The DOI matches its Crossref record; 99 of the 99 references it cites were found in a registry or an index.
Global predictions of primary soil salinization under changing climate in the 21st century
Open accessAbstract
Soil salinization has become one of the major environmental and socioeconomic issues globally and this is expected to be exacerbated further with projected climatic change. Determining how climate change influences the dynamics of naturally-occurring soil salinization has scarcely been addressed due to highly complex processes influencing salinization. This paper sets out to address this long-standing challenge by developing data-driven models capable of predicting primary (naturally-occurring) soil salinity and its variations in the world’s drylands up to the year 2100 under changing climate. Analysis of the future predictions made here identifies the dryland areas of South America, southern and western Australia, Mexico, southwest United States, and South Africa as the salinization hotspots. Conversely, we project a decrease in the soil salinity of the drylands in the northwest United States, the Horn of Africa, Eastern Europe, Turkmenistan, and west Kazakhstan in response to climate change over the same period.
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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
River flow forecasting through conceptual models part I — A discussion of principles
Nash, J. E., & Sutcliffe, J. V. (1970) · Journal of Hydrology · doi:10.1016/0022-1694(70)90255-6 · Cited by 23,674
An Overview of CMIP5 and the Experiment Design
Taylor, K. E., Stouffer, R. J., & Meehl, G. A. (2012) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-11-00094.1 · Cited by 14,052
Mechanisms of Salinity Tolerance
Munns, R., & Tester, M. (2008) · Annual Review of Plant Biology · doi:10.1146/annurev.arplant.59.032607.092911 · Cited by 13,792
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., & Taylor, K. E. (2016) · Geoscientific Model Development · doi:10.5194/gmd-9-1937-2016 · Cited by 11,981
Diagnosis and Improvement of Saline and Alkali Soils
RICHARDS, L. A. (1954) · Soil Science · doi:10.1097/00010694-195408000-00012 · Cited by 9,315
Kuhn, M., & Johnson, K. (2013) · doi:10.1007/978-1-4614-6849-3 · Cited by 6,788
Another look at measures of forecast accuracy
Hyndman, R. J., & Koehler, A. B. (2006) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2006.03.001 · Cited by 5,757
SoilGrids250m: Global gridded soil information based on machine learning
Hengl, T., Mendes de Jesus, J., Heuvelink, G. B. M., Ruiperez Gonzalez, M., Kilibarda, M., Blagotić, A., Shangguan, W., Wright, M. N., Geng, X., Bauer-Marschallinger, B., et al. (2017) · PLOS ONE · doi:10.1371/journal.pone.0169748 · Cited by 4,733
Mechanisms of Salt Tolerance in Nonhalophytes
Greenway, H., & Munns, R. (1980) · Annual Review of Plant Physiology · doi:10.1146/annurev.pp.31.060180.001053 · Cited by 4,003
Cited by
Romanello, M., Di Napoli, C., Drummond, P., Green, C., Kennard, H., Lampard, P., Scamman, D., Arnell, N., Ayeb-Karlsson, S., Ford, L. B., et al. (2022) · The Lancet · doi:10.1016/s0140-6736(22)01540-9 · Cited by 1,710
Leawtrakun, J., Aesomnuk, W., Khanthong, S., Dumhai, R., Songtoasesakul, D., Phosuwan, S., Nuanpirom, J., Charoensawan, V., Siangliw, J. L., Ruanjaichon, V., et al. (2024) · Agronomy · doi:10.3390/agronomy14050929 · Cited by 5
Metadata
- Authors
- Hassani, A., Azapagic, A., & Shokri, N.
- Venue
- Nature Communications
- Published
- 2021-11-18
- Volume
- 12
- Issue
- 1
- Pages
- 6663-6663
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W3212566395
- Cited by
- 1,021
- References
- 99
- FWCI
- 65.50
- Type
- journal article
- Language
- en
- Open access
- gold
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
