https://orcid.org/0000-0003-4897-6945
Figures
Abstract
The boreal environment is high in dissolved organic carbon (DOC) and iron concentrations. This DOC is enriched in functional groups allowing it to bind strongly with iron and act as a significant source of iron to the coastal and marine environment. As climate change intensifies more extreme weather events will affect the northern hemisphere and boreal environment. These weather events can lead to massive fluxes of material but the impact it will have is currently unknown. Hurricane Larry made landfall on Newfoundland (NL) in 2021 providing an opportunity to investigate how the boreal environment will react to extreme weather events. We sampled three rivers before and after the hurricane to see how DOC and iron concentrations, and colour (a350) were affected by the hurricane. Temperature and pH were found to decrease in a statistically significant manner following the storm with iron and a350 having a significant increase, DOC despite increasing following the storm failed to meet the statistical significance threshold. The rivers chosen had a high abundance of natural landcover which buffered the catchment’s response from the hurricane. Pearson’s correlations were calculated between the landcover and the ∆ of the variables to access the resilience of the catchments to extreme weather events. Due to the small sample size Pearson’s corelations could not achieve statistical significance but the results still provide a novel look into the resilience of the northern boreal regions to extreme weather events. These preliminary results show that a high percentage of forest and peatlands buffered against increases in DOC and colour, with wetlands buffering an increase in iron concentrations. This study represents one of the first to observe boreal catchment responses to extreme weather events such as hurricanes and can serve as basis for more robust studies based in the boreal region.
Citation: Heerah KM, Clarke KG, Reader HE (2026) The response of small boreal catchments to extreme weather event: Hurricane Larry. PLoS One 21(4): e0345113. https://doi.org/10.1371/journal.pone.0345113
Editor: Muammar Qadafi, National Research and Innovation Agency, INDONESIA
Received: September 18, 2025; Accepted: March 2, 2026; Published: April 13, 2026
Copyright: © 2026 Heerah et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: Yes - all data are fully available without restriction; The data used in this manuscript can be accessed from Memorial University of Newfoundland Borealis database (https://doi.org/10.5683/SP3/ONYKCS).
Funding: This study was funded by the Natural Sciences and Engineering Council of Canada (NSERC) Discovery Grant Program [RGPIN-2019–04947], and the Canada Research Chairs program. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: Authors have no competing interests to declare.
1. Introduction
Dissolved organic matter (DOM) is a complex mixture of compounds of diverse biological origins and diagenetic states. Rivers deliver significant amounts of terrestrial DOM and other important macro- and micro- nutrients to the coastal ocean, globally [1–3]. Terrestrial DOM impacts the coastal ecosystem by acting as a substrate for microbial degradation processes, and limiting light penetration, tipping the balance towards net heterotrophy [4–7]. Terrestrial DOM brings with it a number of important nutrients, and in particular is recognized as an important supply of dissolved iron (dFe), thereby increasing coastal productivity [3,8].
High intensity storm events such as hurricanes have been shown to cause increases in terrestrial DOM loading in rivers and estuaries [9–12]. A number of studies have found that significant amounts of a river’s annual DOM load can be exported as a result of a major storm event, in some cases up to 50% or more [12–16]. These massive fluxes of DOM occur over short time periods, and can have drastic consequences for coastal carbon cycling [10,17–19]. Yan et al. (2020) found that the DOM flux associated with Hurricane Harvey in 2017 increased carbon cycling in Galveston Bay, Texas (USA) with the majority of the flux mineralized within a month of the storm. On the other hand, Avery et al. (2004) found that the majority of the riverine DOM associated with Hurricane Floyd in 1999 (Long Bay, North Carolina, USA) increased the concentration of recalcitrant DOM in Long Bay. Regardless of the fraction of bioavailable DOM for each event, the total fluxes of DOM and freshwater are such that the coastal bacterial community is altered [9,17,20]. The impact of these storm events persist in the coastal environment for long periods of time from days to months [9,10,13]. As climate change and the warming of the ocean continues high intensity weather events, such as hurricanes are likely to affect more northern regions, such as the boreal environment [21–25]. The majority of studies investigating hurricanes and extreme weather events are based in more southern regions. The response of the boreal environment to hurricanes has not been studied before given its previously rare occurrence.
The boreal environment is a rich source of carbon and iron to the coastal/ marine environment. Changes in the export of material from the boreal environment can have impacts on terrestrial biogeochemical cycles and the nearby coastal communities. DOM from boreal and peatland ecosystems have been identified as important for biogeochemical nutrient cycling [3,8,26]. The acidic soils in boreal forest and the anoxic conditions present in peat barrens can allow for the accumulation of iron in the soils [27,28]. Peat and boreal forest produce DOM enriched in aromatics, lignins, humic acids, and humic ligands [26,29]. This DOM can form complexes with iron more resistant to flocculation allowing greater terrestrial inputs of iron and carbon into the coastal environment [3,30–32]. DOM concentrations have been increasing in inland waters across the boreal region due at least in part to warming conditions and increased precipitation, leading to brownification of waterways and increasing terrestrial inputs of DOM to the coastal ocean [33–38]. The impact of the convergence of brownification and extreme weather events is currently unknown but clearly has the potential to impact the coastal environment.
