Controls of the spatial variability of denitrification potential in nontidal floodplains of the Chesapeake Bay watershed, USA
Controls of the spatial variability of denitrification potential in nontidal floodplains of the Chesapeake Bay watershed, USA
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美国切萨皮克湾流域非潮汐洪泛区反硝化潜力空间变异的控制
DOI：
10.1016/j.geoderma.2018.11.015
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发表时间：
2019
期刊：
影响因子：
6.1
通讯作者：
Ahn, Changwoo
中科院分区：
文献类型：
--
作者：
Korol, Alicia R.;Noe, Gregory B.;Ahn, Changwoo
确定具有高脱氮率的洪泛区将有助于优先考虑去除氮的恢复项目。目前，反硝化作用与水文地貌，自然地理和气候（即，大尺度）洪泛区的特征相对未知，即使这些特征具有数据集（例如，地理绘图工具），这些工具是公开可用的（或即将成为），可以用来了解反硝化作用的变化。因此，我们研究了切萨皮克湾流域18个非潮汐洪泛区中这些大尺度特征对反硝化作用的控制（即，在区域尺度，>100 km，尺度），使用在河流河段和各自集水区尺度的测量或汇编数据;河漫滩土壤和草本植被（即，局部）特征进行了额外的研究。土壤反硝化潜力测定在5月，7月和8月使用互补乙炔为基础的技术在缺氧环境下。反硝化潜力测量的线性大尺度预测因子包括河流氮和磷浓度（+）、河道宽深比（+）、河漫滩沉积（+）、森林（−）和城市（+）集水区土地覆盖以及季节性气温（−）。三个预测因子，集水区森林覆盖（强烈相关的农业用地覆盖），集水区城市土地覆盖，河漫滩沉积有关的反硝化潜力测量的最多的。土壤结构，土壤养分浓度和草本植被特征，季节性测量（有少数例外）是线性预测的反硝化潜力在5月和8月，氮和碳的特点是最一致的（积极的）预测跨测量。营养补充剂分析进一步支持氮和碳控制的重要性。使用当地的特点作为统计介质的通径分析，更大的非森林集水区土地覆盖间接增加反硝化作用，通过更大的河漫滩土壤硝酸盐，总磷，和草本植物地上生物量。此外，更大的河漫滩沉积间接增加反硝化作用，通过更大的土壤pH值，总磷和潜在的碳矿化。由于反硝化潜力测量结果与路径模拟结果沿着的关系的一致性，洪泛区反硝化的热点应在城市和农业集水区，河流洪泛区水文连通性促进沉积。大规模的预测解释了43-57%的反硝化潜力的变化，应该是有用的预测洪泛区。利用公开的大规模数据集，在流域中选址恢复项目以最大限度地去除硝酸盐是可行和有效的。
Identifying floodplains with high rates of denitrification will help prioritize restoration projects for the removal of nitrogen. Currently, relationships of denitrification with hydrogeomorphic, physiographic, and climate (i.e., largescale) characteristics of floodplains are relatively unknown, even though these characteristics have datasets (e.g., geographic mapping tools) that are publicly available (or soon-to-become) that could be used to understand denitrification variability. Thus, we investigated control of denitrification by these largescale characteristics in eighteen nontidal floodplains of the Chesapeake Bay watershed (i.e., at regional scale, >100 km, scale), using measurements or compiled data at the scales of the stream reach and respective catchment; floodplain soil and herbaceous vegetation (i.e., local) characteristics were additionally investigated. Soil denitrification potentials were measured in May, July, and August using complementary acetylene-based techniques under an anoxic environment. Linear largescale predictors of denitrification potential measurements included stream nitrogen and phosphorus concentrations (+), channel width-to-depth ratio (+), floodplain sedimentation (+), forested (−) and urban (+) catchment land cover, and seasonal air temperature (−). Three predictors, catchment forested land cover (strongly related to agricultural land cover), catchment urban land cover, and floodplain sedimentation were related to the most number of denitrification potential measurements. Soil structure, soil nutrient concentrations, and herbaceous vegetation characteristics that were seasonally measured (with a few exceptions) were linear predictors of denitrification potentials in May and August, with nitrogen and carbon characteristics the most consistent (positive) predictors across measurements. Nutrient amendment assays further supported the importance of nitrogen and carbon controls. Using the local characteristics as statistical mediators in path analysis, greater non-forested catchment land cover indirectly increased denitrification through greater floodplain soil nitrate, total phosphorus, and herbaceous aboveground biomass. Additionally, greater floodplain sedimentation indirectly increased denitrification through greater soil pH, total phosphorus, and potential carbon mineralization. Due to the consistency of relationships across denitrification potential measurements along with path modeling results, hotspots of floodplain denitrification should be found in urban and agricultural catchments where river-floodplain hydrologic connectivity promotes sedimentation. Largescale predictors explained 43–57% of the variation in denitrification potentials and should be useful for prediction in floodplains. Siting restoration projects in watersheds for maximum nitrate removal using publicly available largescale datasets is both feasible and effective.
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DOI：
--
发表时间：
1961
期刊：
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DOI：
10.1002/jgrg.20090
发表时间：
2013
期刊：
Journal of Geophysical Research: Biogeosciences
影响因子：
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作者：
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DOI：
--
发表时间：
2016
期刊：
影响因子：
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作者：
C. Hupp;S. Dufour;G. Bornette
通讯作者：
G. Bornette