GIS-SUPPORTED FORECASTING OF HYDROPOWER POTENTIAL FOR SUSTAINABLE ENERGY PLANNING IN MOUNTAINOUS REGIONS (2020–2030)

ABSTRACT

This paper proposes a GIS-based methodology for forecasting hydropower utilization in mountainous river basins, integrating Digital Elevation Models, river network data, and climate-driven discharge models to estimate head, flow, and turbine efficiency. Power output predictions, validated with historical data, indicate an increase from 63 MW (2020) to 218 MW (2030). The findings demonstrate how efficiency upgrades, reservoir optimization, and spatial modeling can reduce variability and support sustainable energy planning in mountainous regions.

АННОТАЦИЯ

В статье предлагается методика прогнозирования использования гидроэнергетических ресурсов горных речных бассейнов на основе ГИС, объединяющая цифровые модели рельефа, данные о речных сетях и климатические модели стока для оценки напора, расхода и КПД турбин. Прогнозы мощности, подтверждённые историческими данными, показывают рост потенциала с 63 МВт (2020 г.) до 218 МВт (2030 г.). Результаты демонстрируют, что повышение эффективности оборудования, оптимизация водохранилищ и пространственное моделирование способствуют снижению сезонной изменчивости и устойчивому энергетическому планированию в горных регионах.

Keywords: GIS modeling; hydropower forecasting; mountain river basins; renewable energy; digital elevation model (DEM); water resources management; sustainable energy planning

Ключевые слова: ГИС-моделирование; прогнозирование гидроэнергетики; горные речные бассейны; возобновляемая энергетика; цифровая модель рельефа (ЦМР); управление водными ресурсами; устойчивое энергетическое планирование

Introduction

Global hydropower capacity has exceeded 1,400 GW as of 2023, supplying nearly 16% of the world’s electricity and making it the largest source of renewable energy. Mountainous river basins contribute a significant share of this capacity due to their steep gradients and abundant water flow, offering high energy potential per installed megawatt. In Central Asia alone, hydropower resources are estimated to exceed 170 TWh annually, yet current utilization remains below 40% [1,2]. Long-term planning for multi-purpose hydropower complexes is vital, as these systems provide not only electricity but also irrigation water, flood regulation, and ecosystem support. However, forecasting hydropower output in mountain regions remains challenging due to climatic variability, seasonal snowmelt, and changing precipitation patterns. Traditional hydrological models often fail to capture the spatial heterogeneity of mountain terrain, leading to inefficiencies in energy planning and infrastructure investments.

Advances in Geographic Information Systems (GIS) and remote sensing technologies have enabled a paradigm shift in hydropower forecasting. GIS-integrated models combine high-resolution topographic data, river network mapping, and climate datasets to provide accurate, location-specific predictions of water availability and energy output. For example, in this study, GIS-based forecasting of a representative river basin predicts an increase in hydropower capacity from 63 MW in 2020 to nearly 218 MW by 2030, driven by efficiency upgrades and improved reservoir management [3,4]. These tools enable policymakers and engineers to optimize water resource utilization, reduce seasonal variability, and plan infrastructure upgrades with greater precision. By integrating spatial analysis with predictive hydrological modeling, GIS provides a robust decision-support framework for sustainable energy strategies in mountainous regions.

Methods

The study employed a Geographic Information System (GIS)-integrated hydrological modeling framework to forecast hydropower resource utilization in mountainous river basins for the period 2020–2030. High-resolution Digital Elevation Models (DEMs), river network datasets, and historical flow records were processed to derive hydraulic head (H) and discharge (Q) values for multiple stations along the river system. River flow data were obtained from regional hydrometeorological networks, while head values were calculated from terrain elevation differences using [4,5]:

where  and  represent upstream and downstream elevations, and  is the head loss due to friction and turbulence. Discharge predictions incorporated snowmelt and precipitation models, parameterized through regional climate datasets. Spatial interpolation techniques, such as Kriging, were applied to map hydrological variables across the basin.

The power output of each hydropower station was estimated using the classical equation:

where  is the turbine-generator efficiency,  is water density (1000 kg/m³), g is gravitational acceleration (9.81 m/s²), Q is discharge (m³/s), and H is head (m). Turbine efficiency was modeled as a function of operational flow conditions, with projected improvements incorporated for the forecast period. The GIS platform facilitated the integration of hydrological models with spatial datasets, enabling scenario simulations for energy production optimization. Statistical techniques, including time-series analysis and regression modeling, were used to validate predicted power outputs against observed data, ensuring model accuracy and reliability[5,6].

Results And Discussion

The GIS-based modeling framework demonstrated strong potential for predicting hydropower resource utilization in mountainous river basins. water density (1000 kg/m³), g is gravitational acceleration (9.81 m/s²), Q is river discharge (m³/s), and H is the effective head (m) [6,7]. The simulation from 2020 to 2030 reveals a projected power output range between 55 MW and 190 MW, depending on seasonal flow variability and infrastructure efficiency upgrades. This formula-based approach highlights the direct relationship between flow dynamics and head elevation, which were dynamically extracted from GIS data layers.

Table 1.

Predicted Hydropower Utilization (2020–2030)

The temporal analysis shows a steady increase in predicted hydropower capacity driven by improvements in turbine-generator efficiency (from 0.85 to 0.94) and modest growth in effective head due to planned reservoir optimization projects. River discharge variability remains the primary driver of output fluctuations, with dry years showing reductions of up to 30% relative to wet-year maxima. GIS integration allowed precise modeling of hydraulic head from terrain elevation data, enabling a more accurate spatial representation of hydropower potential across different stations in the network.

The forecast graph illustrates the upward trend in total power output, with peaks aligning with projected climate-driven discharge patterns (Figure 1). These findings emphasize the importance of incorporating GIS-derived hydrological and topographical parameters into long-term energy planning.

Figure 1. Predicted Hydropower Output (2020-2030)

Furthermore, scenario modeling suggests that strategic reservoir management and flow regulation could smooth seasonal variability, improving annual utilization rates by 12–15%. This study demonstrates that advanced GIS-supported forecasting can serve as a reliable decision-support tool for optimizing multi-purpose hydropower complexes in mountainous regions.

Conclusion

This study demonstrates the effectiveness of advanced GIS-based modeling techniques for predicting hydropower resource utilization in mountainous river basins, offering a robust framework for long-term energy planning. By integrating high-resolution spatial datasets, hydrological modeling, and climate-driven discharge forecasts, the methodology provides precise estimates of hydraulic head and river flow dynamics, leading to more accurate power output predictions. The results highlight a significant growth in hydropower potential from 63 MW in 2020 to 218 MW in 2030, emphasizing the impact of efficiency improvements and optimized reservoir management. The combination of GIS spatial analysis and statistical validation enables decision-makers to minimize seasonal variability, improve infrastructure planning, and enhance the sustainability of multi-purpose hydropower complexes. This approach supports national energy security goals while promoting environmentally responsible management of water resources in mountainous regions.

References:

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