Author: Chenghao Wang Page 3 of 6

Our new paper, “Unraveling the discrepancies between Eulerian and Lagrangian moisture tracking models in monsoon- and westerly-dominated basins of the Tibetan Plateau“, is published in Atmospheric Chemistry and Physics (IF: 5.2).

The paper can be downloaded at https://acp.copernicus.org/articles/24/10741/2024/.

Authors: Ying Li, Chenghao Wang, Qiuhong Tang, Shibo Yao, Bo Sun, Hui Peng, Shangbin Xiao

Abstract: Eulerian and Lagrangian numerical moisture tracking models, which are primarily used to quantify moisture contributions from global sources to specific regions, play a crucial role in hydrology and (paleo)climatology studies on the Tibetan Plateau (TP). Despite their widespread applications in the TP region, potential discrepancies in their moisture tracking results and their underlying causes remain unexplored. In this study, we compare the most widely used Eulerian and Lagrangian moisture tracking models over the TP, i.e., WAM2layers (the Water Accounting Model – 2 layers) and FLEXPART-WaterSip (the FLEXible PARTicle dispersion model coupled with the “WaterSip” moisture source diagnostic method), specifically focusing on a basin governed by the Indian summer monsoon (Yarlung Zangbo River basin, YB) and a westerly-dominated basin (upper Tarim River basin, UTB). Compared to the bias-corrected FLEXPART-WaterSip, WAM2layers generally estimates higher moisture contributions from westerly-dominated and distant sources but lower contributions from local recycling and nearby sources downwind of the westerlies. These differences become smaller with higher spatial and temporal resolutions of forcing data in WAM2layers. A notable advantage of WAM2layers over FLEXPART-WaterSip is its closer alignment of estimated moisture sources with actual evaporation, particularly in source regions with complex land–sea distributions. However, the evaporation biases in FLEXPART-WaterSip can be partly corrected through calibration with actual surface fluxes. For moisture tracking over the TP, we recommend using high-resolution forcing datasets, prioritizing temporal resolution over spatial resolution for WAM2layers, while for FLEXPART-WaterSip, we suggest applying bias corrections to optimize the filtering of precipitation particles and adjust evaporation estimates.

DOI: https://doi.org/10.5194/acp-24-10741-2024

Our new paper, “Improving estimation of diurnal land surface temperatures by integrating weather modeling with satellite observations“, is published in Remote Sensing of Environment (IF: 11.1).

The paper can be downloaded at https://www.sciencedirect.com/science/article/pii/S003442572400419X.

Authors: Wei Chen, Yuyu Zhou, Ulrike Passe, Tao Zhang, Chenghao Wang, Ghassem R. Asrar, Qi Li, Huidong Li

Abstract: Land surface temperature (LST) derived from satellite observations and weather modeling has been widely used for investigating Earth surface-atmosphere energy exchange and radiation budget. However, satellite-derived LST has a trade-off between spatial and temporal resolutions and missing observations caused by clouds, while there are limitations such as potential bias and expensive computation in model calibration and simulation for weather modeling. To mitigate those limitations, we proposed a WRFM framework to estimate LST at a spatial resolution of 1 km and temporal resolution of an hour by integrating the Weather Research and Forecasting (WRF) model and MODIS satellite data using the morphing technique. We tested the framework in eight counties, Iowa, USA, including urban and rural areas, to generate hourly LSTs from June 1st to August 31st, 2019, at a 1 km resolution. Upon evaluation with in-situ LST measurements, our WRFM framework has demonstrated its ability to capture hourly LSTs under both clear and cloudy conditions, with a root mean square error (RMSE) of 2.63 K and 3.75 K, respectively. Additionally, the assessment with satellite LST observations has shown that the WRFM framework can effectively reduce the bias magnitude in LST from the WRF simulation, resulting in a reduction of the average RMSE over the study area from 4.34 K (daytime) and 4.12 K (nighttime) to 2.89 K (daytime) and 2.75 K (nighttime), respectively, while still capturing the hourly patterns of LST. Overall, the WRFM is effective in integrating the complementary advantages of satellite observations and weather modeling and can generate LSTs with high spatiotemporal resolutions in areas with complex landscapes (e.g., urban).

