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| Created: | Jul 05, 2026 at 7:04 p.m. (UTC) | |
| Last updated: | Jul 05, 2026 at 7:59 p.m. (UTC) | |
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Abstract
This resource contains the four Jupyter notebooks used in the hands-on learning activities of the HydroLearn module "How Good Is Your Forecast? Evaluating Deterministic and Ensemble Streamflow Models", developed for the
WMO capacity-building course series. The notebooks walk learners through the full evaluation chain on the Leaf River catchment (Mississippi, USA) with the HyMOD conceptual model:
(1) setting up and calibrating HyMOD and computing deterministic metrics (RMSE, NSE, KGE, PBIAS);
(2) generating a forecast ensemble and converting it to probabilities of threshold exceedance;
(3) deterministic and categorical event verification (contingency table, POD, FAR, CSI, ROC, performance diagram); and
(4) probabilistic ensemble verification (Brier score, Brier skill score, reliability diagram, rank histogram, CRPS).
The notebooks can be run using the JupyterHub environment available through HydroShare (Open with: CUAHSI JupyterHub) or on Google Colab; the first cell of each notebook fetches the required helper files automatically when they
are not already present. All data are bundled: the 40-year Leaf River daily forcing and streamflow file (LeafCatch.dat) and two documented helper modules (hymod_tools.py, nb_style.py). No accounts or external downloads are needed. Total hands-on time is about 60 to 90 minutes at the fundamentals level. Spanish and French versions of the notebooks exist in the course repository.
This resource is part of the learning activities in the HydroLearn module "How Good Is Your Forecast? Evaluating Deterministic and Ensemble Streamflow Models": https://edx.hydrolearn.org/courses/course-v1:UniversityofIowa+WMO_01+2026/about
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This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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