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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]

• The SVM multivariate calibration method resulted in better models than PLSR. • The best spectral pre-processing technique was the Savitzki-Golay derivative. • NIR spectroscopy can accurately predict 22 chemical elements in soil and sediment. • Chemical elements in sediments cannot be predicted using soil-calibrated models. Monitoring the transfer of chemical elements from terrestrial to aquatic environments is essential. To this end, it is necessary to develop fast and low-cost techniques to estimate the concentration of elements in soils and sediments. This study aimed to evaluate multivariate methods and pre-processing techniques to increase the accuracy of estimating the concentration of elements in soils and sediments and to estimate the concentration of elements present in the sediments using calibrated soil models using near infrared spectroscopy (NIR). A total of 316 soil samples and 196 sediment samples were collected in the Guaporé watershed in southern Brazil. These were dried, disaggregated and sieved with a 63 µm sieve size or 230 US mesh standard. Organic carbon was determined by wet oxidation and the total concentration of chemical elements (Al, Ba, Be, Ca, Co, Cr, Cu, Fe, K, La, Li, Mg, Mn, Na, Ni, P, Pb, Sr, Ti, V and Zn) by ICP-OES after microwave-assisted digestion with HCl and HNO3 (3:1). NIR spectra (1000–2500 nm) were recorded for all soil and sediment samples. Multivariate models, for instance partial least square regression (PLSR) and support vector machine (SVM), were tested combined with spectral pre-processing techniques (Detrend, DET; Savitzky-Golay Derivative, SGD; and Standard Normal Variate, SNV) and compared to the raw spectrum (RAW). The SVM model resulted in better predictions for soils, sediments and soils + sediments. In addition, the effects of preprocessing increased the accuracy of the specifications in the order: RAW < SNV < DET = SGD. The best models were obtained by combining the SVM multivariate model with SGD pre-processing. The fit of the models calibrated with soils (R2 = 0.88) and sediments (R2 = 0.89) was superior to the combination of soils and sediments (R2 = 0.85). Prediction of the concentration of elements in sediments through models calibrated with soils had low accuracy, with an average R2 of 0.11 (ranging from 0.00 to 0.46). Therefore, it is necessary to calibrate models separately to estimate the concentration of elements in soils and sediments with greater accuracy. The results showed that it is possible to build accurate spectroscopic models to predict the concentration of elements in soils and sediment samples for all determined elements. [ABSTRACT FROM AUTHOR]

Purpose: The impact of agriculture on water resources has long been a problem associated with the formation of runoff, the siltation of lakes and reservoirs, and overall depletion of water quality. In Brazil, these problems are mainly related to soil degradation by water erosion. However, studies of catchment-scale erosion are still rare particularly in grain-producing regions which have adopted conservative tillage systems for soil protection. In order to contribute to a better understanding of the impact of conservation agriculture on water resources, this study determined the runoff coefficient and sediment yield for two agricultural catchments. Materials and methods: Hydrological and sedimentological monitoring was conducted in two catchments: the Conceicao catchment is characterized by grain production in weathered soils and a gently sloping landscape, while the Guapore catchment is characterized by heterogeneous soils and topography. Both catchments have problems associated with water erosion. Results and discussion: The magnitudes of annual runoff coefficients and sediment yield were high, even if compared to similar agricultural regions, including a catchment with widespread adoption of no-tillage. The sediment yield was 140 t km year, and the runoff coefficient was 14 % for the Conceicao catchment, while the sediment yield was 270 t km year, and the runoff coefficient was 31 % for the Guapore catchment. The results indicate that problems such as gullies, soil compaction, runoff, floods, siltation, and water quality depletion associated with the misuse of agricultural areas in terms of soil conservation and water use are still evident and important even in regions with widespread adoption of no-tillage systems. Conclusions: The magnitudes of both runoff and sediment yield clearly indicate the need to adopt complementary practices of soil conservation measures, such as mechanical runoff control. [ABSTRACT FROM AUTHOR]

