# Assessing sediment and nutrient transport in a South Brazilian catchment: hysteresis, fingerprinting, and management implications

Alice Prates Bisso Dambroz, Jean Paolo Gomes Minella, Paul David Capel, Felipe Bernardi, Larissa Werle, João Henrique Stibe Silva, Alexandre Augusto Schlesner, Lidiane Buligon, Fábio Joel Kochem Mallmann, Claudia Alessandra Peixoto de Barros, et al.

► To cite this version:

Alice Prates Bisso Dambroz, Jean Paolo Gomes Minella, Paul David Capel, Felipe Bernardi, Larissa Werle, et al. Assessing sediment and nutrient transport in a South Brazilian catchment: hysteresis, fingerprinting, and management implications. Environmental Monitoring and Assessment, 2025, 197 (8), pp.933. 10.1007/s10661-025-14198-w. cea-05183733

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Distributed under a Creative Commons Attribution 4.0 International License
Assessing sediment and nutrient transport in a
South Brazilian catchment: hysteresis,
fingerprinting, and management implications

Alice Prates Bisso Dambroz¹*, Jean Paolo Gomes Minella¹,
Paul David Capel², Felipe Bernardi¹, Larissa Werle¹,
João Henrique Stibe Silva¹, Alexandre Augusto Schlesner¹,
Lidiane Buligon³, Fábio Joel Kochem Mallmann¹,
Claudia Alessandra Peixoto de Barros⁴, Tales Tiecher⁴,
Olivier Evrard⁵, Gustavo Henrique Merten⁶

¹*Department of Soils, Universidade Federal de Santa Maria, Av. Roraima, 1000, Santa Maria, 97105-900, Rio Grande do Sul, Brazil.

²Department of Civil, Environmental and Geo-Engineering, University of
Minnesota, 500 Pillsbury Dr., SE, Minneapolis, 55455, Minnesota, USA.

³Department of Mathematics, Universidade Federal de Santa Maria, Av. Roraima, 1000, Santa Maria, 97105-900, Rio Grande do Sul, Brazil.

⁴Department of Soils, Universidade Federal do Rio Grande do Sul, Av. Bento Gonçalves, 7712, Porto Alegre, 91540-000, Rio Grande do Sul, Brazil.

⁵Laboratoire des Sciences du Climat et de l'Environnement (LSCE),
Unité Mixte de Recherche 8212 (CEA/CNRS/UVSQ), Université
Paris-Saclay, CEA Saclay - l'Orme des Merisiers - bat. 714,
Gif-sur-Yvette, 91 191, Cedex, Frane.

⁶Department of Civil Engineering, University of Minnesota Duluth, 1303 Ordean Ct., Duluth, 55812, Minnesota, USA.

*Corresponding author(s). E-mail(s): dambroz.alice@gmail.com;
Contributing authors: jean.minella@ufsm.br; capel001@umn.edu;
felipekbernardi@gmail.com; larissawerle@hotmail.com;
joao.stibe@acad.ufsm.br; arroz.ale@gmail.com; buligon.l@ufsm.br;
fabiojkmallmann@gmail.com; claudia.barros@ufrgs.br;
tales.tiecher@ufrgs.br; olivier.evrard@lsce.ipsl.fr; mertengh@gmail.com;
Abstract

Effective management and protection of natural resources – especially soil and water – require a thorough understanding of the occurring hydro-sedimentological processes in a catchment. In South Brazil, the 18.5 km² Guarda Mor catchment, characterized by strong topographic gradients, is vulnerable to soil and water degradation, despite much of its land use under no-till agriculture. Situated at the transition between volcanic to sedimentary lithologies and featuring an escarpment with a near 300-meter altitude difference, this catchment is part of a fluvial system which was heavily and unprecedentedly impacted by extreme rainfall and flooding in April and May 2024. To quantify and analyze the catchment’s hydro-sedimentological behavior, we combined traditional hydrological monitoring of rainfall, streamflow, sediment, and solute (potassium and phosphorus) concentrations with tools for hysteresis analysis, and sediment source tracing. This approach allowed us to examine sediment related-water quality over four years, during and in between rainfall events. We also assessed the hysteresis between streamflow discharge and sediment and solutes concentrations, calculated sediment and nutrient yields, and used sediment fingerprinting technique to estimate the main land use-based sediment sources. Extreme events were responsible for most of the sediment yield, with one event in November 2023 yielding 18% of the monitored total (~10,400 tons) between 2020 and 2024. Crop fields were estimated as the main sediment source (44%), followed by stream channels (31%) and unpaved roads (25%). Total potassium concentrations were linked to runoff volume, while total phosphorus concentrations were related to sediment yield. During a drought period, between August 2022 and April 2023, increases in phosphorus concentrations impacted water quality. These results highlight the need for sustainable management strategies and long-term monitoring to mitigate extreme flood and drought impacts in headwater catchments.

**Keywords:** Hydrological monitoring, water quality, sediment sources, non-point sources

# 1 Introduction

Identifying critical erosion hotspots is key for proposing sustainable landscape management (Golosov and Walling, 2019), especially in the context of intensive agriculture and increasingly intense rainfall events driven by climate change (Li and Fang, 2016; Nearing et al, 2004; Riquetti et al, 2020). River and catchment monitoring can provide a comprehensive assessment of land use, soil management, and climate interactions at the catchment scale, and subsidize better land management decisions (Capel, 2018). In this context, streamflow and suspended sediment concentration are key monitorable variables that reveal the occurrence and intensity of hydrological and erosive processes occur in a given catchment (Minella et al, 2018).

In Brazil, agriculture represents a large part of the country's economy, exporting commodities worldwide. As commodity prices and grain demand rise, the area of grain production expands towards environmentally fragile areas, potentially triggering soil erosion and water contamination, as water, sediments, and associated chemicals are increasingly supplied to the river systems. Headwater catchments are especially
important sources of runoff and solutes from their hillslopes and zero-order catchments (Gomi et al, 2002). Processes in headwater catchments directly affect downstream water quality, influencing the supply, transport, and fate of water, sediments and solutes to downstream environments and the entire catchment (Alexander et al, 2007; de Barros et al, 2020; Gomi et al, 2002).

These transfers are influenced by the availability of solutes, particularly in regions where hillslopes and zero-order catchments are predominantly used for agriculture. In South Brazil, agricultural fields have shown to be prone to runoff generation (Londero et al, 2018; Deuschle et al, 2019; Londero et al, 2021a), with rivers transporting fertilizers and pesticides that were applied to the soil surface (de Bastos et al, 2021; Didoné et al, 2021). While no-till farming has been effective in controlling diffuse soil erosion, it remains insufficient to mitigate surface runoff (Merten et al, 2015; Deuschle et al, 2019). Factors such as rainfall intensity and volume, soil moisture, and connectivity between fields and the drainage network further increase the potential for water and pollutant transfer (Capel, 2018). Phosphorus, applied to agricultural fields as a fertilizer, when transferred to water bodies, can lead to eutrophication, harmful algal growths, higher treatment costs for potable water, and ecological degradation (Novotny, 2002).

Relying on the tools of environmental monitoring, we can understand and quantify how water flows connect the landscape to stream, and how solutes are transported and diluted. Rainfall-induced runoff can accelerate erosion in fragile areas due to increased steepness, shallow soils, and intense agricultural use (Minella and Merten, 2012) and can cause negative effects both on and off the field (Boardman et al, 2019), leading to decreases in soil productivity and fertility (Pimentel et al, 1995). Rainfall-induced surface runoff can also impact terrestrial and aquatic ecosystems through processes such as eutrophication, siltation of reservoirs and river channels, increased flooding, flash floods, and increased water treatment costs.

In Brazil, since the 1980s, no-till systems have been implemented as a conservation practice to reduce soil erosion (ITPS, 2015). Yet, soil losses are still observed (Didoné et al, 2019), as well as runoff generation (Londero et al, 2018; Deuschle et al, 2019) and sediment transfer from crop fields to rivers (Tiecher et al, 2018). No-till has the potential to lead to significant losses and transfer of nutrients (Coblinski et al, 2019; Martíni et al, 2021; Utzig et al, 2023) and pesticides (Shipitalo and Owens, 2006) via surface runoff. The no-till system has been simplified in agricultural fields, with reduced crop rotation and use of cover crops. Besides, there is not an extensive use of other management practices that reduce agriculture's impact on the environment, such as infiltration terraces (Londero et al, 2018; Koppe et al, 2022; Werle et al, 2024) and vegetative filter strips (Bortolozzo et al, 2014). We need to better understand and quantify the important hydrological processes to propose the optimal conservation practices, while still considering their feasibility to farmers and public managers.

There are several agricultural areas on the border of the volcanic plateau, in South Brazil, which comprise important headwater catchments. In one of them (Arvorezinha catchment), long-term monitoring and robust modeling strategies have led to the development of a sediment budget (Minella et al, 2014), quantification of land use and management changes in sediment yield (Minella et al, 2018), description of chemical
element's transport during rainfall events (de Barros et al, 2020), and to the simulation of land management and erosion mitigation practices (Silva et al, 2021). At Guarda Mor catchment, also straddling the edge of the volcanic plateau, intense agricultural activity occurring on a landscape with strong topographic gradients, makes the headwater catchment prone to processes of soil and water degradation (Dambroz et al, 2022).

