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Water
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Precipitation under Climate Change: Observation, Analysis and Forecasting
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Precipitation under Climate Change: Observation, Analysis and Forecasting

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

Deadline for manuscript submissions: 15 January 2025 | Viewed by 651

Special Issue Editors

Prof. Dr. Xinmin Zeng

E-Mail Website
Guest Editor
College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
Interests: land–atmosphere interactions; weather forecasting; regional climate; hydrologic and water resource modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Irfan Ullah

E-Mail Website
Guest Editor
College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
Interests: climate change; drought; heat waves; extreme events; hydrologic and water resource modeling and simulation; climate dynamics; evapotranspiration; validation studies
Special Issues, Collections and Topics in MDPI journals
Dr. Jian Zhu

E-Mail Website
Guest Editor
College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
Interests: climate change; drought; medium and long-term hydrological forecasting

Special Issue Information

Dear Colleagues,

Precipitation variability and its distribution govern the hydrological cycle, which is critical for human needs regarding agriculture, freshwater, and ecosystems. Precipitation is a key parameter of the water cycle and is fundamental for streamflow, changing climates, and weather forecasting. However, it is among the most difficult parameters to measure accurately. Thus, authors are invited to submit research for this Special Issue on “Precipitation under Climate Change: Observation, Analysis and Forecasting” focusing on observational datasets, novel precipitation reclamation algorithms, analysis methods, predicting techniques, and physical theories for the Earth’s precipitation. We welcome the topics listed below and other scientific results related to this Special Issue:

  • Long-term observations informing the impacts of climate change;
  • New methods to detect or attribute global-warming-induced precipitation responses;
  • Ground validation of remote sensing precipitation products;
  • Existing precipitation observation network coverage and user requirements;
  • Development of new numerical modeling techniques and physical parameterizations for improving precipitation forecasting;
  • Projecting future precipitation and evaluating the impacts under different climate change scenarios;
  • Investigations on sub-seasonal-to-seasonal prediction of precipitation;
  • Climate-scale projections of future rainfall and snowfall, including extreme events.

Prof. Dr. Xinmin Zeng
Dr. Irfan Ullah
Dr. Jian Zhu
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

  • observed precipitation
  • forecasting
  • remote sensing
  • water-related hydrometeorological hazards
  • extreme precipitation
  • climate change detection and attribution

Published Papers (1 paper)

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Research

20 pages, 5025 KiB  
Article
A Study of Precipitation Forecasting for the Pre-Summer Rainy Season in South China Based on a Back-Propagation Neural Network
by Bing-Zeng Wang, Si-Jie Liu, Xin-Min Zeng, Bo Lu, Zeng-Xin Zhang, Jian Zhu and Irfan Ullah
Water 2024, 16(10), 1423; https://doi.org/10.3390/w16101423 - 16 May 2024
Viewed by 346
Abstract
In South China, the large quantity of rainfall in the pre-summer rainy season can easily lead to natural disasters, which emphasizes the importance of improving the accuracy of precipitation forecasting during this period for the social and economic development of the region. In [...] Read more.
In South China, the large quantity of rainfall in the pre-summer rainy season can easily lead to natural disasters, which emphasizes the importance of improving the accuracy of precipitation forecasting during this period for the social and economic development of the region. In this paper, the back-propagation neural network (BPNN) is used to establish the model for precipitation forecasting. Three schemes are applied to improve the model performance: (1) predictors are selected based on individual meteorological stations within the region rather than the region as a whole; (2) the triangular irregular network (TIN) is proposed to preprocess the observed precipitation data for input of the BPNN model, while simulated/forecast precipitation is the expected output; and (3) a genetic algorithm is used for the hyperparameter optimization of the BPNN. The first scheme reduces the mean absolute percentage error (MAPE) and the root mean square error (RMSE) of the simulation by roughly 5% and more than 15 mm; the second reduces the MAPE and RMSE by more than 15% and 15 mm, respectively, while the third improves the simulation inapparently. Obviously, the second scheme raises the upper limit of the model simulation capability greatly by preprocessing the precipitation data. During the training and validation periods, the MAPE of the improved model can be controlled at approximately 35%. For precipitation hindcasting in the test period, the anomaly rate is less than 50% in only one season, and the highest is 64.5%. According to the anomaly correlation coefficient and Ps score of the hindcast precipitation, the improved model performance is slightly better than the FGOALS-f2 model. Although global climate change makes the predictors more variable, the trend of simulation is almost identical to that of the observed values over the whole period, suggesting that the model is able to capture the general characteristics of climate change. Full article
(This article belongs to the Special Issue Precipitation under Climate Change: Observation, Analysis and Forecasting)
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