U.S. Geological Survey, 2130 SW 5th Avenue, Portland, Oregon 97201, United States.
U.S. Geological Survey, 3916 Sunset Ridge Road, Raleigh, North Carolina 27607, United States.
Environ Sci Technol. 2021 Jan 19;55(2):902-911. doi: 10.1021/acs.est.0c02495. Epub 2020 Dec 24.
Elevated nitrogen concentrations in streams and rivers in the Chesapeake Bay watershed have adversely affected the ecosystem health of the bay. Much of this nitrogen is derived as nitrate from groundwater that discharges to streams as base flow. In this study, boosted regression trees (BRTs) were used to relate nitrate concentrations in base flow ( = 156) to explanatory variables describing nitrogen sources, geology, and soil and catchment characteristics. From these relations, a BRT model was developed to predict base flow nitrate concentrations in streams throughout the Chesapeake Bay watershed. The highest base flow nitrate concentrations were associated with intensive agricultural land use, carbonate geology, and sparse riparian canopy, which suggested that reduced nitrogen inputs, particularly over carbonate terrane, are critical for limiting nitrate concentrations. The lowest nitrate concentrations in the BRT model were associated with extensive riparian canopy, high levels of organic carbon in soils, and suboxic conditions at shallow depths, which suggested that denitrification in the subsurface, particularly in the riparian zone, is limiting base flow nitrate concentrations. Nitrate transport from aquifers to streams can take decades to occur, resulting in decades-long lag times between the time when a land-use activity is implemented and when its effects are fully observed in streams. Predictive models of base flow nitrate concentrations in streams will help identify which portions of a watershed are likely to have large fractions of total stream nitrogen load derived from pathways with significant lag times.
切萨皮克湾流域的溪流和河流中氮浓度的升高对该海湾的生态系统健康产生了不利影响。这些氮的大部分来自于作为硝酸盐从地下水排放到溪流中的基流。在这项研究中, boosted regression trees (BRTs) 被用来将基流中的硝酸盐浓度(= 156)与描述氮源、地质、土壤和集水区特征的解释变量相关联。根据这些关系,建立了一个 BRT 模型来预测切萨皮克湾流域溪流中的基流硝酸盐浓度。基流中硝酸盐浓度最高的地方与集约化农业用地、碳酸盐地质和稀疏的河岸植被有关,这表明减少氮素输入,特别是在碳酸盐地层上,对于限制硝酸盐浓度至关重要。BRT 模型中硝酸盐浓度最低的地方与广泛的河岸植被、土壤中高含量的有机碳和浅层的亚缺氧条件有关,这表明地下(特别是在河岸带)的反硝化作用限制了基流中的硝酸盐浓度。含水层中的硝酸盐向溪流中的迁移可能需要几十年的时间,因此,土地利用活动实施与溪流中完全观察到其影响之间存在长达几十年的滞后时间。溪流基流硝酸盐浓度的预测模型将有助于确定流域的哪些部分可能有很大一部分总溪流氮负荷来自具有显著滞后时间的途径。