Articles | Volume 18, issue 5
https://doi.org/10.5194/essd-18-3355-2026
© 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/essd-18-3355-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Riverine phosphorus gain and loss across the conterminous United States
Ohio Agricultural Research and Development Center, School of Environment and Natural Resources, The Ohio State University, Wooster, OH 44691, USA
Xuesong Zhang
CORRESPONDING AUTHOR
USDA-ARS Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705-2350, USA
Kaiguang Zhao
Ohio Agricultural Research and Development Center, School of Environment and Natural Resources, The Ohio State University, Wooster, OH 44691, USA
Robert D. Sabo
United States Environmental Protection Agency, Office of Research and Development, Center for Public Health and Environmental Assessment, Health and Environmental Effects Assessment Division, Washington, DC 20460, USA
Yuxin Miao
Precision Agriculture Center, Department of Soil, Water and Climate, University of Minnesota, St. Paul, MN 55108, USA
Christopher M. Clark
United States Environmental Protection Agency, Office of Research and Development, Center for Public Health and Environmental Assessment, Health and Environmental Effects Assessment Division, Washington, DC 20460, USA
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Daniel T. Myers, David Jones, Diana Oviedo-Vargas, John Paul Schmit, Darren L. Ficklin, and Xuesong Zhang
Hydrol. Earth Syst. Sci., 28, 5295–5310, https://doi.org/10.5194/hess-28-5295-2024, https://doi.org/10.5194/hess-28-5295-2024, 2024
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We studied how streamflow and water quality models respond to land cover data collected by satellites during the growing season versus the non-growing season. The land cover data showed more trees during the growing season and more built areas during the non-growing season. We next found that the use of non-growing season data resulted in a higher modeled nutrient export to streams. Knowledge of these sensitivities would be particularly important when models inform water resource management.
Han Qiu, Dalei Hao, Yelu Zeng, Xuesong Zhang, and Min Chen
Earth Syst. Dynam., 14, 1–16, https://doi.org/10.5194/esd-14-1-2023, https://doi.org/10.5194/esd-14-1-2023, 2023
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The carbon cycling in terrestrial ecosystems is complex. In our analyses, we found that both the global and the northern-high-latitude (NHL) ecosystems will continue to have positive net ecosystem production (NEP) in the next few decades under four global change scenarios but with large uncertainties. NHL ecosystems will experience faster climate warming but steadily contribute a small fraction of the global NEP. However, the relative uncertainty of NHL NEP is much larger than the global values.
Tao Zhang, Yuyu Zhou, Kaiguang Zhao, Zhengyuan Zhu, Gang Chen, Jia Hu, and Li Wang
Earth Syst. Sci. Data, 14, 5637–5649, https://doi.org/10.5194/essd-14-5637-2022, https://doi.org/10.5194/essd-14-5637-2022, 2022
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We generated a global 1 km daily maximum and minimum near-surface air temperature (Tmax and Tmin) dataset (2003–2020) using a novel statistical model. The average root mean square errors ranged from 1.20 to 2.44 °C for Tmax and 1.69 to 2.39 °C for Tmin. The gridded global air temperature dataset is of great use in a variety of studies such as the urban heat island phenomenon, hydrological modeling, and epidemic forecasting.
Sangchul Lee, Dongho Kim, Gregory W. McCarty, Martha Anderson, Feng Gao, Fangni Lei, Glenn E. Moglen, Xuesong Zhang, Haw Yen, Junyu Qi, Wade Crow, In-Young Yeo, and Liang Sun
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-187, https://doi.org/10.5194/hess-2022-187, 2022
Manuscript not accepted for further review
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Watershed modeling is important to protect water resources. However, errors are involved in watershed modeling. To reduce errors, remotely sensed evapotranspiration data are widely used. However, the use of remotely sensed evapotranspiration data still includes errors. This study applied two remotely sensed data (evapotranspiration and leaf area index) into watershed modeling to reduce errors. The results showed advancement of watershed modeling by two remotely sensed data.
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Short summary
Excess riverine phosphorus is a worldwide threat to water quality. Leveraging 51 394 phosphate (PO₄³⁻) and 285 675 total phosphorus (TP) data points and hydrological topology information, we developed a new dataset mapping PO₄³⁻ and TP gain/loss across catchments in the conterminous United States. The new net phosphorus gain/loss maps allow us to assess sources, drivers, and model fidelity, thereby supporting future research and management of phosphorus contamination.
Excess riverine phosphorus is a worldwide threat to water quality. Leveraging 51 394 phosphate...
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