Flood Modelling Research Articles

Unveiling the role of storm surges as a driver of flooding on the western Mediterranean: a case study of the Ebro Delta

AbstractStorm surges in the Western Mediterranean are generally low in magnitude, making their contribution to coastal flooding less significant compared to wave overtopping. Nonetheless, low-lying, sheltered coasts such as deltas and wetlands, which are frequent along the Mediterranean basin are particularly vulnerable to storm surges. This study, focusing on the Ebro Delta as representative of this type of coastal environment, investigates the flooding caused by storm surge alone and in conjunction with other non-wave related factors like astronomical tides and sea level rise (SLR), using the LISFLOOD-FP model. The findings highlight the significant flooding potential of storm surges on passive, and unprotected coasts, while also indicate that astronomical tides have a minor effect on flood extent under prevalent microtidal conditions. SLR greatly increases the impact of storm surges, amplifying temporary inundation in the short term and becoming the dominant factor over time. The study underscores the importance of accurately representing surge duration and small topographic features in flood models to ensure robust coastal inundation assessments in low-lying areas.

A Dynamic Early Warning Model for Flash Floods Based on Rainfall Pattern Identification

AbstractFlash floods are one of the most devastating natural hazards in mountainous and hilly areas. In this study, a dynamic warning model was proposed to improve the warning accuracy by addressing the problem of ignoring the randomness and uncertainty of rainfall patterns in flash flood warning. A dynamic identification method for rainfall patterns was proposed based on the similarity theory and characteristic rainfall patterns database. The characteristic rainfall patterns were constructed by k-means clustering of historical rainfall data. Subsequently, the dynamic flood early warning model was proposed based on the real-time correction of rainfall patterns and flooding simulation by the HEC-HMS (Hydrologic Engineering Center’s Hydrologic Modeling System) model. To verify the proposed model, three small watersheds in China were selected as case studies. The results show that the rainfall patterns identified by the proposed approach exhibit a high correlation with the observed rainfall. With the increase of measured rainfall information, the dynamic correction of the identified rainfall patterns results in corresponding flood forecasts with Nash-Sutcliffe efficiency (NSE) exceeding 0.8 at t = 4, t = 5, and t = 6, thereby improving the accuracy of flash flood warnings. Simultaneously, the proposed model extends the forecast lead time with high accuracy. For rainfall with a duration of six hours in the Xinxian watershed and eight hours in the Tengzhou watershed, the proposed model issues early warnings two hours and three hours before the end of the rainfall, respectively, with a warning accuracy of more than 0.90. The proposed model can provide technical support for flash flood management in mountainous and hilly watersheds.

Flood Mapping using HEC-RAS and GIS: A Case Study of Palembang Watersheds

Flooding is a significant issue in Palembang City, affecting areas such as Gandus, Lambidaro, Boang, Sekanak, Bendung, Kidul, Buah, Juaro, Selinca, Nyiur, Aur, Sriguna, Kedukan, Keramasan, Kertapati and Jakabaring watershed. This research contributes to flood control measures by providing flood mapping and modelling for decision-makers and local authorities. The main objective of this paper is to perform a steady flow analysis to study the hydraulic characteristics of flow; velocity and water surface profile of the adjacent research areas. There are three processes involved in the data analysis: hydrological, hydraulic and spatial. The calculation of average rainfall is performed using the Isohyet method with the assistance of the IDW 3D Analyst tool in ArcGIS and the design discharge is calculated using the HSS Nakayasu method. The hydraulic analyses involve river morphology extraction using RAS Mapper tools in HEC-RAS. Steady flow simulations are conducted for normal water depth conditions and the highest tidal conditions. The simulation result is then reclassified and the flood location points are verified with observed data. For normal water depth conditions, the maximum water level in the Lambidaro watershed reaches 2,09 meters, which covers a flood area of 721,53 ha. Meanwhile, based on the highest tidal simulations, the maximum water level in the Selinca watershed reaches 3,21 meters, covering a flood area of 209,36 ha.

