CHARACTERIZING PARTICULATE NUTRIENT LOADS IN RIVERS TO DEVELOP PREDICTIVE CONTAMINANT TRANSPORT MODELS USING LANDSAT SURFACE REFLECTANCE DATA

Excess nutrient (i.e., nitrogen (N) and phosphorous (P)) contributions from agricultural activities can trigger eutrophication, leading to severe impacts on human and ecosystem health when algal blooms release cyanotoxins or induce hypoxia as they decompose. While rivers are less frequently impacted by algal blooms than lentic or marine systems, they act as the main conduit for nutrient inputs to these waterbodies. Indeed, interactions between nutrient species and the suspended load in rivers play an important role in the release, transport, and storage of N and P, ultimately controlling their delivery to downstream ecosystems. This study therefore aims to characterize N and P levels in the suspended loads of the agriculturally-impacted Mississippi River and Missouri River. Our nutrient data can then be used with an existing model for estimating suspended sediment concentrations (SSC) from Landsat imagery to produce predictive models of particulate nutrient transport from the Mississippi River basin to the Gulf of Mexico. For our study, we collected weekly water samples from both rivers <15 km upstream of their confluence from March 2021 to November 2021. Samples were characterized for SSC, particulate N and P concentrations, and dissolved N and P concentrations. In both the Mississippi River and Missouri River, N was mainly transported in the dissolved fraction (respectively 77.7% and 61.2% of the total N load), while P was mostly moved in the particulate fraction (respectively 65.0% and 78.7% of the total P load). For both rivers, we found positive (r ≥ 0.59) and significant (p < 0.05) correlations between particulate P and SSC. Particulate N was positively (r = 0.61) and significantly (p < 0.05) correlated with SSC in the Missouri River, but we did not observe a similar relationship for the Mississippi River (r = 0.07; p > 0.05). Our preliminary data suggest that SSC derived from surface reflectance models could be used as a proxy for particulate P transport in both rivers, but the relationship between SSC and particulate N is more complex. Ongoing work involves relating our SSC and particulate nutrient correlations to an existing model for estimating SSC from Landsat imagery. These efforts will allow us to predict particulate nutrient loads delivered to downstream environments using only remotely-sensed datasets.