Urbanization and climate change implications in flood risk management: Developing an efficient decision support system for flood susceptibility mapping

Multi-hazard assessment for flood and Landslide risk in Kalimantan and Sumatra: Implications for Nusantara, Indonesia's new capital

Situated within the Ring of Fire and characterized by a tropical climate and high seismic activity, Indonesia is uniquely vulnerable to natural disasters such as floods and landslides. These events pose significant threats to both the population and infrastructure. This study predicts areas exposed to flood and landslide risk by considering various environmental factors related to climate, topography, and land use. The predictive performance of three machine learning models-naïve Bayes, k-nearest neighbors, and random forest (RF)-was evaluated by comparing the AUC, RMSE, and R2 values of each model. Ultimately, the RF model, which demonstrated the highest accuracy, was used to prioritize disaster impact factors and generate hazard maps. The results identified the interaction of rainfall, land use, and slope aspect as the most critical determinants of hazard occurrence. The predicted hazard maps revealed that approximately 26.7 % of the study area was vulnerable to either floods or landslides, with 16.8 % of the area experiencing both. The new capital of Nusantara showed a relatively higher multi-hazard risk than did the overall study area and protected zones, with 22.1 % of the hazard area vulnerable to both flooding and landslides. In single hazard zones, areas classified as at risk for floods had a higher mean probability of experiencing both hazards (43 %), as compared to areas classified as at risk for landslides (22 %). As a result, urban planners and relevant stakeholders can now utilize the hazard maps developed in this study to prioritize infrastructure reinforcement and disaster risk areas, integrating land use planning with risk assessment to mitigate the impact of disasters. By employing these strategies, Indonesia and other countries facing similar challenges can now enhance their disaster preparedness and response capabilities in new capital regions and other areas, ultimately planning for more sustainable urban development.

Climate uncertainty and vulnerability of urban flooding associated with regional risk using multi-criteria analysis in Mumbai, India

The study made a comprehensive effort to examine climatic uncertainties at both yearly and monthly scales, along with mapping flood risks based on different land use categories. Recent studies have progressively been engrossed in demonstrating regional climate variations and associated flood probability to maintain the geo-ecological balance at micro to macro-regions. To carry out this investigation, various historical remote sensing record, reanalyzed and in-situ data sets were acquired with a high level of spatial precision using the Google Earth Engine (GEE) web-based remote sensing platform. Non-parametric techniques and multi-layer integration methods were then employed to illustrate the fluctuations in climate factors alongside creating maps indicating the susceptibility to floods. The study reveals an increased pattern in LST (Land Surface Temperature) (0.03 °C/year), albeit marginal declined in southern coastal regions (-0.15 °C/year) along with uneven rainfall patterns (1.42 mm/year). Moreover, long-term LULC change estimation divulges increased trends of urbanization (16.4 km2/year) together with vegetation growth (8.7 km2/year) from 2002 to 2022. Furthermore, this inquiry involves numerous environmental factors that influence the situation (elevation data, topographic wetness index, drainage density, proximity to water bodies, slope, and soil properties) as well as socio-economic attributes (population) to assess flood risk areas through the utilization of Analytical Hierarchy Process and overlay methods with assigned weights. The outcomes reveal nearly 55 percent of urban land is susceptible to flood in 2022, which were 45 and 37 percent in 2012 and 2002 separately. Additionally, 106 km2 of urban area is highly susceptible to inundation, whereas vegetation also occupies a significant proportion (52 km2). This thorough exploration offers a significant chance to formulate flood management and mitigation strategies tailored to specific regions during the era of climate change.

