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Abstract: Land‐use/cover change (LUCC), and more specifically deforestation and multidecadal agriculture, is one of the various controlling factors of water fluxes at the hillslope or catchment scale. We investigated the impact of LUCC on water pathways and stream stormflow generation processes in a subtropical region in southern Brazil. We monitored, sampled and analysed stream water, pore water, subsurface water, and rainwater for dissolved silicon concentration (DSi) and 18O/16O (δ18O) signature to identify contributing sources to the streamflow under forest and under agriculture. Both forested and agricultural catchments were highly responsive to rainfall events in terms of discharge and shallow groundwater level. DSi versus δ18O scatter plots indicated that for both land‐use types, two run‐off components contributed to the stream discharge. The presence of a dense macropore network, combined with the presence of a compact and impeding B‐horizon, led to rapid subsurface flow in the forested catchment. In the agricultural catchment, the rapid response to rainfall was mostly due to surface run‐off. A 2‐component isotopic hydrograph separation indicated a larger contribution of rainfall water to run‐off during rainfall event in the agricultural catchments. We attributed this higher contribution to a decrease in topsoil hydraulic conductivity associated with agricultural practices. The chemical signature of the old water component in the forested catchment was very similar to that of the shallow groundwater and the pore soil water: It is therefore likely that the shallow groundwater was the main source of old water. This is not the case in the agricultural catchments where the old water component had a much higher DSi concentration than the shallow groundwater and the soil pore water. As the agricultural catchments were larger, this may to some extent simply be a scale effect. However, the higher water yields under agriculture and the high DSi concentration observed in the old water under agriculture suggest a significant contribution of deep groundwater to catchment run‐off under agriculture, suggesting that LUCC may have significant effects on weathering rates and patterns. [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]

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]