Soil erosion is a global concern because of its consequences for the environment and the economy of countries. In the Argentine Pampas Region, soil erosion process is a priority issue, although there is little information about sediment concentration (SC) in agricultural catchments. The study aimed at assessing the factors that have a major influence on SC and discussing the dynamics of hydrological and sedimentological connectivity during 2012, a year with precipitation over the mean and significant erosive events. The study was conducted in a watershed of Buenos Aires province, Argentina. A linear regression model, that considered autocorrelation, was obtained. Maximum rainfall intensity in 30 minutes and peak flow were related to SC. An analysis of satellite images was carried out to discuss the hydrological connectivity, and a connectivity index was calculated to assess changes in sedimentological connectivity. The analyses suggested increments in hydrological and sedimentological connectivity, associated with the drainage area expansion and with water erosion rills. Hydrological connectivity is needed for sedimentological connectivity. However, increments in sedimentological connectivity may have been conditioned by the input of energy to detach and to transport particles. This may have been evidenced when flows exceeded a threshold runoff coefficient.
Soil erosion is a serious challenge for the world, because it affects food production, the quality of the environment, human health and the economy of countries (Capra 2013). Global climate change is expected to lead to the increment of precipitation and runoff and, consequently, water erosion rates, in some regions of the world (Nearing et al. 2004). In this context, it is important to assess the dynamics and factors involved in this degradation process during years with high precipitations, to gather useful information to predict soil erosion behaviour in global changing scenarios.
Regression models may be used as a first step towards understanding factors controlling suspended sediment yield in watersheds (Haregeweyn et al. 2008; de Vente et al. 2011), and may be performed with an explanatory purpose, or to predict the sediment yield. These models are known as empirical models (de Vente & Poesen 2005), and are the alternative to more complex process-based soil erosion models, which require a large amount of data, often unavailable (de Vente et al. 2011).
During humid periods, the interactions between rainfall, runoff and antecedent moisture may generate watershed responses that evidence threshold behaviours, revealing significant changes in natural ecosystems. Threshold behaviours can be defined as rapid and significant changes in the dynamics of processes (infiltration, runoff, particle detachment and erosion), which implies that the response associated with those processes becomes faster or slower (Zehe & Sivapalan 2009). Threshold values may show sensitivity of agroecosystems during less frequent events, which provide useful information for land use and management planning (Cerdan et al. 2002).
The significant changes mentioned may be related to the development of spatial linkages, and the expression of those changes may determine the dynamics of connection and disconnection between landscape components (Cammeraat 2004). Western et al. (2001) refer to connectivity in the context of the variability in spatial distribution of features hydrologically relevant (soil moisture, hydraulic conductivity in aquifer formations), whose continuity in their spatial organization outlines flow paths. Then, by the term connectivity they denote ‘the extent to which connected features, such as arbitrary shaped bands or pathways having similar values, are present in a hydrologically relevant spatial pattern’. Knudby & Carrera (2005) also refer to the term in the framework of spatial patterns, as they explain it as ‘spatially connected features which concentrate flow and reduce travel times'. At watershed scale, Tetzlaff et al. (2007) define connectivity as the flow of matter and energy, such as water, nutrients, sediments, heat, between different landscape units. Croke et al. (2013) mention that connectivity is often employed as a powerful concept in hydrology and geomorphology to describe water and sediment movement through different landscape compartments and the watershed. Therefore, connectivity may lead to increments in runoff (Appels et al. 2011) and sediment yield when flow is effective enough to remove blockages within the watershed (Fryirs 2013; Zimmermann et al. 2014). Soil erosion may have an important role in coupling slopes to channel network, by formation of rills and gullies that act as flowpaths increasing hydrological and sedimentological connectivity (Bracken & Croke 2007; Rodríguez-Blanco et al. 2010).
