Articles | Volume 30, issue 9
https://doi.org/10.5194/hess-30-2651-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-30-2651-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 May 2026
Research article |
| 06 May 2026
| 06 May 2026
Introducing the Model Fidelity Metric (MFM) for robust and diagnostic land surface model evaluation
Zezhen Wu
School of Mathematics (Zhuhai), Sun Yat-sen University, Zhuhai, China
Zhongwang Wei
CORRESPONDING AUTHOR
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, China
Xingjie Lu
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, China
Nan Wei
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, China
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, China
Shupeng Zhang
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, China
Hua Yuan
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, China
Shaofeng Liu
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, China
Yongjiu Dai
CORRESPONDING AUTHOR
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, China
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Hongbin Liang, Shulei Zhang, Omarjan Obulkasim, Qiancheng Ye, Linyi Song, Zhongwang Wei, and Yongjiu Dai
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China has built the world’s largest reservoir system, yet modeling of its climate and environmental impacts lacks attribute values. We developed a database of 5,142 reservoirs across China with over 20 key attributes, covering 87.5 % of total storage and 62 % of hydropower capacity. It enables more realistic identification of reservoir-served irrigation areas and improves streamflow simulations, offering a valuable resource for hydrology, agriculture, energy, climate, and environmental research.
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We developed a new land data assimilation framework based on deep generative models. Compared with existing methods, it better handles non-Gaussian model errors and noisy observations. Tests in both an idealized system and a realistic land data assimilation experiment showed more accurate and consistent estimates, demonstrating its potential for improving land-state estimation with complex land surface models.
Xiaoying Shi, Daniel M. Ricciuto, Yaoping Wang, Joe R. Melton, Fortunat Joos, Yiqi Luo, Xiaofeng Xu, Dafeng Hui, Qianlai Zhuang, Chunjing Qiu, Hongxing He, Siya Shao, Xingjie Lu, Yongjiu Dai, Akihiko Ito, Qing Sun, Nitin Chaudhary, Ayesha Hussain, Mousong Wu, Jian Zhou, Jingwei Zhang, Ye Yuan, Rachael H. Turton, Eleanor J. Burke, Jiafu Mao, Yuefeng Hao, Natalie A. Griffiths, Verity Salmon, Paul J. Hanson, Melanie A. Mayes, and Peter E. Thornton
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Aoqi Sun, Wenjie Xu, Enze Ma, Hua Yuan, and Chen Yang
Hydrol. Earth Syst. Sci., 30, 5215–5227, https://doi.org/10.5194/hess-30-5215-2026, https://doi.org/10.5194/hess-30-5215-2026, 2026
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Lakes influence weather by storing and releasing heat, but models often oversimplify how sunlight travels through water, snow, and ice. We improved how a model handles light entering a lake. Tests across nine diverse lakes show surface waters warm more, deep waters warm less, and under-ice heating is better captured. Benefits vary among lakes, so better light treatment must pair with better mixing and heat storage.
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Groundwater plays an important role in how water and energy move between land and atmosphere, but simulating it in detail is very slow. We developed a faster method that keeps the key processes while reducing computing cost. Tested in a large river basin in China, it ran about twenty times faster and still closely matched detailed simulations. This approach makes it easier to include groundwater in large-scale and long-term environmental modeling.
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Manuscript not accepted for further review
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Flux tower data are widely recognized as benchmarking data for land surface models, but insufficient emphasis on and deficiency in site attribute data limits their true value. We collect site-observed vegetation, soil, and topography data from various sources. The final dataset encompasses 90 sites globally, with relatively complete site attribute data and high-quality flux validation data. This work has provided more reliable site attribute data, benefiting land surface model development.
Yangzi Che, Xuecao Li, Xiaoping Liu, Yuhao Wang, Weilin Liao, Xianwei Zheng, Xucai Zhang, Xiaocong Xu, Qian Shi, Jiajun Zhu, Honghui Zhang, Hua Yuan, and Yongjiu Dai
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Most existing building height products are limited with respect to either spatial resolution or coverage, not to mention the spatial heterogeneity introduced by global building forms. Using Earth Observation (EO) datasets for 2020, we developed a global height dataset at the individual building scale. The dataset provides spatially explicit information on 3D building morphology, supporting both macro- and microanalysis of urban areas.
Liqing Peng, Justin Sheffield, Zhongwang Wei, Michael Ek, and Eric F. Wood
Earth Syst. Dynam., 15, 1277–1300, https://doi.org/10.5194/esd-15-1277-2024, https://doi.org/10.5194/esd-15-1277-2024, 2024
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Integrating evaporative demand into drought indicators is effective, but the choice of method and the effectiveness of surface features remain undocumented. We evaluate various methods and surface features for predicting soil moisture dynamics. Using minimal ancillary information alongside meteorological and vegetation data, we develop a simple land-cover-based method that improves soil moisture drought predictions, especially in forests, showing promise for better real-time drought forecasting.
