Improving real-time inflow forecasting into hydropower reservoirs through a complementary modelling framework

Reservoir management is a critical component of flood management, and information on reservoir inflows is particularly essential for reservoir managers to make real-time decisions given that flood conditions change rapidly. This study's objective is to build real-time data-driven services that enable managers to rapidly estimate reservoir inflows from available data and models. We have tested the services using a case study of the Texas flooding events in the Lower Colorado River Basin in November 2014 and May 2015, which involved a sudden switch from drought to flooding. We have constructed two prediction models: a statistical model for flow prediction and a hybrid statistical and physics-based model that estimates errors in the flow predictions from a physics-based model. The study demonstrates that the statistical flow prediction model can be automated and provides acceptably accurate short-term forecasts. However, for longer term prediction (2 h or more), the hybrid model fits the observations more closely than the purely statistical or physics-based prediction models alone. Both the flow and hybrid prediction models have been published as Web services through Microsoft's Azure Machine Learning (AzureML) service and are accessible through a browser-based Web application, enabling ease of use by both technical and nontechnical personnel.(KEY TERMS: flooding; data-driven model services; AzureML; reservoir inflow.)

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“…Gragne et al . () proposed an error model to improve hourly reservoir inflow forecasts over one day ahead.…”

Section: Introductionmentioning

confidence: 98%

“…Gragne et al (2015a) implemented a filter updating procedure to update error forecast to improve reservoir inflow forecasts. Gragne et al (2015b) proposed an error model to improve hourly reservoir inflow forecasts over one day ahead.…”

Section: Introductionmentioning

confidence: 99%

Statistical and Hybrid Methods Implemented in a Web Application for Predicting Reservoir Inflows during Flood Events

Zhao

Minsker

Salas

et al. 2017

J American Water Resour Assoc

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“…Hence, it is important to determine the optimal operational schedule of single‐stage or multistage hydroelectric power plants. An accurate and reliable reservoir inflow forecast model is in crucial need to enable optimal and efficient scheduling of hydroelectric resources (Gragne, Sharma, Mehrotra, & Alfredsen, ; Madsen, Richaud, & Pedersen, ; Valipour, Banihabib, & Behbahani, ).…”

Section: Introductionmentioning

confidence: 99%

Impact of aerosols on reservoir inflow: A case study for Big Creek Hydroelectric System in California

Kabir

Yao

et al. 2018

Hydrological Processes

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Accurate and reliable reservoir inflow forecast is instrumental to the efficient operation of the hydroelectric power systems. It has been discovered that natural and anthropogenic aerosols have a great influence on meteorological variables such as temperature, snow water equivalent, and precipitation, which in turn impact the reservoir inflow. Therefore, it is imperative for us to quantify the impact of aerosols on reservoir inflow and to incorporate the aerosol models into future reservoir inflow forecasting models. In this paper, a comprehensive framework was developed to quantify the impact of aerosols on reservoir inflow by integrating the Weather Research and Forecasting model with Chemistry (WRF-Chem) and a dynamic regression model.The statistical dynamic regression model produces forecasts for reservoir inflow based on the meteorological output variables from the WRF-Chem model. The case study was performed on the Florence Lake and Lake Thomas Alva Edison of the Big Creek Hydroelectric Project in the San Joaquin Region. The simulation results show that the presence of aerosols results in a significant reduction of annual reservoir inflow by 4-14%. In the summer, aerosols reduce precipitation, snow water equivalent, and snowmelt that leads to a reduction in inflow by 11-26%. In the spring, aerosols increase temperature and snowmelt which leads to an increase in inflow by 0.6-2%. Aerosols significantly reduce the amount of inflow in the summer when the marginal value of water is extremely high and slightly increase the inflow in the spring when the run-off risk is high. In summary, the presence of aerosols is detrimental to the optimal utilization of hydroelectric power systems.

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“…Chang and Chen, 2001;Hsu et al, 2002;Shamseldin et al, 2002;Dessie et al, 2014;Yaseen et al, 2016), reservoir inflow (e.g. Tsai et al, 2014;Gragne et al, 2015;, floods (e.g. Quiroga et al, 2013;Alvarez-Garreton et al, 2015;Kasiviswanathan et al, 2016), baseflow (e.g.…”

Section: Introduction 35mentioning

confidence: 99%

Modelling salinity in river systems using hybrid process and data-driven models

Hunter

Maier

Gibbs

et al. 2017

Preprint

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Abstract. Salinity modelling in river systems is complicated by a number of processes, including in-stream salt transport and various mechanisms of saline accession that vary dynamically as a function of water level and flow, often at different temporal scales. Traditionally, salinity models in rivers have either been process-or data-driven. The primary problem with process-10 based models is that in many instances, not all of the underlying processes are fully understood or able to be represented mathematically, and that there are often insufficient historical data to support model development. The major limitation of data-driven models, such as artificial neural networks (ANNs), is that they provide limited system understanding and are generally not able to be used to inform management decisions targeting specific processes, as different processes are generally modelled implicitly. In order to overcome these limitations, a hybrid modelling approach is introduced and applied in this 15paper. As part of the approach, the most suitable sub-models are developed for each sub-process affecting salinity at the location of interest based on consideration of model purpose, degree of process understanding and data availability, which are then combined to form the hybrid model. The approach is applied to a 46 km reach of the River Murray in South Australia, which is affected by high levels of salinity. In this reach, the major processes affecting salinity include in-stream salt transport, Hydrol. Earth Syst. Sci. Discuss., https://doi

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Improving real-time inflow forecasting into hydropower reservoirs through a complementary modelling framework

Cited by 27 publications

References 41 publications

Statistical and Hybrid Methods Implemented in a Web Application for Predicting Reservoir Inflows during Flood Events

Statistical and Hybrid Methods Implemented in a Web Application for Predicting Reservoir Inflows during Flood Events

Impact of aerosols on reservoir inflow: A case study for Big Creek Hydroelectric System in California

Modelling salinity in river systems using hybrid process and data-driven models

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