Connection may change over time, defining temporal scales over which connectivity–disconnectivity prevail (Lexartza-Artza & Wainwright 2009). Regarding sedimentological connectivity, Croke et al. (2013) point out the need to study its temporal dynamics, as there is limited understanding of the changes needed to activate–deactivate connectivity. As well, Wainwright et al. (2011) mention the need to analyse this temporality to understand changes in systems response. Then, it is important to assess which circumstances define these temporal changes, in which interactions between seasonal variations in land use (Steegen et al. 2000) and natural events, like rainfall (Lexartza-Artza & Wainwright 2009), may be involved.
The objective of this work is to assess the factors that have a major influence on sediment concentration (SC) and to discuss the hydrological and sedimentological connectivity, and their temporal dynamics during a year with precipitation over the mean and significant erosive events in an arable watershed of Argentina. For this purpose a statistical regression analysis was carried out considering correlations between the events recorded in a watershed of 560 ha located in the Pampas Region. An analysis of satellite images obtained during that year was done to discuss the hydrological connectivity, and a connectivity index was calculated to assess changes in sedimentological connectivity in the studied watershed.
As stated earlier, very little is known about factors and mechanisms involved in the activation–deactivation of hydrological and sedimentological connectivity. This paper attempts to make an original contribution in this regard. Moreover, this work centres the analysis on the new approach to the study of hydro-sedimentological connectivity recently proposed by Bracken et al. (2015). This approach considers aspects related to the frequency and magnitude of the events, the mechanisms involved in sediment detachment and the temporal sequencing of the events, to reach comprehensive knowledge of connectivity–disconnectivity at small watershed scale, in this case. Such an analysis is the first for this region of Argentina and it may provide novel information for soil and water conservation.
Argentine Pampas Region is a plain of over 50 million hectares, with lands of high fertility and productivity (Hall et al. 1992). There, 90% of the country's grain production takes place (Magrin et al. 2005), and 48% of the cattle stock is raised (Canosa et al. 2013), because it is the most productive rain-fed and the strongest economic region of Argentina (Holzman et al. 2014). Soil erosion process is a priority problem in this region (SAGyP-CFA 1995). However, in this area as in the rest of Argentina, there is little information about sediment yield and transport in agricultural small catchments, even knowing that water erosion impacts severely on land quality and productivity (Lal 2001) as well as on water quality. Ares et al. (2014) have recently analysed the water erosion types in the study area of this work, and Ares et al. (2016) characterized the possible suspended sediment-discharge hysteresis patterns. This paper considers the approach of connectivity for the analysis of the erosion process using the first data registered at small watershed scale in the country. This work focuses on the study of data corresponding to a humid year to understand the hydrological and erosive response under these conditions, which can provide useful knowledge for decision-makers to design sustainable land use and management planning strategies in the area.
The water level was measured every 30 minutes using a digital water level recorder with pressure sensor located at the outlet of the watershed (Figure 1(b)). Records were turned into flow through the stage–discharge rating curve of the section obtained by stream discharge measurements conducted with current meters. Total runoff separation in direct and base flow was performed by applying a digital filter (Rodríguez et al. 2000) based on one of the methods reviewed by Chapman (1999). The filter removes the high frequency component of the hydrograph, i.e., direct runoff, and determines the low frequency component, i.e., the base flow. The obtained direct flow data were then considered for the present analysis.
An automatic water sampler, located at the outlet of the watershed, was used to collect samples during flood events (Figure 1(b)). The device has a suction pump and a sensor that triggers sampling when making contact with floodwater. Sampling started when the level of the watercourse reached 0.3 m from the bottom of the riverbed. This sampling level provided water samples from events of significant magnitude for this watershed, so the analysis included the runoff events that equalled or exceeded 0.3 m. The pump has its own sample bottle of 3.8 l, and control for setting the size of individual samples. It was set to collect a composite sample consisting of smaller samples taken every 5 minutes. This collection lasted for 1.5 hours, from the initial water level of 0.3m. Other analyses not reported in this paper showed that the highest SCs were registered between the beginning of the event and near its peak discharge. Composite sampling occurred during the rising limb of events of high magnitude, or between peak flow and the initial part of the falling limb of small magnitude events.
