Special Issue "Advances in Hydrologic Forecasts and Water Resources Management"

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water Resources Management and Governance".

Deadline for manuscript submissions: 31 March 2020.

Special Issue Editors

Prof. Fi-John Chang
E-Mail Website
Guest Editor
Distinguished Professor, Department of Bioenvironmental Systems Engineering, National Taiwan University, Taiwan
Tel. +886-2-2363-9461
Interests: artificial intelligence; artificial neural network; hydrology; water resources management; ecohydrology; real-time flood forecasting; system analysis; multiobjective reservoir operation; water–food–energy nexus
Special Issues and Collections in MDPI journals
Prof. Shenglian Guo
E-Mail Website
Guest Editor
Professor, State Key Laboratory of Water Resources and Hdropower Engineering Science, Wuhan University, China
Tel. +86-27-6877-3568
Interests: spatial and temporal distribution of water resources and its integrated management; hydrologic modeling and flood forecasting; design and operation of cascade reservoirs

Special Issue Information

Dear Colleagues,

In the face of climate change and population growth in many parts of the world, we need appropriate tools that can assist in dealing with the difficulties introduced by the increasing complexity of water problems. This Special Issue will feature the latest advances and developments in operational hydrologic forecasts and water resources management. The focus is centered on artificial intelligence (AI) techniques in data-mining for operational hydrologic forecasting and evolutionary algorithms (EAs) for reservoir operation. The main themes of this Special Issue include but are not limited to the following: 

  • AI techniques for multiobjective reservoir operation;
  • Machine learning approaches for operational hydrologic forecasting;
  • Data assimilation for real-time hydrologic forecasting;
  • Uncertainty assessment on hydrological forecasts;
  • Advances in flood forecasting and flood risk assessment;
  • Drought forecasting and warning;
  • Integrated water resources management.

This Special Issue aims at gathering the latest developments in sustainable water resources management as well as operational hydrologic forecasts at different spatiotemporal scales and contexts. The integration of natural sciences with economic and social sciences is also very much appreciated.

Prof. Fi-John Chang
Prof. Shenglian Guo
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. Water 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 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

  • Artificial intelligence
  • Machine learning
  • Water resources management
  • Multiobjective reservoir operation
  • Hydrologic forecasting
  • Uncertainty
  • Risk

Published Papers (5 papers)

