Hydrologic Hydrodynamic Model Research Articles

An Improved Coupled Hydrologic-Hydrodynamic Model for Urban Flood Simulations Under Varied Scenarios

An Improved Coupled Hydrologic-Hydrodynamic Model for Urban Flood Simulations Under Varied Scenarios

An improved dynamic bidirectional coupled hydrologic–hydrodynamic model for efficient flood inundation prediction

Abstract. To improve computational efficiency while maintaining numerical accuracy, coupled hydrologic–hydrodynamic models based on non-uniform grids are used for flood inundation prediction. In these models, a hydrodynamic model using a fine grid can be applied to flood-prone areas, and a hydrologic model using a coarse grid can be used for the remaining areas. However, it is challenging to deal with the separation and interface between the two types of areas because the boundaries of the flood-prone areas are time dependent. We present an improved Multigrid Dynamical Bidirectional Coupled hydrologic–hydrodynamic Model (IM-DBCM) with two major improvements: (1) automated non-uniform mesh generation based on the D-infinity algorithm was implemented to identify the flood-prone areas where high-resolution inundation conditions are needed and (2) ghost cells and bilinear interpolation were implemented to improve numerical accuracy in interpolating variables between the coarse and fine grids. A hydrologic model, the 2D nonlinear reservoir model, was bidirectionally coupled with a 2D hydrodynamic model that solves the shallow-water equations. Three cases were considered to demonstrate the effectiveness of the improvements. In all cases, the mesh generation algorithm was shown to efficiently and successfully generate high-resolution grids in those flood-prone areas. Compared to the original M-DBCM (OM-DBCM), the new model had lower root-mean square errors (RMSEs) and higher Nash–Sutcliffe efficiencies (NSEs), indicating that the proposed mesh generation and interpolation were reliable and stable. It can be adequately adapted to the real-life flood evolution process in watersheds and provide practical and reliable solutions for rapid flood prediction.

Urban Flood Mitigation Strategies with Coupled Gray–Green Measures: A Case Study in Guangzhou City, China

The integration of gray and green infrastructure has proven to be a feasible approach for managing stormwater in established urban areas. However, evaluating the specific contributions of such coupled strategies is challenging. This study introduced a novel integrated hydrological-hydrodynamic model that takes into account the layout of low-impact development (LID) facilities along with pipeline alignment and rehabilitation. Reliable results from modeling were used to assess the individual contribution of LID and improved drainage facilities to urban flooding mitigation. We selected a natural island in Guangzhou City, China, as the study site. The results indicate that combining three LID measures, namely green roofs, sunken green spaces, and permeable pavements, can reduce total runoff by 41.7% to 25.89% for rainfall recurrence periods ranging from 1 year to 100 years, and decrease the volume of nodal overflow by nearly half during rainfall events of less than 10-year return period. By integrating LID measures with the upgraded gray infrastructure, the regional pipeline overloading condition is substantially alleviated, resulting in a significant improvement in pipeline system resilience. For urban flooding control, it is recommended to integrate sufficient green space and avoid pipe-laying structural issues during urban planning and construction. The findings may assist stakeholders in developing strategies to best utilize gray and green infrastructure in mitigating the negative effects of urban flooding.

The influence of change in river networks on waterway connectivity and flooding processes

ABSTRACT Urbanization disrupts river networks, alters waterway connectivity, and elevates flood control pressure. Quantitatively assessing river network changes and their impact on waterway connectivity and flooding processes is imperative. We constructed a waterway connectivity evaluation model and a hydrologic–hydrodynamic model to assess the river network, the waterway connectivity and the flooding change in the Qinhuai River Basin, and used scenario analysis to determine the effectiveness of different topology changes. The results showed a 0.09 decline in connectivity from 2000 to 2010, which led to a peak occurrence time 1 h earlier, an increase in peak discharge by 2.59 m3/s and a rise in peak water level by 51 mm. Scenario analysis demonstrated that adding tributaries can enhance both connectivity and flood resilience. Increasing river width effectively reduces peak water levels while minimally affecting connectivity. Extending river length has a negligible effect on peak water levels but may decrease waterway connectivity.