Catchments in Newfoundland, Canada are dominated by peat barrens and boreal forest [39], both rich in organic carbon and iron. In September 2021, Hurricane Larry made landfall on the Avalon Peninsula of Newfoundland, Canada (Fig 1). This is the first hurricane within 11 years to reach the shores of Newfoundland, the previous one being Hurricane Igor in 2010. Hurricane Larry reached Newfoundland as a category 1 hurricane with a maximum sustained wind speed of 120 km/hr and gusts reaching a maximum of 180 km/hr. Rainfall was around 25–35 mm over a very short period of time causing localized flooding with significant storm surges with waves reaching 3.6 m [40].
Hurricane path and wind extent data from the National Hurricane Center Atlantic HURDAT2 database [41]. Map created using M_Map package for MATLAB [42] using the NOAA GSHHG high-resolution coastline database [43].
While the island of Newfoundland experiences storm events with regular frequency, typically most of these events occur in the winter months, when productivity on land is limited and dissolved organic carbon (DOC) and iron concentrations are relatively low [44–48]. With the predicted increases in hurricane activity in the Atlantic Ocean, the island can expect to experience more of these events during the warm summer months, potentially leading to considerably increased carbon and iron export to the coastal zone that would not be experienced in a typical winter storm event.
Taking advantage of the well-predicted path of Hurricane Larry which made landfall in close proximity to Memorial University, we collected DOM samples from three rivers before and after landfall. We measured the change in concentration of DOC, iron, and colour to gauge the effects the hurricane has on the export of material from the island. Through the comparison of three rivers, one dominated by peatlands, one dominated by forest, and one with a mix of forest and peatlands we aim to identify how the different catchments react to the hurricane. The relationships between peatlands, forest and wetlands, and changes in DOM and iron export will be explored to see how landcover affects the catchment’s behaviour to extreme weather events. This is the first study measuring the effects of a hurricane on carbon cycling in Newfoundland and in the North American boreal environment. The limited number of sample sites and time points due to the opportunistic nature of this study prevents a more robust investigation into the mechanistic behaviours of the catchments. Rather, this serves as a preliminary exploration of how such environments will react to the threat of more extreme weather events brought on by climate change and warming oceans.
2. Methods
2.1. Sample Collection
Samples were collected from three river sites on the Avalon Peninsula of Newfoundland, Canada before (September 10th, 2021) and after the landfall of Hurricane Larry (September 11th, 2021). The three sites are equipped with water level and discharge gauges by Environment and Climate Change Canada (ECCC): Seal Cove Brook near Cappahayden, NL (SC, ECCC 02ZM009), St Shotts River near Trepassey, NL (SS, ECCC 02ZN002), and South River near Holyrood, NL (SR, ECCC 02ZM016). Water level and discharge data was extracted from the ECCC Real-time Hydrometric Data Website [49]. These three rivers were chosen out of fourteen gauged rivers on the Avalon Peninsula based on a number of factors. Of the fourteen rivers equipped with discharge and water level sensors, seven are in the heavily urbanized metro region making it unsuitable for such a study. Of the 7 remaining rivers, one is located downstream of a fish plant, while another is downstream of a mine, leaving 5 potential rivers to choose from. The three rivers chosen cover different landcover types of interest to the study, within safe driving distance given the dangers of sampling during an extreme weather event. SC has the longest historical record of discharge of the three rivers running continuously since 1979, SR started measuring discharge in 1983 with SS starting in 1985. The average discharge for the month of September spanning the historical range is calculated for the three rivers. No chemical parameters of the river such as temperature, pH, alkalinity, DOC, dFe, etc are collected at SR or SS. SC has a small chemical database spanning from 1986 to 1995 measuring lead and cadmium (Email communication with K. Brake Department of Environment and Climate Change, Government of Newfoundland and Labrador, November 2025).
Hurricane Larry made landfall shortly after midnight local time (23:00 Newfoundland Standard Time, NST) on September 11th, 2021. Samples were collected 12–18 hours after landfall as soon as it was determined that the road was passable to all three sites. There are no permits required for the collection of samples in any of the catchments as they all reside on public land. One litre of river water was collected at each site before and landfall, with the sample being stored in two 500 mL acid washed polycarbonate bottles to prevent iron and carbon contamination. The bottles were transported on ice back to the lab where they were filtered on the day of collection using ashed glass fibre filters (GF/F) filters (Whatman, nominal pore size 0.7 µm). Where appropriate, glassware was ashed (heated to 450°C for five hours) to burn off any residual carbon preventing carbon contamination. The filtered sample was then subdivided for dissolved iron (dFe), dissolved organic carbon (DOC), and a350 (a spectroscopic proxy for water colour and the presence of chromophoric DOM). Vials used for DOC, dFe, and a350 were stored in ashed vials capped with Teflon lined caps to further ensure sample integrity. Samples used for dFe and a350 were stored in amber coloured vials to reduce light degradation of iron or chromophoric DOM. Temperature and pH were measured in situ using a Thermo Scientific Orion Star A329 meter and pH probe.
2.2. Catchment characteristics
The catchments are dominated by boreal forest and peatlands, The three catchments occupy 2 distinct ecoregions, SR and SC are situated in the Maritime Barrens ecoregion and SS is situated in the Eastern Hyper-Oceanic Barrens ecoregion. The two ecoregions are distinguished by their proximity to the southernmost coast of the Avalon peninsula, while both regions are characterized by peat barrens and boreal tree species, the Eastern Hyper-Oceanic Barrens are exposed to strong winds, sea spray, and heavy fog most of the year [50]. Tree vegetation in the region is limited to sparse patches of tuckamore (or Krumholtz formations), which are low-lying shrub/thicket formations, as opposed to the typical though somewhat stunted boreal forest found in the Maritime Barrens. The breakdown of the landcover can be seen in Table 1.
