# data[].attributes.attachment_caption
Sensing with boots and trousers — qualitative field observations of shallow soil moisture patterns

# data[].attributes.headline
Favourite paper: Sensing with boots and trousers

# data[].attributes.subject
Favourite paper: Sensing with boots and trousers

# data[].attributes.body
Name: Belle Holthuis

Email: belle.holthuis@uzh.ch or belle.holthuis@wur.nl

Career Stage: Master Student Earth & Environment

Wageningen University & Research

DOI: https://doi.org/10.1002/hyp.9531

<100 words:

I really enjoyed reading this paper and it gave a smile on my face, as the method is so practical. It is something that I notice myself during my MSc thesis (on ET and soil moisture) field work, are my trousers getting wet? Or is water squeezed out the soil when I walk on it with my boots? I like it because it is a quick and inexpensive way of finding out the soil moisture patterns and people recognize the observations when you are a person that spends time in nature to go hiking for example, or do field work!

# data[].attributes.headline
Pioneering Hydrology: Unveiling the Power of Machine Learning Across 50+ Indian Watersheds

# data[].attributes.subject
Pioneering Hydrology: Unveiling the Power of Machine Learning Across 50+ Indian Watersheds

# data[].attributes.body
Name: Siddik Barbhuiya

Email: siddikbarbhuiya@gmail.com

Career state: PhD scholar

Affiliation: IIT MANDI , India

DOI: https://doi.org/10.1002/hyp.14936

The paper by Mangukiya et al.2023 is groundbreaking as it is the first study to explore more than 50 watersheds in India using advanced machine learning models like LSTM and DI-LSTM. It profoundly influenced my research by demonstrating how integrating lagged observations can significantly improve prediction accuracy, even in data-scarce regions. This work inspired me to apply similar data-driven approaches in my research, showing the immense potential of machine learning to revolutionize hydrological predictions in complex and diverse environments like India's. The study’s insights are pivotal for addressing water resource management challenges under climate change.

# data[].attributes.headline
Streams as Mirrors: Reading Subsurface Water Chemistry from Stream Chemistry

# data[].attributes.subject
Streams as Mirrors: Reading Subsurface Water Chemistry from Stream Chemistry

# data[].attributes.body
Name: Amanda Donaldson

Email: amdonald@ucsc.edu

Career Stage: Graduate student

Affiliation: University of California, Santa Cruz

DOI: 10.1029/2021WR029931

< 100 word blurb:

I am an hydrologist interested in how the critical zone structure (e.g. thickness, porosity) influences streamflow (e.g. quality, quantify, behavior). I was inspired by this paper for a number of reasons. First, this paper uses field data within two catchments to investigate a rather simple hypothesis: stream concentration-discharge relationships are shaped by distinct source waters from different depths. Second, it uses interdisciplinary approaches to learn more about the difficult to characterize critical zone subsurface. Third, the large author list suggests that it was a collaborative process which is essential to advance catchment hydrology forward.

# data[].attributes.body
Name: José Gescilam Uchôa

Email: gescilam@usp.br

Career Stage: Graduate student

Affiliation: University of São Paulo, Brazil

DOI: 10.1002/wat2.1386

In this paper, Professor Fan extends the classic study by Toth (1963) with hypotheses that could indicate whether catchments export water. She evaluates these hypotheses using several case studies reported in the literature. Ultimately, her work challenges the hydrology community by highlighting that we will never know whether leaky catchments are the norm or the exception if there are no studies on this topic. The provocative nature and technical quality of her paper motivated me to contact her. To my surprise, she replied to me, which led to the birth of my research project that I am now developing.

# data[].attributes.headline
Prediction in ungauged basins: a grand challenge for theoretical hydrology

# data[].attributes.subject
Prediction in ungauged basins: a grand challenge for theoretical hydrology

# data[].attributes.body
Name: Franziska Clerc-Schwarzenbach

Email: franziska.clerc@geo.uzh.ch

Career stage: PhD student

Affiliation: University of Zurich, Switzerland

DOI: 10.1002/hyp.5155

This paper was written at the beginning of the PUB initiative in 2003, twenty years before I started my PhD. Still, it is one of the most inspiring papers for me. It motivates why PUB research was – and still is – important. I like the statement that we need to accept catchments as heterogenic systems formed by nature that we will not be able to understand fully, but that we should at the same time continue to learn from data, models, and new methods. The paper motivates me to contribute my part to the grand puzzle of hydrology.

