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The flowpaths by which water moves from watersheds to streams has important consequences for the runoff dynamics and biogeochemistry of surface waters in the Amazon Basin. The clearing of Amazon forest to cattle pasture has the potential to change runoff sources to streams by shifting runoff to more surficial flow pathways. We applied end-member mixing analysis (EMMA) to 10 small watersheds throughout the Amazon in which solute composition of streamwater and groundwater, overland flow, soil solution, throughfall and rainwater were measured, largely as part of the Large-Scale Biosphere-Atmosphere Experiment in Amazonia. We found a range in the extent to which streamwater samples fell within the mixing space determined by potential flowpath end-members, suggesting that some water sources to streams were not sampled. The contribution of overland flow as a source of stream flow was greater in pasture watersheds than in forest watersheds of comparable size. Increases in overland flow contribution to pasture streams ranged in some cases from 0% in forest to 27-28% in pasture and were broadly consistent with results from hydrometric sampling of Amazon forest and pasture watersheds that indicate 17- to 18-fold increase in the overland flow contribution to stream flow in pastures. In forest, overland flow was an important contribution to stream flow (45-57%) in ephemeral streams where flows were dominated by stormflow. Overland flow contribution to stream flow decreased in importance with increasing watershed area, from 21 to 57% in forest and 60-89% in pasture watersheds of less than 10 ha to 0% in forest and 27-28% in pastures in watersheds greater than 100 ha. Soil solution contributions to stream flow were similar across watershed area and groundwater inputs generally increased in proportion to decreases in overland flow. Application of EMMA across multiple watersheds indicated patterns across gradients of stream size and land cover that were consistent with patterns determined by detailed hydrometric sampling.
Overland flow is an important hydrological pathway in many forests of the humid tropics. Its generation is subject to topographic controls at differing spatial scales. Our objective was to identify such controls on the occurrence of overland flow in a lowland tropical rainforest. To this end, we installed 95 overland flow detectors (OFDs) in four nested subcatchments of the Lutzito catchment on Barro Colorado Island, Panama, and monitored the frequency of overland flow occurrence during 18 rainfall events at each OFD location temporal frequency. For each such location, we derived three non-digital terrain attributes and 17 digital ones, of which 15 were based on Digital Elevation Models (DEMs) of three different resolutions. These attributes then served as input into a Random Forest ensemble tree model to elucidate the importance and partial and joint dependencies of topographic controls for overland flow occurrence.
Lutzito features a high median temporal frequency in overland flow occurrence of 0.421 among OFD locations. However, spatial temporal frequencies of overland flow occurrence vary strongly among these locations and the subcatchments of Lutzito catchment. This variability is best explained by (1) microtopography, (2) coarse terrain sloping and (3) various measures of distance-to-channel, with the contribution of all other terrain attributes being small. Microtopographic features such as concentrated flowlines and wash areas produce highest temporal frequencies, whereas the occurrence of overland flow drops sharply for flow distances and terrain sloping beyond certain threshold values.
Our study contributes to understanding both the spatial controls on overland flow generation and the limitations of terrain attributes for the spatially explicit prediction of overland flow frequencies.
Clearing for large-scale soy production and the displacement of cattle-breeding by soybeans are major features of land-use change in the lowland Amazon that can alter hydrologic properties of soils and the runoff generation over large areas. We measured infiltrability and saturated hydraulic conductivity (Ksat) under natural forest, pasture, and soybeans on Oxisols in a region of rapid soybean expansion in Mato Grosso, Brazil. The forest-pasture conversion reduced infiltrability from 1258 to 100 mm/h and Ksat at all depths. The pasture-soy conversion increased infiltrability from 100 to 469 mm/h (attributed to shallow disking), did not affect Ksat at 12.5 cm, but decreased Ksat at 30 cm from 122 to 80 mm/h, suggesting that soybean cultivation enhances subsoil compaction. Permeability decreased markedly with depth under forest, did not change under pasture, and averaged out at one fourth the forest value under soybeans with a similar pattern of anisotropy. Comparisons of permeability with rainfall intensities indicated that land-use change did not alter the predominantly vertical water movement within the soil. We conclude that this landscape is well buffered against land-use changes regarding near-surface hydrology, even though short-lived ponding and perched water tables may occur locally during high-intensity rainfall on pastures and under soybeans.
