Document Type : Research Paper
Authors
1 hD Student, Department of Physics, University of Trento, Trento, Italy
2 Assistant Professor, Department of Surveying, Faculty of Civil Engineering, Shahrood University of Technology, Shahrood, Iran
3 Senior Researcher, SKR, Cardiff, UK
4 PhD Student, Department of Water Management, Faculty of Civil Engineering, Isfahan University, Isfahan, Iran
5 Assistant Professor, Department of Water and Environmental Engineering, Faculty of Civil Engineering, Shahrood University of Technology, Shahrood, Iran
Abstract
Introduction
The rapid growth of the global population has led to a significant surge in water consumption across various sectors such as agriculture, industry, and domestic use. This heightened demand for water has profound implications, particularly in ensuring food security, meeting industrial needs, and providing safe drinking water. However, alongside this population growth, climate change has emerged as a critical factor, altering precipitation patterns and exacerbating water scarcity issues. In response to these challenges, there is a growing need to identify and manage accessible water resources effectively. This involves understanding the complex interactions between different components of the hydrological cycle, including surface water, groundwater, soil moisture, and atmospheric water. Hydrological models have emerged as valuable tools in this regard, offering insights into water availability, flow patterns, and quality assessment. These models play a crucial role in various aspects of water resource management, including mitigating environmental impacts, managing floods, and predicting future water stress scenarios. Additionally, they facilitate the analysis of watershed-scale dynamics and provide a basis for informed decision-making. Global Hydrological Models (GHMs) have gained prominence due to their ability to capture the interconnectedness of water systems across different regions. They enable researchers to assess and predict hydrological processes on a large scale, contributing to a more comprehensive understanding of water resource dynamics. Recent studies have focused on evaluating the performance of hydrological models, such as the Variable Infiltration Capacity (VIC) model, in simulating river discharge, soil moisture, and precipitation patterns. These evaluations often utilize various data sources, including satellite imagery, to validate model outputs and improve their accuracy. Moreover, the integration of advanced optimization algorithms, such as NSGA-II, enhances the modeling process by optimizing model parameters and improving simulation results. In light of limited ground station data in extensive watersheds, researchers increasingly rely on long-term weather data and modeling techniques to bridge data gaps and improve the accuracy of hydrological predictions. Overall, ongoing research efforts aim to refine hydrological modeling approaches, integrate diverse data sources, and develop robust strategies for sustainable water resource management in the face of growing population pressures and climate uncertainties.
Materials and methods
The Heblehroud Watershed, situated in the southern part of the Central Alborz mountain range, covers approximately 326,991 hectares and lies between coordinates 52° 13' to 53° 13' East longitude and 35° 17' to 35° 58' North latitude. It spans across Tehran, Mazandaran, and Semnan provinces. Mount Sefidab, with an elevation of 4047 meters, marks its highest point. The region features a semi-arid climate, receiving 272 mm of annual rainfall predominantly in winter and spring. The Heblehroud River, originating from the northern mountains, serves as the main drainage outlet. The semi-distributed hydrological model (VIC) was employed in this study to optimize the coefficient of efficiency (KGE) in simulating runoff on daily and monthly scales in the state of water balance. The study validated the VIC model using data from the Bonekooh station and applied the NSGA-II optimization algorithm to calibrate soil parameters from 1992 to 1996, considering the impact of watershed management. Soil data were obtained from the HWSD database available on the FAO website and categorized into 36 classes based on physical and chemical soil properties. Land cover data were sourced from the MODIS satellite database and classified into 17 categories according to the IGBP standard. Elevational bands are crucial in the VIC hydrological model for assessing soil water pressure distribution and surface runoff. In the Heblehroud basin, elevation differences can reach several thousand meters, impacting flow estimation. Therefore, using elevation bands derived from SRTM data is essential for accurate simulation. The accuracy of precipitation data from each database at the cell scale was evaluated using the IDW method.
Results and discussion
The results indicated that the APHRODITE database had the highest accuracy, while PERSIANN-CDR had the lowest. Additionally, the runoff simulation results demonstrated that the VIC hydrological model performed well in simulating daily and monthly runoff. The KGE efficiency index for simulated daily runoff was 0.78 during the calibration period and 0.76 during the validation period. Evaluating the simulated runoff using climatic precipitation data revealed that PERSIANN-CDR satellite precipitation data was less accurate in detecting precipitation amounts but performed better in simulating runoff. The KGE for this data on a daily scale was 0.64 during the calibration period and 0.77 during the validation period. The KGE index for APHRODITE precipitation data based on ground stations ranked second, with values of 0.62 and 0.75 during the calibration and validation periods, respectively. ERA5-Land precipitation data, which is reanalyzed data, ranked third with a KGE index of 0.50 during the calibration period and 0.66 during the validation period.
Conclusions
These findings indicate that climatic precipitation data can be effectively utilized in watershed management studies with low cost and appropriate accuracy, particularly in basins lacking a regular network or long-term data availability.
Keywords
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