The Hydrologic Cycle in a Changing Climate (2nd Edition)

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Climatology".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 770

Special Issue Editors


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Guest Editor
1. Laboratory of Hydrology, Lithuanian Energy Institute, Breslaujos St. 3, LT-44403 Kaunas, Lithuania
2. Department of Physics, Mathematics and Biophysics, Faculty of Medicine, Lithuanian University of Health Sciences, Eiveniu Str. 4, LT-44307 Kaunas, Lithuania
Interests: climate change; extreme hydrological phenomena; low flow indices; hydromorphology; droughts; spring floods
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Laboratory of Hydrology, Lithuanian Energy Institute, Breslaujos St. 3, LT-44403 Kaunas, Lithuania
Interests: climatology; climate change; teleconnection patterns; hydrometeorological phenomena; catchment hydrology; hydrological modelling; spring floods
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This is a follow-up to the first edition of the Special Issue entitled “The Hydrologic Cycle in a Changing Climate” (https://www.mdpi.com/journal/atmosphere/special_issues/9J0FU34GKW), published in Atmosphere in 2024.

The hydrological cycle describes the continuous movement of water in the Earth's hydrosphere, a continuous process comprising atmospheric, surface, and groundwater movement. The changing climate directly affects the drivers and components of the hydrological cycle (evapotranspiration, water vapor concentrations, clouds, air temperature, precipitation patterns, surface runoff, stream flow patterns, etc.).

The climate crisis has led to an increase in average global temperatures and an increase in high-temperature-related extreme events such as heat waves. Higher temperatures are also predicted to change the geographic distribution of climate zones, leading to an acceleration in evaporation, which increases the risk of severe drought in one region and causes unexpected flooding in another due to transported moisture. Amidst climate change, droughts are already becoming more frequent and longer lasting in many regions of the world. Drought is an unusual and temporary lack of water resulting from insufficient rainfall and increased evaporation (due to high temperatures). Conversely, over the last century, an increase in evaporation and precipitation has been intensifying the hydrological cycle. This is an undesirable consequence of global warming, as higher temperatures encourage evaporation; i.e., the evaporation from the land surface and sea is transporting the moisture as rain and snow to inland areas. Additionally, warmer air can hold more water vapor, leading to heavy rainfall, extreme flooding, and other risks. Another example of changes in the hydrological cycle is the retreat of glaciers when the water supplied via solid precipitation is not sufficient to replenish the ice lost through melting and sublimation.

In this Special Issue, we invite you to submit contributions on new insights into any types of hydrologic cycle processes, their response to climate change, interactions between their components, and many more factors. Research related to any aspect of hydrological cycle observations and modelling is welcome, including new or interdisciplinary approaches, feedback processes, various hydro-meteorological phenomena, the human role in the hydrologic cycle, and other topics that improve our understanding about changes in the hydrologic cycle. Review papers will also be considered.

Dr. Diana Meilutytė-Lukauskienė
Dr. Vytautas Akstinas
Guest Editors

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Keywords

  • climate change
  • hydrologic cycle
  • droughts
  • flooding
  • water resource management
  • river runoff

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Published Papers (2 papers)

