Characterizing rainfall-runoff signatures from micro-catchments with contrasting land cover characteristics in southern Amazonia

Abstract

Abstract:On the basis of interactions between landscape characteristics and precipitation inputs, watersheds respond differently to different climatic inputs. The objective of this study was to quantitatively characterize controls on runoff generation from two first order micro-catchments in the Amazonia region. The study investigated the variation of hydrological signatures at micro-catchment scale and related these to landscape and land cover differences and weather descriptors that control the observed responses. One catchment is a pasture cleared of all natural vegetation in the early 1980s, and the second catchment is a primary tropical forest with minor signs of disturbance. Water levels and meteorological variables were continuously monitored during the study period (December 2012-May 2013. Water level measurements were converted to discharge, evapotranspiration was quantified using Penman-Monteith equation and catchment pedohydrological properties were also determined. During the study period, mean total rainfall was 1200 mm, and direct runoff ratios were 0.29 and 0.12 for the pasture and forest catchments, respectively. Base flow index was relatively high in the forest catchment (0.76) compared with pasture catchment (0.63). Results from this study showed that the pasture catchment had a 35% higher mean stream flow. Analysis of selected individual rainstorm events also showed peak discharges, which were attained much faster in the pasture catchment compared with the forest catchment. At both sites, rainfall-runoff responses were highly dependent on surface and subsurface flow generation. Overland flow was observed in the pasture site during intense rainfall events. The pasture catchment exhibited higher event water contribution than the forest catchment. Findings from this research suggest that shallow lateral pathways play a significant role in controlling runoff generation processes in the forest catchment, whereas infiltration excess runoff generation processes dominate in the pasture catchment. The findings in this study suggest that the conversion of forest to pasture may lead to important changes in runoff generation processes and water storage in these head water catchments.

Continuous versus event‐based sampling: how many samples are required for deriving general hydrological understanding on Ecuador's páramo region?

“…The value of the correlation coefficient increases with the areal fraction of Histosols, and vice versa with Andosols. Similar findings are reported by authors such as Blume et al (2007), Guzha et al (2015) and Mosquera et al (2015), showing that runoff in most cases is controlled by the soil and land cover in undisturbed catchments. Ali et al (2012a) found that soil drainability properties influenced the temporal variability of runoff pathways in catchments with analogous hydropedological characteristics in Scotland.…”

“…(), Guzha et al . () and Mosquera et al . (), showing that runoff in most cases is controlled by the soil and land cover in undisturbed catchments.…”

Carbon-optimised land management strategies for southern Amazonia

“…Results suggest a relevant increase of runoff for pure pasture catchments compared to natural vegetation (Nobrega et al 2015;Guzha et al 2014), but no increase of discharge in the cropland catchment due to adapted land management (no-till soybean-maize double cropping). Macro-catchment modelling using SWAT (Lamparter et al 2018) with CC and dynamic LUC showed only small changes in discharge components due to the large groundwater buffer in the Cerrado region.…”

Linking hydrologic signatures to hydrologic processes: A review

“…One technique is to look at the signature changes that occur in response to known changes in the watershed. For example, Guzha et al () reported that low flows in the Amazon decreased following ecosystem degradation, therefore linking low flow signatures with landscape change. Yilmaz, Gupta, and Wagener () and McDonnell et al () look more generally for spatial–temporal patterns in the data that could be explained by differences in processes.…”