This study integrates traditional hydrological monitoring of runoff, sediment, and nutrient concentrations with sediment source tracing to assess water, sediment, and solute dynamics during rainfall events at Guarda Mor catchment. This approach quantifies the influence of agricultural activities on water quality and identifies the main sources of fine sediment deposited at the catchment outlet, prior to the extreme events of April and May 2024. The findings support recommendations for sustainable land use practices that balance economic activities with the preservation of the headwater catchment's natural hydrological functions.

# 2 Material and methods

## 2.1 Study site

Located in a transition between the volcanic and sedimentary geologies of the Southern Plateau and Central Depression, the Guarda Mor headwater river is a tributary of the Soturno River, in South Brazil. This area is located in the State of Rio Grande do Sul and drains towards the Jacuí River and the Guaíba Lake, which were heavily impacted by extreme rainfall and floods in April and May 2024 (Collischonn et al, 2024a,b; Colussi et al, 2024; Collischonn et al, 2025). With intense no-till agricultural land use and steep topography, Guarda Mor experimental catchment (Figure 1) is an erosion and surface runoff generation-prone environment.

Crop fields and grasslands cover 39% and 11% of the area's land use, which is predominantly covered by forests (44%), those being located near the drainage network and the border of the Southern Plateau (Figure 1). Other mapped land uses are unpaved roads (1% of the area), water bodies (1%), paved/residential areas (2%), and the stream channels. The grain production-cropping system involves a succession of soybeans (in the summer) and wheat or oats (in the winter). According to Rosgen (1994) classification, this is a B-type stream. At its outlet, it has a step-pool morphology (Buffington and Montgomery, 2013) and a stream bed of particles with gravel and boulder sizes. Step-pool, cascade, and riffle morphologies on bedrock streambed are found upstream, with finer sediment accumulation as well. Further description of this fluvial system given by Bernardi (2022) and Bernardi et al (2025).
<figure><img src="image_2.png" /><figcaption>**Fig. 1** Location of South America, highlighting the State of Rio Grande do Sul and the Soturno and Guarda Mor river catchments, and the digital elevation map and land use of Guarda Mor headwater catchment (Gmex) where the hydrological monitoring was conducted</figcaption></figure>

The upper portion lies on volcanic rocks of the Southern Plateau and has elevations up to 511 meters. Below an escarpment and geological transition with slopes of more than 75%, sedimentary rocks cover the lower portion of the catchment, where the lower elevation amounts to 197 meters. Besides a diverse lithology, six soil classes, following the WRB classification system (FAO et al, 2015), are found within the catchment (Figure 2, Table 1). The detailed soil map developed by Pedron et al (2021) shows the classes of Regosols, Leptosols, Nitisols, Gleysols, Cambisols, and Acrisols are found across the catchment.

The predominant soils in this catchment are shallow and classified as Regosols and Leptosols. Gleysols, which are poorly drained, are found in areas of moisture accumulation, while Cambisols are well-drained shallow soils. Nitisols, located over volcanic rocks, are deep and highly weathered, and Acrisols, found over the sedimentary portion, are also deep and weathered, but exhibit a textural gradient, with coarser textures in the upper horizons and a clay-enriched B horizon.
<table><caption>**Table 1** Soil density, soil saturated hydraulic conductivity, and total soil porosity for different soil classes and land uses in the catchment</caption><thead><tr><th></th><th>Soil density<br>g cm<sup>3</sup></th><th>Soil hydraulic conductivity<br>cm h<sup>-1</sup></th><th>Total soil porosity<br>%</th></tr></thead><tbody><tr><td colspan="4">Soil class</td></tr><tr><td>Acrisols</td><td>1.7 to 2.3</td><td>4.8 to 84.0</td><td>38 to 67</td></tr><tr><td>Cambisols</td><td>1.8 to 2.3</td><td>0.04 to 10.2</td><td>47 to 63</td></tr><tr><td>Regosols/Leptosols</td><td>1.5 to 2.6</td><td>0.02 to 105.2</td><td>40 to 74</td></tr><tr><td>Gleysols</td><td>1.6 to 2.0</td><td>2.0 to 22.9</td><td>55 to 76</td></tr><tr><td>Nitisols</td><td>1.6 to 2.3</td><td>0.00 to 81.9</td><td>41 to 69</td></tr><tr><td colspan="4">Land use</td></tr><tr><td>Crop fields</td><td>1.9 to 2.6</td><td>0.00 to 51.9</td><td>38 to 59</td></tr><tr><td>Grasslands</td><td>1.8 to 2.3</td><td>0.2 to 52.6</td><td>44 to 66</td></tr><tr><td>Unpaved roads</td><td>2.1 to 2.3</td><td>0.00 to 2.3</td><td>49 to 52</td></tr><tr><td>Forests</td><td>1.5 to 2.3</td><td>0.01 to 105.2</td><td>40 to 76</td></tr></tbody></table>

<figure><img src="image_3.png" /><figcaption>**Fig. 2** Geology and soil classes of Guarda Mor headwater catchment (GMex), showing the distribution of soil samples used for sediment fingerprinting studies</figcaption></figure>

Werle (2024) characterized the physical-hydric properties of 88 sampling points across different soil classes and land uses in this catchment. These findings are summarized in Table 1.

## 2.2 Hydrological monitoring

At Guarda Mor catchment, automated rainfall and water level sensors collected data at a high temporal resolution to capture the flow variations as a result of rainfall-runoff events, from June 10, 2020 until April 29, 2024. Two rainfall gauges record precipitation in the upper and lower portions of this catchment (Figure 2). The gauges were
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equipped with an automatic rain gauge to capture rainfall intensity, recording data in a 5-minute interval, and a rain gauge that was manually verified for the accumulated 24-hour rainfall volume. At the outlet, a Campbell Scientific CS451 pressure transducer recorded water levels with a 5-minute interval.

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A series of rainfall events that started on April 27, 2024 resulted in rainfall volumes of 555.1 and 490.7 mm at the upper and lower stations, respectively. On April 29, 2024 severe floods carried away equipment and sensors installed at the gauge of Guarda Mor River, hence, the end of continuous, automatic data collection. During the events, manual observations of reference points were taken and used, along with topographic surveys, for estimating streamflow discharge, peak discharge, and sediment yield for the period of April 30 until May 06, 2024.

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At the outlet, water samples were taken manually (using US DH-48 or US DH-49 samplers) and automatically (using the sampler Hach AS950) during storm events and also every two weeks. The frequency and number of samples collected during events were adjusted to represent the magnitude and duration of each event. During an event, a sample was taken prior to the start of rainfall, then sampling frequency was intensified at the onset of rainfall, with high-frequency collection during the rising and falling limbs of the hydrograph to capture the variability in suspended sediment and solute concentrations. Towards the end of each event, sampling frequency decreased as streamflow stabilized. From these samples, suspended sediment concentration (SSC) was analyzed by the evaporation method (Shreve and Downs, 2005). Water samples (containing water and sediments) were submitted to an aqua regia acid microwave-assisted digestion, with a 1:2 proportion of HCl and HNO₃, to obtain total phosphorus (P) and total potassium (K) concentrations. From the extracts, to determine the P concentration, the methodology proposed by Murphy and Riley (1962) was used. The determination of K was obtained by atomic emission spectroscopy, following Tedesco (1995).

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Although monitoring started in June 2020, a drought period followed the onset of monitoring and, due to incomplete sediment data, the first event analyzed in this study occurred in January 2021. The sediment and solute concentrations for different rainfall-runoff events were analyzed for the quantification of loads (based on concentrations and streamflow volume) and patterns of enrichment and dilution, by establishing chemographs (Walling and Foster, 1975) for rainfall-runoff events. These patterns can change due to residence times of water and the source of flows - either from the surface or subsurface (Walling and Foster, 1975). To quantify the total P loads (mg s₁, in between events, total P concentration (mg L⁻¹) was multiplied by the total streamflow discharge (L s⁻¹) for the time step during which the sample was collected.

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To explore the relationship between streamflow discharge (Q) and sediment or solute concentrations (C), the hysteresis index $HI_{mid}$ was calculated, for measuring the size of the hysteresis loop at the mid-point of streamflow discharge, based on the method proposed by Lawler et al (2006). This method is based on the average points of Q during the rising (RL) and falling (FL) limbs of the hydrograph. To obtain the $HI_{mid}$, average points of Q and the closest C for each point are found. If C in the rising limb is higher than C during the falling limb, then there is clockwise hysteresis, and
the $HI_{mid}$ is calculated as:

$$HI_{mid} = (C_{RL}/C_{FL}) - 1 \quad (1)$$

The opposite, lower C in the rising limb and higher during the falling limb, characterizes an anticlockwise hysteresis, for which $HI_{mid}$ is:

$$HI_{mid} = (-1/(C_{RL}/C_{FL})) + 1 \quad (2)$$

A clockwise hysteresis, which is commonly observed, indicates higher C during the rise of discharge, which can be due to either sediment or solute exhaustion during the event or the proximity of the sources to the river. A counterclockwise hysteresis shows the opposite relationship and can indicate that sediment or solute sources are far away - or are transported at lower velocities - to the sampling point in the river (Williams, 1989; Malutta et al, 2020). Besides these two forms, an event may have complex or figure-eight shapes, which are associated with events with more than one peak in Q or C, and variations in event intensity and sediment source; or it can have a linear hysteresis, when there is no impediment for sediment transport and, therefore, no sediment source exhaustion (Gellis, 2013).

