Mathematical modeling of shallow-water flows on steep slopes

Ni, Yufang; Cao, Zhixian; Liu, Qingquan

doi:10.2478/johh-2019-0012

Cited by 11 publications

(4 citation statements)

References 37 publications

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“…Therefore, the FEI value obtained using S‐06 and S‐07 suggests a numerical error due to a combination of high flow velocity and large grid size. This may be related to assumptions in the Shallow Water Equations that do not hold for steep slopes (Denlinger & O'Connell, 2008; Ni et al, 2019). Interestingly, 29‐IN is no longer prioritised in the top 5 by simulation S‐08 (30 m grid size), implying that this grid size smooths out the slope sufficiently enough to reduce this error.…”

Section: Discussionmentioning

confidence: 99%

Tailings storage facilities flood exposure assessment using a 2D hydrodynamic model

Murillo

McIntyre

Cohen

et al. 2022

J Flood Risk Management

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Minimizing environmental pollution from Tailings Storage Facilities (TSFs) is one of the key goals of the mining sector. While much attention is given to risks from potential catastrophic failures of large TSFs, there is also a need to assess risks due to noncatastrophic but more frequent releases of contaminants. In response to this need, this article proposes a method for prioritizing TSFs in terms of flood exposure, based on flood maps produced by the Shallow Water Integrated Flood Tool. The method was implemented using a case study of a flood event in the Copiap o city region (Atacama, Chile). A sensitivity analysis was carried out to evaluate the influence of different model configurations and input parameters. The results showed that 10% of all the TSFs in the case study region were exposed to flood waters due to the fluvial dynamics of the Copiapo River. Additionally, the sensitivity analysis revealed that in the event of extreme local precipitation, capable of generating runoff, up to 88% of all the TSFs could have some degree of flood exposure. The results also suggest that flood simulations using satellite-derived terrain data at moderate resolution can be used for a preliminary large-scale assessment of TSF flood exposure. K E Y W O R D Scontaminated land, extreme events, mapping of flood hazards, SWIFT, TSFs

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Section: Discussionmentioning

confidence: 99%

Tailings storage facilities flood exposure assessment using a 2D hydrodynamic model

Murillo

McIntyre

Cohen

et al. 2022

J Flood Risk Management

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“…This is especially relevant if sediment transport processes and granular flows are investigated. The differences between the use of a global coordinate system and a local coordinate system can be compensated by increasing the roughness parameter in a realistic range [91]. Additionally, the effects are relevant for events with long durations.…”

Section: Model Calibration and Validationmentioning

confidence: 99%

“…In particular, the optimization of the surface roughness in steep locations is impactful. This conclusion must be re-evaluated if a model is used with the formulation of the mathematical equations in a local coordinate system because this represents the flow characteristics more realistic at a steep surface [90,91]. In contrast, an optimization of the model parameters for an evaluation concerning the water depth does not have a large impact (UC = 12.41).…”

Section: High-impact Uncertainty Sourcesmentioning

confidence: 99%

Identification of High-Impact Uncertainty Sources for Urban Flood Models in Hillside Peri-Urban Catchments

Reinstaller

Krebs

Pichler

et al. 2022

Water

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Climate change, as well as increasing urbanization, lead to an increase in urban flooding events around the world. Accurate urban flood models are an established tool to predict flooding areas in urban as well as peri-urban catchments, to derive suitable measures to increase resilience against urban flooding. The high computational cost and complex processes of urban flooding with numerous subprocesses are the reason why many studies ignore the discussion of model uncertainties as well as model calibration and validation. In addition, the influence of steep surface (hillside) conditions on calibration parameters such as surface roughness are frequently left out of consideration. This study applies a variance-based approach to analyze the impact of three uncertainty sources on the two variables—flow and water depth—in a steep peri-urban catchment: (i) impact of DEM validation; (ii) calibration of the model parameter; (iii) differences between 1D/2D and 2D models. The results demonstrate the importance of optimizing sensitive model parameters, especially surface roughness, in steep catchments. Additional findings of this work indicate that the sewer system cannot be disregarded in the context of urban flood modeling. Further research with real heavy storm events is to be pursued to confirm the main results of this study.

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“…Studies have documented that runoff processes in steep slopes or catchments are quite different from those with moderate slope [80,81]. Considering catastrophic flash floods often occur in steep catchments, it is therefore necessary to study the parameters estimation approach in order to better predict the occurrence of flash floods using hydrological models.…”

Section: Limitations and Future Directionsmentioning

confidence: 99%

Estimating the Routing Parameter of the Xin’anjiang Hydrological Model Based on Remote Sensing Data and Machine Learning

Fang

Huang²,

et al. 2022

Remote Sensing

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The parameters of hydrological models should be determined before applying those models to estimate or predict hydrological processes. The Xin’anjiang (XAJ) hydrological model is widely used throughout China. Since the prediction in ungauged basins (PUB) era, the regionalization of the XAJ model parameters has been a subject of intense focus; nevertheless, while many efforts have targeted parameters related to runoff yield using in-site data sets, classic regression has predominantly been applied. In this paper, we employed remotely sensed underlying surface data and a machine learning approach to establish models for estimating the runoff routing parameter, namely, CS, of the XAJ model. The study was conducted on 114 catchments from the Catchment Attributes and MEteorology for Large-sample Studies (CAMELS) data set, and the relationships between CS and various underlying surface characteristics were explored by a gradient-boosted regression tree (GBRT). The results showed that the drainage density, stream source density and area of the catchment were the three major factors with the most significant impact on CS. The best correlation coefficient (r), root mean square error (RMSE) and mean absolute error (MAE) between the GBRT-estimated and calibrated CS were 0.96, 0.06 and 0.04, respectively, verifying the good performance of GBRT in estimating CS. Although bias was noted between the GBRT-estimated and calibrated CS, runoff simulations using the GBRT-estimated CS could still achieve results comparable to those using the calibrated CS. Further validations based on two catchments in China confirmed the overall robustness and accuracy of simulating runoff processes using the GBRT-estimated CS. Our results confirm the following hypotheses: (1) with the help of large sample of catchments and associated remote sensing data, the ML-based approach can capture the nonstationary and nonlinear relationships between CS and the underlying surface characteristics and (2) CS estimated by ML from large samples has a robustness that can guarantee the overall performance of the XAJ mode. This study advances the methodology for quantitatively estimating the XAJ model parameters and can be extended to other parameters or other models.

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Mathematical modeling of shallow-water flows on steep slopes

Cited by 11 publications

References 37 publications

Tailings storage facilities flood exposure assessment using a 2D hydrodynamic model

Tailings storage facilities flood exposure assessment using a 2D hydrodynamic model

Identification of High-Impact Uncertainty Sources for Urban Flood Models in Hillside Peri-Urban Catchments

Estimating the Routing Parameter of the Xin’anjiang Hydrological Model Based on Remote Sensing Data and Machine Learning

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