[1] Many details about the flow of water in soils in a hillslope are unknowable given current technologies. One way of learning about the bulk effects of water velocity distributions on hillslopes is through the use of tracers. However, this paper will demonstrate that the interpretation of tracer information needs to become more sophisticated. The paper reviews, and complements with mathematical arguments and specific examples, theory and practice of the distribution(s) of the times water particles injected through rainfall spend traveling through a catchment up to a control section (i.e., "catchment" travel times). The relevance of the work is perceived to lie in the importance of the characterization of travel time distributions as fundamental descriptors of catchment water storage, flow pathway heterogeneity, sources of water in a catchment, and the chemistry of water flows through the control section. The paper aims to correct some common misconceptions used in analyses of travel time distributions. In particular, it stresses the conceptual and practical differences between the travel time distribution conditional on a given injection time (needed for rainfall-runoff transformations) and that conditional on a given sampling time at the outlet (as provided by isotopic dating techniques or tracer measurements), jointly with the differences of both with the residence time distributions of water particles in storage within the catchment at any time. These differences are defined precisely here, either through the results of different models or theoretically by using an extension of a classic theorem of dynamic controls. Specifically, we address different model results to highlight the features of travel times seen from different assumptions, in this case, exact solutions to a lumped model and numerical solutions of the 3-D flow and transport equations in variably saturated, physically heterogeneous catchment domains. Our results stress the individual characters of the relevant distributions and their general nonstationarity yielding their legitimate interchange only in very particular conditions rarely achieved in the field. We also briefly discuss the impact of oversimple assumptions commonly used in analyses of tracer data.
[1] We study the influence exerted by space-time rainfall patterns on the hydrologic response to determine the scales for which the spatial heterogeneity of rainfall may play a significant role in shaping the hydrographs generated in basins of varying characteristics. We perform numerical experiments using models based on the geomorphological theory of the hydrologic response, in which the spatial resolution of the input rainfall fields is coarse grained from 100 m to 50 km. The variation in the resulting hydrographs shows that rainfall spatial variability does not significantly influence the flood response for basin areas up to about 3500 km 2 in the cases considered, provided that the rainfall volume at each time interval is preserved. We then search for the physical interpretation of these results using the Jensen-Shannon divergence measure to characterize differences in travel time distributions sampled by real and idealized disk-shaped rainfall patterns of different size. Because the total residence time of a water parcel is often controlled by the travel time within hillslopes, we find that when typical hillslope size is smaller than the characteristic size of rainfall structures (say, a correlation length of rainfall intensity), the rainfall pattern effectively samples all possible residence times and the response of the catchment does not depend on the specific rainfall pattern. In larger basins (say, typically larger than 10 3 km 2 ) the travel time in the channels is expected to be an important part of the total residence time. In this case the response of a catchment will also be controlled by the specifics of the spatial distribution of rainfall.
This paper addresses the signatures of catchment geomorphology on base flow recession curves. Its relevance relates to the implied predictability of base flow features, which are central to catchment‐scale transport processes and to ecohydrological function. Moving from the classical recession curve analysis method, originally applied in the Finger Lakes Region of New York, a large set of recession curves has been analyzed from Swiss streamflow data. For these catchments, digital elevation models have been precisely analyzed and a method aimed at the geomorphic origins of recession curves has been applied to the Swiss data set. The method links river network morphology, epitomized by time‐varying distribution of contributing channel sites, with the classic parameterization of recession events. This is done by assimilating two scaling exponents, β and bG, with |dQ/dt| ∝ Qβ where Q is at‐a‐station gauged flow rate and N(l) ∝ N(l)∝G(l)bG where l is the downstream distance from the channel heads receding in time, N(l) is the number of draining channel reaches located at distance l from their heads, and G(l) is the total drainage network length at a distance greater or equal to l, the active drainage network. We find that the method provides good results in catchments where drainage density can be regarded as spatially constant. A correction to the method is proposed which accounts for arbitrary local drainage densities affecting the local drainage inflow per unit channel length. Such corrections properly vanish when the drainage density become spatially constant. Overall, definite geomorphic signatures are recognizable for recession curves, with notable theoretical and practical implications.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.