Forecasting lake evaporation under a changing climate with an integrated artificial neural network model: A case study Lake Nasser, Egypt
Graphical abstract
Introduction
Our planet is changing due to climate variations. These variations are affected mainly by anthropogenic interventions. Climate variability refers to statistical deviations of climate over a specific period of time. But climate change denotes a considerable climatological shift. Water is the main stem of living on the Earth. It is essential to most anthropogenic activities. Water plays a complex and multifunctional role in human life and society (El-Mahdy, 2014). The Nile River is the prime source of fresh water in Egypt. Most of Egypt's area is defined as semi-arid to arid area, with too little rainfall and underground water (Entz, 1976). After building High Aswan Dam (HAD) as a multipurpose project, Lake Nasser the long-term storage reservoir was created. The lake is born in both Egypt and Sudan between latitudes 20° 27' N and 23° 58' N, and longitudes 30° 07' E and 33°15’ E (Badawy, 2009). Its maximum surface area is about 6500 Km2 and its maximum capacity is 162 BCM at elevation 182m above mean sea level (amsl); its mean depth 25m. The Lake is about 500 Km long (about 350 Km in Egypt and about 150 Km in Sudan), while the maximum width is reaching about 35 Km (El-Mahdy et al., 2018). The lake is considered the main stem of the Egyptian economy. Fig. 1 shows a site map of Lake Nasser. Its importance to Egypt cannot be denied. The arable lands around the Lake region represent the future food source for Egypt.
If the Nile River was not in Egypt, the whole country may not be there. The population of Egypt is predicted to be 124 million within 30 years. Although HAD was finished in 1970, but the surrounding agricultural land around the area is not exceeding 4% of the available area. The main restriction in front of agriculture extension is the available water, not the arable soil. Climate change is a very challenging and complex problem facing all the researchers in the forecasting of future hydrologic cycles worldwide. Current predictions of climate change's impact on hydrologic cycles are largely pessimistic in many parts of the world. In general, evaporation is the phenomenon by which a substance is converted from the liquid phase into the vapor phase (Monteith, 1965). Evaporation is pertinent to different disciplines of various scales. It reduces the available water quantity and quality because the evaporation process takes pure water only (Monteith, 1981). Climate change proves to impact the continent of Africa as shown in Fig. 2. For all the above-mentioned reasons, better quantification of Lake Nasser evaporation, in addition to the impact of climate change on it, is a must. In this study, the environmental influences, incorporating meteorological and hydrological records over the Lake, were taken into consideration. These records are used to explore the evaporation and to predict the climate change impact on the lake hydrology. Most of the evaporation prediction models used worldwide depend on the basics of energy balance, aerodynamic, a combination of both, or empirical formulas. The equations, that were often used for calculating evaporation, should undergo careful calibrations, so they have constraints on a global scale. Taking these constraints into account, the traditionally used evaporation techniques need improvements using lesser complex techniques. The impact of climate change on lakes is studied in many papers (Beyene, 2016; Erler et al., 2019; Fontana et al., 2019). The hydrologists used many methods to calculate hydrological phenomena, one of them is the Artificial Neural Network (Asadi et al., 2019; Humphrey et al., 2016; Pradhan et al., 2020; Zema et al., 2020). Many papers studied the impact of meteorological parameters on Lake Nasser (Khedr et al., 2013, 2014). The objective of the study is to develop an accurate evaporation model for Lake Nasser and to apply it to predict the future evaporation of the lake for better planning and management.
Section snippets
Evaporation prediction methods
Evaporation prediction Methods are often used data that is not always available for all regions. The evaporation rate from the free water surface depends on meteorological parameters of the overlying air. It also depends on the balance of energy between the air and water, and the quantity of stock energy inside the body of water. Due to the obscurity of evaporation gauging, the calculation models are the only available alternatives. The number of suggested models to quantify evaporation from
Artificial neural network (ANN)
ANN is a largely parallel-distributed data processing system. It imitates the process of the neuron system in the brain. Neurons' network in the human brain is responsible for their ability to learn. This considerable property is the base of machine learning in ANN (McCulloch and Pitts, 1943). (ANN) is also a computational method devised in the researches of the nervous system of living organisms. ANNs introduce perfect mathematical models simulating complex systems. The most significant
Hydrological data
The evaporation data used in this study for each month from January 1997 to July 2007 taken from the Ministry of Water Resources and Irrigation reports.
Meteorological data
The meteorological data used in this study for each month from January 1997 to July 2007 measured at Aswan meteorological station are as follows:
- 1.
Mean monthly air temperature.
- 2.
Mean monthly wind speed at a height of 2 m.
- 3.
Mean monthly sea level pressure.
- 4.
Mean monthly relative humidity.
- 5.
Mean monthly solar radiation.
Predicted meteorological data
Global Climate Models (GCM) can
Methods
MATLABFX is a highly qualified software for mathematical modeling. It is founded and originally evolved by mathematical programmers (Moler, 2004). MATLAB is devoted to calculations of matrices. The matrix is a mathematical term that denotes an array of numbers as follows:
Results and discussion
In this study, the ideal number of hidden layer neurons in the ANN model was specified by trial and error procedure. The procedure launched initially with two neurons and increased to 22 with a step size of two. For each trial, the ANN model was trained in a bulk mode to idealize the MSE at the output neurons. To examine the over-fitting, cross-validation was applied through the training process. Always, the available modeling data is divided into three datasets (training, validation, and
Conclusions
Lake Nasser's evaporation prediction model using artificial neural networks technique was built. Statistical analysis has been done in the calibration and validation stages to find the ideal model of the Lake evaporation calculation. The predictions of future evaporation were extracted from the model using predicted climatological data. The data from CORDEX regional climate models with two emission scenarios; scenario RCP 4.5 and scenario RCP 8.5 were used. Trend analysis was done to assure the
Data availability statement
Some or all data, models, or code generated or used during the study are available from the corresponding author by request.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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