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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]

Abstract In areas with extensive soil erosion, there is a need to identify the sources supplying sediment to rivers in order to improve our understanding of erosion processes and to guide the implementation of effective soil management measures. Geochemical elements are often used as tracers in sediment fingerprinting studies. Among them, P has often been used as it may specifically trace sediment originating from cropland. Although P-fractions can provide more detailed information than the total P content, their potential to distinguish between different potential sources of sediments has rarely been tested. The objective of the current research was to evaluate the contribution of sediment sources in a rural catchment (Arvorezinha, 1.23 km2) of Southern Brazil, using different sets of tracers (i.e. P-fractions only, geochemical tracers only, and a combination of both tracers). Three potential sediment source types were identified in the catchment: (i) cropland, (ii) unpaved roads and (iii) stream channel banks. Thirty-one suspended sediment samples were collected in the river draining this catchment during 9 significant floods between 2009 and 2011. Estimates of P-fractions were obtained by Hedley fractionation using anion exchange resin membrane (P resin); 0.5 mol L−1 NaHCO 3 (pH 8.5) (P bic); 0.1 mol L−1 NaOH (P NaOH0.1); 1.0 mol L−1 HCl (P HCl); 0.5 mol L−1 NaOH (P NaOH0.5); and residual P (P residual). Total organic (P org) and inorganic P (P inorg) were obtained by the ignition method. The concentrations of P HCl , P NaOH0.5 and P inorg in suspended sediments were higher than those found in the sources. Accordingly, these fractions were considered not to be conservative and were excluded from further analysis. With the exception of P residual , all P-fractions passed the Kruskal-Wallis H -test and were considered as potential tracers of sediment sources. When the P-fractions were combined with geochemical tracers, the discrimination power was much higher than when using geochemical tracers only. The Mahalanobis distance was 9% higher than that observed in the geochemical approach. The calculated contribution of sediment sources using the three approaches was very similar, and cropland was the dominant source of sediment, with calculated contributions of 56 ± 19, 46 ± 16 and 48 ± 16% for those approaches based on geochemical elements, P-fractions, and geochemical + P-fractions, respectively. These results demonstrate the effectiveness of some P-fractions to trace potential sediment sources in rural catchments, as they have a high discriminant power while remaining conservative during sediment transport in the catchment. Graphical abstract Unlabelled Image Highlights • Combining P-fractions with geochemical tracers increased the discrimination power. • P-fractions alone provided results similar to those obtained with geochemical tracers. • Three P-fractions in suspended sediments were considered non-conservative. • All P-fractions were considered as potential tracers of sediment, except residual P. [ABSTRACT FROM AUTHOR]

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One of the challenges of modern agriculture is to adapting to climatic effects, including the capacity of rainy events in causing erosion. The understanding of these phenomena relies on monitoring rain variables that express the magnitude and pattern of erosive agents. Kinetic energy (E) is a fundamental variable to represent the erosivity and to enable the estimation of erosion and sediment yield in mathematical models. In Brazil, there are no direct and continuous measurements of E, and empirical equations to estimate rainfall intensity (I) are used instead. As a result, empirical equations to estimate E from I are used. To assess local behavior between these variables, rain variables (energy, volume and intensity) and associated processes (water flow (Q) and suspended sediment concentration (SSC)) were measured in a small rural catchment. The work was developed in a small rural catchment (1.23 km 2 ) located in southern Brazil where intensive land use with tobacco cultivation has caused high rates of erosion and sediment yield. This study proposes an alternative equation E = f(I) for the study region and provides a comparison with previous equations already proposed by Foster et al. (1981); van Dijk et al. (2002); Wilkinson (1975); Brown and Foster (1987) and USDA-ARS (2013). Furthermore, this work explores the seasonal variations and the differences in the magnitude of the events of these relationships. The measured E values were similar to those estimated by the equations of Foster et al. (1981); USDA-ARS (2013) and van Dijk et al. (2002), whereas Brown and Foster (1987) underestimate the estimation of E. Finally, it was evaluated the predictive ability of the total kinetic energy, 30 min maximum intensity (I 30 ) and total volume of the rainfall (P total ) in explaining the response variables which reflect the hydrology and sediment yield processes considering the Q and SSC. The main results of the work were: i) a new equation to estimate E based on the I measured, its seasonal variation and similarity with the previously known equations and ii) the relationship between E and the hydrological variables at catchment scale. [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: Hydrosedimentological modeling is a tool that can be used to understand better important processes occurring at the catchment scale, such as runoff and sediment yield. The aim of this study was to use the Limburg Soil Erosion Model (LISEM) to describe the runoff and sediment yield during rainfall-runoff events in a small rural catchment in southern Brazil. Materials and methods: The study was conducted in the Lajeado Ferreira Creek catchment (drainage area of 1.19 km) where intense land use has caused a negative impact on water resources. Thirteen rainfall-runoff events that occurred in 2010 and 2011, including high-magnitude events, were used to model hydrosedimentological processes. Results and discussion: Results: from the calibration and validation stages indicate that the model had a good performance when representing the hydrograph, including events with greater complexity. The use of a second soil layer in the model increased its efficiency, which is in accordance with the importance of subsurface flow in this catchment and its sensitivity to the physical properties of the soil, which are essential for controlling hydrosedimentological processes at the catchment scale. The simulation of sediment yield was overestimated by the model, constrained by the lack of sensitivity of the model to soil cohesion and the stability of soil aggregates. During the model calibration stage, these parameters had values different from those measured in the field. Conclusions: The LISEM model performed well in representing runoff for events of different magnitudes. The discretization of the physical-hydrologic properties in the soil profile enabled the evaluation of the effect of subsurface impediment layers on water infiltration and runoff. The simulation was less accurate for suspended sediment concentration than for runoff. This indicates the need for further studies to either identify other factors controlling erosion and sediment yield that have not been identified by the model, or identify if the representation of the physical parameters is inadequate, especially the values of soil cohesion and aggregate stability. [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]

