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. MethodsTopographic attributes were acquired from a digital elevation map, WaterSed model was parametrized to simulate runoff, diffuse erosion and sediment yield, and sediment source contributions were estimated using sediment fingerprinting based on near-infrared spectroscopy. Results 2According 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, 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. ConclusionAreas 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.
<p>Erosion processes are accelerated by the presence of unpaved roads in catchments with shallow soils and steep slopes, favoring overland flow and sediment connectivity between hillslopes and the river network. Soil erosion modeling studies conducted at the catchment scale focus primarily on the hydrological behavior of cultivated hillslopes. Few studies address unpaved roads and suitable practices to limit their impact on hydro-sedimentary transfers in a catchment system. This study simulates soil conservation measures on unpaved roads and hillslopes and their effect on the hydrological and erosive dynamics in a small order catchment. The rainfall events were monitored at Lajeado Ferreira&#8217;s creek, in Arvorezinha, Southern Brazil (1.2 km&#178;). The catchment is characterized by shallow soils, steep slopes, intense agricultural activity and sediment yields (SY) of around 150 t km<sup>-2 </sup>y<sup>-1</sup>. Unpaved roads cover about 3% of the catchment surface area and supply 36% of the annual average SY. The Limburg Soil Erosion Model (LISEM) was used to simulate the roads&#8217; impact on soil erosion. Eight rainfall events, monitored from 2014 to 2017, were calibrated. Rainfall ranged from 9 to 97 mm, total runoff volume (Q) varied from 1462 to 60765 m&#179; and SY from 0.6 to 81 tons. These events represent different precipitation and land use conditions, so that the road&#8217;s effects on the hydrological dynamics of this catchment may be investigated. Then, modeling scenarios consisting of three levels of intervention to mitigate sediment supply were tested. &#160;The low intervention (LI) level was based on cost-efficient practices, applied to the road only through the installation of rockfill and energy-deflecting small reservoirs. The medium intervention (MI) included the sowing of grass for gutter protection on the road area and filter strips were installed near the drainage channels. For the high intervention scenario (HI), additional grass strips were installed on hillslopes. Their impact was evaluated by comparing the hydrosedimentological variables Q (m&#179;), peak flow (L s<sup>-1</sup>) and SY (tons), modelled at the catchment&#8217;s outlet. Rockfill and energy-deflecting small reservoirs were not enough for reducing peak flow (Qp), on average. Indeed, the main proportion of overland flow originates from other landscape components, such as hillslopes. Under the MI and HI scenarios, Qp decreased by 2 and 46%, respectively. The LI and MI scenarios led to an average Q reduction of 12%, compared to 53% under scenario HI. For one event, HI promoted a reduction of 92% of calculated Q, representing 15,693 m&#179;. HI also showed the most positive effects on limiting SY. It becomes evident that hillslope interventions are necessary, as they allow increasing infiltration, reducing both runoff volume and stream power when the flow reaches the roads. For rainfall events of higher magnitude, it was observed that HI was responsible for reducing Qp between 9 and 25%, while during smaller events, this reduction reached 61 to 93%. This indicates the importance of managing roads in order to reduce runoff energy and concentration, but also to take measures on hillslopes to limit overland flow and erosion inputs, as well as to delay peak Q.</p>
<p>Although sediment yield reflects a catchment&#8217;s erosive processes, material transfer from hillslopes to rivers depends on a series of phenomena occurring on variable and continuous range of scales. Physically based, distributed models can be used to evaluate erosion&#8217;s spatial variability within a catchment and to identify hotspots. Sediment fingerprinting allows source type discrimination based on sediment and soil properties. The analysis of these dynamic systems could be coupled by addressing hillslope processes with modeling, while fingerprinting enlightens the connection between them and the drainage network. We aimed to evaluate the erosive susceptibility and its spatial distribution in three environmentally fragile paired headwater catchments, nested within Guarda Mor catchment, located in the border of the volcanic plateau in southern Brazil. This catchment is characterized by intense agricultural use, diverse geology, and complex terrain. WATERSED model was used as a dynamic method to evaluate the spatial distribution of hydrologic and erosive fragility during rainfall events. WATERSED was parameterized for modeling surface runoff volume, sediment yield and interrill erosion, based on monitored data from a zero-order no-till catchment and literature data. Modeling results were analyzed for each land use. For fingerprinting, two sediment sampling strategies and source groupings were considered. One considered spatial sources, and the endmembers were the