Forest, peatlands, and wetlands are strongly associated with DOM and iron quality and quantity, and thus were chosen for statistical analysis for the changes in DOM before and after the Hurricane Larry [2,3,8,51–53]. Full landcover details can be found in the Government of Canada 2015 Land Cover of Canada report [39]. River discharge for the year for each catchment was used to calculate a relative Richard-Baker flashiness index (RB index) [54]. The RB index is a dimensionless indicator of how quickly a river returns to baseflow and can be used to compare catchment responses to storm events. This equation differs from the typical RB index equation being more suited for daily discharge comparisons, where q is the discharge of the river measured in m3/s, n is the total sample number and i represents the current sample in the calculation iteration [54].
2.3. Laboratory methods
2.3.1. Dissolved organic carbon.
DOC was quantified as non-purgeable organic carbon on a Shimadzu TOC-L-CPH analyzer. The instrument was calibrated with a recrystalized acetanilide primary standard C8H9NO, Across Organics, 99 + % pure. The instrument measures samples using 3–5 injections ensuring a co-variance of 2% between injections, to ensure sample accuracy.
2.3.2. Optical Properties.
DOM absorbance was measured on a Cary 300 spectrophotometer (Agilent), from 200–800 nm, with 1 nm resolution in a 5 cm quartz cuvette. Absorbance (A) was converted to decadal absorption (a) by a = A/0.05, where 0.05 is the pathlength of the cuvette in m. The absorption coefficient at 350 nm (a350) was used as a proxy for chromophoric DOM (CDOM). The conversion of absorbance to decadal absorption was calculated using MATLAB 2020A.
2.3.3. Iron quantification.
Iron was quantified using the ferrozine method as outlined by Violler et al. [55] and Kritzberg et al. [3]. Briefly, 2.5 mL of sample is mixed with 515 µL of hydroxylamine hydrochloride solution and 250 µL ferrozine reagent and is then heated to just below boiling for 10 minutes. This step reduces all Fe to Fe (II) allowing complexation to the ferrozine reagent. Once the sample is removed from heat it is allowed to cool for 90 seconds before the pH is adjusted using 200 µL of basic ammonium acetate solution, and the resulting ferrozine-iron complex is measured by its absorbance at 562 nm. The method is calibrated using FeCl3. The FeCl3 calibration ranges from 0 to 5µmols/L using 6 points. The 6 points are 0,0.25µmol/L, 0.5µmol/L, 1µmol/L,2µmol/L and 5µmol/L. This method allows for a limit of detection of 0.0841µmol/L. To reduce the interference of DOM absorbance in Fe measurements, a reagent blank for each sample was also run. DOM of terrestrial origin can exhibit absorbance in the longer wavelength range and can add some interference to the iron measurements [56]. A reagent blank, i.e., running a sample with no reagents, can thus provide a correction factor for the iron measurements.
2.4. Statistical analysis
All statistics were carried out in MATLAB 2020A. After the hurricane, all parameters experienced a change: DOC, Fe, a350 increased while temperature, and pH experienced a decrease. To assess whether these changes were statistically significant one tailed paired t-tests (significance level of 0.05) were conducted. The t-test showed if the changes observed after the hurricane were statistically significant. One tailed t-tests were chosen based on the parameters having a clear direction of change, making it unnecessary to see if the variables changed in both directions which would be the case in a two-tail test. The paired t-test computes the difference in a measurement before and after treatment (the hurricane in this case) for each river separately before testing if the mean of the calculated differences of the full dataset varied from 0, to reject the null hypothesis of Ho = 0, where Ho is the mean of the calculated differences. The test computes a t-score which can be used to determine the p value and thus the significance of the change. In this way each river then serves as its own reference point, for an n = 3 dataset. The ∆ for the parameters were compared with landcover using Pearson’s correlations test. ∆ was calculated for each parameter and each individual river. The % coverage of the landcover types was individually compared to the ∆ of a parameter at a time, making an n = 3 dataset for each analysis. The low degrees of freedom for the Pearson’s correlation reduces statistical power the ρ are reported but the p-values are not as a result.
3. Results
3.1. Change in river state
The hydrographs as seen in Fig 2, show that all rivers were in a period of stable baseflow for the 24 hours prior to the hurricane, experienced a peak in discharge within hours of landfall, and then remained in a high flow state for the 24 hours after landfall. In all three catchments, water level and discharge increased following the hurricane with discharge increasing by 235% in SR, followed by 70% for SC, and 40% for SS between the first sampling and the initial peak in discharge. In SC, the discharge stabilized after the initial discharge peak which occurred ~5.5 hours after landfall of the storm. In both SR and SS, discharge continued to increase after the initial peaks (2 and 5 hours after landfall, respectively) for the 24-hour period after landfall. While we were not able to sample at the time of the initial peaks, all rivers were sampled at higher discharges after the hurricane than before. The change in pH, temperature, discharge, and the RB index is shown in Table 2.
The blue and yellow asterisks represent the time (NST) of sampling prior to and after landfall, respectively. The orange asterisk marks the time (NST) of landfall.
