A Conflict between Traditional Flood Measures and Maintaining River Ecosystems? A Case Study Based upon the River Lærdal, Norway

Juárez, Ana; Alfredsen, Knut; Stickler, Morten; Adeva‐Bustos, Ana; Suárez, Rodrigo; Seguín-Garcia, Sonia; Hansen, Bendik

doi:10.3390/w13141884

Cited by 11 publications

(6 citation statements)

References 30 publications

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“…Water 2022, 14, x FOR PEER REVIEW 6 of 22 (4) Select the variables (at corresponding lag times) that are closely related to the model errors by analyzing the relationship between the model residuals and different state variables (e.g., observed discharge and water level, previous residuals, etc.) at varying lag times by using the PMI-based input variable selection method; (5) Construct data-driven models (artificial neural networks) that estimate the residuals of the hydrodynamic model by using the best-related variables selected in the previous step; (6) Improve the outputs of the hydrodynamic model by adding the estimated errors as corrections.…”

Section: Basic Ideamentioning

confidence: 99%

“…Floods are one of the most severe natural disasters, and more frequent floods occur due to climate change and changes in land use [1][2][3]. River flooding can significantly disrupt important services, including energy, infrastructure, water provision, and transport [4]. Therefore, flood forecasting is a critical issue for the security of individuals and infrastructures and water resources management.…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Approach to Improve Flood Forecasting by Combining a Hydrodynamic Flow Model and Artificial Neural Networks

Jun

2022

Water

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Climate change is driving worsening flood events worldwide. In this study, a hybrid approach based on a combination of the optimization of a hydrodynamic model and an error correction modeling that exploit different aspects of the physical system is proposed to improve the forecasting accuracy of flood water levels. In the parameter optimization procedure for the hydrodynamic model, Manning’s roughness coefficients were estimated by considering their spatial distribution and temporal variation in unsteady flow conditions. In the following error correction procedure, the systematic errors of the optimized hydrodynamic model were captured by combining the input variable selection method using partial mutual information (PMI) and artificial neural networks (ANNs), and therefore, complementary information provided by the data was achieved. The developed ANNs were used to analyze the potential non-linear relationships between the considered state variables and simulation errors to predict systematic errors. To assess the hybrid forecasting approach (hydrodynamic model with an ANN-based error correction model), performances of the hydrodynamic model, two ANN-based water-level forecasting models (ANN1 and ANN2), and the hybrid model were compared. Regarding input candidates, ANN1 considers the historical observations only, and ANN2 considers not only the historical observations that used in ANN1 but also the prescribed boundary conditions required for the hydrodynamic forecast model. As a result, the hybrid model significantly improved the forecasting accuracy of flood water levels compared to individual models, which indicates that the hybrid model is able to take advantage of complementary strengths of both the hydrodynamic model and the ANN model. The optimization of the hydrodynamic model allowing spatially and temporally variable parameters estimated water levels with acceptable accuracy. Furthermore, the use of PMI-based input variable selection and optimized ANNs as error correction models for different sites were proven to successfully predict simulation errors in the hydrodynamic model. Hence, the parameter optimization of the hydrodynamic model coupled with error correction modeling for water level forecasting can be used to provide accurate information for flood management.

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Section: Basic Ideamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Hybrid Approach to Improve Flood Forecasting by Combining a Hydrodynamic Flow Model and Artificial Neural Networks

Jun

2022

Water

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“…Both traditionally engineered flood control structures and nature-based solutions can play an important role in such adaptation strategies. There is a need to ensure that the flood control measures do not infringe on other important measures to protect biodiversity, habitats, and land use (Juárez et al 2021). Knowledge of the interaction between hydrology, ecology, and other river ecosystem services will be necessary in the planning and design of flood mitigation measures for the future.…”

Section: River Regulation and Flood Controlmentioning

confidence: 99%

Nordic hydrological frontier in the 21st century

et al. 2022

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The 21st century has brought new challenges and opportunities and has also increased demands on the Nordic hydrological community. Our hydrological science focus and approaches need rethinking and adaptation to the changing requirements of society in response to climate change and human interventions, in search of more comprehensive and cross-disciplinary solutions. This commentary highlights new possibilities and suggests vital steps forward for the scientific discipline within Nordic hydrological research. By providing a common direction, we hope to increase awareness, accelerate progress in the hydrological community, and emphasize the importance of hydrological knowledge for serving other fields of science and society at large. We hope that our vision and the opportunities we identify will raise awareness of the scientific discipline and assist in the long-term development of the Nordic hydrological frontier in the 21st century.

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“…Bathymetric LiDAR, on the other hand, is commonly characterized by a laser of 532 nm that penetrates the water and gives bottom detection [16]. The bathymetric LiDAR is the most widespread LiDAR type used in river studies that require high degrees of mapping accuracy such as environmental river studies [17][18][19], sediment transport studies [20,21], and flood modelling [22,23].…”

Section: Introductionmentioning

confidence: 99%

Quantitative Evaluation of Bathymetric LiDAR Sensors and Acquisition Approaches in Lærdal River in Norway

et al. 2023

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The development of bathymetric LiDAR technology has contributed significantly to both the quality and quantity of river bathymetry data. Although several bathymetric LiDAR sensors are available today, studies that evaluate the performance of the different bathymetric LiDAR sensors comparatively are still lacking. This study evaluates the performance of three bathymetric LiDAR sensors, CZMIL Supernova, Riegl VQ880-G, and Riegl VQ840-G, used with different acquisition approaches, in mapping Lærdal River bathymetry in Norway. The performance was evaluated based on comparing the sensors against a multibeam echosounder (MBES), a terrestrial laser scanner (TLS), and by an intercomparison between the individual sensors. The comparison was completed by comparing point clouds from the instruments and through the comparison of DEMs created from the point clouds. For the comparison against the MBES, the results show that the median residuals range between 3 to 13 cm, while against the TLS the median residuals range between 0 to 5 cm. The comparison of the CZMIL sensor against the two Riegl sensors shows median residuals of around 12 cm where the CZMIL map is shallower against the VQ880-G and deeper against the VQ840-G sensor. For the two Riegl sensors, the results show a median difference of 2 cm with the VQ880-G map deeper. We do observe that areas with high residuals are linked to river features such as large substrate variability, steep banks, and whitewater/turbulent flow. The study shows that all the LiDAR instruments provide high-quality representations of the river geometry and create a solid foundation for planning, modelling, or other work in rivers where detailed bathymetry is needed.

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A Conflict between Traditional Flood Measures and Maintaining River Ecosystems? A Case Study Based upon the River Lærdal, Norway

Cited by 11 publications

References 30 publications

A Hybrid Approach to Improve Flood Forecasting by Combining a Hydrodynamic Flow Model and Artificial Neural Networks

A Hybrid Approach to Improve Flood Forecasting by Combining a Hydrodynamic Flow Model and Artificial Neural Networks

Nordic hydrological frontier in the 21st century

Quantitative Evaluation of Bathymetric LiDAR Sensors and Acquisition Approaches in Lærdal River in Norway

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