University of Twente Student Theses
A satellite-guided derivation of reservoir dam operation rules in ungauged locations : A Case study in Vietnam
Teunissen, C.T.E. (2024) A satellite-guided derivation of reservoir dam operation rules in ungauged locations : A Case study in Vietnam.
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| Abstract: | Lots of reservoir dams in the world are operated by operation rules. These operation rules are not always publicly available due to several reasons. When they are unknow, data based methods can be used to derive them although in-rsitu data is also not always available in regions lacking direct measurement capabilities. Deriving accurate operational rules for reservoirs remains a challenge for ungauged locations. Traditional methods relying on in-situ data are often inadequate and do have a low temporal resolution, prompting the need for alternative approaches. This study addresses this gap by proposing a methodology that combines remote sensing data, specifically from Sentinel-1 and Sentinel-2 satellites, with fuzzy logic techniques. The objective is to derive the operation rules of a reservoir dam based on readily available satellite data, thereby overcoming the limitations of data scarcity in ungauged basins. The research method begins with the acquisition of satellite data, utilizing the high temporal resolution of Sentinel-1 and the high spatial resolution of Sentinel-2 to derive the Water Surface Area (WSA) of the reservoir behind the dam. This two satellite datasets are then processed by water volume curves to calculate the water volume. The integration of a water balance approach ensures accurate capturing of inflow and outflow dynamics, which forms the foundation for second model of this thesis, the fuzzy logic model. This model is used to obtain a certain set of operation rules of the dam and is calibrated using historical data obtained from satellites and validated against observed measurements, incorporating fuzzy rules to simulate reservoir outflows under various operational conditions. Additionally, the study examines how the model's accuracy can be determined in the absence of in-situ data. The application of the proposed methodology to the Ban Chat reservoir shows an increase in the accuracy of reservoir modelling. The remote sensing-derived water volume time series achieves a Nash-Sutcliffe Efficiency (NSE) of 0.83, indicating a strong correlation with observed data and minor overestimation tendencies (1.93%). This underscores the robustness of using Sentinel satellites for generating reliable water volume estimates in data-scarce environments. Moreover, the fuzzy logic model shows small improvement over traditional demand curve methods, with an NSE of 0.59 and Mean Absolute Error (MAE) of 34.51 x 106 m3 , which are an increase of 7.27% an 1.48% respectively compared to the use of the long term average for modelling the outflow, suggesting its effectiveness in simulating reservoir outflows. The findings highlight several strengths and limitations of the proposed methodology. While remote sensing proves effective in providing continuous data streams for reservoir management, challenges such as data accuracy and resolution persist. The study acknowledges the sensitivity of the fuzzy logic model to input variables and the need for further refinement in defining membership functions to enhance model accuracy. Moreover, the reliance on derived measurements for validation underscores the importance of improving validation methods through complementary approacheslike altimetry satellites. In conclusion, this research demonstrates the potential of integrating remote sensing data with fuzzy logic modelling to derive operational rules for reservoirs in ungauged locations. By leveraging the strengths of Sentinel-1 and Sentinel-2 satellites, the study establishes a framework for managing reservoirs where traditional data sources are limited. The calibrated fuzzy logic model shows promising results in simulating reservoir outflows, providing insights into effective water management strategies. However, the study also identifies several limitations, including data availability and model sensitivity, which require attention in future research efforts. To enhance the applicability of the methodology, future research should focus on refining input variables and membership functions in the fuzzy logic model. Additionally, exploring alternative validation methods, such as integrating altimetry data with remote sensing outputs, could improve the accuracy of reservoir management models. Moreover, expanding the study to different geographical regions and reservoir types would validate the generalizability of the proposed approach. Finally, incorporating hydrological models to predict inflows more accurately could further refine operational rules for reservoirs. |
| Item Type: | Essay (Bachelor) |
| Faculty: | ET: Engineering Technology |
| Programme: | Civil Engineering BSc (56952) |
| Link to this item: | https://purl.utwente.nl/essays/103864 |
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