Discharge is fairly low not exceeding 1m3/s at any point in the sample period (Table 2). The RB index can be used to compare the river’s response to storm events. SS is seen to be the flashiest returning to baseflow the quickest with SC taking the longest to return to baseflow. Both temperature and pH (Table 2) exhibited statistically significant decreases across all three catchments between sampling times (t-tests, p = 0.0145, p = 0.0356, respectively). Increases in DOC, iron concentrations and a350 were seen in all rivers as shown in Fig 3. Fe and a350 increases were found to be statistically significant (t-test, p = 0.0132, p = 0.0389, respectively) with DOC failing to achieve statistical relevance at the 0.05 significance interval (p = 0.0905). While the increase in DOC failed to meet the 0.05 significance interval it is important to highlight at a 0.1 significance interval it would have statistically significant differences. The DOC behaviours will still be discussed but they should not be seen as robust as the other parameters measured. DOC concentrations for SR and SS increased (+237 µmol/L and +159 µmol/L, respectively) with corresponding increases in a350 (+4.55 m-1 and +3.44 m1, respectively). Increase in iron was highest in SR (+1.57 µmol/L), and nearly the same in SC and SS (+1.01 µmol/L and +0.96 µmol/L, respectively).
a) dissolved organic carbon (µmol/L), b) iron (µmol/L), and c) a350 (m-1). Blue bar represents the before conditions with the red bar showing after Hurricane Larry.
3.2. Catchment characteristics and its effect on river conditions
Correlation analysis (Table 3) on the changes pre- and post- hurricane revealed that changes in DOC and a350 (i.e., ΔDOC and Δa350) both had relatively strong negative correlations with the percent of peat in the catchment, as well as the total catchment area, and slightly less strong negative correlations to the percent forest cover. None of the correlations in the DOM analyses were statistically significant, due to the small dataset and low degrees of freedom. Neither ΔDOC nor Δa350 correlated strongly with the amount of wetland in the catchment, with small negative relationships. In contrast, changes in iron (ΔFe) showed the opposite behaviour, with no relationship to forest or total catchment area, but a stronger negative relationship to the amount of wetland in the catchment and a weak negative correlation to peat. Change in discharge was negatively correlated to amount of wetland in the catchment (ρ = 0.998) and had no relationship to any other landcover variables.
4. Discussion
Hurricane Larry made landfall in the late summer/early autumn of Newfoundland, a period where storms are not expected and where the productivity on land is peaking [44]. Following Hurricane Larry, all catchments experienced an increase in discharge, water level, and carbon and iron concentrations which is in line with current literature but the magnitude of change as well as the time it took to return to baselevel varied [12,14,18,27,57]. The difference in flashiness is expected based on the catchment size, land cover differences and catchment complexity [54]. SC, as the largest of the three catchments was the least flashy incorporating the increase in precipitation easily. SR had the highest increases in fluxes of material following landfall despite being the second largest catchment. The increase in flux highlights that in addition to catchment size, landcover plays a major role in catchment response. The differences in landcover appear to regulate the increases seen in the catchment behaviour. The relationships between different landcover types and parameters point to how the landcover controls the release of material following an extreme weather event. The limited sampling points reduces statistical power of tests investigating these relationships. The behaviours observed and discussed should be seen as a preliminary look into how the boreal region will react to such events. The various changes experienced by the rivers are discussed below.
4.1. River state
All three rivers experienced an increase in discharge following Hurricane Larry, but the discharge does not exceed historical averages for the month of September. All three rivers appear to have a much lower discharge compared to historical records prior to the arrival of the hurricane. When discharge is low the rivers are more likely to export DOM from the baseflow which is heavily dominated by groundwater inputs and deeper peat layers [14,58]. The decrease of pH is due to the increase of DOM entering the rivers. DOM from peatlands is known to be rich in carboxylic acids, an acidic compound, with more inputs from land the pH can be seen to decrease [59]. The decrease in temperature is due to the cloud cover and less thermal heating occurring following the storm.
4.2. Buffering of DOM and a350
Export of material following Hurricane Larry is buffered by the natural land cover present in all three catchments [60,61]. While the Pearson’s correlation failed to have high p values, the relatively high ρ values suggest there is a relationship to be explored with the collection of more data. The buffering capacity of natural landcover and storm events has been noted in prior literature [60]. The peat and forest appear to be the dominant source of DOM and colour to the waterways with iron being supplied by wetlands in the catchments. SS has more peat and wetland proportionally than the two other catchments. As the percentage of land cover increases there is a reduced Δ for the associated property. ΔDOC is buffered by an increase in the amount of peat present, a similar trend being seen for Δa350. The close relationship between DOC and a350 is to be expected as DOC provides highly coloured compounds around the 350nm range [62]. The buffering effect may be due to having a steady input of DOC from the peat layers. This buffering has been explored in past studies [58,63].The landscape of NL is dominated by peat and this can explain the small ∆s seen in response to extreme weather events such as Hurricane Larry. Typically, the deeper layers of peat provide DOM to the waterways through groundwater infiltration with the top layers only being accessed during storm events and overland flow [14,58]. With Newfoundland’s shallow soil and high-water table levels [64,65], the baseflow is expected to be more dominant and access a large portion of the soil column. Excess water will only act to speed up the export from the topsoil rather than access normally disconnected pools as in other catchments [12]. As these layers are capable of absorbing large quantities of water and are known to produce high levels of carbon, they are able to facilitate the extra water without significantly changing their export of DOC [58]. In systems such as these it is also common for a delayed peak to be observed where the water is absorbed and released later [57,66]. These delayed peaks could be the increase being seen in SR and SS following landfall.