Contributions of climate change and urbanization to urban flood hazard changes in China's 293 major cities since 1980

The growing incidence of urban flood disasters poses a major challenge to urban sustainability in China. Previous studies have reported that climate change and urbanization exacerbate urban flood risk in some major cities of China. However, few assessments have quantified the contributions of these two factors to urban flood changes in recent decades at the nationwide scale. Here, surface runoff caused by precipitation extremes was used as the urban flood hazard to evaluate the impacts of climate change and urbanization in China's 293 major cities. This study assessed the contributions of these drivers to urban flood hazard changes and identified the hotspot cities with increased trends under both factors during the past four decades (1980-2019). The results showed that approximately 70% of the cities analyzed have seen an increase of urban flood hazard in the latest decade. Urbanization made a positive contribution to increased urban flood hazards in more than 90% of the cities. The contribution direction of climate change showed significant variations across China. Overall, the absolute contribution rate of climate change far outweighed that of urbanization. In half of the cities (mainly distributed in eastern China), both climate change and urbanization led to increased urban flood hazard over the past decade. Among them, 33 cities have suffered a consecutive increase in urban flood hazard driven by both factors.

Identifying changes in flood characteristics and their causes from an event-based perspective in the Central Taihu Basin

Increasing rainstorms induced by climate change and modification in the land surface due to urbanization have greatly altered floods at different spatio-temporal scales. However, investigating flood events in urbanized plains is challenging as anthropogenic behaviors can change river flow without rainfall. In addition, while the frequency and magnitude of floods have been well examined, knowledge about variations in the rate of flood change is still limited. To fill these gaps, we proposed a scheme that focused on flood responses to rainfall to detect changes in flood characteristics in the Central Taihu Basin, a highly urbanized region in the Yangtze River Delta of China. Four characteristic metrics were adopted to summarize the flood hydrograph, including the peak, increment, rising rate, and falling rate. We then examined trends of these metrics based on the selected rainfall-flood events from ten hydrological stations during 1970-2020. Subsequently, the reduction method was used to separate the impacts of regional climate change and human activities on flood characteristics alterations. Furthermore, the importance of fifteen factors was quantified by the random forest model. We found that there is a significant upward trend in the evolution of flood characteristics, except for the increment of floods. Flood characteristics exhibit higher values when rainfall accumulates, indicating stronger responses of floods to a large amount of rainfall. The results also show that human activities dominate and impact the peak, rising rate, and falling rate of floods more than climate change. Meanwhile, although cumulative precipitation is the most important factor, flood characteristics are also susceptible to anthropogenic factors, such as land use change and hydraulic engineering construction. Our findings, which provide insights into flood event identification and enhance the understanding of regional flood changes, will serve as a reference for water resource management and flood mitigation in urbanized areas.

Is the existing methods sustainable? A hybrid approach to flood risk mapping

The hydraulic and integrated modeling approaches appear to stand out in the sequence of flood risk models that have been presented because of their predictive accuracy. The former has a high probability of under predicting and the latter has a high tendency to over-predict. This study proposed a methodological approach that combines the hydraulic and integrated models using analytical hierarchical raster fusion techniques to strengthen the weaknesses of the individual models. This study seeks to undertake a flood inundation model, a runoff model, and raster fusion models using GIS and HEC-RAS rain-on-grid methods to map flood risk in the Ona river basin of Ibadan city. •A hydraulic model was used to identify flood depth and inundation areas along a major stream channel, which was then extracted, rasterized, resampled, and reclassified to a spatial resolution of 5 m.•Several raster datasets (indicators) were created from land use, elevation, soil, and geological data layers using advanced GIS techniques.•AHP assisted raster data fusion model was used to combine all of the raster indicators into a single consolidated hybrid flood raster layer that revealed flood risk areas by magnitude.

