High Intensity Rainfall Events Research Articles

Towards transient connectivity of river networks during rainfall events: Insight from hydrological observation and functional data analysis

River connectivity, which is critical for river networks to perform their functions, is deteriorating in highly urbanized areas. While it is well known about the structural and functional connectivity of river networks using traditional indicators, much less is known about the process connectivity and its influence factor. To this end, we propose a new approach based on hydrological observations and functional data analysis that has the advantage of capturing transient changes in process connectivity and responses to rainfall events. Applying the method to a typical river-dense plain in China, the results show that 1) the highest and the lowest values of process connectivity were concentrated in 0.6–0.95 and 0.04–0.61 during 2015–2020, respectively. The average values of process connectivity were lower in 2015, 2016, and 2020 with 0.49, 0.44, and 0.46, respectively, and higher in 2017 to 2019 with 0.52, 0.57, and 0.58, respectively. 2) There is a significant negative correlation between rainfall intensity and process connectivity, especially when heavy rainfall occurs. Under the same rainfall intensity, the process connectivity was weaker in the river network with lower structural connectivity, lower overflow capacity of main streams, strong concentration of rivers, more sluices and larger unit areas. 3) The main influencing factors on process connectivity were structural connectivity and rainfall, with contributions of 34.67% and 22.67%, respectively. Following closely are unit features and human regulation, contributing 14.07% and 12.61% to process connectivity, respectively. 4) Structural connectivity and unit features are the primary factors influencing process connectivity during low-intensity rainfall events. During high-intensity rainfall events, rainfall and unit features become the predominant factors affecting process connectivity. Our results provide a new insight for quantifying process connectivity in the plain and are expected to support the study of the changing mechanism of hydrological connectivity.

Impact of Hillslope Agriculture on Soil Compaction and Seasonal Water Dynamics in a Temperate Vineyard

Major losses of agricultural production and soils are caused by erosion, which is especially pronounced on hillslopes due to specific hydrological processes and heterogeneity. Therefore, the aim of this study was to assess the impact of agricultural management on the compaction, infiltration, and seasonal water content dynamics of the hillslope. Measurements were made at the hilltop and footslope, i.e., soil water content and potential were measured using sensors, wick lysimeters were used to quantify water flux, while a mini-disk infiltrometer was used to measure the infiltration rate and calculate the unsaturated hydraulic conductivity (K_unsat). Soil texture showed differences between hillslope positions, i.e., at the hilltop after 50 cm depth, the soil is classified as silty clay loam, and from 75 cm onward, the soil is silty clay, while at the footslope, the soil is silt loam even at the deeper depths. The results show a higher K_unsat at the footslope as well as higher average water volumes collected in wick lysimeters compared to the hilltop. Average water volumes showed a statistically significant difference at p < 0.01 between the hilltop and the footslope. The soil water content and water potential sensors showed higher values at the footslope at all depths, i.e., 8.0% at 15 cm, 8.4% at 30 cm, and 27.3% at 45 cm. The results show that, even though the vineyard is located in a relatively small area, soil heterogeneity is present, affecting the water flow along the hillslope. This suggests the importance of observing water movement in the soil, especially today when facing extreme weather (e.g., short-term high-intensity rainfall events) in order to protect soil and water resources.

Modelling antecedent soil hydrological conditions to improve the prediction of landslide susceptibility in typhoon-prone regions

Most regional landslide susceptibility models do not consider the evolving soil hydrological conditions leading up to a multiple occurrence regional landslide event. This results in inaccurate predictions due to the non-linear behaviour of the terrain. To address this, we have developed a simple and efficient model that incorporates the mid-term evolution of soil hydrological conditions. The model combines a water balance model and a geotechnical model based on infinite slope theory. The analysis of 561 high-intensity rainfall events in a typhoon-prone region of the Philippines revealed that the percolation of water during the 5-month wet season is crucial in determining landslide susceptibility. Consequently, high-intensity rainfall events at the start of the wet season are less likely to trigger landslides, while later events are more hazardous. We analysed the change in landslide susceptibility during the 2018 rainy season by comparing the probability of failure (PoF) before and after three high-intensity rainfall events (July, August and September). Only the event in September caused a significant increase in the probability of failure (PoF). The model showed an accuracy of 0.63, with stable cells better represented than unstable cells. The antecedent hydrological conditions on the lower soil layers are responsible for changes in landslide susceptibility. Our findings support the hypothesis that new approaches to developing hydro-meteorological thresholds for landslide early warning systems should be evaluated, especially in regions with strong seasonality.