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Open AccessArticle
Evaluation of GloFAS-Seasonal Forecasts for Cascade Reservoir Impoundment Operation in the Upper Yangtze River
Water 2019, 11(12), 2539; https://doi.org/10.3390/w11122539 - 01 Dec 2019
Abstract
Standard impoundment operation rules (SIOR) are pre-defined guidelines for refilling reservoirs before the end of the wet season. The advancement and availability of the seasonal flow forecasts provide the opportunity for reservoir operators to use flexible and early impoundment operation rules (EIOR). These [...] Read more.
Standard impoundment operation rules (SIOR) are pre-defined guidelines for refilling reservoirs before the end of the wet season. The advancement and availability of the seasonal flow forecasts provide the opportunity for reservoir operators to use flexible and early impoundment operation rules (EIOR). These flexible impoundment rules can significantly improve water conservation, particularly during dry years. In this study, we investigate the potential application of seasonal streamflow forecasts for employing EIOR in the upper Yangtze River basin. We first define thresholds to determine the streamflow condition in September, which is an important period for decision-making in the basin, and then select the most suitable impoundment operation rules accordingly. The thresholds are used in a simulation–optimization model to evaluate different scenarios for EIOR and SIOR by multiple objectives. We measure the skill of the GloFAS-Seasonal forecast, an operational global seasonal river flow forecasting system, to predict streamflow condition according to the selected thresholds. The results show that: (1) the 20th and 30th percentiles of the historical September flow are suitable thresholds for evaluating the possibility of employing EIOR; (2) compared to climatological forecasts, GloFAS-Seasonal forecasts are skillful for predicting the streamflow condition according to the selected 20th and 30th percentile thresholds; and (3) during dry years, EIOR could improve the fullness storage rate by 5.63% and the annual average hydropower generation by 4.02%, without increasing the risk of flooding. GloFAS-Seasonal forecasts and early reservoir impoundment have the potential to enhance hydropower generation and water utilization. Full article
(This article belongs to the Special Issue Advances in Hydrologic Forecasts and Water Resources Management)
Open AccessArticle
Parameter Uncertainty of a Snowmelt Runoff Model and Its Impact on Future Projections of Snowmelt Runoff in a Data-Scarce Deglaciating River Basin
Water 2019, 11(11), 2417; https://doi.org/10.3390/w11112417 - 18 Nov 2019
Abstract
The impacts of climate change on water resources in snow- and glacier-dominated basins are of great importance for water resource management. The Snowmelt Runoff Model (SRM) was developed to simulate and predict daily streamflow for high mountain basins where snowmelt runoff is a [...] Read more.
The impacts of climate change on water resources in snow- and glacier-dominated basins are of great importance for water resource management. The Snowmelt Runoff Model (SRM) was developed to simulate and predict daily streamflow for high mountain basins where snowmelt runoff is a major contributor. However, there are many sources of uncertainty when using an SRM for hydrological simulations, such as low-quality input data, imperfect model structure and model parameters, and uncertainty from climate scenarios. Among these, the identification of model parameters is considered to be one of the major sources of uncertainty. This study evaluates the parameter uncertainty for SRM simulation based on different calibration strategies, as well as its impact on future hydrological projections in a data-scarce deglaciating river basin. The generalized likelihood uncertainty estimation (GLUE) method implemented by Monte Carlo sampling was used to estimate the model uncertainty arising from parameters calibrated by means of different strategies. Future snowmelt runoff projections under climate change impacts in the middle of the century and their uncertainty were assessed using average annual hydrographs, annual discharge and flow duration curves as the evaluation criteria. The results show that: (1) the strategy with a division of one or two sub-period(s) in a hydrological year is more appropriate for SRM calibration, and is also more rational for hydrological climate change impact assessment; (2) the multi-year calibration strategy is also more stable; and (3) the future runoff projection contains a large amount of uncertainty, among which parameter uncertainty plays a significant role. The projections also indicate that the onset of snowmelt runoff is likely to shift earlier in the year, and the discharge over the snowmelt season is projected to increase. Overall, this study emphasizes the importance of considering the parameter uncertainty of time-varying hydrological processes in hydrological modelling and climate change impact assessment. Full article
(This article belongs to the Special Issue Advances in Hydrologic Forecasts and Water Resources Management)
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Open AccessArticle
Multi-Objective Operation of Cascade Hydropower Reservoirs Using TOPSIS and Gravitational Search Algorithm with Opposition Learning and Mutation
Water 2019, 11(10), 2040; https://doi.org/10.3390/w11102040 - 29 Sep 2019
Cited by 1
Abstract
In this research, a novel enhanced gravitational search algorithm (EGSA) is proposed to resolve the multi-objective optimization model, considering the power generation of a hydropower enterprise and the peak operation requirement of a power system. In the proposed method, the standard gravity search [...] Read more.
In this research, a novel enhanced gravitational search algorithm (EGSA) is proposed to resolve the multi-objective optimization model, considering the power generation of a hydropower enterprise and the peak operation requirement of a power system. In the proposed method, the standard gravity search algorithm (GSA) was chosen as the fundamental execution framework; the opposition learning strategy was adopted to increase the convergence speed of the swarm; the mutation search strategy was chosen to enhance the individual diversity; the elastic-ball modification strategy was used to promote the solution feasibility. Additionally, a practical constraint handling technique was introduced to improve the quality of the obtained agents, while the technique for order preference by similarity to an ideal solution method (TOPSIS) was used for the multi-objective decision. The numerical tests of twelve benchmark functions showed that the EGSA method could produce better results than several existing evolutionary algorithms. Then, the hydropower system located on the Wu River of China was chosen to test the engineering practicality of the proposed method. The results showed that the EGSA method could obtain satisfying scheduling schemes in different cases. Hence, an effective optimization method was provided for the multi-objective operation of hydropower system. Full article
(This article belongs to the Special Issue Advances in Hydrologic Forecasts and Water Resources Management)
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Open AccessArticle
Improving Parameter Transferability of GR4J Model under Changing Environments Considering Nonstationarity
Water 2019, 11(10), 2029; https://doi.org/10.3390/w11102029 - 28 Sep 2019
Abstract
Hydrological nonstationarity has brought great challenges to the reliable application of conceptual hydrological models with time-invariant parameters. To cope with this, approaches have been proposed to consider time-varying model parameters, which can evolve in accordance with climate and watershed conditions. However, the temporal [...] Read more.
Hydrological nonstationarity has brought great challenges to the reliable application of conceptual hydrological models with time-invariant parameters. To cope with this, approaches have been proposed to consider time-varying model parameters, which can evolve in accordance with climate and watershed conditions. However, the temporal transferability of the time-varying parameter was rarely investigated. This paper aims to investigate the predictive ability and robustness of a hydrological model with time-varying parameter under changing environments. The conceptual hydrological model GR4J (Génie Rural à 4 paramètres Journalier) with only four parameters was chosen and the sensitive parameters were treated as functions of several external covariates that represent the variation of climate and watershed conditions. The investigation was carried out in Weihe Basin and Tuojiang Basin of Western China in the period from 1981 to 2010. Several sub-periods with different climate and watershed conditions were set up to test the temporal parameter transferability of the original GR4J model and the GR4J model with time-varying parameters. The results showed that the performance of streamflow simulation was improved when applying the time-varying parameters. Furthermore, in a series of split-sample tests, the GR4J model with time-varying parameters outperformed the original GR4J model by improving the model robustness. Further studies focus on more diversified model structures and watersheds conditions are necessary to verify the superiority of applying time-varying parameters. Full article
(This article belongs to the Special Issue Advances in Hydrologic Forecasts and Water Resources Management)
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Open AccessCase Report
Emergency Disposal Solution for Control of a Giant Landslide and Dammed Lake in Yangtze River, China
Water 2019, 11(9), 1939; https://doi.org/10.3390/w11091939 - 18 Sep 2019
Abstract
Although landslide early warning and post-assessment is of great interest for mitigating hazards, emergency disposal solutions for properly handling the landslide and dammed lake within a few hours or days to mitigate flood risk are fundamentally challenging. In this study, we report a [...] Read more.
Although landslide early warning and post-assessment is of great interest for mitigating hazards, emergency disposal solutions for properly handling the landslide and dammed lake within a few hours or days to mitigate flood risk are fundamentally challenging. In this study, we report a general strategy to effectively tackle the dangerous situation created by a giant dammed lake with 770 million cubic meters of water volume and formulate an emergency disposal solution for the 25 million cubic meters of debris, composed of engineering measures of floodgate excavation and non-engineering measures of reservoirs/hydropower stations operation. Such a disposal solution can not only reduce a large-scale flood (10,000-year return period, 0.01%) into a small-scale flood (10-year return period, 10%) but minimize the flood risk as well, guaranteeing no death raised by the giant landslide. Full article
(This article belongs to the Special Issue Advances in Hydrologic Forecasts and Water Resources Management)
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