Combinations of runoff characteristics are commonly used to represent distinct conceptual models of stormflow generation. In this study, three runoff characteristics: hydrograph response, time source ...of runoff water, and flow path are used to classify catchments. Published data from the scientific literature are used to provide evidence from small, forested catchments. Each catchment was assigned to one of the eight conceptual models, depending on the combination of quick/slow response, old/new water, and overland/subsurface flow. A standard procedure was developed to objectively diagnose the predominant conceptual model of stormflow generation for each catchment and assess its temporal and spatial support. The literature survey yielded 42 catchments, of which 30 catchments provide a complete set of qualitative runoff characteristics resulting in one of the eight conceptual models. The majority of these catchments classify as subsurface flow path dominated. No catchments were found for conceptual models representing combinations of quick response‐new water‐subsurface flow (SSF), slow‐new‐SSF, slow‐old‐overland flow (OF) nor new‐slow‐OF. Of the 30 qualitatively classified catchments, 24 provide a complete set of quantitative measures. In summary, the field support is strong for 19 subsurface‐dominated catchments and is weak for 5 surface flow path dominated catchments (six catchments had insufficient quantitative data). Two alternative explanations exist for the imbalance of field support between the two flow path classes: (1) the selection of research catchments in past field studies was mainly to explain quick hydrograph response in subsurface dominated catchments; (2) catchments with prevailing subsurface flow paths are more common in nature. We conclude that the selection of research catchments needs to cover a wider variety of environmental conditions which should lead to a broader, and more widely applicable, spectrum of resulting conceptual models and process mechanisms. This is a prerequisite in studies where catchment organization and similarity approaches are used to develop catchment classification systems in order to regionalize stormflow.
Key Points:
Larger field support at subsurface than at surface flow path dominated sites
The variety of methods to document runoff characteristics complicates comparing of sites
Field studies at sites covering a broader range of characteristics are necessary
End member mixing analysis (EMMA) is a commonly applied method to identify and quantify the dominant runoff producing sources of water. It employs tracers to determine the dimensionality of the ...hydrologic system. Many EMMA studies have been conducted using two to six tracers, with some of the main tracers being Ca, Na, Cl−, water isotopes, and alkalinity. Few studies use larger tracer sets including minor trace elements such as Li, Rb, Sr, and Ba. None of the studies has addressed the question of the tracer set size and composition, despite the fact that these determine which and how many end members (EM) will be identified. We examine how tracer set size and composition affects the conceptual model that results from an EMMA. We developed an automatic procedure that conducts EMMA while iteratively changing tracer set size and composition. We used a set of 14 tracers and 9 EMs. The validity of the resulting conceptual models was investigated under the aspects of dimensionality, EM combinations, and contributions to stream water. From the 16,369 possibilities, 23 delivered plausible results. The resulting conceptual models are highly sensitive to the tracer set size and composition. The moderate reproducibility of EM contributions indicates a still missing EM. It also emphasizes that the major elements are not always the most useful tracers and that larger tracer sets have an enhanced capacity to avoid false conclusions about catchment functioning. The presented approach produces results that may not be apparent from the traditional approach and it is a first step to add the idea of statistical significance to the EMMA approach.
Key Points
Resulting model of EMMA is highly sensitive to tracer set size and composition
Large tracer sets help to avoid false conclusions about runoff processes
Methodology to add statistical significance to the EMMA approach
Stormflow generation in headwater catchments dominated by subsurface flow has been studied extensively, yet catchments dominated by surface flow have received less attention.
We addressed this by ...testing whether stormflow chemistry is controlled by either (a) the event‐water signature of overland flow, or (b) the pre‐event water signature of return flow. We used a high‐resolution hydrochemical data set of stormflow and end‐members of multiple storms in an end‐member mixing analysis to determine the number of end‐members needed to explain stormflow, characterize and identify potential end‐members, calculate their contributions to stormflow, and develop a conceptual model of stormflow. The arrangement and relative positioning of end‐members in stormflow mixing space suggest that saturation excess overland flow (26–48%) and return flow from two different subsurface storage pools (17–53%) are both similarly important for stormflow. These results suggest that pipes and fractures are important flow paths to rapidly release stored water and highlight the value of within‐event resolution hydrochemical data to assess the full range and dynamics of flow paths.
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 1
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.
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