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Course Weekly Lecture Plan

Week Topic
1 1 Modeling Overview: important physical processes, conceptual model, physically based distributed models, the modeling steps, automated optimization tools (PEST, Python and Matlab), simulation (prediction) vs forecast, downscaling vs upscaling data, pre-cooked model examples
2 Input Preparation and Preprocessing – Temporal: common forcing data, rain/snow partitioning, ET estimation, data issues like missing data, generating future scenarios, time series basics, aggregation vs disaggregation
3 Input Preparation and Preprocessing – Spatial: DEM analysis, sub-basin delineation, flow accumulation and direction, land use, land cover and soil data, spatial interpolation, spatial data issues, model parameterization and regionalization
4 Conceptual (Single Basin) Model: HBV and GR4J, case studies from Europe
5 Distributed Modeling: mHM, case studies from Europe
6 Single Run Sensitivity Analysis
7 Parallel Run Sensitivity Analysis
8 Conventional Objective Functions, new performance metrics (SPAEF), satellite based optimization targets, calibration vs validation,
9 Gradient Based Model Calibration: local vs global search algorithms, post-processing model outputs, model evaluation, matlab optimization toolbox
10 Internal mHM internal calibration toolbox, SCE-UA, DDS etc.
11 Model Calibration using PEST toolbox and Global search algorithms: SCE-UA and CMAES, single and parallel runs, master vs slave definitions, pst, bat, rec, sen, out files
12 Uncertainty Classification and Importance Assessment (Ranking)
13 Uncertainty Quantification and Propagation through all Modelling Layers
14 Communication of the Uncertainty to the End Users. Illustrative examples and application using GLUE method (Monte Carlo based approach).
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