In the laboratory, each sample was shaken, a 250cm3 aliquot was taken and oven dried at 60 °C to constant weight, according to ASTM D3977-97 (2007), to measure SC. The determination was performed in duplicate.
The rainfall was measured by an automatic weather station located 5 km away from the outlet of the watershed (Figure 1(a)). It is the closest station to the watershed that has detailed data for the analysed period. It has a raingauge constructed according to the standards of the World Meteorological Organization, which records the rain every 10 minutes with an accuracy of 0.20 mm through a tipping-bucket recording raingauge.
The rainfall–runoff events were characterized by variables associated with precipitation, runoff, antecedent precipitation as surrogate of antecedent conditions, and soil loss ratio from the Universal Soil Loss Equation (Wischmeier & Smith 1978) to consider the effect of soil cover and management on the erosion process.
Direct runoff was characterized by the surface runoff sheet (R, mm), peak flow (Qp, m3 s−1), mean surface flow (Qms, m3 s−1), runoff coefficient (RC, %), calculated by the ratio of surface runoff sheet and total precipitation event. The accumulated precipitation of 5 days previous to the analysed events (P5d, mm) was calculated to evaluate the antecedent condition.
The soil loss ratio was defined by Wischmeier & Smith (1978) as ‘the ratio of soil loss from an area with specified cover and management to that from an identical area in tilled continuous fallow’. Records of land use and rotations of the plots in the watershed were obtained for each rainfall–runoff event studied. Sowing dates, crop stages and fallow periods were considered to calculate the soil loss ratios using the information in tables published in Handbook 537 (Wischmeier & Smith 1978). According to each parcel area and its soil loss ratio obtained, a weighted soil loss ratio was calculated for each event for the whole watershed.
The concentration of suspended solids was calculated for the composite sampling (SC, g L−1).
Data were obtained for the 2011–2013 period. Data corresponding to the dryer years (2011 and 2013) were used to make comparisons with those corresponding to 2012, the year with precipitations over the mean in the study area, to identify the differences between the variables monitored during those contrasting years. Data of the year 2012 were used for the statistical and the connectivity analysis.
A multiple regression analysis was developed with an explanatory purpose, to identify the variables related to SC, with data corresponding particularly to 2012.
Pearson's correlations were analysed to identify the significant relationships between the variables. To handle multicollinearity, four independent variables were selected: P, I30, P5d and Qp, that represent the rainfall and runoff erosive forces and the antecedent conditions during the events.
Analysis of connectivity
Hydrological connectivity: drainage network dynamics
The dynamics of the drainage network was analysed to assess hydrological connectivity during 2012. According to the definitions of Western et al. (2001) and James & Roulet (2007), who state that connectivity refers to hydrologically significant spatial arrangements of properties or state variables that facilitate flow and transport in a watershed, variations in the area of the drainage network may be useful to evaluate changes in connectivity at a watershed scale.
Changes in the drainage area are associated with variations in moisture of riparian zones and of temporal flow paths which may saturate from below, by the rise of the water table (Dunne 1978) or by the infiltration of rainwater in the soil.
Spot's 4 and 5 satellite images were used for this purpose, with spatial resolution of 10 m that is appropriate for studies at semi-detailed scales. According to the revisit period and the availability of cloud-free scenes, three images corresponding to 2012 were selected. The studied dates were 01/02/2012, 13/09/2012 and 21/12/2012, which coincided with different wetness conditions in association with the rainfall events registered during the previous days to the scenes' capture. Then, the image of February was representative of dry conditions, and the images of September and December were representative of humid conditions.
Bands 3 and 4 corresponding to near-infrared (NIR) and short-wave infrared (SWIR), respectively, were used for the spatial delimitation of the drainage network. The scenes of high surface wetness condition (September and December 2012) were considered to define the maximum surface of drainage network during the study period. The use of NIR and SWIR bands is based on the fact that they are located in the region of strong absorption by free water and vegetation liquid water (Gao 1996). The combination of both bands are useful to detect not only free water surfaces but also vegetation water content, which is highly correlated with surface and sub-surface soil moisture availability (Fensholt & Sandholt 2003; Khanna et al. 2007; Holzman et al. 2014).
