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Soils play a crucial role in biogeochemical cycles as spatially distributed sources and sinks of nutrients. Any spatial patterns depend on soil forming processes, our understanding of which is still limited, especially in regards to tropical rainforests. The objective of our study was to investigate the effects of landscape properties, with an emphasis on the geometry of the land surface, on the spatial heterogeneity of soil chemical properties, and to test the suitability of soil-landscape modeling as an appropriate technique to predict the spatial variability of exchangeable K and Mg in a humid tropical forest in Panama. We used a design-based, stratified sampling scheme to collect soil samples at 108 sites on Barro Colorado Island, Panama. Stratifying variables are lithology, vegetation and topography. Topographic variables were generated from high-resolution digital elevation models with a grid size of 5 m. We took samples from five depths down to I m, and analyzed for total and exchangeable K and Mg. We used simple explorative data analysis techniques to elucidate the importance of lithology for soil total and exchangeable K and Mg. Classification and Regression Trees (CART) were adopted to investigate importance of topography, lithology and vegetation for the spatial distribution of exchangeable K and Mg and with the intention to develop models that regionalize the point observations using digital terrain data as explanatory variables. Our results suggest that topography and vegetation do not control the spatial distribution of the selected soil chemical properties at a landscape scale and lithology is important to some degree. Exchangeable K is distributed equally across the study area indicating that other than landscape processes, e.g. biogeochemical processes, are responsible for its spatial distribution. Lithology contributes to the spatial variation of exchangeable Mg but controlling variables could not be detected. The spatial variation of soil total K and Mg is mainly influenced by lithology.
Sediment-discharge hysteresis loops are frequently analyzed to facilitate the understanding of sediment transport processes. Hysteresis patterns, however, are often complex and their interpretation can be complicated. Particularly, quantifying hysteresis patterns remains a problematic issue. Moreover, it is currently unknown how much data is required for analyzing sediment-discharge hysteresis loops in a given area. These open questions and challenges motivated us to develop a new method for quantifying suspended-sediment hysteresis. Subsequently, we applied the new hysteresis index to three suspended-sediment and discharge datasets from a small tropical rainforest catchment. The datasets comprised a different number of events and sampling sites. Our analyses show three main findings: (1) datasets restricted to only few events, which is typical for rapid assessment surveys, were always sufficient to identify the dominating hysteresis pattern in our research area. Furthermore, some of these small datasets contained multiple-peak events that allowed identifying intra-event exhaustion effects and hence, limitations in sediment supply. (2) Datasets comprising complete hydrological years were particularly useful for analyzing seasonal dynamics of hysteresis. These analyses revealed an exhaustion of hysteresis on the inter-event scale which also points to a limited sediment supply. (3) Datasets comprising measurements from two consecutive gauges installed at the catchment outlet and on a slope within that catchment allowed analyzing the change of hysteresis patterns along the flowpath. On the slope, multiple-peak events showed a stronger intra-event exhaustion of hysteresis than at the catchment outlet. Furthermore, exhaustion of hysteresis on the inter-event scale was not evident on the slope but occurred at the catchment outlet. Our results indicate that even small sediment datasets can provide valuable insights into sediment transport processes of small catchments. Furthermore, our results may serve as a first guideline on what to expect from an analysis of hysteresis patterns for datasets of varying quality and quantity. (c) 2014 Elsevier B.V. All rights reserved.