Benchmarking CO₂ storage simulations: Results from the 11th Society of Petroleum Engineers Comparative Solution Project - Jan M. Nordbotten; Martin A. Fernø; Bernd Flemisch; Anthony R. Kovscek; Knut-Andreas Lie; Jakub W. Both; Olav Møyner; Tor Harald Sandve; Etienne Ahusborde; Sebastian Bauer; Zhangxing Chen; Holger Class; et al. (International Journal of Greenhouse Gas Control, 2025)

Tundra Vegetation Community Type, Not Microclimate, Controls Asynchrony of Above‐ and Below‐Ground Phenology - Elise C. Gallois; Isla H. Myers‐Smith; Colleen M. Iversen; Verity G. Salmon; Laura L. Turner; Ruby An; Sarah C. Elmendorf; Courtney G. Collins; Madelaine J. R. Anderson; Amanda Young; Lisa Pilkinton; Gesche Blume‐Werry; et al. (Global Change Biology, 2025)
The below‐ground growing season often extends beyond the above‐ground growing season in tundra ecosystems and as the climate warms, shifts in growing seasons are expected. However, we do not yet know to what extent, when and where asynchrony in above‐ and below‐ground phenology occurs and whether variation is driven by local vegetation communities or spatial variation in microclimate. Here, we combined above‐ and below‐ground plant phenology metrics to compare the relative timings and magnitudes of leaf and fine‐root growth and senescence across microclimates and plant communities at five sites across the Arctic and alpine tundra biome. We observed asynchronous growth between above‐ and below‐ground plant tissue, with the below‐ground season extending up to 74% (~56 days) beyond the onset of above‐ground leaf senescence. Plant community type, rather than microclimate, was a key factor controlling the timing, productivity, and growth rates of fine roots, with graminoid roots exhibiting a distinct ‘pulse’ of growth later into the growing season than shrub roots. Our findings indicate the potential of vegetation change to influence below‐ground carbon storage as the climate warms and roots remain active in unfrozen soils for longer. Taken together, our findings of increased root growth in soils that remain thawed later into the growing season, in combination with ongoing tundra vegetation change including increased shrub and graminoid abundance, indicate increased below‐ground productivity and altered carbon cycling in the tundra biome.

Mathematical vs. machine learning models for particle size distribution in fragile soils of North-Western Himalayas - Owais Bashir; Shabir Ahmad Bangroo; Shahid Shuja Shafai; Tajamul Islam Shah; Shuraik Kader; Lizny Jaufer; Nicola Senesi; Alban Kuriqi; Negar Omidvar; Soora Naresh Kumar; Ayyanadar Arunachalam; Ruby Michael; et al. (Journal of Soils and Sediments, 2024)
Purpose Particle size distribution (PSD) assessment, which affects all physical, chemical, biological, mineralogical, and geological properties of soil, is crucial for maintaining soil sustainability. It plays a vital role in ensuring appropriate land use, fertilizer management, crop selection, and conservation practices, especially in fragile soils such as those of the North-Western Himalayas. Materials and methods In this study, the performance of eleven mathematical and three Machine Learning (ML) models used in the past was compared to investigate PSD modeling of different soils from the North-Western Himalayan region, considering that an appropriate model must fit all PSD data. Results and discussion Our study focuses on the significance of evaluating the goodness of fit in particle size distribution modeling using the coefficient of determination (R 2 adj = 0.79 to 0.45), the Akaike information criterion (AIC = 67 to 184), and the root mean square error (RMSE = 0.01 to 0.09). The Fredlund, Weibull, and Rosin Rammler models exhibited the best fit for all samples, while the performance of the Gompertz, S-Curve, and Van Genutchen models was poor. Of the three ML models tested, the Random Forest model performed the best (R 2 = 0.99), and the SVM model was the lowest (R 2 = 0.95). Thus, the PSD of the soil can be best predicted by ML approaches, especially by the Random Forest model. Conclusion The Fredlund model exhibited the best fit among mathematical models while random forest performed best among the machine learning models. As the number of parameters in the model increased better was the accuracy.

Contributory presentations/posters - N. Manoj; V. R. Srinivas; A. Surolia; M. Vijayan; K. Suguna; R. Ravishankar; K. Suguna; A. Surolia; M. Vijayan; R. Schwarzenbacher; K. Zeth; Diederichs; et al. (Journal of Biosciences, 1999)