Climate Change and Hydrological Processes

1. Introduction

In recent decades, many regions have experienced a noticeable increase in the frequency and intensity of extreme events, posing serious challenges to water resource management [1,2]. Changes in hydrological patterns—manifesting as prolonged periods of drought, devastating floods, rapid glacial melting, and the ominous rise in sea levels—are fundamentally reshaping the dynamics of water security and ecological stability [3,4,5].

Climate change affects virtually all components of the hydrological cycle, particularly through changes in temperature and precipitation. These climatic variables directly influence evaporation, evapotranspiration, runoff, and groundwater recharge, altering water availability and distribution [5]. As global temperatures continue to rise, the hydrological cycle is expected to intensify, leading to faster and more variable water movement across the Earth’s surface and atmosphere [6,7,8,9].

Precipitation is a key connector between the atmospheric and hydrological systems, making its variability a critical concern for water management strategies in the face of climate-related stressors. The interplay between water and climate is fundamentally cyclical: water contributes to climate regulation through the exchange of heat and mass among the ocean, atmosphere, and land, while climate factors dictate the behavior of water systems [9,10]. Furthermore, this variability substantially impacts the runoff and the development of flooding, which are increasingly difficult to predict and manage [11,12].

Quantifying specific processes remains a complex challenge due to their sensitivity to multiple meteorological factors and anthropogenic activities [13,14]. To achieve resilience in the face of climate change and to promote sustainable development, it is essential to develop integrated strategies focused on the efficient utilization of water resources [15]. These strategies should also aim to mitigate the effects of extreme weather events and create adaptive frameworks for water management that can adjust to evolving conditions [16,17].

This Special Issue was dedicated to deepening the knowledge on effects of climate change on hydrological processes. The contributions within it include cutting-edge research that examines the implications of climate change on water runoff, evaluates the risks and uncertainties associated with hydro-meteorological events, assesses flood susceptibility, investigates the appropriateness of existing methodologies for evaluating the intensity and duration of these events, explores the impact of human activities on watershed dynamics, and proposes innovative mitigation and adaptation strategies with which to address the effects of climate change.

2. Main Contributions to the Special Issue

Eleven articles were accepted for publication after the peer review process. In the following, we shall summarize the main findings and contributions to advancements in the study area.

Babaousmail and Ojara [1] analyzed rainfall patterns over Uganda’s Lake Kyoga Basin from 1981 to 2017 using ground-recorded and satellite data. They applied CDI, RAI, and rainfall frequency metrics to identify trends in droughts and floods. The study found a general decrease in seasonal rainfall over the years, followed by a rising trend during 2006–2017 in both MAM and SOND seasons. Northeastern areas experienced dry days more frequently than other parts of the basin. These rainfall fluctuations pose risks to agriculture and livelihoods. The authors recommend using localized climate information to support adaptive planning and development.

Xu et al. [2] have examined runoff changes during the freeze–thaw period in the Changbai Mountains. Using long-term hydrological and climate data, the authors identified strong seasonal and spatial variability in runoff patterns, influenced primarily by snowmelt accumulation and its dynamics. While potential evapotranspiration has limited impact due to cold and frozen conditions, subsurface changes, such as permafrost degradation and land-use shifts, play a growing role in runoff variability. The research underscores the need to consider both climatic and subsurface processes for effective water management in cold-region basins facing environmental change.

The article “Multiscale Factors Driving Extreme Flooding in China’s Pearl River Basin During the 2022 Dragon Boat Precipitation Season” [3] offers an in-depth analysis of the extreme flooding that occurred in the Pearl River Basin during the 2022 Dragon Boat Festival period, investigating multiscale atmospheric factors that contributed to this unique event. The authors identify several key atmospheric systems operating on different temporal and spatial scales that played a role in the extreme rainfall and flooding. These include large-scale phenomena such as the South Asian High (SAH), the Western Pacific Subtropical High (WPSH), and the South China Sea summer monsoon, which directly influenced precipitation patterns in the region.