Regions of interest were delimited by digitizing on the three analysed images taking into account the spectral response of wet areas. Then, the regions of interest were integrated to define the drainage network. In addition, high pass filtering was used to extract the minimum area of drainage network in the image of February, when dry conditions prevailed. This filter removes the low frequency components of an image, and preserves the high frequency (local variations). It accentuates edges between different areas (Jensen 1996), so it was useful to demarcate the boundaries of the drainage network in this case. Finally, the delimited areas corresponding to the drainage network of the three scenes analysed were calculated to assess the dynamics in the hydrological connectivity during 2012.
The index was calculated to compare differences in sedimentological connectivity during three dates of 2012, using data corresponding to the drainage area, and, in this case, the concentration of suspended solids, which was the variable registered at the outlet of the catchment. The index was expressed in units of kg m−1. The dates of the events selected for the analysis were those chronologically near to the day of estimate of the drainage network area. Then, the variables required to calculate the index corresponded to different dates. To establish whether conditions were comparable between the dates, the depth to groundwater of the dates associated for the index calculation was examined. It was considered that differences in this variable may guide about relevant changes in moisture of riparian zones and temporal flow paths. According to Dunne (1978), the moisture dynamics of these zones is related to variations of groundwater depth. There, the water table is located at a relatively shallow depth, and saturation may occur by the rise of the water table to the surface when rainfall occurs over the watershed. Then, percentage differences of depth to groundwater between the values corresponding to the date of estimate of the drainage area and the date of the selected event to measure connectivity were calculated.
Table 1 shows the percentage differences of depth to groundwater between the dates of analysis of the drainage network area and the erosion events selected to calculate the connectivity index. Depth to groundwater percentage differences were between 9.8 and 2.4%. As well, percentage differences of depth to groundwater corresponding to the dates of analysis of the drainage network were calculated, which were between 68 and 139%. According to the results, it was considered that conditions between the selected dates to obtain the index value were comparable. Therefore, the connectivity index was calculated for the dates: 05/03/2012, 03/09/2012 and 19/12/2012.
|Date||Percentage difference of depth to groundwater (%)|
|Date||Percentage difference of depth to groundwater (%)|
RESULTS AND DISCUSSION
During the humid year of 2012, 13 flood events were registered, whereas only eight events during the dry years of 2011 and 2013 were registered. The inter-annual variability of rainfall was high: 807 mm precipitated during 2011, 1,351 mm during 2012 and 668 mm during 2013. Mean annual precipitation corresponding to the 1993–2013 period of Monasterio Trapense pluviometer station is 905.2 mm. This station is 20 km away from the Cerro del Águila station (Figure 1(a)), and is the nearest with complete and reliable rainfall records for the area (Varni & Custodio 2013).
Table 2 summarizes the main characteristics of the events studied. The mean values of the variables related to rainfall events that promoted runoff (P, EI30, E) had higher means in 2011 and 2013 than during 2012. However, P5d, R and associated variables (Qp, Qms and RC) and SC showed higher mean values in 2012 than during 2011 and 2013. This analysis suggests that during the dryer years, rainfalls of higher magnitude and erosivity were needed to generate hydrological and sedimentological response in the watershed.
|No. of events||Parameter||P||PDur||EI30||E||I30||P5d||R||Qp||Qms||RC||SLR||SC|
|No. of events||Parameter||P||PDur||EI30||E||I30||P5d||R||Qp||Qms||RC||SLR||SC|
P, precipitation that promoted runoff (mm); PDur, precipitation duration (h); EI30, erosivity index (MJ mm (ha h)−1); E, total rainfall kinetic energy (MJ ha−1); I30, maximum intensity in 30 minutes (mm h−1); P5d, accumulated precipitation of 5 days previous to the events (mm); R, surface runoff sheet (mm); Qp, peak flow (m3 s−1); Qms, mean surface flow (m3 s−1); RC, runoff coefficient (%); SLR, soil loss ratio; SC, composite sampling concentration of suspended solids (g L−1).
