 |
| Titel |
On the sources of hydrological prediction uncertainty in the Amazon |
| VerfasserIn |
R. C. D. Paiva, W. Collischonn, M. P. Bonnet, L. G. G. Gonçalves |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1027-5606
|
| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 16, no. 9 ; Nr. 16, no. 9 (2012-09-05), S.3127-3137 |
| Datensatznummer |
250013458
|
| Publikation (Nr.) |
 copernicus.org/hess-16-3127-2012.pdf |
|
|
|
|
|
| Zusammenfassung |
| 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. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|