Assessment of socio-economic strategies for managing regional flood risk in an urban coastal catchment

With the cascading flooding events over the past decades, several structural and non-structural measures have been effectively implemented for improved flood management. However, the uneven impacts of the flooding events have highlighted that socio-economic inequality amongst the community needs to be addressed along with hazards to devise long term and sustainable flood management strategies. This study aims to evaluate the impact of socio-economic adaptive scenarios over an urban coastal catchment for reduction in flood risk. Flood risk is estimated utilizing socio-economic aspects (vulnerability) and flood inundation (hazard), for the highly flood-prone coastal city of Mumbai, India. A multivariate approach is used for assessing socio-economic vulnerability, starting with Principal Component Analysis followed by Data Envelopment Analysis, for various socio-economic scenarios, i.e., by reducing percentage of certain feasible socio-economic sensitivity indicators, to identify the key indicators, which contribute to significant change in vulnerability. The flood hazard is assessed through a comprehensive hydrodynamic flood modelling approach to quantify the flood inundation depth and extent. The flood risk is, thereafter, evaluated by combining socio-economic vulnerability and flood hazard using bivariate risk mapping approach. The results indicate that, with the decrease in alteration percentage of certain feasible socio-economic sensitivity indicators, the overall vulnerability and flood risk may be reduced. The areas under very high and high vulnerability classes reduce gradually for all scenarios as the percentage reduction for various indicators is varied from 5 % to 15 %, total illiteracy being the most significant indicator. This study marks the first effort to discern socio-economic vulnerability indicators that have the potential to significantly diminish flood risk, thereby constituting a viable long-term flood risk mitigation strategy. This novel framework is generic and applicable to any flood prone catchments, to devise appropriate long-term flood management measures.

Integrating the cellular vortex method with remote sensing and geographical information systems in the modelling of coastal flooding around Niger Delta

Coastal areas are increasingly vulnerable to flooding, necessitating accurate simulation methods to understand flood dynamics and their potential impacts. This study employed a Lagrangian framework integrating the cellular vortex method with remote sensing and GIS to simulate flood height distribution in a coastal region. Leveraging climatic and remotely sensed data, alongside ArcMap 10.6.1 for map processing, the research estimated flood magnitude and frequency using the L-moment approach, applied to a forty-year tidal record dataset. Essential input parameters, such as the roughness coefficient and curve number, were derived from land use and land cover characteristics. Additionally, river flow velocity was observed at 0.12m/s, with measured wind speed and direction recorded at 4m/s in the northwest direction. Notably, analysis of the initial flood height distribution map revealed a significant expansion of wetland areas, attributed to observed land use changes between May 2002 and July 2005. Projections for flood height distribution in 2025 and 2050 highlighted the emergence of tidal floods, emphasizing the critical role of considering future climate and land use scenarios in flood dynamics assessment. This research contributes to advancing understanding of flood modeling techniques and underscores the urgency of adaptive measures to mitigate the potential impacts of coastal flooding.

Do disparities exist in flood risks during monsoon and post-monsoon seasons? comprehending diametric behaviors over coastal multi-hazard catchments

Abstract Global coastal catchments experience peculiar flood risk attributes due to the confluence of multiple flood drivers. In these regions, the monsoon and post-monsoon seasons impose diverse flood risks that evoke meticulous research for the prudent execution of appropriate flood management options. For the first time in flood management literature, we develop an integrated approach to quantify the distinguishing nature of flood risks during both seasons. We employ a sophisticated 1D-2D coupled flood model to generate high-resolution flood hazards while considering the compound interactions between rainfalls and storm-tides. Flood vulnerability is mapped at the finest administrative scale while considering flood-susceptible physical and socio-economic indicators. With the concept of Bivariate Risk Classifier, we introduce two novel metrics: (a) Area Index and (b) Multi-Hazard Risk Index that advance our understanding of the multi-hazard nature of flood risks during monsoon and post-monsoon seasons. These incisive indices propound case-specific flood management measures for long-term resilience.

