Application of Hydrological Modelling to Water Resources Management

1. The Need for Decision-Ready Hydrological Modelling

The demand for reliable hydrological information is increasing throughout the water sector as climate-driven non-stationarity reduces the predictability of hydrological systems. Nearly two decades after the widely cited declaration that “stationarity is dead” [1], its practical consequences for water resources planning are increasingly apparent. Climate change is altering precipitation patterns, evapotranspiration rates, snow and glacier contributions, soil-moisture retention, groundwater recharge, and streamflow seasonality [2,3]. It is also increasing the frequency and severity of hydrological extremes [4]. In many regions, floods and droughts have become recurring indicators of an increasingly unstable water cycle, resulting in cascading effects on water supply, infrastructure safety, agriculture, energy systems, ecosystems, and public health [5]. The extent of this exposure is significant: recent global assessments estimate that approximately 1.81 billion people, or 23% of the world’s population, are directly exposed to 1-in-100-year floods, with disproportionate impacts observed in low- and middle-income countries [6].

This instability has transformed expectations for hydrological models. Traditionally, models were evaluated based on their ability to reproduce observed hydrographs, achieve water balance closure, or represent local hydrological responses. Currently, models are increasingly required to support decision-making, such as issuing flood warnings, allocating limited water resources, regulating groundwater abstraction, prioritizing urban flood mitigation, operating reservoirs under uncertainty, and designing adaptation strategies for future climate scenarios. The central question is no longer solely whether a model can simulate hydrological processes, but whether it can provide credible, interpretable, and actionable evidence to inform water resources decisions. Recent advances in operational hydrological forecasting are exemplified by the integration of artificial intelligence methods, which enable reliable multi-step-ahead predictions in ungauged and poorly gauged basins [7,8]. These developments have demonstrated particular effectiveness for extreme flood events [9] and are increasingly being applied to streamflow and other hydrological variables. Such progress highlights the accelerating evolution of forecasting capabilities in the hydrological sciences [10].

This shift forms the central premise of the Special Issue “Application of Hydrological Modelling to Water Resources Management,” which emphasizes the practical application of hydrological models rather than their development alone. This Special Issue solicited contributions that operationalize physically based, conceptual, lumped, distributed, deterministic, stochastic, data-driven, and hybrid modelling approaches, particularly when integrated with artificial intelligence, machine learning, optimization, probabilistic frameworks, remote sensing, decision-support systems, or digital twin platforms. Submissions were especially encouraged where the resulting evidence supports Sustainable Development Goals 6, 7, 13, and 15. The first edition comprises nine contributions that vary in method, scale, and geographic focus, yet collectively underscore a single message: hydrological models achieve their greatest value when they serve as tools for evidence-based, resilient, and sustainable water resources management.

2. From Hydrographs to Decisions: The Rise of Operational Hydrological Intelligence

Hydrological modelling is evolving from retrospective process diagnosis toward operational and policy-relevant decision support [11]. Recent advances indicate that artificial intelligence can deliver reliable multi-step-ahead forecasts [12], digital-twin frameworks support near-real-time basin monitoring and management [13,14], and hybrid modelling improves the local applicability of large-scale hydrological models [15]. These innovations do not diminish the significance of understanding hydrological processes; instead, they broaden the means by which observations, simulations, remote sensing, and data-driven methods are translated into actionable hydrological intelligence.

This transition is particularly important given that water resources management decisions are often made with limited data availability. This challenge is most pronounced in low- and middle-income regions, where populations face the highest levels of hydrological risk and monitoring infrastructure remains insufficient [16]. Actions such as flood warnings, reservoir releases, drought restrictions, groundwater regulations, urban drainage interventions, and emergency responses are often required before events fully unfold. In these contexts, the value of a model depends not only on retrospective accuracy but also on timeliness, spatial relevance, effective communication of uncertainty, interpretability, computational efficiency, and institutional usability [17]. A clear illustration is the emergence of prediction-to-map approaches, which generate rapid, spatially explicit flood information by integrating observed time series with numerical model outputs. The focus is thus shifting from forecasting discharge at isolated points toward spatially explicit hazard information that supports emergency response, infrastructure planning, and risk communication [18].

