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Hydrology plays a crucial role in understanding Earth's intricate water system and addressing water‐related problems, including against the backdrop of ongoing climate change. A retrospective review of the evolution of hydrology up to the current state of research is of great importance for understanding this role. While there have been some quantitative reviews of large numbers of hydrological publications, there still remains a lack of overarching hydrological research assessment, particularly with the focus on hydrological basins as fundamental spatial‐geographic units of hydrological analysis. Large language models, represented by OpenAI's ChatGPT, have demonstrated powerful textual understanding capabilities, making it possible to extract such overarching and basin information from hydrological publications. Here, we considered publications related to hydrology from Web of Science spanning January 1980 to October 2023, and parsed the information from this extensive body of literature by integrating a large language model and geocoding. These techniques enable quantitative analysis of research characteristics across different spatio‐temporal scales, focusing on hotspot topics, collaboration networks, and various basins worldwide. Our study revealed an increase in hydrological research since the 1990 s, with shifts in research priorities from groundwater and nutrients to climate change and ecohydrology. Some basins in North America and Europe have consistently been hotspots for hydrological research. Since the 2010s, there has been a noteworthy increase in interest toward basins in China and South Asia, but attention to many regions with frequent extreme rainfall remains insufficient. Geographical patterns show different preferred research topics for different basins, but climate change has emerged as the most prominent topic across all regions in the last decade. In conclusion, our study provides an effective approach to quantitative analysis of research trends, offering a fresh view on the evolution of hydrology as a research field, its focus on various hydrological basins around the world, and the emergence of overarching and basin‐specific hot topics over time. Key Points: We explore the literature review capability of a large language model combined with geocoding over the whole field of hydrological researchWe quantify the evolution of hydrological research, hotspot topics, collaboration networks, and basins in focus, worldwide over timeHydrological research publication exhibits major and accelerating growth with WRR emerging as the top influential journal over time [ABSTRACT FROM AUTHOR]

The article titled "Thank You to Our 2023 Reviewers" expresses gratitude to the individuals who reviewed manuscripts for the journal Water Resources Research (WRR) in 2023. The efforts of these reviewers have contributed to the high quality and impact of the journal's papers and the overall research in the field of water science. In total, 2,809 individuals provided 4,453 peer reviews, with 354 individuals reviewing three or more papers. The editors, associate editors, and authors of WRR sincerely thank all the reviewers and anticipate the publication of exciting advances in 2024. [Extracted from the article]

The out‐of‐sight groundwater and visible but much less extensive surface waters on land constitute a linked terrestrial water system around the planet. Research is crucial for our understanding of these terrestrial water system links and interactions with other geosystems and key challenges of Earth System change. This study uses a scoping review approach to discuss and identify topical, methodological and geographical gaps and priorities for research on these links and interactions of the coupled ground‐ and surface water (GSW) system at scales of whole‐catchments or greater. Results show that the large‐scale GSW system is considered in just a small part (0.4%–0.8%) of all studies (order of 105 for each topic) of either groundwater or surface water flow, storage, or quality at any scale. While relatively many of the large‐scale GSW studies consider links with the atmosphere or climate (8%–43%), considerably fewer address links with: (a) the cryosphere or coastal ocean as additional interacting geosystems (5%–9%); (b) change drivers/pressures of land‐use, water use, or the energy or food nexus (2%–12%); (c) change impacts related to health, biodiversity or ecosystem services (1%–4%). Methodologically, use of remote sensing data and participatory methods is small, while South America and Africa emerge as the least studied geographic regions. The paper discusses why these topical, methodological and geographical findings indicate important research gaps and priorities for the large‐scale coupled terrestrial GSW system and its roles in the future of the Earth System. Plain Language Summary: The water on the land surface (surface water) and that beneath it (groundwater), along with the water that is continuously and increasingly used and managed in human societies, are connected and constitute a coherent natural‐social water system around the world. Many unknowns and open questions remain for how the small‐scale variations add up to large‐scale variability and change of this water system on land, as an integral part of the whole Earth System. Relevant research is crucial for reducing the unknowns and answering the questions, and this study's scoping review aims to assess how they have been addressed in published research so far. The aim is to identify key research gaps and priorities for further research on how the integrated water system on land functions and evolves on large scales, from whole hydrological catchments and in multiple catchments around the world up to global scale. The scoping review results show key research gaps and priorities to be the coupling of surface water and groundwater on land, and the interactions of this coupled water system with other parts and major challenges of the Earth System. Geographically, the gaps and priorities emerge as particularly large and urgent for South America and Africa. Key Points: Coupling of the ground‐surface water system is a key gap in terrestrial water research, particularly at large scalesResearch on terrestrial water interactions with other geospheres and key challenges of Earth System change is rare but impactfulMajor geographic gaps in research on the large‐scale coupled terrestrial water system emerge for South America and Africa [ABSTRACT FROM AUTHOR]