4.3. Iron buffering
Iron is strongly correlated with the proportion of wetlands present. Iron is found to accumulate in such environments due to the saturated soils [27,51,53,67]. The saturated soils of wetlands are typically highly acidic and anoxic allowing for the accumulation of Fe (II) maintained in solution as opposed to precipitating out as iron hydroxides. The iron which can be built up in these reduced conditions can be quickly transported as the storm increases connectivity [27,51]. Like carbon, it is possible that with a high water table the pools of iron are already exporting material to the rivers through the river’s baseflow. Following the storm, the catchments with a high portion of wetland rather than access disconnected pools drastically increasing concentrations, the iron is more quickly mobilised leading to a smaller increase. In a catchment such as SS where the landscape is typically saturated a smaller increase may be seen as opposed to SR which comprises more forest areas. SS wetlands provide a consistent supply of iron to the river, and the increased precipitation facilitates an increase in iron mobilization only as opposed to SR. SR’s iron is mostly likely separated into different pools, mineral soils and wetlands, and the increased precipitation can increase hydraulic connectivity between these pools and thus lead to a greater increase in iron concentrations.
4.4. Eventual fate of the exports
Despite the increases being buffered by the presence of peat and wetlands the high concentrations of DOM and Fe present in these environments requires ongoing research to understand the controls on their fluxes. Studies have shown that the storm flux of DOM can either be mineralized or contribute to carbon storage based on the lability, and composition of the DOM and the microbial community present [9,10,13,20]. The fate of the DOM exported by Hurricane Larry is unknown.
The three rivers studied are all in catchments heavily dominated by boreal landscapes. The strong relationships between forest and peatlands and DOM seen in the t-test and Pearson’s correlations points to these landscapes acting as the dominant source of DOM to the catchment. While there are compositional differences between peatland-DOM and DOM from boreal forest there are key similarities which will affect the DOM fate in the coastal environment [28,58,68]. Both environments produce highly coloured DOM enriched in lignin and tannins which are microbially resistant to degradation [69]. The DOM exported by all three catchments should be high in these compounds and resistant to degradation. The formation of complexes with Fe in the boreal environment can aid in shielding DOM from microbial mineralization further ensuring the DOM exported should act as carbon sequestration [70,71] in the NL coastal environment. There are still gaps in understanding the behaviour of the boreal DOM through the estuary though with some DOM remaining in suspension while others will flocculate out in the estuary and contribute to carbon burial [72]. It is difficult to predict the eventual fate of this DOM as prior studies have focused on bays and large estuaries where different biogeochemical processes can occur with a longer residence time.
The study of carbon dynamics in estuaries in fact remains a key knowledge gap globally [7]. Small northern catchments such as these export high levels of DOM and Fe dismissing them can lead to underestimations in biogeochemical models [73,74]. The direct export of material from small catchments into the coast is understudied.
The lack of continuous monitoring during the hurricane and the inability to capture the initial peak discharge following landfall may have prevented us from capturing the exact dynamics present [19,47,75]. An increase in labile protein-like DOM has been observed in storm events being mobilized from the upper layers of peat [12,45]. This flush of labile protein has been seen in the first peak of outflow as it is quickly shunted to the coast in other studies. Due to sampling constraints, we were unable to capture the initial peak, missing key information following a storm event. The DOM exported is also expected to change on the falling limb of the hydrograph [57].Without continuous monitoring of these sites during storm events small but important changes in the dynamics of carbon and iron export may be missed.
5. Conclusions
Three catchments on the Avalon Peninsula of Newfoundland were sampled before and after Hurricane Larry made landfall. All three catchments are characterized by high levels of carbon and iron and all three increased their export following the hurricane. Our current results show that high intensity events such as Hurricane Larry will increase the export of material, but effects are buffered by saturated landcover. The landscapes prevalent in the boreal region impart resilience in carbon and iron exports, accommodating the increase in discharge and precipitation from extreme weather events. These results demonstrate the importance of natural landcover in regulating storm fluxes in the boreal landscape.
Due to the opportunistic nature of the study, sample sizes were small with only two time points at three rivers. Results from the study are not statistically robust in Pearson’s correlation but represent a preliminary look into the natural resilience of the boreal environment to extreme weather events. As climate change intensifies, it is likely that more hurricanes will affect the peat and boreal ecosystems found in the upper latitudes. Understanding how these environments will be affected by these intense storms will be pertinent as we consider the effects of global change on these ecosystems. The direct input of material into the coast should be studied further with higher-resolution data collection as the frequency of storm events reaching Newfoundland increases. High temporal resolution data that can be collected remotely can aid in capturing key moments in a catchment’s storm response, highlighting the processes occurring in these environments. Nevertheless this study represents the first study of high northern environments reacting to hurricane.
References
- 1. Charette MA, Kipp LE, Jensen LT, Dabrowski JS, Whitmore LM, Fitzsimmons JN. The transpolar drift as a source of riverine and shelf-derived trace elements to the Central Arctic Ocean. J Geophys Res Oceans. 2020.
- 2. Hedges JI, Keil RG, Benner R. What happens to terrestrial organic matter in the ocean?. Org Geochem. 1997;27:195–212.