# data[].attributes.wish_content.attachment_props.alternative_text
Prediction in ungauged basins: a grand challenge for theoretical hydrology

# data[].attributes.headline
Testing Common Water Balance Assumptions

# data[].attributes.subject
Testing Common Water Balance Assumptions

# data[].attributes.body
Name: Paige Becker

Email: pabecker@mines.edu

Career State: Postdoctoral Fellow

Affiliation: Colorado School of Mines

DOI: https://doi.org/10.1002/hyp.14199

As someone who spent her Ph.D. testing common assumptions and practices in hyporheic studies, this paper resonated with me, but at the catchment. Groundwater volumes and fluxes are not easily measured compared to precipitation and runoff, but are arguably, as important for water quality and quantity in catchments. This work challenges how we quantify groundwater and the interpretations made from using GW to close the balance. This inspired the third chapter of my dissertation, which challenged how we interpret hyporheic flows and underflow and how using these flows to close the water balance can alter our understanding.

# data[].attributes.headline
Occam's razor of the catchment storage dynamics 

# data[].attributes.subject
Occam's razor of the catchment storage dynamics 

# data[].attributes.body
Name: Ryoko Araki

Email: raraki8159@sdsu.edu

Career State: PhD candidate

Affiliation: San Diego State University & University of California, Santa Barbara

DOI: https://doi.org/10.1002/HYP.7154

Before reading this paper, I viewed hydrologic models as mere computer programs running simulations. However, Clark et al. (2009) completely transformed my perspective. This paper demonstrates that (1) nonlinear storage-discharge dynamics can be modelled as a parallel array of linear "bucket" systems and (2) the number of storage systems in a catchment can be inferred from time series analysis.

This ultimate simplification opened up intriguing research questions on scaling laws for catchment storage, (non-)linearity of the system, and hydrologic signature analysis of various catchments. These insights inspired the first two chapters of my dissertation and sparked many engaging discussions.

# data[].attributes.headline
A recipe for why and how to set up and sustain an experimental catchment

# data[].attributes.subject
A recipe for why and how to set up and sustain an experimental catchment

# data[].attributes.body
Name: Adriane Hövel

Email: adriane.hoevel@boku.ac.at

Career stage: PhD student

Affiliation: University of Natural Resources and Life Sciences, Vienna, Austria

DOI: 10.1002/hyp.15163

In times of big data and machine learning, this recent paper emphasizes the importance of experimental catchments and the essential value they provide to us as catchment hydrologists. It outlines the many benefits that come with setting up and maintaining an experimental catchment, but also highlights potential difficulties and failures that may be encountered. As an early stage researcher, I especially enjoyed reading the practical “recipe” on how to set up and manage a new experimental catchment. Working with data measured in experimental catchments myself, this paper makes me appreciate even more the important insights gained from these catchments.

# data[].attributes.body
Name: Zhi Li

Email: li1995@stanford.edu

Career State: Postdoc researcher

Affiliation: Stanford University

DOI: https://hess.copernicus.org/articles/13/157/2009/

As a hydrologic modeler, I consider this paper a must-read. Prof. Savenije, whose thought-provoking insights profoundly influenced my decision to pursue academia, masterfully describes the process of hydrologic modeling as an art. He argues that modeling is not a technical exercise (like hydraulics) but one demands imagination, creativity, ingenuity, and experience. The idea is that no one-size-fits-all model exists; each must be crafted with a deep understanding, informed by on-ground observation. This paper concludes hydrology is a multidisciplinary Earth science, intricately linked to geology, ecology, atmosphere, and society. The advance of hydrology necessitates a blend of science, technology, and art.