Evaluation of ERA5 and CHIRPS rainfall estimates against observations across Ethiopia

Satellite-based precipitation estimates and global reanalysis products bear the promise of supporting the development of accurate and timely climate information for end users in sub-Sharan Africa. The accuracy of these global models, however, may be reduced in data-scarce regions and should be carefully evaluated. This study evaluates the performance of ERA5 reanalysis data and CHIRPS precipitation data against ground-based measurements from 167 rain gauges in Ethiopia, a region with complex topography and diverse climates. Focusing over a 38-year period (1981–2018), our study utilizes a point-to-pixel analysis to compare daily, monthly, seasonal, and annual precipitation data, conducting an evaluation based on continuous and categorical metrics. Our findings indicate that over Ethiopia CHIRPS generally outperforms ERA5, particularly in high-altitude areas, demonstrating a better capability in detecting high-intensity rainfall events. Both datasets, however, exhibit lower performance in Ethiopia's lowland regions, possibly the influence of sparse rain gauge networks informing gridded datasets. Notably, both CHIRPS and ERA5 were found to underestimate rainfall variability, with CHIRPS displaying a slight advantage in representing the erratic nature of Ethiopian rainfall. The study’s results highlight considerable performance differences between CHIRPS and ERA5 across varying Ethiopian landscapes and climatic conditions. CHIRPS’ effectiveness in high-altitude regions, especially for daily rainfall estimation, emphasizes its suitability in similar geographic contexts. Conversely, the lesser performance of ERA5 in these areas suggests a need for refined calibration and validation processes, particularly for complex terrains. These insights are essential for the application of satellite-based and reanalysis of rainfall data in meteorological, agricultural, and hydrological contexts, particularly in topographically and climatically diverse regions.

Spatial and Temporal Characteristics of Channel Disturbance Zones in Big River, Southeast Missouri (1937–2018)

abstract: Ozarks watersheds have responded to land clearing and settlement disturbances by transporting large amounts of gravel and sediment to main valleys of medium-large river systems. Historical gravel and sediment accumulate in disturbance zones, which are areas of excessive channel activity that can be detected over time using historical aerial photographs. This project uses a series of aerial photos from 1937–2018 to identify disturbance zones in the Big River of southeast Missouri, which has a history of lead mining and ore processing that has caused widespread contamination of the channel and floodplain deposits. Variations in active areas of the historical meander belt show that disturbance zones account for 24 percent of the channel length at an interval of one per 2.5 km of channel. Megabars and extensions are the most prominent types of disturbance zones, but translations and cutoffs are larger with higher potential for sediment storage. From 1937 to 1976, areas of channel activity have cycled between expansion and contraction. However, after a period of recovery prior to 1976, disturbance zone areas have been expanding over the last 40 years likely in response to an increase in flood magnitude and frequency from more high intensity rainfall events since 1973.

CHIRPS rainfall product application for analyzing rainfall concentration and seasonality in Johor river basin, Malaysia

It is crucial to carry out a spatiotemporal evaluation of rainfall concentration in the Johor river basin (JRB) given that erratic rainfall can lead to floods. The study utilized the CHIRPS rainfall dataset from 1983 to 2018 to determine rainfall concentration using the Concentration Index (CI). To further define the rainfall regime, the Seasonality Index (SI) and Precipitation Concentration Index (PCI) were examined. The Mann-Kendall (MK) test was utilized to examine the trend at a 95% level of significance. The results showed the better accuracy of SI than PCI in classifying the rainfall regime in the JRB, defined as “equable but with a definite wetter season” type. The CI values ranged from 0.61 to 0.72 and were typically high all over the basin, with the northwestern and western regions having the highest values. At 27 grid points on the eastern border of the upper and middle parts of the basin, CI has significantly increased as a result of an upsurge in rainfall concentration brought on by climate change. More than 67% and up to 83% of the yearly rainfall for all grid locations was accounted for by the North East monsoon (NEM), with the 25% wettest days caused by high-intensity rainfall events.

Geo-spatial assessment of pluvial floods in city district Lahore, Pakistan.