Reconstruction of long-term hydrologic change and typhoon-induced flood events over the entire island of Taiwan

Study regionThe island of Taiwan. Study focusThis study presents long-term and high-resolution modeling of flood occurrence, interdecadal patterns of river-floodplain dynamics, and analysis of flooding during two typhoon events—Nari and Morakot over Taiwan. The modeling system combines a hydrological model (HiGW-MAT) and a river hydrodynamics model (CaMa-Flood), simulating hydrologic-hydrodynamic processes at ∼ 5 km resolution with flood occurrences downscaled to ∼ 90 m. New hydrological insights for the regionAs the first investigation to conduct spatially comprehensive and temporally continuous modeling in Taiwan, this study presents important advances on the application of large-scale hydrological-hydrodynamic models in settings like that of Taiwan with important implications on flood prediction under climate change. The assessment of interdecadal changes in streamflow indicates no consistent trends over the past four decades; however, the variabilities in monthly-scale streamflow are significant and regionally diverse across Taiwan. Decadal changes in flood occurrence are also minimal at the island-scale, but the changes vary substantially across different regions and exhibit an increased variability over time. Furthermore, the simulated flood patterns in response to Typhoons Nari and Morakot suggest that the modeling framework can be used to reproduce the spatial dynamics and temporal progression of flooding under extreme events in relatively small regions like Taiwan.

Construction of hydrological and hydrodynamic models based on ARM architecture processor – A case study of inner Harbor area of Macao Peninsula

ABSTRACT Green computing is the current research hotspot; adapting the professional computing model to the low-power ARM architecture processor is a research trend. This article constructs a hydrological–hydrodynamic model by choosing SWMM and TELEMAC-2D as submodules, taking stormwater grates and inspection wells as water flow exchange nodes. The container technology was selected to couple the model on the ARM architecture processor, and the adapted coupling model is named the ARM hydrological–hydrodynamic model (AHM). To verify the reasonableness and accuracy of the model, taking the inner harbor of Macao Peninsula as the study area, a numerical simulation study on the evolution of waterlogging was carried out in various scenarios. The analysis showed that the maximum flow velocity was concentrated in the streets with low topography in the city center, and the areas of standing water were distributed in a point-like manner. Risk distribution maps during different storm recurrence periods were also constructed. Finally, the direct economic losses caused by Typhoon Mangkhut were calculated based on the model results and compared with the statistical values, with an error of only 2.3%, and the direct flooding losses in the inner harbor area under different rainfall scenarios were derived accordingly.

Urban inundation rapid prediction method based on multi-machine learning algorithm and rain pattern analysis

Urban inundation disasters caused by extreme rainfall events are becoming increasingly severe. However, the numerical inundation model based on physical process has relatively slow computational speed and struggle to meet the demands of current forecasting and early warning systems. Addressing how to integrate machine learning technology into rapid urban inundation prediction, especially when dealing with limited training samples, and selecting the most appropriate machine learning algorithms for different rain patterns, is a crucial and pressing issue. To tackle this problem, this work combines a coupled hydrological-hydrodynamic model known for its high-computational accuracy with efficient machine learning algorithms. Taking different rain patterns into account, a rapid urban inundation prediction method is proposed. This method utilizes data-driven results from the coupled hydrological-hydrodynamic model and constructs multiple machine learning algorithms based on the conclusions drawn from the analysis regarding the most suitable machine learning algorithms for different rain patterns. The results show that for single-peak, double-peak, and uniform rain patterns, the Ridge algorithm, KNN algorithm, and RF algorithm should be respectively employed. The method achieves a mean absolute percentage error of 5.32 %, 7.73 %, and 2.49 % for single-peak, double-peak, and uniform rainfall scenarios and can predict inundation within a 3.68 km2 area in 14.07 s. This method offers high prediction accuracy and fast computational speed, not only meeting the requirements of daily forecasting and early warning but also providing a new direction for the application of machine learning technology in urban inundation prevention.

