Special Issue "Advances in Hydrological Forecasting"

A special issue of Forecasting (ISSN 2571-9394). This special issue belongs to the section "Environmental Forecasting".

Deadline for manuscript submissions: 31 December 2020.

Special Issue Editors

Dr. Minxue He
Website
Guest Editor
California Department of Water Resources, 1416 9th Street, Sacramento, CA 95814, USA
Interests: hydrological; hydraulic; hydrodynamic; and water quality modeling;climate change;stochastic modeling; deep learning
Dr. Haksu Lee

Guest Editor
NOAA National Weather Service, Office of Water Prediction, 1325 East-West Hwy, Silver Spring, MD 20910, USA
Interests: hydrological modeling; data assimilation; ensemble modeling; climate change; extreme events; post-processing; precipitation; soil moisture
Dr. Sungwook Wi
Website
Guest Editor
University of Massachusetts Amherst, 130 Natural Resources Road, Amherst, MA 01003-9293, USA
Interests: hydrological modeling and forecasting; coupled human-hydrologic systems planning; optimization; uncertainty; high-performance computing; decision-making support

Special Issue Information

Dear Colleagues,

Hydrological forecasting is of primary importance to better inform decision-making on flood management, drought mitigation, water system operations, water resources planning, and hydropower generation, among others. Typical hydrological forecasting translates single deterministic or an ensemble of short, intermediate, and long lead-time meteorological forecasts into estimates of hydrological variables of interest (e.g., streamflow, river stage, snowmelt, etc.) via forecast models at the corresponding temporal scales. These models range from process-based hydrological models to purely data-driven models. Model predictive skill and uncertainty are normally verified by comparing archived forecasts to field data or in a hindcasting mode. During forecasting, real-time in situ or remote sensing measurements for forecast hydrological variables can be assimilated into the forecast model to update model states or parameters for improved forecasts. Before being disseminated for operational use, hydrological forecasts are often post-processed to best reflect the perceptions of forecasters on the future state of those forecast variables. Although there has been immense progress in forecasting systems, services, and sensors to date, hydrological forecasting today faces convoluted challenges induced by the increasing trend of extreme events (calibration/verification), changing basin climate and hydrology (non-stationarity), and demands of a unified and versatile hydrological forecasting system operating at local to continental scales (hyper-resolution large-scale forecasting). To understand and advance science and practices of present hydrological forecasting, this Special Issue invites studies on the following:

  • Methodological advances in hydrological forecasting, including innovative forecasting methods (e.g., machine learning-based or hybrid), data assimilation, post-processing, and uncertainty analysis techniques;
  • Research and development of hyper-resolution large-scale hydrological forecasting, urban hydrological modeling, and forecasting of hydrological extremes;
  • Incremental analysis of utilizing new observations, modules, or atmospheric model outputs in hydrological forecasting systems;
  • Performance evaluation or verification of current operational hydrological forecast systems, services, or products at different scales and forecast horizons via large sample analysis, long-term hindcasting, or real-time forecasting;
  • Latest applications of deterministic or probabilistic hydrological forecasts in decision-support practices.

Dr. Minxue He
Dr. Haksu Lee
Dr. Sungwook Wi
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 papers will be 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. Forecasting 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 1000 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

  • Hydrological forecasting
  • machine learning
  • data assimilation
  • post-processing
  • uncertainty assessment
  • forecast verification
  • hyper-resolution modeling
  • hydrological extremes
  • decision support

Published Papers (3 papers)

