Wednesday, July 29, 2020

Tom Gleeson shared list of teaching links for hydrology

I quote from his (his site) tweet: "Do you need online resources for emergency remote teaching of hydrology and water sustainability courses ? You can use and share these online curated resources (websites, videos, games, articles and quizzes). "
To access the document click on the image by Rarindra Prakarsa.

The document is editable and it seems that anyone can suggest modification and addition. A nice collaborative work.

Thursday, July 16, 2020

Ph.D. position available for working on the Enhancing GEOframe to deal with anthropogenic influences on the hydrologic cycle of Tevere and Adige River

The candidate will be asked to extend the GEOframe system with tools to understand the anthropogenic impacts on the hydrological cycle on Adige and Tevere basins via the integration of hydrological modelling and satellite/ground based observations. They will start from an already existing solid base (operational at the ARPAB and implemented in various catchment around the world) and tries to answer the following research questions: how much human activities is impacting mountainous region and which will be the main challenges in the future ? How water resources should be allocated to respond to the future needs ? How to best manage water resources among the competing interests ?The Ph.D. candidate will take care of the implementation of a suite of modelling solutions for the Adige and the Tevere river basin with specific focus on some subcatchments.
In general the research activities will aim to fuse information from any available source and especially remote sensing of snow, soil moisture, surface temperature, vegetation with model components of new type developed inside the GEOframe platform The project is part of a wide spectrum of collaborative research activities between UniTrento, IRPI-PG and EURAC.
The candidate will take care of implementing, besides the code, the appropriate procedures for continuous integration of the evolving source code, and s/he will be also asked to maintain a regular rate of commits to the common open platform. Despite these conditions, and being free and open source, the code will be intellectual property by the coder. This will be guaranteed also by the components-based infrastructure offered by OMS3, which allows to better define the contributions of anyone.
The implementation part will be followed, accompanied by testing activities, either for mathematical consistency, than for physical consistency with experiments and field measurements.

The Ph.D. student is intended to produce, besides working and tested codes, also at least three papers in major journals (VQR Class A), of which, at least one as first Author. Duration of the doctoral studies could be three or four years.

Further information of the policies of the Trento research group can be found:

Supervisor of the Ph.D. will be Riccardo Rigon, Christian Massari and Silvia Barbetta. Same information can be found at the AboutHydrology blog.
Information for application can be found at the AES doctoral studies page. Please pay attention to the deadline for applications.

Wednesday, July 15, 2020

Michele Bottazzi Ph.D. Thesis

Finally Michele Bottazzi finished his Ph.D. dissertation and he is going towards graduation. The Thesis has some valuable things: an overview on how to estimate evaporation and transpiration which, I think, is easy to read and plane. 

He discusses also the extension of the theory to cover canopies and catchments and provide an implementation of the model called  Prospero. In the final chapter he applies the model to a point case and to a catchment case. In both there are some interesting results to analyze.

You can access the thesis by clicking on the image above.  The slides of his defense are here. Finally below, please find the video of his defense.

Here below the discussion that followed:

If you are interested in understanding a little more about transpiration from a hydrological point of view, this is the right place to start.

Friday, July 10, 2020

Ph.D. Scholarship @ Unitrento: Assessment of social and economic impacts caused by natural hazards in mountain regions

Death tolls and economic losses from natural hazards continue to rise in many parts of the world. Only in 2018 they caused almost 12000 deaths across the world and over 130 US billion dollars of economic losses (CRED, 2018). European states are experiencing a continuous and significant burden from multiple natural disasters (Wolfgang et al., 2019): in 2016 Germany, Belgium, and Switzerland have been hit by a series of flash floods and storms causing over $2.2 billion in losses; in 2013 Storm Xaver caused to northern Europe at least 15 fatalities, dozens of injured, and more than €800 million total economic losses (e.g. Rucińska, D., 2019); the July-August 2003 European heat wave that caused a total of 70,000 deaths (e.g. Russo et al., 2019; Bouchama, 2004).  