Previsões de vazões sub sazonais para barragens hidrelétricas no Sistema Interligado Elétrico Brasileiro

ABSTRACT Inflow prediction on sub-seasonal timescale have the potential for important contributions to the management of water resources in hydroelectric dam operations. These forecasts challenge the limitations of the medium-term and extend it, bridging a long-standing technical-scientific gap in the forecasting field. In Brazil, the use of sub-seasonal hydrological predictions can boost the hydroelectric production of the National Interconnected System (SIN), since inflow forecast in reservoirs of up to 2 weeks are routinely used using a rain-flow model. This study aimed at the statistical evaluation of hydrological forecasts of up to 6 weeks using a hydrological-hydrodynamic model on a continental scale associated with ensemble precipitation forecasts generated by an atmospheric model, producing future streamflow in the continent basins, and consequently at the SIN’s hydroelectric dams. The statistical evaluation was based on deterministic scores typically used by the SIN operating agent, and additionally we assessed the skill of forecasts based on atmospheric models in relation to simpler forecasts based on the climatology of observed inflows. The performance of the forecasts varies according to the season and geographic location, that is, depending on different hydrological regimes. The best performances were obtained in dams located in the southwest and central-west regions, which have well-defined seasonality, while dams in the south showed greater sensitivity in metrics according to the season. The study presented serves as a technical-scientific contribution for agents and decision makers who seek to improve water resource management by incorporating extended forecasts into the operational chain.

Improved modeling of Congo's hydrology for floods and droughts analysis and ENSO teleconnections

Study regionThe Congo River basin (CRB), the world's second-largest river system, is subject to extreme hydrological events that strongly impact its ecosystems and population. Study focusHere we present an improved 40-year (1981–2020) hydrological reanalysis of daily CRB discharge and analyze the spatiotemporal dynamics of recent major CRB floods and droughts, and their teleconnection with El Niño-Southern Oscillation (ENSO), the dominant driver of tropical precipitation. We employ a large-scale hydrologic-hydrodynamic model (MGB) with lake storage dynamics representation and a data assimilation (DA) technique using in-situ and remote sensing observations. New Hydrological InsightsThe MGB model demonstrates satisfactory performance, with Kling-Gupta efficiency metric of 0.84 and 0.71 for calibration and validation, respectively. Incorporating lake representation substantially enhances simulations, increasing the Pearson correlation coefficient from 0.3 to 0.63. Additionally, DA yields a ∼13% reduction in discharge errors via cross-validation. We find that the 1997–1998 flood impacting the south and central CRB is statistically linked to a major El Niño event during that period. However, no such association is found for the 2019–2020 flood. Severe droughts in 1983–1984 and 2011–2012, affecting northern and southern CRB respectively, exhibit strong correlation with preceding El Niño and La Niña events, with a ∼10–12 months lag. This study advances understanding of the intricate interplay between spatiotemporal hydrological variability in CRB and large-scale climate phenomena like ENSO.

Impacts of large-scale climatic circulation on floods through precipitation and temperature in the Lancang-Mekong River Basin

The importance of large-scale climatic circulation impacts on precipitation and floods in the Lancang-Mekong River Basin (LMRB) has been widely acknowledged. However, the mechanisms related to the impacts of circulation on floods are not yet fully understood. To address this issue, circulations were characterized by using the climate indices, and floods were represented by the flood volume and simulated using an improved hydrological-hydrodynamic model. The linear regression model was used to assess the impacts of circulation on precipitation and temperature, and also to quantify the impacts of precipitation, temperature, and circulation on floods. The results show that circulation affects precipitation with large spatial variability, while affects temperature with small spatial variability. On average, a unit change in different climate indices can cause a 2.5 %–6.1 % change in precipitation and a 0.3 %–1.2 % (0.09 °C–0.16 °C) change in temperature. Further, precipitation has a positive impact on floods, while temperature mainly has a negative impact. On average, a unit increase in precipitation and temperature can cause a 22.6 % increase and a 9.3 % decrease in floods, respectively. Consequently, circulation affects floods mainly through its impact on precipitation, while its impact on temperature also plays an important role. On average, a unit change in different climate indices can cause a 3.1 %–8.7 % change in floods. In addition, a multi-linear regression model was used to discuss the explained variance and relative contribution of climate. The results show that circulation can explain 12 %–23 % of the precipitation, temperature, and flood variances, while temperature and precipitation can explain 69 % of the flood variance. Among all circulations, the monsoon systems, North Atlantic Oscillation, and El Niño–Southern Oscillation dominate precipitation, temperature, and floods in the LMRB with the largest contributions and the most affected regions.

