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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]

Purpose: Erosion and its spatial distribution in three agricultural headwater catchments were assessed in the border of the volcanic plateau in Southern Brazil. We analyzed terrain, hydrological processes, and land use influence to provide a comprehensive assessment of the catchments' sensitivity to erosion. Methods: Topographic attributes were acquired from a digital elevation map, WaterSed model was parameterized to simulate runoff, diffuse erosion, and sediment yield, and sediment source contributions were estimated using sediment fingerprinting based on near-infrared spectroscopy. Results: According to the modeled results, areas covered by crop fields, grasslands, and those adjacent to the drainage network are the most sensitive to erosion. Short distances from the source to the river network and the occurrence of high magnitude rainfall events (80 mm) promoted increases in connectivity for runoff/sediment transfer. Erosion simulations show that areas of low infiltration, such as unpaved roads, were important runoff generators during lower volume rainfall events (25 mm). Sediment fingerprinting provided satisfactory results to quantify the contributions of unpaved roads to sediment (~ 39%). Topsoil and stream channels were also significant sediment sources for the set of analyzed samples, corresponding to average contributions of 38 and 23%, respectively. Conclusion: Areas sharing geomorphological similarities did not lead to similar sediment contributions. Vegetation cover controlled erosion in topographically sensitive areas. Unpaved roads provide a significant sediment source, followed by topsoil and stream channels. The complementary results provide useful insights to better coordinate planning environmental conservation strategies in these fragile landscapes. [ABSTRACT FROM AUTHOR]

No‐till (NT) is a soil management system designed to protect soil resources from water erosion and provide numerous benefits compared to conventional tillage through the increase of organic matter inputs into the soil. However, NT in isolation is not sufficient to control erosion processes caused by an excessive production of surface runoff. This study evaluated soil losses on agricultural hillslopes under no‐till characterized by contrasted water, soil, and crop management conditions. To this end, water and soil losses were monitored between 2014 and 2018 at two scales, including four macroplots (0.6 ha; 27 events) and two paired zero‐order catchments (2.4 ha; 63 events). The resulting dataset covered a wide range of rainfall conditions that occurred in contrasted soil, crop, and runoff management conditions. Hyetographs, hydrographs, and sedigraphs were constructed, and these data were used to evaluate the impact of management on sediment yields, including that of terraces, scarification, and phytomass on sediment yield. The installation of terraces reduced sediment yield by 58.7%, mainly through surface runoff control. Crop management including an increased phytomass input efficiently controlled soil losses (63%), although it did not reduce runoff volume and peak flow. In contrast, scarification had no impact on runoff and soil losses. The current research demonstrated the need to combine the installation of terraces and leaving a high amount of phytomass on the soil to control surface runoff and erosion and reduce sediment yield. The current research therefore reinforces the relevance of the monitoring strategy conducted at the scale of macroplots and zero‐order catchments to evaluate the impact of contrasted water, soil, and crop management methods and select the most effective conservation agriculture practices. [ABSTRACT FROM AUTHOR]

Copyright of Revista Brasileira de Engenharia Agricola e Ambiental - Agriambi is the property of Revista Brasileira de Engenharia Agricola e Ambiental and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista Brasileira de Engenharia Agricola e Ambiental - Agriambi is the property of Revista Brasileira de Engenharia Agricola e Ambiental and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]