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

A special issue of Hydrology (ISSN 2306-5338).

Deadline for manuscript submissions: closed (30 June 2021).

Special Issue Editors

Dr. Lorena Liuzzo
E-Mail Website
Guest Editor
Facoltà di Ingegneria ed Architettura, Università degli Studi di Enna Kore, 94100 Enna, Italy
Interests: hydrology; rainfall-runoff modeling; flood risk modeling; water resources; climate change
Special Issues and Collections in MDPI journals
Dr. Huidae Cho
E-Mail Website
Guest Editor
Assistant Professor of Geospatial Science and Computing, Institute for Environmental and Spatial Analysis, University of North Georgia, 3820 Mundy Mill Road, Oakwood, GA 30566, USA
Interests: spatial hydrology; optimization; uncertainty analysis; GIS; data assimilation

Special Issue Information

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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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Hydrology is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Data assimilation
  • Hydrological modeling
  • Hydrological observations
  • Information transfer
  • Model uncertainty
  • Multi-model ensemble
  • Multi-source information
  • Rainfall-runoff modeling
  • Remote sensing data
  • Water resources systems

Published Papers (4 papers)

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Research

Article
A Streamflow Bias Correction and Performance Evaluation Web Application for GEOGloWS ECMWF Streamflow Services
Hydrology 2021, 8(2), 71; https://doi.org/10.3390/hydrology8020071 - 25 Apr 2021
Viewed by 855
Abstract
We present the development and testing of a web application called the historical validation tool (HVT) that processes and visualizes observed and simulated historical stream discharge data from the global GEOGloWS ECMWF streamflow services (GESS), performs seasonally adjusted bias correction, computes goodness-of-fit metrics, [...] Read more.
We present the development and testing of a web application called the historical validation tool (HVT) that processes and visualizes observed and simulated historical stream discharge data from the global GEOGloWS ECMWF streamflow services (GESS), performs seasonally adjusted bias correction, computes goodness-of-fit metrics, and performs forward bias correction on subsequent forecasts. The HVT corrects GESS output at a local scale using a technique that identifies and corrects model bias using observed hydrological data that are accessed using web services. HVT evaluates the performance of the GESS historic simulation data and provides more accurate historic simulation and bias-corrected forecast data. The HVT also allows users of the GEOGloWS historical streamflow data to use local observed data to both validate and improve the accuracy of local streamflow predictions. We developed the HVT using Tethys Platform, an open-source web application development framework. HVT presents data visualization using web mapping services and data plotting in the web map interface while functions related to bias correction, metrics reporting, and data generation for statistical analysis are computed by the back end. We present five case studies using the HVT in Australia, Brazil, Colombia, the Dominican Republic, and Peru. In these case studies, in addition to presenting the application, we evaluate the accuracy of the method we implemented in the HVT for bias correction. These case studies show that the HVT bias correction in Brazil, Colombia, and Peru results in significant improvement in historic simulation across the countries, while bias correction only resulted in marginal historic simulation improvements in Australia and the Dominican Republic. The HVT web application allows users to use local data to adjust global historical simulation and forecasts and validate the results, making the GESS modeling results more useful at a local scale. Full article
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Article
Data Assimilation of Satellite-Based Soil Moisture into a Distributed Hydrological Model for Streamflow Predictions
Hydrology 2021, 8(1), 52; https://doi.org/10.3390/hydrology8010052 - 20 Mar 2021
Viewed by 1179
Abstract
The authors examine the impact of assimilating satellite-based soil moisture estimates on real-time streamflow predictions made by the distributed hydrologic model HLM. They use SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture Ocean Salinity) data in an agricultural region of the state [...] Read more.
The authors examine the impact of assimilating satellite-based soil moisture estimates on real-time streamflow predictions made by the distributed hydrologic model HLM. They use SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture Ocean Salinity) data in an agricultural region of the state of Iowa in the central U.S. They explore three different strategies for updating model soil moisture states using satellite-based soil moisture observations. The first is a “hard update” method equivalent to replacing the model soil moisture with satellite observed soil moisture. The second is Ensemble Kalman Filter (EnKF) to update the model soil moisture, accounting for modeling and observational errors. The third strategy introduces a time-dependent error variance model of satellite-based soil moisture observations for perturbation of EnKF. The study compares streamflow predictions with 131 USGS gauge observations for four years (2015–2018). The results indicate that assimilating satellite-based soil moisture using EnKF reduces predicted peak error compared to that from the open-loop and hard update data assimilation. Furthermore, the inclusion of the time-dependent error variance model in EnKF improves overall streamflow prediction performance. Implications of the study are useful for the application of satellite soil moisture for operational real-time streamflow forecasting. Full article