# data[].attributes.attachment_link.readable_text
|

Opinion article | | 18 Feb 2009

Abstract. Hydrological modelling is the same as developing and encoding a hydrological theory. A hydrological model is not a tool but a hypothesis. The whole discussion about the inadequacy of hydrological models we have witnessed of late, is related to the wrong concept of what a model is. Good models don't exist. Instead of looking for the "best" model, we should aim at developing better models. The process of modelling should be top-down, learning from the data while at the same time connection should be established with underlying physical theory (bottom-up). As a result of heterogeneity occurring at all scales in hydrology, there always remains a need for calibration of models. This implies that we need tailor-made and site-specific models. Only flexible models are fit for this modelling process, as opposed to most of the established software or "one-size-fits-all" models. The process of modelling requires imagination, inspiration, creativity, ingenuity, experience and skill. These are qualities that belong to the field of art. Hydrology is an art as much as it is science and engineering.

# data[].attributes.attachment_link.description
Opinion article | | 18 Feb 2009 Abstract. Hydrological modelling is the same as developing and encoding a hydrological theory. A hydrological model is not a tool but a hypothesis. The whole discussion about the inadequacy of hydrological models we have witnessed of late, is related to the wrong concept of what a model is.

# data[].attributes.headline
What Role Does Hydrological Science Play in the Age of Machine Learning?

# data[].attributes.subject
What Role Does Hydrological Science Play in the Age of Machine Learning?

# data[].attributes.body
Name: Surabhi Upadhyay

Email: surabhi_upadhyay@mines.edu

Career Stage: PhD Student

Affiliation: Colorado School of Mines, US

DOI: https://doi.org/10.1029/2020WR028091

This paper is a crucial and timely contribution to hydrology, highlighting the integration of machine learning with traditional hydrological models to better understand intricate catchment dynamics. By showcasing how deep learning models surpass calibrated conceptual models in predicting rainfall-runoff dynamics, the paper highlights the transformative potential of data-driven approaches. The authors' call for evidence-based methodologies aligns with my belief to evolving with data science. This paper has inspired me to think along these lines and has prompted more discussions on incorporating machine learning into my research to better leverage data and address water resource challenges.

# data[].attributes.body
Name: Saurav Bhattarai

Email: saurav.bhattarai@students.jsums.edu

Career Stage: Graduate Student

Affiliation: Jackson State University, Jackson

DOI: 10.1029/2019WR025326

As a hydrologist with a background in data analysis, the paper by Xiang et al. (2019) on leveraging LSTM sequence-to-sequence learning for enhanced rainfall-runoff modeling has been a cornerstone of my work. This study provided the foundational framework for my own research, demonstrating how advanced deep learning techniques can significantly improve the accuracy and reliability of hydrological forecasts, particularly in predicting flood events. The model's ability to capture complex nonlinear relationships within catchment systems has been truly inspirational, guiding my efforts to integrate machine learning with traditional hydrological processes and advance the field of water resource management.

# data[].attributes.body
Name: Aline Meyer Oliveira

Email: aline.meyer@geo.uzh.ch

Career Stage: PhD student

Affiliation: University of Zurich, Switzerland

DOI: 10.1002/2017WR021543

The hydrograph lumps substantial data from the headwaters to the rivers in a catchment. However, we probably do not explore this already existing data to its full potential. In this paper by Collischonn et al., they investigated the drivers for different shapes in the hydrograph. Through modeling experiments, they found that hydraulic geometry plays an important role in shaping hydrograph skewness, and that this is highly influenced by the celerity-discharge relationship. I find this paper very inspiring, because the authors showed that we can understand even more than we thought about a catchment by just looking at its hydrograph.

# data[].attributes.headline
Hydrology from Space in the Amazon Basin

# data[].attributes.subject
Hydrology from Space in the Amazon Basin

# data[].attributes.body
Name: Júlia Brusso Rossi

Email: julia.rossi@ufrgs.br

Career stage: PhD Student

Affiliation: Federal University of Rio Grande do Sul, Brazil

DOI: https://doi.org/10.1029/2020RG000728

This paper highlights recent advancements in using satellites to monitor the water budget in the Amazon. The authors identify the Amazon basin as a natural laboratory for remote sensing development and emphasize the importance of upcoming hydrology satellite missions. Remote sensing is a great tool for understanding hydrological processes and is essential in my research for estimating evapotranspiration at large scale. As a hydrologist who relies on remote sensing data, I find this paper extremely valuable for addressing the limitations of satellite observations while underscoring their critical role in monitoring large, remote areas like the Amazon.