Purpose: Information on the effects of eucalyptus forests on hydrosedimentological processes is scarce, particularly at the catchment scale. Monitoring and mathematical modeling are efficient scientific tools used to address the lack of information for natural resource management and the representation and prediction of those processes. This study evaluates the effects of eucalyptus cultivation on hydrosedimentological processes in watersheds and to use the Limburg soil erosion model (LISEM) to represent and predict hydrological processes. Material and methods: The study was conducted in two forested watersheds: the main watershed (94.46 ha) and a nested sub-watershed (38.86 ha), both cultivated with eucalyptus and residual riparian native forest, located in southern Brazil. Hydrosedimentalogical monitoring was conducted from 16th February 2011 to 31st December 2012, and LISEM model calibrations were performed on the bases of six storms events. Results and discussion: The sediment yield for 2011 was 41.6 Mg km and 38.5 Mg km for the watershed and sub-watershed, respectively. An extreme event in 2012 provided greater sediment yield for the sub-watershed (99.8 Mg km) than that for the watershed (51.7 Mg km). Rainfall events with a greater maximum intensity generated rapid discharge and suspended sediment concentration responses in the sub-watershed due to the smaller drainage area and steeper landscape. In the main watershed, the accumulation of flood waves occurred for most events, with less steep hydrographs, and a later occurrence of the discharge peak after that of the sub-watershed. The LISEM adequately reproduced the peak discharge and runoff for the calibrated events; however, the peak time and the shape of the hydrograph were not adequately represented. Conclusions: The hydrosedimentological patterns of the watershed and sub-watershed, both cultivated with eucalyptus, was characterized by sedimentographs preceding hydrographs during rainfall-runoff events where scale effects occur, with maximum discharge and specific sediment yield greater in the watershed than that in the sub-watershed. Empirical models based on hydrologic variables may be used for estimating the suspended sediment concentration and sediment yield. Therefore, LISEM may be used for the prediction of hydrological variables in these forested watersheds. [ABSTRACT FROM AUTHOR]

• Geochemical tracers were not conservative in a catchment with highly weathered soils. • Magnetic and UV tracers had low discriminating potential between land uses. • The low discrimination between sources results in great uncertainties in the results. • Quantifying sediment contributions from more than three sources remains challenging. • Despite limited discrimination, results of all tracers combinations remained consistent. An important step in the sediment source fingerprinting approach is the selection of the appropriate tracing parameters to maximize source discrimination. The use of multiple tracing properties may reduce uncertainties and increase discrimination between sources. Accordingly, this study investigates the discrimination and quantifies the contribution of sediment sources delivering sediment to a river draining a homogeneous subtropical agricultural catchment based on the combination of ultraviolet–visible spectra derived parameters (UV), magnetic (M), and geochemical tracers (GEO). The investigated catchment (Conceição River − 804 km2), located in Southern Brazil, has predominantly deep and strongly weathered Ferralsols. The main land-uses found in the area are cropland (89%), pasture (5%) and forest (5%). A total of 187 samples were collected to characterise the five main sediment sources, including cropland, pastures, unpaved roads, gullies and stream banks. A total of 53 tracers, including 21 geochemical tracers, two magnetic properties and 30 parameters derived from UV spectra, were analysed. Tracers were selected following a three step procedure, including: (i) an interquartile range test, (ii) a Kruskal–Wallis H test, and (iii) a linear discriminant function analysis (LDA). The LDA was performed using six different sets of variables: (i) GEO only; (ii) UV only; (iii) M + UV (MUV); (iv) GEO + UV (GUV); (v) GEO + M (GM) and (vi) GEO + M + UV (GMUV). The selected tracers were introduced into a mass balance mixing model to estimate the source contributions to in-stream sediment by minimizing the sum of square residuals. Most geochemical tracers were considered not conservative by using the interquartile range test in this catchment with highly weathered soils. The GM approach resulted in the highest percentage of samples correctly classified (SCC), with 74%, followed by the approaches with GMUV and GUV, with 73%. Alternative tracers, UV individually or combined with M tracers, correctly classified only 59 and 60% of the samples, respectively. Moreover, they did not provide significant additional discrimination power even when combined with the GEO tracers. The apportionment model resulted in similar source contribution results for all approaches, with the absence of significant difference when comparing the mean source contributions obtained for the entire set of sediment samples (Cropland: 17–23%; Pastures: 24–34%; Unpaved Roads: 3–12%; Stream Banks: 26–31%; Gullies: 14–19%). Due to the strong homogeneity of soil types found in the Conceição catchment, these differences in source contributions remained very low and the results of the mixing model were impacted by the high number of potential sources and the relatively limited quality of the sediment source discrimination. According to the model results, the low discrimination between the potential sediment sources illustrates the difficulties for discriminating land-used based sediment sources, with more than three potential sources, in homogeneous catchments with highly weathered soils (e.g. Ferralsols, Nitisols) under tropical conditions. [ABSTRACT FROM AUTHOR]

