Non-floodplain Wetlands Affect Watershed Nutrient Dynamics: A Critical Review

Abstract

Wetlands have the capacity to retain nitrogen and phosphorus and are thereby often considered a viable option for improving water quality at local scales. However, little is known about the cumulative influence of wetlands outside of floodplains, i.e., non-floodplain wetlands (NFWs), on surface water quality at watershed scales. Such evidence is important to meet global, national, regional, and local water quality goals effectively and comprehensively. In this critical review, we synthesize the state of the science about the watershed-scale effects of NFWs on nutrient-based (nitrogen, phosphorus) water quality. We further highlight where knowledge is limited in this research area and the challenges of garnering this information. On the basis of previous wetland literature, we develop emerging concepts that assist in advancing the science linking NFWs to watershed-scale nutrient conditions. Finally, we ask, “Where do we go from here?” We address this question using a 2-fold approach. First, we demonstrate, via example model simulations, how explicitly considering NFWs in watershed nutrient modeling changes predicted nutrient yields to receiving waters–and how this may potentially affect future water quality management decisions. Second, we outline research recommendations that will improve our scientific understanding of how NFWs affect downstream water quality.

Nitrate removal and young stream water fractions at the catchment scale

“…Chemical weathering rates therefore negatively depend on dimensionless numbers such as the Damköhler number (Maher, 2011; Salehikhoo & Li, 2015; Salehikhoo, Li, & Brantley, 2013). This is opposite for nitrate removal, in particular denitrification, which positively depends on long residence times in wetlands, riparian zones (Cohen et al, 2016; Golden et al, 2019; Pinay et al, 2015), and deeper groundwater as illustrated here. These distinct traits of different solutes can be used as signatures of different water sources and they can reveal differing water paths under diverse hydrologic regimes.…”

“…In deeper groundwater where O 2 is typically depleted, denitrification can become the dominant nitrate removal pathway albeit low organic carbon level (Kolbe et al, 2016). In wetlands, lakes, and reservoirs where nitrate has prolonged contact time with organic carbon, the extent of nitrate removal often reaches its maxima (Cheng & Basu, 2017; Cohen et al, 2016; Golden et al, 2019). In other words, the occurrence of denitrification depends on both transit times and reactions rates, but typically more removal occurs at longer contact times between nitrate, organic carbon, and microbes.…”

Seasonal watershed-scale influences on nitrogen concentrations across the Upper Mississippi River Basin

“…In the anaerobic sediments underlying these wetlands, biologically available nitrogen in the form of nitrate (NO3 -) may-in the presence of labile carbon and anoxic conditions for microbial activity (Soares, 2000)-be converted to nitrogen gas (N2) by the denitrification process (Sanchez-Perez et al, 2003). Evidence has therefore emerged that wetlands may substantially reduce nitrogen loading of surface waters at the watershed scale (Hansen et al, 2018;Quin et al, 2015;Fisher and Acreman, 2004;Golden et al, 2019). The efficacy of wetlands in reducing nitrogen loading has been observed across a range of flow conditions and seasons (Uuemaa et al, 2018).…”

“…Investigations of fertilizer inputs and wetland impacts on nitrogen loading are commonly conducted at an annual timescale (Basu et al, 2010;Thompson et al, 2011;Van Meter et al, 2016;Van Meter et al, 2018, 2019Golden et al, 2019). Yet temporal variability is critically important because it influences the timing and frequency of events in which nutrient concentrations exceed levels safe for human consumption (e.g., 10 mg l -1 in the US; 4.4 mg l -1 in Germany) and ecological integrity.…”

“…Despite recent advances in understanding the role of wetlands in influencing water quality across spatial scales, improved understanding of the seasonal variability remains a priority (Bloschl et al, 2019). Golden et al (2019) suggest the possible role of big data in improving understanding of the influence of non-floodplain wetlands on water quality. To deal with the challenges associated with big data, the hydrologic science community has tentatively and successfully tested machine learning (Shen, 2018;Tyralis et al, 2019), a data driven approach that contrasts with and supplements the normative physically based methods.…”