A combination of several factors contribute to explain the increasing social and economic toll caused by natural hazards: increase in exposed assets, i.e. rising population and capital at risk (e.g. Visser et al., 2014), effects of anthropogenic climate change on climatic extremes (e.g. Donat et al., 2016; Bouwer, 2011), better impact reporting procedures (e.g. Doktycz and Abkowitz, 2019).

International agreements on disaster loss reduction (Sendai Framework for Disaster Risk Reduction 2015–2030) explicitly recognizes the benefits of multi-hazard early warning and forecasting systems (MHEW&F-S)”. In 2017 Member States of the United Nations stated the deemed need of MHEW&F-S and agreed on its the definition as integrated system that “address several hazards and/or impacts of similar or different type in contexts where hazardous events may occur alone, simultaneously, cascadingly or cumulatively over time, and taking into account the potential interrelated effects” (UNISDR, 2017). Here the term early warning (EW) is extend with the term forecasting (&F) to explicitly acknowledge that each hazard have a specific forecast lead time which can varies from minutes/hours for flash-floods, days for pluvial floods or heat/cold waves, to months for drought hazard. The scientific community also agree in the need of novel approaches and local scale models for assessing impacts caused by climate change (e.g. Schewe et al., 2019).  In order to answer to this call and to move towards a rigorous framework for multi-hazard risk assessment in this project I propose to implement a novel local scale multi-hazard impact-centered forecasting system. It aims to:
·       Quantify the three fundamental components of the risk, i.e. hazard, exposure, and vulnerability, and to combine them in a multi-hazard framework, exploiting the most recent dataset and the more appropriate models;
·        Provide timely effective warnings (not just of the hazards but also) of the most probable sectorial impacts that may be triggered by multiple hazard conditions.
The system will be unique and novel because it will be the first operative system for multi-risk quantification including:
·       a local high-resolution meteorological forecasting system with operationally runs at 1 km resolution capable to explicitly model convective phenomena;
·       a detailed and component-based open-source framework for multi-hazard quantification, locally and automatically calibrated for estimating the probability of occurrence of floods, droughts, shallow-landslides/debris-flow, heatwave/coldwaves and windstorms;
·       a new set of exposure and vulnerability layers variable in space and time to account for accounting of socio-economic changes in the risk analysis;
·       an innovative framework based on a probabilistic graphical model (Bayesian Network) dynamic in time and variable in space, which consider all the risk components (hazards, exposure, and vulnerability as in Formetta and Feyen, 2019) as stochastic variables and models all their possible interactions using probabilistic expressions. The latter will be inferred using a Bayesian learning process involving: 1) a database of reported impacts (fatalities and economic losses) occurred in the past (1980-2018) in the study area specifically organized in the project and 2) the corresponding hazard probabilities, exposure, and vulnerability at the time of the reported event (computed by using points ii and iii).

The project study area is the Trentino Alto-Adige region located in the eastern Italian Alps. The choice of the area is motivated by different reasons: i) there is no such a system currently running (this is also valid for all the others Italian regions); ii) in the near future mountain regions will be even more exposed to the occurrence of climatic extremes due to climate warning. The selected geographical domain is only a test-bed where the framework will be implemented, set up, tested, and verified against observed data for each single component, i.e. meteorological forecasting skills (against rainfall or air temperature measurements), hydrological calibration and validation (against historical measured river discharge), hydrological forecasting skills (against observed river-discharge using forecasted meteorological forcing data), historical and forecasted impacts (against reported fatalities and economic losses

Please write to for information or see directly the Department call (here)