A significant factor identified in the research is the Boreal Summer Intraseasonal Oscillation (BSISO), a subseasonal atmospheric oscillation, whose phase stagnation enhanced the intensity and persistence of rainfall during this period. The study also draws attention to the role of the La Niña phenomenon, which, although not directly responsible for the flooding, influenced the atmospheric dynamics by modulating the monsoon and affecting the propagation of the BSISO, further amplifying the precipitation events.

Zheng et al. [3] proposed a conceptual model that highlights the interactions between various factors, illustrating how their combined effects resulted in extreme rainfall and subsequent flooding. They underscore the importance of understanding these interactions for improving flood prediction and management. The authors call for more integrated forecasting systems that can account for these complex atmospheric interactions and provide more accurate early warnings to mitigate the impacts of such hydrological disasters in the future.

The study titled “Intercomparison of Runoff and River Discharge Reanalysis Datasets at the Upper Jinsha River, an Alpine River on the Eastern Edge of the Tibetan Plateau” [4] compares the output of four reanalysis datasets in evaluating the hydrologic processes in a hydrographic basin in Tibet. The research shows that model calibration significantly influences the accuracy of runoff and discharge estimates, with calibrated datasets like GloFAS and GRFR generally outperforming uncalibrated ones. However, GRFR’s river discharge estimates were overestimated due to suboptimal river routing configurations. The evaluation was substantially improved when rerouted using the Muskingum–Cunge method. The study also notes that vector-based river routing models offer advantages over grid-based models in representing river networks and catchment areas. These findings underscore the importance of proper model calibration and routing configurations in hydrological modeling, especially in data-scarce and topographically complex regions like the Tibetan Plateau.

The article of Monges et al. [5] presents the results of a Random Forest (RF) model enhanced with time-lagged hydrometeorological inputs for daily streamflow forecasting and compares its performance with the physically based SWAT model across diverse catchments. The RF model outperformed the SWAT model in daily simulations, achieving higher Nash–Sutcliffe Efficiency values, particularly where short-term dynamics dominate. The RF approach required a fraction of the computational time compared to the SWAT approach—under 12 s versus up to 24 h—highlighting its suitability for rapid assessment, especially in data-limited regions. Conversely, the SWAT model demonstrated superior performance for monthly predictions in catchments with irregular flow, leveraging its process-based structure. However, both models exhibited reduced accuracy in snow-influenced basins, particularly for peak flow estimation. These findings support the use of time-lag-informed RF models for efficient, short-term hydrological forecasting while reinforcing the SWAT model’s value for long-term planning and in-depth watershed analysis.

In the study “Intelligent Methods for Estimating the Flood Susceptibility in the Danube Delta, Romania”, Costache et al. [6] evaluate the susceptibility to flood based on eight geographic factors. Various machine learning models are employed, including Gradient Boosting Machine, Random Forest, and Support Vector Machine, to assess and predict flood-prone areas in the Danube Delta (Romania). They found that all factors contribute to flood susceptibility, the most significant predictors being elevation, topographic wetness index, and distance from rivers. The models demonstrated high accuracy in predicting flood-prone zones, with the Random Forest model outperforming the others in terms of predictive performance. The study’s results underscore the effectiveness of integrating machine learning approaches with geographic information system data to enhance flood risk assessment and management. By identifying areas with higher flood susceptibility, the research provides valuable insights for policymakers and stakeholders to develop targeted flood mitigation strategies and improve disaster preparedness in the Danube Delta region.