## 2.3 Sediment tracing

The sediment fingerprinting technique used the characteristics and properties of both the soils and the target materials to estimate the contributions of each potential sediment source in a catchment delivering material to the river system (Walling, 2013; Collins et al, 2017). This approach is particularly valuable for addressing issues related to water quality, as sediments can significantly affect water quality. Agriculture is a well-known contributor to turbidity and sediment-associated P (de Bastos et al, 2021). Other landscape components with varying compositions may also deliver sediments to the stream, with some providing a dilution effect from non-contaminated sources. Furthermore, processes of connectivity, like deposition and remobilization can alter sediment sources over time, adding complexity and uncertainty to the analysis and emphasizing the need for comprehensive monitoring to accurately assess sediment-driven water quality changes. Figure 3 presents a diagram of the procedure used for the processes of sample collection, preparation, and analysis used in this study.

### 2.3.1 Soil and sediment sampling

Sediment samples deposited on the stream bed (n = 10), referred to as “sink samples”, were collected to quantify their sources. In contrast, soil samples from different land uses, referred to as “source samples” to represent the potential sediment sources, were collected from areas showing evidence of erosion and connectivity to the drainage network. These “source samples” were taken from crop fields (n = 16), unpaved roads (n = 12), and stream channel banks (n = 8). Their spatial distribution is shown in Figure 2. Other land uses were not considered due to the lack of erosion processes that justified their sampling.
Soil samples were collected from the upper 2 cm of the soil surface, where material is most likely mobilized during erosion. Stream channel samples were taken along two to three-meter sections of the banks. Unpaved road samples were taken from either banks or roadside ditches, depending on where erosion was observed. Each sample was composed of five sub-samples, which were mixed and homogenized to form a representative sample. Sediment samples were collected from deposits over the stream bed at the catchment's outlet, care was taken to ensure minimal loss of fine material. All samples were oven-dried at 40°C, with forced air circulation, and were later gently disaggregated and sieved to obtain the 0.63 µm fraction, to minimize the potential impact of particle size sorting during erosion, transport and deposition processes (Laceby et al, 2017).

### 2.3.2 Soil and sediment sample analysis

To obtain potentially discriminant variables for the different sources, all samples were analyzed for their P fractions, geochemical composition, total Carbon (C), total Nitrogen (N), ¹³C and ¹⁵N isotope ratios, and with visNIR spectroscopy.

Four P fractions were obtained: total P, available P (Mehlich-3 extract), organic P (NaOH 0.5 mol L⁻¹ extract), and P extractable by HCl 1 mol L⁻¹. Apart from total P, the fractionation followed the methodology proposed by Gatiboni and Condron (2021). For total P, extracts were obtained after digestion with nitric acid (HNO₃) and the addition of hydrogen peroxide (H₂O₂), based on the U.S. EPA 3050b methodology (EPA, 1996). Concentrations were determined following Murphy and Riley (1962). Analyses were conducted at the Soil Chemistry Laboratory of the Universidade Federal do Rio Grande do Sul (Porto Alegre, Brazil).

The geochemical composition was analyzed by two methods. Using the extracts obtained after digestion with HNO₃ and H₂O₂, the concentration of the elements Al, Ba, Ca, Co, Fe, K, Li, Mg, Mn, Na, Ni, P, S, and V were determined by Inductively Coupled Plasma - Optical Emission Spectrometry (ICP-OES, Perkin-Elmer). While dry, sieved samples were used for determining the elements Al, Ca, Co, Cu, Fe, K, Mg, Mn, Ni, Pb, Rb, Si, Sr, Ti, Zn, and Zr by X-ray fluorescence spectrometry (Epsilon 3, Malvern Panalytical) at the Laboratoire des Sciences du Climat et de l'Environnement (LSCE, Gif-sur-Yvette, France). X-ray intensities of the samples were transformed to concentrations using the Epsilon 3 software program (Malvern Panalytical).

Total C and total N were determined by dry combustion using the elemental analyzer Flash EA 1112 (Thermo Electron Corporation, Milan, Italy). The ¹³C and ¹⁵N isotopic abundance was determined using an isotope ratio mass spectrometer Delta V Advantage (Thermo Fisher Scientific, Bremen, Germany) coupled to the elemental analyzer Flash 2000 IRMS (Thermo Fisher Scientific, Bremen, Germany). These analyses were conducted at the Laboratório de Análises de Carbono e Nitrogênio of the Universidade Federal de Santa Maria (LABCEN, Santa Maria, Brazil).

The visNIR spectral wavelengths (350 to 2500 nm) were quantified on a spectrometer (FieldSpec 4, Malvern Panalytical) with a spectral resolution of 1 nm at the Pedology Laboratory of the Universidade Federal de Santa Maria (Santa Maria, Brazil). From the visible spectral range, color parameters based on Viscarra Rossel et al (2006) were obtained, including R, G, and B from the RGB system; and then, using
300 R package "farver": the Commission Internationale de l'Eclairage (CIE)'s "XYZ",
301 "L*a*b*", "Lu*v*", "lch", "yxy", "hsb", "hsv", "hsl"; and "Lab" from HunterLab.

<figure><img src="image_4.png" /><figcaption>Fig. 3 Sediment fingerprinting technique diagram. “n” represents the number of variables</figcaption></figure>

2.3.3 Tracer selection and source estimation

At first, all 46 analyzed variables were submitted to a conservativity analysis based on
(i) maximum and minimum values and (ii) standard deviation. In this analysis, source
and sediment samples are compared and the values obtained for sediment samples
were constrained to fall within the range of source samples' values (Sellier et al, 2020).
Samples that did not lie within this range were removed from further analysis.

Then, considering only the source samples, variables that proved conservative for
both ranges were then submitted to a Kruskal Wallis test, by applying the kruskal.test
function of the "stats" R package. This analysis tests whether the variables that repre-
sent the sources belong to the same population. If they prove different, it indicates that
the variable is able to discriminate the various sources (Minella et al, 2007). A signifi-
cant difference was considered when p ¡ 0.05. Samples that did not prove discriminant
were removed from analysis.

Next, a Wilks' lambda minimization test was applied to the variables, using the
greedy.wilks function of the "klaR" R package. The test is used to identify the small-
est number of variables that maximizes source discrimination (Collins et al, 2010;
Minella et al, 2007). Variables are added to the analysis until the optimum composite
fingerprinting and their discriminatory power is found, based on the minimization of Wilks's lambda towards zero.

After these steps, the most significant set of parameters was used in a multivariate unmixing model to estimate the relative contribution of each source. This set was composed of available P (Mehlich-3 extract), moderately label P (NaOH 0.5 mol L⁻¹ extract), ¹³C isotope ratio, total C, total N, and total P (ICP determined). The unmix modeling approach is based on the Clarke and Minella (2016) model, using generalized least squares to estimate the source's apportionment. Details of this modeling approach are found in Buligon et al. (2024).

# 3 Results

## 3.1 Sediments and nutrients

A total of 57 rainfall-runoff events were analyzed from 2021 to 2024. The hydrographs of four events are shown in Figure 4. For these events, discharge and SSC were measured, and hydrological variables were calculated (Table 2). While a set of 28 events were analyzed for K and P concentrations as well (Table 2), as shown in a subset in Figure 5. Average SSC during that period was 0.21 g L⁻¹, while maximum concentration reached 30 g L⁻¹ on 04/25/2023. The average of the maximum SSC values across all events was 2.42 g L⁻¹. The hydrographs for all events, considering SSC and P and K concentrations are found in Figures A1 and A2 in the Appendix A.
<figure><img src="image_5.png" /><figcaption>**Fig. 4** Hydrographs and suspended sediment concentration for events 8, 15, 35, and 50. Rainfall is shown in black (upper gauge) and grey (lower gauge), when data is available. The shaded blue area represents streamflow, whereas the maroon lines show the suspended sediment concentrations</figcaption></figure>

Event 8 - Rainfall: 21 mm (max. intensity 15 mm h⁻¹) at both gauges. Mean SSC: 0.08 g L⁻¹, Max SSC: 0.18 g L⁻¹, SY: 2 tons.

Event 15 - Rainfall: 26 mm (6 mm h⁻¹) upper, 55 mm (13 mm h⁻¹) lower. Mean SSC: 0.14 g L⁻¹, Max SSC: 0.46 g L⁻¹, SY: 35 tons.

