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

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.

Suitability of 17 gridded rainfall and temperature datasets for large-scale hydrological modelling in West Africa

“…The model is calibrated only on Q data despite the known limitations of the Q-only calibration (Demirel et al, 2018). However, calibrating the model on additional variables would result in additional model performance improvement that would not be separable from the contribution of the input datasets to the model performance.…”

“…A dynamical scaling function (F DS ) (cf. Demirel et al, 2018) is used to account for vegetation-climate interactions (Bai et al, 2018;Jiao et al, 2017). E p is formulated as follows:…”

Improving the Applicability of Hydrologic Models for Food–Energy–Water Nexus Studies Using Remote Sensing Data

“…Several studies have shown that streamflow, being an integrative response of the entire watershed, cannot adequately inform the spatial representation of ET and SM [37,38]. Recent studies have shown that using the spatial structure of ET and SM from remote sensing data along with streamflow data in calibration can produce physically consistent estimates of all the components [9,39,40]. In this regard, our study agrees with the findings of the hydrologic modeling community, with the spatial RMSE plots (Figure 4), variograms, and correlograms ( Figure 5) showing that considering ET and SM moisture improves their spatial structure.…”

“…Multivariate calibration of hydrologic models is generally carried out by combining different hydrologic variables into a single objective function by using weights [9,40,41]. Such an approach makes it difficult to analyze the trade-offs in accuracy among the different variable involved.…”

Multivariate and spatially calibrated hydrological model for assessing climate change impacts on hydrological processes in West Africa

“…This study analysis the future evolution of the spatiotemporal dynamics of multiple hydrological processes (i.e. streamflow, soil moisture, evaporation and terrestrial water storage) with the fully distributed mesoscale hydrologic Model (mHM), which is constrained with a novel multivariate calibration approach based on the spatial patterns of satellite remote sensing data (Dembélé et al, 2020). The experiment is done in the large and transboundary Volta River Basin (VRB) in West Africa, which is a hotspot of climate vulnerability.…”