The urgency of the problem is grounded in a shifting hazard landscape. Climate change is intensifying climate-driven natural hazards, and these events increasingly strike simultaneously, in quick succession, or trigger one another in cascading chains. Recent studies cited by the authors show that multi-hazard events are becoming more frequent across Europe and globally. The United Nations Office for Disaster Risk Reduction defines multi-hazards as the selection of multiple major hazards a country faces, considering the specific contexts where hazardous events may occur simultaneously, cascadingly or cumulatively over time, taking into account their potential interrelated effects. In response, risk research has been shifting from single-hazard to multi-hazard thinking, yet most large-scale assessments remain dominated by quantitative methods such as machine learning, and data gaps often force studies into narrow approaches that exclude low- and middle-income countries.

The difficulty, the authors argue, is that multi-hazard risk assessment is inherently complex. It demands data, a deep understanding of how risks interact across space and time, and awareness of the local context in which disasters actually unfold. Decision makers face competing adaptation priorities and potential conflicts between measures designed for different hazard types. Moreover, the effectiveness of any disaster risk reduction strategy depends on how communities perceive and respond to risk, a dimension that standardized quantitative methods aimed at larger scales routinely miss. Risk perception, the essay notes, does not always align with modelled risk. A study in Bangladesh combining media narratives, interviews and field observations found that risk perception is often ignored in local adaptation planning, while research on coastal property markets in the United States shows that despite growing hazards, coastal properties continue to rise in value.

The temporal dynamics of perception are equally striking. Immediately after a flood event, property prices in inundated areas drop by an average of 24.9 percent compared to non-flooded properties, yet that discount loses statistical significance within five years. In multi-hazard contexts there is a further complication: focusing on one hazard can decrease people’s attention to the risk posed by another. To structure their investigation of these dynamics, the researchers anchored their survey work in Protection Motivation Theory, a psychological framework with two key components. Threat appraisal addresses how likely people believe an event is and how severely they expect to be hit, while coping appraisal probes whether people believe anything can be done, whether it is affordable, and whether government adaptation measures would actually work.

Mixed-methods research, defined as projects that bring together qualitative approaches such as interviews with quantitative techniques such as risk modelling and survey analysis, is not new. Greene and colleagues laid the terminological foundations in 1989, reviewing studies that already combined methodologies, and interest has grown notably over the past decade, particularly in the social sciences. The main designs include convergent parallel, explanatory sequential and exploratory sequential approaches. What García Álvarez and de Ruiter propose is a simultaneous, iterative process in which quantitative and qualitative strands inform each other continuously. A qualitative interview can improve the interpretation of structured survey data or reshape the survey itself for later stages, as stakeholders identify problems the researchers had not anticipated. Quantitative results, in turn, can guide which questions matter most in interviews and which adaptation solutions to model.

The framework was tested during a ten-day fieldwork trip to Tenerife in March 2025, an island the authors describe as a complex multi-hazard environment exposed to volcanic eruptions, coastal hazards, wildfires and landslides. Recent events, including the devastating 2023 wildfires and the 2021 eruption on the neighboring island of La Palma, have highlighted gaps in preparedness. The team applied a sequential design in which quantitative methods influenced qualitative instruments and vice versa: GIS-based observations and survey results shaped interview questions, while exploratory interviews determined which key questions to address quantitatively and which adaptation solutions to model. A total of 174 respondents completed a 25-question survey, administered in person and online in Spanish and English versions, coded in KoboToolbox and analyzed in Python to test Protection Motivation Theory hypotheses. Five semi-structured interviews targeted three key economic sectors highly exposed to climate risk: agriculture, tourism and critical infrastructure, with thematic analysis covering risk perception, adaptation levels and trust in authorities.

The complementarity of the methods surfaced repeatedly. Desktop analysis and GIS-based exposure mapping provided an initial understanding of each region and identified locations to verify in the field, where observations revealed missing infrastructure, unregistered buildings and local bottlenecks. Several regions of the island contained buildings absent from Google Maps that would have been neglected in modelling otherwise. Questionnaire results showed that respondents were unaware of any emergency plan for most climate hazards despite recent wildfires and coastal storms, yet residents knew volcanic eruption procedures because drills had been conducted by some municipalities after the La Palma eruption. That contrast, the authors suggest, could directly shape future emergency communication plans. In the northern town of Tegueste, nestled in a valley between forested mountains and exposed to wildfires and flooding from steep ravines called barrancos, field observations confirmed residential areas and infrastructure inside high-risk zones, along with accumulated vegetation and debris that could block water flows.

Interviews then revealed opportunities invisible to any dataset. Conversations with locals and field observations uncovered large areas of abandoned agricultural terraces near Taganana, offering low-conflict sites for reforestation, since converting productive farmland to nature-based solutions would require compensating farmers. For Tegueste specifically, the survey confirmed that locals ranked wildfires as the top perceived hazard following the nearby 2023 blaze, and econometric analysis showed that residents with prior hazard experience were more likely to have taken adaptive measures. Yet overall worry levels were low, a contrast between perceived and modelled risk that the authors say reveals communication and awareness gaps pure modelling would overlook. The final recommendations combined these strands: clearing vegetation at barranco bottlenecks, installing five small retention basins to slow runoff, and introducing grazing herbivores to manage vegetation regrowth.

The approach carries particular weight for nature-based solutions, defined by the European Commission as solutions inspired and supported by nature that are cost-effective while providing environmental, social and economic benefits. Their feasibility depends on community acceptance and perceived co-benefits, dimensions that are difficult to quantify but critical for uptake, and the lack of standardized indicators has limited both the evidence base and private investment. Mixed-methods can monetize non-market co-benefits through tools such as choice experiments and contingent valuation, feeding into cost-benefit analysis, while also exposing inequities, since nature-based solutions can raise property prices and displace lower-income residents. In one Dutch study cited by the authors, conversations with waterboard members revealed land-use conflicts that led researchers to add agricultural land conversion as a survey attribute, uncovering a significant decline in respondent utility as more farmland was converted, an insight that would otherwise have been missed. The framework also aligns with the IUCN Global Standard for Nature-based Solutions, addressing criteria on societal challenges, local context, economic viability, inclusive governance and trade-offs.