This operational perspective is reflected in the present Special Issue, where the published papers demonstrate how hydrological modelling can support urban inundation analysis, adaptive stormwater infrastructure, vulnerability prioritization, groundwater assessment, water allocation, and socio-hydrological governance. These applications are discussed thematically in the following sections as contributions to resilient and sustainable water resources management.

3. The Physics–AI Convergence: From Competing Paradigms to Hybrid Modelling

A central objective in applied hydrological modelling is to integrate process-based understanding with data-driven methodologies [19]. Physically based and conceptual models provide hydrological structure, process interpretability, and compliance with water-balance constraints [20]. However, these models are constrained by parameter uncertainty, structural simplifications, computational requirements, and incomplete boundary condition specification [21]. Conversely, data-driven models can capture complex nonlinear relationships from large and heterogeneous datasets, but often lack interpretability, are insufficiently grounded in hydrological theory, and demonstrate limited robustness when extrapolated beyond their training domain [22]. The primary challenge is to synthesize the strengths of both modelling paradigms in a scientifically rigorous manner that supports robust decision-making.

Hybrid modelling addresses this challenge by integrating process-based models, machine learning, remote sensing, uncertainty quantification, and explainable artificial intelligence. These approaches retain process structure through hydrological models, incorporate data-driven components to improve predictive accuracy or correct systematic biases, and utilize satellite and reanalysis datasets to compensate for limited ground observations in data-scarce regions. Probabilistic methods are used to quantify and communicate uncertainty. Recent developments in differentiable and hybrid physics–AI hydrological frameworks indicate that neural networks can be embedded within process-based modelling architectures to learn internal flux corrections or regionalized parameters, while maintaining physical interpretability through explicit model states, fluxes, and parameters [23].

The contributions in this Special Issue reflect this broader hybridization trend in applied forms. Yeşilyurt and Onuşluel Gül [24] evaluated satellite-derived meteorological inputs across SWAT, XGBoost, WGAN, and hybrid modelling frameworks for climate-change-driven streamflow simulation in a data-scarce region. Their study demonstrates how process-based hydrological modelling, machine learning, satellite observations, climate projections, explainability analysis, and uncertainty assessment can be combined to improve streamflow simulation where conventional monitoring networks are limited. The value of this contribution lies not only in model comparison but also in demonstrating how hybrid modelling can support climate-impact assessment and water resources planning under data scarcity.

Kim et al. [25] approach model reliability from a complementary perspective by comparing DWAT, PRMS, and TANK models across watersheds with different physical and land-use characteristics. Their findings reinforce a core principle of applied hydrology: model performance is context-dependent, and no single model structure should be assumed to be universally optimal. This point is especially important as advanced AI and hybrid approaches become more common. Algorithmic sophistication does not remove the need for watershed diagnosis, calibration, validation, sensitivity analysis, uncertainty assessment, and multi-metric evaluation. The practical value of hybrid modelling therefore lies not only in improved prediction but also in producing models that are interpretable, transferable, uncertainty-aware, and fit for the water-management decision being supported.

4. Observation-Rich but Gauge-Poor: Remote Sensing, Transfer Learning, and Data-Scarce Hydrology

Many of the regions most exposed to floods, droughts, and water scarcity are also those with the weakest hydrometric monitoring networks [7]. This creates a persistent asymmetry in water resources management: the places where reliable hydrological information is most urgently needed are often the places where streamflow, precipitation, groundwater, snow, soil moisture, and evapotranspiration observations are most limited or discontinuous. Data scarcity therefore remains not only a technical modelling problem but also a barrier to equitable climate adaptation and water-security planning.

At the same time, hydrology is becoming increasingly observation-rich beyond the traditional gauge network [26]. Satellite precipitation, evapotranspiration, soil moisture, snow cover, vegetation indices, land-surface temperature, surface-water extent, altimetry, and reanalysis products now provide unprecedented opportunities to support hydrological modelling in poorly gauged regions [27]. However, these products do not automatically resolve data scarcity. They introduce their own biases, retrieval uncertainties, scaling limitations, temporal inconsistencies, and region-specific errors. Their value depends on how carefully they are selected, corrected, and embedded within hydrological modelling frameworks.

