HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Journal articles

On the sources of hydrological prediction uncertainty in the Amazon

Abstract : Recent extreme events in the Amazon River basin and the vulnerability of local population motivate the development of hydrological forecast systems using process based models for this region. In this direction, the knowledge of the source of errors in hydrological forecast systems may guide the choice on improving model structure, model forcings or developing data assimilation systems for estimation of initial model states. We evaluate the relative importance of hydrologic initial conditions and model meteorological forcings errors (precipitation) as sources of stream flow forecast uncertainty in the Amazon River basin. We used a hindcast approach that compares Ensemble Streamflow Prediction (ESP) and a reverse Ensemble Streamflow Prediction (reverse-ESP). Simulations were performed using the physically-based and distributed hydrological model MGB-IPH, comprising surface energy and water balance, soil water, river and floodplain hydrodynamics processes. The model was forced using TRMM 3B42 precipitation estimates. Results show that uncertainty on initial conditions plays an important role for discharge predictability, even for large lead times (∼1 to 3 months) on main Amazonian Rivers. Initial conditions of surface waters state variables are the major source of hydrological forecast uncertainty, mainly in rivers with low slope and large floodplains. Initial conditions of groundwater state variables are important, mostly during low flow period and in the southeast part of the Amazon where lithology and the strong rainfall seasonality with a marked dry season may be the explaining factors. Analyses indicate that hydrological forecasts based on a hydrological model forced with historical meteorological data and optimal initial conditions may be feasible. Also, development of data assimilation methods is encouraged for this region.
Complete list of metadata

Contributor : Nathalie Pothier Connect in order to contact the contributor
Submitted on : Saturday, March 26, 2022 - 6:54:25 AM
Last modification on : Monday, May 16, 2022 - 8:20:27 AM


Publication funded by an institution


Distributed under a Creative Commons Attribution 4.0 International License




R. C. D. Paiva, W. Collischonn, Marie-Paule Bonnet, L. G. G. de Gonçalves. On the sources of hydrological prediction uncertainty in the Amazon. Hydrology and Earth System Sciences, European Geosciences Union, 2012, 16, pp.3127-3137. ⟨10.5194/hess-16-3127-2012⟩. ⟨insu-03620301⟩



Record views


Files downloads