This study is an effort of geo-spatial assessment of pluvial floods in District Lahore, Pakistan, caused by urban expansion and the growing frequency and intensity of high-intensity rainfall events. The use of geospatial techniques such as watershed modeling, maximum likelihood image classification, and weighted overlay analysis based on secondary data has enabled the researchers to assess the extent and severity of pluvial floods in the study area. The study's findings highlight the high risk of pluvial floods in the central part of the study area, which is dominated by built-up land and concrete roads. The increase in the area of built-up land from 34.913 km2 in 2018 to 37.442 km2 in 2022 has further intensified the risk of pluvial floods. The findings of this study can assist policymakers in developing effective strategies to reduce the risks associated with pluvial floods. Alongside, it also highlights the importance of geospatial techniques to better understand and address the complex challenges of urbanization and climate change. Flood risk zone-specific strategies are recommended to reduce the risk of pluvial floods.

Comparing Different Coupling and Modeling Strategies in Hydromechanical Models for Slope Stability Assessment

The dynamic interaction between subsurface flow and soil mechanics is often simplified in the stability assessment of variably saturated landslide-prone hillslopes. The aim of this study is to analyze the impact of conventional simplifications in coupling and modeling strategies on stability assessment of such hillslopes in response to precipitation using the local factor of safety (LFS) concept. More specifically, it investigates (1) the impact of neglecting poroelasticity, (2) transitioning from full coupling between hydrological and mechanical models to sequential coupling, and (3) reducing the two-phase flow system to a one-phase flow system (Richards’ equation). Two rainfall scenarios, with the same total amount of rainfall but two different relatively high (4 mm h−1) and low (1 mm h−1) intensities are considered. The simulation results of the simplified approaches are compared to a comprehensive, fully coupled poroelastic hydromechanical model with a two-phase flow system. It was found that the most significant difference from the comprehensive model occurs in areas experiencing the most transient changes due to rainfall infiltration in all three simplified models. Among these simplifications, the transformation of the two-phase flow system to a one-phase flow system showed the most pronounced impact on the simulated local factor of safety (LFS), with a maximum increase of +21.5% observed at the end of the high-intensity rainfall event. Conversely, using a rigid soil without poroelasticity or employing a sequential coupling approach with no iteration between hydromechanical parameters has a relatively minor effect on the simulated LFS, resulting in maximum increases of +2.0% and +1.9%, respectively. In summary, all three simplified models yield LFS results that are reasonably consistent with the comprehensive poroelastic fully coupled model with two-phase flow, but simulations are more computationally efficient when utilizing a rigid porous media and one-phase flow based on Richards’ equation.

Flood susceptibility mapping using a novel integration of multi-temporal sentinel-1 data and eXtreme deep learning model

Flash floods (FFs) are amongst the most devastating hazards in arid regions in response to climate change and can cause the loss of agricultural land, human lives and infrastructure. One of the major challenges is the high-intensity rainfall events affecting low-lying areas that are vulnerable to FF. Several works in this field have been conducted using ensemble machine learning models and geohydrological models. However, the current advancement of eXtreme deep learning, which is named eXtreme deep factorisation machine (xDeepFM), for FF susceptibility mapping (FSM) is lacking in the literature. The current study introduces a new model and employs a previously unapplied approach to enhance FSM for capturing the severity of floods. The proposed approach has three main objectives: (i) During- and after-flood effects are assessed through flood detection techniques using Sentinel-1 data. (ii) Flood inventory is updated using remote sensing-based methods. The derived flood effects are implemented in the next step. (iii) An FSM map is generated using an xDeepFM model. Therefore, this study aims to apply xDeepFM to estimate susceptible areas using 13 factors in the emirates of Fujairah, UAE. The performance metrics show a recall of 0.9488), an F1-score of 0.9107), precision of (0.8756) and an overall accuracy of 90.41%. The accuracy of the applied xDeepFM model is compared with that of traditional machine learning models, specifically the deep neural network (78%), support vector machine (85.4%) and random forest (88.75%). Random forest achieves high accuracy, which is due to its strong performance that depends on factors contribution, dataset size and quality, and available computational resources. Comparatively, the xDeepFM model works efficiently for complicated prediction problems having high non-collinearity and huge datasets. The obtained map denotes that the narrow basins, lowland coastal areas and riverbank areas up to 5 km (Fujairah) are highly prone to FF, whilst the alluvial plains in Al Dhaid and hilly regions in Fujairah show low probability. The coastal city areas are bounded by high-rise steep hills and the Gulf of Oman, which can elevate the water levels during heavy rainfall. Four major synchronised influencing factors, namely, rainfall, elevation, drainage density, distance from drainage and geomorphology, account for nearly 50% of the total factors contributing to a very high flood susceptibility. This study offers a platform for planners and decision makers to take timely actions on potential areas in mitigating the effects of FF.

