FOR 5288: Fast and invisible: Conquering Subsurface Stormflow
through an Interdisciplinary Multi-Site Approach

Abstract:

Subsurface stormflow (SSF) is a streamflow generation process that is difficult to observe. It has therefore been challenging to evaluate the relevance and magnitude of SSF contributions to streamflow quantity and quality. However, some earlier studies have shown that it can deliver substantial amounts of water to the stream at the time scale of an event. One possible approach for detecting SSF in streamflow has been to sample SSF on hillslopes, characterize it by analyzing various tracers and search for this SSF fingerprint in stream water samples. In this study, we ask the following questions: Does subsurface stormflow generated on hillslopes and moving downslope towards the stream have a typical chemical fingerprint or signature by which we could recognize it in the stream? And does this signature vary over time? Here, we present data from a headwater catchment near Freiburg, Germany, where we installed three trenches to measure SSF flow rates and to obtain SSF samples for chemical analysis. We collected SSF samples from the three trenches over multiple events during spring 2023, fall 2023 and spring 2024 and analyzed them for dissolved organic carbon and major ions. We compared chemical SSF signatures through the events, across seasons and between the three trenches. Preliminary analyses indicate that the SSF signature changed during events, with SSF signatures at the beginning and at the end of events being remarkably similar to each other. Results also hint at a seasonal stability of SSF signatures. In our presentation, we are going to present a detailed analysis of the dynamics of the chemical SSF signature. This dataset provides a unique opportunity to evaluate the chemical composition of subsurface stormflow in sub-daily resolution at three different hillslopes and to improve our capability to recognize contributions of SSF to streamflow.

Abstract:

Subsurface stormflow (SSF) is a key runoff generation mechanism in small catchments, yet its dynamics and thresholds remain poorly understood due to the challenges of observing and measuring subsurface processes. To address this, we conducted a study in a headwater catchment in the Black Forest, Germany, where we investigated the depth of SSF flow paths, if SSF is dominated by event water and if all SSF events have similar tracer behavior. Three trenches were installed on slopes with different landuse and topography. They were excavated down to bedrock ( 15 m wide, 2.5 m deep), collecting SSF separately from upper and lower soil layers. Continuous measurements of SSF volume and temperature were combined with water sampling for natural tracers: stable water isotopes, dissolved organic carbon, electric conductivity, and major ions. During the study period, 6 large SSF events were recorded in each trench. Using heat as a tracer we found stable SSF flowpath depths across events. Multitracer analysis suggested that SSF consisted mostly of pre-event water, with antecedent wetness governing event water contributions. Our results also show that C–Q relationships for solutes varied considerably between events and trenches, with some (e.g., Si) remaining chemostatic while others (e.g. DOC, SO24− and NO−3 ) behaved chemodynamically. This indicates that the chemical composition of SSF depends on both the antecedent hydrometeorological conditions and local factors such as landuse, reinforcing the complexity of these processes. Our results highlight the dynamic nature of subsurface stormflow and the challenges involved in its characterization.

Abstract:

Environmental tracers have been used to separate streamflow components for many years. They allow us to quantify the contribution of water originating from different sources, such as direct runoff from precipitation, subsurface storm flow, or groundwater to total streamflow at variable flow conditions. Although previous studies have explored the value of incorporating experimentally derived fractions of event and pre-event water into hydrological models, a thorough analysis of the value of incorporating hydrographseparation-derived information on multiple streamflow components at varying flow conditions into model parameter estimation has not yet been performed. This study explores the value of such information to achieve more realistic simulations of catchment discharge. We use a modified version of the process-oriented HBV model that simulates catchment discharge through the interplay of hillslope, riparian-zone discharge, and groundwater discharge at a small forested catchment which is located in the mountainous north of South Korea, subject to a monsoon season between June and August. Applying a Monte-Carlo-based parameter estimation scheme and the Kling–Gupta efficiency (KGE) to compare discharge observations and simulations across two seasons (2013 and 2014), we show that the model is able to provide accurate simulations of catchment discharge (KGE 0.8) but fails to provide robust predictions and realistic estimates of the contribution of the different streamflow components. Using a simple framework that compares simulated and observed contributions of hillslope, riparian zone, and groundwater to total discharge during two sub-periods, we show that the precision of simulated streamflow components can be increased, while remaining with accurate discharge simulations.We further show that the additional information increases the identifiability of all model parameters and results in more robust predictions. Our study shows how tracer-derived information on streamflow contributions can be used to improve the simulation and predictions of streamflow at the catchment scale without adding additional complexity to the model. The complementary use of temporally resolved observations of streamflow components and modeling provides a promising direction to improve discharge prediction by representing model internal dynamics more realistically.

Abstract:

Summary The links between soil water movement at the plot scale and runoff generation at the hillslope scale are highly non-linear and still not well understood. As such, a framework for the general characterization of hillslopes is still lacking. Here we present a number of virtual experiments with a 3D physically-based finite element model to systematically investigate the interactions between some of the dominant controls on subsurface stormflow generation. We used the well-studied Panola experimental hillslope to test our base case simulation and used the surface and subsurface topography and the stormflow data of this site as a framework for a subsequent series of virtual experiments. The parameterization of the soil and bedrock properties was based on field measurements of soil moisture and saturated hydraulic conductivity. After calibration and testing against multiple evaluation criteria including distributed trench flow data and internal tensiometric response, we varied slope angle, soil depth, storm size and bedrock permeability across multiple ranges to establish a set of response surfaces for several hillslope flow metrics. We found that connectivity of subsurface saturation was a unifying descriptor of hillslope behavior across the many combinations of slope type. While much of the interplay between our four hillslope variables was intuitive, several interactions in variable combinations were found. Our analysis indicated that, e.g. interactions between slope angle, soil depth and storm size that caused unexpected behavior of hydrograph peak times were the result of the interplay between subsurface topography and the overlying soil mantle with its spatially varying soil depth distribution. Those interactions led to new understanding of process controls on connectivity .