A stochastic model of catchment baseflow dynamics

Groundwater discharge profoundly influences river flow, especially during dry spells, potentially exacerbating drought conditions, an issue compounded by escalating climate change-induced hydrological extremes. Amidst this, understanding aquifer systems' efficacy in mitigating (sub-)seasonal fluctuations and their ecological impacts gains significance even in moderate climates. This work introduces a stochastic modeling approach for groundwater-fed baseflow, as an alternative to the traditional hydraulic theory by accounting for spatial heterogeneity of subsurface storage properties and associated uncertainties. Leveraging on readily available rainfall-generated recharge and river discharge series, stochastic tools determine baseflow characteristics. The model’s foundation lies on representing groundwater recharge and subsurface storage as stochastic variables. With the subsurface storage represented as multiple linear reservoirs with stochastic storage parameters, the proposed model reveals the baseflow dynamics and the interplay between heterogeneous reservoir timescales and recharge variability, elucidating temporal variance of baseflow at the catchment scale. Furthermore, the study investigates equivalent parameters for an upscaled unique reservoir to model catchment behavior. Utilizing established stochastic analysis tools in subsurface hydrology, this research advances our understanding of heterogeneous hydrological catchments. In addition, the investigation analyzes the temporal statistical moments of baseline discharge dependent on input recharge and sub-catchments' response times. This detailed analysis unveils the system's attenuation effect as a metric for catchment resilience during prolonged droughts, significantly influenced by underlying hydrogeological properties. As a practical consequence, quantifying the dependency of the attenuation factor on both temporal and hydrogeological variability can help in identifying particularly sensitive watersheds, crucial for tailored adaptation strategies.