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To truly understand the hydrologic and erosive processes that occur at the catchment scale regarding land use and soil management changes, intensive monitoring is required over a long period. Variables such as precipitation, flow rate and suspended sediment concentration are the fundamentals needed to estimate sediment yield (SY). However, in order to shed more light on the effects of soil management changes on SY, traditional hydrologic monitoring techniques can be paired with sediment tracing to identify sediment sources. In addition, mathematical models that predict erosion and SY can also help understand the dynamics of erosion and deposition between the hillslope and stream channel. Given the above, this article provides an overview of almost 20 years of monitoring (2002–2021) in the Arvorezinha experimental catchment (1.23 km2) located on the edge of the Brazilian meridional plateau in southern Brazil, which is subject to extreme erosion due to a combination of factors related to intensive agriculture and steep slopes. The catchment was selected as a study site to evaluate the effects of land‐use changes and soil conservation management on hydrology and SY. The text reviews the history of the study, synthesizes the main results and describes the evolution of the monitoring techniques used while giving special attention to the fingerprinting approach. In addition, the article discusses the importance of catchment‐scale studies for teaching, generating technical‐scientific knowledge and fostering collaboration between national and international research groups. [ABSTRACT FROM AUTHOR]

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Most existing erosion models implicitly assume that runoff is generated due to infiltration excess, i.e. Hortonian overland flow. However, at catchment scale the dynamics is more complex because there exist others water sources and process involved, for example, translatory, return, and macropore flow, which can move slowly from soil towards to catchment outlet. Therefore, those must be included in studies of flow dynamics and description of erosion process. Because an error at representation of water sources can lead to errors on erosion process modelling, when slowly movements, are the main contributor at catchment outlet. Thus, we aim to investigate the role played by water sources in the catchment and how it may affect the process-based erosion models performance. The study was conducted in Arvorezinha catchment, Brazil, and has a drainage area of ca. 1.23 km². Currently, the catchment is characterized by the cultivation of tobacco, especially through the minimum cultivation system. Accumulated rainfall is measured in a rain gauge and used as a reference to adjust the measurements of two pluviograph. Monitoring of water discharge (Q) and suspended sediments concentration (SSC) is performed in the outlet of the catchment through a Parshall flume with a limnigraph and turbidimeter, respectively. Using dissolved silicon concentration (DSi) the hydrograph can be separated into two components: i) surface runoff, i.e., fast movement water (new water) and ii) groundwater+soil water, i.e., slow movement water (old water). Twenty-six samples were collected during subsurface+base flow conditions to characterize DSi in groundwater+soil water from 9/7/2011 to 9/7/2012. Sampling during events started on 10/01/2011 and lasted until to 10/26/2013, in total six rainfall events to characterize surface runoff. An aliquot of each sample (sediment + water) was collected and filtered through a 0.22 µm porosity. The determination of DSi content was through an ICP-OES. Modeling of the erosion process (sediment yield-SY) was performed using the Limburg Soil Erosion Model (2.01 version). A manual calibration procedure was used, and its procedure was repeated for both approaches: with and without runoff separation. For the six rainfall events evaluated, 62% of the hydrograph is formed by groundwater+soil water, especially in autumn-winter and with low-medium magnitude (up to Q = 800 L.s-1). Runoff separation by DSi was essential for obtaining the correct estimation of surface runoff volumes. Furthermore, the effect of dilution during the events was observed, i.e. as Q increases, DSi concentration decreases. Hydrossedimentological monitoring quantifies the sediment at the catchment outlet, which is the result of sediment from soil detachment in the catchment, carried by surface runoff and diluted in the flows that move slowly in outlet (subsurface and base flow). On the other hand, erosion process modeling done by the LISEM model is based only on sediment transported by surface runoff. Since the model simulates only surface flow, the dilution effect does not occur and thus SSC and SY are simulated incorrectly. Therefore, it is essential to consider different sources of water on the occurrence of runoff events, because the dynamics of the sediment is directly influenced by those. [ABSTRACT FROM AUTHOR]