Predicting stormwater nitrogen loads from a cold-region urban catchment in year 2050 under the impacts of climate change and urban densification

Urban stormwater is a primary source of pollution for receiving water, but there is a shortage of studies on pollutant loads from urban catchments in cold regions. In this study, we coupled a build-up and wash-off model (in Mike Urban) with a climate change model to assess the impacts of climate change and urban densification on stormwater nitrogen loads (TN, TKN, NOx-N, and TAN) in an urban catchment in Canada. We calibrated and validated the Mike Urban model against observed event mean concentrations and nitrogen loads from 2010 to 2016. Results show that the nitrogen loads were mainly governed by rainfall intensity, rainfall duration, and antecedent dry days. Future precipitation data were downscaled using the Global Climate Models (GCMs), and three different Representative Concentration Pathways (RCP 2.5, RCP 4.5, and RCP 8.5) were used. Modeling results show that the TN, TKN, NOx-N, and TAN loads in 2050 will increase by 28.5 - 45.2% from May to September under RCP 2.5 compared to those from 2010 to 2016, by 34.6 - 49.9% under RCP 4.5, and by 39.4 - 53.5% under RCP 8.5. The increase of our projected TN load (from 1.33 to 2.93 kg·N/ha) is similar or slightly higher than the limited studies in other urban catchments. This study provides a reference for predicting stormwater nitrogen loads in urban catchments in cold regions.

Modeling flood susceptibility zones using hybrid machine learning models of an agricultural dominant landscape of India

Flooding events are determining a significant amount of damages, in terms of economic loss and also casualties in Asia and Pacific areas. Due to complexity and ferocity of severe flooding, predicting flood-prone areas is a difficult task. Thus, creating flood susceptibility maps at local level is though challenging but an inevitable task. In order to implement a flood management plan for the Balrampur district, an agricultural dominant landscape of India, and strengthen its resilience, flood susceptibility modeling and mapping are carried out. In the present study, three hybrid machine learning (ML) models, namely, fuzzy-ANN (artificial neural network), fuzzy-RBF (radial basis function), and fuzzy-SVM (support vector machine) with 12 topographic, hydrological, and other flood influencing factors were used to determine flood-susceptible zones. To ascertain the relationship between the occurrences and flood influencing factors, correlation attribute evaluation (CAE) and multicollinearity diagnostic tests were used. The predictive power of these models was validated and compared using a variety of statistical techniques, including Wilcoxon signed-rank, t-paired tests and receiver operating characteristic (ROC) curves. Results show that fuzzy-RBF model outperformed other hybrid ML models for modeling flood susceptibility, followed by fuzzy-ANN and fuzzy-SVM. Overall, these models have shown promise in identifying flood-prone areas in the basin and other basins around the world. The outcomes of the work would benefit policymakers and government bodies to capture the flood-affected areas for necessary planning, action, and implementation.

GIS-based hydrodynamic modeling for urban flood mitigation in fast-growing regions: a case study of Erbil, Kurdistan Region of Iraq

Floods threaten urban infrastructure, especially in residential neighborhoods and fast-growing regions. Flood hydrodynamic modeling helps identify flood-prone locations and improve mitigation plans' resilience. Urban floods pose special issues due to changing land cover and a lack of raw data. Using a GIS-based modeling interface, input files for the hydrodynamic model were developed. The physical basin's properties were identified using soil map data, Land Use Land Cover (LULC) maps, and a Digital Elevation Model (DEM). So, the HEC-RAS 2-D hydrodynamic model was developed to estimate flood susceptibility and vulnerability in Erbil, Iraq. The case study examines the quality of flood modeling results using different DEM precisions. Faced with the difficulty, this study examines two building representation techniques: Building Block (BB) and Building Resistance (BR). The work presented here reveals that it is possible to apply the BR technique within the HEC-RAS 2-D to create urban flood models for regions that have a lack of data or poor data quality. Indeed, the findings confirmed that the inundated areas or areas where water accumulated in past rainfall events in Erbil are the same as those identified in the numerical simulations. The study's results indicate that the Erbil city is susceptible to flood hazards, especially in areas with low-lying topography and substantial precipitation. The study's conclusions can be utilized to plan and develop flood control structures, since it identified flood-prone areas of the city.