This is why recent progress in data-scarce hydrology has increasingly emphasized transfer learning [8], regional pre-training [28], hybrid modelling [29], explainable artificial intelligence [30], and uncertainty-aware prediction [31]. These approaches are designed to improve model robustness when local observations are insufficient, while reducing the risk that satellite-derived or reanalysis inputs are used as direct substitutes for ground truth without adequate validation. In this context, data fusion is not simply a matter of adding more variables. It requires physically meaningful feature selection, scale-aware calibration, uncertainty assessment, and evaluation under hydrologically relevant extremes.

Recent studies in data-scarce hydrological forecasting have shown that satellite-derived hydroclimate information and advanced neural architectures can support long-range streamflow and flood prediction where conventional monitoring networks are limited [26,28,31,32,33,34,35,36]. These studies have investigated attention-based deep learning with geo-spatiotemporal mesoscale inputs, feature extraction from satellite-derived hydroclimate data, Informer-based transfer learning, SageFormer-based flood forecasting, and probabilistic Transformer frameworks for uncertainty-aware long-range flood prediction. Collectively, this research trajectory reflects a broader movement in hydrology toward combining remote sensing, deep learning, transferability, and uncertainty quantification to improve prediction reliability in poorly gauged and data-limited basins.

Within the present Special Issue, this theme is represented most directly by Yeşilyurt and Onuşluel Gül [24], who evaluated satellite-derived meteorological inputs across SWAT, XGBoost, WGAN, and hybrid modelling frameworks for climate-change-driven streamflow simulation in a data-scarce region. Their study is important because it treats satellite data not as a simple replacement for station observations but as part of a broader modelling strategy involving process-based simulation, machine learning, hybridization, climate projections, explainability, and uncertainty assessment. This contribution illustrates how remote sensing and hybrid modelling can support climate-impact assessment and water resources planning in regions where dense hydrometric infrastructure is unavailable.

The broader implication is that data-scarce hydrology should not be viewed as a marginal problem affecting only a few under-monitored basins. It is a central challenge for global water resources management. As climate risks intensify, the ability to build credible models from incomplete, heterogeneous, and uncertain data will strongly influence the fairness and effectiveness of flood preparedness, drought management, reservoir planning, groundwater protection, and adaptation investment. Remote sensing and transfer learning are, therefore, not merely technical innovations; they are part of the scientific infrastructure required for more inclusive and resilient water management [37].

5. Extremes as the Stress Test: Floods, Droughts, and Urban Resilience

Hydrological extremes are among the clearest tests of model usefulness. A model that performs well under average conditions but degrades during floods, droughts, or rapid hydrological transitions may have limited value for water resources management. Floods require models that can represent rainfall–runoff generation, drainage capacity, overland flow, river hydraulics, compound hazards, infrastructure interactions, and exposure [38]. Droughts require attention to precipitation deficits, evapotranspiration demand, soil-moisture memory, groundwater storage, reservoir dynamics, water withdrawals, and socio-economic vulnerability [39]. In both cases, uncertainty-aware modelling is essential because management decisions often need to be made before the full event is observed and before all consequences are known.

Recent advances in AI-enhanced flood forecasting, rapid flood mapping, probabilistic drought prediction, and hybrid flood modelling demonstrate that hydrological models are increasingly designed not only for retrospective simulation but also for early warning, emergency response, infrastructure assessment, and resilience planning [40]. This transition is particularly important in urban environments, where intensifying rainfall, expanding impervious surfaces, ageing drainage networks, and increasing exposure of people and assets can transform hydrological extremes into infrastructure and governance crises.

