| 12 Jan 2026
Groundwater Hysteresis Increasingly Decouples Flowing Network Length from Streamflow as Snow Shifts to Rain
Abstract. Flowing stream networks expand and contract in response to dynamic groundwater levels. Field studies generally associate greater flowing network length (L) with higher streamflow (Q), but this neglects potential hysteresis caused by nonequilibrium groundwater flow after rain and snowmelt. Using a new version of the Distributed Hydrology Soil Vegetation Model (DHSVM), we predict that groundwater hysteresis may decouple L from Q across large (> 100 %) variations in Q. In a 27 km2 snowy volcanic watershed, seasonal anomalies in measured stream ionic concentration indicate an outsized contribution from longer subsurface flowpaths during recession, supporting our L-Q hysteresis hypothesis and refining our model calibration. The model can reproduce observed stream network elasticity (from field surveys), and the predicted network length anomaly mirrors seasonal anomalies in measured stream ionic concentration (r = −0.92), suggesting that the model can capture the seasonal reconfiguration of groundwater flowpaths. A warmer climate is expected to cause a partial transition from snow to rain resulting in flashier streamflow, but our simulations predict that seasonal groundwater hysteresis would dampen storm-scale stream network elasticity, thereby significantly increasing L-Q hysteresis on daily to monthly timescales (p < 0.01). Conceptual models of stream networks should consider the potential effects of groundwater hysteresis in headwaters catchments, especially in a changing environment. More broadly, our investigation highlights how spatially distributed process-based hydrological modeling can sometimes reveal emergent hydrological behaviors that are not apparent from sparse field data.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Hydrology and Earth System Sciences.
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General comments
- Very good research on a general topic that spans between several fields of geosciences (hydrogeology, siliciclastic sedimentology and hydrology as a minimum). However, some minor issues need to be fixed.
- Snow hydrology in a mountain region is a growing topic due to the climate change.
Specific comments
Line 35. “other fluvial processes”. You need to specify all of them (erosion, sedimentation, landslide triggering or whatever you retain relevant).
Line 39. “Longer time scales”. Please, specify the order of magnitude for these scales.
Lines 40-41. “and hydrogeological properties(transmissivity)”. You should back-up this statement with references. See research on aquifer transmissivity and groundwater that converges:
- Dietrich, C. R., and Garry Neil Newsam 1989. A stability analysis of the geostatistical approach to aquifer transmissivity identification. Stochastic Hydrology and Hydraulics 3, 293-316.
- Agbotui, P.Y., Firouzbehi, F. and Medici, G., 2025. Review of effective porosity in sandstone aquifers: insights for representation of contaminant transport. Sustainability, 17(14), 6469.
Lines 134. Ok the research questions are clear. Have you been so explicit for the description of the general goal?
Line 140. You need inserting basic equations when describing routing modelling. The four below are not sufficient in the manuscript.
Line 360. Double information with same equation on line 49.
Figures and tables
Figure 1. I prefer the conceptual models below, and the graphs above.
Figure 2. The second option would be splitting the figure in two parts.
Figure 4. Months on horizontal axes difficult to read. Please, enlarge them.
Figure 4. Splitting the figure in two parts to fix the issue?
Figure 7. I suggest the conceptual models below, and the graphs above also for this figure.
Figure 9. Do you need an approximate spatial scale?