Individual and combined impacts of urbanization and climate change on catchment runoff in Southeast Queensland, Australia

Assessing the impacts of climate change and land-use change is of critical importance, particularly for urbanized catchments. In this study, a novel framework was used to examine and quantify these impacts on the runoff in six catchments in Southeast Queensland, Australia. For each catchment, temporal variations in impervious areas were derived from six satellite images using a sub-pixel classification technique and incorporated into the SIMHYD hydrological model. This model was satisfactorily calibrated and validated with daily runoff observations (0.63 ≤ Nash-Sutcliffe efficiency coefficient ≤ 0.94, percent bias ≤ ±18 %) and was used to produce baseline runoff for 1986-2005 in these six catchments. The projected population increase was used to predict future imperviousness based on the linear relationship between the two. The projected rainfall and evapotranspiration were derived from the ensemble means of the eight general circulation models. Catchment runoff was projected under two climate change scenarios (RCP4.5 and 8.5), three urbanization scenarios (low, medium, and high), and six combined scenarios for two future periods (2026-2045 and 2046-2065). Comparing with the baseline, it was found that (1) climate change alone would lead to a -3.8 % to -17.6 % reduction in runoff among the six catchments, for all scenarios and both future periods; (2) a 11.8 % to 78 % increase in runoff was projected under the three urbanization scenarios, and (3) a decrease in runoff due to climate change would moderate the increase in runoff caused by urbanization. For example, the combined effect would be a 54 % increase in runoff, with a -17.2 % decrease due to climate change and 78 % increase due to urbanization. Overall, runoff in the six catchments may be significantly affected by urban expansion. From this study, decision makers could gain a better understanding of the relative importance of the effects of climate and land-use change, which can be applied when developing future long-term water management plans at the catchment scale.

Influence and prediction of meteorological factors on brucellosis in a northwest region of China

This paper aims to study the cumulative lag effect of meteorological factors on brucellosis incidence and the prediction performance based on Random Forest model. The monthly number of brucellosis cases and meteorological data from 2015 to 2019 in Yongchang of Gansu Province, northwest China, were used to build distributed lag nonlinear model (DLNM). The number of brucellosis cases of lag 1 month and meteorological data from 2015 to 2018 were used to build RF model to predict the brucellosis incidence in 2019. Meanwhile, SARIMA model was established to compare the prediction performance with RF model according to R² and RMSE. The results indicated that the population had a high incidence risk at temperature between 5 and 13 °C and lag between 0 and 18 days, sunshine duration between 225 and 260 h and lag between 0 and 1 month, and atmosphere pressure between 789 and 793.5 hPa and lag between 0 and 18 days. The R² and RMSE of train set and test set in RF model were 0.903, 1.609, 0.824, and 2.657, respectively, and the R² and RMSE in SARIMA model were 0.530 and 7.008. This study found significant nonlinear and lag associations between meteorological factors and brucellosis incidence. The prediction performance of RF model was more accurate and practical compared with SARIMA model.

Comments and recommendations on Sponge City - China's solutions to prevent flooding risks

Background

/Objective: Flooding risk is a global issue, and various approaches have been established to prevent flooding risk around the world. China is one of the heavily flood-affected countries and has been implementing the Sponge City program since 2015 to defend against flooding. Unfortunately, flooding has been common in China in recent years, causing severe health risks to citizens. This research mainly focuses on (a) evaluating the implementation of China's Sponge City program and the associated impacts on human health and (b) exploring the future improvement of the Sponge City program in China.

Methods

The Interpretive Document Approach was used to explore an inclusive review of the Sponge City program and its implications on human health.