Event 35 - Rainfall: 85 mm (55 mm h⁻¹) upper, 67 mm (56 mm h⁻¹) lower. Mean SSC: 0.93 g L⁻¹, Max SSC: 18.98 g L⁻¹, SY: 1073 tons.

Event 50 - Rainfall: 51 mm (96 mm h⁻¹) upper, 54 mm (89 mm h⁻¹) lower. Mean SSC: 0.50 g L⁻¹, Max SSC: 1.57 g L⁻¹, SY: 78 tons.

The total sediment yield (SY) summing all 57 events was ~10,400 tons, corresponding to an average sediment loss of 560 tons/km². Specifically, SY totaled 2515 tons in 2021 (136 tons/km²), 837 tons in 2022 (45 tons/km²), 5794 tons in 2023 (313 tons/km²) and 1227 tons (66 tons/km²) until the last monitored event in 2024. The highest SY recorded in this data set was 1913 tons during the 11/12/2023 event, following a rainfall of 99 mm at the upper gauge and 81 mm at the lower gauge. The second highest occurred on 05/28/2021, with 1557 tons of SY and 120 mm at the upper gauge (Figure 6). Other significant events with SY exceeding 1000 tons occurred on 07/12/2023 (150 and 126 mm at the upper and lower gauges, respectively), 05/05/2023 (85 and 67 mm), and 04/16/2024 (85 and 73 mm).
<figure><img src="image_6.png" /><figcaption>**Fig. 5** Chemographs of total phosphorus (P) and total potassium (K) concentrations during events 32 (05/02/2023), 41 (07/12/2023), and 45 (09/12/2023). Rainfall is shown in black (upper gauge) and grey (lower gauge), when data is available. The shaded blue area represents streamflow, whereas the red and orange lines show the P and K concentrations, respectively</figcaption></figure>

The extreme rainfall event that started on April 29, 2024, total rainfall volume reached 440 mm, resulting in a very large peak discharge of 700 m³s⁻¹, total streamflow volume of 2,905,000 m³, and sediment yield of 12,365 tons - approximately 120% of the total SY (10,400 tons) accumulated over the previous four years. Unlike other events, this extreme rainfall and sediment yield triggered additional erosion processes, in addition to surface sources, including mass movement and channel erosion, plus wide-scale flooding (Collischonn et al., 2025). This highlights the disproportionate contribution of extreme events to sediment transport.

On 11/12/2023, two distinct rainfall events were registered. The first (Table 2, event 50) happened during a precipitation event of about 50 mm, leading to a peak Q of 9.59 m³ s⁻¹ and a maximum SSC of 2.74 g L⁻¹, which resulted in a SY of 78 tons. The subsequent event (event 51), following a precipitation of nearly 100 mm in the upper rain gauge and 80 mm in the lower gauge, with peak Q of 183 m³ s⁻¹ and maximum SSC of 1.55 g L⁻¹. Event 51 resulted in the highest SY of the dataset (~1910 tons), accounting for 18% of the total SY for all 57 events. Cumulatively, the five events with highest SYs contributed 68% of the total SY.