The authors are candid about the costs and pitfalls. Fieldwork is resource-intensive and time-bounded; access to key locations was sometimes blocked by private property, forcing contingency plans. Time constraints limited questionnaire numbers and raised sampling bias risks, since environmentally engaged residents were more willing to participate, and any mid-fieldwork survey revision cost a full day of data. Language barriers complicated interviews with stakeholders uncomfortable speaking English, and technical terms such as multi-hazards and nature-based solutions required clarification. Integrating diverse data types, from ecological measurements to soil stability and tourism impacts, into a coherent framework risks inconsistencies, and local results resist generalization. Their research agenda responds with concrete prescriptions: interdisciplinary teams familiar with the region, standardized mixed-methods protocols for local multi-hazard risk, pre-testing surveys before fieldwork begins, preliminary analysis during the trip to catch data problems while they can still be fixed, and genuine incorporation of local knowledge. Done well, they conclude, the iterative integration of methods bridges the gap between scientific modelling and community realities, producing adaptation strategies that are simultaneously evidence-based, socially equitable and, crucially, accepted by the people they are meant to protect.

Subject of Research: Mixed-methods approaches to local multi-hazard climate risk assessment and nature-based solutions in Tenerife

Article Title: Breaking the methodological silos: Integrating Mixed-Methods in Multi-Hazard Risk Assessment Lessons learnt from fieldwork in Tenerife

Article References: García Álvarez, G., & de Ruiter, M. C. (2026). Breaking the methodological silos: Integrating Mixed-Methods in Multi-Hazard Risk Assessment Lessons learnt from fieldwork in Tenerife. PLOS Climate, 5(10), e0001070. https://doi.org/10.1371/journal.pclm.0001070

Image Credits: AI Generated

DOI: 10.1371/journal.pclm.0001070

Keywords: multi-hazard risk, mixed methods, Tenerife, nature-based solutions, climate adaptation, risk perception, disaster risk reduction, fieldwork, stakeholder interviews, GIS mapping, PLOS Climate, Canary Islands

The conversation’s central conceptual move is a rejection of the idea that authoritarianism is a place. Rather than speaking of authoritarian states or authoritarian contexts, Wolfe prefers the phrase spaces shaped by authoritarian practices, following the political scientist Marlies Glasius, who urges analysts to focus on practices rather than regime types. This reframing matters because it dissolves the familiar mental map that divides the world into democratic and authoritarian nation-states, a binary the authors call a container imaginary. Once the lens shifts to practices, such as restricted access to information, censorship, and the concentration of power over knowledge production, authoritarian behavior becomes visible in unexpected places. Wolfe points to the 2024 Paris Olympics, arguing that the way certain Paris neighborhoods were treated bore a stunning resemblance to the treatment of communities ahead of the 2014 Sochi Games, a parallel that traditional comparative frameworks would never prompt researchers to draw.

Kuletskaya deepens the definition by locating authoritarianism in the control of knowledge itself. She invokes Robert Caro’s account of Robert Moses in The Power Broker and Bent Flyvbjerg’s study of planning in Rationality and Power, both set in democracies, to argue that Moses’s methods, including the blatant withholding of information and the silencing of journalists and researchers who questioned his projects, fully fit the definition of authoritarian practice. For her, the core question is who holds the power to create knowledge about the processes we observe, and how tightly that production is controlled. Wolfe adds that this concentration of power explains why authoritarian-leaning governments so often turn hostile toward universities, which represent an alternative form of knowledge production. At its best, academic knowledge challenges dominant narratives and the power to define reality, which the authors describe as the heart of the authoritarian project: to dictate what is real and what is not.

Yet reality has a stubborn way of resisting. The two scholars trade examples of authoritarian projects colliding with material facts. Wolfe recalls the Soviet agricultural theorist Trofim Lysenko, who insisted that farming follow Marxist-Leninist ideology rather than genetics, with catastrophic results, because seeds grow under conditions and do not conform to ideology. Kuletskaya counters with a contemporary case: reporting by the Financial Times on Saudi Arabia’s Neom project, in which even an extreme concentration of power and resources in one person’s hands could not bend the laws of physics in pursuit of a linear city. The lesson, they suggest, is that authoritarian truth projects can punish dissent and enforce an official reality, but a diversity of thought persists, and part of the academy’s role is to identify and amplify the excluded voices that carry it.

The methodological heart of the conversation concerns what happens to research when a field closes. Kuletskaya began studying Minsk in 2018, before her PhD, examining the government-aligned real estate developer Dana Holdings, owned by the Serbian Karić oligarch family. At the time the context was relatively open: she could travel to Belarus, collect data, and even present at the government-supported Minsk Design Week in 2019. Then came the 2020 protests and the subsequent closure. She has not been able to return since 2021, and her publications now rest either on material gathered earlier or on remotely accessible sources, including documentation produced by witnesses who shared their data. This raises, she says, difficult questions about how researchers should use data collected by others, particularly when the original collectors risk persecution.

Data availability and data reliability emerge as the twin pillars of the problem. In Minsk, some information vanishes faster than it can be recorded: when COVID-19 arrived, life expectancy statistics disappeared from public view because they could be used to calculate excess mortality the government intended to withhold. Reliability is equally fraught. In one case, an activist group verified official mortality statistics and found major discrepancies with the actual figures, but verification required creative methods, including hacktivism, far beyond the toolkit of conventional Western scholarship. Kuletskaya’s advice is blunt: save and document everything as you go, and if material carries any political dimension, question and verify every piece of it, even something as seemingly straightforward as mortality statistics. Wolfe learned the same lesson painfully in Russia, where independent blogs hosted much of the country’s best investigative journalism; when those platforms were shut down, anything not saved locally was gone forever.

