Statistically extracted fundamental watershed variables for estimating the loads of total nitrogen in small streams
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Abstract
Accurate estimation of total nitrogen loads is essential for evaluating conditions in the aquatic environment. Extrapolation of estimates beyond measured streams will greatly expand our understanding of total nitrogen loading to streams. Recursive partitioning and random forest regression were used to assess 85 geospatial, environmental, and watershed variables across 636 small (<585 km2) watersheds to determine which variables are fundamentally important to the estimation of annual loads of total nitrogen. Initial analysis led to the splitting of watersheds into three groups based on predominant land use (agricultural, developed, and undeveloped). Nitrogen application, agricultural and developed land area, and impervious or developed land in the 100-m stream buffer were commonly extracted variables by both recursive partitioning and random forest regression. A series of multiple linear regression equations utilizing the extracted variables were created and applied to the watersheds. As few as three variables explained as much as 76 % of the variability in total nitrogen loads for watersheds with predominantly agricultural land use. Catchment-scale national maps were generated to visualize the total nitrogen loads and yields across the USA. The estimates provided by these models can inform water managers and help identify areas where more in-depth monitoring may be beneficial.
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | Statistically extracted fundamental watershed variables for estimating the loads of total nitrogen in small streams |
| Series title | Environmental Modeling & Assessment |
| DOI | 10.1007/s10666-016-9525-3 |
| Volume | 21 |
| Issue | 6 |
| Year Published | 2016 |
| Language | English |
| Publisher | Springer |
| Contributing office(s) | National Water Quality Assessment Program |
| Description | 10 p. |
| First page | 681 |
| Last page | 690 |
| Google Analytic Metrics | Metrics page |