I made this because of the large endorsement Python had among hydrologist and because:

- its object oriented structure is much more firm than the R one.
- Besides, Python seems to be easy to learn by engineering students.
- Some of my colleagues seem to agree to converge toward the use of Python in their classes

Here it is the foreseen schedule of the class:

Corso di Idrologia 2017

Legend: T - Theoretical lecture - L - Laboratory class (this can include theoretical parts, but mostly students will exercise with tools)

- T - Introduction to the class.
- Water on Earth (optional).
- The hydrological cycle (YouTube)
- The hydrosphere parts (optional).
- Modern hydrological information (YouTube).
- The water budget (YouTube).
- The energy budget (YouTube)
- Fluxes, Reservoirs, Residence times (optional)
- The Budyko scheme.
- Further Readings
- T - A terrain analysis primer.
- Elevation, Slopes, Curvatures. (YouTube)
- River network delineation.
- Contributing areas.
- Geomorphic laws (optional)
- Further Readings
- L - Introduction to QGIS. Introduction to the JGrasstools in OMS.
- T - A little of Statistics and Probability.
- Descriptive Statistics
- Location indicators (YouTube)
- Form and Shapes of data
- Tests of hypothesis
- Stationarity And Ergodicity
- Further readings on Statistics (see also here)
- Probability's Axioms (optional)
- Univariate distributions
- Further readings on Probability
- L - Delineation of catchments' characteristics with JGrasstools and QGIS.
- T - Precipitations. Mechanisms of formation of precipitation. Ground based statistics. Extreme precipitations.
- See the points 6 to 11 in this post.
- Further readings (Point 1-5 and 17 in the Precipitations' post)
- L - Intro to Python - Loading/reading files. Time series and their visualisation. (See Notebook 0 an 1 here.)
- T - Extreme precipitation statistics (parameters' estimation)
- See points 12-15 in the Precipitations post
- Further readings (Point 1-5 and 17 in the Precipitations' post)
- L - Estimation of extreme distributions parameters. (See Notebook 2 to 5 here.)
- T - Radiation (YouTube 2017).
- The Sun (YouTube 2017)
- Stefan-Boltzmann law and radiation spectrum (YouTube 2017)
- Sun to Earth (YouTube 2017)
- Coping with latitude and longitude (YouTube 2017)
- Atmospheric Absorptions (YouTube 2017)
- Clouds (YouTube 2017)
- Coping with terrain (YouTube 2017)
- Long wave radiation (YouTube 2017)
- Table of symbols
- Further readings:
- Corripio, J. G. (2002). Modelling the energy balance of high altitude glacierised basins in the Central Andes. Ph.D Dissertation, 1–175.
- Corripio, J. G. (2003). Vectorial algebra algorithms for calculating terrain parameters from DEMs and solar radiation modelling in mountainous terrain. Int. J. Geographical Information Science, 17(1), 1–23.
- Formetta, G., Rigon, R., Chávez, J. L., & David O. (2013). Modeling shortwave solar radiation using the JGrass-NewAge system. Geoscientific Model Development, 6(4), 915–928. http://doi.org/10.5194/gmd-6-915-2013
- Formetta, G., Bancheri, M., David, O., & Rigon, R. (2016). Performance of site-specific parameterizations of longwave radiation. Hydrology and Earth System Sciences, 20(11), 4641–4654. http://doi.org/10.5194/hess-20-4641-2016
- L - Estimation of shortwave and longwave radiation in a catchment (data, executables, sim files are available through Zenodo. Who is interested in the source code and further information, plese refers to GEOframe or the Github GEOframe components site).
- A brief rehearsal of the matter given by Michele Bottazzi (M.B.) (YouTube)
- Estimation of solar radiation with JGrass-NewAGE components (YouTube) by M.B. Part I
- Estimation of solar radiation with JGrass-NewAGE components by M.B. (YouTube) Part II
- T - Spatial interpolation of environmental data
- Some concepts about the spatial representation of environmental quantities (YouTube 2017)
- Simple Kriging (YouTube 2017) (This is more or less covered in Raspa work from page 75)
- More on variance and covariance (YouTube 2017)
- Further readings
- L - Practical spatial interpolation of rainfall and temperature.
- T - Water in soils. - Darcy-Buckhingham law- Soil water retention curves and hydraulic conductivity.
- Soils (YouTube 2017)
- Texture and Structure of soils (YouTube 2017)
- Aquifers (optional)
- Definitions (YouTube 2017)
- Darcy-Buckingham law (YouTube 2017)
- Soil Water RetentionCurves (YouTube 2017)
- Hydraulic Conductivity
- Further readings
- L - Numerical experiments on soil water retention curves and hydraulic conductivity.
- T - Richards equation and its extensions.
- Mass conservation - Richards equation (YouTube2017)
- Pedotransfer Functions (Used during the lab 15) - (YouTube2017)
- Simplifications of Richards 1D in a hillslope (YouTube2017)
- Phenomenology of infiltration (according to Richards equation) in a hillslope (YouTube2017)
- Macropores (Optional)
- Water Tables equations (Optional)
- Water in soils measures
- Notation Summary
- Further Readings (or view)
- L - Experiments with a Richards 1D simulator
- Readme First
- Data
- Notebooks: Input; Outputs
- Explanation of the sim file
- Executable, Source Code and Data
- Another Richards 1D solver
- T - Elements of theory of evaporation from water and soils - Dalton. Penman-Monteith. Priestley-Taylor
- The Thermodynamical origin of evaporation (YouTube2017)
- Vapor transport by turbulence (YouTube2017)
- Evaporation from free water surfaces (YouTube2017)
- Evaporation from soils (YouTube2017)
- Penman-Monteith (YouTube2017)
- Further Readings
- T - Estimation of evaporation and Transpiration at hillslope scale
- Transpiration (YouTube2017)
- Estimation of ET over large areas (YouTube2017)
- Evaporation and Transpiration from the energy budget (YouTube2017)
- L - Estimation of evaporation and transpiration at catchment scale
- T - Water movements in a hillslope and runoff generation
- T - On the impact of climate change on the hydrological cycle

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