Climate Change and Hydrological Processes, 2nd Edition

1. Introduction

Climate change is reshaping the global water cycle in profound and often unpredictable ways. Rising temperatures, shifting precipitation regimes, and intensifying extreme events are altering the distribution, availability, and quality of water resources across regions [1,2]. These changes have direct implications for ecosystems, agriculture, energy production, urban water supply, and human well-being. Understanding the hydrological consequences of a warming climate is therefore a central challenge in both scientific research and policy development. Hydrological processes—such as evapotranspiration, infiltration, groundwater recharge, and river discharge—are highly sensitive to climatic drivers. Small shifts in temperature and precipitation patterns can trigger nonlinear responses in catchment behavior, water storage, and flood or drought risks [3,4]. Moreover, feedback among the land surface, atmosphere, and human interventions complicates the prediction of future water dynamics. Bridging the gap between climate projections and hydrological impacts requires advances in modeling, long-term monitoring, and the integration of interdisciplinary perspectives [5,6,7,8].

Research over the past two decades has demonstrated that climate change is exerting significant and multifaceted impacts on global and regional water resources. Observational evidence shows alterations in precipitation regimes, with many areas experiencing increases in the intensity and frequency of heavy rainfall events, even in regions where the mean precipitation has remained stable or declined [9]. These changes amplify flood risks and challenge existing water management infrastructure designed under assumptions of stationarity. Conversely, prolonged dry spells and shifts in seasonal precipitation have contributed to more severe and persistent droughts in diverse settings, reducing surface water availability and placing stress on groundwater resources [10,11].

Declining snowpack, earlier snowmelt, and accelerated glacier retreat are altering runoff seasonality in mountain catchments worldwide [12]. While some basins are experiencing a temporary increase in meltwater supply, many are projected to face substantial reductions in summer flows as glaciers diminish, with cascading consequences for ecosystems, agriculture, and hydropower generation. Groundwater systems, often relied upon to buffer surface water variability, are increasingly recognized as vulnerable: recharge is affected by altered precipitation and evapotranspiration patterns, while over-abstraction in water-stressed regions amplifies depletion trends [13,14].

Methodologically, advances have been achieved through detection and attribution studies, which identify anthropogenic signals in streamflow, snowpack, and drought indices, although disentangling climate-driven effects from direct human activities such as land use change, reservoir regulation, and irrigation remains complex [15,16]. Coupled climate–hydrological models, remote sensing datasets such as GRACE for terrestrial water storage, and socio-hydrological approaches have significantly enhanced our understanding of climate–water interactions on multiple scales [17,18]. Nevertheless, major uncertainties persist, particularly regarding the projection of compound extremes, feedback between climate and human water use, and the representation of groundwater processes in global and regional models [19].

Overall, the research record highlights both the urgency of addressing climate-induced water challenges and the limitations of our current knowledge. Future work is needed to strengthen the integration of physical and social sciences, improve the representation of human–environment feedback, advance water quality modeling under climate extremes, and develop actionable, uncertainty-informed guidance for policy and management [20]. As hydrological nonstationarity becomes the defining condition of the twenty-first century, the study of climate change and water resources stands at the center of global sustainability challenges [21].

2. Main Contributions to This Special Issue

The papers showcased in this second edition of “Climate Change and Hydrological Processes” cast a wide net as they explore these issues, combining case studies, methodological advances, and regional comparisons. Together, they underscore both the progress that we have made in diagnosing hydrological change and the challenges that still lie ahead.

Using historical cartography, remote sensing, statistical trend tests, and GIS-based morphometric indices, Radu and Comănescu (contribution 1) document a long-term simplification of the Ialomița riverbed (Romania): a shift from braided/multi-threaded channel patterns toward a sinuous single-thread channel, accompanied by narrowing, incision, a reduced area of active and bankfull channels, reduced migration of the thalweg, and so on. Global warming (increasing temperature, rising precipitation) is evident in climatic data; human interventions (dams, embankments, gravel extraction, land use expansion) are intensifying these transitions. The authors project that unless river restoration or management is implemented, degradation will continue in the short-term.