Wolfe’s own trajectory illustrates another key concept: the shifting authoritarian red line, borrowed from Glasius, which divides what can be said and done from what cannot. When he first moved to Russia in 1998, the country was far freer, and he never applied a political label to his everyday experiences, such as being stopped by police and asked for papers. He noticed no widespread change until Putin’s return for a third term in 2012, after the Bolotnaya protests, when processes that had been bubbling under the surface became visible and the screws tightened for real. When Wolfe moved back in 2013 as a researcher, his ordinary practices, walking, talking to people, photographing buildings, suddenly brought him to the attention of the authorities. He crossed a red line that had moved beneath him. The line, he stresses, is not stable, and it can even shift retroactively, a point with devastating implications.

Retroactivity is where the ethical stakes become sharpest. In Belarus, legal cases are still being initiated five years after the 2020 mass protests based on photographs or social media posts made when those actions were legal. Iconic protest images that circulated globally were later used to identify and prosecute people in them, and a journalist who scraped publicly available protest videos into a documentary drew renewed official attention that exposed people in the footage. Wolfe notes that social media data carry an expectation of privacy even when publicly accessible, and here the harm was not theoretical but real. He reads retroactive law as part of a deeper authoritarian ambition to control time itself, exerting will over both past and future, with the spread of fear as the primary goal. Kuletskaya observes that the rewriting of the past to serve the present is an old mechanism of historiography; only the timescale is new.

The conversation also confronts the tangled position of researchers themselves. Wolfe recounts the anthropologist Katherine Verdery, who studied communist Romania believing herself too insignificant to monitor, only to receive roughly three thousand pages of her Securitate surveillance file after the regime fell, revealing that some of her close friends had informed on her and that her presence had endangered them. Verdery describes the researcher as radioactive, and Wolfe knows the feeling: after being expelled from Russia, he no longer contacts friends there because his association could put them at risk. Neither scholar calls themselves an activist, but both reject neutrality as the alternative. Kuletskaya’s formulation is precise: a researcher can be non-neutral but must strive toward objectivity, being explicit about sources, practices, data, and how opinions are formed. The state, meanwhile, may classify the same work as activism or espionage, as Russian authorities did with Wolfe, transforming him into a legal category that justified expulsion.

Their warning to Western audiences is stark. They describe the scrubbing of climate, gender, and inequality data from United States government sites after the 2025 inauguration, the temporary 404 errors on the White House website, and Gallup’s decision to end 88 years of tracking presidential approval ratings, developments they read as following the authoritarian playbook. Kuletskaya, viewed from the East, finds the American events strikingly familiar but shocked by their speed: what took decades in Eastern Europe takes months in the United States. The authors close by invoking Václav Havel’s idea of living within the truth, arguing that the minimal response is to name authoritarian strategies as such. Naming things as they are, Kuletskaya concludes, is the core purpose of the academic project, and one of its main values lies in holding a diversity of perspectives, from the optimistic American to the doomsday Eastern European, against the narrowing of reality that authoritarianism demands.

Subject of Research: Methodological and ethical challenges of academic research in contexts shaped by authoritarian practices

Article Title: Researching the authoritarian: a conversation on methodology and ethics in closed contexts

Article References: Wolfe, S. D., & Kuletskaya, D. (2026). Researching the authoritarian: a conversation on methodology and ethics in closed contexts. Geographica Helvetica, 81(2), 297-304. https://doi.org/10.5194/gh-81-297-2026

Image Credits: AI Generated

DOI: 10.5194/gh-81-297-2026

Keywords: authoritarianism, research ethics, fieldwork, Russia, Belarus, data censorship, surveillance, academic freedom, self-censorship, knowledge production, post-Soviet studies, democratic backsliding

The research, led by Rafael K. Horota of the University Centre in Svalbard (UNIS) and the University of Bergen, together with colleagues Christian H. Eide, Kim Senger, Marius O. Jonassen, and Marie A. Vander Kloet, surveyed 66 students enrolled in bachelor’s, master’s, and PhD-level Arctic geology and geophysics courses. The team framed their investigation around two questions: how students perceive the usefulness of digital field representations in their learning, and how these tools help manage what education researchers call the novelty space, the cognitive and emotional burden of encountering an unfamiliar environment for the first time.

The technology at the heart of the study is part of the Svalbox initiative, an effort begun at UNIS in 2017 to systematically digitise Arctic outcrops. VR Svalbard, launched in 2021, weaves together drone-based photogrammetry, 360-degree photospheres, map layers, and three-dimensional terrain data into Virtual Field Guides and Virtual Field Tours. Students can rotate around digital outcrop models, measure bed orientations, trace structural features, and zoom seamlessly between landscape-scale overviews and centimetre-scale detail, all from a browser. The platform currently offers six Virtual Field Guides and 175 Virtual Field Tours covering Svalbard’s remarkably diverse terrain, from sedimentary basins to traces of ancient mountain-building events, glaciations, and magmatic activity.

To evaluate how students responded, the researchers combined the Technology Acceptance Model, a well-established framework for measuring whether people find a tool useful and easy to use, with pre-field and post-field questionnaires built around Kolb’s Experiential Learning Theory. That theory describes learning as a four-stage cycle: concrete experience, reflective observation, abstract conceptualisation, and active experimentation. In field education, these stages traditionally unfold as in-situ observation, interpretation, and iterative testing of geological ideas. The study asked whether virtual tools could scaffold each stage before, during, and after physical excursions.

The results were striking. Of the surveyed students, 86 percent had used the platform at least once, 71 percent during field preparation lectures, and 55 percent in post-fieldwork exercises. Perhaps most tellingly, 53 percent explored the platform independently out of personal curiosity, and more than half engaged with it beyond their scheduled class time. Students also used it to plan their own fieldwork, revisit specific localities, and extract visual data for assignments. On the acceptance measures, items such as using VR Svalbard would enhance my effectiveness in the field scored a mean of 6.0 on a seven-point scale, while ease-of-use statements scored around 5.9, indicating that the tool was intuitive enough that technological friction did not eat into cognitive resources needed for geological reasoning.

