Assimilating in situ and radar altimetry data into a large-scale hydrologic-hydrodynamic model for streamflow forecast in the Amazon

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SILVA, Joecila/B-1478-2014 FINEP; ANA; CNPq-IRD The authors are grateful for: the financial and operational support from the Brazilian agencies FINEP and ANA ("Projeto de Integracao e Cooperacao Amazonica para a Modernizacao do Monitoramento Hidrologico" ICA-MMH) and CNPq-IRD ("Assimilacao de Dados de monitoramento Espacial para a analise do regime hidrologico da Bacia Amazonica e a previsao de curto e medio prazos"); the ENVISAT satellite altimetry data supplied by ESA; the TRMM data supplied by NASA and associated agencies; the TRMM Merge data provided by CPTEC/INPE; the discharge and stage data provided by ANA, Hybam, SENHAMI-Peru and SENHAMI-Bolivia; the EnKF Fortran codes provided by Geir Evensen; the support from Fernando Fan in processing precipitation data; as well as for the constructive comments from Adalberto Meller and Jeffrey Neal, and also from Ross Woods (editor from HESS) and two anonymous reviewers. 0 COPERNICUS GESELLSCHAFT MBH GOTTINGEN HYDROL EARTH SYST SCIn 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 Delta rms = -49 %) and also transferring information to ungauged rivers reaches (Delta rms = -16 %). Altimetry data assimilation improves results, in terms of water levels (Delta rms = -44 %) and discharges (Delta rms = -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 Solimoes/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