https://bura.brunel.ac.uk/handle/2438/25433 Sat, 03 Oct 2026 09:40:06 GMT 2026-10-03T09:40:06Z https://bura.brunel.ac.uk/handle/2438/33887 Title: Life cycle assessment of virgin and recycled polypropylene kitchenware in the UK: impacts of domestic recycling, international supply chains, and consumer use Authors: Jaramillo, Caitlin; Aljoubory, Janna; Ng, Kok Siew; Qi, Kun; Fern, George; Iacovidou, Eleni Abstract: ... Description: ... Thu, 01 Jan 2026 00:00:00 GMT https://bura.brunel.ac.uk/handle/2438/33887 2026-01-01T00:00:00Z https://bura.brunel.ac.uk/handle/2438/33808 Title: Comparative analysis of GAMLSS modeling approaches for nonstationary runoff dynamics in the Yellow River Basin of China Authors: Niu, Ben; Li, Yi; Fan, Yurui; Gong, Lei; Wang, Lei; Wang, Taishan Abstract: Quantifying the driving effects of climate change and human activities on nonstationary runoff dynamics is essential. However, the systematic assessments of nonstationary characteristics and their multiple driving mechanisms at the basin scale remain insufficient. This study compared two Generalized Additive Model for Location, Scale, and Shape (GAMLSS) modeling approaches–Continuous-series modeling (Mode1) and monthly-segmented modeling (Mode2)–to analyze the hydrological nonstationarity characteristics of the Yellow River Basin in China and elucidate the driving mechanisms of runoff processes by incorporating covariates of time, circulation, climate, and human factors. The results indicated that: (1) Taking time as a covariate, Mode2 significantly enhanced model robustness by isolating seasonal dynamics. Mode2 raised the average correlation of the location parameter (μ) with the monthly runoff series to R = 0.82 (range 0.70–0.90), a 30–50% increase over Mode1. Furthermore, the nonstationary standardized runoff index (NSRI) aligned more accurately with actual hydrological fluctuations. (2) Taking circulation indices as covariates, circulation indices (AMO, PDO and NINO3) predominantly governed large-scale hydrological trends, with PDO exerting a more pronounced regulatory effect on extreme events in the downstream region under Mode2. (3) The climate-human composite-driven model had the best fitting effect on runoff, particularly at lower timescales (1-, 3-, and 6-month scales) in the middle and lower reaches, where the interactions among total precipitation (TP), snowmelt (SMT), and soil water storage capacity (SWC) explained over 80% of runoff variations (This model yielded the lowest AICc—15% lower than the single climate model—and the highest explanatory power, with R2 = 0.85 at 1-month, and 0.70 at 3-month scales). This study suggests that Mode2, with its precise characterization of seasonal differentiation and human dynamics, is more suitable for refined water resource management and extreme drought-flood prediction, whereas Mode1 remains efficient for analyzing interdecadal circulation effects. By addressing three key challenges—capturing monthly runoff nonstationarity, integrating multi-factor drivers, and validating runoff simulations—this study greatly improves runoff modeling and drought detection accuracy, laying a scientific foundation for adaptive management under the combined pressures of climate change and human activities. Description: Data availability: The authors do not have permission to share data.; Supplementary data are available online at: https://www.sciencedirect.com/science/article/pii/S0022169426001459?via%3Dihub#s0185 . Thu, 29 Jan 2026 00:00:00 GMT https://bura.brunel.ac.uk/handle/2438/33808 2026-01-29T00:00:00Z https://bura.brunel.ac.uk/handle/2438/33807 Title: Non-stationary framework of quantifying heat-driven drought propagation mechanisms in the Yellow River Basin of China Authors: Niu, Ben; Li, Yi; Qi, Xingyun; Song, Maokai; Zhang, Guiyuan; Fan, Yurui; Li, Zhi; Liong, Shie-Yui Abstract: Drought evolution in the Yellow River Basin has become increasingly complex under climatic warming and intensified human disturbance, challenging conventional stationary monitoring approaches. In this study, we developed an integrated framework to characterize meteorological (MD), agricultural (AD), and hydrological (HD) droughts by combining GAMLSS-based non-stationary drought indices (NSPEI, NSSMI, and NSRI), three-dimensional spatiotemporal event identification, hierarchical grade-cascade analysis, and explainable machine learning. A two-level attribution strategy was further applied to diagnose dominant drivers at both the event- and