48 citations as recorded by crossref.

1. Increasing river sediment concentration and flux across the pan-Arctic S. Tian et al.
2. CAMELS-Chem: augmenting CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) with atmospheric and stream water chemistry data G. Sterle et al.
3. Estimating lateral nitrogen transfers over the last century through the global river network using a land surface model M. Ma et al.
4. River water quality shaped by land–river connectivity in a changing climate L. Li et al.
5. GLOBal river SALiniTy and associated ions (GlobSalt) A. Moyano-Salcedo et al.
6. Adapting machine learning for environmental spatial data - A review M. Jemeļjanova et al.
7. Implementing machine learning methods for in-depth analysis and classification of surface water quality in Central Java V. Perdana et al.
8. River reach-level machine learning estimation of nutrient concentrations in Great Britain C. Tso et al.
9. Heavy metal speciation and risk assessment in suspended particulate matter: A case study of small-medium freshwater bodies in Southwestern China C. Yao et al.
10. Human alterations to global riverine phosphorus fluxes to the ocean G. Liu et al.
11. Artificial intelligence for geoscience: Progress, challenges, and perspectives T. Zhao et al.
12. Fit-for-Purpose Information for Offshore Wind Farming Applications—Part-II: Gap Analysis and Recommendations J. Schulz-Stellenfleth et al.
13. Global basin-scale mapping of pH and alkalinity in inland waters M. Batalla et al.
14. A Community Dataset for Large-Scale River Nitrogen Modeling in the United States S. Chang et al.
15. Carbonate weathering enhances nitrogen assimilatory uptake in rivers globally H. Qi et al.
16. QUADICA: water QUAlity, DIscharge and Catchment Attributes for large-sample studies in Germany P. Ebeling et al.
17. Hydrography90m: a new high-resolution global hydrographic dataset G. Amatulli et al.
18. Future directions for river carbon biogeochemistry observations J. Dean & T. Battin
19. How does the choice of DEMs affect catchment hydrological modeling? D. Moges et al.
20. Random forest-based modeling of stream nutrients at national level in a data-scarce region H. Virro et al.
21. Application of machine learning for real-time water quality monitoring in developing countries: A review S. Ooko et al.
22. LakeBeD-US: a benchmark dataset for lake water quality time series and vertical profiles B. McAfee et al.
23. Economic risks hidden in local water pollution and global markets: A retrospective analysis (1995–2010) and future perspectives on sustainable development goal 6 J. Yang et al.
24. Human and natural activities regulate organic matter transport in Chinese rivers D. Liu et al.
25. Deep representation learning enables cross-basin water quality prediction under data-scarce conditions Y. Zheng et al.
26. A Comprehensive Water Chemistry Dataset for Iranian Rivers E. Zarei et al.
27. Making China’s water data accessible, usable and shareable J. Lin et al.
28. Mitigating nitrogen losses with almost no crop yield penalty during extremely wet years W. Liu et al.
29. The impact of anthropogenic pressures on microbial diversity and river multifunctionality relationships on a global scale Q. Qu et al.
30. Data limitations in developing countries make river restoration planning challenging. Study case of the Cesar River, Colombia S. Vega et al.
31. Sharing FAIR monitoring program data improves discoverability and reuse J. Bayer et al.
32. Temperature has an enhanced role in sediment N2O and N2 fluxes in wider rivers S. Zhang et al.
33. OzRiCa: an Australian riverine carbon database of concentrations, gas fluxes and isotopes F. Ulloa-Cedamanos et al.
34. Identifying Priority Nutrients for Achieving Water Quality Improvement and Climate Change Mitigation W. Li et al.
35. Disaster mitigation comics as a communication medium for youth regarding the impact of water pollution S. Mataram & R. Margaretha
36. Estimation of global riverine total phosphorus concentration based on multi-source data and stacked ensemble learning Q. Li et al.
37. Examining the open-source datasets for water quantity and quality using the soil and water assessment tool (SWAT) İ. Peker & S. Gülbaz
38. An extensive spatiotemporal water quality dataset covering four decades (1980–2022) in China J. Lin et al.
39. Geological regulation of nitrous oxide emission risks in rivers globally H. Qi et al.
40. OLIGOTREND, a global database of multi-decadal chlorophyll a and water quality time series for rivers, lakes, and estuaries C. Minaudo et al.
41. Determination of Optimal Water Intake Layer Using Deep Learning-Based Water Quality Monitoring and Prediction Y. Kim et al.
42. Harmonization of aggregated freshwater biotic data to support continental and global assessment J. Lento et al.
43. Overlooked riverine contributions of dissolved neodymium and hafnium to the Amazon estuary and oceans A. Xu et al.
44. Modeling hydraulic heads with impulse response functions in different environmental settings of the Baltic countries M. Jemeļjanova et al.
45. QUADICA v2: extending the large-sample data set for water QUAlity, DIscharge and Catchment Attributes in Germany P. Ebeling et al.
46. Global riverine land-to-ocean carbon export constrained by observations and multi-model assessment M. Liu et al.
47. Deep learning for water quality W. Zhi et al.
48. Satellites reveal widespread deoxygenation of large global reservoirs from 1984 to 2023 L. Liao & X. Cai