MODELING FLOOD SUSCEPTIBILITY IN HYDROLOGICAL DATA-SCARCE STUDY AREAS

Hydrological information is essential for estimating the magnitude of floods, thus helping reduce losses and damage. However, when this data is scarce, it challenges flood modeling and risk assessment. In Brazil's west of Rio Grande do Sul, we used the Santa Maria hydrographic basin as a case study to estimate the river flow for different RP based on the statistical distribution of rainfall and river flow data and to simulate flood scenarios using a 2D IBER hydrological model.River flow estimates for 5, 10, 20, 30, and 50-year RP were obtained using the Giandotti equation. The results showed a high correlation between the river flows calculated from rainfall and the fluviometric data, using GEV and GumbelMax distribution. The areas susceptible to flooding are consistent with past flood events, validated with fieldwork and Sentinel imagery. River flows estimated from rainfall data can promote hydrological studies where fluviometric data is absent. Keywords: River Flow Estimation; Generalized Extreme Values; Flood Susceptibility; Rosário Do Sul; Brazil.

Evaluation of WRF model performance with different microphysics schemes for extreme rainfall prediction in Lagos, Nigeria: Implications for urban flood risk management

In Nigeria, particularly in urban areas like Lagos, flooding is a frequent natural hazard. In 2011, Lagos experienced one of its worst floods resulting in significant economic losses and displacement of people. In recent years, Lagos has continued to grapple with flooding challenges, with an equally significant flood episode occurring in 2021. This study focuses on predicting floods and forecasting extremely heavy rainfall in West Africa's equatorial zone using the Weather Research and Forecasting (WRF) model, particularly in humid tropical environments like Lagos. The study discusses the need to review existing flood models and adopt alternative flood models to address the limitations of flood prediction. As potential causes of these rainfall episodes, the interconnections between synoptic systems such as tropical easterly waves, southwesterly winds related to the West African Monsoon, and local topography and oceanic conditions are investigated. Three key metrics: root mean square error (RMSE), mean bias (MB), and mean absolute error (MAE) are used to assess the effectiveness of the computational model. Results indicate that the WRF model, specifically when using the Thompson parameterisation, can estimate the amount of rainfall accumulated over a 24-h period. This suggests that the model can predict the size of daily precipitation during intense rain events. The Thompson scheme shows better performance compared to the WSM6 scheme while evaluating the stations and episodes. During the rainfall episode on July 10, 2011, Thompson's spatial rainfall predictions were better than WSM6, resulting in a decrease in root mean square error (RMSE) of 15–31% depending on the area. Simulations of the July 2021 episode also show better performance, with a decrease in RMSE of 11–25% when comparing Thompson to WSM6 scheme. The Thompson scheme’s improved ability is directly linked to a more accurate depiction of the microphysical mechanisms that control the rainfall formation. By explicitly simulating the dynamics of ice crystals and graupel, it is possible to accurately replicate the processes of orographic lifting and moist convection that are responsible for driving intense monsoon precipitation. In addition, Thompson scheme shows a reduced degree of systemic bias in comparison to WSM6, with a 75% reduction in the average bias in rainfall accumulation over the research area. The combination of the advanced Thompson microphysics method and WRF's atmospheric dynamics shows a high level of accuracy in predicting intense rainfall and the risk of floods in this area with diverse tropical topography.

Evaluating the Enhanced Bathtub Model for Coastal Flood Risk Assessment in Table Bay, South Africa

ABSTRACTCoastal zones are susceptible to increasing pressures from urban development and natural hazards, such as storm events, climate change, and rising sea levels. The GIS‐based enhanced bathtub model (eBTM) enables the identification of areas at risk of flooding as a baseline for disaster management and coastal adaptation. This study aims to establish the methodological robustness of the eBTM for coastal flood modeling, by analyzing eight sites flooded during a recent storm event in Table Bay, Cape Town by comparing eBTM outputs with observed flood extent data collected after the storm. The validation showed that for 74% of the 332 validation points the spatial modeling error was < 6 m and for 56% below 3 m. The root mean square error for the model was 4.88 m, indicating an acceptable level of accuracy of the eBTM outputs for coastal risk assessments where more sophisticated models are unavailable.

