Those who are engineers or policy makers deep in their heart but live in Academia - IV

 It could be argued that a hydrologist is not merely a scientist but also a technologist or engineer who must manage water resources. This broader perspective expands the scope of our work to include the many aspects of life where water plays a crucial role—agriculture, urban planning, energy production, and more. Effective water management requires not only technical expertise but also the ability to address risks associated with floods, droughts, and the development of policies that consider the social dimensions of these challenges. Achieving long-term success in water management also demands the consensus and cooperation of the public, highlighting the importance of sociological considerations.

These technological aspects, often applied directly to end-users, rely heavily on the foundational processes that science has uncovered and translated into models. While it is common to build upon established scientific knowledge, it is also essential for practitioners to bridge the gap between basic science and practical applications. In many cases, this is not only convenient—such as when seeking grants and research support—but also necessary. The needs of society often dictate the direction of research, guiding scientists to gather and interpret the data required to address pressing issues. The current state of the art is particularly promising, as modern technologies have provided an unprecedented amount of data that must be harnessed effectively.

For academics, contributing new insights into these "more technical" but equally important topics remains a vital pursuit. Scholarly publications are expected to offer something novel, potentially groundbreaking, in their approach. Regardless the focus  being on technological/practical applications, scientific rigor, the appropriate use of tools, and the reproducibility of results are mandatory. For who wants to publish on these topics, the fundamental question always persists: Why is this work important? What is the ultimate goal?

Engineering big applications in water management encompass several critical areas, including the infrastructure for water discovery, transportation, and supply, as well as defense against water-related risks and their interactions with other hazards. These applications often transcend traditional engineering disciplines. Key concepts include adopting nature-based solutions and ensuring equity in both access to resources and protection from hazards.

When considering the broader implications of scientific advancements, it's important to recognize that certain tools and knowledge are often taken for granted. Consider the analogy of cooking: some individuals focus on building the kitchen and tools (the hardware builders), while others use these tools to create meals (the chefs). The goals of hardware builders differ fundamentally from those of the chefs. The chef's ability to prepare a dish is sometimes constrained by the availability of the right tools.

Personally, I identify more with the role of the hardware builder than with that of the chef. This distinction implies a different exposure to success. Those who rely on models created by others, utilize remote sensing products, or apply pre-packaged machine learning tools are akin to chefs, but not all are master chefs—many are simply preparing everyday meals. This is not the goal of academic life. Academics should be like chefs who meticulously select ingredients, create innovative dishes, and focus on addressing significant problems. In other words, academics are not just practitioners; they pave new paths for practice, improving lives and advancing society.

Though my focus lies elsewhere, I recognize that these new technological solutions in water management are well-suited for scholarly publications and will garner significant interest, given water's central role in Earth's ecosystems, life and human activities.

At every stage of the process, whether building or cooking, technical precision and attention to detail are paramount. Consistency is essential. Achieving the state of the art in any discipline requires years of training, engagement with experts, deep problem-solving, and continuous practice. As with any endeavor, success in hydrology engineering and related fields requires a blend of talent (1%) and discipline (99%). As someone once said, it's 99% sweat.

Those who aim to apply - III

 As a follow-up to my previous post, see also here and here, I'd like to share some additional reflections on the experience of doing hydrology in academia. I've attempted to classify different types of researchers, and below, you'll find the third part of this classification.

There are those who apply cutting-edge knowledge to case studies, which in turn reveal new and unusual dynamics. These cases should not be relegated, as often happens, to tedious and routine treatments where tools are taken for granted and hydrological models as commodities. Instead, they should be viewed as integral to the process of acquiring knowledge about the cases themselves and evaluating the effectiveness of the tools used to describe them. In these times, the ability to apply hydrological theories systematically to wide areas  open the gates to unprecedented knowledge of Earth system and its cycles well beyond episodic application of forecasting just to a hydrological event or two but trying to discern the whole hydrological behavior during droughts and floods and intermediate periods (the latter the more frequent indeed).

Examples of current applications in the standard include the use of legacy code, calibration procedures, and validation criteria. Significant academic efforts have gone into developing these codes and defining validation standards, primarily to steer clear of deep philosophical debates and unanswerable concerns from reviewers. However, as mentioned previously, building these tools is no simple task, and the process is lengthy and complex, one that you may not wish to pursue.

Can you publish any application anywhere? Certainly not. Or, if you do manage to publish it, it may simply become a drop in the ocean, unnoticed. As with any endeavor, the application must offer something novel.

