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Hydrology
Special Issues
Advances in the Measurement, Utility and Evaluation of Precipitation Observations
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Advances in the Measurement, Utility and Evaluation of Precipitation Observations

A special issue of Hydrology (ISSN 2306-5338). This special issue belongs to the section "Hydrology–Climate Interactions".

Deadline for manuscript submissions: 12 August 2025 | Viewed by 3107

Special Issue Editors

Prof. Dr. Jiangjiang Zhang

E-Mail Website
Guest Editor
Yangtze Institute for Conservation and Development, Hohai University, Nanjing 210098, China
Interests: rainfall-runoff modeling; data assimilation; machine learning
Prof. Dr. Junliang Jin

E-Mail Website
Guest Editor
Yangtze Institute for Conservation and Development, Hohai University, Nanjing 210098, China
Interests: hydroloigcal modeling; water resources management; drought and flood management and prevention; remote sensing data analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Precipitation is a critical component of the hydrological cycle, directly impacting various hydrologic processes such as runoff, groundwater recharge, and flood management. The accurate measurement and evaluation of precipitation is vital for enhancing water resource management, refining climate models, and improving disaster preparedness strategies. This Special Issue seeks contributions that address the challenges and innovations in precipitation measurement, including the latest technological advancements (e.g., deep learning, Internet of Things), the effectiveness of different observational techniques, and the evaluation of precipitation data across diverse climatic and geographic contexts. Topics of interest include, but are not limited to, remote sensing technologies, ground-based observations, the integration of multiple data sources, and the development of novel methodologies to improve precipitation measurement accuracy.

The goal of this Special Issue is to collect papers (original research articles and review papers) to give insights about a comprehensive understanding of how precipitation observations can be enhanced, applied, and critically assessed to support hydrologic research and applications.

This Special Issue will welcome manuscripts that link the following themes:

  • Statistical Modeling of Rainfall Patterns;
  • Deep Learning Approaches for Precipitation Measurement;
  • Leveraging Internet of Things (IoT) for Enhanced Precipitation Estimation;
  • Multi-Source Rainfall Observation Fusion;
  • Uncertainty and Bias Analysis in Rainfall Data;
  • Impacts of Climate Change on Precipitation Trends;
  • Remote Sensing Techniques for Precipitation Observation;
  • Satellite-Based Precipitation Monitoring and Evaluation.

We look forward to receiving your original research articles and reviews.

Prof. Dr. Jiangjiang Zhang
Prof. Dr. Junliang Jin
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

  • precipitation observations
  • deep learning
  • data fusion
  • remote sensing
  • uncertainty quantification
  • rainfall-runoff modeling

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Published Papers (6 papers)

