The Role of Stream Water Isotopes in Integrated Hydrological Model Calibration and Flowpath Identification

In hydrological modeling, multiple combinations of parameter sets can alter catchment processes and local velocities, while still achieving the same overall streamflow (i.e., celerity), underscoring the equifinality theorem. Improving model calibration by incorporating new dimensions of information helps narrow down the range of viable models, leading to a more accurate representation of system behavior. This study investigates how different combinations of hydrometric and isotopic cross-output targets—including groundwater levels (GWL), streamflow rates (Q), and stream stable water isotopic compositions (δ¹⁸O)—influence parameter sensitivity patterns, calibration processes, and subsequently inform the dominant flowpath of the system. The research was conducted on the Pallas sub-arctic catchment in northern Finland using HydroGeoSphere (HGS), the fully integrated, physically based hydrological model. The study employed a workflow consisting of global sensitivity analysis (SA), automated parameter estimation (PE), and parameter uncertainty analysis (UA), assisted by PEST++, across multiple scenarios targeting different combinations of observables (GWL, Q, and δ¹⁸O).

The SA results showed that a combined target of isotopic (δ¹⁸O) and hydrometric data (GWL + Q) produced similar sensitivity patterns to targeting stream isotopes alone (δ¹⁸O), underscoring the crucial role of isotopes in constraining system behavior. UA results revealed that models calibrated with both hydrometric and isotopic data (Q, GWL, and δ¹⁸O) yielded the narrowest parameter uncertainty bounds, followed by the isotope-only calibration. The hydrometric-only calibration (Q and GWL) exhibited the widest uncertainty range, highlighting the role of isotope data in constraining parameter distributions, and correspondingly reducing model forecast uncertainty.

While models with different calibration targets showed similar performance in streamflow rates, stages, and isotope composition across various hydrograph stages, water balance analysis revealed variations in internal processes and flowpaths. Ground surface water partitioning (e.g., infiltration rates) was consistent across setups, but subsurface processes differed notably. Models calibrated with isotope data exhibited greater groundwater recharge via rapid deep percolation, facilitated by enhanced soil water–groundwater connectivity. While Other setups showed minimal groundwater recharge and increased soil water storage. Incorporating isotopic data emphasized vertical flowpaths essential for matching isotope observations, altering subsurface water partitioning and storage dynamics.

Isotope-enabled calibration in fully integrated physically based models enhances flowpath representation, narrows plausible parameter combinations, and provides more constrained prediction envelopes, offering a robust approach for reliably informing sustainable water management strategies.