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Precipitation under Climate Change: Observation, Analysis and Forecasting

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

Deadline for manuscript submissions: closed (15 January 2025) | Viewed by 7143

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Special Issue Editors

Prof. Dr. Xinmin Zeng

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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

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Published Papers (6 papers)

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Research

19 pages, 6096 KiB

Open AccessArticle

The Analysis of Hydrometeorological Characteristics in the Yarlung Tsangpo River Basin

by Xiangwei Liu, Yilong Li, Li Wang, Junfu Gong, Yihua Sheng and Zhijia Li

Water 2025, 17(3), 344; https://doi.org/10.3390/w17030344 - 26 Jan 2025

Viewed by 688

Abstract

Understanding the hydrometeorological processes of the Yarlung Tsangpo River Basin, located on the “Third Pole” Qinghai–Tibet Plateau, is crucial for effective water resource management and climate change adaptation. This study provides a comprehensive analysis of the basin’s hydrometeorological characteristics using long-term observational data from six representative stations across the upper, middle, and lower reaches. We examined trends, periodicity, variability, and correlations of key elements—precipitation, temperature, evaporation, and discharge—employing methods such as linear regression, Mann–Kendall tests, wavelet analysis, and Kendall rank correlation coefficient tests. The results indicated that precipitation and discharge exhibited non-significant upward trends, with fluctuations across decades, while temperature showed a significant increase of 0.39 °C per decade, surpassing the national and global rates. Evaporation generally decreased with increasing precipitation; however, at Lazi Station, evaporation significantly increased due to low precipitation and rising temperatures causing decreased relative humidity. Periodic analysis revealed cycles at multiple temporal scales, particularly at 2–5 years, 10 years, and over 20 years. Correlation analysis demonstrated a strong positive relationship between precipitation and discharge, and a negative correlation between evaporation and discharge. The hydrometeorological characteristics are significantly influenced by climatic factors, especially precipitation and temperature, with the warming trend potentially affecting water’s availability and distribution. These findings offer valuable insights for water resource management and highlight the need for continuous monitoring to understand hydrological responses to climatic and anthropogenic changes in this critical region. Full article

(This article belongs to the Special Issue Precipitation under Climate Change: Observation, Analysis and Forecasting)

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17 pages, 7926 KiB

Open AccessArticle

Development of Multi-Temporal-Scales Precipitation-Type Separation Method in the Qinghai–Tibetan Plateau

by Juan Zhang, Weizhen Wang, Tao Che, Xianfeng Su and Wenjiang Su

Water 2024, 16(24), 3690; https://doi.org/10.3390/w16243690 - 21 Dec 2024

Viewed by 594

Abstract

The accurate identification of precipitation types is very important for understanding the hydrological processes in cold regions. Existing identification methods have been established based on daily precipitation and meteorological data, which cannot match the high temporal resolution (such as hourly) simulations of hydrological processes. Based on the minutely surface meteorological data in the QTP from 2012 to 2021, we established three sub-models of the dynamic threshold method with wet-bulb temperature (T_w) and three sub-models of the frequency threshold method with air temperature (T_a) for distinguishing among precipitation types. The results revealed that the mean accuracy (ACC) of the three precipitation types was 0.86, and that these models provided a refined and accurate precipitation identification performance for the Qinghai–Tibet Plateau (QTP). However, these models performed well in the identification of rain and snowfall but performed poorly in the identification of sleet. In addition, the smaller the time scale and regional scales, the better the identification rate. In particular, snowfall is overestimated when daily precipitation-type separation thresholds are input into hourly or minute hydrological models. Therefore, to improve simulation performance, it is important to develop multi-temporal scale precipitation-type partitioning models, take regional variations into account when setting temperature thresholds, and conduct analyses at the finest possible time resolutions to minimize scale-related uncertainties. Full article

(This article belongs to the Special Issue Precipitation under Climate Change: Observation, Analysis and Forecasting)

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20 pages, 11913 KiB

Open AccessArticle

Long-Term Spatiotemporal Analysis of Precipitation Trends with Implications of ENSO-Driven Variability in the Department of Magdalena, Colombia

by Geraldine M. Pomares-Meza, Yiniva Camargo Caicedo and Andrés M. Vélez-Pereira

Water 2024, 16(23), 3372; https://doi.org/10.3390/w16233372 - 23 Nov 2024

Viewed by 1197

Abstract

The Magdalena department, influenced by southern trade winds and ocean currents from the Atlantic and Pacific, is a climatically vulnerable region. This study assesses the Magdalena Department’s precipitation trends and stationary patterns by analyzing multi-year monthly records from 55 monitoring stations from 1990 to 2022. To achieve this, the following methods were used: (i) homogeneous regions were established by an unsupervised clustering approach, (ii) temporal trends were quantified using non-parametric tests, (iii) stationarity was identified through Morlet wavelet decomposition, and (iv) Sea Surface Temperature (SST) in four Niño regions was correlated with stationarity cycles. Silhouette’s results yielded five homogeneous regions, consistent with the National Meteorological Institute (IDEAM) proposal. The Department displayed decreasing annual trends (−32–−100 mm/decade) but exhibited increasing monthly trends (>20 mm/decade) during the wettest season. The wavelet decomposition analysis revealed quasi-bimodal stationarity, with significant semiannual cycles (~4.1 to 5.6 months) observed only in the eastern region. Other regions showed mixed behavior: non-stationary in the year’s first half and stationary in the latter half. Correlation analysis showed a significant relationship between SST in the El Niño 3 region (which accounted for 50.5% of the coefficients), indicating that strong phases of El Niño anticipated precipitation responses for up to six months. This confirms distinct rainfall patterns and precipitation trends influenced by the El Niño–Southern Oscillation (ENSO), highlighting the need for further hydrometeorological research in the area. Full article

