Same Streamflow, Different Water Stories: The Hidden Impacts of Streamflow-Only Calibration in Distributed Hydrological Modeling

Abstract. Distributed hydrological models enable the characterization of spatial heterogeneities in states and fluxes, including streamflow at inner points of a basin. Despite the growing number of remotely sensed observations, calibrating the model parameters using only streamflow observed at the catchment outlet remains a popular practice. In this paper, we examine how streamflow-only calibration impacts the average seasonality and spatial patterns of simulated evapotranspiration (ET), soil moisture (SM), land surface temperature (LST), and fractional snow-covered area (fSCA). To this end, we conduct calibration experiments with the Variable Infiltration Capacity (VIC) model in six basins located in Chile, using (i) different streamflow-based objective functions, and (ii) regularizing parameters associated with different physical processes. For the latter step, we develop and test a novel spatial regularization strategy based on principal component analysis of physiographic attributes associated with the modeling units contained within each basin. Our results suggest that these decisions may have large effects on the spatial representation of ET, SM₁ (i.e., SM from the first soil layer in VIC), LST, and fSCA, without degrading the performance of streamflow simulations. The average streamflow seasonality can be simulated reasonably well, with large biases in ET, fSCA, SM₁, and LST (in that order). In particular, different calibration configurations can yield the same annual cycle of streamflow through very different ET seasonalities, affecting the catchment-scale seasonal water balance. Additional calibration experiments incorporating ET and SM₁ besides streamflow reaffirm tradeoffs in the fidelity of different simulated variables. Overall, the results presented here reinforce the benefits of including spatial patterns of hydrological variables in the calibration of distributed hydrological models and highlight the need to verify the seasonality of other simulated variables besides streamflow.