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Research

Open AccessArticle
Forecasting of Future Flooding and Risk Assessment under CMIP6 Climate Projection in Neuse River, North Carolina
Forecasting 2020, 2(3), 323-345; https://doi.org/10.3390/forecast2030018 - 28 Aug 2020
Abstract
Hydrological extremes associated with climate change are becoming an increasing concern all over the world. Frequent flooding, one of the extremes, needs to be analyzed while considering climate change to mitigate flood risk. This study forecast streamflow and evaluate risk of flooding in [...] Read more.
Hydrological extremes associated with climate change are becoming an increasing concern all over the world. Frequent flooding, one of the extremes, needs to be analyzed while considering climate change to mitigate flood risk. This study forecast streamflow and evaluate risk of flooding in the Neuse River, North Carolina considering future climatic scenarios, and comparing them with an existing Federal Emergency Management Agency study. The cumulative distribution function transformation method was adopted for bias correction to reduce the uncertainty present in the Coupled Model Intercomparison Project Phase 6 (CMIP6) streamflow data. To calculate 100-year and 500-year flood discharges, the Generalized Extreme Value (L-Moment) was utilized on bias-corrected multimodel ensemble data with different climate projections. Out of all projections, shared socio-economic pathways (SSP5-8.5) exhibited the maximum design streamflow, which was routed through a hydraulic model, the Hydrological Engineering Center’s River Analysis System (HEC-RAS), to generate flood inundation and risk maps. The result indicates an increase in flood inundation extent compared to the existing study, depicting a higher flood hazard and risk in the future. This study highlights the importance of forecasting future flood risk and utilizing the projected climate data to obtain essential information to determine effective strategic plans for future floodplain management. Full article
(This article belongs to the Special Issue Advances in Hydrological Forecasting)
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Open AccessArticle
Machine Learning-Based Error Modeling to Improve GPM IMERG Precipitation Product over the Brahmaputra River Basin
Forecasting 2020, 2(3), 248-266; https://doi.org/10.3390/forecast2030014 - 25 Jul 2020
Cited by 2
Abstract
The Integrated Multisatellite Retrievals for Global Precipitation Measurement (GPM) (IMERG) Level 3 estimates rainfall from passive microwave sensors onboard satellites that are associated with several uncertainty sources such as sensor calibration, retrieval errors, and orographic effects. This study aims to provide a comprehensive [...] Read more.
The Integrated Multisatellite Retrievals for Global Precipitation Measurement (GPM) (IMERG) Level 3 estimates rainfall from passive microwave sensors onboard satellites that are associated with several uncertainty sources such as sensor calibration, retrieval errors, and orographic effects. This study aims to provide a comprehensive investigation of multiple machine learning (ML) techniques (Random Forest, and Neural Networks), to stochastically generate an error-corrected improved IMERG precipitation product at a daily time scale and 0.1°-degree spatial resolution over the Brahmaputra river basin. In this study, we used the operational IMERG-Late Run version 06 product along with several meteorological and land surface parameters (elevation, soil type, land type, soil moisture, and daily maximum and minimum temperature) to produce an improved precipitation product in the Brahmaputra basin. We trained, tested, and optimized ML algorithms using 4 years (from 2015 through 2019) of reference rainfall data derived from the rain gauge. The ML generated precipitation product exhibited improved systematic and random error statistics for the study area, which is a strong indication for using the proposed algorithms in retrieving precipitation across the globe. We conclude that the proposed ML-based ensemble framework has the potential to quantify and correct the error sources for improving and promoting the use of satellite-based precipitation estimates for water resources applications. Full article
(This article belongs to the Special Issue Advances in Hydrological Forecasting)
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Open AccessArticle
Tuning the Bivariate Meta-Gaussian Distribution Conditionally in Quantifying Precipitation Prediction Uncertainty
Forecasting 2020, 2(1), 1-19; https://doi.org/10.3390/forecast2010001 - 15 Jan 2020
Cited by 1
Abstract
One of the ways to quantify uncertainty of deterministic forecasts is to construct a joint distribution between the forecast variable and the observed variable; then, the uncertainty of the forecast can be represented by the conditional distribution of the observed given the forecast. [...] Read more.
One of the ways to quantify uncertainty of deterministic forecasts is to construct a joint distribution between the forecast variable and the observed variable; then, the uncertainty of the forecast can be represented by the conditional distribution of the observed given the forecast. The joint distribution of two continuous hydrometeorological variables can often be modeled by the bivariate meta-Gaussian distribution (BMGD). The BMGD can be obtained by transforming each of the two variables to a standard normal variable and the dependence between the transformed variables is provided by the Pearson correlation coefficient of these two variables. The BMGD modeling is exact provided that the transformed joint distribution is standard normal. In real-world applications, however, this normality assumption is hardly fulfilled. This is often the case for the modeling problem we consider in this paper: establish the joint distribution of a forecast variable and its corresponding observed variable for precipitation amounts accumulated over a duration of 24 h. In this case, the BMGD can only serve as an approximate model and the dependence parameter can be estimated in a variety of ways. In this paper, the effect of tuning this parameter is studied. Numerical simulations conducted suggest that, while the parameter tuning results in limited improvements in goodness-of-fit (GOF) for the BMGD as a bivariate distribution model, better results may be achieved by tuning the parameter for the one-dimensional conditional distribution of the observed given the forecast greater than a certain large value. Full article
(This article belongs to the Special Issue Advances in Hydrological Forecasting)
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