# data[].attributes.headline
Permafrost promotes shallow groundwater flow and warmer headwater streams

# data[].attributes.subject
Permafrost promotes shallow groundwater flow and warmer headwater streams

# data[].attributes.body
Name: Andras Szeitz

Email: szeitza@mcmaster.ca

Career stage: PhD Candidate

Affiliation: McMaster University

DOI: https://doi.org/10.1029/2020WR027463

With some understanding of stream temperature processes and permafrost, the results of this study may strike one as counter-intuitive. Or rather, perhaps it is intuitive? Permafrost is cold and all the water I’ve observed at the top of permafrost is also cold, so how does it have a warming influence? What else is going on? This study raised so many questions in my mind about the complexities of these interactions that I was immediately hooked – this study inspired my PhD!

# data[].attributes.attachment_caption
Coastal Groundwater Under Pressure (Michael et al. 2017).

# data[].attributes.body
Name: Jeeban Panthi

Email: jeebanp@ksu.edu

Career stage: Postdoctoral Fellow

Affiliation: Kansas State University

DOI: https://doi.org/10.1002/2017WR020851

This commentary by Michael et al. (2017) is important to me as it inspired my research on how drought impacts marginal freshwater in barrier island aquifers, which became the focus of the fourth chapter in my PhD dissertation and was published in the Journal of Hydrology. The concept of the "coastal groundwater squeeze" emphasized the urgent need for interdisciplinary approaches and collaboration with policymakers, guiding my work. From reading it, I've learned the critical importance of integrating scientific advances in coastal hydrology with broader environmental management strategies.

# data[].attributes.headline
Satellite-based estimates of groundwater depletion in India

# data[].attributes.subject
Satellite-based estimates of groundwater depletion in India

# data[].attributes.body
Name: Ramya Chandrasekaran

Email: rchandrasekara1597@sdsu.edu

Career Stage: Graduate Student

Affiliation: San Diego State University

DOI: https://doi.org/10.1038/nature08238

Groundwater has always been tricky to track but as aquifers are frequently used for every water need, we must find accessible and accurate monitoring methods. This paper uses observed surface water changes from NASA's GRACE satellites and modeled soil moisture fluxes to detect groundwater depletions across India. The results appear promising, suggesting remote sensing to be a viable tool in groundwater monitoring. Groundwater is my passion, and aquifers are often overdrafted and understudied. Remote sensing can build datasets describing the fluctuation of the state of aquifers over past and future years and is convincing me to pick up remote sensing.

# data[].attributes.attachment_link.title
Satellite-based estimates of groundwater depletion in India - Nature

# data[].attributes.attachment_link.description
Indirect evidence suggests that groundwater is being consumed faster than it is naturally being replenished in northwest India, but there has been no regional assessment of the rate of groundwater depletion. Terrestrial water storage-change observations and simulated soil-water variations from a modelling system are now used to show that groundwater is indeed being depleted and that its use for irrigation and other anthropogenic uses is likely to be the cause.

# data[].attributes.headline
Twenty-three unsolved problems in hydrology (UPH) – a community perspective

# data[].attributes.subject
Twenty-three unsolved problems in hydrology (UPH) – a community perspective

# data[].attributes.body
Name: Shubham Goswami

Email: gshubham@iisc.ac.in

Career Stage: PhD Student

Affiliation: Indian Institute of Science Bangalore, India

DOI: https://doi.org/10.1080/02626667.2019.1620507

The 2019 paper by Blöschl et al. has been a guiding light in my research, consistently sparking new ideas and providing a focused path forward. My PhD work on integrated hydrological modeling under climate change and anthropogenic impact in semi-arid regions of India closely relates to the unsolved problems 3, 4 and 5 from the list. Initially, I didn’t fully grasp the paper's significance, but as I progressed, I realized how deeply my research objectives align with these critical challenges. This paper has been a catalyst for my growth and continues to inspire my work as a hydrologist.

