Journal of Hydrology最新文献

{"title":"Empowering a coupled hydrological-geotechnical model to simulate long-term vegetation dynamics and their impact on catchment-scale flood and landslide hazards","authors":"Guoding Chen ,&nbsp;Ke Zhang ,&nbsp;Yunping Li ,&nbsp;Jin Feng ,&nbsp;Thom Bogaard","doi":"10.1016/j.jhydrol.2025.133225","DOIUrl":"10.1016/j.jhydrol.2025.133225","url":null,"abstract":"<div><div>Vegetation plays a critical role in regulating the catchment water balance and enhancing soil stability through root reinforcement. The dynamic nature of vegetation, particularly its seasonal change, significantly affects the magnitude of this influence. However, quantifying the long-term impacts of dynamic vegetation on both flood and landslide occurrences at the catchment scale remains challenging due to the complexity of root structures and the varying dimensions of landslides. In this study, we improved the coupled hydrological-geotechnical model iHydroSlide3D v1.0 by incorporating key vegetation components, such as Leaf Area Index (LAI), root characteristics, and their seasonal dynamics. The improved model was validated using historical observations and applied to a 100-years simulation driven by a weather generator. Three computational scenarios were employed to assess the influence of vegetation on key hydrological and slope-stability variables. Results show that vegetation reduces soil moisture and runoff during low to moderate rainfall events but has a limited impact during larger rainfall events. Additionally, slope stability is found to be more influenced by root reinforcement than soil water uptake. The dynamic nature of vegetation plays a decisive role in modulating its effects on hydrological processes and soil stability, depending on the growth or decay trend of vegetation. This modeling framework offers a robust tool for assessing long-term flood and landslide risks in vegetated catchments.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"658 ","pages":"Article 133225"},"PeriodicalIF":5.9,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777271","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":"Effect of matrix permeability on non-Darcian flow behavior and flow partitioning patterns in fractured porous media: Insight from experimental and numerical tests","authors":"Linyu Xiang ,&nbsp;Changdong Li ,&nbsp;Jie Meng","doi":"10.1016/j.jhydrol.2025.133234","DOIUrl":"10.1016/j.jhydrol.2025.133234","url":null,"abstract":"<div><div>Comprehending the seepage characteristics in fractured porous media is crucial for engineering construction and environmental protection. Despite extensive research, quantifying matrix contributions to flow partitioning patterns and elucidation of mechanisms remains challenging. Based on a self-developed seepage experimental system, non-Darcian flow behavior in fractures with different matrix permeabilities and fracture apertures were investigated. The results indicate that both the viscous and the inertial permeability exhibit a positive correlation with matrix permeability and fracture aperture. Increased matrix permeability weakens the resistance to flow at the matrix-fracture interface and inhibits non-Darcian flow. The flow partitioning patterns in fractured porous media were quantified experimentally. As the Reynolds number increases, the fracture flow percentage exhibits a ‘two-phase’ distribution. The relative permeability threshold corresponding to matrix flow exceeding fracture flow was scaled down from 1 in previous research to about 10⁻³. Further analysis revealed that matrix permeabilities and fracture apertures determine the stable phase of flow partitioning, while the relative permeability influences the trend of the abrupt phase. Moreover, a double-parameter equation was established to quantitatively characterize the effects of the permeable matrix on the flow regime evolution in fractures. The research will provide a basis for solving engineering geological problems in fractured porous aquifers.