A Coupled CA Urban Development Model (UDM) and Urban Fabric Generator (UFG) for the Assessment of Climate Hazards on Future Urban Development

Abstract. Cellular Automata (CA) spatial models have become a de facto means by which to simulate future scenarios of land use activity, particularly urban development. Increasingly, such models are being employed within large scale climate impact, adaption and resilience studies to provide future scenarios of land use change and urban development, facilitating the calculation of the economic impact of future climate hazards and development land use adaption options. However, many spatial CA models only provide information on whether land has undergone a transition from one use/activity to a new one. In the case of urban development, they are unable to provide information on the spatial pattern of new development at an intra-cell level of spatial fidelity. However, in many cases such information is important as the spatial impacts of hazards and the adaption and resilience of new urban development will be dependent on the precise spatial configuration of buildings, roads and urban green space. In this paper an Urban Fabric Generator (UFG) is presented that takes the outputs of an Urban Development Model (UDM) and simulates plausible spatial configurations of buildings, roads and urban green space. The utility of the UFG is demonstrated via two flooding case studies, where generated spatial patterns of urban form are used to parameterise a hydrodynamic pluvial flood model that evaluates the impact of future climate driven rainfall on property damage and the utility of infrastructure investment with regards to improving surface water flood resilience.

A prediction method of oil recovery for hot water chemical flooding in heavy oil reservoirs: Semi-analytical stream tube model

Abstract Chemical agents (polymer and surfactant) assisted hot water flooding is an effective means of enhancing oil recovery in heterogeneous and heavy oil reservoirs. The rapid prediction of oil recovery through hot water chemical flooding is very important. The stream tube model is a fast analytical method for predicting water flooding recovery, but it is not suitable for hot water chemical flooding. In this paper, a permeability distribution model for multi-stream tubes is established based on permeability variation coefficient using normal distribution function for reservoir heterogeneity, Using GOMPERTZ growth function model to characterize the changes in stream tube temperature, polymer viscosity, and surfactant concentration with injection volume, Using Brooks-Corey model to describe the influence of interfacial tension and temperature on the relative permeability curve. Finally, an analytical stream tube model for hot water chemical flooding of heavy oil reservoirs was established. The time discrete method is used to solve the model, then the graphs of the relationship between oil recovery and water cut are obtained. Compared with numerical simulation methods, the prediction error of oil recovery is less than 2%, and the calculation time is reduced by 89%. This model has been successfully applied to X oilfield. Based on historical fitting, it is predicted that hot water chemical flooding can enhance oil recovery by 6.3% compared to water flooding. This paper provides a fast calculation method for predicting and evaluating the effectiveness of hot water chemical flooding in heavy oil reservoirs.

Geospatial Mapping and Meteorological Flood Risk Assessment: A Global Research Trend Analysis.

Flooding is a global threat causing significant economic and environmental damage, necessitating a policy response and collaborative strategy. This study assessed global research trends and advances in geospatial and meteorological flood risk assessment (G_MFRA), considering the ongoing debate on flood risk management and adaptation strategies. A total of 1872 original articles were downloaded in BibTex format using the Web of Science (WOS) and Scopus databases to retrieve G_MFRA studies published from 1985 to 2023. The annual growth rate of 15.48% implies that the field of G_MFRA has been increasing over time during the study period. The analysis of global trends in flood risk research and practice highlights the key themes, methodologies, and emerging directions. There exists a notable gap in data and methodologies for flood risk assessment studies between developed and developing countries, particularly in Africa and South America, highlighting the urgency of coordinated research efforts and cohesive policy actions. The challenges identified in the body of extant literature include technical expertise, complex communication networks, and resource constraints associated with the application gaps of the study methodologies. This study advocates for a holistic research approach to flood disaster management through ecosystem-based adaptation that underpins the Sustainable Development Goals to develop innovative flood techniques and models with the potential to influence global decision-making in the G_MFRA domain. Addressing these global challenges requires a networked partnership between the research community, institutions, and countries.

