HS.2 New approaches to
hydrological prediction in data sparse regions
Uncertainty assessment
of hydrological predictions due to sparse precipitation stations
S. Wagner1), H. Kunstmann1), A. Bárdossy2)
1) Institute for Meteorology and Climate Research
(IMK-IFU),
2) Institute for Hydraulic Engineering,
University
The spatial variability of precipitation is often termed as the major
source of uncertainty in investigations of rainfall-runoff processes and water
balance estimations. Therefore, two geostatistical approaches are applied for
the uncertainty assessment of hydrological predictions due to the network
structure and density of precipitation stations. First, different spatial
interpolation methods (Thiessen polygon, inverse distance weighting, ordinary,
and external drift kriging) for areal precipitation are applied, and their
impact on water balance estimates is analysed. Second, geostatistical
simulations using the turning band method for areal precipitation are performed
in order to investigate the propagation of consequential uncertainties in water
balance estimations. These results provide ranges of the temporal and spatial
distribution of water balance variables as consequence of uncertainties from
the calculation of areal precipitation interpolation and simulation from
station data.
This study is
performed for the White Volta basin (100,000 km˛), a hydrometeorological data
sparse region in the semi-arid environment of