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"658 ","pages":"Article 133234"},"PeriodicalIF":5.9,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777353","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":"N2O emissions from a multi-habitat lake: Patterns and controls","authors":"Fanyan Yang ,&nbsp;Xiaolan Zheng ,&nbsp;Enqin Yao ,&nbsp;Dongqi Wang ,&nbsp;Suyun Chang ,&nbsp;Wei Huang ,&nbsp;Lei Zhang ,&nbsp;Jianjun Wang ,&nbsp;Jicheng Zhong","doi":"10.1016/j.jhydrol.2025.133209","DOIUrl":"10.1016/j.jhydrol.2025.133209","url":null,"abstract":"<div><div>Lakes are a significant source of N₂O emissions, and the spatial and temporal variability of lake N₂O emissions contributes to the uncertainty in global N₂O budget estimates. Currently, researchers have a limited understanding of N₂O emission patterns and controlling factors in different habitats of large, shallow lakes. This study focused on the phytoplankton-dominated zone (PDZ), submerged plants-dominated zone (SDZ), and emergent plants-dominated zone (EDZ) of Lake Taihu, China. The N₂O fluxes in each lake zone were recorded monthly for one year using the static floating chamber method, to explore the spatio-temporal variations in N₂O emissions and their controlling factors in lakes with complex habitat types. Additionally, a microcosm experiment was used to identify the effect of algal addition on the water column nitrogen (N) load and N₂O emissions. Results showed that the annual average N₂O fluxes at the water–air interface were 0.66 ± 0.62 μmol m⁻² h⁻¹, 0.09 ± 0.06 μmol m⁻² h⁻¹, and 0.56 ± 0.93 μmol m⁻² h⁻¹, from the PDZ, SDZ, and EDZ, respectively. The N₂O fluxes in the PDZ were significantly (<em>p</em> &lt; 0.05) higher than those from the SDZ (<em>p</em> &lt; 0.05), while those in the EDZ did not differ significantly from the other two zones. During summer, the N₂O concentrations and fluxes in all lake zones were higher than those in the other seasons. Statistical analysis indicated that water TN, temperature (T_W), and TP are crucial factors regulating N₂O emissions. The algae addition experiment demonstrated that phytoplankton aggregation promoted N₂O emissions by altering DO content and water column N load. Overall, this study emphasizes the importance of considering habitat differences in regional and global lake N₂O emission estimates.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"658 ","pages":"Article 133209"},"PeriodicalIF":5.9,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777363","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":"Analyzing the impact of across-fault flow in carbon geological storage: A simulation study","authors":"Zhicheng W. Wang,&nbsp;Seyyed A. Hosseini,&nbsp;Ramón H. Treviño,&nbsp;Susan D. Hovorka","doi":"10.1016/j.jhydrol.2025.133108","DOIUrl":"10.1016/j.jhydrol.2025.133108","url":null,"abstract":"<div><div>Carbon geological storage (CGS) aims to mitigate climate change by sequestering carbon dioxide in underground formations. The simulation study plays a crucial role during the EPA Class VI permit application process in defining the area of review (AOR). Boundary conditions play a significant role in delineating the AOR and specifically faults (as a boundary condition) and how they are modeled poses a considerable challenge to accurately model CO₂ storage and containment. Addressing this challenge is vital to understanding how faults impact storage processes and pressure build-up in the formation. This study identifies the gap between geological modeling and dynamic simulations, particularly concerning fault representation in numerical simulation models. We selected a field model with typical geological formation features of the onshore Gulf of Mexico Basin, including multiple faults. Two options were leveraged: fault transmissibility multiplier (TM), and Across-fault pressure difference (AFPD) for fluid flow. We considered various scenarios of these options’ in containing CO₂ and brine multiphase flow and assessed the containment of CO₂ and evolution of the AOR and the dynamic pressure build-up. This study not only provides lessons on how to better model fault boundary conditions, but also on how our modeling assumptions related to faults would impact flow behaviors and AOR.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"658 ","pages":"Article 133108"},"PeriodicalIF":5.9,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777348","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":"Performance enhancement of deep learning model with attention mechanism and FCN model in flood forecasting","authors":"Cheng Chen ,&nbsp;Binquan Li ,&nbsp;Huiming Zhang ,&nbsp;Maihuan Zhao ,&nbsp;Zhongmin Liang ,&nbsp;Kuang Li ,&nbsp;Xindai An","doi":"10.1016/j.jhydrol.2025.133221","DOIUrl":"10.1016/j.jhydrol.2025.133221","url":null,"abstract":"<div><div>Accurate and timely inflow flood forecasting is a critical foundation for the flood operation of multi-reservoir systems. To address the issue of low accuracy in long lead-time and extreme event flood forecasting with existing deep learning models, this study employed a Gated Recurrent Unit (GRU) as the base model and integrated the Attention mechanism and one-dimensional fully convolutional network (FCN) module to enhance its performance. The upper