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Research

17 pages, 2681 KiB  
Article
Ensemble Learning-Based Soft Computing Approach for Future Precipitation Analysis
by Shiu-Shin Lin, Kai-Yang Zhu, Chen-Yu Wang, Chou-Ping Yang and Ming-Yi Liu
Atmosphere 2025, 16(6), 669; https://doi.org/10.3390/atmos16060669 - 1 Jun 2025
Viewed by 207
Abstract
This study integrated the strengths of ensemble learning and soft computing to develop a future regional rainfall model for evaluating the complex characteristics of island precipitation. Soft computing uses the well-developed adaptive neuro-fuzzy inference system, which has been successfully applied in atmospheric hydrology [...] Read more.
This study integrated the strengths of ensemble learning and soft computing to develop a future regional rainfall model for evaluating the complex characteristics of island precipitation. Soft computing uses the well-developed adaptive neuro-fuzzy inference system, which has been successfully applied in atmospheric hydrology and combines the features of neural networks and fuzzy logic. This combination enables artificial intelligence (AI) to effectively represent reasoning derived from complex data and expert experience. Due to the multiple atmospheric and hydrological factors that influence rainfall, the nonlinear interrelations among them are highly intricate. Nonlinear principal component analysis can extract nonlinear features from the data, reduce dimensionality, and minimize the adverse effects of data noise and excessive input factors on soft computing, which may otherwise result in poor model performance. Ultimately, ensemble learning enhances prediction accuracy and reduces uncertainty. This study used Tamsui and Kaohsiung in Taiwan as case study locations. Historical monthly rainfall data (January 1950 to December 2005) from Tamsui Station and Kaohsiung Station of the Central Weather Administration, along with historical and varied emission scenario data (RCP 4.5 and RCP 8.5) from three AR5 GCM models (ACCESS 1.0, CSIRO-MK3.6.0, MRI-CGCM3), were used to evaluate future regional rainfall trends and uncertainties through the method proposed in this study. The research findings indicate the following: (1) Ensemble learning results demonstrate that all examined general circulation models effectively simulate historical rainfall trends. (2) The average rainfall trends under the RCP 4.5 emission scenario are generally consistent with historical rainfall trends. (3) The exceedance probabilities of future rainfall during the mid-term (2061–2080) and long-term (2081–2100) suggest that Kaohsiung may experience precipitation events with higher rainfall than historical data during dry seasons (October to April of next year), while Tamsui Station may exhibit greater variability in terms of exceedance probabilities. (4) Under both the RCP 4.5 and RCP 8.5 emission scenarios, the percentage changes in future rainfall variability at Kaohsiung Station during dry seasons are higher than those during wet seasons (May to September), indicating an increased risk of extreme precipitation events during dry seasons. Full article
(This article belongs to the Special Issue The Hydrologic Cycle in a Changing Climate (2nd Edition))
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23 pages, 13046 KiB  
Article
Evaluating the Performance of Soil and Water Assessment Tool (SWAT) in a Snow-Dominated Climate (Case Study: Azna–Aligoudarz Basin, Iran)
by Yaser Sabzevari, Saeid Eslamian, Saeid Okhravi and Mohammad Hadi Bazrkar
Atmosphere 2025, 16(4), 382; https://doi.org/10.3390/atmos16040382 - 27 Mar 2025
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Abstract
This study aims to investigate the capability of the SWAT (Soil and Water Assessment Tool) model in hydrologic simulation of a cold and mountainous climate, the Azna–Aligoudarz Basin, Iran. For this purpose, daily climatic data from the Aligoudarz synoptic station, discharge data from [...] Read more.
This study aims to investigate the capability of the SWAT (Soil and Water Assessment Tool) model in hydrologic simulation of a cold and mountainous climate, the Azna–Aligoudarz Basin, Iran. For this purpose, daily climatic data from the Aligoudarz synoptic station, discharge data from the Marbare hydrometric station, soil and land use maps, and a 10 m digital elevation model of the study area were used. The results demonstrated that the model exhibited poor performance due to poor simulation of runoff generated from snowmelt. To enhance the model’s performance, the calibration period was split into warm and cold seasons using a temperature threshold of 3.6 °C. As a result, the model’s performance improved, with the Nash–Sutcliffe Efficiency (NSE) increasing from 0.28 to 0.60 and R2 rising from 0.32 to 0.61. The research indicated that refining the conceptual and theoretical framework of the SWAT model is essential to reduce uncertainty and achieve reliable accuracy, particularly in snow-dominated and mountainous areas. Full article
(This article belongs to the Special Issue The Hydrologic Cycle in a Changing Climate (2nd Edition))
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