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Editorial Board Members’ Collection Series in “Climate Simulations for Hydrological Predictions and Projections”

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water and Climate Change".

Deadline for manuscript submissions: closed (20 July 2024) | Viewed by 3888

Special Issue Editors

Dr. Davide Zanchettin

E-Mail Website
Guest Editor
Department of Environmental Sciences, Informatics and Statistics, University Ca’Foscari of Venice, 30123 Venezia, VE, Italy
Interests: decadal climate variability and predictability; natural climate forcing; European hydroclimates; teleconnections; climate of the common era
Prof. Dr. Xiaojun Wang

E-Mail Website
Guest Editor
National Key Laboratory of Water Disaster Prevention, Nanjing Hydraulic Research Institute, Nanjing 210029, China
Interests: water resources planning and management; water resources management in response to climate change
Special Issues, Collections and Topics in MDPI journals
Dr. Na Zhao

E-Mail Website
Guest Editor
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Interests: data fusion; downscaling; climate simulation; climate extremes; spatial analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The hydrological cycle is a critical component of the Earth’s system, contributing to both intrinsic and forced climate variability observed on a broad range of temporal scales and from local/regional to global scales. In turn, precipitation, and hydrological surface processes, including river runoff, are affected by climate change and variability. Near-term predictions and projections of water availability and hydrological extremes from the watershed to the continental scale under climate change must account for the uncertainties and limitations of combining global/regional climate models with hydrological models, where both numerical tools are affected by substantial uncertainties and deficiencies related to functionality, complexity and resolution, whose magnitude is inflated for integrated assessments.

This Special Issue aims to collect studies on the use of output from global and regional climate simulations as a boundary for hydrological predictions and projections of the broad water resources, including water availability and quality, droughts and floods, and surface and groundwater reservoirs. Climate and hydrological model evaluation studies, regional land-use and land-cover change studies and studies of data assimilation and downscaling approaches, and their optimization, including statistical methods and artificial intelligence, are especially welcome.

Dr. Davide Zanchettin
Dr. Xiaojun Wang
Dr. Na Zhao
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. Water is an international peer-reviewed open access semimonthly 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 2600 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

  • climate change
  • water resources
  • hydrological change
  • downscaling
  • climate extremes
  • hydrological forecasts
  • model uncertainty

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Published Papers (2 papers)

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Research

28 pages, 10569 KiB  
Article
Integrating Hydrological and Machine Learning Models for Enhanced Streamflow Forecasting via Bayesian Model Averaging in a Hydro-Dominant Power System
by Francisca Lanai Ribeiro Torres, Luana Medeiros Marangon Lima, Michelle Simões Reboita, Anderson Rodrigo de Queiroz and José Wanderley Marangon Lima
Water 2024, 16(4), 586; https://doi.org/10.3390/w16040586 - 16 Feb 2024
Cited by 2 | Viewed by 2018
Abstract
Streamflow forecasting plays a crucial role in the operational planning of hydro-dominant power systems, providing valuable insights into future water inflows to reservoirs and hydropower plants. It relies on complex mathematical models, which, despite their sophistication, face various uncertainties affecting their performance. These [...] Read more.
Streamflow forecasting plays a crucial role in the operational planning of hydro-dominant power systems, providing valuable insights into future water inflows to reservoirs and hydropower plants. It relies on complex mathematical models, which, despite their sophistication, face various uncertainties affecting their performance. These uncertainties can significantly influence both short-term and long-term operational planning in hydropower systems. To mitigate these effects, this study introduces a novel Bayesian model averaging (BMA) framework to improve the accuracy of streamflow forecasts in real hydro-dominant power systems. Designed to serve as an operational tool, the proposed framework incorporates predictive uncertainty into the forecasting process, enhancing the robustness and reliability of predictions. BMA statistically combines multiple models based on their posterior probability distributions, producing forecasts from the weighted averages of predictions. This approach updates weights periodically using recent historical data of forecasted and measured streamflows. Tested on inflows to 139 reservoirs and hydropower plants in Brazil, the proposed BMA framework proved to be more skillful than individual models, showing improvements in forecasting accuracy, especially in the South and Southeast regions of Brazil. This method offers a more reliable tool for streamflow prediction, enhancing decision making in hydropower system operations. Full article
(This article belongs to the Special Issue Editorial Board Members’ Collection Series in “Climate Simulations for Hydrological Predictions and Projections”)
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16 pages, 2968 KiB  
Article
Climate Impact on Irrigation Water Use in Jiangsu Province, China: An Analysis Using Empirical Mode Decomposition (EMD)
by Tao Zhang, Xiaojun Wang, Zhifeng Jin, Shamsuddin Shahid and Bo Bi
Water 2023, 15(16), 3013; https://doi.org/10.3390/w15163013 - 21 Aug 2023
Cited by 1 | Viewed by 1268
Abstract
In this paper, the quantitative effects of climatic factor changes on irrigation water use were analyzed in Jiangsu Province from 2004 to 2020 using the Empirical Mode Decomposition (EMD) time-series analysis method. In general, the irrigation water use, precipitation (P), air temperature (T), [...] Read more.
In this paper, the quantitative effects of climatic factor changes on irrigation water use were analyzed in Jiangsu Province from 2004 to 2020 using the Empirical Mode Decomposition (EMD) time-series analysis method. In general, the irrigation water use, precipitation (P), air temperature (T), wind speed (Ws), relative humidity (Rh) and water vapor pressure (Vp) annual means ± standard deviation were 25.44 ± 1.28 billion m3, 1034.4 ± 156.6 mm, 16.1 ± 0.4 °C, 2.7 ± 0.2 m·s−1, 74 ± 2%, and 15.5 ± 0.6 hPa, respectively. The analysis results of the irrigation water use sequence using EMD indicate three main change frequencies for irrigation water use. The first major change frequency (MCF1) was a 2-to-3-year period varied over a ±1.00 billion m3 range and showed a strong correlation with precipitation (the Pearson correlation was 0.68, p < 0.05). The second major change frequency (MCF2) was varied over a ±2.00 billion m3 range throughout 10 years. The third major change frequency (MCF3) was a strong correlation with air temperature, wind speed, relative humidity, and water vapor pressure (the Pearson correlations were 0.56, 0.75, 0.71, and 0.69, respectively, p < 0.05). In other words, MCF1 and MCF3 represent the irrigation water use changes influenced by climate factors. Furthermore, we developed the Climate–Irrigation–Water Model based on farmland irrigation theory to accurately assess the direct effects of climate factor changes on irrigation water use. The model effectively simulated irrigation water use changes with a root mean square error (RMSE) of 0.06 billion m3, representing 2.24% of the total. The findings from the model indicate that climate factors have an average impact of 6.40 billion m3 on irrigation water use, accounting for 25.14% of the total. Specifically, precipitation accounted for 3.04 billion m3 of the impact, while the combined impact of other climatic factors was 3.36 billion m3. Full article
(This article belongs to the Special Issue Editorial Board Members’ Collection Series in “Climate Simulations for Hydrological Predictions and Projections”)
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