The educational impact data suggest the virtual previews did exactly what experiential learning theory would predict. Before excursions, students rated the statement that visiting field localities virtually made the excursion more interesting at a mean of 4.0 on a five-point scale, and geographic orientation benefits scored 4.1. A total of 83 percent agreed or fully agreed that the virtual experiences improved their geographic orientation, and 85 percent positively evaluated the multi-scale integration of GIS maps and aerial imagery. After fieldwork, students reported that revisiting sites digitally helped them consolidate observations, with 75 percent saying the virtual visits contributed to their preparedness and 82 percent saying that virtually visiting additional localities complemented their learning. Students felt better prepared overall, with a mean score of 4.1 for post-field revisiting.

Some of the most vivid evidence came from students’ own words. One noted that seeing a site in its summer condition helped them understand what lay beneath the snow they encountered in the field. Another observed that their class had only seen 10 percent of an outcrop during a spring excursion, while VR Svalbard revealed the rest. These comments capture a key advantage of digital representations in the Arctic: seasonal snow cover, limited daylight, weather windows, and logistical constraints mean that physical access to outcrops is often partial and fleeting, whereas a digital model can be revisited indefinitely, in any season, at any scale.

Crucially, the students did not view the technology as a substitute for the real thing. Support for partially replacing fieldwork scored a lukewarm mean of 3.0, and most participants explicitly rejected the idea that virtual experiences could stand in for physical fieldwork. Instead, they positioned the tools as complements: instruments for preparation, aids for reflection, resources for revisiting inaccessible features, and extensions of spatial visualisation. Within the novelty space framework, the researchers argue that virtual previews calibrate rather than eliminate novelty, allowing students to arrive in the field with preliminary mental models already formed, so that limited and expensive field time can be devoted to observation and interpretation rather than situational adjustment.

The study also carries broader implications for equity and sustainability in geoscience education. Fieldwork has long been criticised for excluding people with disabilities, for costs that burden students and institutions, particularly in the global south, and for a disciplinary culture that has historically privileged masculine displays of physical competence while leaving safety concerns around gender, sexuality, and race inadequately addressed. Virtual field experiences cannot dissolve these structural problems, but the authors argue they can widen participation, offer guaranteed access to sites in daylight and good visibility, and function partly like preparatory guides that reduce anxiety for students, including neurodivergent students, facing unfamiliar environments. There is a carbon dimension as well: as scrutiny of air travel intensifies, digital extensions of field learning offer a way to balance experiential depth with ecological responsibility.

The economics are surprisingly favourable. The researchers report that developing the online platforms cost roughly 10,000 euros in total, with about 1,000 euros per year for web hosting, while consumer drones such as the DJI Mavic series used for data acquisition cost around 2,000 euros each. The expensive part is Arctic fieldwork itself, which is precisely why the team recommends opportunistically capturing photospheres and digital outcrop models during courses and research campaigns that are already running. At UNIS, where educating one student-year costs about 10,000 euros and individual courses operate on budgets of 30,000 to 50,000 euros, the authors conclude that virtual field experiences are not a luxury but a necessity. For institutions elsewhere, the moderate resource requirements should make adoption straightforward. The researchers caution that their findings rest on self-reported perceptions within a single institutional context, and they call for future work measuring actual learning gains, explicitly quantifying cognitive load, and co-designing the tools with students and educators. But the message from the edge of the Arctic is clear: when thoughtfully integrated, digital field representations do not dilute the field experience. They extend it across time, space, and circumstance, making one of science education’s most powerful traditions available to more students than ever before.

Subject of Research: Virtual field experiences in High Arctic geoscience field education

Article Title: Digital field representations as a holistic approach to experiential learning in High Arctic geoscience field education

Article References: Digital field representations as a holistic approach to experiential learning in High Arctic geoscience field education. (n.d.). https://doi.org/10.5194/gc-9-311-2026

Image Credits: AI Generated

DOI: 10.5194/gc-9-311-2026

Keywords: virtual field experiences, geoscience education, Svalbard, experiential learning, VR Svalbard, fieldwork, photogrammetry, novelty space, Technology Acceptance Model, inclusivity, Arctic, UNIS

The study, led by Roel Dijksma and colleagues at Wageningen’s Hydrology and Environmental Hydraulics group, took advantage of a rare natural experiment. Since 2009, the university’s Master course in Catchment and Climate Hydrology has culminated in an intensive eight-day excursion across Iceland, where students learn to read complex, largely undisturbed landscapes and reconcile what they see with the simplified hydrological models they have built in class. When pandemic restrictions halted all outdoor teaching in 2020 and 2021, a professional video team produced a virtual version of the excursion, filmed on location with the teaching staff. The cohorts of 2020 and 2021, some 105 students in total, completed the course using only this digital material.

Then came the twist. In April 2022, once restrictions lifted, the university offered an additional voluntary excursion to Iceland for those same students. Roughly half, 54 of the 105, were able to join. That created three distinct groups within a single student population: those who had only attended the real excursion, those who had only studied the virtual version, and those who first worked through the virtual materials and later stood on Icelandic soil themselves. The researchers seized the opportunity to send a detailed questionnaire to excursion participants spanning every cohort from 2011 to 2025, asking how the experience had shaped their careers, their scientific thinking, and their views on virtual versus real fieldwork.

The response was striking. Of 134 questionnaires sent to a randomly selected 20 percent of the 676 total participants, 104 were completed, a response rate of 78 percent. Respondents ranged from current Master students to senior scientists with more than fifteen years of professional experience. More than half strongly agreed that planning and fieldwork remain an important part of their current job or study, and the average response across all respondents was moderately agree. Even among the most senior alumni, whose careers have drifted toward modelling and management, the excursion’s contribution to their scientific development was still clearly recognized, though its perceived intensity softened slightly with the passing years.

