Rainfall-runoff modeling for real-time ecological reserve implementation.
Date
2010-02-03T09:01:07Z
Authors
Halwiindi, Mazunda
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Abstract
Real-time ecological reserve implementation systems are essential for preventing
deterioration, or facilitating the restoration of riverine ecosystems. These
conditions are a result of modified flow regimes that alter essential facets of flow,
and which are in turn due to water resource development projects and other
activities undertaken along river courses. The essential facets of flow for
ecological reserve implementation are presently seen as the low or base flows,
the small increases in flow, referred to as freshes, and the small to medium
floods. Large floods, which may not be impounded, are not considered because
they are unmanageable. The objective therefore is to bring back certain facets of
the original natural flow regime that are essential for the proper functioning of the
riverine ecosystem. These facets rely strongly on the natural variability of river
flow. To accomplish this objective, there is need of effecting optimized reservoir
releases that mimic natural variability of flow, in this way to satisfy both
ecosystem needs as well as human needs for water. Therefore defining an
approach that will provide trigger information to release the required flow
becomes imperative.
The scope of this project is confined to the study of high flows or flood events in a
sub-catchment of the Thukela River in an attempt to build a model that can be
used to forecast an impending flood event and hence trigger appropriate
releases. The models are for making a 1 day forecast of flow from rainfall at two
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rain gauge stations i.e. Heartsease and Monks Cowl stations. The study employs
3 methods: regression analysis, a simple empirical model and Artificial Neural
Networks (ANN) to build the models. The flood events were selected from stream
flow records of a stream gauge at Instream Flow Requirement (IFR) station
V1H010 in the little Thukela River.
The results obtained from the study show that the ANN performed better than the
other two models which yielded unsatisfactory results for prediction of flood
events. ANN analysis produced a coefficient of determination of 0.60 with a
correlation coefficient of 0.78. Results obtained from regression analysis were
0.49 as coefficient of determination and 0.70 for correlation coefficient. Analysis
results from the empirical model showed the worst performance of the three
models with a coefficient of determination of 0.36 and a correlation coefficient of
0.60. The results bring forth the need to further analyze data using more powerful
models in order to achieve better results than those obtained from ANN. The
analysis also indicates that the recommended ecological reserve implementation
from a reserve determination study of the selected catchment cannot even be
met by the natural series and are therefore most likely invalid.