Assimilation of water temperature and discharge data for ensemble water temperature forecasting

Ouellet-Proulx, Sébastien; Chiadjeu, Olivier Chimi; Boucher, Marie‐Amélie; St‐Hilaire, André

doi:10.1016/j.jhydrol.2017.09.027

Cited by 18 publications

(20 citation statements)

References 71 publications

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“…2 the highest water temperature, reaching 29.5 • C. In addition, other highly polluted areas such as Nos. 3 and 6 registered water temperatures higher than 26 • C. Although the water temperature can register to seven different configurations of fluctuation patterns [34] and high daily variations [35], these results can be considered as extreme for a river if we compare our results with those obtained by Vliet et al [36] analyzing different rivers from Asia, Europe, and America. Using the PCA, we observed a positive relationship in the fourth component between water temperature and Fe.…”

Section: Discussionsupporting

confidence: 44%

Identifying the Key Information and Land Management Plans for Water Conservation under Dry Weather Conditions in the Border Areas of the Syr Darya River in Kazakhstan

Yegemova

Kumar

Abuduwaili

et al. 2018

Water

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Due to an increase in poorly planned anthropogenic activities, the water quality of several Asian big rivers is highly being affected. Although the assessment of heavy metal contents is vital to develop and design sustainable water management plans, several areas in Central Asia such as Kazakhstan do not have recent studies available that evaluate this situation. One representative example of this lack of information is the Syr Darya River. Thus, this study carried out the first approach to a water quality assessment in Kazakhstan’s Syr Darya River, where a massive expansion of irrigation canals, pastures in middle- and lower-reaches and an increase in industrialization and population have lowered its potential water capacity. To achieve this goal, various physicochemical parameters were analyzed for forty-three water samples along the river under dry weather conditions at 25 cm water depth. The obtained results were analyzed using standard methods (e.g., Multi N/C 2100 S analyzer or an atomic absorption spectrometer) and evaluated by multivariate techniques (cluster analysis (CA), principal component analysis (PCA), and non-metric multidimensional scaling (NDMS)) and a heavy metal pollution index (HPI). In the CA, five cluster groups were obtained. It is important to remark that the first cluster consists of the highest number of water sampling points (8). The last cluster is made up of only one point, which shows the highest difference against the other sites in our model. The NDMS also confirmed that some specific points along the river are different. Five components were extracted from the PCA: (1) COD (chemical oxygen demand), Zn, Cu, Pb, Ni and Mn; (2) Cu, Cd, Ni and Co; (3) T (water temperature), pH and DO (dissolved oxygen); (4) T and Fe; and (5) COD and OC (organic carbon). The HPI showed very high values (279.9), which were locally confirmed in some hotspots close to the Aral Sea, industrialized areas and agricultural fields. Therefore, our results demonstrate that, under dry weather conditions, surface water resources could be mismanaged in the Syr Darya River in Kazakhstan in specific areas. For the future, considering the important role that agriculture and pasture play in the Kazakh economy, we insist upon the importance of applying water quality control measures applying nature-based solutions and efficient management plans. Moreover, we confirmed the necessity to conduct further research related to sampling under other weather situations such as wet and cold conditions, different river water depths and other locations considering specific land uses, for example, grazing, mining, railways or industries.

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Section: Discussionsupporting

confidence: 44%

Identifying the Key Information and Land Management Plans for Water Conservation under Dry Weather Conditions in the Border Areas of the Syr Darya River in Kazakhstan

Yegemova

Kumar

Abuduwaili

et al. 2018

Water

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“…Real-time forecasts of water temperature with fully-specified uncertainties are particularly valuable for managers that oversee drinking water supply lakes and reservoirs, as waterbody temperatures can be very dynamic due to meteorological forcing, management, the evaluation of both forecast accuracy and the reliability of uncertainty estimation. Despite the importance of quantifying multiple uncertainty sources, few water resource forecasting studies quantify more than one or two sources of uncertainty and when they do, they typically only include initial conditions uncertainty (via data assimilation) and meteorological uncertainty (via ensemble weather forecasts) [e.g., Baracchini et al, 2020b;Komatsu et al, 2007;Ouellet-Proulx et al, 2017a;Ouellet-Proulx et al, 2017b;Page et al, 2018].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, they rarely partition the relative contributions of the individual sources of uncertainty to the total forecast uncertainty [but see Ouellet-Proulx et al, 2017a].…”

