Pub Date : 2026-02-19DOI: 10.1016/j.atmosres.2026.108876
Daniela Monterde, José Francisco León-Cruz, Noel Carbajal, Luis Felipe Pineda-Martínez
{"title":"Features of anticyclonic tornadoes in a complex orography based on numerical simulations","authors":"Daniela Monterde, José Francisco León-Cruz, Noel Carbajal, Luis Felipe Pineda-Martínez","doi":"10.1016/j.atmosres.2026.108876","DOIUrl":"https://doi.org/10.1016/j.atmosres.2026.108876","url":null,"abstract":"","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"42 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146778234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In pothole-dominated catchments, such as those in the Prairie Pothole Region (PPR), potholes strongly influence catchment hydrologic behavior through complex and dynamic fill–spill–connection mechanisms. This complexity—combined with the predominance of ungauged catchments and the lack of high-resolution pothole inventories—poses challenges for both traditional hydrologic models and purely data-driven deep learning approaches. To address this, we developed the δHBV-Pot model within a differentiable modeling framework (δ). This physics-informed deep learning model integrates the conceptual HBV model with a probabilistic algorithm that emulates the aggregate effects of pothole fill–spill–connection processes. Applied to 98 PPR catchments, δHBV-Pot achieves stronger predictive accuracy and physical realism than a purely data-driven Long Short-Term Memory (LSTM) model and two conceptual hydrologic models. The PPR-scale regional δHBV-Pot model successfully simulates hydrologic behavior for the majority of pseudo-ungauged (test) catchments withheld during model development, effectively regionalizing (a) high-flow magnitude and interannual variability, (b) intra-annual flashiness of high-flow and normal flow conditions, and (c) interannual variability in pothole water storage dynamics. Moreover, the model identifies vulnerable catchments with large high-flow magnitude and variability—even in the absence of streamflow data—and delineates catchments with varying temporal variability in pothole water storage without requiring detailed pothole inventories. Our findings highlight the value of combining conceptual hydrology with data-driven deep learning models in pothole-dominated regions. This integrated approach enables the regionalization of high-flow and pothole storage characteristics to ungauged catchments, providing critical insights for vulnerability assessment and the design of sustainable water and ecological management strategies in pothole-dominated landscapes.
{"title":"Regionalization of Hydrologic Behavior and Pothole Water Storage Dynamics in the Prairie Pothole Region","authors":"Javad Rahmani, Chaopeng Shen, Ali A. Ameli","doi":"10.1029/2025wr040280","DOIUrl":"https://doi.org/10.1029/2025wr040280","url":null,"abstract":"In pothole-dominated catchments, such as those in the Prairie Pothole Region (PPR), potholes strongly influence catchment hydrologic behavior through complex and dynamic fill–spill–connection mechanisms. This complexity—combined with the predominance of ungauged catchments and the lack of high-resolution pothole inventories—poses challenges for both traditional hydrologic models and purely data-driven deep learning approaches. To address this, we developed the δHBV-Pot model within a differentiable modeling framework (δ). This physics-informed deep learning model integrates the conceptual HBV model with a probabilistic algorithm that emulates the aggregate effects of pothole fill–spill–connection processes. Applied to 98 PPR catchments, δHBV-Pot achieves stronger predictive accuracy and physical realism than a purely data-driven Long Short-Term Memory (LSTM) model and two conceptual hydrologic models. The PPR-scale regional δHBV-Pot model successfully simulates hydrologic behavior for the majority of pseudo-ungauged (test) catchments withheld during model development, effectively regionalizing (a) high-flow magnitude and interannual variability, (b) intra-annual flashiness of high-flow and normal flow conditions, and (c) interannual variability in pothole water storage dynamics. Moreover, the model identifies vulnerable catchments with large high-flow magnitude and variability—even in the absence of streamflow data—and delineates catchments with varying temporal variability in pothole water storage without requiring detailed pothole inventories. Our findings highlight the value of combining conceptual hydrology with data-driven deep learning models in pothole-dominated regions. This integrated approach enables the regionalization of high-flow and pothole storage characteristics to ungauged catchments, providing critical insights for vulnerability assessment and the design of sustainable water and ecological management strategies in pothole-dominated landscapes.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"1 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146223011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-19DOI: 10.1016/j.jhydrol.2026.135126