- 3. Kritzberg ES, Bedmar Villanueva A, Jung M, Reader HE. Importance of boreal rivers in providing iron to marine waters. PLoS One. 2014;9(9):e107500. pmid:25233197
- 4.
Jennings E, Järvinen M, Allott N, Arvola L, Moore K, Naden P, et al. Impacts of Climate on the Flux of Dissolved Organic Carbon from Catchments. The Impact of Climate Change on European Lakes. Springer Netherlands. 2009. p. 199–220. https://doi.org/10.1007/978-90-481-2945-4_12
- 5. Jiao N, Herndl GJ, Hansell DA, Benner R, Kattner G, Wilhelm SW. The microbial carbon pump and the oceanic recalcitrant dissolved organic matter pool. Nat Rev Microbiol. 2011;9:555.
- 6. Reader HE, Miller WL. The efficiency and spectral photon dose dependence of photochemically induced changes to the bioavailability of dissolved organic carbon. Limnol Oceanogr. 2014;59:182–94.
- 7. Ward ND, Bianchi TS, Medeiros PM, Seidel M, Richey JE, Keil RG. Where carbon goes when water flows: carbon cycling across the aquatic continuum. Front Mar Sci. 2017;4:7.
- 8. Krachler R, Krachler RF, von der Kammer F, Süphandag A, Jirsa F, Ayromlou S, et al. Relevance of peat-draining rivers for the riverine input of dissolved iron into the ocean. Sci Total Environ. 2010;408(11):2402–8. pmid:20206963
- 9. Yan G, Labonté JM, Quigg A, Kaiser K. Hurricanes accelerate dissolved organic carbon cycling in coastal ecosystems. Frontiers in Marine Science. 2020;0:248.
- 10. Asmala E, Osburn CL, Paerl RW, Paerl HW. Elevated organic carbon pulses persist in estuarine environment after major storm events. Limnol Oceanogr Lett. 2021;6:43–50.
- 11. Tang J, Wang YP, Zhu Q, Jia J, Xiong J, Cheng P, et al. Winter storms induced high suspended sediment concentration along the north offshore seabed of the Changjiang estuary. Estuarine, Coastal and Shelf Science. 2019;228:106351.
- 12. Austnes K, Evans CD, Eliot-Laize C, Naden PS, Old GH. Effects of storm events on mobilisation and in-stream processing of dissolved organic matter (DOM) in a Welsh peatland catchment. Biogeochemistry. 2009;99:157–73.
- 13. Avery GB Jr, Kieber RJ, Willey JD, Shank GC, Whitehead RF. Impact of hurricanes on the flux of rainwater and Cape Fear River water dissolved organic carbon to Long Bay, southeastern United States. Global Biogeochemical Cycles. 2004;18(3).
- 14. Clark JM, Lane SN, Chapman PJ, Adamson JK. Export of dissolved organic carbon from an upland peatland during storm events: Implications for flux estimates. J Hydrol. 2007;347:438–47.
- 15. Raymond PA, Saiers JE. Event controlled DOC export from forested watersheds. Biogeochemistry. 2010;100(1–3):197–209.
- 16. Yang L, Chang SW, Shin HS, Hur J. Tracking the evolution of stream DOM source during storm events using end member mixing analysis based on DOM quality. J Hydrol. 2015;523:333–41.
- 17. Bukaveckas PA, Tassone S, Lee W, Franklin RB. The influence of storm events on metabolism and water quality of riverine and estuarine segments of the James, Mattaponi, and Pamunkey Rivers. Estuaries and Coasts. 2020;43(7):1585–602.
- 18. Hounshell AG, Rudolph JC, Van Dam BR, Hall NS, Osburn CL, Paerl HW. Extreme weather events modulate processing and export of dissolved organic carbon in the Neuse River Estuary, NC. Estuarine, Coastal and Shelf Science. 2019;219:189–200.
- 19. Yoon B, Raymond PA. Dissolved organic matter export from a forested watershed during Hurricane Irene. Geophys Res Lett. 2012;39.
- 20. Osburn CL, Rudolph JC, Paerl HW, Hounshell AG, Van Dam BR. Lingering carbon cycle effects of Hurricane Matthew in North Carolina’s coastal waters. Geophys Res Lett. 2019;46:2654–61.
- 21. Bender MA, Knutson TR, Tuleya RE, Sirutis JJ, Vecchi GA, Garner ST, et al. Modeled impact of anthropogenic warming on the frequency of intense Atlantic hurricanes. Science. 2010;327(5964):454–8. pmid:20093471
- 22. Paerl HW, Hall NS, Hounshell AG, Luettich RA Jr, Rossignol KL, Osburn CL, et al. Recent increase in catastrophic tropical cyclone flooding in coastal North Carolina, USA: Long-term observations suggest a regime shift. Sci Rep. 2019;9(1):10620. pmid:31337803
- 23. Holland G, Bruyère CL. Recent intense hurricane response to global climate change. Clim Dyn. 2013;42(6):617–27.
- 24. Mann ME, Emanuel KA. Atlantic hurricane trends linked to climate change. EoS Transactions. 2006;87(24):233–41.
- 25. Smith DM, Eade R, Dunstone NJ, Fereday D, Murphy JM, Pohlmann H. Skilful multi-year predictions of Atlantic hurricane frequency. Nature Geoscience. 2010;3:846–9.
- 26. Worrall F, Burt TP, Adamson J. The rate of and controls upon DOC loss in a peat catchment. J Hydrol. 2006;321:311–25.