sub catchments, the other considered land use source types within each sub catchment. Deposited bed sediment samples were collected at the outlets of each sub catchment and the main outlet. Soil source samples were collected in crop fields, grasslands, stream channels, forests, and unpaved roads. Crop fields and grasslands compose the source type topsoil. Samples were analyzed by near-infrared spectroscopy. Artificial mixtures were made to calibrate the prediction models. Fifteen Support Vector Machine (SVM) models were built and independently trained. Modeled erosion indicates that the steepest areas and those near the drainage network can be the most susceptible to erosion and runoff. The spatial distribution of runoff-prone areas shows the connectivity from upper segments of these catchments increases with higher magnitude events. In fingerprinting, calibration results&#8217; predictors show good performance by the models, validation results vary from poor to good. SVM models for unpaved roads and forest had the best validation performance. For sourcing tributaries, results and poor validation statistical results indicate the need to use different tracers, and to consider unsampled sources associated to soil and geological differences found downstream from the sub catchment&#8217;s outlets. As for the sub catchments, there is a variation among the main sediment sources and a significantly constant contribution from unpaved roads in all of them. Other important sources are topsoil and stream channels, while forests did not show significant contribution. These methodologies were useful in seeking a more holistic process understanding, as physical processes were addressed and later integrated with the resulting sediment yield. Despite the results are modelled, the complementation of their insights indicates that there is a possibility for validating the sediment fingerprinting technique once modelling is validated by monitored and measured data.</p>
<p>No-tillage is an extensively used soil conservation practice in crop fields. Yet, no-tillage is prone to runoff generation, which may lead to downstream concentrated forms of erosion, floods, solute transfer and eutrophication of water bodies. However, infiltration terraces on hillslopes can reduce runoff and erosion. We analyzed nutrient losses, in both dissolved and particulate forms, on terraced and non-terraced agricultural hillslopes under no-tillage in Southern Brazil. Precipitation, runoff, sediment yield and chemical elements&#8217; concentrations were monitored in paired catchments, including a 2.35 ha terraced catchment (TC) and a 2.43 non-terraced catchment (NTC), during rainfall events that occurred from 2017 to 2018. Runoff and suspended sediment samples were manually collected in H-flumes at the outlet of each hillslope, where automatic water level readings were recorded at 5-minute intervals by a limnigraph to estimate runoff discharge. P, K, Ca, Mg, Cu, Zn and N concentrations were analyzed in runoff-water samples and P, K, Ca e Mg in the suspended sediment samples to obtain dissolved and particulate concentrations, respectively, and total nutrient losses. Maximum N concentration in TC&#8217;s runoff samples (8.70 mg L<sup>-1</sup>) were higher than in the NTC (7.41 mg L<sup>-1</sup>). Ca concentrations were higher in the NTC (average 3.9 mg L<sup>-1</sup>). Low and similar Mg, Cu, Zn mean concentrations were observed in the catchments. Mean P concentrations were ~0.11 mg L<sup>-1</sup> in both catchments but reached higher concentrations in the NTC. Mean (~3 mg L<sup>-1</sup>) and maximum (8.74 mg L<sup>-1</sup>) K concentrations were observed the TC. In sediment samples, Ca, Mg, P and K concentrations were higher in the NTC. To compare total dissolved nutrients losses, we chose 13 rainfall-runoff events and 10 events for particulate nutrient losses. Total rainfall for the 13 events was 1020 mm, leading to 110 and 222 mm of runoff in TC and NTC, respectively. Besides higher runoff volume, NTC shows higher losses of all analyzed nutrients in runoff. P losses were of 105 and 352 g ha<sup>-1</sup> in TC and NTC, respectively, while K losses were of 2293 and 4604 g ha<sup>-1</sup>, showing a similar trend. The average increase in Cu losses for NTC was 21 times higher than for TC. Total sediment yield in TC, for the 10 events, was 12 kg ha<sup>-1</sup>, and 39 kg ha<sup>-1</sup> in the NTC. Higher particulate nutrient loss was observed in the NTC outflow. An almost nine-fold increase in particulate P losses was observed in NTC, besides a four-fold increase in Ca, a seven-fold increase in Mg and two-fold K losses. Although higher nutrient concentrations in water were observed in the TC for some samples, overall losses and concentrations were greater in the NTC. This indicates that nutrient flux from agricultural hillslopes is controlled by runoff and that terraces can decrease flow and material connectivity over hillslopes. As soil and water conservation practices are needed to ensure agriculture&#8217;s sustainability and to avoid deleterious environmental impacts, measures for runoff mitigation, such as terraces, were shown to effectively control nutrient &#8211; and, potentially, other solutes &#8211; transfer to water bodies.</p>
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