Modelling and Upscaling Unstable Miscible Displacement Processes: Characterisation of Physical Instabilities

The compositional simulations are required to model CO2 flooding are computationally expensive particularly for fine-gridded models that have high resolutions, and many components. Upscaling procedures can be used in the subsurface flow models to reduce the high computation requirements of the fine grid simulations and accurately model miscible CO2 flooding. However, the effects of physical instabilities are often not well represented and captured by the upscaling procedures. This paper presents an approach for upscaling of miscible displacements is presented which adequately represents physical instabilities such as viscous and heterogeneity induced fingering on coarser grids using pseudoisation techniques. The approach was applied to compositional numerical simulations of two-dimensional reservoir models with a focus on CO2 injection. Our approach is based on the pseudoisation of relative permeability and the application of transport coefficients to upscale viscous fingering and heterogeneity-induced channelling in a multi-contact miscible CO2 injection. Pseudo-relative permeability curves were computed using a pseudoisation technique and applied in combination with transport coefficients to upscale the behaviour of fine-scale miscible CO2 flood simulations to coarser scales. The accuracy of the results of the pseudoisation procedures were assessed by applying statistical analysis to compare them to the results of the fine grid simulations. It is observed from the results that the coarse models provide accurate predictions of the miscible displacement process and that the fingering regimes are adequately captured in the coarse models. The study presents a framework that can be employed to represent the dynamics of physical instabilities associated with miscible CO2 displacements in upscaled coarser grid reservoir models.

Emulation of 2D Hydrodynamic Flood Simulations at Catchment Scale Using ANN and SVR

Two-dimensional (2D) hydrodynamic models are one of the most widely used tools for flood modeling practices and risk estimation. The 2D models provide accurate results; however, they are computationally costly and therefore unsuitable for many real time applications and uncertainty analysis that requires a large number of model realizations. Therefore, the present study aims to (i) develop emulators based on SVR and ANN as an alternative for predicting the 100-year flood water level, (ii) improve the performance of the emulators through dimensionality reduction techniques, and (iii) assess the required training sample size to develop an accurate emulator. Our results indicate that SVR based emulator is a fast and reliable alternative that can predict the water level accurately. Moreover, the performance of the models can improve by identifying the most influencing input variables and eliminating redundant inputs from the training process. The findings in this study suggest that the training data size equal to 70% (or more) of data results in reliable and accurate predictions.

Validation of Flash Flood Simulations Using Satellite Images and Community-Based Observations—Impact of Infiltration and Small-Scale Topographical Features

This work is aimed at investigating flash floods in the region of El Gouna, Egypt, by using a 2D robust shallow-water model that incorporates the Green-Ampt model to find the most realistic infiltration setting for this desert area. The results of different infiltration settings are compared to inundation areas observed from LANDSAT 8 images as well as to community-based information and photographs to validate the results despite scarce data availability. The model tends to overestimate infiltration in the study area if tabulated Green-Ampt parameters for the dominant soil texture class are considered. Specifically, bare soils with no vegetation tend to develop a surface crust, leading to significantly decreased infiltration rates during heavy rainfalls. Comparing the results of different infiltration settings with the observed data showed that the crust approach or the consideration of sandy clay loam instead of sand led to more plausible results for the considered study area than those obtained using the values for sand from two different sources in the literature. Furthermore, small-scale structures, which are not appropriately captured in the original digital surface model, but significantly affect the resulting flow field, have been included based on the available information leading to much more plausible results.

A practical methodology to perform global sensitivity analysis for 2D hydrodynamic computationally intensive simulations

Sensitivity analysis is a commonly used technique in hydrological modeling for different purposes, including identifying the influential parameters and ranking them. This paper proposes a simplified sensitivity analysis approach by applying the Taguchi design and the ANOVA technique to 2D hydrodynamic flood simulations, which are computationally intensive. This approach offers an effective and practical way to rank the influencing parameters, quantify the contribution of each parameter to the variability of the outputs, and investigate the possible interaction between the input parameters. A number of 2D flood simulations have been carried out using the proposed combinations by Taguchi (L27 and L9 orthogonal arrays) to investigate the influence of four key input parameters, namely mesh size, runoff coefficient, roughness coefficient, and precipitation intensity. The results indicate that the methodology is adequate for sensitivity analysis, and that the precipitation intensity is the dominant parameter. Furthermore, the model calibration based on local variables (cross-sectional water level) can be inaccurate to simulate global variables (flooded area).