- 27. Abesser C, Robinson R, Soulsby C. Iron and manganese cycling in the storm runoff of a Scottish upland catchment. J Hydrol. 2006;326:59–78.
- 28. Kaal J, Pérez-Rodríguez M, Biester H. Molecular Probing of DOM Indicates a Key Role of Spruce-Derived Lignin in the DOM and Metal Cycles of a Headwater Catchment: Can Spruce Forest Dieback Exacerbate Future Trends in the Browning of Central European Surface Waters?. Environ Sci Technol. 2022;56(4):2747–59. pmid:35104105
- 29. Krachler R, Jirsa F, Ayromlou S. Factors influencing the dissolved iron input by river water to the open ocean. Biogeosciences. 2005;2(4):311–5.
- 30. Heerah KM, Reader HE. Towards the identification of humic ligands associated with iron transport through a salinity gradient. Sci Rep. 2022;12(1):15545. pmid:36109552
- 31. Herzog SD, Persson P, Kvashnina K, Kritzberg ES. Organic iron complexes enhance iron transport capacity along estuarine salinity gradients of Baltic estuaries. Biogeosciences. 2020;17(2):331–44.
- 32. Krachler R, Krachler RF, Wallner G, Hann S, Laux M, Cervantes Recalde MF, et al. River-derived humic substances as iron chelators in seawater. Mar Chem. 2015;174:85–93. pmid:26412934
- 33. Björnerås C, Weyhenmeyer GA, Evans CD, Gessner MO, Grossart H-P, Kangur K, et al. Widespread increases in iron concentration in European and North American freshwaters. Global Biogeochemical Cycles. 2017;31:1488–500.
- 34. de Wit HA, Valinia S, Weyhenmeyer GA, Futter MN, Kortelainen P, Austnes K. Current browning of surface waters will be further promoted by wetter climate. Environ Sci Technol Lett. 2016;3:430–5.
- 35. Evans CD, Monteith DT, Cooper DM. Long-term increases in surface water dissolved organic carbon: observations, possible causes and environmental impacts. Environ Pollut. 2005;137(1):55–71. pmid:15944040
- 36. Kritzberg ES, Ekström SM. Increasing iron concentrations in surface waters - a factor behind brownification?. Biogeosciences. 2012;9:1465–78.
- 37. Lepistö A, Räike A, Sallantaus T, Finér L. Increases in organic carbon and nitrogen concentrations in boreal forested catchments - Changes driven by climate and deposition. Sci Total Environ. 2021;780:146627. pmid:34030343
- 38. Roulet N, Moore TR. Environmental chemistry: browning the waters. Nature. 2006;444(7117):283–4. pmid:17108948
- 39. Latifovic R, Pouliot D, Olthof I. Circa 2010 Land Cover of Canada: Local Optimization Methodology and Product Development. Remote Sensing. 2017;9(11):1098.
- 40.
Brown D. Hurricane Larry. National Oceanic and Atmospheric Administration. 2021.
- 41. Landsea CW, Franklin JL. Atlantic hurricane database uncertainty and presentation of a new database format. Monthly Weather Review. 2013;141(10):3576–92.
- 42.
Pawlowicz R. M_map: A mapping package for MATLAB. 2020.
- 43. Wessel P, Smith WHF. A global, self‐consistent, hierarchical, high‐resolution shoreline database. J Geophys Res. 1996;101(B4):8741–3.
- 44. Bowering KL, Edwards KA, Wiersma YF, Billings SA, Warren J, Skinner A, et al. Dissolved Organic Carbon Mobilization Across a Climate Transect of Mesic Boreal Forests Is Explained by Air Temperature and Snowpack Duration. Ecosystems. 2022;26(1):55–71.
- 45. Broder T, Knorr K-H, Biester H. DOM quality in a peatland and forest headwater stream: seasonal and event characteristics. Ecohydrology/Instruments and observation techniques; 2016.
- 46. Iavorivska L, Boyer EW, Miller MP, Brown MG, Vasilopoulos T, Fuentes JD. Atmospheric inputs of organic matter to a forested watershed: Variations from storm to storm over the seasons. Atmospheric Environment. 2016;147:284–95.
- 47. Rosset T, Binet S, Antoine J-M, Lerigoleur E, Rigal F, Gandois L. Drivers of seasonal- and event-scale DOC dynamics at the outlet of mountainous peatlands revealed by high-frequency monitoring. Biogeosciences. 2020;17(13):3705–22.
- 48. Singh S, Inamdar S, Mitchell M, McHale P. Seasonal pattern of dissolved organic matter (DOM) in watershed sources: influence of hydrologic flow paths and autumn leaf fall. Biogeochemistry. 2013;118(1–3):321–37.
- 49.
Real-Time Hydrometric Data - Water Level and Flow. https://wateroffice.ec.gc.ca/mainmenu/real_time_data_index_e.html. 2023. Accessed 2024 March 27.
- 50.
South GR, Rogerson RJ, Banfield CE, Roberts BA, Damman AWH, Wells ED, et al. Biogeography and ecology of the Island of Newfoundland. The Hague: Dr. W. Junk. 1983.