Mid-infrared diffuse reflectance spectroscopy with machine learning methods has been studied worldwide for estimating nutrient content in plant tissue of numerous plant species. Therefore, this study aimed to evaluate the best combination of spectral preprocessing and machine learning methods for estimating the concentration of chemical elements in leaf tissues of Ilex paraguariensis. From 2019 to 2021, 111 samples were collected from farms throughout Rio Grande do Sul State in southern Brazil. The total concentration of 11 nutrients (N, P, K, Ca, Mg, B, S, Mn, Fe, Cu, and Zn) and the spectra recorded in the mid-infrared range (2500– 18,000 nm) were obtained. The original spectrum underwent detrending, standard normal variate, and Savitzky-Golay derivative (SGD) spectral preprocessing. Multivariate partial least squares regression and support vector machine models were used to calibrate the spectra based on the nutrient concentrations obtained from the conventional analytical method. The models' performance was evaluated using the coefficient of determination, the ratio of performance to the interquartile range, and the root mean square error of prediction. The support vector machine model combined with SGD preprocessing resulted in a more accurate prediction of the 11 elements studied in leaf tissue samples. • The SVM multivariate calibration method resulted in better models than PLSR. • The best spectral pre-processing technique was the Savitzki-Golay derivative. • MIR spectroscopy predicted 11 chemical elements in Ilex paraguariensis leaf tissues. [ABSTRACT FROM AUTHOR]

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]

The hydrological dynamics of a small rural river catchment in southern Brazil have been used to understand the impact of agricultural activity on soil and water resource degradation. The monitoring and modeling strategies employed to better understand the integrated phenomena focus on surface flow dynamics and the associated processes, such as erosion, sediment yield (SY), and chemical element transfer. In this context, monitoring and modeling techniques are combined in order to include the main hydrological processes involved in soil and water resource degradation. The project began in 2002 and over 500 rainfall-runoff-sediment events and suspended sediment concentration make up the hydrological database under different land use and soil management conditions. The 1.2 km2 catchment of the study is characterized by the presence of shallow soils, high slope, intense agricultural activity, and high SY (~150 t.km-2.y-1). Techniques that enable the identification of the origin and redistribution of sediments (e.g fingerprinting approach and Cs137) and hydrograph analysis are being used to further understand the dynamics of SY in the catchment. The comprehension of the factors involved in the SY dynamics has allowed the improvement and application of different mathematical models (e.g., LISEM and WATERSHED) to simulate surface flow and SY. These models are used to represent the influence of soil and water conservation practices while considering the interaction between the different landscape components (crops, roads, drainage network, etc.). From this set of data and tools, the capacity of the agricultural production system, drainage network, wetlands and riparian forests in accelerating or retarding water flow, sediment, and chemical elements in the landscape are evaluated. All of these studies have been used to improve soil and water conservation practices at catchment scale, meeting the expectations of farmers in erosion control and water storage as well as of the society interested in maintaining the hydrological functions of soils in catchment scale. [ABSTRACT FROM AUTHOR]