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RZ-TRADEOFF: A New Model to Estimate Riparian Water and Air Quality Functions
oleh: Yasaman T. Hassanzadeh, Philippe G. Vidon, Arthur J. Gold, Soni M. Pradhanang, Kelly Addy Lowder
| Format: | Article |
|---|---|
| Diterbitkan: | MDPI AG 2019-04-01 |
Deskripsi
Riparian zones are often used as best management practices due to their ability to remove nitrate (NO<sub>3</sub><sup>−</sup>) from subsurface flow. Research suggests that beyond local biogeochemical controls, the impact of riparian zones on nitrogen removal and other functions, such as phosphorus dynamics and greenhouse gas emissions, largely depends on land-use/land-cover, hydrogeomorphology, and weather. In this study, we therefore present RZ-TRADEOFF, a novel and easily applicable model that connects multiple riparian functions and characteristics (NO<sub>3</sub><sup>−</sup> and phosphate (PO<sub>4</sub><sup>3−</sup>), concentration and removal in subsurface flow, total phosphorus (TP) removal in overland flow, nitrous oxide (N<sub>2</sub>O), methane (CH<sub>4</sub>), and carbon dioxide (CO<sub>2</sub>) emissions, water table) to landscape hydrogeomorphic characteristics, weather, and land-cover/land-use. RZ-TRADEOFF was developed with data from past studies and digital databases, and validated with data collected from the literature. Three functions (water table, PO<sub>4</sub><sup>3−</sup> and CO<sub>2</sub>) were observed to be significantly influenced by climate/weather, while the others were primarily influenced by hydrogeomorphology and land use. The percent bias and normalized root mean square error respectively were −3.35% and 0.28 for water table, 16.00% and 0.34 for NO<sub>3</sub><sup>−</sup> concentration, −7.83% and 20.82 for NO<sub>3</sub><sup>−</sup> removal, 6.64% and 0.35 for PO<sub>4</sub><sup>3−</sup> concentration, 2.55% and 0.17 for TP removal, 40.33% and 0.23 for N<sub>2</sub>O, 72.68% and 0.18 for CH<sub>4</sub>, and −34.98% and 0.91 for CO<sub>2</sub>. From a management standpoint, RZ-TRADEOFF significantly advances our ability to predict multiple water and air quality riparian functions using easily accessible data over large areas of the landscape due to its scalability.