Bouchama, A. (2004). The 2003 European heat wave. Intensive care medicine, 30(1), 1-3.
Bouwer, L. M. (2011). Have disaster losses increased due to anthropogenic climate change?. Bulletin of the American Meteorological Society, 92(1), 39-46.
Donat, M. G., Alexander, L. V., Herold, N., & Dittus, A. J. (2016). Temperature and precipitation extremes in centurylong gridded observations, reanalyses, and atmospheric model simulations. Journal of Geophysical Research: Atmospheres, 121(19), 11-174.
Doktycz, C., & Abkowitz, M. (2019). Loss and Damage Estimation for Extreme Weather Events: State of the Practice. Sustainability, 11(15), 4243.
Formetta, G., & Feyen, L. (2019). Empirical evidence of declining global vulnerability to climate-related hazards. Global Environmental Change, 57, 101920.
Rucińska, D. (2019). Describing Storm Xaver in disaster terms. International journal of disaster risk reduction, 34, 147-153.
Russo, S., Sillmann, J., Sippel, S., Barcikowska, M. J., Ghisetti, C., Smid, M., & O’Neill, B. (2019). Half a degree and rapid socioeconomic development matter for heatwave risk. Nature communications, 10(1), 19.
Schewe, J., Gosling, S. N., Reyer, C., Zhao, F., Ciais, P., Elliott, J., ... & Van Vliet, M. T. (2019). State-ofthe-art global models underestimate impacts from climate extremes. Nature communications, 10(1), 1-14

Friday, July 3, 2020

On Doing Large-Scale Hydrology with Lions: Realising the Value of Perceptual Models and Knowledge Accumulation A Review.

This is a review of the paper by Wagener, Thorsten, Tom Gleeson, Gemma Coxon, Andreas Hartmann, Nicholas Howden, Francesca Pianosi, Shams Rahman, Rafael Rosolem, Lina Stein, and Ross Woods. 2020. “On Doing Large-Scale Hydrology with Lions: Realising the Value of Perceptual Models and Knowledge Accumulation.” EarthArXiv.

Since the Authors uploaded it to EarthArXiv making it available as Preprint, My review can be public too. 

The paper main statement can be formulated by saying that in global hydrology and related science there remain large areas of knowledge which could be easily explored because we have now the data and the tools to do it, but we do not do. There are unexplored geographical regions and substantially the Authors asks for a “everywhere modelling effort” by saying that as in the old maps where it was written “hic sunt leones” there are large areas on Earth whose hydrology is essentially unknown (a known unknown indeed).  They have a point.  The paper's language is good and the writing pleasant but I would prefer a more simpler organization which focuses more on the two or three main statements. A sound knowledge of literature is interesting for the general reader but it is not in my opinion used to focalise the issues. On the contrary there are a lot of paragraph that, reporting the state of art, let with the impression that there is no problem at all. This does not mean that those paragraph, read alone, are not well written, informed, or interesting but they do not serve to goal of highlighting well the issues. You get easily the main ideas but I had difficulties to grasp the whole paper contents, even after many readings. For instance, "the lions" refers to the known-unknown, I cited above, or to an unknown-unknown with regards models' structure and their granularity how the manuscript seems to indicate sometimes ?

I understand that the Authors invoke two main solutions for the issues they rise:
  • a larger sharing of perceptual models of catchments
  • better strategies for organization of the current knowledge which is seen as not efficient with systematic metadata, development of tools for knowledge harvesting, standardization of databases and data in general.

These two directions of work are remarked within two sections, and I would say that without this separation, I would have hard time to obtain this synthesis.

Understanding what a “perceptual model” is,  is  part of making the reader understand the concepts supported by the Authors. What a perceptual model is, however, is not clarified in the paper, and could remain obscure to non-hydrologists.   What is that ? Can they define it more precisely ? Is it a drawing ? Is a set of relations ? Has it a specific mathematical representation ? Overall,  I believe a little more should be said on what a hydrological models at large scale are, without fall into an annoying classification or taxonomy but discussing what these models are or should be. Recently, Frigg et al (2020) tried a general  discussion on scientific models from the point of view of philosophy of science which could help to clarify what these models are.

The domain of the paper is a little slippery. While the title of the paper and the main statements look at the large-scale hydrology, sometimes, the Authors indulge in observations that have to do with a finer granularity of the processes than the one required by this type of modelling. Maybe there is a lack of definition of what “large-scale hydrology” is, especially with respect to the methods, and the granularity of the processes described. The Authors should adopt or try one. Not that it cannot be partially deduced from what it is written in the present manuscript, but this knowledge should not be given for granted in readers who come from other disciplines.

