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New Perspectives in the Flood Forecasting Chain (Weather Prediction, Rainfall-Runoff...
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New Perspectives in the Flood Forecasting Chain (Weather Prediction, Rainfall-Runoff Modeling, and Communication with Stakeholders)

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: 31 December 2024 | Viewed by 9344

Special Issue Editors

Dr. Mohammed Bari

E-Mail Website
Guest Editor
Bureau of Meteorology Australia, Melbourne, Australia
Interests: ensemble flood; streamflow forecasting, water quality and quantity modelling
Prof. Dr. Hans-Peter Nachtnebel

E-Mail Website
Guest Editor
Institute for Hydrology and Water Management (HyWa), University of Natural Resources and Life Sciences (BOKU), Muthgasse 18, A-1190 Vienna, Austria
Interests: hydrological modeling; real-time runoff forecasting; integrated water management; climate change impacts on the water resources; monitoring and modeling of sediment transport; flood risk assessment and management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the last 40 years, more than 50 million people have been affected by floods across the world, with related economic loses accounting for more than USD 1 trillion. Flood severity and damage have increased significantly due to population growth, economic prosperity, and climate change. The significance of rainfall-runoff modeling for flood forecasting was recognized by water resource managers and the World Meteorological Organisation as early as 1970s. Consequently, a large number of rainfall-runoff models have been developed over the last 50 years for operational flood forecasting. The spatial scale of these models ranges from basin to regional to national to continental to global. The structure of these rainfall-runoff models ranges from lumped conceptual to fully distributed physically based.

Rainfall-runoff models and operational flood forecasting systems require rainfall forecasts as input to provide the early warnings of likely flood events, allowing civil protection authorities sufficient preparation time. Over the last two decades, significant advancements have been made in numerical weather prediction (NWP) models, which can currently forecast rainfall up to 15 days ahead, either in a deterministic or ensemble form. Using NWP rainfall products, ensemble flood forecasting systems have been developed and made operational by many regions across the world, including North America, Europe, Australia, and Southeast Asia, just to name a few. These operational systems present the following challenges:

  • Improvement in NWP rainfall forecasts;
  • Precipitation measurement networks and data assimilation;
  • Improvements in rainfall-runoff modeling and combined hydrologic/hydraulic models;
  • Probabilistic forecast and post processing;
  • Verification and quality for forecasts;
  • Effective communication with stakeholders.

In addition to the latest achievements and current challenges in this area, rainfall-runoff modeling and ensemble flood forecasting systems present opportunities for further development, including the following:

  • Best use of observations: data assimilation and integration of alternative sources (rain gauges, radar, satellite, NWP models) also using machine learning and artificial intelligence;
  • Non-stationarity: due to climate change’s impact across the globe, the assumption of stationarity in rainfall-runoff modeling ought to be further tested;
  • NWP models: significant improvements are expected for more accurate and reliable forecasts, particularly in extreme precipitation events forecasting;
  • Off-line inundation mapping and flush flooding: this will require the integration of rainfall-runoff and hydraulic models and a procedure to exploit on-line the obtained results;
  • Improved forecast horizons: integrated flood and streamflow forecasts, from hours to days to weeks to months;
  • User benefits: use of probabilistic forecasts, including ensemble forecasts and informed decision making;
  • Communication: timely information must be supplied to the end users via smartphones and other communication devices.

Please submit papers addressing the research topics mentioned above.

Dr. Mohammed Bari
Prof. Dr. Hans-Peter Nachtnebel
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

  • numerical weather predictions
  • precipitation measurement networks
  • data assimilation
  • rainfall-runoff and hydraulic modeling
  • probabilistic flood forecasting and predictive uncertainty
  • forecast verification
  • use of probabilistic forecasts and informed decision making

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Published Papers (4 papers)

