Abstract
Abstract. In this work, we introduce and evaluate a data assimilation framework for gauged and radar altimetry-based discharge and water levels applied to a large scale hydrologic-hydrodynamic model for stream flow forecasts over the Amazon River basin. We used the process-based hydrological model called MGB-IPH coupled with a river hydrodynamic module using a storage model for floodplains. The Ensemble Kalman Filter technique was used to assimilate information from hundreds of gauging and altimetry stations based on ENVISAT satellite data. Model state variables errors were generated by corrupting precipitation forcing, considering log-normally distributed, time and spatially correlated errors. The EnKF performed well when assimilating in situ discharge, by improving model estimates at the assimilation sites (change in root-mean-squared error Δrms = −49%) and also transferring information to ungauged rivers reaches (Δrms = −16%). Altimetry data assimilation improves results, in terms of water levels (Δrms = −44%) and discharges (Δrms = −15%) to a minor degree, mostly close to altimetry sites and at a daily basis, even though radar altimetry data has a low temporal resolution. Sensitivity tests highlighted the importance of the magnitude of the precipitation errors and that of their spatial correlation, while temporal correlation showed to be dispensable. The deterioration of model performance at some unmonitored reaches indicates the need for proper characterisation of model errors and spatial localisation techniques for hydrological applications. Finally, we evaluated stream flow forecasts for the Amazon basin based on initial conditions produced by the data assimilation scheme and using the ensemble stream flow prediction approach where the model is forced by past meteorological forcings. The resulting forecasts agreed well with the observations and maintained meaningful skill at large rivers even for long lead times, e.g. >90 days at the Solimões/Amazon main stem. Results encourage the potential of hydrological forecasts at large rivers and/or poorly monitored regions by combining models and remote-sensing information.
Highlights
Land surface waters play an important role in global water cycle and earth system, regulating freshwater discharge from land into oceans (Oki and Kanae, 2006) and land-atmosphere exchanges of water, energy (Krinner, 2003; Decharme et al, 2011) and gases such as methane (Gedney et al, 2004)
We evaluated the assimilation of three types of data: (1) in situ discharge observations; (2) remotely sensed water levels derived from the ENVISAT radar altimeter; and (3) remotely sensed discharge estimates derived from radar altimetry water levels and rating curves
Results were evaluated in terms of mean changes in root-meansquared error between observed and simulated discharges, computed for two samples, the first including stream gauges used for data assimilation and the latter only the validation ones
Summary
Land surface waters play an important role in global water cycle and earth system, regulating freshwater discharge from land into oceans (Oki and Kanae, 2006) and land-atmosphere exchanges of water, energy (Krinner, 2003; Decharme et al, 2011) and gases such as methane (Gedney et al, 2004). More specific to the Amazon basin, important extreme hydrological events have occurred recently, for instance, the 2009 and 2012 floods and the 1996, 2005 and 2010 droughts (Chen et al, 2010; Tomasella et al, 2010; Marengo et al, 2008, 2011; Espinoza et al, 2011) These events caused several impacts on local population that strongly depends on the rivers and is very vulnerable to floods since most settlements lie along the rivers. In situ measurements of river stage and discharge at stream gauges are the most conventional alternative for monitoring surface waters, observation networks are rather sparse at several regions such as the Amazon River basin. It may be possible to derive discharge estimates from SWOT data by using specially developed algorithms (e.g. Durand et al, 2010b)
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