(This article belongs to the Special Issue Precipitation under Climate Change: Observation, Analysis and Forecasting)

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21 pages, 15148 KiB

Open AccessArticle

Evaluation of Three High-Resolution Satellite and Meteorological Reanalysis Precipitation Datasets over the Yellow River Basin in China

by Meixia Xie, Zhenhua Di, Jianguo Liu, Wenjuan Zhang, Huiying Sun, Xinling Tian, Hao Meng and Xurui Wang

Water 2024, 16(22), 3183; https://doi.org/10.3390/w16223183 - 7 Nov 2024

Viewed by 1088

Abstract

Recently, Integrated Multi-satellite Retrievals for the Global Precipitation Measurement (IMERG) mission and European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) precipitation datasets have been widely used in remote sensing and atmospheric studies, respectively, because of their high accuracy. A dataset of 268 site-gauge precipitation measurements over the Yellow River Basin in China was used in this study to comprehensively evaluate the performance of three high-resolution precipitation products, each with a spatial resolution of 0.1°, consisting of two satellite-derived datasets, IMERG and multisource weighted-ensemble precipitation (MSWEP), and one ERA5-derived dataset, ERA5-Land. The results revealed that the spatial distribution of IMERG annual precipitation closely resembled that of the observed rainfall and generally exhibited a downward trend from southeast to northwest. Among the three products, IMERG had the best performance at the annual scale, whereas ERA5-Land had the worst performance due to significant overestimation. Specifically, IMERG demonstrated the highest correlation coefficient (CC) above 0.8 and the lowest BIAS and root mean square error (RMSE), with values in most regions of 24.79 mm/a and less than 100 mm/a, respectively, whereas ERA5-Land presented the highest RMSE exceeding 500 mm/a, BIAS of 1265.7 mm/a, and the lowest CC below 0.2 in most regions. At the season scale, IMERG also exhibited the best performance across all four seasons, with a maximum of 17.99 mm/a in summer and a minimum of 0.55 mm/a in winter. Following IMERG, the MSWEP data closely aligned with the observations over the entire area in summer, southern China in spring and winter, and middle China in autumn. In addition, IMERG presented the highest Kling–Gupta efficiency coefficient (KGE) of 0.823 at the annual scale and the highest KGE (>0.77) across all four seasons among the three products compared with ERA5-Land and MSWEP, which had KEG values of −2.718 and −0.403, respectively. Notably, ERA5-Land exhibited a significant positive deviation from the observations at both the annual and seasonal scales, whereas the other products presented relatively smaller biases. Full article

(This article belongs to the Special Issue Precipitation under Climate Change: Observation, Analysis and Forecasting)

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17 pages, 6828 KiB

Open AccessArticle

Relationships between Precipitation and Elevation in the Southeastern Tibetan Plateau during the Active Phase of the Indian Monsoon

by Lun Luo, Yanggang Zhao, Yanghai Duan, Zeng Dan, Sunil Acharya, Gesang Jimi, Pan Bai, Jie Yan, Liang Chen, Bin Yang and Tianli Xu

Water 2024, 16(18), 2700; https://doi.org/10.3390/w16182700 - 23 Sep 2024

Viewed by 1457

Abstract

The precipitation gradient (PG) is a crucial parameter for watershed hydrological models. Analysis of daily precipitation and elevation data from 30 stations in the southeastern Tibetan Plateau (SETP) during the active phase of the Indian monsoon reveals distinct patterns. Below 3000 m, precipitation generally decreases with increasing altitude. Between 3000 and 4000 m, precipitation patterns are more complex; in western regions, precipitation increases with elevation, whereas in eastern regions, it decreases. Above 4000 m, up to the highest observation point of 4841 m, precipitation continues to decrease with elevation, with a more pronounced decline beyond a critical height. In the SETP, PGs for LYR and NYR are positive, at 11.3 ± 2.7 mm/100 m and 17.3 ± 3.8 mm/100 m, respectively. Conversely, PLZB exhibits a negative PG of −22.3 ± 4.2 mm/100 m. The Yarlung Zangbo River (YLZBR) water vapor channel plays a significant role in these PGs, with the direction and flux of water vapor potentially influencing both the direction and magnitude of the PG. Additional factors such as precipitation intensity, the number of precipitation days, precipitation frequency, and station selection also significantly impact the PG. Notable correlations between elevation and variables such as the number of precipitation days, non-precipitation days, and precipitation intensity. The precipitation intensity gradients (PIGs) are 0.06 ± 0.02 mm/d/100 m, 0.11 ± 0.04 mm/d/100 m, and −0.18 ± 0.04 mm/d/100 m for the three catchments, respectively. Future research should incorporate remote sensing data and expand site networks, particularly in regions above 5000 m, to enhance the accuracy of precipitation–elevation relationship assessments, providing more reliable data for water resource simulation and disaster warning. Full article

(This article belongs to the Special Issue Precipitation under Climate Change: Observation, Analysis and Forecasting)

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20 pages, 5024 KiB

Open AccessArticle

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

Cited by 4 | Viewed by 1268

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 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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