In their study, Ajin et al. (contribution 2) develop flood susceptibility maps for the Buzău River catchment in Romania. They use four modern ensemble boosting methods (AdaBoost, CatBoost, LightGBM, and XGBoost), combined with multi-tier feature selection and explainable AI (XAI) tools (e.g., SHAP values) to pinpoint which factors matter most. The models perform very well (AUC ~0.97 for CatBoost), and key predictors turn out to be the slope, distance from rivers, topographic wetness index, and land use/land cover. The novelty is in combining high predictive performance with interpretability, which helps in applying such models for planning and risk mitigation.

In their article, Dobrică et al. (contribution 3) examine a lake in Romania (part of the Razim–Sinoe complex) that has undergone substantial hydrological modifications, including canalization, polder construction, changing connections to the Black Sea, and irrigation infrastructure. After human intervention and then the cessation of irrigation, the lake dried in 2020. The authors trace chemical evolution: decreasing salinity, a decline in sapropelic mud production, and changing inputs of fresh vs. saline water. It presents a cautionary tale of how hydrological connectivity and human changes (both direct infrastructure and indirect management) can alter chemical and ecological processes to the same extent that volumetric changes can.

The paper by Popescu and Bărbulescu (contribution 4) focuses on the Vărbilău River catchment (Romania), which is prone to flash floods and terrain instability. Using GIS-based travel-time modeling, they assess how accessibility to emergency intervention units is affected under normal and hazard-constrained conditions (e.g., blocked roads, steep terrain). The study quantifies changes in response times, identifies vulnerable zones in terms of accessibility, and points to how infrastructure planning should consider dynamic hazard conditions. In effect, the paper extends the flash-flood context from susceptibility mapping to operational readiness and resilience.

Ma et al. (contribution 5) investigate patterns in rapid alternations between drought and flood (DFAAs) from 1970 to 2019 in the Heilongjiang River Basin (China) and how vegetation growth responds with delays. Key findings: drought→flood events increased over time; flood→drought events decreased. Vegetation responds with lags (3–4 months in spring–summer, ~3 months in summer–autumn). These lags are important for predicting ecosystem impacts, achieving food security, and designing early warning systems.

Ande et al. (contribution 6) compare multiple precipitation datasets (satellite, gauge, merged) for the Godavari basin (India), apply bias correction, and build predictive models (Random Forest, M5P, etc.) to forecast streamflow under CMIP6 scenarios. They find that merged products such as MSWEP often outperform others and that machine learning models offer competitive performance if carefully tuned and validated. Their methodological comparative approach offers practical guidance for hydrological modeling under climate change.

In their article, Taheri et al. (contribution 7) review AI-based methods (neural networks, tree-based, kernel/support vector methods, and hybrid models) for the estimation of evapotranspiration (ET). They discuss the limitations of classical methods (e.g., Penman–Monteith) in data-poor contexts and the promise of data-driven models. They warn, however, of the dangers of overfitting, variable selection inconsistency, interpretability, and the need to embed physical constraints. They call for the standardization of inputs, better integration of remote sensing, and hybrid approaches combining AI with physical models.

Wavelet analysis, Granger causality, and singular value decomposition (SVD) are used in (contribution 8) to determine statistical time-lagged relationships between winter Arctic sea ice anomalies and subsequent spring precipitation anomalies over China. The authors identify key sea ice regions (Barents, Kara, East Siberian, Chukchi Seas) whose anomalies influence atmospheric circulation, jet stream positioning, and precipitation patterns in China. They find that including sea ice variability enhances the explanatory power of precipitation models beyond classical climate indices such as ENSO, PDO, and AO. This study adds an intriguing large-scale teleconnection dimension to the hydrology–climate nexus.

3. Concluding Remarks

Through the quality and diversity of its eight articles, this Special Issue, “Climate Change and Hydrological Processes, 2nd Edition” paints a remarkably balanced and forward-looking portrait of hydrological science under climate stress. The combination of retrospective diagnostics, methodological innovation, and forward projections enables both a deeper understanding of this topic and paths toward actionable forecasting.

The key takeaway is that hydrological vulnerability is dynamic. Risk zones shift, channel forms evolve, extreme behaviors change, human interventions modulate and sometimes redirect climate signals, and the very tools that we use to map susceptibility must evolve, too.

This Special Issue reminds us that future progress will be achieved not only by using stronger predictive models but by integrating dynamical change, uncertainty, intervention modeling, and socioecological relevance. As we move forward, dialog between modelers, field scientists, decision makers, and stakeholders will become ever more critical.