Spatial and Temporal Characteristics of Channel Disturbance Zones in Big River, Southeast Missouri (1937-2018)

Ozarks watersheds have responded to land clearing and settlement disturbances by transporting large amounts of gravel and sediment to main valleys of medium-large river systems. Historical gravel and sediment accumulate in disturbance zones, which are areas of excessive channel activity that can be detected over time using historical aerial photographs. This project uses a series of aerial photos from 1937-2018 to identify disturbance zones in the Big River of southeast Missouri, which has a history of lead mining and ore processing that has caused widespread contamination of the channel and floodplain deposits. Variations in active areas of the historical meander belt show that disturbance zones account for 24 percent of the channel length at an interval of one per 2.5 km of channel. Megabars and extensions are the most prominent types of disturbance zones, but translations and cutoffs are larger with higher potential for sediment storage. From 1937 to 1976, areas of channel activity have cycled between expansion and contraction. However, after a period of recovery prior to 1976, disturbance zone areas have been expanding over the last 40 years likely in response to an increase in flood magnitude and frequency from more high intensity rainfall events since 1973.

Accuracy of satellite and reanalysis rainfall estimates over Africa: A multi-scale assessment of eight products for continental applications

Study RegionContinental Africa Study FocusThis study evaluates the accuracy of eight gauge-corrected rainfall products across Africa through direct comparisons with in situ observations for the period 2001–2020. The effect of validation datasets on the performance of the rainfall products was also quantified in ten African countries. Four categorical and five continuous metrics were estimated at multiple spatial and temporal scales as part of the evaluation. New hydrological insights for the RegionResults indicate that the performance of the rainfall products varied in space and time. Evaluation at temporal scales revealed that, on average, most rainfall products showed poor results (KGE < 0.35) at the daily timescale. In contrast, RFE v2.0, ARC v2.0, and MSWEP v2.8 were reliable (KGE > 0.75) at the monthly and annual timescales. Among the rainfall products, the performance of TAMSATv3.1, PERSIANN-CDR, and ERA 5 was relatively poor in capturing in situ observations. Evaluation at various spatial scales revealed mixed results. The ARC v2.0 and CHIRPS v2.0 rainfall products were reliable in detecting no rains (< 1 mm/day) for all 19 spatial scales, indicating a high level of confidence for drought studies. IMERG-F v6B and RFE v2.0 were reliable in detecting heavy and high-intensity rainfall events for all spatial scales. Using the KGE performance metrics at the regional level, MSWEP v2.8 in the Northern Africa region, RFE v2.0 in the Western and Southern Africa regions, ARC v2.0 in Central Africa, and CHIRPS v2.0 in the Eastern Africa region showed better performances at monthly timescale. Moreover, the performance of the gauge-corrected rainfall datasets was reduced when compared with independent validation data (gauge data not used by rainfall products) than dependent validation data. This study provides several new insights into choosing a rainfall product for continental to regional applications and identifies the need for bias correction.