reaches of the Luohe River basin in the middle and lower Yellow River basin in China were chosen as the study area, utilizing observed data from 14 rain gauge stations, 1 evaporation station, and 1 hydrological station from 2013 to 2021 to build the dataset. GRU, GRU-FCN, Attention-GRU, and Attention-GRU-FCN were applied to flood events and daily streamflow forecasting. In addition, Informer was introduced and compared with other models. The results showed that the Attention mechanism enhanced GRU’s ability to predict extreme flood events while achieving more stable forecasting results, thereby improving the model’s performance for long lead times. The FCN module further strengthened the performance of GRU and Attention-GRU. Among the four GRU-based models, Attention-GRU-FCN demonstrated the best performance in extreme flood forecasting and long lead-time predictions, with the smallest peak timing error. Informer exhibited a significant advantage in long lead-time predictions but had lower accuracy in peak flood forecasting compared to Attention-GRU-FCN.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"658 ","pages":"Article 133221"},"PeriodicalIF":5.9,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143768478","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":"Blending daily satellite precipitation product and rain gauges using stacking ensemble machine learning with the consideration of spatial heterogeneity","authors":"Chuanfa Chen,&nbsp;Jinda Hao,&nbsp;Shufan Yang,&nbsp;Yanyan Li","doi":"10.1016/j.jhydrol.2025.133223","DOIUrl":"10.1016/j.jhydrol.2025.133223","url":null,"abstract":"<div><div>Blending satellite precipitation products (SPPs) with rain gauge observations through machine learning (ML)-based methods offers a proficient means of achieving high-accuracy precipitation data. However, traditional ML methods often neglect the spatial heterogeneity of precipitation across the study area, and the unique strengths of individual ML models remain underutilized. To address these challenges, this paper proposes a stacking ensemble learning approach that accounts for spatial heterogeneity for blending SPPs with rain gauge data to produce highly accurate precipitation estimates. Specifically, the study area is segmented into several homogeneous zones to mitigate spatial heterogeneity, with each grid cell within these zones assigned a uniform identifier (ID). Furthermore, a stacking ensemble ML framework which takes the ID as an input feature is developed to merge SPPs and rain gauge observations. To evaluate the performance of our proposed method, we blended daily IMERG data and rain gauge observations spanning from 2016 to 2020 across the Chinese mainland, benchmarking it against seven ML methods and the original IMERG data. The experimental results provide several key insights: (i) Data-driven adaptive clustering emerges as an efficient tool for addressing the challenge of spatial heterogeneity in high-quality precipitation estimation. (ii) Across multiple temporal scales, the proposed method outperforms the classical ML-based methods. Notably, at the daily scale, it improves upon the classical approaches by at least 2.4 % in Mean Absolute Error (MAE), 0.76 % in Root Mean Square Error (RMSE), 1.4 % in Correlation Coefficient (CC), and 1.4 % in Kling-Gupta Efficiency (KGE). Furthermore, at the monthly and seasonal scales, it reduces MAE by at least 2.3 % and 2.8 %, respectively, and enhances KGE by at least 0.9 % and 1.1 %. (iii) The spatial distribution of precipitation estimated by the proposed method aligns more closely with rain gauge observations compared to the classical methods. (iv) The ID feature plays a crucial role in precipitation estimation, ranking first and second in terms of feature importance for 39.6 % and 33.9 % of days, respectively, over the five-year period. (v) The proposed method generates positive incremental values at 69 % of rain gauge stations, demonstrating greater added value compared to the classical methods. Overall, the proposed method can be regarded as an effective tool for generating high-accuracy daily precipitation products.