Integrating Intelligent Hydro-informatics into an effective Early Warning System for risk-informed urban flood management

The urban drainage system constantly facing flooding issues in coastal and urban areas. Robust and accurate urban flood management, particularly considering fast-moving compound floods, is crucial to minimize the impact of flood disasters in coastal cities. Till now, Ho Chi Minh City (HCMC) lacks an effective means of urban flood management because of flood risk communication among residents. Existing flood risk communication tools rely on post-disaster flood model outcomes and data. Therefore, this research proposes a real-time Early Urban Flooding Warning System (EUFWS) integrated with a user-friendly web and app interface. The backbone of this system consists of flood models developed using machine learning (ML) algorithms, combined with big data and Web-GIS visualization, with ML serving as the core for constructing the EUFWS. EUFWS offer several key advantages: they are available at all times, accessible from anywhere, and provide a real-time, multi-user working platform. Additionally, the system is flexible, allowing for the easy addition of components and services and scalable, adjusting to workload demands. EUFWS have been successfully deployed in Thu Duc City, Vietnam, as a case study and are operating effectively. EUFWS have been successfully deployed in Thu Duc City, Vietnam, as a case study and are operating effectively. Research results indicate that EUFWS supported decision-makers to be effectively risk informed and make intelligent decisions during urban flood emergencies. This underscores the significant potential of integrating ML and information technology to enhance the management of smart urban drainage systems in flood-prone cities worldwide.

Flood Risks Analysis in Upton Upon Severn: Assessing the Baseline Conditions and Future Scenarios for 100 and 1000-year Flood Events with a 2050 Climate Change Perspective.

This paper investigates the flood risks in Upton upon Severn, a town in Worcestershire, England, which is prone to fluvial flooding from the River Severn. Historical events, such as the significant floods of 1947 and 2007, highlight the town’s vulnerability and underscore the importance of analysing current and future flood risks. This research uses a one-dimensional hydraulic model, using Flood Modeller and QGIS software, to assess baseline flood conditions and predict the potential impacts of severe and extreme flood events, including 1 in 100-year and 1 in 1000-year flood scenarios. Additionally, it examines the projected effects of climate change by incorporating a 20% increase in flood risk estimates for the year 2050. The model results reveal that the baseline scenario poses limited flood risks, with only 17 properties affected, and key infrastructure like the A4104 road remains passable. However, under the 1 in 100-year event, approximately 336 properties are at risk, with significant road blockages and a flooded area of 5.09 Km2. The 1 in 1000-year flood scenario predicts a peak flow of 1807 m3/s, threatening more properties and covering a larger area of land. When climate change is factored into the 1 in 100-year scenario, the risks become similar to those of the 1 in 1000-year event, with 500 properties and key infrastructure, including the fire station and primary school, at risk and the A4104 road becoming impassable. These findings provide vital insights for flood risk management, especially given the possible effects of climate change, and stress the need for long-term solutions to flooding.

Conceptual framework to incorporate drainage solutions in the urban open space system