Dynamic N transport and N2O emission during rainfall events in the coastal river

The coastal zone exhibited a high population density with highly impacted by anthropogenic activities, such as river impoundment to prevent saline intrusion, which resulted in weak hydrological conditions. Rainfall events can result in dramatic changes in hydrological and nutrient transportation conditions, especially in rivers with weak hydrological conditions. However, how the nitrogen transport and N2O emissions or biogeochemistry responds to the different types of rainfall events in the weak hydrodynamics rivers is poorly understood. In this study, the hydrological, nitrogenous characteristic, as well as N2O dynamics, were studied by high-frequency water sampling during two distinct rainfall events, high-intensity with short duration (E1) and low-intensity with long duration (E2). The results displayed that the hydrologic condition in E1 with a wider range of d-excess values (from −9.50 to 32.1 ‰), were more dynamic than those observed in E2. The N2O concentrations (0.01–3.33 μmol/L) were higher during E1 compared to E2 (0.03–1.11 μmol/L), which indicated that high-intensity rainfall has a greater potential for N2O emission. On the contrary, the concentrations of nitrogen (e.g., TN and NO3−-N) were lower during E1 compared to E2. Additionally, hysteresis was observed in both water and nitrogen components, resulting in a prolonged recovery time for pre-rainfall levels during the long-duration event. Moreover, the results showed that the higher average N2O flux (78.3 μmol/m2/h) in the rainfall event period was much larger than that in the non-rainfall period (1.63 μmol/m2/h). The frequency dam regulation resulted in the water level fluctuation, which could enhance wet-dry alternation and simulated N2O emissions. This study highlighted the characteristic of N dynamic and hydrological responses to diverse rainfall events occurrences in the coastal river. Rainfall could increase the N2O emission, especially during high-intensity rainfall events, which cannot be ignored in the context of annual N2O release.

Rainfall intensity affects the recharge mechanisms of groundwater in a headwater basin of the North China plain

Understanding the origins of groundwater and its movement from mountain to plain during different high intensity rainfall events is critical for conserving water supplies, determining water-use policies and controlling pollution. These factors are also the keys for understanding the dominant processes in hydrological models. In this study, groundwater resources and recharge processes during heavy precipitation were explored by using stable isotope tracers in the hilly area of Taihang Mountain. It was found that the δ2H and δ18O values of precipitation exhibited obvious precipitation amount effect during different precipitation intensity events. The stable isotopic values in groundwater and river water showed significantly varied during the single extreme heavy precipitation and the continuous heavy precipitation events. In the rainy season, precipitation amounts greater than 40 mm/d could effectively recharge the shallow groundwater in the study area. By comparing the spatial isotopic distribution of groundwater, soil water, and river water with precipitation, we showed distinct groundwater recharge patterns in terms of their water resources, timing, and the degree of river water and groundwater interaction during the single extreme heavy precipitation and the continuous heavy precipitation. After the single extreme heavy precipitation, the δ2H and δ18O values of groundwater, soil water, and river water showed stable over time and had the same similar variations, suggested that the groundwater recharge was mainly dominated by precipitation with preferential flow or bypass flow. While after the continuous heavy precipitation, the variation of δ2H and δ18O in all water is consistent with the previous precipitation, which shown a mixing effect of previous enrich precipitation and depleted heavy precipitation, suggested that groundwater source was dominated by a continuous recharge of previous heavier precipitation with translatory flow. The groundwater main recharge mechanism is not constant, but changes with rainfall intensity. The rainfall intensity play an important role in groundwater recharge change affecting runoff process. Overall, this paper presents a new insight to understand the effect of rainfall intensity on hydrological process, which could be used to provide vital information in the semi-humid and semi-arid regions where water resources are critical in climate change adaptation strategies.

Quantifying geomorphic change in Andean river valleys using UAV-PPK-SfM techniques: An example from the western Peruvian Andes

The western Peruvian region is prone to erosion and geomorphic change. Extreme precipitation events lead to rapid change in river channel and floodplain morphology due to bank erosion and debris flows delivering detrital material to the fluvial system. Monitoring geomorphic events and their associated topographic changes at high spatial and temporal resolutions remains a challenge. Here, we used an Uncrewed Aerial Vehicle - Post-Processing Kinematic - Structure from Motion (UAV-PPK-SfM) approach that includes co-registration of point clouds by using relative Ground Control Points (GCPs). This workflow adjusts each elevation model to a reference model using invariant features that did not change their position or form over time. We applied this technique to monitor landscape change (2019–2021) in an area of 0.3 km2 located in the Cañete River basin. Our results showed that a minimum observable elevation change of 0.56 m (95 % confidence interval) can be achieved using this workflow, beyond which an actual elevation change can be separated from systematic error. Using object-based classification techniques on the aerial images, we separated geomorphic dynamics from land cover changes. This allowed us to isolate the effect of geomorphic processes, and quantify rates related to gully erosion, river scouring, bank erosion, and sediment deposition. Within the study area, a hotspot of geomorphic change corresponded to an ephemeral tributary channel. The gully channel incising an alluvial fan is highly dynamic, showing bank erosion of 0.75 to 3.2 m and net export of 37 m3 of sediment in the 25-month study period. Given that the monitoring period did not include high intensity rainfall events, the study illustrates how geomorphic activity in ungauged Andean river basins, such as the Cañete valley, may be considerably underestimated in literature.

