Reliable precipitation (P) and evapotranspiration (ET) datasets are essential for understanding, modeling, and predicting hydrological processes. Recently, the number of products has expanded rapidly, including products based on observations, remote sensing, reanalysis, land surface models, machine learning, and hybrid approaches. These products allowed for new opportunities, while also raising challenges related to evaluation, uncertainty characterization, and fitness for purpose. Addressing these challenges requires methods that assess hydrological consistency, transferability across regions, and requirements for diverse applications. This session brings together advances in the development, evaluation, integration, and application of P and ET datasets across scales, climates, and hydrological contexts. We invite contributions in the following:
1 Evaluation and benchmarking of P and ET datasets
How can we systematically evaluate the growing number of datasets across hydroclimates and spatial scales? Which statistical and hydrological metrics are most appropriate for assessing quality and suitability? How can we quantify and propagate uncertainty and biases in water-related analyses? Can community datasets and evaluation frameworks enhance comparability and reproducibility?
2 Approaches for dataset development and integration
How can hydroinformatics, including artificial intelligence, uncertainty quantification, data fusion, and data assimilation, improve datasets? How can cloud computing, high-performance computing, and open-source workflows facilitate evaluations? What role can FAIR principles accelerate the development and adoption of new datasets?
3 Applications in hydrological modeling and assessments
How does uncertainty in datasets impact model calibration, evaluation, and prediction? Which datasets are most appropriate for various applications, including flood forecasting or drought monitoring? How can multiple datasets be combined to improve hydrological consistency and reduce uncertainty in changing environmental conditions?
4 Emerging datasets and future directions
How can next-generation satellite missions, reanalysis products, and hybrid datasets improve P and ET estimation and evaluation? What methods are required to evaluate increasingly high-resolution and multi-source datasets? How can we establish standards, benchmark protocols, and collaborative platforms to support transparent evaluation and fitness for purpose assessment?