A Simple Streamflow Forecasting Scheme For The Ganges Basin.
Jiang, Yudan.
2013
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Abstract: This thesis develops two statistical models to predict Ganges river
discharge at Hardinge Bridge in Bangladesh near India/Bangladesh border using only the
previous local flow and upstream rainfall. Current operational flood forecasting in
Bangladesh provides no more than 3-day lead-time forecast, matching the time it takes water
in the Ganges to flow through Bangladesh, mainly due to... read morethe unreleased river flow
information from India. The upstream rainfall used in this thesis is derived from freely
available gridded precipitation data estimated from observed daily precipitation from the
rain-gauge-observation network. The Ganges is a highly seasonal river with high discharge
during July to October, and for other time the monthly mean discharge is smaller than one
eighth of the critical flood level at Hardinge Bridge. Historical discharge data shows that
almost all floods at Hardinge Bridge happened in August and September during the peak flow
season, which is one month lagged in respect to peak season of upstream rainfall. As a
result, the selected forecasting period is August-September. The first model, termed Q--Q,
utilizes the persistence of previous stream flow (Q) to make predictions of future
discharge. The model is able to provide promising forecasting performance within a 3-day
horizon. The second model, termed Q+P--Q, combines upstream rainfall (P) with previous
values of discharge to predict river flow. Results show that the Q--Q model has comparable
forecast ability to Q+P--Q model within 5-day horizon, but Q+P--Q model begins to show
advantage beyond 5-day horizon. For 10-day lead-time forecast, Q+P--Q model can explain 20%
more variance in river discharge than Q--Q model when no rainfall forecast is included. The
utility of rainfall forecast in flow predictions is also examined, model performances of
10-day lead-time flow forecast are compared when different lead-time rainfall forecasts are
incorporated, and results suggest that 8-day rainfall forecast yields the best predicting
capability while 6-day rainfall forecast is able to generate comparable model result. This
thesis also assesses the impact of basin scale on flow forecasting capability. Two more
rivers of different drainage area are studied, Mississippi River at Clinton and Des Moines
River at Keosauqua. Experiments indicate that flow forecasting capability deteriorates with
the decreasing catchment size. In addition, this thesis compares hybrid model, which was
proposed by Nash and Barsi (1983) and particularly developed for highly seasonal rivers,
with the proposed two models in their performances. Results show that for 10-day lead-time
flow forecast, Q+P--Q model outperforms hybrid model during August and September, but
hybrid model provides better forecast ability than Q--Q model for whole-year
forecasting.
Thesis (M.S.)--Tufts University, 2013.
Submitted to the Dept. of Civil Engineering.
Advisor: Shafiqul Islam.
Committee: Shafiqul Islam, David Small, and Levine Stephen.
Keywords: Hydrologic sciences, Water resources management, and Statistics.read less - ID:
- 2r36v850w
- Component ID:
- tufts:21930
- To Cite:
- DCA Citation Guide EndNote
