Constraining Conceptual Hydrological Models With Multiple Information Sources

Abstract

The calibration of hydrological models without streamflow observations is problematic, and the simultaneous, combined use of remotely sensed products for this purpose has not been exhaustively tested thus far. Our hypothesis is that the combined use of products can (1) reduce the parameter search space and (2) improve the representation of internal model dynamics and hydrological signatures. Five different conceptual hydrological models were applied to 27 catchments across Europe. A parameter selection process, similar to a likelihood weighting procedure, was applied for 1,023 possible combinations of 10 different data sources, ranging from using 1 to all 10 of these products. Distances between the two empirical distributions of model performance metrics with and without using a specific product were determined to assess the added value of a specific product. In a similar way, the performance of the models to reproduce 27 hydrological signatures was evaluated relative to the unconstrained model. Significant reductions in the parameter space were obtained when combinations included Advanced Microwave Scanning Radiometer ‐ Earth Observing System and Advanced Scatterometer soil moisture, Gravity Recovery and Climate Experiment total water storage anomalies, and, in snow‐dominated catchments, the Moderate Resolution Imaging Spectroradiometer snow cover products. The evaporation products of Land Surface Analysis ‐ Satellite Application Facility and MOD16 were less effective for deriving meaningful, well‐constrained posterior parameter distributions. The hydrological signature analysis indicated that most models profited from constraining with an increasing number of data sources. Concluding, constraining models with multiple data sources simultaneously was shown to be valuable for at least four of the five hydrological models to determine model parameters in absence of streamflow.

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

“…This work is a follow-up on several recent studies on multiobjective calibration and spatial pattern improvement in hydrological modeling (e.g., Demirel et al, 2018;Koch et al, 2018;Nijzink et al, 2018;Stisen et al, 2018;Yassin et al, 2017;Zink et al, 2018). The proposed multivariate calibration approach is a step forward in improving the realism of hydrological model predictions (Baroni et al, 2019;Clark et al, 2015;Rakovec et al, 2016) because not only a reliable temporal dynamic in the modeling objective but also plausible spatial patterns of several hydrological processes simultaneously are sought for.…”

“…For parameter estimation (i.e., model calibration) with SRS data, the existing approaches consist in using SRS data alone or in combination with in situ data, usually streamflow data (Immerzeel & Droogers, 2008;Li et al, 2018;Rajib, Evenson, et al, 2018;Wambura et al, 2018). Calibration of hydrological models without concomitant streamflow data remains challenging, and attempts to do so have only shown limited success (Nijzink et al, 2018;Silvestro et al, 2015;Sutanudjaja et al, 2014;Wanders et al, 2014).…”

A hydrological model for root zone water storage simulation on a global scale

“…A global parameter optimization algorithm (Tolson and Shoemaker, 2007), dynamically dimensioned search (DDS), has been applied in this study for model parameters calibration. DDS is designed for computationally expensive optimization problems and has been used in many studies related to the distributed hydrological model calibration at global and regional scales (Moore et al, 2010;Kumar et al, 2013;Rakovec et al, 2016;Nijzink et al, 2018;Smith et al, 2018). Since the reference data, i.e.…”

Impact of satellite and in situ data assimilation on hydrological predictions

“…The set-up of the hydrological models used in most cases is based on dynamic forcing, land cover classifications and parametrizations of vegetation dynamics that are partially (or even entirely) derived from some remote sensing. Satellite data are used to varying degrees in model calibration [14][15][16], validation [9,10,17] and data assimilation [3,4,12,13,18].…”