[Display omitted] • On average 62% of the streamflow is composed of subsurface + base flow. • Subsurface + base flows are responsible for increased streamflow, especially in autumn–winter. • The sediment dynamic is affected by the dilution process that occurs in the outlet. • The knowledge of flow sources allowed to improve calibration of erosion parameters. • Hydrographs and sedimentographs are not adequately represented by LISEM model. Water flows that move slowly in the landscape are not simulated by spatially and physical distributed erosion models and can contribute significantly during rainfall events in the streamflow, making model calibration complex. The goals of this study were: (i) to assess the relative contribution of subsurface and surface runoff to the streamflow at the outlet and (ii) to investigate to what extent the presence of runoff, other than Hortonian overland flow, affects erosion model performance. The Arvorezinha catchment (1.23 km2 – Southern Brazil) is characterized by the cultivation of tobacco under minimum cultivation system on shallow soils. From October 2011 until October 2013, we set up a complementary monitoring program of water chemistry and samples were collected during rainfall-runoff events to analyze dissolved silicon (DSi). The observed hydrograph was separated into two components using DSi: i) surface flow and ii) groundwater + soil water flow. The OpenLISEM model was used to assess the impact of different runoff sources and understand the hydrological and erosive process dynamics in the catchment. For this, a manual calibration procedure was used and repeated for two approaches: with and without runoff separation. For the six rainfall events evaluated, 62% of the hydrograph is formed by groundwater + soil water, especially in autumn–winter and for low-medium magnitude events (up to Q peak = 800 L s−1). The dilution effect was observed during rainfall events, when liquid discharge increases, DSi concentration and suspended sediment concentration decreases. The performance of physics-based models in a catchment with very fragile soils and with frequent changes in land use, is impacted by runoff separation. [ABSTRACT FROM AUTHOR]

• Spectral pre-processing, multivariate method, and spectral ranges were tested. • Quality of model calibration depends mainly on the spectral preprocessing technique. • NIR range alone or combined with other spectral ranges resulted in better models. • The lowest errors were obtained by combining UV–Vis, NIR and MIR spectra. • Savitzky-Golay 1st derivative and normalization were the best spectral pre-processing. In recent years, several sediment fingerprinting studies have used ultraviolet–visible (UV–Vis), near-infrared (NIR) and middle-infrared (MIR) spectroscopy as a low cost, non-destructive and fast alternative to obtain tracer properties to estimate sediment source contributions. For this purpose, partial last square regression (PLSR) has often been used to build predictive parametric models. However, spectra preprocessing and more robust and non-parametric models such as support vector machines (SVM) has gained little attention in these studies. Accordingly, the objectives of the current research were to evaluate (i) the accuracy of two multivariate methods (PLSR and SVM), (ii) the effect of eight spectra preprocessing techniques, and (iii) the effect of using the information contained in the UV–Vis, NIR and MIR regions considered either separately or in combination on sediment source apportionment. The estimated source contribution was then compared with contributions obtained by the conventional fingerprinting approach based on geochemical tracers. This study was carried out in the Arvorezinha catchment (1.23 km2) located in southern Brazil. Forty soil samples were collected in three main potential source (cropland surface, unpaved roads and stream channels) and twenty-nine suspended sediment samples collected at the catchment outlet during nine rainfall-runoff events were used in this study. Both PLSR and SVM models showed a higher accuracy when calibrated and validated with the spectra submitted to spectral processing when compared to the direct use of the raw spectra. The best model results were obtained with PLSR and SVM mathematical models associated with the spectral preprocessing techniques 1st derivative Savitzky-Golay (SGD1), normalization (NOR) and combining NOR + SGD1 in the UV–Vis + NIR + MIR. The lowest errors were observed when the UV–Vis + NIR + MIR bands were combined due to the gain in information and, consequently, the increase in discriminant power achieved by the models. Despite the good accuracy of the models calibrated and validated with the artificial mixtures, significant errors remain when results of source contributions are compared to those obtained with the conventional sediment fingerprinting technique based on geochemical tracers. Nevertheless, the magnitude of the contributions calculated by the spectroscopy and geochemical approaches remains very similar for all sources, especially when using the SVM-UV–Vis + NIR + MIR model. Therefore, spectroscopy proved to be a fast, cheap and accurate technique, offering an alternative to the conventional geochemical approach for discriminating sediment source contributions in agricultural catchments located in subtropical regions. [ABSTRACT FROM AUTHOR]