Samuel Fagbemi, Oubai Elagab, Ziqiang Qin, Bradley McCaskill, Mohammad Piri
{"title":"Multi-scale imaging and analysis of core-sized samples using successive image registration and machine learning for pore scale modeling","authors":"Samuel Fagbemi, Oubai Elagab, Ziqiang Qin, Bradley McCaskill, Mohammad Piri","doi":"10.1016/j.jhydrol.2026.135126","DOIUrl":"https://doi.org/10.1016/j.jhydrol.2026.135126","url":null,"abstract":"","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"6 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2026-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146778404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-19DOI: 10.1016/j.chemgeo.2026.123312
Anthony Boxleiter, Yinghao Wen, Martin Yan Hei Li, Joell Ashcraft, Yuanzhi Tang, W. Crawford Elliott
{"title":"Assessing rare earth (yttrium) speciation in natural ores and processed waste streams using X-ray absorption spectroscopy","authors":"Anthony Boxleiter, Yinghao Wen, Martin Yan Hei Li, Joell Ashcraft, Yuanzhi Tang, W. Crawford Elliott","doi":"10.1016/j.chemgeo.2026.123312","DOIUrl":"https://doi.org/10.1016/j.chemgeo.2026.123312","url":null,"abstract":"","PeriodicalId":9847,"journal":{"name":"Chemical Geology","volume":"8 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146778170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-19DOI: 10.1016/j.tecto.2026.231127
Takato Takemura, David Healy
{"title":"Anisotropic stress-induced permeability change in cracked rock during the crack closure stage: A tensorial approach incorporating tortuosity and bottlenecks","authors":"Takato Takemura, David Healy","doi":"10.1016/j.tecto.2026.231127","DOIUrl":"https://doi.org/10.1016/j.tecto.2026.231127","url":null,"abstract":"","PeriodicalId":22257,"journal":{"name":"Tectonophysics","volume":"20 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146778218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-19DOI: 10.1016/j.atmosres.2026.108874
H.H. Yıldırım, K. Ishida, A. Ercan
{"title":"Spatiotemporal assessment of aridification in Europe (1950–2024) using bias-corrected high-resolution reanalysis dataset","authors":"H.H. Yıldırım, K. Ishida, A. Ercan","doi":"10.1016/j.atmosres.2026.108874","DOIUrl":"https://doi.org/10.1016/j.atmosres.2026.108874","url":null,"abstract":"","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"1 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146777940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-19DOI: 10.1016/j.chemgeo.2026.123308
Gabriel R. Moizinho, Germain Bayon, Martin Roddaz, Marina Rabineau, Daniel Aslanian, Anne Trinquier, Marie-Laure Rouget, Roberto V. Santos
{"title":"Tracing black shale weathering using REE and Nd isotopes in riverine Fe oxides: Insights from the Amazon Basin","authors":"Gabriel R. Moizinho, Germain Bayon, Martin Roddaz, Marina Rabineau, Daniel Aslanian, Anne Trinquier, Marie-Laure Rouget, Roberto V. Santos","doi":"10.1016/j.chemgeo.2026.123308","DOIUrl":"https://doi.org/10.1016/j.chemgeo.2026.123308","url":null,"abstract":"","PeriodicalId":9847,"journal":{"name":"Chemical Geology","volume":"24 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146778171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-19DOI: 10.1016/j.jhydrol.2026.135164
Lei Zhao, Yuankun Wang, Yang You, Sen Wang, Yixu Wang, Changqing Meng, Yanke Zhang, Dong Wang
{"title":"Quantifying the drivers of river thermal regimes in the Hanjiang River Basin under climate change and reservoir construction","authors":"Lei Zhao, Yuankun Wang, Yang You, Sen Wang, Yixu Wang, Changqing Meng, Yanke Zhang, Dong Wang","doi":"10.1016/j.jhydrol.2026.135164","DOIUrl":"https://doi.org/10.1016/j.jhydrol.2026.135164","url":null,"abstract":"","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"97 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2026-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146778206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Mapping nationwide 1 Km hourly human mobility (2022–2024) across South Korea using multi-source data","authors":"Siwoo Lee, Yoojin Kang, Taejun Sung, Soomin Hwang, Seyoung Yang, Jungho Im","doi":"10.1080/15481603.2026.2633052","DOIUrl":"https://doi.org/10.1080/15481603.2026.2633052","url":null,"abstract":"","PeriodicalId":55091,"journal":{"name":"GIScience & Remote Sensing","volume":"10 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2026-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146260838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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