Results

/Findings: The Sponge City program in China is still insufficient to prevent flooding risks effectively. In the past eight years, 24/34 provinces have recorded flooding, which caused a total of 4701 deaths and over 525.5 billion RMB (around 72.9 billion US$) in economic loss. Till now, only 64/654 cities have promulgated local legislation to manage sponge city construction, although the Sponge City was implemented in 2015. Besides, the completed Sponge City program constructions cannot fully prevent flooding risks, the flood prevention capacity is limited. The Sponge City program is not granted priority, lacking national legislation hinders Sponge City program implementation in China.

Conclusions

China needs to make national legislation on the Sponge City program and update the Sponge City program technology guidelines. Local governments should implement Sponge City construction according to local geographic environments.

Bridge-specific flood risk assessment of transport networks using GIS and remotely sensed data

A novel framework for the expedient assessment of flood risk to transportation networks focused on the response of the most critical and vulnerable infrastructure assets, the bridges, is developed, validated and applied. Building upon the recent French guidelines on scour risk (CEREMA, 2019), this paper delivers a thorough methodology, that incorporates three key, risk parameters: (i) the hydrodynamic loading, a hazard component of equal significance to scour, for the assessment of hazard; (ii) the correlation of select scour indicators with a new index relating to flow velocity, a primary measure of the adverse impacts of flow-structure interaction, enabling a more accurate and automated, assessment of bridge susceptibility to scour; (iii) the use of a new, comprehensive indicator, namely the Indicator of Flood Hazard Intensity (IFHI) which incorporates, in a simple yet efficient way, the key parameters controlling the severity of flood impact on bridges, namely flow velocity, floodwater height, flow obstruction, and sediment type. The framework is implemented for the analysis of flood risk in a case study area, considering an inventory of 117 bridges of diverse construction characteristics, which were affected by a major flood that impacted Greece in September 2020. The reliability of the method is validated against an extensive record of inspected and documented bridge damages. Regional scale analysis is facilitated by the adoption of the Multi-Criteria Decision-Making method for flood hazard indexing, considering geomorphological, meteorological, hydrological, and land use/cover data, based on the processing of remotely sensed imagery and openly available geospatial datasets in GIS.

Determining the effect of urbanization on flood hazard zones in Kahramanmaras, Turkey, using flood hazard index and multi-criteria decision analysis

Floods are the most destructive natural hazard throughout the world. Identifying flood hazard zones is the first step in flood risk management. Land use changes, especially urbanization, is the key factor in the destructiveness of flood events and change in flood risk. Therefore, determining the effect of urbanization on the changes in flood hazard zones presents a valuable data for effective flood risk management and urban development planning. In this context, the aim of this study is to reveal the effect of urbanization on flood hazard zones in Kahramanmaras city. In this study, an index-based approach was used to identify the flood hazard zones. To calculate a flood hazard index, the susceptibility map was multiplied by the land use map by using the raster calculator tool in the ArcGIS. The susceptibility map was generated by combining the maps of flow accumulation, distance to stream, slope, and elevation parameters based on parameter weights obtained from the analytical hierarchy process (AHP) method. Two land use maps for the years 1990 and 2018 were separately overlaid with the susceptibility map to reveal the effects of urbanization on flood hazard zones. According to the results, it was found that very low, low, and moderate hazard zones decreased by 0.01%, 0.09%, and 1.2%, respectively whereas the high and very high zones increased by 3.30% and 0.58%, respectively due to urbanization. It was also determined that the main reason for the increase in the high zone was the expansion of urban areas into agricultural areas in the study area.

A comprehensive web-based system for flood inundation map generation and comparative analysis based on height above nearest drainage