The paper by Stancu et al. [7] examines the influence of various factors on the navigability in one of the Danube channels (in Romania), using hydrological and hydrodynamic models. The rapid sediment build-up indicates the necessity of frequent dredging to maintain navigability in the channel. The study further indicates that ships with deeper drafts, specifically those around 11.5 m, generate propeller-induced currents that significantly erode the seabed, exacerbating the sedimentation problem. Moreover, the research highlights the significant role of the prevailing northeastern marine waves and currents, which contribute to the movement of sediments into the Sulina channel, further complicating the sedimentation challenges. The study confirms that the current dikes remain robust, despite these natural forces’ actions. In conclusion, the research provides valuable insights into the complex interactions between marine dynamics and shipping activities in the Sulina channel, offering a deeper understanding of the challenges associated with maintaining safe and efficient navigation. It underscores the importance of ongoing sediment management and infrastructure maintenance in safeguarding this critical maritime route.

The authors of [8] indicate that the Emirate of Sharjah in the United Arab Emirates (UAE) has experienced several such events, most notably the severe urban flooding that occurred on 17 April 2024. These developments highlight the urgent need to reassess existing hydrological tools, particularly Intensity–Duration–Frequency curves, which serve as critical inputs for flood risk assessment, stormwater design, and urban drainage planning. They applied the Gumbel distribution to annual maximum rainfall data, supplemented by a neural network-based extension technique using Self-Organizing Maps (SOMs), which improves data continuity and accuracy for IDF curve updates. Given the implications of outdated IDF models on flood resilience and infrastructure safety, this work underscores the necessity of incorporating recent climatic variability into hydrological design frameworks.

The article by Dimond et al. [9] explores how uncertainties in climate models influence adaptation pathways in managing the water resources. The authors demonstrate that different climate models can lead to varying adaptation decisions, highlighting the importance of considering a range of scenarios. They advocate for flexible adaptive management strategies that can be adjusted as new information becomes available. The study emphasizes the need for decision-making frameworks that account for uncertainty to ensure resilient and sustainable water management practices.

The study “Water Management Instructions as an Element of Improving the State of the Pakoski Reservoir (Central–Western Poland)” by Nowak et al. [10] addresses the growing challenges of managing the Pakoski Reservoir, which has been a vital water resource in the region for over five decades. The reservoir faces deteriorating conditions due to a combination of factors, including climate change, human activities, and the increasing pressure on water resources in its catchment area. The study emphasizes the need for more effective management strategies that consider both hydrological data and environmental considerations. A key proposal is the development of comprehensive water management instructions, which would provide guidelines for water quality improvement, sustainable use, and the prevention of ecological degradation in the reservoir. The authors stress that these measures would help maintain the balance of the reservoir’s ecosystems while supporting biodiversity and ensuring a stable water supply for local communities. The article also highlights the importance of adaptive management approaches, where water management practices are flexible and can be adjusted in response to changing conditions, particularly in uncertain future climate patterns.

Article [11] presents the development and implementation of a cheap system for the hydrological monitoring of a protected area in Colombia, which is affected by climate change. Despite the simplicity of the tools, the collected data provided valuable insights into the hydrological behavior of the lagoon. The study emphasizes the importance of understanding water exchange processes between the lagoon and the sea, especially considering the potential impacts of climate change.

3. Concluding Remarks

As its editors, we are particularly happy with the balance struck in this collection—between theoretical depth and applied relevance; between global overviews and locally rooted case studies. The inclusion of perspectives from both data-rich and data-scarce regions ensures that the Special Issue remains globally relevant while recognizing the specific challenges of local contexts.

We are deeply grateful to the authors for their high-quality contributions and to the reviewers for their critical, constructive feedback. We also thank the editorial team for their support in the production of this Special Issue. It is our hope that the knowledge shared within these pages will inspire further research, strengthen cross-disciplinary collaboration, and ultimately contribute to building more resilient hydrological systems and societies.

We invite readers from across disciplines—hydrology, climatology, environmental science, engineering, and beyond—to engage with the work presented here. The challenges we face in adapting to hydrological change are formidable, but through scientific innovation, collaborative action, and sustained dialogue, meaningful progress is possible.