Seasonal streamflow forecasting in South America’s largest rivers

Study regionSouth America large basins (>5000 km²) Study focusThis work represents a first assessment of seasonal streamflow forecasts in South America based on a continental-scale application of a large scale hydrologic-hydrodynamic model and ECMWF's seasonal forecasting system precipitation forecasts (SEAS5-SSF) with bias correction. Seasonal streamflow forecasts were evaluated against a reference model run. Forecast skill was estimated relative to the Ensemble Streamflow Prediction (ESP) method. New hydrological insightsWe observed that bias correction was essential to obtain positive skill of SEAS5-SSF over ESP, which remained a hard to beat benchmark, especially in regions with high seasonality, and highly dependent on initial conditions. SEAS5-SSF skill was found to be dependent on initialization month, basin and lead time. Rivers where the skill is higher were Amazon, Araguaia, Tocantins and Paraná.

Lake Impoundment in Advance of Post-Flood Period Based on Large-Scale Numerical Simulation

Influenced by climate warming and human disturbance, the contradiction between flood control and water supply of the rivers-connected lake is becoming more prominent, and the prevailing dispatch system mainly aimed at flood control is a serious challenge for storage allocation. This paper presents a lake impoundment in advance method during flood recession period, which preliminarily explores the traditional dispatch model and couples it with a high-precision hydrodynamic model. A one- and two-dimensional coupled hydrodynamic model was constructed for integrated flood regulation in the “river-lake-flood detention basin” system. Then a sample library of water storage strategies was developed, which combined impounding times and gate operation schemes based on previous engineering experience. Thus, by combining large-scale numerical simulations of flood scenarios and risk-benefit assessment, the dispatching schemes facilitating the combined use of gates during the post-flood period under different wet-dry conditions were determined. The results show that: (1) the hydrological-hydrodynamic model has high accuracy for realizing the elaborate simulation of flood evolution and inundation dynamic between lands and lakes, with the average error controlled from 0.03 to 0.14 m, (2) the strategy extraction method of post-flood impoundment in advance for the lake basin is proposed, which takes the impounding times, gate scheduling rules, and inflow scenarios into account comprehensively, and (3) under the optimal storage strategies, the water storage efficiency is significantly improved, with the water level overrun rate controlled at 19.24%, the terminal storage level raised by 0.96 m, and the storage capacity increased by 17.0×108 m3 on average. The outcomes will provide support for flood control and water storage improvement during the post-flood period, which is beneficial to the utilization and management of flood resources.

Modelling the climate change impacts on river discharge and inundation extent in the Magdalena River basin – Colombia

ABSTRACT Climate change may have significant impacts on water balance and may considerably influence flooding dynamics of river systems by increasing extreme precipitation. This study evaluates the potential effects of climate change on river discharge and inundation in the Magdalena River basin, the main river in Colombia, using the synergy between the MGB (Modelo de Grandes Bacias) hydrological–hydrodynamic model and downscaled Eta-regional climate model (RCM) projections based on four global climate models (GCMs): BESM (Brazilian Earth System Model), CanESM2 (Canadian Earth System Model), MIROC5 (Model for Interdisciplinary Research on Climate Version Five), and HadGEM2-ES (Hadley Centre Global Environment Model version 2). We used two different greenhouse gas scenarios (RCP4.5 and RCP8.5 (Representative Concentration Pathway)) for the “historical” (1986–2005) and “mid-term prospective” (2046–2065) periods. Model results for the “mid-term prospective” period under scenarios RCP4.5 and RCP8.5 indicate increase in mean river discharges in the east portion of the basin, decreased river discharges (mainly in the dry season) in the upper Magdalena basin, and increased inundation extent. By coupling hydrological–hydrodynamic and GCMs/RCMs models, modelling frameworks like the one used in this study provide an effective management tool for stakeholders interested in potential climate change impacts on tropical river basins.

Joint assimilation of satellite soil moisture and streamflow data for the hydrological application of a two-dimensional shallow water model

Data assimilation (DA) in physically-based hydrodynamic models is conditioned by the difference in temporal and spatial scales of the observed data and the resolution of the model itself. In order to use remote sensing data in small-scale hydrodynamic modelling, it is necessary to explore innovative DA methods that can lead to a more plausible representation of the spatial variability of the parameters and processes involved. In the present study, satellite-derived soil moisture and in situ-observed streamflow data were jointly assimilated into a high-resolution hydrological-hydrodynamic model based on the Iber software, using the Tempered Particle Filter (TPF) for the dual estimation of model state variables and parameters. Twelve storm events occurring in a 199 km2 catchment located in NW Spain were used for testing the proposed approach. A 3-step procedure was followed: (1) sensitivity analysis of the model parameters; (2) joint assimilation of soil moisture and discharge data to estimate correlations between observations and model parameters; (3) joint assimilation of soil moisture and discharge data using an initial set of particles and parameter standard deviations derived from prior information. The numerical model correctly reproduces the observed data, with an average Nash-Sutcliffe efficiency (NSE) value of 0.74 over the 12 events when the prior information is used. The approach described is shown to be most efficient with storm events that produce isolated peak discharges.