Cities are increasingly dealing with challenges regarding the negative impact of rapid and mismanaged urbanization. Therefore, city planning must cope with the natural environment limitations, seeking a balance between the human activities and the well-functioning of the hydrologic cycle. This work aims to present a conceptual framework able to properly integrate the stormwater dynamics into the open spaces system in a functional way, establishing a Hydrological Interest Area, HIA, to structure urban expansion integrated into and respecting watershed natural processes. The initial step is to define a HIA, primarily consisting of open spaces that can be used for supporting urban drainage functions and to order land use in the urban expansion process. This delimitation offers the background for interpreting the watershed in three functional arches, especially covering the upstream, mid-reach and downstream areas of the basin, guiding the design of a set of flood mitigation interventions focusing on the use of Blue-Green Infrastructure. To illustrate and validate the proposed methodological framework, the design is evaluated by a flood modeling tool, using a hydrological-hydrodynamic cell-model. A case study was driven in the Bambu Watershed, a rapidly developing area in the municipality of Maricá, Rio de Janeiro, Brazil. The proposed intervention includes an urban expansion scenario for a low impact development on flood behavior alongside with four parks: an upstream park with reservoirs, two multifunctional floodable urban parks, and a park dedicated to lagoon restoration. This plan complements riverbed modifications designed to enhance water discharge. The simulation showed significant reduction of water depths with a consequent decrease in exposure of buildings and roads, especially in the most critical region of the watershed. This framework highlights the importance of a multifunctional approach in land use and serves as a robust foundation for controlling urban expansion and proposing projects.

Assessing the Impact of Rainfall Inputs on Short-Term Flood Simulation with Cell2Flood: A Case Study of the Waryong Reservoir Basin

This study explored the impacts of various rainfall input types on short-term runoff simulations using the Cell2Flood model in the Waryong Reservoir Basin, South Korea. Six types of rainfall data were assessed: on-site gauge measurements, spatially interpolated data from 39 Automated Synoptic Observing System (ASOS) and 117 Automatic Weather System (AWS) stations using inverse distance weighting (IDW), and Hybrid Surface Rainfall (HSR) data from the Korea Meteorological Administration. The choice of rainfall input significantly affected model accuracy across the three rainfall events. The point-gauged ASOS (P-ASOS) data demonstrated the highest reliability in capturing the observed rainfall patterns, with Pearson’s r values of up to 0.84, whereas the radar-derived HSR data had the lowest correlations (Pearson’s r below 0.2), highlighting substantial discrepancies. For runoff simulation, the P-ASOS and ASOS-AWS combined interpolated dataset (R-AWS) achieved relatively accurate predictions, with P-ASOS and R-AWS exhibiting Normalized Peak Error (NPE) values of approximately 0.03 and Peak Time Error (PTE) within 20 min. In contrast, the HSR data produced large errors, with NPE up to 4.66 and PTE deviations exceeding 200 min, indicating poor temporal accuracy. Although input-specific calibration improved performance, significant errors persisted because of the inherent uncertainty of rainfall data. These findings underscore the importance of selecting and calibrating appropriate rainfall inputs to enhance the reliability of short-term flood modeling, particularly in ungauged and data-sparse basins.

Using a multiphysics coupling-oriented flood modelling approach to assess urban flooding under various regulation scenarios combined with rainstorms and tidal effects

Climate change and rapid urbanisation have intensified urban flooding. Flood models constructed using hydrological and hydrodynamic approaches are crucial tools for simulating and regulating urban flooding processes. Urban flooding arises from the interplay of various physical processes in urban water systems, influenced by heterogeneous urban surfaces, underground structures, and intricate river networks influencing surface flow, which must be considered in flood models. For coastal cities with intricate drainage systems, the disasters of combined rainstorms and tidal flooding can be mitigated through river scheduling and real-time control of hydraulic structures. However, a lack of research on the effectiveness of flood regulations limits the application of flood models. This study proposes a flood modelling approach oriented towards the coupling of multiphysics processes, achieving a comprehensive simulation and regulation of the flood process. A surface flood control model was created by coupling hydrological and hydrodynamic processes using the Storm Water Management Model and the orthogonal storage cell model, enabled by data exchange synchronisation technology. Subsequently, the model’s accuracy was validated using observed rainstorm events, identifying drainage system overflows and land surface inundation. Finally, the developed model was applied to analyse the enhancing effect of tides on rainstorm-induced flooding and the impact of various control scenarios. This study offers theoretical direction for large-scale urban flood modelling and model development, offering significant references for revisiting urban planning and formulating flood prevention strategies under the joint impact of rainstorms and tides.

