Data integrity and model structural adequacy are fundamental determinants of predictive reliability in hydrologic and hydraulic systems. This special volume solicits contributions that critically examine data acquisition, preprocessing, uncertainty characterization, and curation, alongside rigorous model calibration, validation, and selection across physically based, data-driven, and hybrid (AI-/ML-integrated) frameworks. Submissions addressing flume-scale experiments, catchment-scale field observations, process-based numerical simulations, and machine learning applications are encouraged, with emphasis on parameter identifiability, equifinality, sensitivity analysis, and cross-validation under data-scarce and heterogeneous conditions. The volume particularly welcomes studies that integrate multi-source datasets and advance reproducible workflows, benchmarking protocols, and transferable methodologies to enhance robustness in water resource modelling.

Data integrity and model structural adequacy are fundamental determinants of predictive reliability in hydrologic and hydraulic systems. This special volume solicits contributions that critically examine data acquisition, preprocessing, uncertainty characterization, and curation, alongside rigorous model calibration, validation, and selection across physically based, data-driven, and hybrid (AI-/ML-integrated) frameworks. Submissions addressing flume-scale experiments, catchment-scale field observations, process-based numerical simulations, and machine learning applications are encouraged, with emphasis on parameter identifiability, equifinality, sensitivity analysis, and cross-validation under data-scarce and heterogeneous conditions. The volume particularly welcomes studies that integrate multi-source datasets and advance reproducible workflows, benchmarking protocols, and transferable methodologies to enhance robustness in water resource modelling.