Analyzing the impact of polder‐type flood control pattern on river system's regulation and storage capacity under urbanization

AbstractThe rapid development of urbanization has led to changes in the morphology and structure of the river system, exacerbating flooding in the basin and seriously affecting the regulation and storage capacity of the river system. We propose the evaluation indexes of the regulation and storage capacity, establish a hydrological–hydrodynamic model to investigate the effects of the polder‐type flood control pattern on the regulation and storage capacity during urbanization. The results show that the river system's static regulation and storage capacity exhibits a decreasing trend during the urbanization process. The change in static regulation and storage capacity of low‐grade rivers has a more significant impact on the total regulation and static storage capacity. The construction of polders increases the flood pressure of the river system and reduces the dynamic regulation and storage capacity. The smaller the size of the flood, the more pronounced the effect. With the increase in flood‐protected areas, the dynamic regulation and storage capacity gradually decreases, and minor flooding is more sensitive to the number of polders.

Impact assessment of climate change on compound flooding in a coastal city

Flood disasters in coastal cities are generally caused by the combined effect of typhoon rainfall, storm surge and other factors. Coastal cities will face more severe compound flooding under climate change. Although some studies have analyzed compound flooding under climate change, there is a lack of research considering the combined effect of sea level rise (SLR) and future rainfall changes, especially lack of the jacking effect of SLR on the volume and duration of compound flooding. This paper investigates the impact of future rainfall changes and SLR on compound flood risk, via a case study of a coastal city, Haikou, China. Future rainfall in Haikou is obtained by bias correction of rainfall from five global climate models (GCMs) in CMIP5. The SLR in 2050 and 2090 is predicted according to IPCC. Then, a 1D/2D hydrological-hydrodynamic model for dynamic simulation of surface runoff is applied to simulate compound flooding. Results show that both future rainfall changes and SLR will lead to the increase of flood extent, flood depth and flood duration. But future rainfall changes will have a greater impact on flood, and the combined effect of future rainfall changes and SLR on flood is greater than the sum of their individual effects. The individual effects of SLR and future rainfall changes would increase flood depth in 10.6 % and 32.1 % of the study area, respectively, while their combined effect would increase this proportion to 34.9 %. Also, the jacking effect of SLR on compound flooding will lead to an increase in flood volume, but it presents a nonlinear relationship that gradually slows down with the increase of SLR. A SLR of 0.3 m and 0.63 m will lead to a 2.34 % and 3.22 % increase in flood volume, respectively. And the jacking effect of SLR is more obvious when future rainfall changes are considered, which will lead to a sharp increase in the duration of compound flooding. Moreover, water storage facilities and tidal gates are effective ways to reduce the impact of climate change on compound flooding.

Model-Based Mechanism Analysis of “7.20” Flash Flood Disaster in Wangzongdian River Basin

With digital information technology based on limited data, disaster simulation review is an important guideline for analyzing disaster mechanisms, planning post-disaster reconstruction, and improving defense capability. Taking the “7.20” flash flood in the Wangzongdian river basin as a research area, a hydrological-hydrodynamic model was established using limited measured data. The results showed that the extreme rainstorm caused flooding in mountainous areas and the collapse of subgrade water damming, and the high-level flood quickly flowed into Wangzongdian Village in a short distance, which was the main cause of this serious disaster. Considering the collapse due to the congestion of the upstream bridge, the simulated flood flow in Wangzongdian Village reached 782 m3/s, which was basically consistent with the post-disaster survey results, with a relative error of only +8%. The modeling strategy proposed in this paper is applicable in the review of sudden heavy rainstorms and flash floods and can provide technical guidance for future flash flood simulation review analysis in other areas.