grid-levels, and sensitivity experiments were conducted to evaluate the robustness of the framework. The results showed that MD occurred most frequently and covered the widest area, while AD and HD were generally less frequent but more persistent. Drought propagation exhibited clear pathway dependence: meteorological drought acted as the primary trigger and propagated more widely to agricultural and hydrological droughts, whereas the AD–HD linkage was less frequent and showed greater temporal persistence and complexity. MA and MH propagation events were dominated by short durations and positive propagation times, with most events concentrated at 2–3 months. Across attribution analyses, high-temperature days emerged as the most consistent dominant driver, while precipitation- and surface-moisture-related factors played stronger roles in AD and HD. Sensitivity analysis further showed that data perturbations and threshold adjustments had limited influence on drought characteristics and propagation structure. These findings highlight the intensifying role of heat stress in cross-type drought evolution and provide a basis for adaptive drought early warning and water-resource management under non-stationary climate conditions. Description: Data availability: Data will be made available on request.; Supplementary data are available online at: https://www.sciencedirect.com/science/article/pii/S0022169426008802#s0225 . Sat, 06 Jun 2026 00:00:00 GMT https://bura.brunel.ac.uk/handle/2438/33807 2026-06-06T00:00:00Z https://bura.brunel.ac.uk/handle/2438/33571 Title: Food web complexity underlies biodiversity effects on ecosystem functioning Authors: Barnes, AD; Brose, U; Eisenhauer, N; Berti, E; Brauns, M; Eggert, SL; Garcia-Callejas, D; Giling, DP; Hall, RO; Hines, J; Jochum, M; Korobushkin, DI; Kortsch, S; Kratina, P; Manca, M; Mor, J-R; Nordström, MC; O’Gorman, EJ; Ott, D; Perkins, DM; Rosenbaum, B; Saifutdinov, RA; Saito, VS; Tanentzap, AJ; Vinagre, C; Gauzens, B Abstract: Biodiversity change has elicited widespread concern over the consequences for functions and services provided by ecosystems¹ ² ³. Despite extensive evidence for a positive effect of biodiversity on ecosystem functioning within a single trophic level⁴ ⁵, how this biodiversity effect varies with multi-trophic food web structure remains unresolved⁶ even though most ecosystems contain two to six trophic levels⁷. We investigate how food web complexity modulates biodiversity–ecosystem functioning relationships in nature by quantifying energy fluxes as proxies for two principal ecosystem functions⁸ —primary consumption and predation—in 318 highly resolved, complex food webs from marine, lake, stream and soil ecosystems. Ecosystem functioning increased consistently with taxon richness across all trophic levels and ecosystems, which arose from greater vertical diversity (that is, maximum trophic level⁹) and trophic complementarity of predators in more taxonomically diverse food webs. Furthermore, predator trophic complementarity¹⁰ ¹¹ increased predation fluxes in all freshwater ecosystem types. These findings highlight the threat of trophic downgrading to critical ecosystem functions (for example, biological control and maintenance of biodiversity and ecosystem stability) provided by predators¹² ¹³, which are typically most vulnerable to anthropogenic disturbances¹⁴ ¹⁵. Our study demonstrates that the consequences of biodiversity change are deeply entangled within the web of life, emphasizing the need to conserve the trophic complexity underlying biodiversity–ecosystem function relationships. Description: Data availability: All data used in this study are available at Zenodo (https://doi.org/10.5281/zenodo.20130985). Source data (https://www.nature.com/articles/s41586-026-10710-5#Sec12) are provided with this paper.; Code availability: Code for computing food web properties, energy fluxes, stability, NPP and statistical analyses is available at Zenodo (https://doi.org/10.5281/zenodo.20130985).; Supplementary information is available online at: https://www.nature.com/articles/s41586-026-10710-5#Sec15 .; Acknowledgements: This work was initiated from the Food web Structure, Ecosystem functioning and Diversity across ecosystems (FuSED) workshop at the German Centre for Integrative Biodiversity Research (iDiv) Halle–Jena–Leipzig, funded by the German Research Foundation (DFG–FZT 118, 202548816). Wed, 01 Jul 2026 00:00:00 GMT https://bura.brunel.ac.uk/handle/2438/33571 2026-07-01T00:00:00Z