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"658 ","pages":"Article 133223"},"PeriodicalIF":5.9,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748108","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":"Highly trapped terrestrial dissolved organic matter fuels dissolved greenhouse gases of ice-covered boreal steppe lakes","authors":"Xiaoguang Xu ,&nbsp;Jiasen Yang ,&nbsp;Jin Gao ,&nbsp;Xin Zhang ,&nbsp;Wenlin Wang ,&nbsp;Yulong Tao ,&nbsp;Wen Ao ,&nbsp;Bo Liu ,&nbsp;Guoxiang Wang","doi":"10.1016/j.jhydrol.2025.133227","DOIUrl":"10.1016/j.jhydrol.2025.133227","url":null,"abstract":"<div><div>Although lakes are widely recognized as a significant source of greenhouse gas (GHG) emissions, it is worth nothing that boreal ice-covered period lakes are frequently overlooked in annual GHG budgets, leading to considerable uncertainly in estimating their fluxes. This uncertainty is closely linked to the dominant pool of dissolved organic matter (DOM), as its biodegradability and stability can significantly influence GHG budgets. For better understanding the potential impact of DOM sources and structure on GHG emissions, this study systematically investigated the primary dissolved GHGs, as well as the sources and distribution of DOM in a boreal steppe lake basin during the ice-covered period. The concentrations of dissolved CO₂, CH₄, and N₂O ranged from 27.9 to 33.1, 0.070–0.139, and 0.054–0.056 μmol L^-1, respectively, and exhibited spatial similarities with higher levels observed in the inflowing rivers compared to those in lakes. Three aromatic humus components were identified via the spectral characteristics of DOM, and subsequent molecular composition analysis further revealed that tannin and lignin were the primary components of DOM. The aromatic humus DOM showed a significant positive correlation with GHGs, as did the microbial indicators, suggesting that the trapped terrestrial DOM in the boreal steppe lake basin during the ice-covered period contributed to the storage of dissolved GHGs. Microorganisms isolated under the ice utilized DOM for respiration, degradation, nitrification and denitrification, resulting in the production of a considerable amount of GHGs. Furthermore, the presence of ice cover accelerates the accumulation of dissolved GHGs. Therefore, it is crucial to consider the massive GHG release during the thawing period to accurately evaluate GHG emissions in freshwater bodies.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"658 ","pages":"Article 133227"},"PeriodicalIF":5.9,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143760547","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":"Deglacial and Holocene precipitation seasonality on the central Tibetan Plateau","authors":"Qingfeng Ma ,&nbsp;Liping Zhu ,&nbsp;Junbo Wang ,&nbsp;Jianting Ju ,&nbsp;Ruimin Yang ,&nbsp;Yong Wang ,&nbsp;Xinmiao Lü","doi":"10.1016/j.jhydrol.2025.133224","DOIUrl":"10.1016/j.jhydrol.2025.133224","url":null,"abstract":"<div><div>Variations in precipitation seasonality have a profound impact on aspects of the climatic component, social activities and ecological processes. Reconstructing the precipitation seasonality during the deglaciation and Holocene, can improve our understanding of how and why precipitation seasonality changes under different climate scenarios. Here we develop an indicator of seasonal precipitation weight (growing season precipitation to annual precipitation) based on a modern pollen dataset for the central and western Tibetan Plateau (TP), which is further applied to a fossil pollen record spanning the last deglaciation and Holocene from the central TP to reconstruct long-term variations in seasonal precipitation weight. Our reconstruction result, combined with modelled data from a transient simulation, shows that changes in the precipitation seasonality associated with shifts in the atmospheric circulation systems are distinct from those in annual precipitation amount. Our results suggest that changes in the westerlies driven by the Atlantic meridional overturning circulation (AMOC) dominate the precipitation seasonality during the last deglaciation and the Indian summer monsoon (ISM) dominates mainly in the Holocene, and confirm the important moisture contributions of the westerlies to the early Holocene humid conditions.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"658 ","pages":"Article 133224"},"PeriodicalIF":5.9,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748105","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":"HR-PrecipNet: A machine learning framework for 1-km high-resolution satellite precipitation estimation","authors":"Hamidreza Mosaffa ,&nbsp;Luca Ciabatta ,&nbsp;Paolo Filippucci ,&nbsp;Mojtaba Sadeghi ,&nbsp;Luca Brocca","doi":"10.1016/j.jhydrol.2025.133217","DOIUrl":"10.1016/j.jhydrol.2025.133217","url":null,"abstract":"<div><div>Accurate and high-resolution precipitation estimation is critical for various applications in hydrology, meteorology, agriculture, and climate studies. This work proposes a novel machine learning (ML) framework for generating high-resolution (1 km) daily precipitation estimates over Italy by