Most of the monitored events showed a clockwise hysteresis loop (75%), in the relationship between C and SSC, as illustrated by event 19 (Figure 7-A). In contrast, event 34 exhibited a counterclockwise loop (Figure 7-B). However, several events present more complex patterns, such as eight-figure loops or combinations of clockwise
<table><caption>**Table 2** Hydrological variables for 57 monitored events: precipitation volume, peak streamflow discharge, total streamflow volume, sediment yield, and Q-SSC hysteresis. For 29 events, potassium and phosphorus loads and hysteresis are also shown</caption><thead><tr><th rowspan="2">Event<br>#</th><th rowspan="2">Date</th><th colspan="2">Rainfall (mm)</th><th rowspan="2">Peak discharge<br>m<sup>3</sup> s<sup>-1</sup></th><th rowspan="2">Total volume<br>m<sup>3</sup></th><th colspan="3">Sediment yield</th><th colspan="3">Q-SS hysteresis</th><th colspan="3">K load</th><th colspan="3">Q-K hysteresis</th><th colspan="3">P load</th><th colspan="3">Q-P hysteresis</th></tr><tr><th>Upper gauge</th><th>Lower gauge</th><th>t</th><th>Loop</th><th>HI</th><th>t</th><th>Loop</th><th>HI</th><th>t</th><th>Loop</th><th>HI</th><th>t</th><th>Loop</th><th>HI</th><th>t</th><th>Loop</th><th>HI</th></tr></thead><tbody><tr><td>1</td><td>01/28/2021</td><td>NA</td><td>83.83</td><td>9.62</td><td>115.99</td><td>67.29</td><td>C*</td><td>0.06</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>2</td><td>02/03/2021</td><td>NA</td><td>54.21</td><td>6.51</td><td>246.89</td><td>51.27</td><td>C</td><td>2.32</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>3</td><td>05/28/2021</td><td>119.73</td><td>NA</td><td>65.20</td><td>1543.76</td><td>1556.77</td><td>C</td><td>13.68</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>4</td><td>06/26/2021</td><td>59.80</td><td>61.64</td><td>9.25</td><td>484.41</td><td>45.57</td><td>C</td><td>25.33</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>5</td><td>08/24/2021</td><td>219.63</td><td>NA</td><td>16.94</td><td>805.74</td><td>347.27</td><td>C*</td><td>1.69</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>6</td><td>09/07/2021</td><td>138.72</td><td>124.37</td><td>31.94</td><td>1469.02</td><td>407.17</td><td>AC*</td><td>-0.10</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>7</td><td>09/13/2021</td><td>41.72</td><td>33.01</td><td>10.48</td><td>181.09</td><td>37.30</td><td>C*</td><td>0.09</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>8</td><td>10/15/2021</td><td>20.96</td><td>20.74</td><td>1.19</td><td>23.82</td><td>2.07</td><td>C*</td><td>0.43</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>9</td><td>04/25/2022</td><td>NA</td><td>97.49</td><td>13.27</td><td>930.90</td><td>156.24</td><td>C</td><td>1.74</td><td>2.64</td><td>C</td><td>0.95</td><td>0.06</td><td>C</td><td>1.09</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>10</td><td>04/30/2022</td><td>NA</td><td>63.26</td><td>2.98</td><td>519.05</td><td>111.51</td><td>C*</td><td>2.50</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>11</td><td>05/18/2022</td><td>10.14</td><td>16.12</td><td>0.65</td><td>165.08</td><td>9.25</td><td>AC</td><td>-2.00</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>12</td><td>05/25/2022</td><td>52.05</td><td>NA</td><td>0.85</td><td>287.12</td><td>5.22</td><td>AC*</td><td>-0.40</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>13</td><td>06/05/2022</td><td>65.43</td><td>68.45</td><td>8.76</td><td>247.69</td><td>121.89</td><td>C*</td><td>9.29</td><td>1.26</td><td>C</td><td>1.81</td><td>0.05</td><td>C</td><td>2.59</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>14</td><td>06/16/2022</td><td>44.06</td><td>55.49</td><td>2.92</td><td>198.30</td><td>18.81</td><td>C*</td><td>1.89</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>15</td><td>06/21/2022</td><td>25.76</td><td>55.31</td><td>4.61</td><td>208.01</td><td>35.48</td><td>C</td><td>0.77</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>16</td><td>07/08/2022</td><td>0.72</td><td>7.26</td><td>4.90</td><td>212.44</td><td>43.49</td><td>C</td><td>2.71</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>17</td><td>07/11/2022</td><td>47.14</td><td>54.42</td><td>7.06</td><td>417.52</td><td>147.67</td><td>C</td><td>0.60</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>18</td><td>07/13/2022</td><td>63.01</td><td>70.35</td><td>60.12</td><td>1092.04</td><td>63.90</td><td>C</td><td>1.50</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>19</td><td>07/16/2022</td><td>32.77</td><td>19.43</td><td>13.54</td><td>362.97</td><td>79.51</td><td>C</td><td>2.43</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>
<table><thead><tr><th rowspan="2">Event</th><th rowspan="2">Date</th><th colspan="2">Rainfall (mm)</th><th rowspan="2">Peak discharge m³ s<sup>-1</sup></th><th rowspan="2">Total volume m³</th><th rowspan="2">Sediment yield t</th><th colspan="2">Q-SS hysteresis</th><th colspan="2">Q-K hysteresis</th><th rowspan="2">P load t</th><th colspan="2">Q-P hysteresis</th></tr><tr><th>Upper gauge</th><th>Lower gauge</th><th>Loop t</th><th>HI t</th><th>Loop t</th><th>HI t</th><th>Loop t</th><th>HI t</th></tr></thead><tbody><tr><td>20</td><td>07/22/2022</td><td>18.56</td><td>NA</td><td>1.61</td><td>151.48</td><td>26.73</td><td>AC</td><td>-1.67</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>21</td><td>07/28/2022</td><td>28.89</td><td>NA</td><td>1.02</td><td>103.95</td><td>2.14</td><td>AC*</td><td>-2.50</td><td>0.27</td><td>-1.34</td><td>0.00</td><td>0.00</td><td>-0.75</td></tr><tr><td>22</td><td>08/03/2022</td><td>45.81</td><td>NA</td><td>4.21</td><td>237.71</td><td>12.99</td><td>C</td><td>1.31</td><td>0.09</td><td></td><td></td><td></td><td></td></tr><tr><td>23</td><td>08/09/2022</td><td>14.40</td><td>NA</td><td>0.90</td><td>94.53</td><td>1.87</td><td>C*</td><td>3.00</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>24</td><td>09/19/2022</td><td>24.58</td><td>NA</td><td>0.80</td><td>32.46</td><td>0.68</td><td>C</td><td>5.00</td><td>0.01</td><td>AC</td><td>-0.20</td><td>0.00</td><td>AC</td></tr><tr><td>25</td><td>02/14/2023</td><td>48.41</td><td>60.14</td><td>0.01</td><td>0.32</td><td>0.03</td><td>AC</td><td>-0.40</td><td>0.00</td><td>C</td><td>0.11</td><td>0.00</td><td>C</td></tr><tr><td>26</td><td>03/03/2023</td><td>23.23</td><td>17.37</td><td>0.08</td><td>1.10</td><td>0.09</td><td>AC*</td><td>-1.14</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>27</td><td>03/04/2023</td><td>27.97</td><td>24.62</td><td>0.11</td><td>3.09</td><td>0.10</td><td>C</td><td>0.67</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>28</td><td>03/25/2023</td><td>17.37</td><td>15.79</td><td>0.01</td><td>0.20</td><td>0.01</td><td>AC</td><td>-0.67</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>29</td><td>03/29/2023</td><td>25.73</td><td>31.80</td><td>0.09</td><td>1.35</td><td>0.06</td><td>C</td><td>0.11</td><td>0.00</td><td>C</td><td>0.38</td><td>0.00</td><td>C</td></tr><tr><td>30</td><td>04/13/2023</td><td>9.08</td><td>11.60</td><td>0.03</td><td>0.33</td><td>0.01</td><td>C</td><td>0</td><td>0.00</td><td>C</td><td>0.92</td><td>0.00</td><td>C</td></tr><tr><td>31</td><td>04/25/2023</td><td>93.92</td><td>82.27</td><td>4.79</td><td>101.51</td><td>334.53</td><td>C*</td><td>38.31</td><td>0.58</td><td>AC</td><td>-0.53</td><td>0.03</td><td>AC</td></tr><tr><td>32</td><td>05/02/2023</td><td>10.12</td><td>12.74</td><td>0.05</td><td>2.20</td><td>0.00</td><td>-</td><td>-</td><td>0.01</td><td>AC</td><td>-0.26</td><td>0.00</td><td>AC</td></tr><tr><td>33</td><td>05/03/2023</td><td>20.28</td><td>11.35</td><td>0.26</td><td>11.97</td><td>1.10</td><td>C</td><td>25.00</td><td>0.12</td><td>AC</td><td>-0.76</td><td>0.01</td><td>AC</td></tr><tr><td>34</td><td>05/04/2023</td><td>12.55</td><td>12.03</td><td>0.52</td><td>19.32</td><td>0.99</td><td>AC</td><td>0.00</td><td>0.11</td><td>C</td><td>1.41</td><td>0.00</td><td>C</td></tr><tr><td>35</td><td>05/05/2023</td><td>85.14</td><td>66.60</td><td>17.25</td><td>479.12</td><td>1073.15</td><td>C</td><td>0.66</td><td>0.90</td><td>C</td><td>16.21</td><td>0.15</td><td>C</td></tr><tr><td>36</td><td>05/07/2023</td><td>49.94</td><td>33.05</td><td>4.19</td><td>174.08</td><td>137.47</td><td>C</td><td>11.93</td><td>0.86</td><td>C</td><td>0.12</td><td>0.02</td><td>C</td></tr><tr><td>37</td><td>05/26/2023</td><td>47.17</td><td>16.50</td><td>1.17</td><td>60.30</td><td>11.00</td><td>C*</td><td>1.91</td><td>0.23</td><td>C</td><td>1.01</td><td>0.01</td><td>C</td></tr><tr><td>38</td><td>06/10/2023</td><td>32.30</td><td>26.23</td><td>0.90</td><td>33.51</td><td>2.26</td><td>C*</td><td>24.00</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>39</td><td>06/14/2023</td><td>81.97</td><td>69.87</td><td>5.98</td><td>389.19</td><td>302.50</td><td>C*</td><td>11.60</td><td>0.45</td><td>AC</td><td>-0.11</td><td>0.03</td><td>C</td></tr><tr><td>40</td><td>07/07/2023</td><td>92.37</td><td>82.72</td><td>7.26</td><td>437.96</td><td>140.12</td><td>C</td><td>10.22</td><td>1.59</td><td>C</td><td>1.32</td><td>0.03</td><td>C</td></tr><tr><td>41</td><td>07/12/2023</td><td>149.77</td><td>126.39</td><td>109.95</td><td>2107.46</td><td>1490.97</td><td>C</td><td>2.19</td><td>9.67</td><td>AC</td><td>0.00</td><td>0.61</td><td>C</td></tr><tr><td>42</td><td>08/10/2023</td><td>16.20</td><td>20.00</td><td>0.45</td><td>4.70</td><td>0.33</td><td>AC</td><td>-0.13</td><td>0.02</td><td>AC</td><td>-0.10</td><td>0.00</td><td>AC</td></tr><tr><td>43</td><td>09/03/2023</td><td>153.54</td><td>NA</td><td>14.77</td><td>1128.39</td><td>72.03</td><td>C</td><td>7.40</td><td>3.77</td><td>C</td><td>1.00</td><td>0.04</td><td>C</td></tr></tbody></table>
<table><thead><tr><th rowspan="2">Event</th><th rowspan="2">Date</th><th colspan="2">Rainfall (mm)</th><th rowspan="2">Peak discharge m³ s⁻¹</th><th rowspan="2">Total volume m³</th><th rowspan="2">Sediment yield t</th><th colspan="2">Q-SS hysteresis</th><th rowspan="2">K load t</th><th colspan="2">Q-K hysteresis</th><th rowspan="2">P load t</th><th colspan="2">Q-P hysteresis</th></tr><tr><th>Upper gauge</th><th>Lower gauge</th><th>Loop</th><th>HI</th><th>Loop</th><th>HI</th><th>Loop</th><th>HI</th><th>Loop</th><th>HI</th></tr></thead><tbody><tr><td>44</td><td>09/07/2023</td><td>48.82</td><td>NA</td><td>18.46</td><td>277.54</td><td>22.39</td><td>C</td><td>1.10</td><td>1.02</td><td>C</td><td>0.25</td><td>0.03</td><td>C</td><td>0.42</td></tr><tr><td>45</td><td>09/12/2023</td><td>101.13</td><td>NA</td><td>41.67</td><td>2066.10</td><td>135.64</td><td>C</td><td>4.00</td><td>6.38</td><td>C</td><td>0.28</td><td>0.13</td><td>AC</td><td>0.00</td></tr><tr><td>46</td><td>09/18/2023</td><td>29.30</td><td>NA</td><td>5.16</td><td>197.09</td><td>11.48</td><td>AC</td><td>-0.09</td><td>0.63</td><td>C</td><td>0.21</td><td>0.01</td><td>C</td><td>0.11</td></tr><tr><td>47</td><td>10/03/2023</td><td>30.12</td><td>33.74</td><td>3.56</td><td>204.38</td><td>8.51</td><td>C</td><td>0.78</td><td>0.64</td><td>C</td><td>0.05</td><td>0.02</td><td>AC</td><td>-1.71</td></tr><tr><td>48</td><td>10/11/2023</td><td>NA</td><td>67.43</td><td>4.53</td><td>304.02</td><td>8.77</td><td>C</td><td>1.20</td><td>1.03</td><td>C</td><td>0.37</td><td>0.03</td><td>C</td><td>0.47</td></tr><tr><td>49</td><td>11/02/2023</td><td>23.52</td><td>27.05</td><td>1.03</td><td>9.42</td><td>1.30</td><td>AC</td><td>-0.96</td><td>0.03</td><td>AC</td><td>-0.36</td><td>0.00</td><td>AC</td><td>-3.50</td></tr><tr><td>50</td><td>11/12/2023-1</td><td>50.95</td><td>53.64</td><td>9.59</td><td>187.42</td><td>78.46</td><td>C*</td><td>6.14</td><td>1.77</td><td>C</td><td>4.34</td><td>0.03</td><td>C</td><td>2.08</td></tr><tr><td>51</td><td>11/12/2023-2</td><td>98.88</td><td>80.83</td><td>183.45</td><td>2295.63</td><td>1912.63</td><td>C</td><td>0.17</td><td>14.62</td><td>C</td><td>0.97</td><td>0.50</td><td>AC</td><td>-0.32</td></tr><tr><td>52</td><td>12/06/2023</td><td>31.57</td><td>18.80</td><td>5.18</td><td>46.51</td><td>40.78</td><td>C</td><td>2.85</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>53</td><td>12/15/2023</td><td>6.22</td><td>5.80</td><td>2.55</td><td>73.09</td><td>7.10</td><td>C</td><td>2.00</td><td>0</td><td>C</td><td>0.04</td><td>0</td><td>C</td><td>0.12</td></tr><tr><td>54</td><td>03/21/2024</td><td>54.17</td><td>NA</td><td>7.70</td><td>210.57</td><td>39.64</td><td>C*</td><td>20.50</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>55</td><td>04/07/2024</td><td>52.32</td><td>NA</td><td>11.85</td><td>102.84</td><td>114.84</td><td>C</td><td>3.52</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>56</td><td>04/16/2024</td><td>84.92</td><td>73.13</td><td>91.46</td><td>1103.95</td><td>1054.40</td><td>C</td><td>2.58</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>57</td><td>04/29/2024**</td><td>298.94</td><td>252.46</td><td>32.58</td><td>166.86</td><td>18.61</td><td>-</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

