Abstract
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.
Highlights
Recent extreme hydrological events have occurred in the past years in the Amazon River basin, such as the 2009 flood (Chen et al, 2010) and the 1996 (Tomasella et al, 2010), 2005 (Marengo et al, 2008; Zeng et al, 2008; Chen et al, 2009) and 2010 (Espinoza et al, 2011; Marengo et al, 2011) droughts
In upper Solimoes River, discharge starts to rise in September, and the spread of the Ensemble Streamflow Prediction (ESP) run rapidly surpasses the spread of the reverse-ESP run, showing that the importance of uncertainties in meteorological forcings is larger than from initial conditions (Fig. 3a)
This situation changes in the other forecasts when the uncertainty in initial conditions appears to be more important than meteorological forcings
Summary
Recent extreme hydrological events have occurred in the past years in the Amazon River basin, such as the 2009 flood (Chen et al, 2010) and the 1996 (Tomasella et al, 2010), 2005 (Marengo et al, 2008; Zeng et al, 2008; Chen et al, 2009) and 2010 (Espinoza et al, 2011; Marengo et al, 2011) droughts. These extreme events caused several impacts on local population, since most settlements lie along the Amazon and its main tributaries where susceptibility to floods is large. The authors conclude that, in the Amazon, it is possible to forecast seasonal runoff one season in advance with a certain degree of accuracy using empirical models, SST and/or precipitation data, with correlation coefficient between observed and estimated discharges ranging from −0.38 to 0.74 in Uvo and Grahan (1998) and from 0.53 to 0.86 in Uvo et al (2000). Schongart and Junk (2007) presented retrospective forecasts
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