Therefore, the wet area expansion, in addition to the presence of channelized pathways corresponding to rills linked to the water course, may have facilitated connectivity in runoff. Zimmermann et al. (2014) report that connectivity development by expansion of the drainage network increased runoff coefficients in a forested catchment of Panama. Borselli et al. (2008) point out that few events are responsible for the generation of flowpaths that increase the connectivity between hillslopes and channels, which may have been evidenced by the RC and Qp observed from May 2012. As well, this connectivity may have also contributed to correlations between observations.
Other key aspects when discussing connectivity are the moment and frequency of disturbances (Wainwright et al. 2011). It is important to analyse the moment of occurrence of the events with regard to the temporal variation of land use, and thus, the presence of different vegetation cover that provides surface roughness and decreases overland flow velocity. Bracken & Croke (2007) and Lesschen et al. (2009) point out the role of vegetation cover in catchment hydrological connectivity, and Lexartza-Artza & Wainwright (2009) mention that temporal changes in land use have to be considered for a better understanding of connectivity in watersheds. In this case, at the beginning of the rainy period from event 5 in May and until November, the watershed was predominantly under fallow. Approximately 50% of the area was covered with soybean stubble, which provides limited surface roughness (Alberts & Neibling 1994). Corn, wheat or barley residues occupied the rest of the watershed area. Less than 20% of the area was sown with barley in early July. Summer crops were sown between October and November, so, at the moment of the rainfall events, they were still in the early stages of growth. Then, over the period when relevant erosive events occurred more frequently, as stated above, vegetation may have had little effect as a barrier to prevent connectivity. Observations in the field showed that on plots under fallow or recently sown, rills remained or were formed during these events. Moreover, the lack of conservation practices, such as terraces and diversions, may have favoured the connection of the drainage network. These analyses may suggest the persistence of hydrological connectivity during the period considered.
Regarding sedimentological connectivity, the values of the connectivity index were 7.9, 16.4 and 175.9 for the events registered on 05/03/2012, 03/09/2012 and 19/12/2012, respectively. The values show the increments in sedimentological connectivity of three contrasting situations. The first value corresponds to an event previous to May 2012, when sheet erosion prevailed. The second value of the index is also associated with a case of sheet erosion, but following the event of 23/08/2012, which was classified as a rill erosion event. The third index reported corresponds to the event of highest SC registered during 2012, which belonged to the rill erosion events group.
Moreover, Jain & Tandon (2010) and Bracken et al. (2015) point out that high-magnitude events increase sedimentological connectivity, as a result of the input of energy that increases sediment detachment and transport. Then, connectivity may be related to these episodic events that pulse sediment to the system. Considering Figure 7, the events with RC greater than 20% were the five previously classified as rill erosion events. Concentrated flow is characterized by high energy to detach and transport particles (Bracken & Croke 2007). Therefore, during those events, flow delivered by the channel network previously analysed and by rills formed during event 5 and eroded during subsequent events, may have been responsible for the high SCs registered.
Some final aspects regarding connectivity should be pointed out. The increment in hydrological connectivity from May 2012 may be related to rill formation during different rainfall events in addition to the presence of an active drainage network. The persistence of these active flowpaths may have caused the higher RC and Qp values than those registered between January and April 2012, even during sheet erosion events. In contrast, the increment in sedimentological connectivity may be associated with the occurrence of events of energy enough to detach and transport soil particles. In this case, rill erosion events were responsible for the input of that energy, evidenced by RC over 20%. The exceedance of this threshold value determined the increment of sedimentological connectivity, a significant aspect that may distinguish this type of connectivity from hydrological connectivity between May and December 2012.
