Evaluation of the Storms Direct Runoff Prediction Methods used for Goizha-Dabashan Watershed

In semi-arid and arid areas, there is little surface and groundwater. Consequently, efforts are being undertaken to protect and collect rainwater as much as possible. When the rainfall is irregular in arid and semi-arid regions, a large portion of it is lost as surface run-off creating a water shortage that puts people at risk. Water collection is a substitute in these circumstances and is crucial. Water harvesting is the process of collecting rainfall and run-off using various storage structures, such as tiny dams, primarily for human, agricultural, and livestock use. For the construction of storage structures to impound and harvest rain and run-off water, forming surface storage reservoirs, four sites with special hydrological and geomorphologic characteristics have been identified and pointed in the drainage map of the studied areas (Tal-Kaif, Zummar, Al-Shor, and Wadi-Almur). A dam model was created using a Global Mapper and Watershed Modeling System program, WMS. Each reservoir's geometrical dimensions and the overall volume of water held behind each site's dams were measured and computed. The four sites' combined storage capacity for water behind the dams might exceed (75×106) m3. Several dam heights were selected in each basin, and the submerged areas for each height were determined to select the optimal height in each basin, i.e., 11.75, 13.9, 9.8, and 12.3 m.

Rainwater Harvesting of Some Catchment Areas in Mosul City/ Iraq by Small Dams Construction

AL-Zakar,

Alsaidi

2023

Application of Machine Learning for Daily Forecasting Dam Water Levels

Almubaidin,

Ahmed,

Winston

et al. 2023

The evolving character of the environment makes it challenging to predict water levels in advance. Despite being the most common approach for defining hydrologic processes and implementing physical system changes, the physics-based model has some practical limitations. Multiple studies have shown that machine learning, a data-driven approach to forecast hydrological processes, brings about more reliable data and is more efficient than traditional models. In this study, seven machine learning algorithms were developed to predict a dam water level daily based on the historical data of the dam water level. Multiple input combinations were investigated to improve the model’s sensitivity, and statistical indicators were used to assess the reliability of the developed model. The study of multiple models with multiple input scenarios suggested that the bagged trees model trained with seven days of lagged input provided the highest accuracy. The bagged tree model achieved an RMSE of 0.13953, taking less than 10 seconds to train. Its efficiency and accuracy made this model stand out from the rest of the trained model. With the deployment of this model on the field, the dam water level predictions can be made to help mitigate issues relating to water supply.

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

Yahya,

AL-Zakar,

AL-Mohseen

2024

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.