# data[].attributes.headline
Post-wildfire Recovery in Mediterranean Climates

# data[].attributes.subject
Post-wildfire Recovery in Mediterranean Climates

# data[].attributes.body
Name: Brittany Barreto Martinez

Email: bbarretomartinez@sdsu.edu

Career stage: Postdoctoral Researcher

Affiliation: San Diego State University

DOI: https://doi.org/10.1016/j.jhydrol.2021.126772

This paper reviews post-wildfire hydrologic recovery in Mediterranean climates. The study identifies a gap in understanding long-term recovery processes, influenced by fire severity, vegetation regrowth, and climate. With longer time periods of research, there would be the necessary background to predict recovery and guide effective post-fire management. My remote sensing background can expand these findings by providing continuous, large-scale monitoring of vegetation, soil moisture, and erosion, for targeted rehabilitation strategies. Using the information outlined from this review, it has helped outline my objectives in applying remote sensing in California to monitor post-wildfire effects on watersheds to develop effective rehabilitation strategies.

# data[].attributes.headline
HESS Opinions: Never train an LSTM on a single basin

# data[].attributes.subject
HESS Opinions: Never train an LSTM on a single basin

# data[].attributes.body
Name: Kshitij Dahal

Email: kdahal3@asu.edu

Career Stage: Graduate student

Affiliation: Arizona State University, USA

DOI: https://doi.org/10.5194/hess-2023-275

< 100 word blurb:

No offense to process-based models, but the success of LSTMs in hydrology really highlights the flaws in traditional, single-basin methods and our limited understanding of catchment hydrology. Seeing how regional, multi-basin LSTM models consistently outperform our process-based approaches in this article has deeply inspired me to explore their potential to better understand hydrological processes. It’s clear we need to rethink how complex hydrological processes are. I see a huge opportunity to leverage machine learning to model rainfall–runoff relationships across different basins, improve our understanding of catchment hydrology, and possibly improve our process-based models.

# data[].attributes.attachment_link.title
HESS Opinions: Never train an LSTM on a single basin

# data[].attributes.attachment_link.description
Abstract. Machine learning (ML) has played an increasing role in the hydrological sciences. In particular, certain types of time series modeling strategies are popular for rainfall–runoff modeling. A large majority of studies that use this type of model do not follow best practices, and there is one mistake in particular that is common: training deep learning models on small, homogeneous data sets (i.e., data from one or a small number of watersheds). In this position paper, we show that Long Short Term Memory (LSTM) streamflow models are best when trained with a large amount of hydrologically diverse data.

# data[].attributes.attachment_caption
The Demographics of Water: A Review of Water Ages in the Critical Zone

# data[].attributes.headline
The Demographics of Water: A Review of Water Ages in the Critical Zone

# data[].attributes.subject
The Demographics of Water: A Review of Water Ages in the Critical Zone

# data[].attributes.body
Name: Sara, Mohandes Samani

Email: Mohan197@umn.edu

Career Stage: PhD student

Affiliation: University of Minnesota, Twin Cities

DOI: https://doi.org/10.1029/2022WR033096

This review caught my attention because it explored the linkages between water age studies across different hydrologic disciplines. It helped me understand how compartmentalizing a catchment could clarify the problem and its underlying assumptions. In studying water age, it's easy to make problematic assumptions due to the high variability of hydrologic systems and many unknowns, leading to potential errors in modeling. This paper highlighted the importance of reconsidering traditional methods by viewing the catchment as separate components and analyzing each water cycle element's fate when applying tracer data to study water age.

# data[].attributes.headline
Beyond the natural flow regime? Broadening the hydro-ecological foundation to meet environmental flows challenges in a non-stationary world

# data[].attributes.subject
Beyond the natural flow regime? Broadening the hydro-ecological foundation to meet environmental flows challenges in a non-stationary world

# data[].attributes.body
Name: Uchechukwu Ogbenna

Email: uogbenna2@huskers.unl.edu

Affiliation: University of Nebraska-Lincoln

Career stage: PhD Student

htpps://doi.org/10.1111/fwb.13038

Building off the natural regime concept, this paper highlights the need to understand dynamic processes that drive ecosystem functions in the face of present realities like climate change and population growth. The paper influenced the way I view aquatic ecosystems. In my study of small shallow reservoirs was instrumental in guiding my focus on understanding how surface area dynamics (connectivity) impact nutrient dynamics. My study links ecosystem dynamics to processes and function, providing the scientific background necessary to emphasize the importance of adaptability and resilience in managing reservoir ecosystems in the face of shifting environmental conditions.