Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.9
3.1
Journals
Hydrology
Special Issues
Multi-Source Data Assimilation for the Improvement of Hydrological Modeling Predictions
share announcement

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

A special issue of Hydrology (ISSN 2306-5338). This special issue belongs to the section "Hydrological and Hydrodynamic Processes and Modelling".

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 23037

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, Collections and Topics 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 Issues, Collections and Topics in MDPI journals

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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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 monthly 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 1800 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

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (6 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Editorial

Jump to: Research, Other

3 pages, 176 KiB  
Editorial
Editorial for Special Issue: “Multi-Source Data Assimilation for the Improvement of Hydrological Modeling Predictions”
by Huidae Cho and Lorena Liuzzo
Hydrology 2022, 9(1), 4; https://doi.org/10.3390/hydrology9010004 - 24 Dec 2021
Viewed by 2402
Abstract
Physically-based or process-based hydrologic models play a critical role in hydrologic forecasting [...] Full article
(This article belongs to the Special Issue Multi-Source Data Assimilation for the Improvement of Hydrological Modeling Predictions)

Research

Jump to: Editorial, Other

21 pages, 35161 KiB  
Article
A Streamflow Bias Correction and Performance Evaluation Web Application for GEOGloWS ECMWF Streamflow Services
by Jorge Sanchez Lozano, Giovanni Romero Bustamante, Riley Chad Hales, E. James Nelson, Gustavious P. Williams, Daniel P. Ames and Norman L. Jones
Hydrology 2021, 8(2), 71; https://doi.org/10.3390/hydrology8020071 - 25 Apr 2021
Cited by 17 | Viewed by 5653
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
(This article belongs to the Special Issue Multi-Source Data Assimilation for the Improvement of Hydrological Modeling Predictions)
►▼ Show Figures

Figure 1

18 pages, 12335 KiB  
Article
Data Assimilation of Satellite-Based Soil Moisture into a Distributed Hydrological Model for Streamflow Predictions
by Navid Jadidoleslam, Ricardo Mantilla and Witold F. Krajewski
Hydrology 2021, 8(1), 52; https://doi.org/10.3390/hydrology8010052 - 20 Mar 2021
Cited by 10 | Viewed by 4199
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
(This article belongs to the Special Issue Multi-Source Data Assimilation for the Improvement of Hydrological Modeling Predictions)
►▼ Show Figures

Graphical abstract

20 pages, 3966 KiB  
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
by Jean Bergeron, Robert Leconte, Mélanie Trudel and Sepehr Farhoodi
Hydrology 2021, 8(1), 36; https://doi.org/10.3390/hydrology8010036 - 24 Feb 2021
Cited by 6 | Viewed by 2420
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
(This article belongs to the Special Issue Multi-Source Data Assimilation for the Improvement of Hydrological Modeling Predictions)
►▼ Show Figures

Figure 1

15 pages, 1896 KiB  
Article
Simulation of Rainfall-Induced Floods in Small Catchments (the Polomet’ River, North-West Russia) Using Rain Gauge and Radar Data
by Elena Grek and Sergey Zhuravlev
Hydrology 2020, 7(4), 92; https://doi.org/10.3390/hydrology7040092 - 27 Nov 2020
Cited by 8 | Viewed by 3400
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
(This article belongs to the Special Issue Multi-Source Data Assimilation for the Improvement of Hydrological Modeling Predictions)
►▼ Show Figures

Figure 1

Other

Jump to: Editorial, Research

10 pages, 2011 KiB  
Technical Note
GFPLAIN and Multi-Source Data Assimilation Modeling: Conceptualization of a Flood Forecasting Framework Supported by Hydrogeomorphic Floodplain Rapid Mapping
by Antonio Annis and Fernando Nardi
Hydrology 2021, 8(4), 143; https://doi.org/10.3390/hydrology8040143 - 22 Sep 2021
Cited by 4 | Viewed by 2623
Abstract
Hydrologic/hydraulic models for flood risk assessment, forecasting and hindcasting have been greatly supported by the rising availability of increasingly accurate and high-resolution Earth Observation (EO) data. EO-based topographic and hydrologic open geo data are, nowadays, available on large scales. Data Assimilation (DA) models [...] Read more.
Hydrologic/hydraulic models for flood risk assessment, forecasting and hindcasting have been greatly supported by the rising availability of increasingly accurate and high-resolution Earth Observation (EO) data. EO-based topographic and hydrologic open geo data are, nowadays, available on large scales. Data Assimilation (DA) models allow Early Warning Systems (EWS) to produce accurate and timely flood predictions. DA-based EWS generally use river flow real-time observations and 1D hydraulic models to identify potential inundation hot spots. Detailed high-resolution 2D hydraulic modeling is usually not used in EWS for the computational burden and the numerical complexity of injecting multiple spatially distributed sources of flow observations. In recent times, DEM-based hydrogeomorphic models demonstrated their ability in characterizing river basin hydrologic forcing and floodplain domains providing data-parsimonious opportunities for data-scarce regions. This work investigates the use of hydrogeomorphic floodplain terrain processing for optimizing the ability of DA-based EWSs in using diverse distributed flow observations. A flood forecasting framework with novel applications of hydrogeomorphic floodplain processing is conceptualized for empowering flood EWSs in preliminarily identifying the computational domain for hydraulic modeling, rapid flood detection using satellite images, and filtering geotagged crowdsourced data for flood monitoring. The proposed flood forecasting framework supports the development of an integrated geomorphic-hydrologic/hydraulic modeling chain for a DA that values multiple sources of observation. This work investigates the value of floodplain hydrogeomorphic models to tackle the major challenges of DA for EWS with specific regard to the computational efficiency issues and the lack of data in ungauged river basins towards an improved flood forecasting able to use advanced hydrodynamic modeling and to inject all available sources of observations including flood phenomena captures by citizens. Full article
(This article belongs to the Special Issue Multi-Source Data Assimilation for the Improvement of Hydrological Modeling Predictions)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-6
Back to TopTop