Merging rainfall from diverse sources to improve hydrological prediction

Biswa Bhattacharya, Tegegne Tarekegn

Thursday 2 july 2015

14:05 - 14:20h at Central America (level 0)

Themes: (T) Water resources and hydro informatics (WRHI), (ST) Catchment hydrology

Parallel session: 12H. Water resources - Catchment

Hydrological modelling depends heavily on rainfall data from raingauges. Raingauges measure rainfall in a fairly straightforward manner. However, they measure what is falling on a tiny funnel, which is used to infer rainfall over a large area. Weather radars and satellite based rainfall estimates such as from Tropical Rainfall Measuring Mission (TRMM) provide new ways of complementing rainfall data from raingauges. These estimates of rainfall may differ a lot compared to the values measured at raingauges. Combining the diverse rainfall sources is not easy as they employ different measurement techniques and space-time scales. A method of merging these two rainfall products is explored in this study under the framework of Bayesian Data Fusion (BDF) principle. The usefulness of the approach has been explored in a case study on Lake Tana Basin of Upper Blue Nile Basin in Ethiopia. The merged data, along with the data from TRMM and rain gauges, were used in a lumped conceptual model of the study area. Visual inspection of the simulated and observed flow plots and statistical indices for goodness of fit were used to evaluate the predictive capability of the rainfall sources. The model results with the BDF rainfall showed improved prediction of the observed discharge. The results showed the capability of the proposed merging technique in estimating rainfall from diverse sources.