DOI: https://doi.org/10.1016/j.rse.2024.114393

Our new paper, “Methane dynamics
altered by reservoir operations in a typical tributary of the Three Gorges Reservoir“, is published in Water Research (IF: 11.4).

The paper and its supplement can be downloaded at https://www.sciencedirect.com/science/article/pii/S0043135424010625.

Authors: Jia Liu, Fei Xue, Xiaojuan Guo, Zhengjian Yang, Manchun Kang, Min Chen, Daobin Ji, Defu Liu, Shangbin Xiao, and Chenghao Wang

Abstract: Substantial nutrient inputs from reservoir impoundment typically increase sedimentation rate and primary production. This can greatly enhance methane (CH₄) production, making reservoirs potentially significant sources of atmospheric CH₄. Consequently, elucidating CH₄ emissions from reservoirs is crucial for assessing their role in the global methane budget. Reservoir operations can also influence hydrodynamic and biogeochemical processes, potentially leading to pronounced spatiotemporal heterogeneity, especially in reservoirs with complex tributaries, such as the Three Gorges Reservoir (TGR). Although several studies have investigated the spatial and temporal variations in CH₄ emissions in the TGR and its tributaries, considerable uncertainties remain regarding the impact of reservoir operations on CH₄ dynamics. These uncertainties primarily arise from the limited spatial and temporal resolutions of previous measurements and the complex underlying mechanisms of CH₄ dynamics in reservoirs. In this study, we employed a fast-response automated gas equilibrator to measure the spatial distribution and seasonal variations of dissolved CH₄ concentrations in XXB, a representative area significantly impacted by TGR operations and known for severe algal blooms. Additionally, we measured CH₄ production rates in sediments and diffusive CH₄ flux in the surface water. Our multiple campaigns suggest substantial spatial and temporal variability in CH₄ concentrations across XXB. Specifically, dissolved CH₄ concentrations were generally higher upstream than downstream and exhibited a vertical stratification, with greater concentrations in bottom water compared to surface water. The peak dissolved CH₄ concentration was observed in May during the drained period. Our results suggest that the interplay between aquatic organic matter, which promotes CH₄ production, and the dilution process caused by intrusion flows from the mainstream primarily drives this spatiotemporal variability. Importantly, our study indicates the feasibility of using strategic reservoir operations to regulate these factors and mitigate CH₄ emissions. This eco-environmental approach could also be a pivotal management strategy to reduce greenhouse gas emissions from other reservoirs.

DOI: https://doi.org/10.1016/j.watres.2024.122163

Our new paper, “A modeling framework to assess fenceline monitoring and self-reported upset emissions of benzene from multiple oil refineries in Texas“, is published in Atmospheric Environment X (IF: 3.8).

The paper and its supplement can be downloaded at https://www.sciencedirect.com/science/article/pii/S2590162124000480.

Authors: Qi Li, Lauren Padilla, Tammy Thompson, Shuolin Xiao, Elizabeth Mohr, Xiaohe Zhou, Nino Kacharava, Yuanfeng Cui, and Chenghao Wang

Abstract: Benzene as one type of hazardous air pollutants (HAPs) is produced by industrial production processes and/or emitted during upset events caused by man-made or natural accidents. Although upset emissions of benzene can be a significant contributor to the total emission, it is still challenging to quantify. This study first develops a fast modeling framework using obstacle-resolving computational fluid dynamics modeling to compare the modeled within-facility-scale passive pollutant dispersion with the observed levels based on self-reported emissions for fourteen facilities in Texas, United States. Results of numerical simulations demonstrate that neglecting the obstacle effect can underpredict (overpredict) the near-(far-)field concentrations for a low source. For a source located above obstacles, underprediction occurs at all distances. The diagnostic framework is applied to 107 self-reported upset emission events for fourteen petroleum refineries in Texas from year 2019–2022. Considering different metrics across all events, it can be concluded that the modeled concentrations based on self-reported emissions likely underpredict the observed concentration increments. Depending on the possible source height, the median factor of underprediction ranges from 3 to 95 based on the average-plume metric. The agreement between model and observation is better for events characterized by high emission amounts and rates, which also correspond to high observed concentration increments. Overall, the research highlights the importance of considering obstacles and demonstrates the potential application of the current approach as an efficient diagnostic method for self-reported upset emissions using fenceline observations of HAPs.

DOI: https://doi.org/10.1016/j.aeaoa.2024.100281