Parameter uncertainty and sensitivity analysis of the three Gorges Reservoir and Xiangxi River EFDC model

Xu, Song; He, Guojian; Fang, Hongwei; Bai, Sen; Wu, Xinghua

doi:10.1016/j.jhydrol.2022.127881

Cited by 24 publications

(8 citation statements)

References 40 publications

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“…Environmental Fluid Dynamics Code (EFDC) model, which has been applied in various aquatic ecosystems (e.g., lakes, rivers, and estuaries) for hydrodynamic and environmental processes simulation (e.g., carbon, nitrogen, and phosphorus cycling, and algal production), − was introduced.…”

Section: Methodsmentioning

confidence: 99%

Crucial Role of Tributary Bays in the Carbon Burial of Three Gorges Reservoir

Wang,

Huang,

et al. 2024

ACS EST Water

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Tributary bays, which are formed in river tributaries for reservoir construction with a relatively lentic environment, have been estimated to hold unique biogeochemical processes and to be involved in ecological issues (e.g., algal bloom). Nevertheless, the role of tributary bays in carbon cycling is unclear, precluding an accurate assessment of the carbon budgets of reservoirs. To fill this knowledge gap, we take the Three Gorges Reservoir (TGR) as an example and assess the carbon budget of a typical tributary bay (Xiangxi River, XXR) of TGR. With laboratory experiments and numerical models, we demonstrate that XXR may be an important carbon sink. This study exhibits that primary productivity enhancement (e.g., algal bloom) in tributary bays plays a critical role in CO 2 sequestration and contributes significantly to carbon burial. Moreover, the organic carbon burial flux of tributary bays in TGR is estimated to be equivalent to (22 ± 9) % of that in the mainstream, demonstrating the crucial role of tributary bays in the carbon burial of TGR. Considering the scientific debate on whether the reservoir is a carbon sink or source, we suggest that the overlooking of tributary bays might lead to a significant error in assessing the reservoir carbon budget.

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Section: Methodsmentioning

confidence: 99%

Crucial Role of Tributary Bays in the Carbon Burial of Three Gorges Reservoir

Wang,

Huang,

et al. 2024

ACS EST Water

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“…According to the actual regional climate conditions, the CNFF model calls for the spatiotemporal variable-source mixed-runoff method in the algorithm library to construct a distributed hydrological model [29], which mainly includes plane mixing, vertical mixing, and period mixed runoff. This paper adopts the RSA method [35], selects nine watersheds in the study area for parameter sensitivity analysis, and selects three flood events for analysis. Three indicators, NSE, RE Q , and E T , were selected to conduct multiple sensitivity analyses on the parameters.…”

Section: Sensitivity Analysismentioning

confidence: 99%

Hydrological Simulation Study in Gansu Province of China Based on Flash Flood Analysis

Zhang,

Wei,

Liu

et al. 2024

Water

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The calibration and validation of hydrological model simulation performance and model applicability evaluation in Gansu Province is the foundation of the application of the flash flood early warning and forecasting platform in Gansu Province. It is difficult to perform the simulation for Gansu Province due to the fact that it covers a wide range, from north to south, with multiple climate types and diverse landforms. The China Flash Flood Hydrological Model (CNFF) was implemented in this study. A total of 11 model clusters and 289 distributed hydrological models were divided based on hydrology, climate, and land-use factors, among others. A spatiotemporally mixed runoff method and the Event-Specific Geomorphological Instantaneous Unit Hydrograph (GIUH) were applied based on large-scale fast parallel computation. To improve model calibration and validation efficiency, the RSA method (Regionalized Sensitivity Analysis) was used for CNFF model parameter sensitivity analysis, which could reduce the number of model parameters that need to be adjusted during the calibration period. Based on the model sensitivity analysis results, the CNFF was established in Gansu Province to simulate flood events in eight representative watersheds. The average NSE, REQ, and ET were 0.76 and 0.73, 9.1% and 12.6%, and 1.2 h and 1.7 h, respectively, in the calibration and validation period. In general, the CNFF model shows a good performance in multiple temporal and spatial scales, thus providing a scientific basis for flash flood early warning and analysis in Gansu Province.

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“…Compared with the hydrodynamic model, the process of water quality simulation was substantially more complex. Widely used numerical models include Water Quality Analysis Simulation Program (WASP) models, Environmental Fluid Dynamics Code (EFDC) models, and MIKE models [12][13][14][15][16][17]. The WASP model was flexible in application and can be widely used in various water quality simulations, but its hydrodynamic simulation was poor.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Early Warning Method for Algal Bloom Risk Based on a Neural Network and a Two-Dimensional Hydrodynamic Model

Luo,

Gu,

Gao

et al. 2024

Pol. J. Environ. Stud.

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Eutrophication of lakes and reservoirs and the occurrence of cyanobacteria blooms are two of the major environmental problems facing the whole world. However, if cyanobacteria bloom outbreaks can be predicted in advance, there is enough time to implement various measures to reduce ecological harm and health risks and greatly reduce economic losses. Yuqiao Reservoir was a typical shallow-water lake reservoir that was also faced with a greater risk of cyanobacteria bloom outbreaks in summer and autumn. In this study, a BP neural network model and a two-dimensional hydrodynamic numerical model were constructed based on meteorological, hydrological, water quality, and terrain data for Yuqiao Reservoir. The neural network model was used to simulate the biogeneration and extinction processes of cyanobacteria bloom biomass, while the two-dimensional hydrodynamic numerical model was used to simulate the migration and accumulation processes of cyanobacteria. The two models were coupled by an algal attenuation coefficient for the shortterm warning of algal bloom. The results of the Nash coefficient evaluation showed that the coupled model had good overall efficiency and could effectively simulate the algal density both in time and space. The results of scenario analysis showed that the change in flow field in a shallow water reservoir had a great influence on algae migration and accumulation, and water transfer, wind speed, and direction all affected the flow field distribution and then the formation of bloom. The model application results showed that the short-term bloom prediction method constructed in this study had advantages in forecasting accuracy and displayed an effect.

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Parameter uncertainty and sensitivity analysis of the three Gorges Reservoir and Xiangxi River EFDC model

Cited by 24 publications

References 40 publications

Crucial Role of Tributary Bays in the Carbon Burial of Three Gorges Reservoir

Crucial Role of Tributary Bays in the Carbon Burial of Three Gorges Reservoir

Hydrological Simulation Study in Gansu Province of China Based on Flash Flood Analysis

Short-Term Early Warning Method for Algal Bloom Risk Based on a Neural Network and a Two-Dimensional Hydrodynamic Model

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