Improving the Predictive Skill of a Distributed Hydrological Model by Calibration on Spatial Patterns With Multiple Satellite Data Sets

TLDR

Hydrological model calibration that integrates Earth observations with in situ data shows promise, but combining multiple sources requires careful integration to leverage reliable spatial patterns and avoid propagating uncertainties. The study proposes a multivariate calibration framework that selects model parameters using only spatial patterns from satellite data, ignoring absolute values, to improve calibration. The framework employs a bias‑insensitive multicomponent spatial pattern matching metric to formulate a multiobjective function, incorporates streamflow and satellite products (GLEAM, ESA CCI, GRACE), evaluates with MODIS land surface temperature, and is tested on the mHM in the Volta River basin. The calibration reduced streamflow and terrestrial water storage performance by 7% and 6% respectively, but improved soil moisture and evaporation performance by 105% and 26%, demonstrating the benefits of robustly integrating satellite data.

Abstract

Abstract Hydrological model calibration combining Earth observations and in situ measurements is a promising solution to overcome the limitations of the traditional streamflow‐only calibration. However, combining multiple data sources in model calibration requires a meaningful integration of the data sets, which should harness their most reliable contents to avoid accumulation of their uncertainties and mislead the parameter estimation procedure. This study analyzes the improvement of model parameter selection by using only the spatial patterns of satellite remote sensing data, thereby ignoring their absolute values. Although satellite products are characterized by uncertainties, their most reliable key feature is the representation of spatial patterns, which is a unique and relevant source of information for distributed hydrological models. We propose a novel multivariate calibration framework exploiting spatial patterns and simultaneously incorporating streamflow and three satellite products (i.e., Global Land Evaporation Amsterdam Model [GLEAM] evaporation, European Space Agency Climate Change Initiative [ESA CCI] soil moisture, and Gravity Recovery and Climate Experiment [GRACE] terrestrial water storage). The Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature data set is used for model evaluation. A bias‐insensitive and multicomponent spatial pattern matching metric is developed to formulate a multiobjective function. The proposed multivariate calibration framework is tested with the mesoscale Hydrologic Model (mHM) and applied to the poorly gauged Volta River basin located in a predominantly semiarid climate in West Africa. Results of the multivariate calibration show that the decrease in performance for streamflow (−7%) and terrestrial water storage (−6%) is counterbalanced with an increase in performance for soil moisture (+105%) and evaporation (+26%). These results demonstrate that there are benefits in using satellite data sets, when suitably integrated in a robust model parametrization scheme.