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Research

13 pages, 1512 KiB  
Article
Uncertainty in Kinetic Energy Models for Rainfall Erosivity Estimation in Semi-Arid Regions
by José Bandeira Brasil, Ana Célia Maia Meireles, Carlos Wagner Oliveira, Sirleide Maria de Menezes, Francisco Dirceu Duarte Arraes and Maria Simas Guerreiro
Hydrology 2025, 12(7), 181; https://doi.org/10.3390/hydrology12070181 - 4 Jul 2025
Abstract
The Brazilian semi-arid Northeast plays a critical role in regional hydrology, where rainfall is marked by pronounced temporal variability and short duration, presenting significant challenges for understanding and managing hydrological and erosive processes. This study aims to evaluate the performance of empirical models [...] Read more.
The Brazilian semi-arid Northeast plays a critical role in regional hydrology, where rainfall is marked by pronounced temporal variability and short duration, presenting significant challenges for understanding and managing hydrological and erosive processes. This study aims to evaluate the performance of empirical models for estimating rainfall kinetic energy (KE) and erosivity index (EI30) in this region, for all events and erosive events, using high-resolution rainfall data collected at the Federal University of Cariri (UFCA), Ceará. A total of 283 natural rainfall events were analyzed, with KE and EI30 values calculated using multiple models: Wischmeier and Smith, USDA, Van Dijk, a temporal variation-based model (KE_VT), and a regional model developed for Brazil’s semi-arid zone, which served as the reference. The results show a predominance of small rainfall events (<5.2 mm), though maximum EI30 values exceeded 1300 MJ ha−1 mm h−1, highlighting the potential for extreme erosive events. Comparative analysis revealed that all international models significantly underestimated KE and EI30 values compared to the regional reference, with the KE_VT model showing the closest approximation (13% underestimation), for all events and erosive events. Statistical assessments using the Wilcoxon test, Nash–Sutcliffe efficiency, and Willmott concordance index confirmed the superior performance of the KE_VT, for all events and erosive events. These findings underscore the importance of considering intra-event rainfall variability and regional calibration when modeling erosivity in semi-arid climates, contributing to more effective soil conservation and hydrological planning. Full article
(This article belongs to the Special Issue Advances in the Measurement, Utility and Evaluation of Precipitation Observations)
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24 pages, 10218 KiB  
Article
Rainfall Organization and Storm Tracking in Urban Barcelona, NE Spain, Using a High-Resolution Rain Gauge Network
by María del Carmen Casas-Castillo, Xavier Navarro and Raül Rodríguez-Solà
Hydrology 2025, 12(7), 178; https://doi.org/10.3390/hydrology12070178 - 3 Jul 2025
Abstract
Extreme rainfall in urban areas can cause major economic damage, a problem expected to intensify with climate change. Despite this, high-resolution studies at the city scale remain limited. This study analyzes rainfall organization and storm dynamics over Barcelona using data from a dense [...] Read more.
Extreme rainfall in urban areas can cause major economic damage, a problem expected to intensify with climate change. Despite this, high-resolution studies at the city scale remain limited. This study analyzes rainfall organization and storm dynamics over Barcelona using data from a dense rain gauge network (1994–2019). The aim is to identify dominant spatial patterns and understand how storms evolve in relation to local urban and topographic features. Principal component analysis and simple scaling analysis revealed signs of a rainfall island effect, possibly linked to the urban heat island and modulated by orographic and coastal influences. Tailored rainfall indices highlighted a division between inland areas shaped by orography and coastal zones influenced by the sea. These spatial structures evolved with rainfall duration, shifting from localized contrasts at a 10 min resolution to more homogeneous distributions at daily scales. Storm tracking showed that 90% of speeds ranged from 5 to 60 km/h and intense rainfall events typically moved east–southeast toward the sea and north–northeast. Faster storms tended to follow preferred directions reflecting mesoscale circulations and possible modulations by local terrain. These findings underscore how urban morphology, local relief, and a coastal setting may shape rainfall at the city scale, in interaction with broader Mediterranean synoptic dynamics. Full article
(This article belongs to the Special Issue Advances in the Measurement, Utility and Evaluation of Precipitation Observations)
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14 pages, 2407 KiB  
Article
Refining Rainfall Derived from Satellite Radar for Estimating Inflows at Lam Pao Dam, Thailand
by Nathaporn Areerachakul, Jaya Kandasamy, Saravanamuthu Vigneswaran and Kittitanapat Bandhonopparat