C = Clockwise, AC = Anticlockwise/counterclockwise hysteresis loop. HI = hysteresis index. C* = Clockwise loop shapes defined by suspended sediment concentrations during rising and falling limbs, yet visual analysis shows complex shapes. ** Incomplete series for streamflow discharge, due to loss of equipment during extreme flood event. NA = missing data
and counterclockwise. An example of this complexity is seen in event 31 (04/25/2023), shown in Figure 7-C.

The highest HI<sub>mid</sub> values were observed during events that occurred between April and July 2023 as well as in March 2024. During the event of 04/25/2023 (Figure 7-C), SSC during the rising limb was almost 40 times higher than during the falling limb, likely reflecting sediment availability after soybeans were harvested in the catchment and soil cover was scarce. Following the event of 04/25/2023, a series of events that followed in the month of May 2023 exhibited a distinct pattern: the high values of HI<sub>mid</sub> on April 25th and May 3rd were succeeded by events characterized by low SSC, low HI<sub>mid</sub> and counterclockwise hysteresis. This pattern suggests possible sediment exhaustion or a shift in sediment sources after the large events. In this dataset, events occurring in close succession typically exhibited an initial high HI<sub>mid</sub> followed by a lower one, as seen in pairs, such as events 14 and 15, 16 and 17, 20 and 21, 31 to 34 (Figure 7-B and C), 38 and 39, 30 and 41, 43 and 44, 50 to 53. However, the opposite trend was noted in pairs, such as events 6 and 7, 17 to 19, 26 to 29, 35 and 36.

<figure><img src="image_7.png" /><figcaption>**Fig. 6** Violin plot showing sediment yield (tons) from rainfall events monitored at the outlet of Guarda Mor catchment between 2021 and 2024. The plot illustrates data distribution and probability density, with the median marked and the interquartile range highlighted by the white box. The x-axis indicates the number of events monitored each year</figcaption></figure>

Additional observations from this dataset include precipitation volumes from both gauges, peak streamflow discharge ($Q_{max}$), total streamflow volume, time difference from maximum discharge and maximum SSC, and the meteorological season during which the event occurred. Correlations derived from this data (Figure 8) reveal a positive relationship between SY and the rainfall volume, particularly from the lower rain gauge. Rainfall volumes from both gauges were also positively correlated with
<figure><img src="image_8.png" /><figcaption>**Fig. 7** Hysteresis loops from events 19, 34, and 31, displaying clockwise (A), counterclockwise (B) and complex (C) patterns</figcaption></figure>

average SSC. Seasonally, there is a slight positive correlation between Qmax, total streamflow volume, and events that occurred in the spring (for example, 11/12/2023) and winter. Fourty events had their peak SSC before peak Q, hence the slight negative correlation.

Average total K and total P concentrations for all collected samples were 3.31 and 0.02 mg L⁻¹, respectively. The lowest concentrations for both solutes were less than the quantitation limit (0.00 mg L⁻¹). The maximum K concentration was observed during event 50 (11/12/2023-1) with ~31 mg L⁻¹, and for P, it occurred during event 33 (05/05/2023) with a concentration of 1.25 mg L⁻¹.

For this catchment, the total loads of K and P, obtained from samples taken from April 2022 to March 2024, were calculated to be 55 and 2 tons, respectively, representing average losses of approximately 30 kg ha⁻¹ of K and 1 kg ha⁻¹ of P, over the entire catchment area. During the monitoring period, not all events were completely sampled, so a subset of 28 events from 2022 and 2023 was analyzed in detail (Table 2). Events 51 and 41, which occurred on 11/12/2023 and 07/12/2023, recorded the highest K and P loads, with around 15 and 10 tons of K and 0.5 and 0.6 tons of P, respectively. The total loads of these events were approximately 47 tons of K and 1.7 tons of P. These events were compared with the same hydrological variables mentioned earlier (Figure 8), revealing positive correlations between total loads and precipitation, with a slightly stronger correlation between rainfall volume at the lower gauge and P loads compared to K loads. Correlations between loads and total streamflow volume and SY showed that K loads were more positively correlated with water volume, while P loads were more strongly correlated with SY.

Positive correlations were also observed between mean and maximum P concentrations and mean SSC. Additionally, maximum P concentration was positively correlated with SY and rainfall volume at the lower rain gauge.

Between September and December 2022, during drought conditions, biweekly samples showed that as streamflow decreased, P concentrations increased from 0.02 to 0.05 mg L⁻¹, impairing water quality. Converting P concentrations into P loads, those ranged from 0.32 to ~111 mg s⁻¹. When examining P loads between August 2022 and April 2023 - a period encompassing both drought conditions and rainfall events - it became evident that P loads were higher at the onset of the drought (Figure 9). This suggests that following rainfall events, sediments may be deposited on the stream bed, leading to a gradual release of P from those sediments to the water over time. These
<figure><img src="image_9.png" /><figcaption>**Fig. 8** Pearson correlations between hydrological variables for a set of 57 rainfall-runoff events and for a subset of 28 events considering P and K loads and concentrations</figcaption></figure>

*season = Summer (1), Fall (2), Winter (3), and Spring (4). volU and volL= Events' total rainfall volume in Upper and Lower rain gauges. Qmax and Qmean = Events' peak and mean streamflow discharges. Qvol = total streamflow volume. SY = sediment yield in tons. SSCtime = SSC peak before Qmax (-1) or after (1). SSCmean = Events' mean SSC. Kload and Pload = Events' total potassium and phosphorus loads in tons. Kmax and Pmax = maximum potassium and phosphorus concentrations. Kmean and Pmean = mean potassium and phosphorus concentrations.

findings highlight the importance of event-based monitoring, as P loads are signifi-
cantly elevated during such events. In the following period, between April 2023 and
March 2024, average P loads reached 2031 mg s⁻¹.

3.2 Sediment tracing

From the dataset of 46 potential tracers (Figure 3), a total of 33 properties proved
to be conservative and were submitted to a Kruskal Wallis test, which resulted in 13
significantly different variables. The most discriminant set of variables, determined
by Wilks' lambda minimization, consists of P (Mehlich-3 extract), moderately label
P (NaOH 0.5 mol L⁻¹ extract), ¹³C isotope ratio, total C, total N, and total P (ICP
determined). The optimal tracers set was then used in the multivariate unmixing
model described by Buligon et al. (2024), which estimates the contributions of different
sediment sources considering the deleterious effects of collinearity between tracers and
quantifies the associated uncertainties. The results provide the information on the
<figure><img src="image_10.png" /><figcaption>**Fig. 9** Each point represents the total phosphorus load in mg s⁻¹ during or in-between events from August 2022 until April 2023. Y-axis is shown in logarithmic scale</figcaption></figure>

likely source or the relative importance of the different sediment sources (Walling, 2013).

These tracers are variables related to the land uses, especially P to agricultural fields, as this is an important fertilizer input. C and N are related to soil organic matter. Tiecher et al (2019) mention that total P is sometimes removed from this type of analysis due to its non-conservative behavior. The authors then used P fractions and found that separating P fractions improved the predictive ability of tracers and models (Tiecher et al, 2019).

The result is an estimate of average contributions, for the deposited sediments sampled at the outlet, were of 44% for crop fields, 31% for stream channels, and 25% for unpaved roads (Figure 10). The major source ranged from contributions of 40 to 47%, among the 10 target samples, while stream channels and unpaved roads supplied between 27 and 34% and 22 and 28%, respectively. The coefficients of variation for each source were 7, 7, and 5% for stream channels, unpaved roads, and crop fields, respectively.