Section: Introductionmentioning

confidence: 99%

“…First, FLARE was built using all open‐source software and model components and is completely automated via its cyberinfrastructure: once set up, the forecasting system automatically retrieves real‐time observations for data assimilation, runs models, and triggers the delivery of daily forecasts and system status updates. Second, unlike many real‐time water forecasting systems that depend on governmental agencies for the collection of data used for assimilation (e.g., Ouellet‐Proulx, Chimi Chiadjeu, et al, 2017; Pike et al, 2013), FLARE forecasts can be run and updated with water temperature data commonly collected by local water managers. These features enable scalability to a range of waterbodies without existing infrastructure.…”

Section: Introductionmentioning

confidence: 99%

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A Near‐Term Iterative Forecasting System Successfully Predicts Reservoir Hydrodynamics and Partitions Uncertainty in Real Time

Thomas

Figueiredo

Daneshmand

et al. 2020

Water Resources Research

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“…Temperature (i.e., water temperature, soil temperature and Urban Heat Island, etc.) forecasting plays an important role in eco-environment-related research involving the functioning of the eco-environment system [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 ]. In industry, accurate forecasts of temperature are part of an energy-management strategy to reduce energy consumption while maintaining an internal temperature within a specified comfort range [ 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature Forecasting Using Long Short-Term Memory Neural Network Based on Ensemble Empirical Mode Decomposition

Zhang

Gui

et al. 2018

IJERPH

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Daily land surface temperature (LST) forecasting is of great significance for application in climate-related, agricultural, eco-environmental, or industrial studies. Hybrid data-driven prediction models using Ensemble Empirical Mode Composition (EEMD) coupled with Machine Learning (ML) algorithms are useful for achieving these purposes because they can reduce the difficulty of modeling, require less history data, are easy to develop, and are less complex than physical models. In this article, a computationally simple, less data-intensive, fast and efficient novel hybrid data-driven model called the EEMD Long Short-Term Memory (LSTM) neural network, namely EEMD-LSTM, is proposed to reduce the difficulty of modeling and to improve prediction accuracy. The daily LST data series from the Mapoling and Zhijiang stations in the Dongting Lake basin, central south China, from 1 January 2014 to 31 December 2016 is used as a case study. The EEMD is firstly employed to decompose the original daily LST data series into many Intrinsic Mode Functions (IMFs) and a single residue item. Then, the Partial Autocorrelation Function (PACF) is used to obtain the number of input data sample points for LSTM models. Next, the LSTM models are constructed to predict the decompositions. All the predicted results of the decompositions are aggregated as the final daily LST. Finally, the prediction performance of the hybrid EEMD-LSTM model is assessed in terms of the Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Pearson Correlation Coefficient (CC) and Nash-Sutcliffe Coefficient of Efficiency (NSCE). To validate the hybrid data-driven model, the hybrid EEMD-LSTM model is compared with the Recurrent Neural Network (RNN), LSTM and Empirical Mode Decomposition (EMD) coupled with RNN, EMD-LSTM and EEMD-RNN models, and their comparison results demonstrate that the hybrid EEMD-LSTM model performs better than the other five models. The scatterplots of the predicted results of the six models versus the original daily LST data series show that the hybrid EEMD-LSTM model is superior to the other five models. It is concluded that the proposed hybrid EEMD-LSTM model in this study is a suitable tool for temperature forecasting.

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Assimilation of water temperature and discharge data for ensemble water temperature forecasting

Cited by 18 publications

References 71 publications

Identifying the Key Information and Land Management Plans for Water Conservation under Dry Weather Conditions in the Border Areas of the Syr Darya River in Kazakhstan

Identifying the Key Information and Land Management Plans for Water Conservation under Dry Weather Conditions in the Border Areas of the Syr Darya River in Kazakhstan

A Near‐Term Iterative Forecasting System Successfully Predicts Reservoir Hydrodynamics and Partitions Uncertainty in Real Time

A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature Forecasting Using Long Short-Term Memory Neural Network Based on Ensemble Empirical Mode Decomposition

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