- 51. Olivie-Lauquet G, Gruau G, Dia A, Riou C, Jaffrezic A, Henin O. Release of trace elements in wetlands: role of seasonal variability. Water Res. 2001;35(4):943–52. pmid:11235890
- 52. Clark JM, Lane SN, Chapman PJ, Adamson JK. Link between DOC in near surface peat and stream water in an upland catchment. Sci Total Environ. 2008;404(2–3):308–15. pmid:18076974
- 53. Weyer C, Peiffer S, Lischeid G. Stream water quality affected by interacting hydrological and biogeochemical processes in a riparian wetland. J Hydrol. 2018;563:260–72.
- 54. Baker DB, Richards RP, Loftus TT, Kramer JW. A new flashiness index: characteristics and applications to midwestern rivers and streams1. J American Water Resour Assoc. 2004;40(2):503–22.
- 55. Viollier E, Inglett PW, Hunter K, Roychoudhury AN, Van Cappellen P. The ferrozine method revisited: Fe(II)/Fe(III) determination in natural waters. Appl Geochem. 2000;15:785–90.
- 56. Xiao YH, Sara-Aho T, Hartikainen H, Vähätalo AV. Contribution of ferric iron to light absorption by chromophoric dissolved organic matter. Limnol Oceanogr. 2013;58(2):653–62.
- 57. Vaughan MCH, Bowden WB, Shanley JB, Vermilyea A, Schroth AW. Shining light on the storm: in-stream optics reveal hysteresis of dissolved organic matter character. Biogeochemistry. 2019;143(3):275–91.
- 58. Broder T, Knorr KH, Biester H. DOM quality in a peatland and forest headwater stream: seasonal and event characteristics. Hydrol Earth Syst Sci. 2017;21:2035–51.
- 59. Boguta P, D’Orazio V, Senesi N, Sokołowska Z, Szewczuk-Karpisz K. Insight into the interaction mechanism of iron ions with soil humic acids. The effect of the pH and chemical properties of humic acids. J Environ Manage. 2019;245:367–74. pmid:31158689
- 60. Fasching C, Wilson HF, D’Amario SC, Xenopoulos MA. Natural Land Cover in Agricultural Catchments Alters Flood Effects on DOM Composition and Decreases Nutrient Levels in Streams. Ecosystems. 2019;22(7):1530–45.
- 61. Worrall F, Burt T, Adamson J. Can climate change explain increases in DOC flux from upland peat catchments?. Sci Total Environ. 2004;326(1–3):95–112. pmid:15142769
- 62. Del Vecchio R, Blough NV. On the origin of the optical properties of humic substances. Environ Sci Technol. 2004;38(14):3885–91. pmid:15298197
- 63. Broder T, Biester H. Hydrologic controls on DOC, As and Pb export from a polluted peatland – the importance of heavy rain events, antecedent moisture conditions and hydrological connectivity. Biogeosciences. 2015;12(15):4651–64.
- 64. Heringa PK. Soils of the Avalon Peninsula, Newfoundland. Res Branch Agric Can. 1981;Publication 113:1–119.
- 65. Price JS. Blanket bog in Newfoundland. Part 2. Hydrological processes. J Hydrol. 1992;135:103–19.
- 66. Wagner S, Fair JH, Matt S, Hosen JD, Raymond P, Saiers J, et al. Molecular Hysteresis: Hydrologically Driven Changes in Riverine Dissolved Organic Matter Chemistry During a Storm Event. JGR Biogeosciences. 2019;124(4):759–74.
- 67. Neal C, Lofts S, Evans CD, Reynolds B, Tipping E, Neal M. Increasing iron concentrations in UK upland waters. Aquat Geochem. 2008;14:263–88.
- 68. Krachler R, Krachler RF, Wallner G, Steier P, El Abiead Y, Wiesinger H, et al. Sphagnum-dominated bog systems are highly effective yet variable sources of bio-available iron to marine waters. Sci Total Environ. 2016;556:53–62. pmid:26971209
- 69. Nebbioso A, Piccolo A. Molecular characterization of dissolved organic matter (DOM): a critical review. Anal Bioanal Chem. 2013;405(1):109–24. pmid:22965531
- 70. Barber A, Brandes J, Leri A, Lalonde K, Balind K, Wirick S, et al. Preservation of organic matter in marine sediments by inner-sphere interactions with reactive iron. Sci Rep. 2017;7(1):366. pmid:28336935
- 71. Lalonde K, Mucci A, Ouellet A, Gélinas Y. Preservation of organic matter in sediments promoted by iron. Nature. 2012;483(7388):198–200. pmid:22398559
- 72. Krachler R, Krachler RF. Northern high-latitude organic soils as a vital source of river-borne dissolved iron to the ocean. Environ Sci Technol. 2021;55(14):9672–90. pmid:34251212
- 73. Khoo CLL, Sipler RE, Faulkner SJM, Boyd SG, Foroutani MB, McBride CE. Size fractionated biogeochemical constituents across adjacent coastal systems informs approaches for integrating small catchment studies into regional models. Limnol Oceanogr. 2023;68:1285–300.
- 74. Sanders CJ, Santos IR, Maher DT, Sadat-Noori M, Schnetger B, Brumsack HJ. Dissolved iron exports from an estuary surrounded by coastal wetlands: Can small estuaries be a significant source of Fe to the ocean?. Mar Chem. 2015;176:75–82.
- 75. Vaughan MCH, Bowden WB, Shanley JB, Vermilyea A, Sleeper R, Gold AJ, et al. High‐frequency dissolved organic carbon and nitrate measurements reveal differences in storm hysteresis and loading in relation to land cover and seasonality. Water Resources Research. 2017;53(7):5345–63.