It is critical to obtain accurate flood extent predictions in a timely manner in order to reduce flood-related casualties and economic losses from floods with greater magnitudes and fall outside the handling capacity of our existing mitigation systems. Running a real-time flood inundation mapping model is helpful in supporting quick response decisions for unplanned floods, such as how to distribute limited resources and labor so that the most flood-prone areas receive adequate mitigation efforts and how to execute evacuations that keep people safe while causing the least amount of unneeded disruption. Most inundation systems, on the other hand, are either overly demanding in terms of data and computing power or have limited interaction and customization with various input and model configurations. This paper describes a client-side web-based real-time inundation mapping system based on the Height Above the Nearest Drainage (HAND) model. The system includes tools for hydro-conditioning terrain data, modifying terrain data, custom inundation mapping, online model performance evaluation, and hydro-spatial analyses. Instead of only being able to work on a few preprocessed datasets, the system is ready to run in any region of the world with limited data needs (i.e., elevation). With the system's multi-depth inundation mapping approach, we can use water depth measurements (sensor-based or crowdsourced) or model predictions to generate more accurate flood inundation maps based on current or future conditions. All of the system's functions can be performed entirely through a client-side web browser, without the need for GIS software or server-side computing. For decision-makers and the general public with limited technical backgrounds, the system provides a one-stop, easy-to-use flood inundation modeling and analysis tool.

Comprehensive Risk Assessment of Urban Waterlogging Disaster Based on MCDA-GIS Integration: The Case Study of Changchun, China

Urban waterlogging will harm economic development and people’s life safety; however, the waterlogging risk zoning map provides the necessary decision support for the management of urban waterlogging, urban development and urban planning. This paper proposes an urban waterlogging risk assessment method that combines multi-criteria decision analysis (MCDA) with a geographic information system (GIS). The framework of urban waterlogging risk assessment includes four main elements: hazard, exposure, vulnerability, and emergency response and recovery capability. Therefore, we selected the urban area of Changchun City, Jilin Province as the study area. The Analytic Hierarchy Process (AHP) is a generally accepted MCDA method, it is used to calculate the weight and generate a result map of hazards, exposure, vulnerability, and emergency responses and recovery capability. Based to the principle of natural disaster risk formation, a total of 18 parameters, including spatial data and attribute data, were collected in this study. The model results are compared with the recorded waterlogging points, and the results show that the model is more reliable.

Sponge City Practices in China: From Pilot Exploration to Systemic Demonstration

In recent years, China has been committed to strengthening environmental governance and trying to build a sustainable society in which humans and nature develop in harmony. As a new urban construction concept, sponge city uses natural and ecological methods to retain rainwater, alleviate flooding problems, reduce the damage to the water environment, and gradually restore the hydrological balance of the construction area. The paper presents a review of sponge city construction from its inception to systematic demonstration. In this paper, research gaps are discussed and future efforts are proposed. The main contents include: (1) China’s sponge city construction includes but is not limited to source control or a drainage system design. Sponge city embodies foreign experience and the wisdom of ancient Chinese philosophy. The core of sponge city construction is to combine various specific technologies to alleviate urban water problems such as flooding, water environment pollution, shortage of water resources and deterioration of water ecology; (2) this paper also introduces the sponge city pilot projects in China, and summarizes the achievements obtained and lessons learned, which are valuable for future sponge city implementation; (3) the objectives, corresponding indicators, key contents and needs of sponge city construction at various scales are different. The work at the facility level is dedicated to alleviating urban water problems through reasonable facility scale and layout, while the work at the plot level is mainly to improve the living environment through sponge city construction. The construction of urban and watershed scales is more inclined to ecological restoration and blue-green storage spaces construction. Besides, the paper also describes the due obligations in sponge city construction of various stakeholders.

Analysis of Climate Variability in a Time Series of Precipitation and Temperature Data: A Case Study in Cartagena de Indias, Colombia

