submit:

Please make sure that your paper follows the Instructions to Authors of the journal, before submitting your paper directly to Hydrology Research peer review system. Then choose the article type – ‘Special Issue Article OA’ and the submission category – Special Issue: Data Fusion. This will send your paper to one of the Guest Editors.

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