This novelty lies in your ability to address fundamental questions such as: Is catchment hydrology a continuum of processes that cannot be disentangled? Can we instead identify functional components within catchments that explain spatial and temporal behaviors? Put differently, what are the dominant elements of catchment hydrology? Does the catchment exhibit specific features? What are the seasonal changes in the organization of hydrological flows? What are the effects of land use and land cover? What are the primary time scales of catchment responses (e.g., response time)? Are catchment responses variable across space and time? Where do models fail to provide reasonable answers (potentially indicating a new scientific pathway)?

The strength of a potential paper lies in the authors’ ability to thoroughly dissect some of these questions or explore other relevant aspects that I may not have considered.

Examples of noteworthy applications include those that generate new datasets made publicly available for future research. [[Sharing data and ensuring reproducible workflows are essential. No reproducibility? No publication. We can no longer accept impressive simulations and superior results that can't be replicated. That isn't science; it's, at best, storytelling—or as some might call it, vaporware.]] Another compelling example is large-scale applications on extensive river systems [[especially when these studies capture the hydrology of vast regions of the Earth, influencing numerous ecosystems and human communities]]. Papers that explore future scenarios with increasing reliability [[not just retrospective hindcasts, but predictive studies that anticipate future conditions and are subsequently validated]] are particularly valuable. Additionally, papers that integrate hydrology with ecosystem studies or catchment biogeochemistry stand out. As always, novelty is key.

Many argue that validating a model solely on discharge outputs is inadequate. If you agree with this perspective, it necessitates a commitment to a more rigorous approach.

Recently, I have strongly advocated in all our papers for the inclusion of a comprehensive water budget alongside the simulation of desired discharge levels. While this requires considerable effort, it significantly enhances the discussion around the consistency of the results.

If I were to fully indulge my preferences, I would no longer approve manuscripts that only replicate discharge results. Not anymore! If you share this view, you might consider updating your legacy codes to simulate and discuss the entire water budget, and possibly the energy budget. This ties into the research discussed in previous posts, highlighting the interconnected nature of our work.

Those who aim to model - II

As a follow-up to my previous post, see also here, I'd like to share some additional reflections on the experience of doing hydrology in academia. I've attempted to classify different types of researchers, and below, you'll find the second part of this classification.

 

There are those who hypothesize relationships, laws, models. In this context, statistical analysis plays a key role, even in its modern algorithmic forms, producing various models that uncover causal relationships and correlations. Possessing good mathematics skills avoid to be trivial and adapt your solutions to your ability (if you have a hammer, you tend to see any problem like a nail).  It is not an infrequent attitude in Hydrologist being impatient to get number and result as if the mathematics were a commodity. Any problem needs its mathematics. Opposite of the previous attitude, someone is in love with mathematistry, and try to impress people with unrequired complexity or concepts. Maybe they gain some paper on major journal but that will rarely be cited.   Where the proper use of mathematics  stands, is also the  good science that should be worthwhile to pursue.

Example of mathematics used in hydrology is the solution of partial differential equations like de Saint-Venant equation, Richards equation and groundwater equations with the additions of the Naviers-Stokes equations when transport in atmosphere is pursued. To these equations and their direct simplification, the heat transfer equation and various diffusion-like equation were intended to be the last word when dealing with the energy budget and various transport phenomena.

Their use was seen as a needed progress with respect to the use of empirical formula and supported in the famous blueprint by Freeze and Harlan, that however found several criticism and several defenders*.

While the previous description of the physics of the processes  was though as superior to empirical equation (mostly simple regressions) or closed formulas of pre-digital era, still it has often claimed that not the whole information contained in those equations was relevant to produce macroscopic estimator of the water budget (often just the discharge)   and only a few degree of freedom survive to the dynamics and the coarse graining that works in catchments. However,  not consistent proof was so far produced that could lead to the right simplification of equations or methods. [[Only the Witthaker / Gray integration methods, that have found just few factual believers. ]]

At the catchment scale, ordinary differential equations (ODEs) have remained the dominant mathematical framework. The prevailing idea is that a set of ODEs, potentially organized within a graph of interactions, can effectively capture most of a catchment's hydrological dynamics. However, no formal proof has yet been established to confirm that this state-of-the-art approach is universally consistent—its widespread acceptance is largely based on empirical success. What many don't realize is that the ODE approach can also be viewed as an integral part of the so-called Instantaneous Unit Hydrograph (IUH) theories. These theories have recently experienced a resurgence, partly due to their intersection with new measurement techniques involving stable isotopes.

I wrote somewhere:

Aristotile had it all wrong. 

Dalton, Horton, Sherman and Leopold plaid the starting gong. 

Eagleson, Rodriguez-Iturbe went for a grand theory, in which they believe. 

