On the sources of hydrological prediction uncertainty in the Amazon

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.

An experimental seasonal hydrological forecasting system over the Yellow River basin – Part 1: Understanding the role of initial hydrological conditions

“…As compared with snow, soil moisture has less impact on the hydrological predictability during the snow melting season but can affect the predictability significantly during other seasons, where its dominance can last over 6 months over certain river basins (Mahanama et al, 2012). In addition, the IC of groundwater is also important during the low-flow period where the subsurface runoff dominates the streamflow (Paiva et al, 2012).…”

“…Since then, the revESP framework has been widely used to assess the role of ICs at regional to global scales (Li et al, 2009;Koster et al, 2010;Shukla and Lettenmaier, 2011;Paiva et al, 2012;Singla et al, 2012;Shukla et al, 2013;Yossef et al, 2013;Staudinger and Seibert, 2014;Yang et al, 2014). However, most assessments did not explicitly investigate the role of the IC of the surface water state variables in the streamflow forecasting, where it could be a major source of hydrological forecast uncertainty over rivers with low slope and large floodplains (Paiva et al, 2012). In addition, the ICs may have different impacts on the hydro- Table 1.…”

On the sources of global land surface hydrologic predictability

“…Hydrologic predictability at seasonal lead times (1 to 6 months) is derived from knowledge of initial hydrologic conditions (IHCs), which includes soil moisture (SM), snow water content (SWE), ground water and surface water (Paiva et al, 2012;Singla et al, 2012;Rosenberg et al, 2013) and seasonal climate forecast skill (FS) of meteorological variables like temperature, precipitation. In the past, numerous studies have investigated the contributions of the IHCs and/or FS in seasonal hydrologic predictability over different regions of the globe.…”

Near-real-time adjusted reanalysis forcing data for hydrology

“…The hydrological skill sensitivity to the initial state and/or the meteorological forecast varies as a function of the season, which has been shown for both seasonal and short-term forecasts (Li et al, 2009;Shukla and Lettenmaier, 2011;Paiva et al, 2012;Demirel et al, 2013;Pechlivanidis et al, 2014). In most cases, however, hydro-logical forecast models are initialized by hindcast simulations covering some period before the forecast issue date, for which appropriate meteorological forcing data are needed.…”