Bangjun Cao, Yaping Shao, Xianyu Yang, Xin Yin, and Shaofeng Liu
Atmos. Chem. Phys., 24, 275–285, https://doi.org/10.5194/acp-24-275-2024, https://doi.org/10.5194/acp-24-275-2024, 2024
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Our novel scheme enhances large-eddy simulations (LESs) for atmosphere–land interactions. It couples LES subgrid closure with Monin–Obukhov similarity theory (MOST), overcoming MOST's limitations. Validated over diverse land surfaces, our approach outperforms existing methods, aligning well with field measurements. Robustness is demonstrated across varying model resolutions. MOST's influence strengthens with decreasing grid spacing, particularly for sensible heat flux.
Qingliang Li, Gaosong Shi, Wei Shangguan, Vahid Nourani, Jianduo Li, Lu Li, Feini Huang, Ye Zhang, Chunyan Wang, Dagang Wang, Jianxiu Qiu, Xingjie Lu, and Yongjiu Dai
Earth Syst. Sci. Data, 14, 5267–5286, https://doi.org/10.5194/essd-14-5267-2022, https://doi.org/10.5194/essd-14-5267-2022, 2022
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SMCI1.0 is a 1 km resolution dataset of daily soil moisture over China for 2000–2020 derived through machine learning trained with in situ measurements of 1789 stations, meteorological forcings, and land surface variables. It contains 10 soil layers with 10 cm intervals up to 100 cm deep. Evaluated by in situ data, the error (ubRMSE) ranges from 0.045 to 0.051, and the correlation (R) range is 0.866-0.893. Compared with ERA5-Land, SMAP-L4, and SoMo.ml, SIMI1.0 has higher accuracy and resolution.
Yi Nan, Zhihua He, Fuqiang Tian, Zhongwang Wei, and Lide Tian
Hydrol. Earth Syst. Sci., 26, 4147–4167, https://doi.org/10.5194/hess-26-4147-2022, https://doi.org/10.5194/hess-26-4147-2022, 2022
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Tracer-aided hydrological models are useful tool to reduce uncertainty of hydrological modeling in cold basins, but there is little guidance on the sampling strategy for isotope analysis, which is important for large mountainous basins. This study evaluated the reliance of the tracer-aided modeling performance on the availability of isotope data in the Yarlung Tsangpo river basin, and provides implications for collecting water isotope data for running tracer-aided hydrological models.
Ziqi Lin, Yongjiu Dai, Umakant Mishra, Guocheng Wang, Wei Shangguan, Wen Zhang, and Zhangcai Qin
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-232, https://doi.org/10.5194/essd-2022-232, 2022
Manuscript not accepted for further review
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Spatial soil organic carbon (SOC) data is critical for predictions in carbon climate feedbacks and future climate trends, but no conclusion has yet been reached on which dataset to be used for specific purposes. We evaluated the SOC estimates from five widely used global soil datasets and a regional permafrost dataset, and identify uncertainties of SOC estimates by region, biome, and data sources, hoping to help improve SOC/soil data in the future.
Shuang Ma, Lifen Jiang, Rachel M. Wilson, Jeff P. Chanton, Scott Bridgham, Shuli Niu, Colleen M. Iversen, Avni Malhotra, Jiang Jiang, Xingjie Lu, Yuanyuan Huang, Jason Keller, Xiaofeng Xu, Daniel M. Ricciuto, Paul J. Hanson, and Yiqi Luo
Biogeosciences, 19, 2245–2262, https://doi.org/10.5194/bg-19-2245-2022, https://doi.org/10.5194/bg-19-2245-2022, 2022
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The relative ratio of wetland methane (CH4) emission pathways determines how much CH4 is oxidized before leaving the soil. We found an ebullition modeling approach that has a better performance in deep layer pore water CH4 concentration. We suggest using this approach in land surface models to accurately represent CH4 emission dynamics and response to climate change. Our results also highlight that both CH4 flux and belowground concentration data are important to constrain model parameters.
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Short summary
Land surface models simulate exchanges at the Earth's surface. Traditional evaluation methods can be misleading because they may hide errors or be overly sensitive to outliers. We introduce the Model Fidelity Metric, which measures model performance in terms of accuracy, variability and distribution similarity. Tests with synthetic data and real streamflow observations show that this metric provides more stable and informative assessments of model performance.
Land surface models simulate exchanges at the Earth's surface. Traditional evaluation methods...
