Multi-Source Data Assimilation for the Improvement of Hydrological Modeling Predictions

Dear Colleagues,

Data assimilation is a procedure in which observations of a system are analyzed through mathematical and statistical algorithms to obtain the optimal assessment of the system state. Over the last decades, data assimilation has been recognized as a valuable and reliable tool for the improvement of the predictive performance of hydrological models, addressing some of the main issues related to modeling uncertainties (forcing input, model parameters, model structure, initial hydrologic conditions, boundary conditions, etc.). In particular, distributed hydrological models have considerably benefited from the availability of multi-source data assimilation. Recent researches in this field include the joint assimilation of soil moisture, water table and river flow data in hydrological models using the ensemble Kalman filter and its variants, particle filters, and variational methods.

The Special Issue “Multi-Source Data Assimilation for the Improvement of Hydrological Modeling Predictions” aims to collect contributions about the development and application of novel methodologies and approaches, the discussion of real-world test cases and the review of the current state of the art about the topic, with a particular focus on new challenges, issues and limitations of data assimilation techniques. 

Topics of interest will include, but will not be limited to: 

• development of novel data assimilation tools and frameworks for hydrological applications;
• data assimilation in real-time control of water resources systems and hydraulic structures;
• multi-model ensemble approaches for generating forcing variables;
• assimilation of satellite-based remote sensing data into hydrological models;
• quantification of model and observation errors, predictive uncertainty identification and evaluation of data assimilation effectiveness.

Dr. Lorena Liuzzo
Dr. Huidae Cho
Guest Editors

Manuscript Submission Information

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