An important step in the sediment source fingerprinting approach is the selection of the appropriate tracing parameters to maximise source discrimination. In order to reduce uncertainties and increase discrimination between sources it may be necessary to use multiple tracing parameters. Accordingly, this study investigates the discrimination and apportionment of sediment sources in a rural agricultural catchment obtained by combining colour, magnetic, and geochemical fingerprinting approaches. The Conceição River catchment (804 km²) has predominantly deep and highly weathered Ferralsols with land-use consisting of croplands (73%), pastures (18%), forests (8%) and other uses (1%). A total of 189 samples were taken from the main sediment sources, including: Croplands (CR, n=78), pastures (P, n=24), unpaved roads (UR, n=38), gullies (G, n=15) and stream bank (SB, n=34). Sediment samples were taken from the surface bed (n=10) of the river and with time integrated samplers (n=4). Twenty-two geochemical tracers, 6 magnetic properties and 24 colour parameters were analyzed. Tracers were selected following a three step procedure, including: (i) a conservative range test (95% confident interval), (ii) a Kruskal–Wallis H test, and (iii) discriminant function analysis (DFA). The DFA was performed using four different sets of variables: (i) geochemical variables only (G); (ii) geochemical+magnetic+colour (GMC); (iii) geochemical+colour (GC); (iv) geochemical+magnetic (GM). The selected tracers were introduced into a modified version of the classical Solver-based mixing model that in order to determine the relative contribution of different sources to in-stream sediment through simultaneously minimizing mixing model difference. The G and GC DFAs both resulted 69% of samples correct classified (SCC) as no colour parameters were selected by the DFA. The GMC and GM approaches improved the discrimination, both resulting in 76% of SCC. For the G and GC approaches, the average source contribution for the 12 sediment samples were P 47%, SB 29%, CR 19%, UR 5% and G 0%. For the GMC and GM approaches, the contribution of each source was P 41%, SB 37%, CR 14%, UR 8% and G 0%. These results are counterintuitive to field observations where cropland is anticipated to contribute more sediment than pastures. Future research should use artificial mixtures to validate these results. Both magnetic and colour parameters hold potential to improve discrimination between sources, particularly magnetic parameters in catchments with high weathered soils. [ABSTRACT FROM AUTHOR]

Information on the main sources supplying deleterious sediment loads to river systems is needed to improve our understanding of soil erosion processes to better target erosion control measures. In particular, it is important to quantify the respective contributions of surface and subsurface sources to material degrading waterbodies. Radiocesium (137Cs), emitted during thermonuclear bomb testing (~1950–1980) and nuclear accidents provides significant discrimination between surface material exposed to atmospheric fallout and subsurface material sheltered from it. A systematic worldwide review of research articles (n = 123) that used 137Cs to trace sediment sources showed that the United Kingdom (n = 24), Australia (n = 23) and the United States (n = 20) had the highest number of publications utilizing 137Cs in a sediment tracing framework. In contrast, few studies were published for catchments in Africa (n = 9) or South America (n = 2). In the northern hemisphere, positive relationships were evident between 137Cs activities in surface and subsurface sources and the proportion of thermonuclear bomb fallout. However, given the low proportions of fallout received in regions between 0 and 20°N and 0–20°S, the potential application of 137Cs tracing techniques may be limited in this area of the world as well as in agricultural regions with severe soil erosion (i.e. Chinese Loess Plateau and South Africa). In total, 94% of the studies researching surface and subsurface sources that analysed 137Cs as a potential tracing property included this parameter in end-member mixing models. The main challenges for the future of this technique are related to the access to ultra-low background gamma spectrometry facilities and the development of surrogate tracers. Future research should ensure that basic catchment information and details on the sampling design are properly documented to ensure studies are reproducible. Moreover, researchers should provide raw measurement data to help improve our global understanding of 137Cs dynamics in soil erosion research. • The meta-analysis compiled 123 articles considering 137Cs for sediment tracing. • 70% of these studies were conducted in Europe, North America and Australia. • Fallout levels are too low in regions near the Equator and with extensive erosion. • 137Cs was included in 71 to 94% of studies modelling source contributions. • Future research should systematically include details on sampling design. [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]