Distributions of water transit times (TTDs), and related storage‐selection (SAS) distributions, are spatially integrated metrics of hydrological transport within landscapes. Recent works confirm that the form of TTDs and SAS distributions should be considered time variant—possibly depending, in predictable ways, on the dynamic storage of water within the landscape. We report on a 28 day periodic‐steady‐state‐tracer experiment performed on a model hillslope contained within a 1 m3 sloping lysimeter. Using experimental data, we calibrate physically based, spatially distributed flow and transport models, and use the calibrated models to generate time‐variable SAS distributions, which are subsequently compared to those directly observed from the actual experiment. The objective is to use the spatially distributed estimates of storage and flux from the model to characterize how temporal variation in water storage influences temporal variation in flow path configurations, and resulting SAS distributions. The simulated SAS distributions mimicked well the shape of observed distributions, once the model domain reflected the spatial heterogeneity of the lysimeter soil. The spatially distributed flux vectors illustrate how the magnitude and directionality of water flux changes as the water table surface rises and falls, yielding greater contributions of younger water when the water table surface rises nearer to the soil surface. The illustrated mechanism is compliant with conclusions drawn from other recent studies and supports the notion of an inverse‐storage effect, whereby the probability of younger water exiting the system increases with storage. This mechanism may be prevalent in hillslopes and headwater catchments where discharge dynamics are controlled by vertical fluctuations in the water table surface of an unconfined aquifer.
Direct runoff and baseflow are the two primary components of total streamflow, and their accurate estimation is indispensable for a variety of hydrologic applications. While direct runoff is the quick response stemming from surface and shallow subsurface flow paths and is often associated with floods, baseflow represents the groundwater contribution from stored sources (e.g., groundwater) to streams and is crucial for environmental flow regulations, and water supply, among others. L'vovich (1979, https:// doi.org/10.1029/SP013) proposed a two-step water balance partitioning, where precipitation is divided into direct runoff and catchment wetting, followed by the disaggregation of the latter into baseflow and evapotranspiration. Here, we investigate the role of the aridity index (ratio between mean-annual potential evapotranspiration and precipitation) in controlling the long-term (mean-annual) fluxes of direct runoff and baseflow. We present an analytical solution beginning with similar assumptions as proposed by Budyko (1974, https://www.elsevier.com/books/climate-and-life/budyko/978-0-12-139450-9), leading to two complementary expressions for the two fluxes. The aridity index explained 77% and 89% of variability in direct runoff and baseflow from 378 catchments within the continental United States, while our formulations were able to reproduce the patterns of water balance partitioning proposed by L'vovich (1979, https://doi.org/10.1029/SP013) at the mean-annual timescale. Our approach can be used to further understand how climate and landscape controls the terrestrial water balance at mean-annual timescales, while also representing a step toward the prediction of baseflow and direct runoff at ungauged basins.
Abstract. In this paper, we present the Catchments Attributes for Brazil (CABra), which is a large-sample dataset for Brazilian catchments that includes long-term data (30 years) for 735 catchments in eight main catchment attribute classes (climate, streamflow, groundwater, geology, soil, topography, land cover, and hydrologic disturbance). We have collected and synthesized data from multiple sources (ground stations, remote sensing, and gridded datasets). To prepare the dataset, we delineated all the catchments using the Multi-Error-Removed Improved-Terrain Digital Elevation Model (MERIT DEM) and the coordinates of the streamflow stations provided by the Brazilian Water Agency, where only the stations with 30 years (1980–2010) of data and less than 10 % of missing records were included. Catchment areas range from 9 to 4 800 000 km2, and the mean daily streamflow varies from 0.02 to 9 mm d−1. Several signatures and indices were calculated based on the climate and streamflow data. Additionally, our dataset includes boundary shapefiles, geographic coordinates, and drainage area for each catchment, aside from more than 100 attributes within the attribute classes. The collection and processing methods are discussed, along with the limitations for each of our multiple data sources. CABra intends to improve the hydrology-related data collection in Brazil and pave the way for a better understanding of different hydrologic drivers related to climate, landscape, and hydrology, which is particularly important in Brazil, having continental-scale river basins and widely heterogeneous landscape characteristics. In addition to benefitting catchment hydrology investigations, CABra will expand the exploration of novel hydrologic hypotheses and thereby advance our understanding of Brazilian catchments' behavior. The dataset is freely available at https://doi.org/10.5281/zenodo.4070146 and https://thecabradataset.shinyapps.io/CABra/ (last access: 7 June 2021).
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