The starkest contrasts emerged when the virtual-only cohort was compared with everyone who had physically travelled to Iceland. Students who had only studied the video materials were neutral about whether the modelling concepts discussed during the excursion echoed into their later work, while their peers who had made the journey were clearly positive. On the contribution to understanding undisturbed hydrological systems and to overall scientific development, the virtual-only group scored substantially lower, falling to the slightly-to-moderately-agree range, whereas the field participants were strongly to fully in agreement. When asked to distribute ten points between the virtual and the real excursion, the students who had experienced both allocated 8.9 points to the real trip and just 1.1 to its digital counterpart.

Why does the difference run so deep? The students themselves pointed to several factors that a camera, however professional, cannot replicate. They missed the active, location-based learning in a landscape of undisturbed catchments, the direct and inspirational interaction with teaching staff, and the chance to bond with peers into a professional network that lasts well beyond graduation. One participant captured the sentiment in a reflection quoted in the paper: you can learn a lot in a classroom, but an excursion makes you remember. Another described the trip as capturing the very essence of studying, being curious, feeling passionate, and loving the process of learning. The virtual-only group also remained neutral on whether a trip to a more heavily managed area closer to home could substitute for Iceland, while the field participants firmly disagreed, suggesting that the visceral experience of an unmanaged landscape shaped their judgment in ways the video never could.

The choice of Iceland is no accident, and the paper devotes considerable attention to why the island is such a powerful teaching environment. Straddling the Mid-Atlantic Ridge and fed by a mantle plume, Iceland is the only place on Earth where an oceanic ridge rises above sea level thanks to a hotspot, producing young mafic rocks roughly 20 million years old with strikingly different hydraulic properties from older formations. The Gulf Stream warms the southwest while cold Arctic air dominates the north, and the mountains and ice caps boost precipitation by about 40 percent, generating a rainfall gradient from under 400 millimetres per year in the dry northeast to more than 4,000 on some glaciers, and locally even above 7,000. The result is a compact natural laboratory of groundwater-fed rivers, direct runoff rivers and glacier-fed streams, braided and meandering channels, and catchments that remain relatively free of human management.

Climate change is written into that landscape in real time. Glaciers cover about 11 percent of the country, and all of them face strong net ablation, so the share of glacier meltwater in river discharge is rising, with rivers below the large ice caps carrying visibly more meltwater than before, while streams fed by small glaciers are losing their summer melt signal altogether. For students trained to conceptualize precipitation-runoff relationships, flow generation processes, drought propagation and flood forecasting, seeing these processes unfold across such contrasting settings, and then confronting how crudely their computer models represent them, appears to leave a lasting imprint. The survey found that all respondents, regardless of cohort, strongly agreed that the excursion deepened their grasp of undisturbed hydrological systems.

The findings come with honest caveats. The authors, all of whom teach on the excursion, acknowledge their own attachment to the course and took steps to limit bias by keeping the survey anonymous and examining the results from multiple angles. Response rates for the pandemic cohorts were lower than hoped, with only 40 percent of contacted students completing the questionnaire, and some declined because the memory of that stressful period remained too raw. One participant noted that although grateful for the effort behind the virtual excursion, the combination with lockdown was devastating. The authors also note that opinions on flying may have shifted over the years, though the survey showed no trend across cohorts. Still, 74 percent of respondents considered it acceptable to fly to Iceland for the excursion, only 13 percent disagreed, and the researchers argue that careful, selective travel for transformative field learning remains defensible.

The overall conclusion is measured but firm. Virtual excursions, especially as production quality improves and tools like animations and artificial intelligence mature, can be a valuable addition to the teaching toolbox, useful for preparation, revision, or reaching students who cannot travel. But as a replacement for the real thing, they fall short. The students who lived through both versions are unambiguous: the field trip fostered relatedness with peers and teachers, boosted intrinsic motivation across the whole course, and delivered learning that no classroom or screen could match. As the paper’s closing sentiment from a participant suggests, the essence of geoscience education still lies in standing before a river, feeling the wind off an ice cap, and working out for yourself how the water got there.

Subject of Research: Comparing the perceived educational impact of real versus virtual hydrology field excursions among Master students

Article Title: Field or virtual? Perceived impact of academic excursions through the eyes of (future) hydrologists

Article References: Dijksma, R., Teuling, A. J., Melsen, L. A., Bense, V. F., & van der Ploeg, M. J. (2026). Field or virtual? Perceived impact of academic excursions through the eyes of (future) hydrologists. Geoscience Communication, 9(4), 437-450. https://doi.org/10.5194/gc-9-437-2026

Image Credits: AI Generated

DOI: 10.5194/gc-9-437-2026

Keywords: hydrology education, field excursions, virtual field trips, Iceland, Wageningen University, geoscience communication, COVID-19, catchment hydrology, climate change, student survey, fieldwork, scientific development

Melbourne’s experience was extraordinary even by pandemic standards. The city was subject to public health restrictions prohibiting people from gathering for work or study for a cumulative total of 262 days across 2020 and 2021, the longest continuous lockdown anywhere in the world. For Currell, who had coordinated two hydrogeology coursework units with between 20 and 55 students since 2013, the implications were immediate. The shift to fully online delivery occurred just before the first week of the Australian academic year in March 2020, leaving almost no time to prepare. Like most of his colleagues worldwide, he was ill-equipped for the task, with little prior experience in delivering fully online content and assessment, and was simultaneously juggling care responsibilities, social isolation, and an intermittently unstable internet connection while teaching from home.