Within this Special Issue, the contributions concentrate mainly on the urban flood problem and together trace a progression from simulation to intervention to spatial prioritization. Yu et al. [41] address the simulation layer using the Grid-Based Urban Drainage System (GUDS), which employs a 2D Weighted Cellular Automata framework and is coupled with EPA-SWMM. Benchmarked against the commercial XP-SWMM model, GUDS showed relatively small differences in maximum inundation depth, inundation area, and propagation speed, while maintaining numerical stability under steep-slope conditions where XP-SWMM was more prone to instability. This quantified precision–stability trade-off, together with the open and GIS-integrable design of GUDS, highlights an important point for decision support: model usefulness depends not only on analytical precision but also on stability, transparency, adaptability, and operational interoperability.

Cha et al. [42] move from flood simulation to runoff control at the source by evaluating smart blue–green roof systems as adaptive stormwater-control infrastructure. Based on a monitored rooftop-scale case study in Gimpo, Republic of Korea, their results show that active water-level control coupled with subsurface storage can substantially reduce rooftop runoff compared with both conventional roofs and passive blue–green roof systems. The advantage of the smart system became particularly evident under heavier rainfall, where active storage management helped maintain high runoff-reduction performance, whereas passive systems were more vulnerable to storage exceedance. This contribution demonstrates how to integrate monitoring, hydrological analysis, decentralized storage, and real-time control to achieve climate-resilient urban stormwater management.

Lee et al. [43] complete this urban flood-resilience sequence by shifting the question from how inundation occurs to where intervention should be prioritized. Their entropy-weighted TOPSIS framework ranks urban sub-watersheds according to hydrological vulnerability using indicators derived from hydrological modelling and spatial analysis. This approach is important because flood mitigation resources are usually limited, and decision-makers require transparent, objective, and spatially explicit methods for identifying areas where interventions are most urgently needed.

Read together, these studies show that urban flood resilience cannot be reduced to flood-depth simulation alone. It requires joint capacity to simulate inundation reliably and stably, to intervene adaptively through distributed infrastructure, and to prioritize vulnerable areas under finite budgets. The broader implication is that hydrological modelling must be more closely linked to urban planning, green infrastructure design, early warning systems, and climate-resilient investment strategies. In this sense, hydrological extremes act as stress tests not only for models but also for the institutions and infrastructures that depend on their outputs.

6. Beyond Surface Flows: Green Water, Groundwater, and Hidden Resilience

Water resources management has traditionally concentrated on the most visible and directly allocable components of the hydrological cycle, particularly river discharge, reservoir storage, surface-water withdrawals, and engineered supply systems. Sustainable management, however, increasingly demands attention to less visible but equally critical components of hydrological resilience, including baseflow, green water flow and storage, groundwater storage, ecosystem water use, and surface–subsurface exchange [44]. These hidden components rarely appear in headline allocation figures, yet they govern low-flow persistence, drought buffering, ecological functioning, and the long-term reliability of water supply.

Several papers in this Special Issue contribute to this broader understanding. Tong and Wan [45] address a long-standing challenge in low-flow hydrology: conventional baseflow estimation methods often have limited capacity to account for both evapotranspiration losses and human water withdrawals. By reformulating the nonlinear storage–discharge relationship into a physically based baseflow equation that incorporates these processes, their study provides a more management-relevant basis for estimating low-flow support under climatic and anthropogenic disturbance. This is important because baseflow sustains rivers during dry periods, supports aquatic ecosystems, and influences drought resilience. A baseflow formulation that explicitly accounts for evapotranspiration and human withdrawals can therefore improve the assessment of eco-hydrological benefits, dry-season water availability, and climate-change impacts in river basins.

Zhang et al. [46] broaden this perspective from a single hydrological flux to basin-scale water allocation. Using the SWAT model to simulate the spatiotemporal distribution of blue and green water resources in the Taolai River Basin, they show that allocation planning cannot be reduced to surface-water availability alone. Spatial heterogeneity, supply–demand balance, ecological requirements, green-water contributions, and transferable blue-water potential all shape the form of a defensible basin-scale strategy. This argument is particularly important in inland and water-stressed basins, where ecological protection, agricultural demand, regional development, and hydrological limits must be balanced within a constrained water budget.