A final comment have to be made on the advancement of science.  The Authors cite Popper and Kuhn but I do not think their topic is in the same domain, which is in my opinion in the area of theories and interpretations of  a theory, but in the application of a given theory (or a set of theories) to the cases to which it is thinkable they could apply. Therefore I would esclude a “Kuhnnian” direction here. Eventually the topic here could be the practice of Popperian theory of falsification. If the repeated application of the models reveals unsatisfactory in fact, it could bring to the necessity of a new theory or a new model.
But I guess this grows too philosophical for me and I do not want to pursue this argument further, I concede I do not have the understanding required to treat it properly.

Therefore, I like very much the issue the paper rises and I think that the topic is of interest for hydrologists and a wider audience. However, I think that the Author should make the effort to reframe and refocus a little bit more their manuscript.

Below some sparse comments on specific statements.

Page 6 - Line 1 “… for new scales of management …” What doest it means ? It seems to me, whatever it means, that it diverts the attention from the fact that “it also contains hydrologic lions" is the point by moving the thinking to the lateral issue of scale of analysis.

Page 8 - Line 5  - I think the main problems with pure data assimilation is that it tends to be erroneously inductive, without any   hypothesis to test. Some phrases that follow in the subsequent pages, seem to support that science advancement is not hypotetical-deductive but inductive.  This is, maybe, a marginal point in the context of the paper, but, because I think it is wrong, I ask the Authors to be more clear on it.

Page 9 - Line 12 - “As Mc Donnell et al. …” - Yes, correct, but: how this statement is coupled with large-scale hydrology ? Does it means a support to the reductionist view that, if we describe well all the hillslope of the world, we have the best large scale-model ? Secondly, it is used to support  inductivism ? Or to deny it ?

Page 9 - Line 44 - “How much can we reduce model uncertainty … “. This phrase conveys the idea that  large scale models should be constrained by some expected large scale behavior. But what does have to do with the hydrologic "lions" ?  I was expecting from the geographical example that these "lions" were referring to unexplored geographical areas, where data or modelling are scanty, not to the general aspect of modelling. Do the Authors mean that we apply our models to large areas but nevertheless we do not know well their validity and foundations (other Lions indeed) ? If it is so, maybe the concept of lions is not so clear too me, and the authors should clarify more its extent.

Page 9 - Line 48 - "Some studies have shown that simpler …” This regards again the “inner Lions” of the large scale hydrological modelling. It is a critique of the way such model are actually done and verified. The Authors suggest (probably with some unexpressed example in mind) a directions to better characterizing those models.  I agree with the single statements, but I do not feel the topic is properly prepared and introduced/discussed in the paper.

Page 9 - Is, to their knowledge, Boorman 1995 the unique paper that deals with conceptualised parameters of a model ?  It was 25 years ago though. Or did I misunderstand what you want to say ?

Page 9 - Line 55 - “It has been widely discussed … “ Frankly, I do not buy this statement. It is not exactly true that complex models, as those, for instance that solve partial differential equation have this degrees of freedom in practice. There are, at least,  two reasons for that: 1 - Models that conserve mass (and BTW energy) usually cannot be stretched to reproduce any measured time series as accurately as one desires (for instance, a model that solves Richards equations cannot reproduce macropores flow with any characterization of the soil parameters); 2 - Even if, in principle, optimization of the parameters of a spatially distributed model can involve arbitrary and different values of the parameters in each site, in practice the calibration is extremely time consuming and usually unfeasible even with a ten of them.  Therefore the possibility to really explore a large set of parameters in high dimension is simply not possible. I’ve tried it several times. If anything, the strength of physically based spatially distributed models is their physics,  the capability to accomodate heterogenous inputs and obtain spatially distributed outputs.

Page 11 - Line 8 - “Supervisors” I would better say “oral communication”, like for instance the one given by Tom Dunne.

Page 12 - line 51 - Among the experiences that merit to be cited in categorizing the hydrological description, at least two should be cited: the CF ( convention and the Basic Model Interface (now in its second version:


Frigg, Roman, and Stephan Hartmann. 2020. “Models in Science.” In The Stanford Encyclopedia of Philosophy, edited by Edward N. Zalta, Spring 2020. Metaphysics Research Lab, Stanford University.