Combining in-situ fluorometry and distributed rainfall data provides new insights into natural organic matter transport dynamics in an urban river

Urbanization alters the quality and quantity of Dissolved Organic Matter (DOM) fluxes to rivers potentially leading to water quality problems and impaired ecosystem function. Traditional synoptic and point sampling approaches are generally inadequate for monitoring DOM source dynamics. To identify links between spatial heterogeneity in precipitation and DOM dynamics, we used a unique approach combining high spatial and temporal resolution precipitation datasets featuring point, catchment, and land-cover weighted precipitation to characterise catchment transport dynamics. These datasets were linked to fluorescence records from an urban stream (Bourn Brook, Birmingham, UK). Humic-like fluorescence (HLF: Ex. 365 nm, Em. 490 nm) and Tryptophan-like fluorescence (TLF: Ex. 285 nm, Em. 340 nm) were measured, (plus river flow and turbidity) at 5 min intervals for 10 weeks during Autumn 2017. The relationship between discharge (Q) and concentration (C) for TLF and HLF were strongly chemodynamic at low Q (<Q50) but TLF was chemostatic when Q exceeded this threshold. Figure of eight hysteresis was the most common response type for both HLF and TLF, indicating that DOM sources shift within and between events. Key drivers of DOM dynamics were identified using regression analysis and model outputs using point, catchment-averaged, and land-use weighted precipitation were compared. Antecedent rainfall was identified as the most important predictor (negative relationship) of TLF and HLF change suggesting DOM source exhaustion. Precipitation weighted by land cover showed that urbanization metrics were linked to increased TLF:HLF ratios and changes in hysteresis index. This study presents a novel approach of using land-cover weighted rainfall to enhance mechanistic understanding of DOM controls and sources. In contrast, catchment-average rainfall data have the potential to yield stronger understanding of TLF dynamics. This technique could be integrated with existing high resolution in-situ datasets to enhance our understanding of DOM dynamics in urban rivers.