Gideon and Ignacio (Vujica teachs) dated with randomness. 

Richards, Richardson, Harlan and Freeze insisted on using PDEs. 

Horton said the the runoff is infiltration excess, 

Dunne said that it is saturation excess, 

Hewlett and Hibbert said that overland flow necessary is not 

Tracers research screwed all it up. 

Darcy and Buckingham it is all matter of gradients they thoughts. 

Beven and Germann set up a mountain of doubts. 

And many, I forgot, I do not know

Now we do not really know what we know, 

except that we know more than before, 

better data we have, 

satellites see it all (but what you see you do not believe).  

Modelers give numbers, without caring 

machine learning thinks it can do all without understanding

and because we did not had it when we thought, 

they probably sing the right song

If you address the existing gaps in this theoretical framework, your work would be well-suited for publication. Similarly, if you introduce novel approaches in the application of PDEs, develop new models that combine ODEs and PDEs, discover innovative methods for parameter estimation, or invent new solvers, a new implementation of a known theory, previously neglected, your contributions would be highly valued and welcomed for publication.If you're truly ambitious, you might consider introducing a new branch of mathematics for describing phenomena—much like fractals, fuzzy logic, and other methods did about thirty years ago. Doing so could earn you a lasting place in the literature. Additionally, there's still significant potential in established fields like information theory and causal inference, which could be further explored and systematically applied, offering you the opportunity to gain recognition and influence.

The latest trend is undoubtedly centered around machine learning techniques, which have proven highly effective at replicating the behavior of hydrological systems—often without fully understanding the underlying mechanisms. Today, the field of machine learning is ripe for exploration, and there's ample opportunity to publish in this area due to its resurgence in popularity. However, papers that merely imitate existing work are unlikely to have lasting impact or relevance.

[[On Earth observations, I have already written]]

In reality, many of the approaches and models that continue to find their way into publications remain, even today, remarkably simplistic. I admit, I have been guilty of this myself. Examples include degree-day models for SWE estimation, outdated infiltration formulas, empirical methods for potential evapotranspiration, and basic time-based formulas for peak flows. These methods are often said to work, but only after episodic parameter calibrations and inadequate, narrow verifications. Wolfgang Pauli might have deemed these methods as "not even wrong," implying that they lack sufficient depth even to be considered incorrect. Dante might have consigned such papers to the ranks of the "ignavi," those souls unworthy of a place even in the circles of Hell.

If you manage to replace these obsolete methods with more robust alternatives, your work is certainly publishable.

Many professionals have built their careers by advancing technical aspects related to models and theories, such as uncertainty assessment, parameter calibration, and data assimilation. This is undoubtedly a viable path for a researcher, one that can lead to significant recognition. Moreover, there are several promising areas within stochastic hydrology, including groundwater studies, time series analysis, and forecasting. Although these areas may not align perfectly with my current focus, they represent fertile ground for research and development, and have recently seen a resurgence of interest.

*A NOTE:  The use of PDEs that actually were derived from the foundations od physics, let the door open to the fact that some aspect were simplified and some parameters of the equation have only a statistical significance. For instance in Richards equation, the soil water retention curves that play a fundamental role are equilibrium relationship between the water content and the chemical potential of water in soil that is realized when the system has the time to relax in order to the smallest capillaries fill first and empties last. There is a famous sequence of papers by Keith Beven treating some of this topics that you should recover (but not necessarily agree upon). So the supposed fundamental PDEs are sometimes not that fundamental and often depends on parameters that have the faint light of some slippery statistical significance. 

Those who aim to discover - I

As a follow-up to my previous post, I'd like to share some additional reflections on the experience of doing hydrology in academia. I've attempted to classify different types of researchers, and below, you'll find the first part of this classification.

There are those who discover. This process involves observing data, identifying unexpected aspects of the water cycle and related sciences, and expanding the empirical base as progress is made. In this sense, science is also about keen observation and m uch of scientific work is conducted in this way. To better grasp this, consider the field of natural sciences. Hydrology is also a natural science and phenomena discovery, observation, classification is an important part of it. Today the field is positively contaminating a lot considering hydrology feedbacks with biology and geochemistry, ecosystems behavior. [[This does not mean that what has to be discovered is all in the interdisciplinary studies, many historical hydrological issues not having being solved yet.]]