Integrated coastal vulnerability index for coastal flooding: A case study of the Croatian coast

The combination of accelerated urbanization and tourism-related activities, along with frequent coastal flooding, has generated pressure on the coastline of the Republic of Croatia (RH). In this paper, an integrated coastal vulnerability model (ICVI) for coastal flooding was developed. The ICVI was generated using a GIS multicriteria approach and derived from two sub-indices: the physical vulnerability index (CVIN) and the socio-economic vulnerability index (CVIS). In total, 30 criteria were used in the derivation of the ICVI, with twelve contributing to CVIS and eighteen to CVIN. The ICVI model is represented as a line divided into smaller segments, each segment indicating ICVI vulnerability levels ranging from 1 (very low) to 5 (very high). The accuracy of CVIN was evaluated using 159 geocoded coastal flood locations obtained from various websites and the official register of Hrvatske Vode flood events from 2008 to 2023. More than 80% of geocoded flood locations are situated in areas with very high (5) or high (4) CVIN. Furthermore, out of 32 settlements with officially registered flood events, 90.6% of them are located in areas with very high or high ICVI. Since all data used in the ICVI derivation were acquired from open-source databases and a user-friendly GIS-MCDA toolbox was utilized, this paper presents a cost-effective approach to modeling integrated coastal vulnerability. This model can guide decision-makers and provide them with new insights for implementing an effective integrated coastal zone management strategy.

Numerical simulation of carbon dioxide flooding in fractured reservoirs using generic projection-based embedded discrete fracture model

This paper, for the first time, integrates the generic projection-based embedded discrete fracture model (pEDFM) with the commercial reservoir simulator ECLIPSE for carbon dioxide (CO2) flooding in fractured reservoirs. The integrated method first obtains inter-grid connections and corresponding transmissibilities within the reservoir model based on the generic pEDFM. It then constructs an equivalent CO2 flooding ECLIPSE model to the original pEDFM reservoir model, thereby calculating the global equations of the compositional flow model to obtain distributions of pressure, saturation, component concentrations, and well performance data. We implemented three numerical examples to verify that the proposed method can achieve significantly higher computational accuracy compared to the widely used embedded discrete fracture model in both high and low permeability fracture scenarios, while also avoiding the difficulties associated with generating matching grids for complex fracture networks. Furthermore, the proposed integrated method uses the solver within ECLIPSE to solve the global equations, thus avoiding the high cost of developing a robust nonlinear solver for complex compositional model of CO2 flooding. This demonstrates the practicality of the method and its significant potential for subsequent application to various reservoir models.

Uncovering the Dynamic Drivers of Floods Through Interpretable Deep Learning

AbstractThe formation of floods, as a complex physical process, exhibits dynamic changes in its driving factors over time and space under climate change. Due to the black‐box nature of deep learning, its use alone does not enhance understanding of hydrological processes. The challenge lies in employing deep learning to uncover new knowledge on flood formation mechanism. This study proposes an interpretable framework for deep learning flood modeling that employs interpretability techniques to elucidate the inner workings of a peak‐sensitive Informer, revealing the dynamic response of floods to driving factors in 482 watersheds across the United States. Accurate simulation is a prerequisite for interpretability techniques to provide reliable information. The study reveals that comparing the Informer with Transformer and LSTM, the former showed superior performance in peak flood simulation (Nash‐Sutcliffe Efficiency over 0.6 in 70% of watersheds). By interpreting Informer's decision‐making process, three primary flood‐inducing patterns were identified: Precipitation, excess soil water, and snowmelt. The controlling effect of dominant factors is regional, and their impact on floods in time steps shows significant differences, challenging the traditional understanding that variables closer to the timing of flood event occurrence have a greater impact. Over 40% of watersheds exhibited shifts in dominant driving factors between 1981 and 2020, with precipitation‐dominated watersheds undergoing more significant changes, corroborating climate change responses. Additionally, the study unveils the interplay and dynamic shifts among variables. These findings suggest that interpretable deep learning, through reverse deduction, transforms data‐driven models from merely fitting nonlinear relationships to effective tools for enhancing understanding of hydrological characteristics.