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Article
On the Choice of Metric to Calibrate Time-Invariant Ensemble Kalman Filter Hyper-Parameters for Discharge Data Assimilation and Its Impact on Discharge Forecast Modelling
Hydrology 2021, 8(1), 36; https://doi.org/10.3390/hydrology8010036 - 24 Feb 2021
Viewed by 583
Abstract
An important step when using some data assimilation methods, such as the ensemble Kalman filter and its variants, is to calibrate its parameters. Also called hyper-parameters, these include the model and observation errors, which have previously been shown to have a strong impact [...] Read more.
An important step when using some data assimilation methods, such as the ensemble Kalman filter and its variants, is to calibrate its parameters. Also called hyper-parameters, these include the model and observation errors, which have previously been shown to have a strong impact on the performance of the data assimilation method. Many metrics can be used to calibrate these hyper-parameters but may not all yield the same optimal set of values. The current study investigated the importance of the choice of metric used during the hyper-parameter calibration phase and its impact on discharge forecasts. The types of metrics used each focused on discharge accuracy, ensemble spread or observation-minus-background statistics. The calibration was performed for the ensemble square root Kalman filter over two catchments in Canada using two different hydrologic models per catchment. Results show that the optimal set of hyper-parameters depended heavily on the choice of metric used during the calibration phase, where data assimilation was applied. These sets of hyper-parameters in turn produced different hydrologic forecasts. This influence was reduced as the forecast lead time increased, because of not applying data assimilation in the forecast mode, and accordingly, convergence of model state ensembles produced in the calibration phase. However, the influence could remain considerable for a few days up to multiple weeks depending on the catchment and the model. As such, a preliminary analysis would be recommended for future studies to better understand the impact that metrics can have within and outside the bounds of hyper-parameter calibration. Full article
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Article
Simulation of Rainfall-Induced Floods in Small Catchments (the Polomet’ River, North-West Russia) Using Rain Gauge and Radar Data
Hydrology 2020, 7(4), 92; https://doi.org/10.3390/hydrology7040092 - 27 Nov 2020
Cited by 3 | Viewed by 994
Abstract
In recent years, rain floods caused by abnormal rainfall precipitation have caused several damages in various part of Russia. Precise forecasting of rainfall runoff is essential for both operational practice to optimize the operation of the infrastructure in urbanized territories and for better [...] Read more.
In recent years, rain floods caused by abnormal rainfall precipitation have caused several damages in various part of Russia. Precise forecasting of rainfall runoff is essential for both operational practice to optimize the operation of the infrastructure in urbanized territories and for better practices on flood prevention, protection, and mitigation. The network of rain gauges in some Russian regions are very scarce. Thus, an adequate assessment and modeling of precipitation patterns and its spatial distribution is always impossible. In this case, radar data could be efficiently used for modeling of rain floods, which were shown by previous research. This study is aimed to simulate the rain floods in the small catchment in north-west Russia using radar- and ground-based measurements. The investigation area is located the Polomet’ river basin, which is the key object for runoff and water discharge monitoring in Valdai Hills, Russia. Two datasets (rain gauge and weather radar) for precipitation were used in this work. The modeling was performed in open-source Soil and Water Assessment Tool (SWAT) hydrological model with three types of input data: rain gauge, radar, and gauge-adjusted radar data. The simulation efficiency is assessed using the coefficient of determination R2, Nash–Sutcliffe model efficiency coefficient (NSE), by comparing the mean values to standard deviations for the calculated and measured values of water discharge. The SWAT model captures well the different phases of the water regime and demonstrates a good quality of reproduction of the hydrographs of the river runoff of the Polomet’ river. In general, the best model performance was observed for rain gauge data (NSE is up to 0.70 in the Polomet’river-Lychkovo station); however, good results have been also obtained when using adjusted data. The discrepancies between observed and simulated water flows in the model might be explained by the scarce network of meteorological stations in the area of studied basin, which does not allow for a more accurate correction of the radar data. Full article
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