Examples of discoveries in data present in literature are, for instance: the observation of self similarity (fractality) in many geophysical sciences which revealed a scale-free response of catchments and hydrologic systems which is not fully explained. Shifts in precipitation timing and intensity, increased frequency of droughts, or altered snowmelt patterns that significantly impact water availability and hydrological cycles in ways not previously anticipated. Anomalous runoff coefficients caused by the presence of karst or melting glaciers. Effects of human activities at various scales. Effects of groundwater water distribution and redistribution in the overall cycle and at various spatial scales. Different stress response of plants with respect to droughts. Unexpected old age of water in runoff  challenging the understanding of runoff production. Missing rainfall events, due to lack of space-time resolution of observations.  Fill and spill and other non intuitive and sometimes counterintuive phenomena in runoff. 

If what you see is new, you can easily publish it. But even if it is a confirmation of something new, you still can. I would distinguish "discover" from "measure". Measures, experiments and field observations have their own place and a different literature. 

It is clear that to discover in data the simple and literal observation is not enough. The help of some tools and some mathematics is necessary, even if researcher who excel in narrative capabilities and metaphor production exist and sometimes do without (do not follow their example if you are not a very gifted writer). 

You want a tenure-track position ? (Sunday Thinking)

You embarked on this postdoc to advance your career and, ideally, secure a tenure-track position somewhere in the near future. But what strategy should you follow?

First and foremost, focus on building a solid publication record. Aim for about three publications per year—fewer if they're with a small number of co-authors, and possibly more if there are many co-authors. Prioritize publishing in reputable journals, preferably in higher citation quantiles, and ensure your work demonstrates a clear research trajectory and distinct academic personality.

It's also important to gradually differentiate yourself from your postdoc advisor. This could involve publishing with other colleagues or clearly highlighting your unique contributions in joint papers with your advisor. Aim to be the first or corresponding author, or the primary driving force behind at least 50% of your papers. However, remember to credit your co-authors appropriately—being selfish won't serve you well.

Managing your relationship with your advisor requires a delicate balance. Both you and your advisor need visibility and recognition, though your needs may differ. Learn to navigate this relationship to ensure mutual satisfaction while avoiding toxic dynamics.

Building a strong professional network is crucial. Be visible in your department and, more importantly, in the wider academic community of your subdiscipline. Engage actively by fostering collaborations, organizing events or sessions at major conferences, and contributing to departmental initiatives. Your advisor can support you in this, but it’s vital to establish connections with influential researchers who can later provide further strong and informed reference letters.

Mentoring students and giving guest lectures will enhance your visibility and demonstrate your ability to fulfill a professor's role. 

Seek funding early on. There are many opportunities, and securing funding not only proves your ability to thrive in the competitive research environment but also signals to potential hiring departments that you can bring in resources and enhance their reputation.

Overall, approach your job search with professionalism. This involves crafting a strong CV, preparing thoroughly for interviews, and respecting the time and resources of the institutions you're applying to—a principle that holds true for both PhD and postdoc candidates.

At the core of all this is doing good science—not just average work, though that is still honorable, but truly innovative and solid research on some topic where you can be reconigned as a  active contributor. This requires dedication, the right tools, intuition, and the capacity to recognize new opportunities while holding firm to your vision (do you have one?). Don’t sacrifice quality and originality for the sake of productivity (up to a point!).

While following trends might offer short-term gains, it won’t serve you in the long run. However, being overly rigid in this belief can also be a mistake, as science constantly evolves, and shifts in language and focus can quickly render even well-founded arguments to look obsolete. So, while trends shouldn't dictate your work, it’s wise to remain aware of them.

Everything works better if you find the right advisor (right is not the better, is the ones that fit with you).

Mumbai GEOframe School !

 We have just completed our effort with the GEOframe Mumbai Monsoon School, inserted in a larger initiative, of the GISE HUB which included one day long SCPP workshop on "Recent Advances in Hydrological Modelling" on 31st July. Besides being trained on GEOframe, hands on training on Dynamic Budyko model was provided by prof. Basudev Biswal (GS) and his postdoc  Prashant Istalkar. Lectures on the 31st July covered a wide range of topics including flood inundation modelling, socio-hydrology, land-surface modeling, climate-change impact assessment, machine learning models, and complex networks.

Great thanks to Sumit Sen and Basudev Biswal for organizing the School. Hospitality was superb, discussions enriching and seeing the dedication and smartness of students an encouraging academic experience. We hope that the School will have follows up both at IIT and UniTrento and exchanges could continue in the future. For further information, see also the Linkedin post by Basudev here. 

The GEOframe material of the School is available to anyone and the slides and videos (when uploaded) will be available at the GEOframe blog page dedicated to the School.

The success of the School, from our side, is the outcome of many that are listed in this "people of GEOframe" presentation available here. 

For students who want to complete a personal exercise with GEOframe, the GEOframe team is available to assist. Upon completion, each student will receive a University of Trento T-shirt.