Short-Term River Flow Forecasting Framework and Its Application in Cold Climatic Regions

Belvederesi, Chiara; Dominic, John Albino; Hassan, Quazi K.; Gupta, Anil; Achari, G P Kumaraswamy

doi:10.3390/w12113049

Cited by 9 publications

(9 citation statements)

References 37 publications

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“…This represents a great limitation when attempting to scale-up existing empirical models. Figure 2 shows the results of the work proposed by Belvederesi et al (2020a), which adopted the BDM, previously developed by Veiga et al (2014), to forecast daily river flow at different locations along the Athabasca river over the year. Although the BDM was successfully applied by Veiga et al (2014) to the Bow river basin (drainage area of study: 7860 km 2 ) in Alberta, Canada, it was not suitable for the Athabasca river basin (drainage area of study: 159 000 km 2 ), which is located in the same province.…”

Section: Regression Methods For River-flow Forecastingmentioning

confidence: 99%

“…2b). The main difference between the BDM and the FDM was that the BDM used the BD as flow average over the winter months, whereas the FDM used the Daily Difference (DD), which was the daily difference in flow between a station upstream and a station downstream (Belvederesi et al 2020a).…”

Section: Regression Methods For River-flow Forecastingmentioning

confidence: 99%

“…In this review, river-flow forecasting models that solely rely on mathematical equations to explain the input-output relationship are considered empirical. This category also includes less complex stochastic and deterministic methods, such as simple linear, multilinear, and nonlinear regression, that assist in reducing calibration parameters, computation time and resources, model requirements, and complexity of user interface (Belvederesi et al 2020a).…”

Section: Introductionmentioning

confidence: 99%

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Modelling river flow in cold and ungauged regions: a review of the purposes, methods, and challenges

et al. 2022

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River flow forecasting models assist in the understanding, predicting, monitoring, and managing of issues related to surface-water resources, such as water quality deterioration and flooding, or developing adaptation strategies to cope with climate change and increasing water demand. This review presents an overview of the current research status and progress in river-flow forecasting, focusing on cold climates and ungauged locations. River-flow forecasting in cold regions represents a challenge because the natural processes that occur within catchments vary greatly both seasonally and annually. This variability, which highly depends on climatic and topo-geomorphological characteristics within a basin, translates into increased model uncertainty and a substantial limitation when attempting to forecast river flow in cold regions, which are often poorly gauged or ungauged. To address this limitation, the “Predictions in Ungauged Basins” initiative offers a variety of studies to improve forecasting performance by adopting regionalization, spatial calibration, interpolation, and regression approaches. Process-based models demonstrate significant improvement by including remote-sensing data to replicate and derive complex hydrological processes. Empirical models, which utilize observed data to formulate a graphical solution, unlike mathematical models that require formulating the relationships between the processes, are also implemented with the most recent developments in machine learning, showing exceptional forecasting accuracy. Although process-based models provide a wide understanding of a watershed hydrology, data are often unavailable, expensive, and time-consuming to collect. They also generate numerous calibration parameters, resulting in complex and computationally demanding methods to operate. River-flow forecasting using empirical models reduces the number of calibration parameters but could produce biased results when insufficient variables are available to explain the physical mechanisms of a watershed’s hydrology. Moreover, empirical models could be potentially sensitive to calibration and validation dataset selection. In this review, Canadian studies are primarily selected to highlight some of the efforts that may be necessary in other similar cold and ungauged regions, including: (i) coping with limited data availability through regionalization methods; (ii) providing user-friendly interfaces; (iii) advancing model structure; (iv) developing a universal method for transferring regionalization parameters; (v) standardizing calibration and validation dataset selection; (vi) integrating process-based and empirical models.

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Section: Regression Methods For River-flow Forecastingmentioning

confidence: 99%

Section: Regression Methods For River-flow Forecastingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modelling river flow in cold and ungauged regions: a review of the purposes, methods, and challenges

et al. 2022

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“…In addition, the PR measurement has many applications in flood estimation, computing plant water requirements, hydrological analyses and studying water-related issues including erosion and quality [ 9 ]. For example, extreme AT and PR events caused warming in the coastal and inland areas [ 10 ], and PR records help in forecasting river flow [ 11 ]. Moreover, wind speed and direction recordings are often required to issue weather-related warnings, and they also play a major role in the movement and distribution of spores, pollen, and pollution elements in the atmosphere [ 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Quantifying relations and similarities of the meteorological parameters among the weather stations in the Alberta Oil Sands region

et al. 2022

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Our objective was to quantify the similarity in the meteorological measurements of 17 stations under three weather networks in the Alberta oil sands region. The networks were for climate monitoring under the water quantity program (WQP) and air program, including Meteorological Towers (MT) and Edge Sites (ES). The meteorological parameters were air temperature (AT), relative humidity (RH), solar radiation (SR), barometric pressure (BP), precipitation (PR), and snow depth (SD). Among the various measures implemented for finding correlations in this study, we found that the use of Pearson’s coefficient (r) and absolute average error (AAE) would be sufficient. Also, we applied the percent similarity method upon considering at least 75% of the value in finding the similarity between station pairs. Our results showed that we could optimize the networks by selecting the least number of stations (for each network) to describe the measure-variability in meteorological parameters. We identified that five stations are sufficient for the measurement of AT, one for RH, five for SR, three for BP, seven for PR, and two for SD in the WQP network. For the MT network, six for AT, two for RH, six for SR, and four for PR, and the ES network requires six for AT, three for RH, six for SR, and two for BP. This study could potentially be critical to rationalize/optimize weather networks in the study area.

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“…The classification is based on the required lead time or waiting time of the basin level in relation to rainfall. Floods can be sudden, medium (basin flood) and large floods [5].…”

Section: Introductionmentioning

confidence: 99%

Water Flow Forecasting Based on River Tributaries Using Long Short-Term Memory Ensemble Model

et al. 2021

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Water flow forecasts are an essential information for energy production, management and hydropower control. Advanced actions to optimize electricity production can be taken based on predicted information. This work proposes an ensemble strategy using recurrent neural networks to generate a forecast of water flow at Jirau Hydroelectric Power Plant (HPP), installed on the Madeira River in Brazil. The ensemble strategy consists of combining three long short-term memory (LSTM) networks that model the Madeira River and two of its tributaries: Mamoré and Abunã rivers. The historical data from streamflow of the Madeira river and its tributaries are used to validate the ensemble LSTM model, where each time series of river tributaries are modeled separated by LSTM models and the result used as input for another LSTM model in order to forecast the streamflow of the main river. The experimental results present low errors for training and test sets for individual LSTM networks and ensemble model. In addition, these results were compared with the operational forecasts performed by Jirau HPP. The proposed model showed better accuracy in four of the five scenarios tested, which indicates a promising approach to be explored in water flow forecasting based on river tributaries.

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Short-Term River Flow Forecasting Framework and Its Application in Cold Climatic Regions

Cited by 9 publications

References 37 publications

Modelling river flow in cold and ungauged regions: a review of the purposes, methods, and challenges

Modelling river flow in cold and ungauged regions: a review of the purposes, methods, and challenges

Quantifying relations and similarities of the meteorological parameters among the weather stations in the Alberta Oil Sands region

Water Flow Forecasting Based on River Tributaries Using Long Short-Term Memory Ensemble Model

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