Anthropogenic climate change is a global trend, hitherto incontrovertible, causing immense social and economic damage. Although the this is evident at the global level, at the local level, there is still debate about the most appropriate analyses to support this fact. This debate is particularly relevant in developing countries, such as Colombia, where there is a significant lack of data at the local level that require analysis and interpretation. Consequently, studies are often superficially conducted to support climate change theory at the local level. However, such studies are then used to design hydraulic infrastructure, with potential catastrophic errors for human and environmental health. In this study, we sought evidence of climate change through an analysis of a series of data on temperature (maximum, mean and minimum), as well as total annual and maximum rainfall in 24 h registered at the Rafael Nuñez Airport station in the city of Cartagena, Colombia, from 1941 to 2015. The hypotheses of homogeneity, trend, stationarity and non-stationarity were analyzed. Problems of non-homogeneity and the presence of periodicity in the analyzed series were found, showing a trend and apparent non-stationarity in the original series. This could be associated with the effects of climate change. In this case, no correlation was found between temperatures and rainfall. Spectral analysis was performed for all series, and residual series were generated by extracting the harmonics of greatest significance. It was found that the series data generated from the third harmonic are generally stationary and without trend. Therefore, the trend and non-stationarity of the original series are due to problems of non-homogeneity and periodicity in the series. In the results of the stationarity test conducted according to the Phillips–Perron criterion, all series were non-stationary. For the two additional criteria of stationarity tests, 40% were shown to be stationary, and 60% were non-stationary. Specifically, non-homogeneity problems and apparent trends associated with climate change could have negative implications for the design of drainage systems.

Investigating the Impact of Spatial Distribution of Sustainable Drainage System (SuDS) Components on Their Flood Mitigation Performance in Communities with High Groundwater Levels

This paper investigated the impact of the spatial distribution of SuDS components on their flood reduction performance and the underlying mechanisms in a community with high groundwater levels. The effects of SuDS components’ connectivity, decentralized level, and installation position along the flow direction on the reduction of total discharge volume (TDV), average discharge flow rate (ADFR), maximum discharge flow rate (MDFR), inundated area (IA), average inundated depth (AID), and maximum inundated depth (MID) were studied by coupling of the storm water management model (SWMM) and high-performance integrated hydrodynamic modelling system (HiPIMS). The results demonstrate that the connectivity has a positive linear correlation with the reduction of TDV (R2 > 0.991), ADFR (R2 > 0.992), and MDFR (R2 > 0.958), while the decentralized level of rain gardens and green roofs present positive one-phase exponential correlation with the reduction of TDV (R2 > 0.935), ADFR (R2 > 0.934) and MDFR (R2 > 0.967). A better-integrated mitigation of TDV and ADFR could be achieved by installing SuDS upstream along the flow direction. The connectivity from green roofs to rain gardens has a positive effect on the reduction of AID and MID but leads to the increase of IA. The findings of this study may contribute to the development of general spatial distribution guidelines and strategies to optimize the overall performance of SuDS components, especially at a community scale.

Application of GIS and Machine Learning to Predict Flood Areas in Nigeria

Floods are one of the most devastating forces in nature. Several approaches for identifying flood-prone locations have been developed to reduce the overall harmful impacts on humans and the environment. However, due to the increased frequency of flooding and related disasters, coupled with the continuous changes in natural and social-economic conditions, it has become vital to predict areas with the highest probability of flooding to ensure effective measures to mitigate impending disasters. This study predicted the flood susceptible areas in Nigeria based on historical flood records from 1985~2020 and various conditioning factors. To evaluate the link between flood incidence and the fifteen (15) explanatory variables, which include climatic, topographic, land use and proximity information, the artificial neural network (ANN) and logistic regression (LR) models were trained and tested to develop a flood susceptibility map. The receiver operating characteristic curve (ROC) and area under the curve (AUC) were used to evaluate both model accuracies. The results show that both techniques can model and predict flood-prone areas. However, the ANN model produced a higher performance and prediction rate than the LR model, 76.4% and 62.5%, respectively. In addition, both models highlighted that those areas with the highest susceptibility to flood are the low-lying regions in the southern extremities and around water areas. From the study, we can establish that machine learning techniques can effectively map and predict flood-prone areas and serve as a tool for developing flood mitigation policies and plans.