This analysis has been carried out at small watershed scale, and the findings of this work may be generalized for other catchments under conditions comparable with those of this study area: size, slopes, soil types, land use, rainfall–runoff characteristics. An analysis for a larger region may be carried out considering the approach proposed by El Haj Tahir et al. (2010), for example, who upscaled soil erosion areas using geographical information systems and remote sensing data with different spatial resolution to study the consequences of an exceptionally wet year. However, a new interpretation of the processes involved in hydrological and sedimentological (dis)connectivity would be necessary, based on field observations or models' results at large watershed scale. This is associated with the scale dependence of hydro sedimentological processes noted by several authors (de Vente & Poesen 2005; Lesschen et al. 2009; García-Ruiz et al. 2010; Yang et al. 2012). Runoff decreases as plot or watershed area increases, in relation to the possibility of reinfiltration and detention in reservoirs and depressions (Esteves & Lapetite 2003; Cerdan et al. 2004; Feng & Li 2008). De Vente & Poesen (2005) discuss the reduction in sediment yield with the increment in basin area. The authors point out different erosion processes that become active at different spatial scales, which affect sediment yield. Thus, high variability in local conditions causes the variability in soil loss measurements at plot scale, where the splash erosion process may be analysed (García-Ruiz et al. 2010). At small watersheds (0.03–10km2), de Vente & Poesen (2005) mention that rill and gully erosion play an important role in suspended sediment yield and connectivity at the basin outlet. Whereas, in large catchments (>10km2) sediment deposition becomes dominant over its transport, which reduces sediment yield at the outlets (Medeiros et al. 2014). In addition, it is important to highlight that spatial patterns of vegetation and land use, soil types, microtopography and topography and rainfall erosivity may affect runoff and sediment yield at the scales specified (Lane et al. 1997; de Vente et al. 2007; Huang et al. 2013), and, thus, are key factors to take into account in upscaling. Therefore, these processes and factors mentioned interact at different spatial scales, and affect water and sediment movement between landscape compartments, that is, the (dis)connectivity. The approach recently proposed by Bracken et al. (2015) may be useful in this interpretation for larger regions, as these authors discuss that spatial scale is intimately related to this framework proposed for the analysis of connectivity.
According to the previous discussion, the conclusions of this work are valid at small watershed scale. It is considered that this is an important scale of study, because it is appropriate to implement agricultural management practices for soil and water conservation (Collins & Owens 2006) and to test their efficiency for the control of runoff excess and water erosion, two degradation processes that affect lands worldwide.
SUMMARY AND CONCLUSIONS
The events recorded during 2012, a year with precipitations over the mean, were studied in a watershed where rainfall, runoff and SC were monitored between 2011 and 2013, in the Argentine Pampas Region.
To identify variables that were related to SC, a regression analysis that considered the correlations between the observations was performed. Maximum rainfall intensity in 30 minutes and peak flow explained SC variation for the year studied.
Some differences between hydrological and sedimentological connectivity during the studied year were considered. Hydrological connectivity was more active from the rill erosion event registered in May 2012, in association with the increment of the drainage network area and the presence of rills. This was favoured by little vegetation cover, consisting predominantly of soybean residues or crops in the early stages of growth, which may have reduced its effect as a barrier to prevent connectivity. On the other hand, although hydrological connectivity is essential for sedimentological connectivity, the latter may have been dependent on the occurrence of flows over a threshold, with energy enough to detach and transport particles from hillslopes to channels. The exceedance of that threshold value led to increased sedimentological connectivity, an important aspect that may differentiate sedimentological from hydrological connectivity during the studied period.
These are the first results analysed considering the connectivity approach for a small watershed in Argentina. It is necessary to continue monitoring the variables in the study area, to get data under humid conditions comparable to 2012, to validate the model obtained, and to corroborate the manifestation of threshold behaviours. However, the results obtained are indicative of the dynamics and conditions that may activate connectivity in agricultural watersheds. In addition, this information can be useful to evaluate soil erosion in global changing scenarios.
This work was partially supported by the Universidad de Buenos Aires under Grant UBACYT 709 and 937, and by the Consejo Nacional de Investigaciones Científicas y Técnicas. We thank Daniel Arias, Fátima Altolaguirre and Natalia De Líbano for collaborating with the processing of water samples. We also thank Enrique Queupán, Joaquín Rodríguez Silveira and Matías Silicani for collaborating with field tasks.