Hydrology Paper of the Day @kinarnicholas.bsky.social and colleagues on extending the range of the compensation heat pulse (CHP) method: the use of two probes in conjunction with novel mathematics; numerical and lab sand column experiments; and comparisons. www.sciencedirect.com/science/arti...

Hydrology Paper of the Day @christost.bsky.social @georgiapapachar.bsky.social on the Nash‑Sutcliffe loss: a measure with clear theoretical underpinnings, and the concept of Nash-Sutcliffe linear regression in context of an estimation framework in hydrology, machine learning and geosciences.

Hydrology Paper of the Day @gescilam.bsky.social and colleagues on communication in an age of water scarcity: issues and challenges identified in conjunction with the scientific community at EGU24; data from interviews and surveys; and the need for trust, appropriate audiences, equity, and clarity

Happy to contribute to this community paper on science communication in hydrology within the IAHS HELPING decade. We propose the FUSS framework: messages should be Few, Unambiguous, Short, and Structured. Link to paper: www.tandfonline.com/doi/full/10.... #WaterScience #ScienceCommunication

arXiv📈🤖 Learning with the Nash-Sutcliffe loss By Tyralis, Papacharalampous

Hristos Tyralis, Georgia Papacharalampous: Learning with the Nash-Sutcliffe loss https://arxiv.org/abs/2603.00968 https://arxiv.org/pdf/2603.00968 https://arxiv.org/html/2603.00968

Hydrology Paper of the Day @christost.bsky.social @georgiapapachar.bsky.social on consistent loss functions: explicit definitions and examples; how these loss functions are utilized to evaluate point predictions; loss functions and transformations; and applications to climatology and hydrology.

Happy (😊) to share our latest paper on variable transformations in consistent loss functions: 👉 doi.org/10.1016/j.kn... 👉 authors.elsevier.com/c/1mSkA3OAb9... #LossFunction #MachineLearning #Prediction @christost.bsky.social

Applying transforms within loss functions motivated our new paper with @georgiapapachar.bsky.social. Free access: authors.elsevier.com/a/1mSkA3OAb9... Available here: doi.org/10.1016/j.kn...

Hydrology Paper of the Day @georgiapapachar.bsky.social on estimating uncertainties associated with gridded precipitation data products derived from satellite remote sensing: non-linear ensemble learning and quantile loss functions; predictor variable reduction; stacking; and assessing performance.

Very happy (😊) to share our new paper, in which we introduce deep Huber quantile regression networks: 👉 doi.org/10.1016/j.ne... 👉 authors.elsevier.com/a/1knnz3BBjK... #DeepLearning #MachineLearning #PredictiveUncertainty