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.

A major challenge for humankind is to provide adequate and reliable supplies of affordable energy and natural resources. These should be obtained in environmentally sustainable ways, which is essential for economic prosperity, environmental quality, and political stability around the world. With this special issue, which reflects the scientific programme of the General Assembly 2026 of the Division on Energy, Resources and the Environment (ERE) of the European Geosciences Union (EGU), we would like to provide a holistic view of activities according to the development, assessment, and management of energy-related programmes. In addition, we provide a forum for the presentation of fundamental scientific advances and engineering developments in the understanding of resources systems. This can also cover aspects of the environmental impacts of socio-economic development.

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.

A major challenge for humankind is to provide adequate and reliable supplies of affordable energy and natural resources. These should be obtained in environmentally sustainable ways, which is essential for economic prosperity, environmental quality, and political stability around the world. With this special issue, which reflects the scientific programme of the General Assembly 2026 of the Division on Energy, Resources and the Environment (ERE) of the European Geosciences Union (EGU), we would like to provide a holistic view of activities according to the development, assessment, and management of energy-related programmes. In addition, we provide a forum for the presentation of fundamental scientific advances and engineering developments in the understanding of resources systems. This can also cover aspects of the environmental impacts of socio-economic development.