Mosaad Khadr

Drought analysis in the Eastern Nile basin using the standardized precipitation index

Drought is considered by many researchers to be the most complex but least understood of all natural hazards, affecting more people than any other hazard. Drought affects many aspects of community and environment, and any future increases in the water demand will be most critical in periods of severe drought. Geospatial analysis of the historical drought events and their causes can be used to mitigate drought impacts and to develop preparedness plans. This study aimed to identify the changes in drought frequency, magnitude, duration, and intensity in the Eastern Nile basin during the period 1965–2000, using the standardized precipitation index (SPI). An SPI program based on C sharp language was developed to monitor drought in the study area. Twenty-eight meteorological stations distributed on the Eastern Nile basin were chosen to collect monthly precipitation data. For drought analysis, SPI series of 3-, 6-, 9-, 12-, and 24-month timescales have been calculated. Results showed that the study area received several drought events during the long rainy season (June to September) and the short rainy season (March to May) as well. Annual analysis of SPI time series indicated that the study area received several drought events, and the most severity event was during the year 1984.

Recent Trends and Fluctuations of Rainfall in the Upper Blue Nile River Basin

During the last decades, water resource managers are facing severe challenges all over the world, and the trends of temperature and precipitation intensify this situation. Knowledge of the spatial variability and temporal trends of rainfall is essential for efficient management of water resource and agriculture. Rainfall plays a crucial role in the process because its variations, coupled with changes in extreme events, can influence water resources, on natural environments and human activities, as well as on human health and safety. In this chapter, an investigation of the rainfall variability of the Upper Blue Nile River Basin, the main source of the Nile River, was carried out by means of a daily rainfall data set of 22 meteorological stations with 48 years of observation. The nonparametric Mann–Kendall test and the Sen’s slope estimator were used to identify the existence of trends and slope magnitude in rainfall at seasonal and annual scale. Results revealed that although there was a mix of positive and negative trends, they were statistically insignificant except at one station. In contrast, the trend and change point analyses of annual series found that 21 of the tested 22 rainfall stations did not show statistically significant changes. Furthermore, analyses of rainy seasonal series (June–September) showed significant changes only at 1 out of 22 stations. The spatial analysis of the trend in the Upper Blue Nile River Basin for several time scales showed that the negative trends are in western side and the positive trends are on the eastern side of the basin.

Data-driven stochastic modeling for multi-purpose reservoir simulation

This paper presents an integration of data-driven modeling and stochastic models for simulation of reservoir operation. The simulation model developed in this study was applied to the Ruhr river reservoirs system in Germany. An adaptive neuro-fuzzy inference system, Thomas–Fiering model and hidden Markov model were integrated in a simulation model. The set of model input included the time of the year, reservoir storage, inflow and Standardized Precipitation Index; and the target output was the reservoir release. Predicted and observed release values were evaluated using several common evaluation criteria. Results of model performance showed that the proposed model is capable of simulating reservoir operation and provides reliable reservoir release prediction. Results showed also that the proposed approach could be a good tool at the real-time operation stage to quickly check operational alternatives due to emergency events or planning and real-time incongruence.

On-Farm Water Management in the Nile Delta

Water resources play an important role in most human activities. During the last decades, water resources managers have faced severe challenges all over the world. As water becomes increasingly scarce in Egypt, the role of agriculture in the use of this essential resource has come under growing scrutiny especially because Egyptian agriculture consumes about 85% of Egypt’s limited water resources. On-farm water resources management plays a vital role in saving and optimizing water resources. The aim of this chapter is to review the irrigation development in the Nile Delta followed by an assessment of the on‐farm irrigation performance and economic aspects in irrigation management for the case study area, using a set of scenarios for improving the agricultural economic efficiency and sustainability of water use, while respecting cultural, hydrological, environmental, and institutional constrains on urban and environmental uses. The results indicate that, compared with the theoretical and actual polices of cultivation, an increase of the economic benefits and a decrease of the consumed water could be gained when using studied scenarios. Alternative cost-benefit scenarios for crop patterns are presented for decision-makers to improve national agricultural policies and to integrate the efforts of both government and farmers.

Modeling of Water Quality Parameters in Manzala Lake Using Adaptive Neuro-Fuzzy Inference System and Stochastic Models

Egyptian coastal lakes, four lakes, and two lagoons represent about 25% of the Mediterranean total wetlands, and the four lakes are located at the north of Nile Delta and are known as Northern Delta Lakes. Manzala Lake, the largest of the Egyptian lakes, is affected qualitatively and quantitatively by drainage water that flows into the lake. In water quality modeling, deterministic models are frequently used to describe the system behavior. However most ecological systems are so complex and unstable. In some cases, the deterministic models have high chance of failure due to absence of prior information. A deterministic model may also have inevitably errors originated from model structures or other causes. For such cases, new modeling paradigm such as data-driven modeling or data mining has recently been a considerable growth in the development and application of computational intelligence and computer tools with respect to water-related problems. This chapter illustrates the capabilities of adaptive neuro-fuzzy inference system (ANFIS) to predict water quality parameters in Manzala Lake based on water quality parameters of drains associated with the Lake. A combination of data sets was considered as input data for ANFIS models, including discharge, pH, total suspended solids, electrical conductivity, total dissolved solids, water temperature, dissolved oxygen, salinity, and turbidity. The models were calibrated and validated against the measured data. The performance of the models was measured using various prediction skills criteria. Results show that ANFIS models are capable of simulating the water quality parameters and provided reliable prediction of total phosphorus and total nitrogen and, thus, suggesting the suitability of the proposed model as a tool for on-site water quality evaluation.