Saturated hydraulic conductivity (Ks) of the soil is a key variable in the water cycle. For the humid tropics, information about spatial scales of Ks and their relation to soil types deduced from soil map units is of interest, as soil maps are often the only available data source for modelling. We examined the influence of soil map units on the mean and variation in Ks along a transect in a tropical rainforest using undisturbed soil cores at 06 and 612 cm depth. The Ks means were estimated with a linear mixed model fitted by residual maximum likelihood (REML), and the spatial variation in Ks was investigated with the maximum overlap discrete wavelet packet transform (MODWPT). The mean values of Ks did not differ between soil map units. The best wavelet packet basis for Ks at 06 cm showed stationarity at high frequencies, suggesting uniform small-scale influences such as bioturbation. There were substantial contributions to wavelet packet variance over the range of spatial frequencies and a pronounced low frequency peak corresponding approximately to the scale of soil map units. However, in the relevant frequency intervals no significant changes in wavelet packet variance were detected. We conclude that near-surface Ks is not dominated by static, soil-inherent properties for the examined range of soils. Several indicators from the wavelet packet analysis hint at the more dominant dynamic influence of biotic processes, which should be kept in mind when modelling soil hydraulic properties on the basis of soil maps.
Disproportionate single-species contribution to canopy-soil nutrient flux in an Amazonian rainforest
(2012)
Rainfall, throughfall and stemflow were monitored on an event basis in an undisturbed open tropical rainforest with a large number of palm trees located in the southwestern Amazon basin of Brazil. Stemflow samples were collected from 24 trees with a diameter at breast height (DBH) > 5 cm, as well as eight young and four full-grown babassu palms (Attalea speciosa Mart.) for 5 weeks during the peak of the wet season. We calculated rainfall, throughfall and stemflow concentrations and fluxes of Na+, K+, Ca2+, Mg2+,, Cl-, SO42-, NO3- and H+ and stemflow volume-weighted mean concentrations and fluxes for three size classes of broadleaf trees and three size classes of palms. The concentrations of most solutes were higher in stemflow than in rainfall and increased with increasing tree and palm size. Concentration enrichments from rainfall to stemflow and throughfall were particularly high (81-fold) for NO3-. Stemflow fluxes of NO3- and H+ exceeded throughfall fluxes but stemflow fluxes of other solutes were less than throughfall fluxes. Stemflow solute fluxes to the forest soil were dominated by fluxes on babassu palms, which represented only 4% of total stem number and 10% of total basal area. For NO3-, stemflow contributed 51% of the total mass of nitrogen delivered to the forest floor (stemflow + throughfall) and represented more than a 2000-fold increase in NO3- flux compared what would have been delivered by rainfall alone on the equivalent area. Because these highly localized fluxes of both water and NO3- persist in time and space, they have the potential to affect patterns of soil moisture, microbial populations and other features of soil biogeochemistry conducive to the creation of hotspots for nitrogen leaching and denitrification, which could amount to an important fraction of total ecosystem fluxes. Because these hotspots occur over very small areas, they have likely gone undetected in previous studies and need to be considered as an important feature of the biogeochemistry of palm-rich tropical forest.
Forests seem to represent low-erosion systems, according to most, but not all, studies of suspended-sediment yield. We surmised that this impression reflects an accidental bias in the selection of monitoring sites towards those with prevailing vertical hydrological flowpaths, rather than a tight causal link between vegetation cover and erosion alone. To evaluate this conjecture, we monitored, over a 2-year period, a 3.3 ha old-growth rainforest catchment prone to frequent and widespread overland flow. We sampled stream flow at two and overland flow at three sites in a nested arrangement on a within-event basis, and monitored the spatial and temporal frequency of overland flow. Suspended-sediment concentrations were modeled with Random Forest and Quantile Regression Forest to be able to estimate the annual yields for the 2 years, which amounted to 1 t ha(-1) and 2 t ha(-1) in a year with below-average and with average precipitation, respectively. These estimates place our monitoring site near the high end of reported suspended-sediment yields and lend credence to the notion that low yields reflect primarily the dominance of vertical flowpaths and not necessarily and exclusively the kind of vegetative cover. Undisturbed forest and surface erosion are certainly no contradiction in terms even in the absence of mass movements.
Landslides are a hazard for humans and artificial structures. From an ecological point of view, they represent an important ecosystem disturbance, especially in tropical montane forests. Here, shallow translational landslides are a frequent natural phenomenon and one local determinant of high levels of biodiversity. In this paper, we apply weighted ensembles of advanced phenomenological models from statistics and machine learning to analyze the driving factors of natural landslides in a tropical montane forest in South Ecuador. We exclusively interpret terrain attributes, derived from a digital elevation model, as proxies to several driving factors of landslides and use them as predictors in our models which are trained on a set of five historical landslide inventories. We check the model generality by transferring them in time and use three common performance criteria (i.e. AUC, explained deviance and slope of model calibration curve) to, on the one hand, compare several state-of-the-art model approaches and on the other hand, to create weighted model ensembles. Our results suggest that it is important to consider more than one single performance criterion.
