Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.9
3.2
Journals
Hydrology
Special Issues
Advances in the Measurement, Utility and Evaluation of Precipitation Observations:...
share announcement

Advances in the Measurement, Utility and Evaluation of Precipitation Observations: 2nd Edition

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

Deadline for manuscript submissions: 31 August 2026 | Viewed by 2552

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
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Junliang Jin

E-Mail Website
Guest Editor
Yangtze Institute for Conservation and Development, Hohai University, Nanjing 210098, China
Interests: extreme precipitation; climate change; hydrological process; flood modeling
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, the development of novel methodologies to improve precipitation measurement accuracy, and the simulation of rainfall–runoff processes.

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

This Special Issue will welcome manuscripts that discuss 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;
  • Disaster and risk analysis induced by precipitation;
  • Applications of precipitation data in hydrological modeling.

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 250 words) can be sent to the Editorial Office for assessment.

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
  • risk analysis

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

20 pages, 12557 KB  
Article
The Atmospheric Water Cycle over South America as Seen in the New Generation of Global Reanalyses
by Mário Francisco Leal de Quadro, Dirceu Luís Herdies, Ernesto Hugo Berbery, Caroline Bresciani, Fabrício Daniel dos Santos Silva, Helber Barros Gomes, Michel Nobre Muza, Cássio Aurélio Suski and Diego Portalanza
Hydrology 2025, 12(12), 316; https://doi.org/10.3390/hydrology12120316 - 29 Nov 2025
Viewed by 335
Abstract
We assess precipitation and key atmospheric water-cycle terms over South America (SA) in three modern reanalyses—MERRA-2, ERA5, and CFSR/CFSv2—during 1980–2021. Two observation-based datasets (CPC Unified Gauge and MSWEP-V2) serve as references to bracket observational uncertainty. Diagnostics include regional means for the Tropical and [...] Read more.
We assess precipitation and key atmospheric water-cycle terms over South America (SA) in three modern reanalyses—MERRA-2, ERA5, and CFSR/CFSv2—during 1980–2021. Two observation-based datasets (CPC Unified Gauge and MSWEP-V2) serve as references to bracket observational uncertainty. Diagnostics include regional means for the Tropical and Subtropical South Atlantic Convergence Zone (TSACZ, SSACZ) and southeastern South America (SESA), Taylor-diagram skill metrics, and a vertically integrated moisture-budget residual as a proxy for closure. All products reproduce the large-scale spatial and seasonal patterns, but disagreements persist over the Andes and parts of the central/northern Amazon. Relative to CPC/MSWEP-V2, MERRA-2 exhibits the smallest precipitation biases and the highest correlations, followed by ERA5; CFSR/CFSv2 shows a warm-season wet bias. Moisture-budget residuals are smallest in MERRA-2, moderate in ERA5, and largest in CFSR/CFSv2, with clear regional and seasonal dependence. These results document improvements in the new generation of reanalyses while highlighting persistent challenges in gauge-sparse and complex-orography regions. For hydroclimate applications that depend on internally consistent P, E, moisture-flux convergence, and runoff, MERRA-2 provides the most coherent depiction among the three, whereas ERA5 is a strong alternative when higher spatial/temporal resolution or dynamical fields are needed and CFSR/CFSv2 should be applied with caution for warm-season precipitation and closure-sensitive analyses. Full article
(This article belongs to the Special Issue Advances in the Measurement, Utility and Evaluation of Precipitation Observations: 2nd Edition)
►▼ Show Figures