Flooding is one of the most serious and frequent natural hazards affecting human life, property, and the environment. This study develops and tests a deep learning approach for large‐scale spatial flood modeling, using Convolutional Neural Network (CNN) and optimized versions combined with the Gray Wolf Optimizer (GWO) or the Imperialist Competitive Algorithm (ICA). With Sweden as an application case for nation‐wide flood susceptibility mapping, this modeling approach considers ten geo‐environmental input factors (slope, elevation, aspect, plan curvature, length of slope, topographic wetness index, distance from river, distance from wetland, rainfall, and land use). The GWO and ICA optimization improves model prediction by 12% and 8%, respectively, compared with the standalone CNN model performance. The results show 40% of the land area, 45% of the railroad, and 43% of the road network of Sweden to have high or very high flood susceptibility. They also show the aspect to have the highest input factor impact on flood susceptibility prediction while, for example, rainfall ranks only seven of the total 10 considered geo‐environmental input factors. In general, accurate nation‐wide flood susceptibility prediction is essential for guiding flood management and mitigation efforts. This study's approach to such prediction has emerged as well‐performing and cost‐effective for the case of Sweden, calling for further application and testing in other world regions. Plain Language Summary: Floods have the potential to negatively affect human wellbeing, infrastructures and the natural environment. Identifying regions prone to flooding is essential in avoiding such catastrophes. In the current study, standard and optimized convolutional neural network models are used to generate maps identifying the regions of Sweden with highest probability of flooding. Various topographic, hydrological, and anthropogenic factors are taken into account for the modeling. The analysis reveals numerous areas in Sweden prone to flooding, especially in the northern, central, and southeastern parts of the country. Malmo (the third largest city in Sweden) and some areas of Stockholm (capital) are the cities most susceptible to flooding. Additionally, considerable extensions of Sweden's roadways and railways might be impacted by floods. Accurate flood susceptibility mapping is required to assist policymakers and urban planners in implementing measures aimed at mitigating flood vulnerability. Furthermore, the findings show the ability of deep learning models for detecting flood‐susceptible areas, which can be implemented worldwide to improve flood management strategies and protect lives. Key Points: Deep learning model of convolutional neural network (CNN) was optimized and improved with gray wolf optimizer (GWO) and imperialist competitive algorithm (ICA) to detect flood‐prone areasDuring both the training and testing phases, the CNN‐GWO model demonstrated superior performance compared to CNN‐ICA and the standalone CNN modelThe results show 40% of the land area, 45% of the railroad, and 43% of the road network of Sweden to have high or very high flood susceptibility [ABSTRACT FROM AUTHOR]

The emergence of large language models (LLMs), such as ChatGPT, has garnered significant attention, particularly in academic and scientific circles. Researchers, scientists, and instructors hold varying perspectives on the advantages and disadvantages of using ChatGPT for research and teaching purposes. ChatGPT will be used by many scientists going forward for creating content and driving scientific progress. This commentary offers a brief explanation of the fundamental principles behind ChatGPT and how it can be applied in the fields of hydrology and other Earth sciences. The article examines the primary applications of this open artificial intelligence tool within these fields, specifically its ability to assist with writing and coding tasks, and highlights both the advantages and concerns associated with using such a model. Moreover, the study brings up some other limitations of the model, and the dangers of potential miss‐uses. Finally, we suggest that the academic community adapts its regulations and policies to harness the potential benefits of LLMs while mitigating its pitfalls, including establishing a structure for utilizing LLMs and presenting clear regulations for their implementation. We also outline some specific steps on how to accomplish this structure. Key Points: Large language models (LLMs) such as ChatGPT, are new technological tools that might fundamentally change academiaChatGPT can assist in academic writing but should not be relied on as the only source of information in hydrology and Earth Science studiesAuthors should exhibit transparency in their utilization of LLMs and uphold ethical responsibility [ABSTRACT FROM AUTHOR]

Drought's intensity and duration have increased in many regions over the last decades. However, the propagation of drought‐induced water deficits through the terrestrial water cycle is not fully understood at a global scale. Here we study responses of monthly evaporation (ET) and runoff to soil moisture droughts occurring between 2001 and 2015 using independent gridded datasets based on machine learning‐assisted upscaling of satellite and in‐situ observations. We find that runoff and ET show generally contrasting drought responses across climate regimes. In wet regions, runoff is strongly reduced while ET is decoupled from soil moisture decreases and enhanced by sunny and warm weather typically accompanying soil moisture droughts. In drier regions, ET is reduced during droughts due to vegetation water stress, while runoff is largely unchanged as precipitation deficits are typically low in these regions and ET decreases are buffering runoff reductions. While these water flux drought responses are controlled by the large‐scale climate regimes, they are additionally modulated by local vegetation characteristics. Land surface models capture the observed water cycle responses to drought in the case of runoff, but not for ET where the ET deficit (surplus) is overestimated (underestimated), related to a misrepresentation of the general soil moisture‐evaporation interplay. In summary, our study illustrates how the joint analysis of machine learning‐enhanced Earth observations can advance the understanding of global eco‐hydrological processes, as well as the validation of land surface models. Key Points: Contrasting drought response for both evaporation (ET) and runoff between wet and dry regionsET increases during drought in tree‐covered and wet regions, which in turn enhances runoff deficitsLand surface models capture runoff deficits but overestimate ET deficits due to misrepresented soil moisture‐evaporation interplay [ABSTRACT FROM AUTHOR]

Translational research (TR) represents a promising systematic process for going from scientific discoveries to practical applications. Through conversations with academics, practitioners, decision‐makers and users, there has emerged a broad level of water science community support for including TR in Water Resources Research (WRR) publications. Based on this, we now open a continuing special collection of TR papers in WRR. The aim is to facilitate a community within hydrology and water science that seeks to provide actionable knowledge for societal benefit across disciplines, scales and contexts, with a focus on water as a key societal resource or a risk (e.g., of floods, droughts, or as pollutant carrier). This Editorial discusses what the multi‐faceted nature of TR may include in the context of WRR, why it is important to encourage TR papers in WRR, and how the opening of a continuing special collection of translational water research papers initiates a process to include such articles in the journal. Key Points: Opening a continuing special collection for translational research (TR) papers in the context of Water Resources Research (WRR)Explaining the multi‐faceted nature of TR and Initiating a process to encourage and include such papers in WRRAiming to facilitate a community within hydrology and water science that seeks to provide actionable knowledge for societal benefit [ABSTRACT FROM AUTHOR]