Disaggregation Model of Tigris River Inflow into a Proposed Makhol Reservoir Using Parametric Approach

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Aaisha Mohand Yahya
Shatha H.D. AL-Zakar
Kamel A. AL-Mohseen


Since Makhol dam is planned to be constructed on Tigris River to the north of Baiji discharge measurement station, it is essential to study the nature of inflow into this reservoir. The information concerning this inflow is of great help in operating and management of the prospective reservoir. From our point of view, it is necessary to know how these inflows are distributed and contributed to Tigris from different upstream sources. Disaggregation flow models are stochastic generation techniques, that used to divided data into lower time scales from higher time scales using parametric approaches with two main categories: spatial and temporal. In the streamflow disaggregation model, historical data statistics (mean, skewness, standard deviation, maximum, and minimum) can be preserved while distributing single-site values to several sites in space and time. In this study, the aggregated streamflows data at a key station will be disaggregated into a corresponding series of discharges at sub-stations that are statistically similar to those observed by applying Stochastic Analysis Modeling and Simulation (SAMS 2010) software. To investigate  the appropriate the disaggregation method for modeling monthly flow data, we used the annual and monthly data flow of five gauging stations in the Tigres River in Iraq (Mosul Dam station on Tigris river, Asmawah on AlKhazir river, Eski Kalak on Upper Zab, Dibs Dam on Lower Zab, and Baiji station on Tigris river) for the duration 2000–2020. The application approach's statistical outcomes were contrasted with their historical counterparts and the results showed that most years and months at all stations were in good agreement with the historical data. Therefore, we argue that this method have ability to be used when making decisions about water management strategies in these regions which is essential for water resource managers and decision makers.

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