Hydrology 2025, 12(7), 163; https://doi.org/10.3390/hydrology12070163 - 25 Jun 2025
Viewed by 213
Abstract
This project aimed to evaluate the use of meteorological satellite-derived rainfall data to estimate water inflows to dams. In this study, the Lam Pao Dam in the Chi Basin, Thailand, was used as a case study. Rainfall data were obtained using the PERSIANN [...] Read more.
This project aimed to evaluate the use of meteorological satellite-derived rainfall data to estimate water inflows to dams. In this study, the Lam Pao Dam in the Chi Basin, Thailand, was used as a case study. Rainfall data were obtained using the PERSIANN technique. To improve accuracy, satellite-derived rainfall estimates were adjusted using ground-based rainfall measurements from stations located near and within the catchment area, applying the 1-DVAR method. The Kriging method was employed to estimate the spatial distribution of rainfall over the catchment area. This approach resulted in a Probability of Detection (POD) of 0.92 and a Threat Score (TS) of 0.72 for rainfall estimates in the Chi Basin. Rainfall data from the Weather Research and Forecasting (WRF) numerical models were used as inputs for the HEC-HMS model to simulate water inflows into the dam. To refine rainfall estimates, various microphysics schemes were tested, including WSM3, WSM5, WSM6, Thompson, and Thompson Aerosol-Aware. Among these, the Thomson Aerosol-Aware scheme demonstrated the highest accuracy, achieving an average POD of 0.96, indicating highly reliable rainfall predictions for the Lam Pao Dam catchment. The findings underscore the potential benefits of using satellite-derived meteorological data for rainfall estimation, particularly where installing and maintaining ground-based measurement stations is difficult, e.g., forests/mountainous areas. This research contributes to a better understanding of satellite-derived rainfall patterns and their influence on catchment hydrology for enhanced water resource analysis. Full article
(This article belongs to the Special Issue Advances in the Measurement, Utility and Evaluation of Precipitation Observations)
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61 pages, 18163 KiB  
Article
Regional Frequency Analysis Using L-Moments for Determining Daily Rainfall Probability Distribution Function and Estimating the Annual Wastewater Discharges
by Pau Estrany-Planas, Pablo Blanco-Gómez, Juan I. Ortiz-Vallespí, Javier Orihuela-Martínez and Víctor Vilarrasa
Hydrology 2025, 12(6), 152; https://doi.org/10.3390/hydrology12060152 - 16 Jun 2025
Viewed by 309
Abstract
The spatial distribution of precipitation is one of the major unknowns in hydrological modeling since meteorological stations do not adequately cover the territory, and their records are often short. In addition, regulations are increasingly restricting the amount of wastewater that can be discharged [...] Read more.
The spatial distribution of precipitation is one of the major unknowns in hydrological modeling since meteorological stations do not adequately cover the territory, and their records are often short. In addition, regulations are increasingly restricting the amount of wastewater that can be discharged each year. Therefore, understanding the annual behavior of rainfall events is becoming increasingly important. This paper presents Rainfall Frequency Analysis (RainFA), a software package that applies a methodology for data curation and frequency analysis of precipitation series based on the evaluation of the L-moments for regionalization and cluster classification. This methodology is tested in the city of Palma (Spain), identifying a single homogeneous cluster integrated by 7 (out of 11) stations, with homogeneity values less than 0.6 for precipitation values greater than or equal to 0.4 mm. In the evaluation of the prediction capacity, the selected cluster of 7 stations performed in the first quartile of the 120 possible combinations of 7 stations, both for the detection of the occurrence of rainfall—in terms of Probability of Detection (POD), False Alarm Ratio (FAR), Critical Success Index (CSI) and Bias Score (BS) statistics—and for the accuracy of rainfall—according to Root Mean Square Error (RMSE), Nash–Sutcliffe Efficiency coefficient (NSE) and Percent Bias (PBIAS). The cluster was also excellent for predicting different rainfall ranges, resulting in the best combination for both light—i.e., [1, 5) mm—and moderate—i.e., [5, 20) mm—rainfall prediction. The Generalized Pareto gave the best probability distribution function for the selected region, and it was used to simulate daily rainfall and system discharges over annual periods using Monte Carlo techniques. The derived discharge values were consistent with observations for 2023, with an average discharge of about 700,000 m3 of wastewater. RainFA is an easy-to-use and open-source software programmed using Python that can be applied anywhere in the world. Full article
