Saturday, May 25, 2019

The Hydrology Lab Class 2019

This is the lab class material for the 2019 Hydrology course of the University of Trento. Most of the material is in Italian. The more traditional part of lectures can be found at the following link.

Go to the Lectures

Used Software

There is no engineering without using models. During the class will be used various open source softwares and resources:
All these resources are free, besides being open. For installations requirements, please see the GEOframe winter school material here. For understanding a little more about this material, please look at "Getting started with Docker OMS and Jupyterlab" post.

Lab Classes and Lectures

Python resources for hydrologists

2019- 06-06  - Exercise with Python
2019 - 05-10  Lab Work on Precipitation extremes
2019-05 -19 - OMS and GEOframe
2019 -05-24  - Lab work on infiltration (Richards 1d)
2019-06 - 7&12&14  - Evaporation and transpiration
 This will be for another year -2D Richards simulations and runoff production:
  • Implementing a 2D simulation with Richards
  • Github
  • Other material and readings
  • Python notebooks
Go to the Lectures.

Saturday, May 18, 2019

Francesco Serafin Ph.D. Defense and Thesis

Francesco Serafin is not anymore a Ph.D. student bu a doctor! His work concerned mainly the structure of the Object Modelling System Version 3 especially in two directions: improving the researchers modelling experience and improving the end users experience. You can learn directly from his video how.

The defense had a more than forty minutes of presentation and the same period of discussion. In order, you can find below, the presentation, the YouTube video of the the defense and the discussion (the latter in Italian).
  • The presentation (by clicking on the Figure):

  • The Video of the defense

  • The Discussion (In Italian)

      • Last but not least, the Dissertation is here

      Tuesday, May 7, 2019

      On Hydrological models structure

      In many papers that deal with semi-distributed hydrological modelling, it is argued about the models structure, a topic which becomes even more relevant when people talk about uncertainty and attribute to the model structure an error that is named epistemic error. But what is the model structure is rarely discussed in depth. Butts et al. (2004) discussed it citing the book by K. Beven (GS): “Beven (2000) breaks down the development of a hydrological model into the following steps:
      1. The Perceptual model: deciding on the processes
      2. The Conceptual model: deciding on the equations
      3. The Procedural model: developing the model code
      4. Model calibration: getting values of parameters
      5. Model validation: confirming applicability and accuracy”
      To these phases Clark et al (2011) also add:
           6. Characterizing the model uncertainty
      which is a requirement that certainly contemporary modelling demands. 

      Evidently, the concepts that are relevant for defining what a model structure is are the first two: "The selection of specific perceptual and conceptual models determines the model structure. " However, when parameters of the model are fixed, they also become part of the model structure. Models often are continuous function of parameters, nevertheless different classes of parameters, especially in non linear models, can trigger qualitatively different dynamics.
      Model structure includes a whole range of choices and assumptions made by the modeller either explicitly or implicitly in applying a hydrological model. Examples of different model structures include:
      • different process choices and descriptions
      • coupling of the processes
      • numerical discretisation
      • representations of the spatial variability-zones, grids, sub-catchments, etc.
      • element scale and sub-grid process representations including distribution functions, different degrees of lumping, effective parameterisation, etc.
      • interpretations and classifications of soil type, geology land use cover, vegetation, etc.
      There is a great variety of models and mathematical approaches to cope with the above issues. For limiting the discussion, let’s assume to concentrate on those models which are implemented as systems of ordinary differential equations. These models differs, in practice for:
      1. the number of equation (let’s called them places according to our classification of such systems)
      2.  the interactions between places (represented by the relative adjacency matrix)
      3. the form of fluxes laws
      4. the values parameters assume
      In particular, point (1) above deals both with the number of processes and the discretisation of the landscape in hydrologic units in space (called representative elementary watersheds REW, e.g. Reggiani et al., 1999 or Hydrologic response units, HRUs, Burges and Kampf, 2008). A recent tradition tried to build a heuristic about how to select appropriately these elements (e.g. Clark et al., 2011; Fenicia et al., 2011, Fenicia et al., 2014, Fenicia et al., 2016).
      Once these hydrologic heuristics are applied, we eventually find ourselves with the nude set of equations, and it could be interesting to see if there exist methods that can discover and classify the main properties of the dynamical systems which depends on their structure. This problem, indeed, has received a lot of attention in system and control theory (see for e.g. Ljung, 1999 and references therein), mostly to autonomous linear systems.

      Some of the aspects, in this case is the discover of T-invariants and P-invariants, or, in a less obscure language, of loops and set of correlated quantities that remains globally (i.e. their sum) stationary (i.e. Gilbert and Heiner, 2006). Other aspects regards reachability, i.e. the prior understanding if a certain distribution of the state variables can be obtained. All these aspect are well dealt within traditional books in system and control theory. Unfortunately the resulting structure of hydrological models is usually non-autonomous (the system are open) and non-linear. All aspects that make investigations more complicate, but probably not unfeasible. A lot of digging in literature and research is necessary though.


      Saturday, May 4, 2019

      Daniele Penna's invited presentation at the EGU Wien General Assembly

      At recent EGU General Assembly in Vienna, Daniele Penna was invited to give a talk on recent developments of tracers hydrology.  He was so kind to share with me the pdf of his slides, that you can find below by clicking on the Figure.
      Below, I am inserting the main papers cited.
      • Allen, S. T., Kirchner, J. W., Braun, S., Siegwolf, R. T. W., & Goldsmith, G. R. (2019). Seasonal origins of soil water used by trees. Hess, 1199–1210.
      • Bargues Tobella, A., Hasselquist, N. J., Bazié, H. R., Nyberg, G., Laudon, H., Bayala, J., & Ilstedt, U. (2017). Strategies trees use to overcome seasonal water limitation in an agroforestry system in semiarid West Africa. Ecohydrology, 10(3), e1808–11.
      • Benettin, P., Queloz, P., Bensimon, M., McDonnell, J. J., & Rinaldo, A. (2019). Velocities, Residence Times, Tracer Breakthroughs in a Vegetated Lysimeter: A Multitracer Experiment. Water Resources Research, 55(1), 21–33.
      • Beyer, M., Koeniger, P., Gaj, M., Hamutoko, J. T., Wanke, H., & Himmelsbach, T. (2016). A deuterium-based labeling technique for the investigation of rooting depths, water uptake dynamics and unsaturated zone water transport in semiarid environments. Journal of Hydrology, 533(C), 627–643.
      • Bowling, D. R., Schulze, E. S., & Hall, S. J. (2016). Revisiting streamside trees that do not use stream water: can the two water worlds hypothesis and snowpack isotopic effects explain a missing water source? Ecohydrology, 10(1), e1771–31.
      • Brinkmann, N., Seeger, S., Weiler, M., Buchmann, N., Eugster, W., & Kahmen, A. (2018). Employing stable isotopes to determine the residence times of soil water and the temporal origin of water taken up by Fagus sylvaticaand Picea abiesin a temperate forest. New Phytologist, 219(4), 1300–1313.
      • BUCKLEY, T. N. (2005). The control of stomata by water balance. New Phytologist, 168(2), 275–292.
      • Dawson, T. E., & Ehleringer, J. R. (1991). Streamside trees that do not use stream water. Nature, 350(6316), 335–337.
      • Dubbert, M., & Werner, C. (2018). Water fluxes mediated by vegetation: emerging isotopic insights at the soil and atmosphere interfaces. New Phytologist, 221(4), 1754–1763.
      • Dubbert, M., Caldeira, M. C., Dubbert, D., & Werner, C. (2019). A pool‐weighted perspective on the two‐water‐worlds hypothesis. New Phytologist, 222(3), 1271–1283.
      • Evaristo, J., Kim, M., Haren, J., Pangle, L. A., Harman, C. J., Troch, P. A., & McDonnell, J. J. (2019). Characterizing the Fluxes and Age Distribution of Soil Water, Plant Water, and Deep Percolation in a Model Tropical Ecosystem. Water Resources Research, 511(4), 605–21.
      • Matthias Sprenger, H. L. K. G. M. W. (2016). Illuminating hydrological processes at the soil-vegetation-atmosphere interface with water stable isotopes, 1–31.
      • North, G., & Nobel, P. (1995). Hydraulic conductivity of concentric root tissues of Agave deserti Engelm. under wet and drying conditions, 130, 47–57.
      • Oerter, E. J., Siebert, G., Bowling, D. R., & Bowen, G. (2019). Soil water vapour isotopes identify missing water source for streamside trees. Ecohydrology, 168(344), e2083–36.
      • Orlowski, N., Breuer, L., Angeli, N., Boeckx, P., Brumbt, C., Cook, C. S., et al. (2018). Inter-laboratory comparison of cryogenic water extraction systems for stable isotope analysis of soil water. Hydrology and Earth System Sciences, 22(7), 3619–3637.
      • Penna, D., Hopp, L., Scandellari, F., Allen, S. T., Benettin, P., Beyer, M., et al. (2018). Ideas and perspectives: Tracing terrestrial ecosystem water fluxes using hydrogen and oxygen stable isotopes – challenges and opportunities from an interdisciplinary perspective. Biogeosciences, 15(21), 6399–6415.
      • Pernilla Brinkman, E., Van der Putten, W. H., Bakker, E.-J., & Verhoeven, K. J. F. (2010). Plant-soil feedback: experimental approaches, statistical analyses and ecological interpretations. Journal of Ecology, 98(5), 1063–1073.