Abstract
Abstract. Data assimilation has the potential to improve flood forecasting. However, it is rarely employed in distributed hydrologic models for operational predictions. In this study, we present variational assimilation of river flow data at multiple locations and of land surface temperature (LST) from satellite in a distributed hydrologic model that is part of the operational forecasting chain for the Arno river, in central Italy. LST is used to estimate initial condition of soil moisture through a coupled surface energy/water balance scheme. We present here several hindcast experiments to assess the performances of the assimilation system. The results show that assimilation can significantly improve flood forecasting, although in the limit of data error and model structure.
Highlights
The potential of data assimilation in hydrology has been demonstrated by several studies (e.g., Clark et al, 2008; Seo et al, 2009; Brocca et al, 2010; Lee et al, 2012; Laiolo et al, 2015)
The usage of data assimilation in distributed hydrologic models for operational flood forecasts is limited by many issues: non-linear and discontinuous model structure, non-Gaussian/multiplicative errors, large dimensionality of the inverse problem, model governed by different equations, complex topology of domains such as surface drainage and river network
The performances of the developed assimilation system are assessed in several hindcast experiments
Summary
The potential of data assimilation in hydrology has been demonstrated by several studies (e.g., Clark et al, 2008; Seo et al, 2009; Brocca et al, 2010; Lee et al, 2012; Laiolo et al, 2015). The usage of data assimilation in distributed hydrologic models for operational flood forecasts is limited by many issues: non-linear and discontinuous model structure, non-Gaussian/multiplicative errors, large dimensionality of the inverse problem, model governed by different equations, complex topology of domains such as surface drainage and river network. This work presents variational assimilation of flow data at multiple locations and of land surface temperature (LST) maps from satellite in a distributed hydrologic model that is part of the operational forecasting chain for the Arno river, in central Italy. We show results from several hindcast experiments in the Arno basin
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