Exploring the Application of a Debris Flow Likelihood Regression Model in Mediterranean Post-Fire Environments, Using Field Observations-Based Validation

Post-fire geomorphic processes and associated risks are an important threat in Mediterranean environments. Currently, post-fire mass movement prediction has limited applications across the Mediterranean despite the abundance of both forest fires and landslide/debris flow disasters. This work applies a debris flow generation likelihood model to evaluate the probability of mass movement phenomena in different catchments of a burnt area, after a catastrophic fire near Schinos (Attica, Greece) in 2021. Then, it uses field observations from the area, recording mass movement phenomena after high-intensity rainfall events, to validate the results. The findings show that the model is successful in determining the probability of debris flow generation in the 21 basins of the study area, ranging from 0.05 to 0.893. The probability values show a statistically significant correlation (sig. = 0.001) with the actual debris flow occurrences in the area, and satisfactory results in terms of the model’s predictive ability, functioning well within the particular geo-environmental characteristics of the Mediterranean environment. The results establish the reliability of the approach as a tool to assess mass movement risks in a region with an abundance of post-fire related hazards and disastrous events.

Impact of Climate Change on Soil Erosion

Global warming is expected to result in a more vigorous hydrological cycle, including total rainfall and more frequent high-intensity rainfall events, which affect soil erosion and cause a threat to agricultural land sustainability and productivity, particularly in tropical and subtropical regions. This study aims to determine the effects of climate change on soil erosion in Taiwan. This experiment assesses the risk of soil erosion in Taiwan over 2 – 200 years. To determine the predictor of soil erosion risk, the study used USPED. Result showed there was an increase in sediment yield because of soil erosion. Hence, targeting conservation practices to erosion-prone areas, expanding conservation coverage, and adapting agronomic practices may be necessary to prevent excessive soil erosion under climate change scenarios that include intensified precipitation.

Assessment of satellite precipitation products at different time scales over a cyclone prone coastal river basin in India

Abstract Precipitation is a fundamental input for many hydrological and water management studies. With the advancement in science, a variety of satellite precipitation products are available. In this study, the ability of three satellite precipitation products (TRMM-3B42v7, PERSIANN-CDR and GPM-IMERGv6) to capture rainfall were evaluated with ground-based Indian Meteorological Department (IMD) gridded data and also by driving the Soil Water Assessment Tool (SWAT) hydrological model for a cyclone prone coastal river basin in the southeast of India. Results indicate that among the three, GPM-IMERG v6 outperformed in all the statistical metrics, followed by TRMM-3B42 v7 at different temporal scales. GPM-IMERG v6 exhibited the highest correlation coefficient (0.48) and lowest root mean square error (9.52 mm/day), followed by TRMM-3B42 v7 (CC = 0.44, RMSE = 9.58 mm/day) at daily scale. In contrast to other studies in similar regions, IMERGV6 showed better performance than the PERSIANN CDR in detection skills of low, medium and high intensity rainfall events as well as False Alarm Ratio. Hydrological evaluation of the three products using the SWAT model over the study area showed satisfactory results at daily and monthly scale during the calibration and validation period. IMERGV6 is found to have better performance in hydrological evaluation as well.

Debris flow simulation 2D (DFS 2D): Numerical modelling of debris flows and calibration of friction parameters

Debris flows are rapid mass movements with a mixture of rock, soil and water. High-intensity rainfall events have triggered multiple debris flows around the globe, making it an important concern from the disaster management perspective. This study presents a numerical model called debris flow simulation 2D (DFS 2D) and applicability of the proposed model is investigated through the values of the model parameters used for the reproduction of an occurred debris flow at Yindongzi gully in China on 13 August 2010. The model can be used to simulate debris flows using three different rheologies and has a user-friendly interface for providing the inputs. Using DFS 2D, flow parameters can be estimated with respect to space and time. The values of the flow resistance parameters of model, dry-Coulomb and turbulent friction, were calibrated through the back analysis and the values obtained are 0.1 and 1000 m/s 2 , respectively. Two new methods of calibration are proposed in this study, considering the cross-sectional area of flow and topographical changes induced by the debris flow. The proposed methods of calibration provide an effective solution to the cumulative errors induced by coarse-resolution digital elevation models (DEMs) in numerical modelling of debris flows. The statistical indices such as Willmott's index of agreement, mean-absolute-error, and normalized-root-mean-square-error of the calibrated model are 0.5, 1.02 and 1.44, respectively. The comparison between simulated and observed values of topographic changes indicates that DFS 2D provides satisfactory results and can be used for dynamic modelling of debris flows. • A new numerical model called Debris Flow Simulation 2D (DFS 2D) is developed. • The proposed DFS 2D is useful for understanding the runout process of debris flows. • DFS 2D considers three different rheologies and the effect of entrainment. • The model is user-friendly and the outputs are saved in interactive formats. • Proposed two different approaches for calibration of friction parameters.