Groundwater completes the picture of hidden resilience. Kishor et al. [47] reviewed two decades of published research on the integration of MODFLOW with artificial neural networks for groundwater flow modelling and classified this integration into three functional strategies: surrogate modelling, parameter estimation, and post-processing or error correction. Their synthesis is balanced and timely: physically based groundwater models remain indispensable for process interpretation and management credibility, while machine learning can support computational acceleration, calibration, and predictive improvement in complex nonlinear settings. This contribution is especially relevant where groundwater is increasingly expected to buffer surface-water variability under climatic uncertainty and anthropogenic pressure.

Taken together, these contributions show that resilient water resources management requires models that represent not only surface runoff and flood response, but also the slower and less visible components of the hydrological system. Baseflow, green water flow and storage, groundwater storage, and surface–subsurface exchange determine how basins absorb climatic variability, sustain ecosystems, and support human water use during periods of stress. By bringing these hidden components into modelling and allocation frameworks, hydrological models can provide a more complete basis for long-term water security, drought preparedness, and ecosystem-oriented management.

7. Digital Twins and Socio-Hydrological Intelligence: Modelling with People in the Loop

Water resources management is not only a physical modelling problem; it is also a governance problem. Hydrological information must be interpreted by institutions, communicated to stakeholders, negotiated across competing sectors, and implemented through policy, infrastructure, and behavioural change [48]. The recognition that human and hydrological systems co-evolve—that water shapes society while society reshapes water—has made socio-hydrology an important frontier in water science [49]. In parallel, digital-twin river-basin concepts are increasingly being proposed as platforms for connecting monitoring, forecasting, early warning, scenario analysis, data assimilation, model integration, and stakeholder interaction within shared decision environments [50]. The implementation of this paradigm adheres to a defined integration hierarchy: a digital model provides a static representation of the system; a digital shadow enables unidirectional real-time data flow for continuous monitoring; and a fully developed digital twin establishes a bidirectional cyber–physical feedback loop, facilitating adaptive decision-making and optimal control [51]. This trajectory suggests that future hydrological modelling will increasingly operate within socio-technical systems, rather than as isolated simulation engines.

The most methodologically novel contribution in this Special Issue is provided by Batista et al. [52], who applied natural language processing and a large language model to examine how stakeholder sentiment relates to hydrological drought conditions in the semi-arid state of Ceará, Brazil. Their study analyzed 36 sets of water-management-body meeting minutes from 2007 to 2024, including 17 records from drought periods and 19 from normal periods, and used Llama 3.2 3B for sentiment analysis. By comparing sentiment scores with reservoir volume conditions, the authors found that institutional perceptions and discussion themes differed between drought and normal phases, with greater water availability associated with more positive sentiment. The reported positive relationship between sentiment and reservoir volume indicates that stakeholder narratives can respond measurably to hydrological conditions.

The significance of this study extends beyond the Ceará case. It shows that qualitative institutional records—meeting minutes, stakeholder discussions, and governance narratives—can be transformed into socio-hydrological indicators that complement physical observations and model outputs. This is important because drought management, water allocation, and adaptation planning are not determined solely by hydrological states. They are shaped by institutional perception, public trust, governance capacity, stakeholder conflict, and the legitimacy of decisions.

This contribution therefore expands the definition of decision support in hydrological modelling. Future water resources platforms may need to integrate physical simulations, real-time monitoring, policy documents, stakeholder narratives, sentiment dynamics, and institutional memory within a common analytical frame. Such integration is most valuable where hydrology and governance are inseparable: drought management, transboundary basins, contested allocation systems, and climate-vulnerable regions where public trust and institutional capacity strongly condition adaptation outcomes. In this sense, the socio-hydrological perspective complements the physics–AI convergence discussed earlier: hydrological modelling is expanding not only toward deeper physical representation, but also toward the social systems that determine how model evidence is interpreted and used.

8. Lessons from the First Edition: What This Collection Adds to the Field

The nine papers in this Special Issue collectively show that application-oriented hydrological modelling is not defined by a single model type, dataset, or management problem. Its value lies in the capacity to connect hydrological understanding with decisions that are spatially explicit, uncertainty-aware, institutionally usable, and relevant under changing climatic and socio-economic conditions. Several cross-cutting lessons emerge from the first edition.