The technical response to the crisis reveals both the ingenuity and the limits of emergency online pedagogy. Rather than recycling pre-existing lecture recordings, Currell chose to rebuild his courses around livestreamed, recorded lectures and tutorial sessions, supplemented with new video content designed for viewing outside scheduled class times. For the introductory unit, the traditional weekly lecture and tutorial structure was retained. The advanced course adopted weekly ‘lectorial’ sessions for five weeks, followed by a three-day online intensive focused on solving groundwater flow and solute transport problems using the industry-standard MODFLOW-MT3D modeling codes. During the mid-year academic break in 2020, special permission allowed the instructor to work with a professional videographer to produce a 60-minute video demonstrating groundwater concepts using a benchtop model of a heterogeneous layered aquifer system, in which dyes injected at different locations trace flow paths and solute movement.

That video production, born of necessity, turned out to be a genuine pedagogical improvement. The format permitted close-up shots and time-lapse photography that had never been possible in a live classroom demonstration, allowing students to see the effects of changing hydraulic conductivity and hydraulic gradient on groundwater flow paths, solute transport, and ground-surface water interaction in unprecedented detail. The video was linked directly to an assessment task testing conceptual understanding, and it stands as a concrete example of how the pandemic accelerated the creation of digital teaching resources that can permanently enrich hydrogeology instruction when they are carefully planned and curated with adequate time and funding.

Not every element of the coursework translated so smoothly. Numerical modeling sessions, previously run as five full days of in-person practical work using MODFLOW and MT3D to build three-dimensional flow and transport models, were compressed into three days to avoid digital fatigue among students, supported by self-paced video tutorials. Small-group collaboration was salvaged through Microsoft Teams breakout rooms, which allowed students to share developing models and let the instructor troubleshoot individual problems remotely. Students appreciated the effort, but their feedback was unambiguous about the trade-off. One student reported that it took far longer to work through MODFLOW issues alone online than it would have to physically show someone the problem and receive immediate help. Both students and instructor concluded that direct, in-person guidance in building and debugging numerical models remains preferable even when screen sharing and breakout rooms are available.

The most profound loss, however, was the field trip. Before the pandemic, each semester of the introductory course included a full-day excursion to groundwater monitoring sites across a small coastal sedimentary aquifer historically affected by saltwater intrusion driven by high rates of seasonal extraction for irrigation. Students measured groundwater levels, collected samples, observed physicochemical characteristics, and recorded lithological features in soil and aquifer material, gradually assembling a working conceptual model of the system. In 2020 the trip could not run, and students instead completed desktop research using maps, reports, and hydrographs published online. While this built valuable skills in desk-based hydrogeological assessment, fundamental practical competencies that most students would encounter nowhere else in their degree simply could not be taught. The field trip was reinstated in 2021, and the instructor’s analysis aligns with a growing body of evidence that virtual field trips can complement but never replace in-person field training.

Assessment practices underwent their own controversial transformation. Supervised in-person examinations, the largest component of assessment before the pandemic, were replaced with 24-hour take-home online exams. Currell’s experience echoes findings from across the university sector: students typically undertook far less revision in preparation, relying on accessing information on demand during the exam rather than learning methods in advance and applying them to problems. The result was relatively poor exam performance and, arguably, a shallower understanding of the material. The concern has only intensified with the arrival of generative artificial intelligence, which makes unsupervised online assessment increasingly difficult to police. Yet a 2025 study by Newton and Draper found that more than 75 percent of surveyed UK universities were still using unsupervised online examinations during 2023 and 2024, raising what the essay describes as serious questions about quality assurance in higher education.

Perhaps surprisingly, the data on engagement told a more nuanced story. Comparing attendance figures and digital analytics tracking unique downloads and views of online material across pre-pandemic, pandemic, and post-pandemic years, Currell found that lecture and class attendance were broadly similar across all formats. The availability of online material, often promoted by universities as a way to broaden access for busy or remote students, had little impact on overall engagement numbers in this case. What mattered was structure: the highest levels of genuine engagement, as opposed to mere attendance, occurred wherever students were encouraged to discuss key ideas and case studies in groups and contribute to class discussions in a structured way, whether through breakout sessions in video call software or in a traditional classroom. The persistent challenge online was reaching quieter students who chose not to activate their cameras or microphones, and the social isolation imposed by lockdowns demonstrably limited the peer-to-peer interaction that underpins deep learning.

Student satisfaction ratings tell a story of resilience with a measurable cost. The two courses achieved overall satisfaction scores of 94.7 and 94.1 percent during fully online delivery, with students praising the efficiency of the transition and the use of real-world case studies. Yet both figures represented a slight decline from the 100 percent satisfaction recorded in the two years prior, a dip the essay attributes in part to the online engagement fatigue documented by researchers as the pandemic wore on. Qualitative comments revealed no serious concerns about overall course quality, but repeatedly flagged the modeling tutorials and the absence of fieldwork and face-to-face collaboration as the weak points of the online experience, with one student noting that a friend dropped the course after losing personal connections when teaching moved online.

Beneath these pedagogical questions lies a sharper equity problem that gives the essay its title. Currell’s experience during the pandemic was that many students, contrary to the stereotype of the well-equipped digital native, lacked the IT hardware, software, connectivity, or simply a stable, disturbance-free space in which to study effectively. University grant schemes sometimes helped with basic equipment but almost never addressed the need for an adequate working environment. This digital inequality, which researchers identify as a pressing and unresolved issue across tertiary education, threatens to create a divide in groundwater education precisely because many of the world’s most urgent groundwater crises affect low-income communities whose aspiring hydrogeologists have the least access to quality learning infrastructure. Currell argues that traditional classroom formats offer a standardized mechanism to level the playing field for many aspects of hydrogeology education, while initiatives such as The Groundwater Project, which publishes free online textbooks in multiple languages, help ensure that students who cannot attend in-person classes are not excluded. As universities continue to expand online offerings in pursuit of new audiences, the essay’s central warning is clear: online tools can enrich groundwater education, but only if the field’s irreplaceable practical, field-based, and interpersonal dimensions, and the students least served by digital delivery, are deliberately protected.