Figure 1

27 pages, 3207 KB  
Article
Interpolation and Machine Learning Methods for Sub-Hourly Missing Rainfall Data Imputation in a Data-Scarce Environment: One- and Two-Step Approaches
by Mohamed Boukdire, Çağrı Alperen İnan, Giada Varra, Renata Della Morte and Luca Cozzolino
Hydrology 2025, 12(11), 297; https://doi.org/10.3390/hydrology12110297 - 10 Nov 2025
Viewed by 598
Abstract
Complete sub-hourly rainfall datasets are critical for accurate flood modeling, real-time forecasting, and understanding of short-duration rainfall extremes. However, these datasets often contain missing values due to sensor or transmission failures. Recovering missing values (or filling these data gaps) at high temporal resolution [...] Read more.
Complete sub-hourly rainfall datasets are critical for accurate flood modeling, real-time forecasting, and understanding of short-duration rainfall extremes. However, these datasets often contain missing values due to sensor or transmission failures. Recovering missing values (or filling these data gaps) at high temporal resolution is challenging due to the imbalance between rain and no-rain periods. In this study, we developed and tested two approaches for the imputation of missing 10-min rainfall data by means of machine learning (Multilayer Perceptron and Random Forest) and interpolation methods (Inverse Distance Weighting and Ordinary Kriging). The (a) direct approach operates on raw data to directly feed the imputation models, while the (b) two-step approach first classifies time steps as rain or no-rain with a Random Forest classifier and subsequently applies an imputation model to predicted rainfall depth instances classified as rain. Each approach was tested under three spatial scenarios: using all nearby stations, using stations within the same cluster, and using the three most highly correlated stations. An additional test involved the comparison of the results obtained using data from the imputed time interval only and data from a time window containing several time intervals before and after the imputed time interval. The methods were evaluated with reference to two different environments, mountainous and coastal, in Campania region (Southern Italy), under data-scarce conditions where rainfall depth is the only available variable. With reference to the application of the two-step approach, the Random Forest classifier shows a good performance both in the mountainous and in the coastal area, with an average weighted F1 score of 0.961 and 0.957, and an average Accuracy of 0.928 and 0.946, respectively. The highest performance in the regression step is obtained by the Random Forest in the mountainous area with an R2 of 0.541 and an RMSE of 0.109 mm, considering a spatial configuration including all stations. The comparison with the direct approach results shows that the two-step approach consistently improves accuracy across all scenarios, highlighting the benefits gained from breaking the data imputation process in stages where different physical conditions (in this case, rain and no-rain) are separately managed. Another important finding is that the use of time windows containing data lagged with respect to the imputed time interval allows capturing the atmospheric dynamics by connecting rainfall instances at different time levels and distant stations. Finally, the study confirms that machine learning models outperform spatial interpolation methods, thanks to their ability to manage data with complicated internal structure. Full article
(This article belongs to the Special Issue Advances in the Measurement, Utility and Evaluation of Precipitation Observations: 2nd Edition)
►▼ Show Figures

Figure 1

35 pages, 18392 KB  
Article
Assessing the Impacts of Land Cover and Climate Changes on Streamflow Dynamics in the Río Negro Basin (Colombia) Under Present and Future Scenarios
by Blanca A. Botero, Juan C. Parra, Juan M. Benavides, César A. Olmos-Severiche, Rubén D. Vásquez-Salazar, Juan Valdés-Quintero, Sandra Mateus, Jean P. Díaz-Paz, Lorena Díez, Andrés F. García and Oscar E. Cossio
Hydrology 2025, 12(11), 281; https://doi.org/10.3390/hydrology12110281 - 28 Oct 2025
Viewed by 1436
Abstract
Understanding and quantifying the coupled effects of land cover change and climate change on hydrological regimes is critical for sustainable water management in tropical mountainous regions. The Río Negro Basin in eastern Antioquia, Colombia, has undergone rapid urban expansion, agricultural intensification, and deforestation [...] Read more.
Understanding and quantifying the coupled effects of land cover change and climate change on hydrological regimes is critical for sustainable water management in tropical mountainous regions. The Río Negro Basin in eastern Antioquia, Colombia, has undergone rapid urban expansion, agricultural intensification, and deforestation over recent decades, profoundly altering its hydrological dynamics. This study integrates advanced satellite image processing, AI-based land cover modeling, climate change projections, and distributed hydrological simulation to assess future streamflow responses. Multi-sensor satellite data (Landsat, Sentinel-1, Sentinel-2, ALOS) were processed using Random Forest classifiers, intelligent multisensor fusion, and probabilistic neural networks to generate high-resolution land cover maps and scenarios for 2060 (optimistic, trend, and pessimistic), with strict area constraints for urban growth and forest conservation. Future precipitation was derived from MPI-ESM CMIP6 outputs (SSP2-4.5, SSP3-7.0, SSP5-8.5) and statistically downscaled using Empirical Quantile Mapping (EQM) to match the basin scale and precipitation records from the national hydrometeorological service of the Colombia IDEAM (Instituto de Hidrología, Meteorología y Estudios Ambientales, Colombia). The TETIS hydrological model was calibrated and validated using observed streamflow records (1998–2023) and subsequently used to simulate hydrological responses under combined land cover and climate scenarios. Results indicate that urban expansion and forest loss significantly increase peak flows (Q90, Q95) and flood risk while decreasing baseflows (Q10, Q30), compromising water availability during dry seasons. Conversely, conservation-oriented scenarios mitigate these effects by enhancing flow regulation and groundwater recharge. The findings highlight that targeted land management can partially offset the negative impacts of climate change, underscoring the importance of integrated land–water planning in the Andes. This work provides a replicable framework for modeling hydrological futures in data-scarce mountainous basins, offering actionable insights for regional authorities, environmental agencies, and national institutions responsible for water security and disaster risk management. Full article
(This article belongs to the Special Issue Advances in the Measurement, Utility and Evaluation of Precipitation Observations: 2nd Edition)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop