Seven Steps in Modelling a Catchment - Reprise

 Recently I delivered a post where I discussed the the steps in doing a catchment analysis, based on the previous recent experiences on the topic.  The seven steps are in brief:

1. Clarify the scope of your analysis and gather the information
2. Analyse the catchment geomorphology
3. Analyze all the available data
4.  Make the simulations plan and setting up the simulations 
5. Running the simulations
6.  Showing and discussing the results and their reliability
7.  Deploy all what done for Open Science

Another step, actually is building or choosing  the model, which previously was given for granted (so actually the 7 steps can be 8 but this obviously does not really matters. During the GWS2022 I went further in developing a presentation in three parts and I deployed it, 

• Steps 1, 2, 3 (slides, video)
• Step 4          (slides, video)
• Step 5, 6, 7  (slides, video)

 

Seven Steps Into Catchments analysis by Hydrological models

 First step: Overall

Define the overall scope of the analysis. (e.g. Blöschl et al., 2019)

Document about the literature existing on the catchments. Including papers appeared in any type of studies.  Choose a large set of  performances indicators (depending on the model use and objectives) (Addor et al., 2017).  Set a strategy for assessing the results uncertainty and variability. (e.g. Clark et al., )

Second step: Geomorphology

Extract the catchment from DEM by state-of-art  analysis tools (as TauDEM or the Horton Machine Toolbox). Investigate if the surface catchment corresponds to the effective catchment (i.e. if including/excluding karst is an option). Control the surface water network extension (talk at the GSS2021). Analyze the presence and the number of lakes/reservoirs. Pay attention to endorheic catchments. Discuss the catchment connectivity.

Third step: Hydrological Data

Analyze the time series of available data and make a comparison  between rainfall and runoff amounts. Analyze any other time series or map time series, like  map of snow and evapotranspiration (Abera et al., 2017). Define the calibration set and the validation set.

Fourth step: Modelling Setup

Setup the space partitioning in hydrologic response units  (e.g. Dal Molin, 2021). Discuss the data density required (or possible) to give robust results or, viceversa, reduce your objectives to something achievable with the data available.   Choose the model among existing ones for adequacy not for legacy (Addor, 2019).  Choose a model structure as hypothesis zero (Fenicia and Kavetski, 2021, VimeoVideo).*  Setup the modelling solution (MS). Choose the better modelling structure (Clark et al., 2011).  Analyze the MS  parameters and discuss their variability.  Plan the model runs thinking to open science protocols (Hall et al., 2021). 

Fifth step: Modelling Execution

Executing the model, including discharges, snow and ET. Annotate the model performances issues. 

Sixth step: Results Delivery

Show the results appropriately (discharge analysis is not the only one quantity to watch at).  Analyze the performances of indicators. Add comments and discussion. Assess results confidence.

Seventh step: Deployment

Deploy the results for open science and public discussion (e.g. Hall et al., 2021). 

Notes

*With regards to the modelling structure, if you are using ODEs for modelling, consider a standard way to visualize and describe the model structure. As many know, the Extended Petri Net can be a sound way to do it. 

Some Slides and Videos about the above topics:

• Seven Steps in Modelling I-III Overall Analysis,, Geomorphology, The Data (Vimeo2023)
  
• (Vimeo2022)
• 4 - Modelling setup, Calibration/Validation 
• (Vimeo2022)
• 5-6-7 Executing, Delivery of the results , Preparing for Open Science 
• (Vimeo2022)

References

Abera, W., G. Formetta, and L. Brocca. 2017. “Modeling the Water Budget of the Upper Blue Nile Basin Using the JGrass-NewAge Model System and Satellite Data.” Hydrology and Earth System Sciences. http://nora.nerc.ac.uk/id/eprint/517346/.

Addor, Nans, Andrew J. Newman, Naoki Mizukami, and Martyn P. Clark. 2017. “The CAMELS Data Set: Catchment Attributes and Meteorology for Large-Sample Studies.” Hydrology and Earth System Sciences 21 (10): 5293–5313.

Addor, N., and L. A. Melsen. 2019. “Legacy, Rather Than Adequacy, Drives the Selection of Hydrological Models.” Water Resources Research 55 (1): 378–90.

Blöschl, Günter, Marc F. P. Bierkens, Antonio Chambel, Christophe Cudennec, Georgia Destouni, Aldo Fiori, James W. Kirchner, et al. 2019. “Twenty-Three Unsolved Problems in Hydrology (UPH) – a Community Perspective.” Hydrological Sciences Journal 64 (10): 1141–58.

Clark, Martyn P., Andrew G. Slater, David E. Rupp, Ross A. Woods, Jasper A. Vrugt, Hoshin V. Gupta, Thorsten Wagener, and Lauren E. Hay. 2008. “Framework for Understanding Structural Errors (FUSE): A Modular Framework to Diagnose Differences between Hydrological Models.” Water Resources Research, Water Sci. Appl., 44 (12): 2135.

Clark, Martyn P., Dmitri Kavetski, and Fabrizio Fenicia. 2011. “Pursuing the Method of Multiple Working Hypotheses for Hydrological Modeling: HYPOTHESIS TESTING IN HYDROLOGY.” Water Resources Research 47 (9). https://doi.org/10.1029/2010wr009827.

Dal Molin, Marco. 2021. “Improvement and Application of Flexible Frameworks for Modelling Regional Streamflow Variability.” Edited by Marco Schirmer Fabrizio Fenicia. Ph.D., Université de Neuchâtel.

Fenicia, Fabrizio, and Dmitri Kavetski. 2021. “Behind Every Robust Result Is a Robust Method: Perspectives from a Case Study and Publication Process in Hydrological Modelling.” Hydrological Processes 35 (8). https://doi.org/10.1002/hyp.14266.

Hall, Caitlyn A., Sheila M. Saia, Andrea L. Popp, Nilay Dogulu, Stanislaus J. Schymanski, Niels Drost, Tim van Emmerik, and Rolf Hut. 2021. “A Hydrologist’s Guide to Open Science.” Hydrol. Earth Syst. Sci. https://doi.org/10.5194/hess-2021-392.

Reservoirology

The current idea in many modern modelling systems (at catchment scale) is that the hydrology of control volumes can be reduced to a set of interconnected reservoirs (Fenicia et al., 2008; Tague et al., 2010; Clark et al., 2008; Hrachowitz et al., 2013). Each one of these reservoir can be thought as “well mixed”, meaning that each water reservoir acts like a chemical reactor where all what comes in is uniformly distributed across the reservoir and perfectly mixes (and instantaneously too) with everything already present. Some of these reservoirs have a geographical identification (as happened in the geomorphologic unit hydrograph, eg. Rinaldo and Rodriguez-Iturbe, 1996), some others have a functional reason and we could call them “embedded”, or with no-geographical reference. They are used especially to disentangle processes, to attribute the right travel time to water, and, more prosaically, to get right quantitative adjustements to the various outputs, discharge, first of all. 

This way of working is quite necessary (but remind Todini’s adage: first they had a linear reservoir -Sherman, 1932 -, then they introduced a sequence of linear reservoirs - Nash, 1957, after, they made the reservoirs non linear, Dooge, 1959; finally was a mess, Sagawara, 1967) but the separation (or the composition, it depends from the point of view) of the domain in parts is somewhat that remains to be demonstrated.

In using these simplifications  nobody of the researchers mentioned goes through a direct simplification of coupled (hydrological) equations at the finer scale and coarse grain them^1, but directly adopts the paradigm of reservoirs and validate it, nowadays technologically, by using a set of GOFs (goodness of fit) indicators. 

This system is, IMO, intrinsically weak but I, like the others, adopted it. A statistical theory is missing, and I believe that it will arise from the travel time theories. 

In the revamp of  these reservoirs' theories, the attention to the spatial distribution of the reservoirs should find again its place. Distinguishing, “vertically”, reservoirs as canopy, surface, vadose zone and groundwater storage is a necessity that is widely recognised and should be deployed. Reservoirs “lateral” aggregation (by using convolutions or following new paradigms) is the follow up (see also Rigon et al., 2016a). Embedded chains of reservoirs should be also necessary, as invoked by those who study small catchments experimentally (e.g. Birkel, 2011). All should be investigated carefully, and complexity added after implementing “ad hoc” experiments, or using appropriate datasets. 

This scenery would not be complete if these models would limit themselves to consider just the forecasting of discharges. They should also convey a reasonable set of processes to close the water budget. Next would be to include appropriate simplifications of the energy budget, a necessary companion. The latter, however, was never tried so far in coarse grained models.

Notes

^1 Perhaps Paolo Reggiani et al, 1998 tried it in a generic way, making experience on Gray’s previous work, and Todini did it, his own way with Topkapy, i.e. Liu and Todini, 2002; see also Todini, 2007 and my talk here. Paolo's work is certainly to be reconsidered.

References

Birkel, C., Soulsby, C., & Tetzlaff, D. (2014). Developing a consistent process-based conceptualization of catchment functioning using measurements of internal state variables. Water Resources Research, 50(4), 3481–3501. http://doi.org/10.1002/2013WR014925

Clark, M. P., A. G. Slater, D. E. Rupp, R. A. Woods, J. A. Vrugt, H. V. Gupta, T. Wagener, and L. E. Hay (2008), Framework for Understanding Structural Errors (FUSE): A modular framework to diagnose differences between hydrological models, Water Resour. Res., 44, W00B02, doi:10.1029/2007WR006735.

Dooge J,  (1959) - This reference was suggested by Ezio Todini, but I did not find it (the one talking of non-linear reservoirs).

Fenicia F, Savenije HHG, Matgen P, Pfister L, 2008. Understanding catchment behavior through stepwise model concept improvement. Water Resour. Res. 44(1): W01402. ISSN 0043-1397. doi:10.1029/2006WR005563. 

Gray, W., Lijennse, A, Kolar, R.L, Blain, C.A., Mathematical tools for changing spatial scales in the analysis of physical systems, CRC Press, Boca Raton, 1994

Hrachowitz, M., Savenije, H., Bogaard, T. A., Tetzlaff, D., & Soulsby, C. (2013). What can flux tracking teach us about water age distribution patterns and their temporal dynamics? Hydrology and Earth System Sciences, 17(2), 533–564. http://doi.org/10.5194/hess-17-533-2013

Liu and Todini (2002), Towards a comprehensive physically-based rainfall-runoff model, Hydrology and Earth System Sciences, 6(5), 859–881

Nash, J.E., 1958, The form of the instantaneous unit hydrograph, IUGG General Assembly of Toronto, Vol III, IAHS pub. no. 45, 1141-121. 

Reggiani, P., M. Sivapalan, and S. M. Hassanizadeh (1998), A unifying

framework of watershed thermodynamics: 1 Balance equations for mass, momentum, energy and entropy and the second law of thermodynamics, Adv. Water Resour., 22(4), 367–398.

Rinaldo A & Rodríguez-Iturbe I, 1996. Geomorphological theory of the hydrological response. Hydrol. Process. 10(6): 803–829. ISSN 1099-1085. doi:10.1002/(SICI)1099- 1085(199606)10:6<803::AID-HYP373>3.0.CO;2-N. 

Rigon R., Bancheri M., Formetta G., & de Lavenne, A. (2015). The geomorphological unit hydrograph from a historical-critical perspective. Earth Surface Processes and Landforms, n/a–n/a. http://doi.org/10.1002/esp.3855

Rigon R., Bancheri M, Green T., Age-ranked hydrological budgets and a travel time description of catchment hydrology,Hydrol. Earth Syst. Sci. Discuss., doi:10.5194/hess-2016-210, in review, 2016.

Sherman, L. K., Streamflow from rainfall by the unit hydrograph method, Eng. News-Record 108, 501-505, 1932. 

Sugawara, 1967, The flood forecasting by a seried storage type model, IAHS Publication no. 85, 1-6

Tague, C., & Dugger, A. L. (2010). Ecohydrology and Climate Change in the Mountains of the Western USA - A Review of Research and Opportunities. Geography Compass, 4(11), 1648–1663. http://doi.org/10.1111/j.1749-8198.2010.00400.x

Todini, E. (2007). Hydrological catchment modelling: past, present and future. Hess, 11(1), 468–482.

Di chi è l'acqua/To whom water belongs ?

This is the presentation given at the Green Week held in Trento between  March 4 and March 6 2016 in Trento.  The topic is the role of quantitative hydrology in understanding how to manage the resource. Everybody see the possible conflicts. Hydrologists give a quantitative basis for discussion. Slides are in Italian.

Click on the figure to see the presentation. References are linked inside the slides-show.

CLIMAWARE project (again) at the Anticipation Conference

Here in Trento, the last three days was organised a Conference on Anticipation promoted by the UNESCO chair on Anticipatory systems. I participate as auditor in one of the sessions, and the experience was, at the same time interesting and different. Interesting and different because, it was disclosing to me the view of economists and sociologists on the topic, and I had to sinchronize my brain on their language and quirks. However, there was also a session on Anticipation in Engineering in which we presented the project CLIMAWARE. Presentation was given by Lavinia Laiti, and I think it gives a consistent overview of the project.

Clicking on the above image, you will have access to the slides view. Enjoy!