Assessment of compound flooding through seamless linkage of coastal hydrodynamic and inland catchment models

Compound flooding refers to the complex interactions among oceanographic, inland (catchment) hydrological, and meteorological processes with anthropogenic factors such as land use changes due to urbanization. To-wit, the impact of higher tide levels, storm surge, and high intensity rainfall events over the inland catchment collectively may result in more extensive flooding than the individual processes acting separately. In the context of climate change and more frequent extreme weather events, coastal cities, particularly those in the ASEAN region, are increasingly vulnerable to compound flooding, yet there is no convenient and user-friendly modelling approach available that would enable the planning community and decision-makers to envision compound flooding as part of resiliency-oriented urban plan development. We addressed this gap by developing the3D Resiliency Visualisation Platform (3DRVP), within which linked features are established using Python scripts to seamless integrate a 2D mixed land use fluvial/pluvial catchment model (PCSWMM) with a 2D/3Dcoastal hydrodynamic model (Delft3D) to simulate the dynamics ofcompound floods for the assessment of coastal inundation. This platform aims to assist planners, urban design professionals, and engineers with a realistic visualization tool to picture the urban infrastructure planning alternatives as well as to facilitate the real-time operational decision-making and evacuation activation with flood control strategies. The integrated platform theory is developed first and the platform then is trialled for a developing coastal area in south Bangkok, Thailand. Similar to many cities of the global south, data availability to calibrate models is limited and as such we used a mixed methods approach to explore model accuracy. The Delft3D model was calibrated successfully using water level data from a nearby gauge in the Gulf of Thailand for Typhoon Linda. The catchment model (PCSWMM) was validated using observed flood areas as reported by the local municipality. A 100-year design rainstorm was subsequently modelled and linked with the Typhoon Linda surge levels with results indicating the combination of rainfall flooding and storm surge would increase the flooded area by 25.6% over the system components modelled individually.

Experimental Investigation on Water Seepage through Transparent Synthetic Rough-Walled Fractures

One of the impacts of climate changes nowadays is the increase in the frequency of high-intensity rainfall events alternating with extreme dry periods, which affect the components of the hydrologic cycle, such as runoff, infiltration, and aquifer recharge. Several experimental investigations and theoretical studies have demonstrated that infiltration flow in fractured media can develop along preferential pathways. However, the prediction of infiltration phenomena in fractured media still remains an open issue. This, together with erratic rainfall patterns due to climate changes, affects the prediction of aquifer recharge and contaminant transport in fractured aquifers. The present work contributes to reducing this research gap by means of experimental investigation and forecast analysis, with a focus on the geometrical properties of single fractures and their influence on flow patterns. Several fracture surfaces based on different fractal dimensions, standard deviations, and mismatch lengths were designed using the SynFrac model and were generated by 3D printing technology. The results revealed that the fracture’s fractal dimension has a significant impact on the number of flow paths, while the fracture inclination only increases the number of intermediate preferential channels, and, hence, modifies the flow rate distribution over the fracture outlet. Additionally, the change in the inclination angle of the dry fracture from 55 to 65 degrees resulted in an 8% reduction in the mean width of first flow path. A sensitivity analysis using an M5 tree indicates that there is a linear relationship between flow rate and the exponential form of the fractal dimension. The location of flow channels is a function of fracture fractal dimension, and the influence of mismatch length on their location is negligible. Finally, an accurate prediction algorithm with a Nash value of 0.81 was developed using Wavelet transform in order to estimate the time series of periodic flow rates over the fracture outlet.