First, the model application must be purpose-driven. The appropriate modelling approach depends on the management question, the dominant hydrological processes, the spatial and temporal scales, data availability, tolerance for uncertainty, and the decision context. A model suitable for long-term basin-scale water allocation may not be suitable for real-time flood forecasting, and a model useful for groundwater scenario analysis may not be appropriate for urban inundation mapping. The first lesson is therefore one of fit-for-purpose modelling: model complexity, structure, and evaluation criteria should be selected based on the decision being supported.

Second, hybrid modelling is becoming a central paradigm in applied hydrology. The contributions in this edition show that process-based models, satellite-derived data, machine learning, comparative model evaluation, and statistical or AI-based correction methods can be combined to improve prediction, interpretation, computational feasibility, and decision relevance. This does not imply that physical understanding is being replaced by data-driven methods. Rather, it shows that the most useful modelling frameworks increasingly combine hydrological reasoning with data-adaptive tools.

Third, data scarcity and uncertainty must be treated as core design conditions, not secondary limitations. Many water-management decisions are made in regions with incomplete, inconsistent, or spatially sparse monitoring networks. Remote sensing, transfer learning, multi-source calibration, probabilistic forecasting, explainable AI, and hybrid model structures are therefore becoming essential components of operational hydrology. The challenge is not only to obtain more data, but to evaluate data quality, propagate uncertainty, and communicate model confidence in forms that decision-makers can use.

Fourth, urban hydrology is emerging as a critical frontier for climate adaptation. Urban flood resilience requires more than simulating inundation depth. It requires stable and transparent drainage modelling, assessment of adaptive and nature-based infrastructure, spatial prioritization of vulnerable areas, and integration with urban planning and emergency management. As rainfall intensifies and impervious surfaces expand, hydrological models must increasingly support intervention design, infrastructure operation, and climate-resilient investment.

Fifth, resilient water resources management requires attention to less visible components of the hydrological system. Baseflow, green water, groundwater storage, soil moisture, evapotranspiration, and surface–subsurface interactions strongly influence drought buffering, ecosystem functioning, low-flow persistence, and long-term water security. The papers in this edition show that sustainable management cannot rely only on visible surface flows or short-term runoff response; it must also account for the slower hydrological processes that determine resilience during periods of stress.

Sixth, decision support must engage governance. Hydrological information becomes useful only when it can be interpreted, communicated, trusted, and acted upon by institutions and stakeholders. The integration of socio-hydrological analysis, stakeholder narratives, and large language models points toward a new generation of governance-aware hydrological intelligence. This does not replace physical modelling; it extends decision support to include the social and institutional systems through which water decisions are made.

Taken together, these lessons suggest that the field is moving toward a broader definition of hydrological modelling. The most valuable models will not necessarily be the most complex but those that are scientifically credible, transparent about uncertainty, compatible with available data, interpretable to users, and connected to management action. In this sense, this first edition contributes to a central shift in water resources science: from modelling hydrological systems in isolation toward modelling water decisions within coupled climatic, environmental, technological, and governance contexts.

9. A Roadmap for the Second Edition: Toward Trustworthy, Scalable, and Equitable Hydrological Modelling

The continuation of this Special Issue into a second edition is timely. The first edition demonstrates a strong interest in application-oriented hydrological modelling and shows that the field is moving rapidly toward more integrated, data-rich, uncertainty-aware, and decision-relevant approaches. At the same time, it identifies several directions for further research to strengthen the scientific credibility, operational value, and societal relevance of hydrological modelling for water resources management.

A first priority is the continued development of hybrid physics–AI modelling. Future contributions should advance approaches that improve predictive performance without sacrificing interpretability, physical consistency, or management credibility. Differentiable hydrological models, physics-informed machine learning, uncertainty-aware deep learning, explainable artificial intelligence, and hybrid process–data frameworks are likely to play increasingly important roles in climate-impact assessment, streamflow forecasting, flood and drought prediction, groundwater simulation, and water allocation. However, these methods should be evaluated not only by accuracy metrics but also by robustness, transferability, uncertainty representation, and usefulness for decision-making.

