Nutrient Removal Potential of Headwater Wetlands in Coastal Plains of Alabama, USA
Headwater streams drain over 70% of the land in the United States with headwater wetlands covering 6.59 million hectares. These ecosystems are important landscape features in the southeast United States, with underlying effects on ecosystem health, water yield, nutrient cycling, biodiversity, and wa...
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Published in | Water (Basel) Vol. 15; no. 15; pp. 1 - 22 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
Switzerland
MDPI AG
25.07.2023
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Subjects | |
Online Access | Get full text |
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Summary: | Headwater streams drain over 70% of the land in the United States with headwater wetlands covering 6.59 million hectares. These ecosystems are important landscape features in the southeast United States, with underlying effects on ecosystem health, water yield, nutrient cycling, biodiversity, and water quality. However, little is known about the relationship between headwater wetlands' nutrient function (i.e., nutrient load removal
and removal efficiency
) and their physical characteristics. Here, we investigate this relationship for 44 headwater wetlands located within the Upper Fish River watershed (UFRW) in coastal Alabama. To accomplish this objective, we apply the process-based watershed model SWAT (Soil and Water Assessment Tool) to generate flow and nutrient loadings to each study wetland and subsequently quantify the wetland-level nutrient removal efficiencies using the process-based wetland model WetQual. Results show that the calculated removal efficiencies of the headwater wetlands in the UFRW are 75-84% and 27-35% for nitrate
and phosphate
, respectively. The calculated nutrient load removals are highly correlated with the input loads, and the estimated
shows a significant decreasing trend with increased input loadings. The relationship between
and wetland physical characteristics such as area, volume, and residence time is statistically insignificant (
> 0.05), while for
, the correlation is positive and statistically significant (
< 0.05). On the other hand, flashiness (flow pulsing) and baseflow index (fraction of inflow that is coming from baseflow) have a strong effect on
removal but not on
removal. Modeling results and statistical analysis point toward denitrification and plant uptake as major
removal mechanisms, whereas plant uptake, diffusion, and settling of sediment-bound P were the main mechanisms for
removal. Additionally, the computed nutrient
is higher during the driest year of the simulated period compared to during the wettest year. Our findings are in line with global-level studies and offer new insights into wetland physical characteristics affecting nutrient removal efficiency and the importance of headwater wetlands in mitigating water quality deterioration in coastal areas. The regression relationships for
and
load removals in the selected 44 wetlands are then used to extrapolate nutrient load removals to 348 unmodeled non-riverine and non-riparian wetlands in the UFRW (41% of UFRW drains to them). Results show that these wetlands remove 51-61% of the
and 5-10% of the
loading they receive from their respective drainage areas. Due to geographical proximity and physiographic similarity, these results can be scaled up to the coastal plains of Alabama and Northwest Florida. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Author Contributions: Conceptualization, S.I., L.K., M.M.H. and C.N.; methodology, S.I., H.H., L.K. and M.M.H.; software, S.I. and H.H.; validation, S.I., H.H. and L.K.; formal analysis, S.I., H.H. and L.K.; investigation, S.I., L.K. and M.M.H.; resources, L.K.; data curation, S.I.; writing—original draft preparation, S.I., H.H. and L.K.; writing—review and editing, S.I., H.H., L.K., M.M.H. and C.N.; visualization, S.I.; supervision, L.K. and M.M.H.; project administration, L.K. and M.M.H.; funding acquisition, L.K. and M.M.H. All authors have read and agreed to the published version of the manuscript. |
ISSN: | 2073-4441 2073-4441 |
DOI: | 10.3390/w15152687 |