merging multi-source of information from top-down and bottom-up approaches. Our two-step framework firstly employs a deep learning (DL) architecture to produce initial 0.1-degree (approximately 10 km) daily precipitation estimates. We evaluate several U-Net DL architectures (2DCNN (Two-Dimensional Convolutional Neural Network), 3DCNN (Three-Dimensional CNN), ConvLSTM (Convolutional Long Short-Term Memory), Siamese, and Siamese-Diff), utilizing features such as infrared (IR), water vapor (WV) observation, soil moisture (SM), elevation, and geographical coordinates. Feature importance analysis underscores the significance of IR, WV, and differences in SM, demonstrating the value of integrating data from both approaches. The top-performing DL model achieves a correlation coefficient of 0.733 with ground-based data during the test period, a root mean square error of 4.06 mm, a bias close to zero, and a Critical Success Index (CSI) of 0.628. Secondly, we refine the estimates to 1 km resolution using a Random Forest (RF) model and high-resolution SM data. This refinement step crucially preserves the quality of the precipitation estimates. This approach effectively captures localized precipitation patterns across Italy and establishes a promising framework for the future development of 1-km high-resolution global precipitation products.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"658 ","pages":"Article 133217"},"PeriodicalIF":5.9,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Potential methane emissions from aquifer and coal seam gas groundwater extraction: Effect of open and closed sampling methods and new emission factors","authors":"Julie K. Pearce ,&nbsp;Harald Hofmann","doi":"10.1016/j.jhydrol.2025.133228","DOIUrl":"10.1016/j.jhydrol.2025.133228","url":null,"abstract":"<div><div>Greenhouse gas fugitive emissions contribute to climate change, of which 18 % are thought to be methane. Aquifer groundwater, and gas production water extraction are two potential fugitive emission sources. Aquifer groundwater is a vital water source composing ∼ 36 % of total water consumption worldwide. The world’s largest artesian basin, the Great Artesian Basin (GAB), Australia, has an estimated 322,327 GL/yr of groundwater extracted in Queensland for domestic, livestock, town water, irrigation and industrial use. Water is also produced with coal seam gas extraction. Dissolved methane was measured from deep and shallow GAB aquifers and coal seam gas production water via both closed and open sampling methods. The commonly used open sampling method can lose methane during the sampling process and underestimate concentrations. The resulting data were used to compare estimated methane fugitive emissions from groundwater extraction. This study compares the emissions obtained using the two sets of data from the two methods. A deep GAB aquifer, the Precipice Sandstone, has an estimated maximum methane emission of 1.89E-02 Tg/y using the closed sampling method, but only 2.28E-07 Tg/y using the open sampling method (Surat Basin, Queensland). The maximum estimated methane emission from coal seam gas (CSG) produced water in the whole state of Queensland is 1.88E-03 Tg/y using the closed method, however using the open sampling method it is only 9.56E-07 Tg/y. Considering only CSG produced water from the Surat Basin, the maximum estimated methane emission is 8.94E-04 Tg/y.</div><div>We suggest a new lower emission factor for CSG produced water based on actual dissolved gas measurements of 0.031 tonnes per ML produced water. <em>This is an order of magnitude less</em> than the Australian National Inventory Report CSG production water emission factor of 0.31 tonnes /ML (derived from a USA simulation study). We also suggest new, different, emission factors that could be applied to deep gassy aquifers, shallow aquifers, and aquifers containing interbedded coal. This study demonstrates that greenhouse gases from groundwater estimates can be affected by the gas concentration sampling method, of which there has still not been a standard robust and accepted method in Australia. More widespread groundwater methane sampling is urgently needed, especially for deep, old aquifers, to address the current uncertainties in emissions, along with the need for further characterization of the stable isotope signatures of the sources that may enable bottom-up attribution and allocation of gas from different sources.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"658 ","pages":"Article 133228"},"PeriodicalIF":5.9,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143760543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}