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Research

14 pages, 2295 KiB  
Article
Kilometer-Scale Precipitation Forecasting Utilizing Convolutional Neural Networks: A Case Study of Jiangsu’s Coastal Regions
by Ninghao Cai, Hongchuan Sun and Pengcheng Yan
Hydrology 2024, 11(10), 173; https://doi.org/10.3390/hydrology11100173 - 13 Oct 2024
Viewed by 820
Abstract
High-resolution precipitation forecasts play a pivotal role in formulating comprehensive disaster prevention and mitigation plans. As spatial resolution enhances, striking a balance between computation, storage, and simulation accuracy becomes imperative to ensure optimal cost-effectiveness. Convolutional neural networks (CNNs), a cornerstone of deep learning, [...] Read more.
High-resolution precipitation forecasts play a pivotal role in formulating comprehensive disaster prevention and mitigation plans. As spatial resolution enhances, striking a balance between computation, storage, and simulation accuracy becomes imperative to ensure optimal cost-effectiveness. Convolutional neural networks (CNNs), a cornerstone of deep learning, are examined in this study for their downscaling capabilities in precipitation simulation. During a precipitation event on 23 June 2022, in Jiangsu Province, China, distinct rain belts emerged in both southern and northern Jiangsu, precisely captured by a numerical model (the Weather Research and Forecasting, WRF) with a 3 km spatial resolution. Specifically, precipitation was prevalent in northern Jiangsu from 00:00 to 11:00 Beijing Time (BJT), transitioning to southern Jiangsu from 12:00 to 23:00 BJT on the same day. Upon dynamic downscaling, the model reproduced precipitation in these periods with an average error of 12.35 mm at 3 km and 12.48 mm at 1 km spatial resolutions. Employing CNN technology for statistical downscaling to a 1 km spatial resolution, samples from the initial period were utilized for training, while those from the subsequent period served for validation. Following dynamic downscaling, CNNs with four, five, six, and seven layers exhibited average errors of 8.86 mm, 8.93 mm, 9.71 mm, and 9.70 mm, respectively, accompanied by correlation coefficients of 0.550, 0.570, 0.574, and 0.578, respectively. This analysis indicates that for this precipitation event, a shallower CNN depth yields a lower average error and correlation coefficient, whereas a deeper architecture enhances the correlation coefficient. By employing deep network architectures, CNNs are capable of capturing nonlinear patterns and subtle local features from complex meteorological data, thereby providing more accurate predictions during the downscaling process. Leveraging faster computation and reduced storage requirements, machine learning has demonstrated immense potential in high-resolution forecasting research. There is significant scope for advancing technologies that integrate numerical models with machine learning to achieve higher-resolution numerical forecasts. Full article
(This article belongs to the Special Issue New Perspectives in the Flood Forecasting Chain (Weather Prediction, Rainfall-Runoff Modeling, and Communication with Stakeholders))
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13 pages, 3539 KiB  
Article
Modeling and Simulating Rainfall and Temperature Using Rotated Bivariate Copulas
by Giovanni De Luca and Giorgia Rivieccio
Hydrology 2023, 10(12), 236; https://doi.org/10.3390/hydrology10120236 - 12 Dec 2023
Cited by 2 | Viewed by 2343
Abstract
Climate change is a significant environmental challenge that affects water resources, agriculture, health, and other aspects of human life. Bivariate modeling is a statistical method used to analyze the relationship between variables such as rainfall and temperature. The Pearson correlation coefficient, Kendall’s tau, [...] Read more.
Climate change is a significant environmental challenge that affects water resources, agriculture, health, and other aspects of human life. Bivariate modeling is a statistical method used to analyze the relationship between variables such as rainfall and temperature. The Pearson correlation coefficient, Kendall’s tau, or Spearman’s rank correlation are some measures used for bivariate modeling. However, copula functions can describe the dependence structure between two or more variables and can be effectively used to describe the relationship between rainfall and temperature. Despite the literature on bivariate modeling of rainfalls and temperature being extensive, finding flexible and sophisticated bivariate models is sometimes difficult. In this paper, we use rotated copula functions that can arrange any type of dependence that is empirically detected, especially negative dependence. The methodology is applied to an Italian municipality’s bivariate daily time series of rainfall and temperature. The estimated rotated copula is significant and, therefore, can be used for simulating the effects of extreme events. Full article