(This article belongs to the Special Issue Advances in the Measurement, Utility and Evaluation of Precipitation Observations)
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20 pages, 5405 KiB  
Article
Assessing the Risk of Natural and Socioeconomic Hazards Caused by Rainfall in the Middle Yellow River Basin
by Yufeng Zhao, Shun Xiao, Xinshuang Wu, Shuitao Guo and Yingying Yao
Hydrology 2025, 12(6), 134; https://doi.org/10.3390/hydrology12060134 - 29 May 2025
Viewed by 850
Abstract
Extreme rainfall events directly increase flood risks and further trigger environmental geological hazards (i.e., landslides and debris flows). Meanwhile, rainfall-induced risks are determined by climate and geographical factors and spatial socioeconomic factors (e.g., population density and gross domestic product). However, the middle stream [...] Read more.
Extreme rainfall events directly increase flood risks and further trigger environmental geological hazards (i.e., landslides and debris flows). Meanwhile, rainfall-induced risks are determined by climate and geographical factors and spatial socioeconomic factors (e.g., population density and gross domestic product). However, the middle stream of Yellow River Basin, where geological hazards frequently occur, lacks systematic analyses of rainfall-induced risks. In this study, we propose a comprehensive quantification framework and apply it to the Loess Plateau of northern China based on 40 years of climate data, streamflow measurements, and multiple spatial and geographical attribute datasets. A deep learning algorithm of long short-term memory (LSTM) was used to predict runoff, and the analytic hierarchy index was utilized to evaluate the comprehensive spatial risk considering natural and socioeconomic factors. Despite a decrease in annual precipitation in our study area of 1.46 mm per year, the intensity of heavy rainfall has increased since the 1980s, characterized by increases in rainstorm intensity (+4.68%), rainfall intensity (+7.07%), and rainfall amount (+5.34%). A comprehensive risk assessment indicated that high-risk areas accounted for 20.30% of the total area, with rainfall, geographical factors, and socioeconomic variables accounting for 53.90%, 29.72%, and 16.38% of risk areas, respectively. Rainfall was the dominant factor that determined the risk, and geographical and socioeconomic properties characterized the vulnerability and resilience of disasters. Our study provided an evaluation framework for multi-hazard risk assessment and insights for the development of disaster prevention and reduction policies. Full article
(This article belongs to the Special Issue Advances in the Measurement, Utility and Evaluation of Precipitation Observations)
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24 pages, 6615 KiB  
Article
Creation and Comparison of High-Resolution Daily Precipitation Gridded Datasets for Greece Using a Variety of Interpolation Techniques
by Giorgos Ntagkounakis, Panagiotis Nastos, John Kapsomenakis and Kostas Douvis
Hydrology 2025, 12(2), 31; https://doi.org/10.3390/hydrology12020031 - 10 Feb 2025
Viewed by 855
Abstract
This study investigates a range of precipitation interpolation techniques with the objective of generating high-resolution gridded daily precipitation datasets for the Greek region. The study utilizes a comprehensive station dataset, incorporating geographical variables derived from satellite-based elevation data and integrating precipitation data from [...] Read more.
This study investigates a range of precipitation interpolation techniques with the objective of generating high-resolution gridded daily precipitation datasets for the Greek region. The study utilizes a comprehensive station dataset, incorporating geographical variables derived from satellite-based elevation data and integrating precipitation data from the ERA5 reanalysis. A total of three different modeling approaches are developed. Firstly, we utilize a General Additive Model in conjunction with an Indicator Kriging model using only station data and limited geographical variables. In the second iteration of the model, we blend ERA5 reanalysis data in the interpolation methodology and incorporate more geographical variables. Finally, we developed a novel modeling framework that integrates ERA5 data, a variety of geographical data, and a multi-model interpolation process which utilizes different models to predict precipitation at distinct thresholds. Our results show that using the ERA5 data can increase the accuracy of the interpolated precipitation when the station dataset used is sparse. Additionally, the implementation of multi-model interpolation techniques which use distinct models for different precipitation thresholds can improve the accuracy of precipitation and extreme precipitation modeling, addressing important limitations of previous modeling approaches. Full article
(This article belongs to the Special Issue Advances in the Measurement, Utility and Evaluation of Precipitation Observations)
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