# 4 Discussion

## 4.1 Sediments

The positive correlation between SY, monitored at the catchment outlet, and rainfall volume from the lower rainfall gauge indicates the susceptibility to erosion and connectivity between the sedimentary (lower) basin of the catchment and the stream. The energy generated by surface runoff, driven by the topographic gradients and the large volume of runoff volume generated on hillslopes, likely contributes to stream channel erosion, supporting the significant contribution of this source to the sediment in the stream, as outlined by the fingerprinting analysis. This is supported by Bernardi (2022)
<figure><img src="image_11.png" /><figcaption>**Fig. 10** Violin plot of the sediment fingerprinting model outputs. The graph shows the data distribution and its probability density, highlighting the median of the data, while the white box indicates the interquartile range</figcaption></figure>

and Bernardi et al (2025), who found this stream to be at a non-equilibrium state due to excess runoff. Besides, the presence of sediments originating from agricultural areas in the stream can facilitate the release of nutrients and pollutants associated with this land use. Additionally, unpaved roads act as connectivity features within river basins, generating and propagating surface runoff and associated sediments.

Overall, it is challenging to outline clear patterns among the hydrological variables across the rainfall events. The frequent clockwise hysteresis found for Q and SSC indicates a potential difficulty for establishing a rating curve for suspended sediments. At the same river and reporting the difficulty for establishing this rating curve, but considering bedload sediment, Bernardi (2022) found sediment delivery to be governed by sediment availability and not by the energy of streamflow. The use of sensors with continuous, high-temporal resolution, and long-term monitoring of turbidity can be useful in establishing these types of relations with greater confidence (Merten et al, 2014), since SSC sampling at high-temporal resolution leads to major sampling and laboratory challenges. Also, at the catchment scale, studies have linked particulate P to turbidity readings (Yamamoto and Suetsugi, 2006; Udeigwe et al, 2007), further highlighting the potential and importance of obtaining these monitoring data.

Based on this study's findings, several key indicators emerge for designing and implementing practices to reduce soil and water degradation. These include addressing the fragility and connectivity of the sedimentary basin; recognizing the role of unpaved roads in runoff and sediment propagation (Silva et al, 2021); and emphasizing the importance of buffer zones adjacent to crop fields and improvements to riparian areas
to better reduce sediment connectivity from agricultural fields to the drainage net-
work (Berry et al, 2003; Bortolozzo et al, 2014). The detailed soil classification map can
provide valuable guidance for designing appropriate practices to reduce peak runoff
and its erosive potential. For example, since many soils in the catchment are shallow
and some exhibit a textural gradient, management practices should focus on control-
ling runoff drainage to minimize erosion and peak discharge. For deeper soils, surface
runoff can be managed more effectively by installing infiltration terraces, which pro-
mote water infiltration and storage. These should reduce sediment transfer and peak
streamflow discharges in the stream during rainfall events.

Thomaz et al (2014) found SSC to increase up to 10 times downstream of unpaved roads. The authors (Thomaz et al, 2014) emphasize the negative impact this landscape component has on hydrogeomorphological processes in streams of third to fifth orders in agricultural areas of South Brazil. Owens et al (2005) reviewed how long-term monitoring studies have captured the influence and connections of human activities and documented increased sediment loads from catchments across the world. Among these studies, the change in fine sediment loads are specifically related to changes in land use and management, but, in some situations, these shifts are attributed to climatic phenomena, such as El Niño-Southern Oscillation (Owens et al, 2005). Our data show Guarda Mor catchment has intense responses to the climatic effect.

From a water quality perspective, this study considers sediment and nutrient flows, and runoff contributions, quantifying these processes using hydrological monitoring and sediment source apportionment. This highlights how water quality is controlled by these inflows. Excess sediments increase water turbidity and cause sedimentation to water bodies, which alter stream morphology, habitat ecology, and, at larger scales, even navigation. Also, fine sediments are responsible for transporting nutrients and pollutants that are adsorbed to it. These sediments may be deposited along the landscape, especially in floodplains, and connected water bodies where they can be a source of P and other nutrients (Owens et al, 2005).

## 4.2 Phosphorus and Potassium

According to Sharpley et al (1993), during its transport through water there is a progressive dilution of P and, in some cases, this process may be due to sediment deposition. The availability of P in the environment depends on the transformations between dissolved and particulate forms, which are accentuated by the preferential transport of smaller, more reactive soil particles, and by sorption-desorption processes (Sharpley et al, 1993).

Utzig et al (2023) examined the concentrations of dissolved K and P in runoff from agricultural hillslopes and zero-order catchments, with K concentrations found to be approximately 10 times higher than those of P. In this study, the total fractions were analyzed, but our results agree with the findings of Lemos et al (2020), who reported higher K losses compared to P, under simulated intense rainfall and no-till practices. This highlights the potential for K transport in runoff over agricultural areas.

The connection between agricultural activities and water quality is well established, as this land use can be determinant to nutrient transport and increased concentrations in agricultural areas (Baker, 2006). In a 1 km² catchment in South Brazil, which
is also located at the border of the Southern Plateau, Bender et al (2018) found P concentrations from 0.08 to 1.5 mg L⁻¹; de Barros et al (2020) found average and maximum P concentrations of 0.2 and 1.9 mg L⁻¹ and K of 3.2 and 29 mg L⁻¹. de Bastos et al (2021) also found concentrations of 3.2 and 3.9 mg L⁻¹ for K, at the same catchment. Also in South Brazil, Capoane et al (2014) found dissolved P concentrations from 0.013 to 0.18 mg L⁻¹, but the authors noticed that higher concentrations were observed close to the occurrence of rainfall events. Besides, concentrations were higher near crop fields and livestock buildings, and during the month of December.

By dividing the total exported loads at Guarda Mor River's catchment outlet by the area occupied exclusively by crop fields, potential exports of 73 kg ha⁻¹ for K and 3 kg ha⁻¹ for P were observed over the two-year (2022 and 2023) monitoring period. When considering the seasons in which the events occurred, the potential exports for the winter of 2023 were 30 kg ha⁻¹ for K and 1 kg ha⁻¹ for P. Yet, runoff does not spread evenly across the surface and some areas that are more prone to surface runoff generation and accumulation, known as hotspots, are critical for the transfer of runoff, sediments, and solutes. These are influenced by surface topography, land use and management practices. In the volcanic, upper portion of this catchment, these areas were mapped, and sediment sources were traced (Dambroz et al, 2022). Therefore, distributing total loads evenly across the surface might be misleading, as the greatest losses are likely concentrated in these fragile areas where best management practices should be prioritized. Still, these findings emphasize the importance of soil and water conservation practices, not only to sustain farm profitability (Hörbe et al, 2021), but also for environmental protection.

Agriculture is a non-point source of pollution due to the potential of fertilizers and solutes for being transported via runoff and sediments to rivers, due to soil erosion (Novotny, 2002). Particularly critical, P is associated with the high input of fertilizers in agricultural fields (Munn et al, 2018) and it is strongly adsorbed to soil particles (Fonseca, 2006). Excess P can lead to the eutrophication of water bodies (Novotny, 2002). With a strong association of P with sediments (Sharpley et al, 1993) and increased streamflow in agricultural catchments (Rose et al, 2018), erosion from agricultural areas can transport P to water bodies. Other controlling factors, associated with this land use, can be the fertilization method and P concentration in the soil (Sharpley et al, 1993).

While P is often transported in lower concentrations compared to other elements in water (Fonseca, 2006; Utzig et al, 2023), it plays a significant role in water quality due to its pollutant potential. A large portion of P transport occurs in particulate form. Sharpley et al (1993) found that 75 to 90% of P moves with sediments, while the dissolved fraction, although smaller, is more readily absorbed by algae (Sharpley et al, 1993). Ding et al (2016) suggested that sampling only during baseflow might mask high concentrations and loads of nutrients that occur during rainfall-runoff events. However, excess P in water bodies from agriculture does not occur only from direct runoff after rainfall events. Excess P can impact water quality during many different hydrological stages (Ding et al, 2016), as observed in this study. Mellander and Jordan (2021) also observed increased P concentration during drought.
## 4.3 Event-based analysis

Event 51 (11/12/2023) resulted in the highest recorded SY, K load, and second-highest P load in the dataset. This event coincided with an El Niño period, which brought heavier rainfall, and followed soybean planting. After planting, soil disturbance increases, and fertilizers applied during this period become more readily available for transport. In this region, a mixture of nitrogen (N), P, and K is typically applied to the planting line, with additional K doses spread on the surface for fertility management. This surface application of K contributes to its availability for transport via surface runoff.

The five events with the highest SY occurred during periods that followed the planting of summer crops (such as soybeans) or winter crops (oats or wheat), or immediately after the harvest of summer crops. These are critical periods of soil vulnerability, during which management practices should be prioritized, reducing fallow periods and using runoff control practices, such as infiltration terraces, where possible. Notably, event 51 yielded nutrient exports of approximately 8 kg ha⁻¹ for K, 0.3 kg ha⁻¹ for P, and 1000 kg ha⁻¹ for sediments. The highest K concentration (~31 mg L⁻¹) was observed during event 50, which occurred on the same date.