A second priority is the development of meaningful water resources digital twins. Digital twins should not be treated merely as visualization platforms or static replicas of physical systems. A scientifically useful water resources digital twin should integrate monitoring, data assimilation, hydrological and hydraulic models, uncertainty analysis, scenario simulation, early warning, stakeholder interfaces, and feedback mechanisms between physical and digital systems. Achieving this requires not only technical development but also data governance, interoperability, institutional coordination, transparent model documentation, and equitable access to digital infrastructure.

A third priority is stronger modelling of the water–energy–food–ecosystem nexus. Reservoir operation, hydropower generation, floating photovoltaic systems, irrigation, environmental flows, groundwater pumping, desalination, drought adaptation, and ecosystem protection increasingly interact within shared water systems. Hydrological models should therefore be more closely connected to optimization, multi-objective decision-making, economic assessment, ecological indicators, energy-system constraints, and SDG-oriented evaluation. This is particularly important where water management decisions create trade-offs among supply reliability, renewable-energy generation, food production, ecosystem integrity, and climate resilience.

A fourth priority is implementation in data-scarce and climate-vulnerable regions. Many regions facing severe water insecurity require scalable modelling tools, but they also require monitoring capacity, open and interoperable data, local technical expertise, institutional trust, and transparent communication of uncertainty. Future research should therefore address not only model sophistication but also model accessibility, reproducibility, capacity building, and the translation of model outputs into usable information for planners, operators, communities, and policy-makers.

The central challenge for the second edition is therefore not simply to present more advanced models but to demonstrate how hydrological modelling can become more trustworthy, scalable, and equitable. Future studies are encouraged to show how models are calibrated, validated, interpreted, communicated, and implemented in real decision contexts. The ultimate value of hydrological modelling lies not only in technical innovation but in its ability to support just, resilient, and sustainable water resources management under changing climatic and societal conditions. Crucially, as these advanced hybrid frameworks scale to massive cyber–physical deployments, future research must elevate architectural frugality to a primary design principle, navigating the security–sustainability paradox to ensure that computational complexity does not impose prohibitive water, energy, and environmental footprints [53].

10. Conclusions

The first edition of the Special Issue “Application of Hydrological Modelling to Water Resources Management” demonstrates that hydrological modelling is entering a decision-ready era. Models are no longer expected only to reproduce hydrological behaviour; they are increasingly expected to support planning, adaptation, risk reduction, infrastructure operation, water allocation, governance, and sustainability. This shift reflects a broader transformation in water resources science: from modelling hydrological systems as isolated physical processes to modelling water decisions within coupled climatic, environmental, technological, and institutional contexts.

The papers collected in this edition reflect this transition across a diverse set of applications. They show how physically based baseflow equations, satellite-assisted and hybrid streamflow modelling, comparative model evaluation, urban drainage simulation, smart stormwater infrastructure, blue–green water allocation, socio-hydrological sentiment analysis, multi-criteria vulnerability assessment, and hybrid groundwater modelling can contribute to practical water resources management. Their collective contribution lies not in a single method, model, or case study, but in demonstrating how hydrological modelling can be translated into evidence for management action.

At the same time, recent advances in global AI flood forecasting, differentiable modelling, digital twin river basins, physics-informed and physics-embedded learning, hybrid process–machine learning approaches, and probabilistic drought forecasting demonstrate that the wider field is advancing rapidly. The challenge for the coming years is to ensure that these advances remain scientifically credible, physically meaningful, uncertainty-aware, transparent, ethically governed, and useful for real-world decisions. Hydrological models will have their greatest impact when they can connect data, process understanding, uncertainty, visualization, stakeholder interpretation, and policy action.

The Guest Editors sincerely thank all authors for their valuable contributions, the reviewers for their careful and constructive evaluations, and the editorial team of Water for their professional support throughout the development of this Special Issue. We hope that this first edition will serve as a useful reference for researchers, practitioners, and decision-makers working toward resilient, equitable, and sustainable water resources management under a changing climate. Its success provides a strong foundation for the second edition, which will continue to promote high-quality research on the application of hydrological modelling to water resources management.