Effects of cryospheric hydrological processes on future flood inundation and the subsequent socioeconomic exposures in Central Asia

Floods and their subsequent socioeconomic exposures are increasing in most parts of the world due to global warming. However, less attention is given in the arid Central Asia (CA), in which floods usually occur in data-scarce high-mountainous regions with complex cryospheric hydrological processes (CHP). In this study, an improved hydrologic-hydrodynamic model coupled with a glacier mass balance module was developed to enhance flood simulations in CA. The effects of the CHP on future flood inundation and the subsequent socioeconomic exposures were also investigated. We found that the simulations of daily streamflow and flood magnitudes improved significantly over the selected hydrological stations after considering the glacier mass balance. Our estimations indicated that the flood inundation and its dynamic evolution generally agreed with satellite observations. Moreover, CHP-induced (rainfall-induced) flood inundation plays a significant role in China’s Xinjiang and Tajikistan (other regions of CA). The CHP would amplify the effects of future flood on socioeconomics in CA, with population (Gross Domestic Productivity, GDP) exposure up to 2.25 million persons/year (150 billion $ PPP/year) for 2071–2100. These findings could provide scientific evidence to improve the understanding of CHP effects on future floods and the subsequent exposures, informing the prioritization and design of flood mitigation strategies in CA.

Assessment of urban flood risk based on data-driven models: A case study in Fuzhou City, China

In response to the growing threat of urban flooding, this study developed an integrated method to evaluate the flood risk of the study area (Fuzhou City, China) to different types of urban flooding. First, a coupled hydrological–hydrodynamic model was developed to obtain the key dynamic flood factors forced by typical rainstorm events with river levee overtopping and pipeline transportation. Second, an integrated framework was developed to assess urban flood vulnerability under different flood hazard scenarios. Fifteen features were selected and their relative importance was analyzed by using the XGBoost model. Finally, a cloud model was used to realize qualitative to quantitative characterization of subregions of urban flood risk. The results highlighted the flooding nodes, and the inundation areas presented increasing trends with the increase of return periods. The inundation degree was divided into four categories based on the inundation degree, for which the flooding area was 324.30, 177.81, 43.42, and 20.93 ha, respectively. Six explanatory variables with greater importance were recognized in all schemes. The risk level of subregions changed dynamically, when the rainfall intensity increased, the risk level of subregions showed an increasing trend. Areas with intermediate and high risk were concentrated in the central and western of the study area. At the drainage subregion scale, two types of subareas are essential in urban flood management, respectively, the subregions with high risk in specific rainfall, and the subregions easily transforming into high-risk areas when rainfall increases. This study provides promising references in urban flood risk management.

Flood inundation in the Lancang-Mekong River Basin: Assessing the role of summer monsoon

• Monsoons have positive impacts on inundation frequency, area and depth. • Inundation timing is either advanced or delayed when monsoons strengthen. • A change of monsoon can cause on average up to 14.2% change in inundation. • Different monsoons dominate different inundation characteristics. Although it is well known that precipitation and flood pulses in the Langcang-Mekong River Basin (LMRB) are largely impacted by monsoons, it is unclear to what extent flood inundation characteristics (i.e., inundation frequency, depth, area, and timing) in the basin respond to different monsoon types and monsoon combined effect, i.e., the Indian summer monsoon (ISM), the Western North Pacific Monsoon (WNPM), and their combined effect (ISWN). In this study, flood inundation in the LMRB during 1967–2015 was simulated by a hydrological-hydrodynamic model, from which the inundation characteristics were extracted and calculated. The monsoon impact on inundation characteristics was then quantified using the slope from linear regression model. The results show the monsoons and the ISWN overall have a positive impact on inundation frequency, depth, and area, while the inundation timing is usually advanced when the WNPM or the ISWN strengthens but delayed when the ISM strengthens. On average, a unit change in different monsoons can cause, 7.7%–14.2% change in inundation frequency, 5.3%–8.1% change in inundation depth and 4.3 days–5.8 days change in inundation timing for depth, which can also lead to 1.0%–4.3% change in inundation area and 2.8 days–3.8 days change in inundation timing for area. Also, the relative contributions of different monsoons and spatial distributions of the dominant monsoon were discussed. The results indicate different monsoons regulate different inundation characteristics, and suggest the coexistence of monsoon impacts. If the impact of the ISWN is ignored, the WNPM will play a more important role than the ISM in affecting the inundation.