Using Remote Sensing Techniques to Improve Hydrological Predictions in a Rapidly Changing World

Remotely sensed geophysical datasets are being produced at increasingly fast rates to monitor various aspects of the Earth system in a rapidly changing world. The efficient and innovative use of these datasets to understand hydrological processes in various climatic and vegetation regimes under anthropogenic impacts has become an important challenge, but with a wide range of research opportunities. The ten contributions in this Special Issue have addressed the following four research topics: (1) Evapotranspiration estimation; (2) rainfall monitoring and prediction; (3) flood simulations and predictions; and (4) monitoring of ecohydrological processes using remote sensing techniques. Moreover, the authors have provided broader discussions, on how to make the most out of the state-of-the-art remote sensing techniques to improve hydrological model simulations and predictions, to enhance their skills in reproducing processes for the fast-changing world.

Impact of Stream-Groundwater Interactions on Peak Streamflow in the Floods

Floods are the one of the most significant natural disasters, with a damaging effect on human life and properties. Recent global warming and climate change exacerbate the flooding by increasing the frequency and intensity of severe floods. This study explores the role of groundwater during the floods at the Miho catchment in South Korea. The Hydrological-Ecological Integrated watershed-scale Flow model (HEIFLOW) model is used for the flood simulations to investigate the impact of groundwater and streamflow interactions during floods. The HEIFLOW model is assessed by the Nash–Sutcliffe model efficiency coefficient (NSE) and Root Mean Square Error (RMSE) for the surface water and groundwater domains, respectively. The model evaluation shows the acceptable model performance (0.64 NSE and 0.25 m–2.06 m RMSE) with the hourly time steps. The HEIFLOW shows potential as one of the methods for the flood risk management in South Korea. The major findings of this study indicate that the stream runoff at the Miho catchment is highly affected by the groundwater flows during the dry and flood seasons. Thus, the interactions between surface water and groundwater domains should be fully considered to mitigate the water hazards at the catchment scale.

A physically-based distributed karst hydrological model (QMG model-V1.0) for flood simulations

Karst trough valleys are prone to flooding, primarily because of the unique hydrogeological features of karst landform, which are conducive to the spread of rapid runoff. Hydrological models that represent the complicated hydrological processes in karst regions are effective for predicting karst flooding, but their application has been hampered by their complex model structures and associated parameter set, especially so for distributed hydrological models, which require large amounts of hydrogeological data. Distributed hydrological models for predicting the Karst flooding is highly dependent on distributed structrues modeling, complicated boundary parameters setting, and tremendous hydrogeological data processing that is both time and computational power consuming. Proposed here is a distributed physically-based karst hydrological model, known as the QMG (Qingmuguan) model. The structural design of this model is relatively simple, and it is generally divided into surface and underground double-layered structures. The parameters that represent the structural functions of each layer have clear physical meanings, and the parameters are less than those of the current distributed models. This allows modeling in karst areas with only a small amount of necessary hydrogeological data. 18 flood processes across the karst underground river in the Qingmuguan karst trough valley are simulated by the QMG model, and the simulated values agree well with observations, for which the average value of Nash–Sutcliffe coefficient was 0.92. A sensitivity analysis shows that the infiltration coefficient, permeability coefficient, and rock porosity are the parameters that require the most attention in model calibration and optimization. The improved predictability of karst flooding by the proposed QMG model promotes a better mechanistic depicting of runoff generation and confluence in karst trough valleys.

Study on the influence of infiltration on flood propagation with different peak shape coefficients and duration

Traditional flood simulations fail to properly consider the impact of soil infiltration in floodplain areas with high soil infiltration rates. Notably, ignoring soil infiltration will lead to considerable uncertainty in flood simulations. In this paper, a fully hydrodynamic model coupled with the Green–Ampt infiltration model was used. Taking a natural reach in northern China (HTH in this paper) as a case study, observed flood discharge data were used to analyze the influence of soil infiltration on flood propagation based on the flood propagation simulation results for various inflow conditions. The maximum difference of inundation area is about 25%. The results show that soil infiltration has little effect on the inundation area during the rising stage of a flood. In the late period of a flood, the inundation area considering the effect of infiltration is smaller than that without infiltration, and the smaller the peak coefficient is, the longer the flood duration is, the larger the impact of infiltration on the inundation area. When the peak shape coefficient is 0.42 and the flood duration is 44.4 h, the maximum difference of the inundation area is about 28%. The research results provide a reference for flood management and post-disaster rescue efforts.