Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2018WR024054 |
Salinity Yield Modeling of the Upper Colorado River Basin Using 30-m Resolution Soil Maps and Random Forests | |
Nauman, Travis W.1; Ely, Christopher P.2; Miller, Matthew P.3; Duniway, Michael C.1 | |
2019-06-01 | |
发表期刊 | WATER RESOURCES RESEARCH |
ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2019 |
卷号 | 55期号:6页码:4954-4973 |
文章类型 | Article |
语种 | 英语 |
国家 | USA |
英文摘要 | Salinity loading in the Upper Colorado River Basin (UCRB) costs local economies upward of $300 million U.S. dollars annually. Salinity source models have generally included coarse spatial data to represent nonagriculture sources. We developed new predictive soil property and cover maps at 30-m resolution to improve source representation in salinity modeling. Salinity loading erosion risk indices were also created based on soil properties, remotely sensed bare ground exposure, and topographic factors to examine potential surface soil erosion drivers. These new maps and data from previous SPARROW models were related to recently updated records of salinity at 309 stream gauges in the UCRB using random forest regressions. Resulting salinity yield predictions indicate more diffuse salinity sources, with slightly higher yields in more arid portions of the UCRB, and less overall load coming from irrigated agricultural sources. Model simulations still indicate irrigation to be the major human source of salinity (661,000 Mg or 12%) and also suggest that 75,000 Mg (1.4%) of annual salinity in the UCRB is coming from areas with excessive exposed bare ground in high-elevation mountain areas. Model inputs allow for field-scale screening of locations that could be targeted for salinity control projects. Results confirm recent studies indicating limited surface erosional influence on salinity loading in UCRB surface waters, but impacts of monsoonal runoff events are still not fully understood, particularly in drylands. The study highlights the utility of new predictive soil maps and machine learning for environmental modeling. |
英文关键词 | water quality digital soil mapping electrical conductivity salinity control SPARROW Colorado River |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000477616900026 |
WOS关键词 | AUTOMATED REFERENCE TOOLSET ; CONTERMINOUS UNITED-STATES ; DISSOLVED-SOLIDS ; EROSION MODEL ; MANCOS SHALE ; PLATEAU ; UNCERTAINTY ; TRANSPORT ; DISTURBANCE ; DEPOSITION |
WOS类目 | Environmental Sciences ; Limnology ; Water Resources |
WOS研究方向 | Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/183980 |
专题 | 资源环境科学 |
作者单位 | 1.US Geol Survey, Southwest Biol Sci Ctr, Moab, UT 84532 USA; 2.US Geol Survey, Calif Water Sci Ctr, San Diego, CA USA; 3.US Geol Survey, Utah Water Sci Ctr, Salt Lake City, UT USA |
推荐引用方式 GB/T 7714 | Nauman, Travis W.,Ely, Christopher P.,Miller, Matthew P.,et al. Salinity Yield Modeling of the Upper Colorado River Basin Using 30-m Resolution Soil Maps and Random Forests[J]. WATER RESOURCES RESEARCH,2019,55(6):4954-4973. |
APA | Nauman, Travis W.,Ely, Christopher P.,Miller, Matthew P.,&Duniway, Michael C..(2019).Salinity Yield Modeling of the Upper Colorado River Basin Using 30-m Resolution Soil Maps and Random Forests.WATER RESOURCES RESEARCH,55(6),4954-4973. |
MLA | Nauman, Travis W.,et al."Salinity Yield Modeling of the Upper Colorado River Basin Using 30-m Resolution Soil Maps and Random Forests".WATER RESOURCES RESEARCH 55.6(2019):4954-4973. |
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