Development and evaluation of a hydrologic data-assimilation scheme for short-range flow and inflow forecasts in a data-sparse high-latitude region using a distributed model and ensemble Kalman filtering

Samuel, Jos; Rousseau, Alain N.; Abbasnezhadi, Kian; Savary, Stéphane

doi:10.1016/j.advwatres.2019.06.004

Cited by 12 publications

(7 citation statements)

References 77 publications

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“…Since many years, HYDROTEL is the main hydrological model used operationally at the Direction de l'Expertise Hydrique (Québec's Water Expertise Direction) for short-term flow forecasting (Turcotte et al, 2004) and for simulating the impacts of climate change on river flows as part of the Hydroclimatic Atlas of Southern Quebec (DEH, 2018a). Recently, the model was implemented for short-term operational hydrological forecasting using ensemble Kalman filtering for the Yukon Energy Corporation in northwestern Canada (Samuel et al, 2019). It is noteworthy that the model had previously been used to assess the added value of using ensemble Kalman filtering to improve operational streamflow forecasts during the snow accumulation and melt periods (Abaza et al, 2015) and during the rainfall-runoff production periods (Abaza et al, 2014).…”

Section: Methodology and Experimental Setupmentioning

confidence: 99%

Application of a High‐Resolution Distributed Hydrological Model on a U.S.‐Canada Transboundary Basin: Simulation of the Multiyear Mean AnnualHydrograph and 2011 Flood of theRichelieu River Basin

Lucas‐Picher

Arsenault

Poulin

et al. 2020

J Adv Model Earth Syst

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During spring 2011, an extreme flood occurred along the Richelieu River located in southern Quebec, Canada. The Richelieu River is the last section of the complex Richelieu basin, which is composed of the large Lake Champlain located in a valley between two large mountains. Previous attempts in reproducing the Richelieu River flow relied on the use of simplified lumped models and showed mixed results. In order to prepare a tool to assess accurately the change of flood recurrences in the future, a state-of-the-art distributed hydrological model was applied over the Richelieu basin. The model setup comprises several novel methods and data sets such as a very high resolution river network, a modern calibration technique considering the net basin supply of Lake Champlain, a new optimization algorithm, and the use of an up-to-date meteorological data set to force the model. The results show that the hydrological model is able to satisfactorily reproduce the multiyear mean annual hydrograph and the 2011 flow time series when compared with the observed river flow and an estimation of the Lake Champlain net basin supply. Many factors, such as the quality of the meteorological forcing data, that are affected by the low density of the station network, the steep terrain, and the lake storage effect challenged the simulation of the river flow. Overall, the satisfactory validation of the hydrological model allows to move to the next step, which consists in assessing the impacts of climate change on the recurrence of Richelieu River floods.Plain Language Summary In order to study the 2011 Richelieu flood and prepare a tool capable of estimating the effects of climate change on the recurrence of floods, a hydrological model is applied over the Richelieu basin. The application of a distributed hydrological model is useful to simulate the flow of all the tributaries of the Richelieu basin. This new model setup stands out from past models due to its distribution in several hydrological units, its high-resolution river network, the calibration technique, and the high-resolution weather forcing data set used to drive the model. The model successfully reproduced the 2011 Richelieu River flood and the annual hydrograph. The simulation of the Richelieu flow was challenging due to the contrasted elevation of the Richelieu basin and the presence of the large Lake Champlain that acts as a reservoir and attenuates short-term fluctuations. Overall, the application was deemed satisfactory, and the tool is ready to assess the impacts of climate change on the recurrence of Richelieu River floods.

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Section: Methodology and Experimental Setupmentioning

confidence: 99%

Application of a High‐Resolution Distributed Hydrological Model on a U.S.‐Canada Transboundary Basin: Simulation of the Multiyear Mean AnnualHydrograph and 2011 Flood of theRichelieu River Basin

Lucas‐Picher

Arsenault

Poulin

et al. 2020

J Adv Model Earth Syst

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“…2 for the specific location of the hydrometric stations). The inflows to Aishihik Lake and Mayo Lake do not represent naturally observed discharge values and were reconstructed based on recorded water levels (see Samuel et al (2019) for a detailed description of the reconstruction methodology). For Mayo Lake, water level data obtained from the 09DC005 station and streamflow observed at the YECMAYO station were used for reconstructing inflows.…”