(This article belongs to the Special Issue New Perspectives in the Flood Forecasting Chain (Weather Prediction, Rainfall-Runoff Modeling, and Communication with Stakeholders))
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19 pages, 3157 KiB  
Article
Benchmarking Three Event-Based Rainfall-Runoff Routing Models on Australian Catchments
by David Kemp and Guna Hewa Alankarage
Hydrology 2023, 10(6), 131; https://doi.org/10.3390/hydrology10060131 - 13 Jun 2023
Cited by 3 | Viewed by 2634
Abstract
In the field of hydrology, event-based models are commonly used for flood-flow prediction in catchments, for use in flood forecasting, flood risk assessment, and infrastructure design. The models are simplistic, as they do not consider longer-term catchment processes such as evaporation and transpiration. [...] Read more.
In the field of hydrology, event-based models are commonly used for flood-flow prediction in catchments, for use in flood forecasting, flood risk assessment, and infrastructure design. The models are simplistic, as they do not consider longer-term catchment processes such as evaporation and transpiration. This paper examines the relative performance of two widely used models, the American HEC-HMS model, the Australian RORB model, and a newer model, the RRR model. The evaluation is conducted on four case study catchments in Australia. The first two models, HEC-HMS and RORB, do not include baseflow, necessitating the estimation of baseflow through alternate means. By contrast, the RRR model includes baseflow, by extracting a separate loss from the rainfall, and then routing the resultant flow through the catchment, much like quickflow, but with a longer delay time. The models are calibrated and then verified with weighted mean parameter values on an independent set of events in each case study catchment. This gives an indication of the ability of the models to correctly predict flow, which is important when the models are used with design rainfalls to predict design flows. The results demonstrate that all models perform adequately on the four examined catchments, but the RRR model exhibits superior calibration, and, to a lesser extent, better validation compared to the other two models. Full article
(This article belongs to the Special Issue New Perspectives in the Flood Forecasting Chain (Weather Prediction, Rainfall-Runoff Modeling, and Communication with Stakeholders))
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16 pages, 2600 KiB  
Article
Non-Stationary Precipitation Frequency Estimates for Resilient Infrastructure Design in a Changing Climate: A Case Study in Sydney
by Shahab Doulabian, Erfan Ghasemi Tousi, Amirhossein Shadmehri Toosi and Sina Alaghmand
Hydrology 2023, 10(6), 117; https://doi.org/10.3390/hydrology10060117 - 24 May 2023
Cited by 4 | Viewed by 2420
Abstract
The intensity–duration–frequency (IDF) curve is a commonly utilized tool for estimating extreme rainfall events that are used for many purposes including flood analysis. Extreme rainfall events are expected to become more intense under the changing climate, and there is a need to account [...] Read more.
The intensity–duration–frequency (IDF) curve is a commonly utilized tool for estimating extreme rainfall events that are used for many purposes including flood analysis. Extreme rainfall events are expected to become more intense under the changing climate, and there is a need to account for non-stationarity IDF curves to mitigate an underestimation of the risks associated with extreme rainfall events. Sydney, Australia, has recently started experiencing flooding under climate change and more intense rainfall events. This study evaluated the impact of climate change on altering the precipitation frequency estimates (PFs) used in generating IDF curves at Sydney Airport. Seven general circulation models (GCMs) were obtained, and the best models in terms of providing the extreme series were selected. The ensemble of the best models was used for comparing the projected 24 h PFs in 2031–2060 with historical values provided by Australian Rainfall and Runoff (ARR). The historical PFs consistently underestimate the projected 24 h PFs for all return periods. The projected 24 h 100 yr rainfall events are increased by 9% to 41% for the least and worst-case scenario compared to ARR historical PFs. These findings highlight the need for incorporating the impact of climate change on PFs and IDF curves in Sydney toward building a more prepared and resilient community. The findings of this study can also aid other communities in adapting the same framework for developing more robust and adaptive approaches to reducing extreme rainfall events’ repercussions under changing climates. Full article
(This article belongs to the Special Issue New Perspectives in the Flood Forecasting Chain (Weather Prediction, Rainfall-Runoff Modeling, and Communication with Stakeholders))
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