Salinity Yield Modeling of the Upper Colorado River Basin Using 30‐m Resolution Soil Maps and Random Forests. Issue 6 (22nd June 2019)

• Record Type: Journal Article
• Authors: Nauman, Travis W.; Ely, Christopher P.; Miller, Matthew P.; Duniway, Michael C.
• Is Part Of: Water resources research. Volume 55:Issue 6(2019)
• Page Start: 4954
• Page End: 4973
• Publication Date: 2019-06-22
• Subjects: water quality -- digital soil mapping -- electrical conductivity -- salinity control -- SPARROW -- Colorado River; Hydrology -- Periodicals; 333.91
• DOI: 10.1029/2018WR024054
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: 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. Key Points: A flexible random forest approach for salinity yield modeling is presented Field‐scale distribution characteristics of new soil property maps were the most important catchment salinity yield predictors New 30‐m soil cover maps highlight areas where salinity control projects may reduce loads by 1.4% or 76, 051 Mg