Approaching our main question, we compare responses of weighted model ensembles that were trained on distinct functional units of landslides (i.e. initiation, transport and deposition zones). This way, we are able to show that it is quite possible to deduce driving factors of landslides, if the consistency between the training data and the processes is maintained. Opening the 'black box' of statistical models by interpreting univariate model response curves and relative importance of single predictors regarding their plausibility, we provide a means to verify this consistency.
With the exception of classification tree analysis, all techniques performed comparably well in our case study while being outperformed by weighted model ensembles. Univariate response curves of models trained on distinct functional units of landslides exposed different shapes following our expectations. Our results indicate the occurrence of landslides to be mainly controlled by factors related to the general position along a slope (i.e. ridge, open slope or valley) while landslide initiation seems to be favored by small scale convexities on otherwise plain open slopes.
The expansion of soybean cultivation into the Amazon in Brazil has potential hydrological effects at local to regional scales. To determine the impacts of soybean agriculture on hydrology, a comparison of net precipitation (throughfall, stemflow) in undisturbed tropical forest and soybean fields on the southern edge of the Amazon Basin in the state of Mato Grosso is needed. This study measured throughfall with troughs and stemflow with collar collectors during two rainy seasons. The results showed that in forest 91.6% of rainfall was collected as throughfall and 0.3% as stemflow, while in soybean fields with two-month old plants, 46.2% of rainfall was collected as throughfall and 9.0% as stemflow. Hence, interception of precipitation in soybean fields was far greater than in intact forests. Differences in throughfall, stemflow and net precipitation were found to be mainly associated with differences in plant structure and stem density in transitional forest and soybean cropland. Because rainfall interception in soybean fields is higher than previously believed and because both the area of cropland and the frequency of crop cycles (double cropping) are increasing rapidly, interception needs to be reconsidered in regional water balance models when consequences of land cover changes are analyzed in the Amazon soybean frontier region. Based on the continued expansion of soybean fields across the landscape and the finding that net precipitation is lower in soy agriculture, a reduction in water availability in the long term can be assumed.
In undisturbed tropical montane rainforests massive organic layers accommodate the majority of roots and only a small fraction of roots penetrate the mineral soil. We investigated the contribution of vegetation to slope stability in such environments by modifying a standard model for slope stability to include an organic layer with distinct mechanical properties. The importance of individual model parameters was evaluated using detailed measurements of soil and vegetation properties to reproduce the observed depth of 11 shallow landslides in the Andes of southern Ecuador. By distinguishing mineral soil, organic layer and above-ground biomass, it is shown that in this environment vegetation provides a destabilizing effect mainly due to its contribution to the mass of the organic layer (up to 973 t ha-1 under wet conditions). Sensitivity analysis shows that the destabilizing effect of the mass of soil and vegetation can only be effective on slopes steeper than 37.9 degrees. This situation applies to 36% of the study area. Thus, on the steep slopes of this megadiverse ecosystem, the mass of the growing forest promotes landsliding, which in turn promotes a new cycle of succession. This feedback mechanism is worth consideration in further investigations of the impact of landslides on plant diversity in similar environments.
Saturated hydraulic conductivity (K-s) is an important soil characteristic affecting soil water storage, runoff generation and erosion processes. In some areas where high-intensity rainfall coincides with low K-s values at shallow soil depths, frequent overland flow entails dense drainage networks. Consequently, linear structures such as flowlines alternate with inter-flowline areas. So far, investigations of the spatial variability of K-s mainly relied on isotropic covariance models which are unsuitable to reveal patterns resulting from linear structures. In the present study, we applied two sampling approaches so as to adequately characterize K-s spatial variability in a tropical forest catchment that features a high density of flowlines: A classical nested sampling survey and a purposive sampling strategy adapted to the presence of flowlines. The nested sampling approach revealed the dominance of small-scale variability, which is in line with previous findings. Our purposive sampling, however, detected a strong spatial gradient: surface K-s increased substantially as a function of distance to flowline; 10 m off flowlines, values were similar to the spatial mean of K-s. This deterministic trend can be included as a fixed effect in a linear mixed modeling framework to obtain realistic spatial fields of K-s. In a next step we used probability maps based on those fields and prevailing rainfall intensities to assess the hydrological relevance of the detected pattern. This approach suggests a particularly good agreement between the probability statements of K-s exceedance and observed overland flow occurrence during wet stages of the rainy season.