Abstract

Data assimilation (DA) of satellite soil moisture (SM) observations represents a great opportunity for improving the ability of rainfall-runoff models in predicting river discharges. Many studies have been carried out so far demonstrating the possibility to reduce model prediction uncertainty by incorporating satellite SM observations. However, large discrepancies can be perceived between these studies with the result that successful DA is not only related to the quality of the satellite observations but can be significantly controlled by many methodological and morphoclimatic factors. In this article, through an experimental study carried out on the Tiber River basin in Central Italy, we explore how the catchment area, soil type, climatology, rescaling technique, observation and model error selection may affect the results of the assimilation and can be the causes of the apparent discrepancies obtained in the literature. The results show that: (i) DA of SM generally improves discharge predictions (with a mean efficiency of about 30%); (ii) unlike catchment area, the soil type and the catchment specific characteristics might have a remarkable influence on the results; (iii) simple rescaling techniques may perform equally well to more complex ones; (iv) an accurate quantification of the model error is paramount for a correct choice of the observation error and, (v) SM temporal variability has a stronger influence than the season itself. On this basis, we advise that DA of SM may be not a simple task and one should carefully test the optimality of the assimilation experiment prior to drawing any general conclusions.

Highlights

  • It is well recognized that soil moisture (SM) is central to the repartition of the mass and energy fluxes between the land surface and the atmosphere, and, as a consequence, it plays a key role in the hydrological cycle

  • The values of the parameters denote a homogenous behaviour for all catchments for Wmax and Kc while different values of Ks and η were obtained for Marroggia at Azzano (MA-AZ)

  • Assuming the validity of the assumptions for the application of the Ensemble Kalman Filter (EnKF)—which are not the goal of this study—we analyzed the effects of some important factors which can introduce complexities, uncertainties and non-optimality conditions for the application of Data assimilation (DA) and which can be propagated across the whole DA process

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Summary

Introduction

It is well recognized that soil moisture (SM) is central to the repartition of the mass and energy fluxes between the land surface and the atmosphere, and, as a consequence, it plays a key role in the hydrological cycle. In rainfall-runoff modelling, it has been demonstrated that the wetness condition of the catchment before a flood event strongly controls the severity of the response of the catchment in terms of runoff [1,2,3,4]. Different initial soil moisture conditions can discriminate between catastrophic and minor effects for the same amount of rainfall. An accurate estimate of the initial SM conditions is paramount for reducing the uncertainties in early warning flood forecasting models and may help to mitigate the flood hazard. Hydrological models are valid solutions for obtaining accurate estimate of the SM conditions and have been used worldwide for diminishing the impact of floods on the population by issuing warnings with appropriate lead-time before floods occur. Hydrological simulations are “imperfect” [5] in the sense that they contain uncertainties which are mainly related to (i) the quality and quantity of the hydrological data used to drive the models [6,7,8] as well as representativeness errors due to scale incompatibility [9]; (ii) the simple representation of the real physical processes leading to inevitable assumptions and simplifications and unavoidably imperfect approximations to the complex reality [5]

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