Subject of Research: The transition of hydrogeology education to online delivery during the COVID-19 pandemic and its implications for teaching quality and equity

Article Title: Preventing a groundwater divide: Ensuring quality hydrogeology education for all in the age of online coursework following the COVID-19 pandemic

Article References: Currell, M. (2026). Preventing a groundwater divide: Ensuring quality hydrogeology education for all in the age of online coursework following the COVID-19 pandemic. Hydrogeology Journal. https://doi.org/10.1007/s10040-026-03162-8

Image Credits: AI Generated

DOI: 10.1007/s10040-026-03162-8

Keywords: hydrogeology education, groundwater, online learning, COVID-19 pandemic, fieldwork, MODFLOW, digital inequality, numerical modeling, virtual field trips, assessment, Australia, higher education

The authors’ central concern is not that AI is ineffective—quite the opposite. Machine learning models now process environmental datasets at scales and speeds no human team could match, compressing research timelines that once stretched over years into weeks or even days. Neural networks can classify satellite imagery of deforestation, predict groundwater contamination plumes, reconstruct historical climate patterns, and optimize pollution control strategies with remarkable accuracy. The visual outputs of these models, the researchers note, have also enhanced the readability of scientific publications, making complex geospatial patterns accessible to broader audiences and accelerating the communication of findings across disciplines. In fields ranging from hydrology to atmospheric science, AI has become an indispensable partner in discovery, echoing earlier demonstrations that machine learning can drive data-driven breakthroughs in solid Earth geoscience.

Yet this efficiency comes with a hidden cost. The paper describes a growing tension between the speed of AI-assisted research and the slow, methodical work of collecting primary data in the field. Field campaigns, borehole sampling, sediment coring, and controlled laboratory experiments are expensive, time-consuming, and often unglamorous, and they are increasingly being displaced by analyses that reuse existing datasets. Younger researchers, facing publication pressures and funding cycles that reward rapid output, may rationally choose to mine legacy data rather than generate new observations. The authors warn that this shift creates a systemic vulnerability: if the community stops producing primary data, the training sets on which future AI models depend will stagnate, become outdated, or fail to capture the rapidly changing conditions of a warming planet.

This concern about data sustainability is perhaps the paper’s most striking argument. AI models are only as good as the data they learn from, and most environmental AI applications rely heavily on legacy datasets collected under climatic and land-use conditions that no longer exist. The authors caution that overreliance on unvalidated global secondary data—datasets compiled from heterogeneous sources, often without rigorous quality control—threatens the reproducibility of scientific findings. Worse, as climate change alters hydrological cycles, soil chemistry, and ecosystem dynamics at unprecedented rates, historical datasets may become systematically unrepresentative of future conditions. A model trained on twentieth-century river discharge records, for example, may fail catastrophically when applied to a watershed transformed by drought, urbanization, or glacial melt. The researchers describe this as a looming risk of future data scarcity: a generation of scientists skilled in algorithms but increasingly disconnected from the empirical grounding needed to generate the data of tomorrow.

The paper also engages with a broader debate about what AI does to scientific understanding itself. Citing recent work on the illusions of understanding that can arise when researchers delegate interpretation to opaque models, the authors argue that AI can automate routine data processing but cannot substitute for human critical thinking in formulating fundamental scientific questions. A machine can identify correlations across millions of environmental measurements, but deciding which correlations matter, which mechanisms deserve investigation, and which hypotheses would genuinely advance the field requires human judgment, intuition, and disciplinary expertise. The authors cite evidence that AI tools, while expanding scientists’ overall impact, may paradoxically contract the focus of science, narrowing the diversity of questions asked and the range of approaches pursued. In environmental geoscience, where problems are inherently interdisciplinary and context-dependent, such narrowing could be particularly damaging.

Explainability and trustworthiness emerge as additional technical challenges. Environmental decisions—where to site a landfill, how to remediate an aquifer, whether to restrict industrial emissions—carry enormous consequences for public health and ecosystems. Black-box models that achieve high predictive accuracy but offer no insight into underlying mechanisms are difficult to defend in regulatory and policy contexts. The authors point to ongoing efforts to develop explainable AI frameworks specifically for environmental and Earth system sciences, arguing that trustworthiness must be designed into models rather than assumed. They emphasize that mechanistic understanding remains essential: a model that predicts contaminant transport without capturing the underlying geochemical processes cannot be reliably extrapolated beyond its training domain, and its predictions cannot be meaningfully validated by independent evidence.

The systemic disruptions extend to the sociology of research itself. The authors observe that AI is changing career trajectories, collaboration patterns, and the distribution of credit within environmental geoscience. Teams that command large datasets and computational resources gain outsized influence, while groups with deep field expertise but limited computational capacity risk marginalization. This asymmetry is particularly pronounced between well-funded institutions in wealthy countries and research communities in data-rich but resource-poor regions of the Global South, where much of the world’s most critical environmental change is unfolding. The paper implicitly raises questions about scientific equity: who owns environmental data, who benefits from AI-derived insights, and whose observations of local environmental degradation make it into the global training sets that shape our collective understanding of the planet.

Despite these warnings, the authors are careful not to position themselves as AI skeptics. Their vision is a balanced, diversified research ecosystem in which AI and empirical approaches are complementary rather than competitive. They advocate for continued investment in primary data collection, long-term monitoring networks, and mechanistic experimentation, even as AI tools are responsibly integrated into analysis and modeling workflows. They call for rigorous validation of secondary datasets, transparent reporting of model limitations, and training programs that equip the next generation of environmental scientists with both computational skills and hands-on field experience. The goal, they argue, is not to slow the AI transition but to ensure that it strengthens rather than hollows out the discipline’s empirical foundations.