Several characteristics influence nutrient transport, including land use, slope, soil texture, and hydrological conditions (Irvine et al, 2019). The interactions between controlling factors create specific hydrological responses (Fenicia and McDonnell, 2022). For instance, in the United States, Banner et al (2009) found that 88% of P discharge occurred during 10% of the time, coinciding with high-magnitude streamflow events. Ockenden et al (2016) also reported that 80% of P transport occurred during larger events. These findings illustrate the strong dependence of P transport on the characteristics of rainfall-runoff events in agricultural catchments.

Irvine et al (2019) found P transport to be associated with runoff in agricultural areas. Similarly, Banner et al (2009) observed higher P discharge with closer proximity of agricultural fields to water courses. Irvine et al (2019) also observed that a critical moment occurs when rainfall events happen when soil moisture is higher, especially during the spring. In Brazil, Bender et al (2018) observed a similar tendency for P transport during the spring, with P peaks occurring before the peak Q due to higher soil moisture and exposed soil conditions. This further emphasizes the importance of year-round monitoring to capture variations in process dynamics and highlights the usefulness of the sediment fingerprinting technique.

At the plot scale, Burwell et al (1975) had observed that the critical moments for nutrient loss (P and K) occurred up to two months after corn planting. Alvarez et al (2014) demonstrated that nutrient transport, during events prior to summer planting, included both dissolved and particulate forms of P, but considered the concentrations to be low. Alvarez et al (2014) and de Barros et al (2020) found P preferential transport to be in the particulate form. Both P and K transport are controlled by runoff, with P concentrations closely linked to SSC (de Barros et al, 2020). This means nutrient behavior varies throughout the year due to the erosivity of rainfall events over agricultural areas. Therefore, P transport is controlled by the same factors as those that control soil erosion. Generally, the greater the SSC, the greater P concentrations (Fonseca, 2006). Bender et al (2018) found SSC and P hysteresis to be clockwise and
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Bertol et al (2004) observed that K losses from agricultural fields were higher when associated with sediments rather than water, with 78% of K concentrations and 65% of P concentrations depending on the volume of runoff. Moreover, higher soil moisture content favors element dissolution and leads to higher nutrient losses by runoff (Fonseca, 2006). Lemos et al (2020) reported higher nutrient losses from no-till management compared to conventional tillage - and the opposite trend for SSC -, as nutrients tend to accumulate at the surface in no-till and, thus, are more available for transport.

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Also at the plot scale and under simulated rainfall, Fonseca (2006) attributed higher P losses from no-till to its availability, either because of the fertilization moment or its form (in the planting line or over the surface). This further emphasizes the great potential for the transfer of this nutrient to rivers, especially considering that the connectivity in areas adjacent to stream channels and in zero-order catchments is high (Gomi et al, 2002).

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Rose et al (2018) found both P and total suspended solids hysteresis to be predominantly clockwise, followed by complex patterns; while for K most hysteresis patterns were complex, followed by anticlockwise. Rose et al (2018) report that source contributions and hydrologic events influence solute export in catchments. de Barros et al (2020) found that particulate P and K both showed clockwise loops, associated with nutrient and SSC enrichment during events.

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## 4.4 Runoff, sediments, and nutrients influencing water quality

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This study focuses on identifying and quantifying water, sediment, and nutrient inflows that control water quality, considering their temporal and spatial distribution. This is evidenced by the results which reveal, from both P enrichment during droughts and sediment fingerprinting results, the significance of the agricultural land use on water quality, emphasizing the need to quantify how much sediment from those areas reaches the stream and releases P. Sediment fingerprinting can highlight contributions from other land uses, such as unpaved roads, which at times may dilute the impact of P-enriched soils and sediments, and the remobilization of material during stream channel erosion.

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Casquin et al (2021) found that P exports are controlled by the spatial configuration of its sources in headwater catchments, and, because P is mostly transported via surface runoff, buffer zones can intercept it. Prosser et al (2020) reviewed that the effectiveness of buffers can range from 10 to 100% for reducing the movement of nutrients, depending upon vegetation type and its width, as Bortolozo et al (2014) also observed in South Brazil. Riparian areas are zones of nutrient accumulation or a temporary sink for those pollutants (Pärn et al, 2012). At Guarda Mor catchment, riparian areas, which vary in width according to the Brazilian legislation, are typically occupied by forests with dense tree cover. However, the shading from these trees often limits the growth of grassy, near-ground vegetation and can lead to the formation of erosion channels from field to stream. Additionally, Cereta (2024) assessed satellite
imagery and found that while most of the riparian areas comply with the legisla-
tion, areas on steeper slopes lack the appropriate riparian width and native vegetation
coverage. Conservation management efforts should consider these factors.

Dabney et al (2006) reviewed the types and effects of buffers in agricultural areas, where their adoption is more widespread in the U.S. than in Brazil. In-field buffers help controlling erosion near its sources, while also improving the efficiency of edge-of-field buffers (Dabney et al, 2006). Contour buffer strips and grassed waterways are examples of in-field buffers that improve water quality by reducing runoff, sediment, and pollutant transport (Dabney et al, 2006). The authors emphasize the benefits of combining conservation practices to promote sediment trapping and to slow down concentrated runoff.

Our study highlights the significant impact of rainfall-runoff events, agricultural practices, and landscape features on nutrient transport. Management practices should focus on critical periods of soil vulnerability, especially during and soon after planting, to mitigate nutrient and sediment losses. In fragile areas with high connectivity, prioritizing soil conservation efforts will be crucial in reducing nutrient export and improving water quality. Importantly, water management, including water and sediment quality, must be organized at the catchment scale in order to be effective (Owens et al, 2005). And as reported by Banner et al (2009), management practices must aim at reducing both peak discharge and nutrient loads. Examples of successful peak discharge reduction at the catchment scale include Londero et al (2018), who demonstrated that infiltration terraces on no-till zero-order catchments can reduce peak discharge by nearly 80%, and Silva et al (2021), whose model outputs showed up to a 300% reduction in peak discharge from the addition of diversion weirs on unpaved roads, road ditch graveling, road ditches, grass waterways, and filter strips in a 1 km² catchment. These examples underscore the effectiveness of targeted structural interventions on crop fields and unpaved roads in minimizing peak discharge, reinforcing the need for target-specific solutions.

# 5 Conclusion

Significant rainfall-runoff events are responsible for a large portion of sediment yield and nutrient loads, particularly during major storm events. Fifty-seven rainfall-runoff events analyzed from June 2021 to April 2024 resulted in a total sediment yield of ~10, 400 tons. The highest sediment yield was recorded on November 12, 2023 (1913 tons), contributing 18% of the total sediment yield from all events. The five largest events accounted for 68% of the total monitored sediment yield, indicating that major events dominate sediment export.

Hysteresis loops, variations in suspended sediment concentration, and sediment sources suggest complex interactions between flow dynamics and sediment availability, especially after major events. For suspended sediment concentration, most events presented a clockwise hysteresis loop, with complex patterns observed in some events, suggesting variations in sediment sources and sediment exhaustion after significant rainfall. The sediment fingerprinting technique proved valuable in estimating the significant contributions of sediments from crop fields (44%), stream channels (31%),
and unpaved roads (25%) to target deposited sediment samples. Future studies should consider additional sources to capture the contribution from mass movements that occurred during the extreme event of May 2024.

K and P concentrations, from a subset of 28 events, averaged concentrations of 3.31 mg L⁻¹ for K and 0.02 mg L⁻¹ for P. Total K and P loads were 55 tons and 2 tons, respectively. On November 12, 2023, the highest K concentration (30.67 mg L⁻¹) was recorded, and on May 03, 2023, the highest P concentration (1.25 mg L⁻¹). K and P loads are driven by both hydrological conditions and sediment transport, with P loads more strongly correlated with sediment yield, and K loads with water volume.

During the 2022 drought, P concentrations increased as streamflow decreased, suggesting that reduced flow can impair water quality. Rainfall events led to higher P loads, reinforcing the significance of event-based monitoring for capturing nutrient dynamics and for managing water quality.

**Funding.** Funding for this research was provided by Conselho Nacional de Desenvolvimento Científico e Tecnológico-CNPq (309759/2021-8) and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-CAPES CLIMAT-AMSUD Program (88887.878904/2023-00).

**Acknowledgements.** Alice Dambroz gratefully acknowledges support from the Doctoral Dissertation Research Award by the Fulbright Program, which is sponsored by the U.S. Department of State and the Brazilian Fulbright Commission.

**Declarations.** All authors have read, understood, and have complied as applicable with the statement on "Ethical responsibilities of Authors" as found in the Instructions for Authors.

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<figure><img src="image_12.png" /><figcaption>**Fig. A1** Hydrographs and suspended sediment concentration for 57 events. Rainfall is shown in black (upper gauge) and grey (lower gauge), when data is available. The shaded blue area represents streamflow, whereas the maroon lines show the suspended sediment concentrations</figcaption></figure>
<figure><img src="image_13.png" /><figcaption>**Fig. A2** Chemographs of total phosphorus (P) and total potassium (K) concentrations during 28 events. Rainfall is shown in black (upper gauge) and grey (lower gauge), when data is available. The shaded blue area represents streamflow, whereas red and orange lines show the P and K concentrations, respectively</figcaption></figure>