Section: Hydrometric Datamentioning

confidence: 99%

Verification of Regional Deterministic Precipitation Analysis Products Using Snow Data Assimilation for Application in Meteorological Network Assessment in Sparsely Gauged Nordic Basins

Abbasnezhadi

Rousseau

Foulon

et al. 2021

Journal of Hydrometeorology

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Sparse precipitation information can result in uncertainties in hydrological modelling practices. Precipitation observation network augmentation is one way to reduce the uncertainty. Meanwhile, in basins with snowpack-dominated hydrology, in the absence of a high-density precipitation observation network, assimilation of in situ and remotely sensed measurements of snowpack state variables can also provide the possibility to reduce flow estimation uncertainty. Similarly, assimilation of existing precipitation observations into gridded numerical precipitation products can alleviate the adverse effects of missing information in poorly instrumented basins. In Canada, the Regional Deterministic Precipitation Analysis (RDPA) data from the Canadian Precipitation Analysis (CaPA) system have been increasingly applied for flow estimation in sparsely gauged Nordic basins. Moreover, CaPA-RDPA data have also been applied to establish observational priorities for augmenting precipitation observation networks. However, the accuracy of the assimilated data should be validated before being applicable in observation network assessment. The assimilation of snowpack state variables has proven to significantly improve streamflow estimates, and therefore, it can provide the benchmark against which the impact of assimilated precipitation data on streamflow simulation can be compared. Therefore, this study introduces a parsimonious framework for performing a proxy-validation of the precipitation assimilated products through the application of snow assimilation in physically-based hydrologic models. This framework is demonstrated to assess the observation networks in three boreal basins in Yukon, Canada. The results indicate that in most basins, the gridded analysis products generally enjoyed the level of accuracy required for accurate flow simulation and therefore were applied in the meteorological network assessment in those cases.

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“…Other approaches have also been the subject of studies for the prediction of flows. The authors of [19], [20] apply a rain-runoff model to determine soil status for short-term water flow forecasting with strategies that apply the ensemble Kalman filter (EnKF) to assimilate soil data. [21] is also an example of the application of this method in problems of forecasting hydrological aspects, but with a focus on water quality.…”

Section: Related Workmentioning

confidence: 99%

Daily Water Flow Forecasting via Coupling Between SMAP and Deep Learning

et al. 2020

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Hydrological models are essential tools to forecast daily water resources' availability, which are used to plan the short-term electrical systems' operation. However, there is a trade-off when choosing a given model. Complex models may provide good results depending on very complicated analytical and optimization procedures beyond sophisticated data, whereas simpler models offer reasonable results with much more amenable tuning approaches. To improve the quality of simpler models this paper proposes the coupling of the Soil Moisture Accounting Procedure (SMAP) hydrological model with a Deep Learning architecture based on Conv3D-LSTM. In the proposed methodology, the SMAP is first optimized to obtain general parameters of the hydrographic basin. This optimized model's output is used as input to the Conv3D-LSTM estimator to provide the final results. This gray estimator model can generate fast and accurate results. Studies whit the goal of forecast the natural flow seven days ahead are carried out for two large Brazilian hydroelectric plants to validate the method. The results obtained by the architecture are better than those obtained with decoupled techniques.

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Development and evaluation of a hydrologic data-assimilation scheme for short-range flow and inflow forecasts in a data-sparse high-latitude region using a distributed model and ensemble Kalman filtering

Abstract: Development and evaluation of a hydrologic data-assimilation scheme for short-range flow and inflow forecasts in a data-sparse high-latitude region using a distributed model and ensemble Kalman filtering,

Cited by 12 publications

References 77 publications

Application of a High‐Resolution Distributed Hydrological Model on a U.S.‐Canada Transboundary Basin: Simulation of the Multiyear Mean AnnualHydrograph and 2011 Flood of theRichelieu River Basin

Application of a High‐Resolution Distributed Hydrological Model on a U.S.‐Canada Transboundary Basin: Simulation of the Multiyear Mean AnnualHydrograph and 2011 Flood of theRichelieu River Basin

Verification of Regional Deterministic Precipitation Analysis Products Using Snow Data Assimilation for Application in Meteorological Network Assessment in Sparsely Gauged Nordic Basins

Daily Water Flow Forecasting via Coupling Between SMAP and Deep Learning

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