The timing of the paper is significant. As generative AI tools spread through every scientific field, environmental geoscience faces a distinctive version of the dilemma because its subject matter—the Earth’s surface, subsurface, and atmosphere—is changing faster than at any point in the instrumental record. The authors’ message to the community is ultimately one of stewardship: the datasets being generated and curated today will determine what AI can achieve in environmental science for decades to come, and the scientific questions being asked now will shape whether the field’s algorithms illuminate the planet’s trajectory or merely recycle the assumptions of a world that no longer exists. Preserving the human capacity to ask fundamental questions, they conclude, is not a nostalgic attachment to old methods but a strategic necessity for a science confronting unprecedented environmental change.

Subject of Research: The opportunities and risks of artificial intelligence integration in environmental geoscience research

Article Title: The impact of artificial intelligence on environmental geoscience research

Article References: Shu, L., Yu, Z., Wu, M., Yuan, Q., Zhu, Y., Zhang, Y., Liang, X., Wang, Y., Liu, B., & Wang, X. (2026). The impact of artificial intelligence on environmental geoscience research. ENGINEERING Environment, 20(12), Article 194. https://doi.org/10.1007/s11783-026-2294-6

Image Credits: AI Generated

DOI: 10.1007/s11783-026-2294-6

Keywords: artificial intelligence, environmental geoscience, data sustainability, machine learning, fieldwork, empirical data, research paradigm, scientific reproducibility, climate change, explainable AI, scientific policy, primary data collection

The grant was first secured in 2023 by Andrea Dávalos, associate professor of biological sciences at SUNY Cortland, and Li Jin, professor of geology, with Mary Beth Voltura, associate professor of biological sciences, joining them to help develop the project. What began as a component of the Coregonine Restoration Framework, a binational plan representing eight U.S. states, three U.S. intertribal organizations and the Canadian province of Ontario to restore and conserve the coregonine subfamily of freshwater fish, has since expanded into wider opportunities for students, while keeping its focus firmly on aquatic life and the ecosystems that sustain it.

This year, the Cortland portion of the grant totaled 73,315 dollars, enough to support five Cortland students and two Oswego students in research placements across the state. Although the budget is modest by the standards of large research programs, the faculty behind the initiative argue that its impact is disproportionate to its size. Jin emphasized that the paid structure of the internships matters as much as the science itself, because it allows students to pursue valuable professional opportunities without the burden of financial stress that often forces undergraduates to choose between earning money and gaining research experience.

The internships are designed to immerse students in the full workflow of professional fisheries science. According to Dávalos, participants engage in original research, hone their communication skills and learn a variety of field and laboratory techniques used in fisheries work. Just as importantly, they interact daily with professionals, graduate students and professors, building the kind of networks that frequently shape career trajectories long before graduation. She described networking as one of the great aspects of the internship, noting that these relationships expose undergraduates to career paths they might never otherwise encounter.

Among this year’s participants, the placements spanned some of New York’s most important aquatic research institutions. Jonathan Hall worked at the Cornell Biological Field Station at Shackelton Point, Isabella Impellizzeri was based at SUNY Oswego, Minju Kim conducted her research at the New York State Water Resources Institute at Cornell University, Vinoj Ramachandran worked with the Department of Environmental Conservation in Cortland, and Ava Schiff was placed at the SUNY College of Environmental Science and Forestry’s Thousand Islands Biological Station. Each site offered a distinct window into the practice of fisheries and water-resources science.

Kim, a senior chemistry major with a minor in geographic information systems, spent her summer studying the effects of dam removal, a topic of growing importance as managers weigh the ecological benefits of restoring free-flowing rivers against the practical realities of aging infrastructure. Her work combined fieldwork, laboratory analysis and data processing, including bioindicator sampling, a technique that uses living organisms to assess environmental conditions over time. She said that participating in an active field research project provided invaluable insight into professional workflows and project execution, giving her a realistic picture of how environmental science is conducted outside the classroom.

The experience has already shaped Kim’s plans for life after graduation. She intends to pursue a career in environmental chemistry that applies environmental monitoring and sustainable ecosystem management, with a particular focus on water quality. Reflecting on the summer, she noted that the internship provided practical, professional skills in fieldwork and laboratory analysis that go beyond standard classroom theory, a distinction that many undergraduates only encounter for the first time in graduate school or their first job.

For Impellizzeri, a senior conservation biology major, the eight weeks at SUNY Oswego delivered a different but equally valuable set of skills centered on boat-based fieldwork. Her research contributed to weekly tows for larval fish at four sites, with the collected samples subsequently tested and identified in the laboratory. Along the way she completed New York boater safety training and drove a boat for the first time, and learned how to manage sample collection and water profile measurements on the water. She described the hands-on experience as invaluable, particularly the multitude of skills she acquired regarding boat procedures and safety.

The internship has also helped Impellizzeri clarify her next steps. Still weighing her career options after graduation, she said that working near the water proved highly interesting, and that she is now considering a job at one of the Great Lakes, whether conservation based or nature based in general. Her trajectory illustrates one of the central aims of the program: to give undergraduates enough direct exposure to fisheries science that they can make informed decisions about graduate study and employment in the field.

Faculty involved in the program say the combination of paid positions, covered housing costs and credit-earning placements has been central to its success, removing financial barriers while ensuring the experience carries formal academic weight. Applications for the Summer 2027 internships, the final year currently funded under the USGS grant, will open in the spring semester. As the Coregonine Restoration Framework continues its multi-state, multi-national effort to conserve a vulnerable subfamily of freshwater fish, the program’s graduates carry with them the field techniques, laboratory skills and professional connections that the next generation of fisheries scientists will need.

Subject of Research: Paid undergraduate fisheries research internships in New York funded by a USGS grant tied to the Coregonine Restoration Framework

Article Title: Students land research internships at fisheries across New York

Article References: Students land research internships at fisheries across New York. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: USGS grant, SUNY Cortland, SUNY Oswego, fisheries internships, undergraduate research, Coregonine Restoration Framework, dam removal, larval fish, water quality, New York State, aquatic science, fieldwork