Urban Flood-Risk Assessment: Integration of Decision-Making and Machine Learning

Urban flood-risk mapping is an important tool for the mitigation of flooding in view of continuing urbanization and climate change. However, many developing countries lack sufficiently detailed data to produce reliable risk maps with existing methods. Thus, improved methods are needed that can help managers and decision makers to combine existing data with more soft semi-subjective data, such as citizen observations of flood-prone and vulnerable areas in view of existing settlements. Thus, we present an innovative approach using the semi-subjective Analytic Hierarchy Process (AHP), which integrates both subjective and objective assessments, to help organize the problem framework. This approach involves measuring the consistency of decision makers’ judgments, generating pairwise comparisons for choosing a solution, and considering criteria and sub-criteria to evaluate possible options. An urban flood-risk map was created according to the vulnerabilities and hazards of different urban areas using classification and regression-tree models, and the map can serve both as a first stage in advancing flood-risk mitigation approaches and in allocating warning and forecasting systems. The findings show that machine-learning methods are efficient in urban flood zoning. Using the city Rasht in Iran, it is shown that distance to rivers, urban drainage density, and distance to vulnerable areas are the most significant parameters that influence flood hazards. Similarly, for urban flood vulnerability, population density, land use, dwelling quality, household income, distance to cultural heritage, and distance to medical centers and hospitals are the most important factors. The integrated technique for both objective and semi-subjective data as outlined in the present study shows credible results that can be obtained without complicated modeling and costly field surveys. The proposed method is especially helpful in areas with little data to describe and display flood hazards to managers and decision makers.

Assessment of Variations in Runoff Due to Landcover Changes Using the SWAT Model in an Urban River in Dublin, Ireland

Investigating the impact of land cover change in hydrological modelling is essential for water resources management. This paper investigates the importance of landcover change in the development of a physically-based hydrological model called SWAT. The study area considered is the Dodder River basin located in southern Dublin, Ireland. Runoff at the basin outlet was simulated using SWAT for 1993–2019 using five landcover maps obtained for 1990, 2000, 2006, 2012 and 2018. Results indicate that, in general, the SWAT model-simulated runoff for a chosen time-period are closer to the real-world observations when the landcover data used for simulation was collated as close to the time-period for which the simulations were performed. For 23 (20) years (from 27 years period) the monthly mean (maximum) runoff for the Dodder River generated by the SWAT model had the least error when the nearby landcover data were used. This study indicates the necessity of considering dynamic and time-varying landcover data during the development of hydrological modelling for runoff simulation. Furthermore, two composite quantile functions were generated by using a kappa distribution for monthly mean runoff and GEV distribution for monthly maximum runoff, based on model simulations obtained using different landcover data corresponding to different time-period. Modelling landcover change patterns and development of projected landcover in the future for river basins in Ireland needs to be integrated with SWAT to simulate future runoff.

Modelling the effect of rainfall patterns on the runoff control performance of permeable pavements

With the implementation of low impact development (LID) in urban areas, it is necessary to quantify the actual effectiveness of LID facilities. In this study, a coupled hydrology-hydrodynamic numerical model was utilized to investigate the runoff control effectiveness of permeable pavements in the city centre of Shijiazhuang, China. Two groups of designed rainfall events with the same duration but different rainfall amounts and peak rainfall intensity locations were presented, and the effectiveness of permeable pavement was demonstrated by the reduction in the total runoff volume, water depth, and inundated area. The results indicate that the rainfall amount is the main factor affecting the runoff control of permeable pavements, and their effectiveness decreases with increasing rainfall amounts and peak intensity coefficients. Moreover, permeable pavements are more effective in reducing the residential waterlogging area, and the proportion of the inundated area above a depth of 0.2 m is considerably diminished. This study reveals the response of the runoff control of permeable pavements to different rainfall patterns, which is essential for supporting the design and practical operation of permeable pavements.

A Framework for Calculating Peak Discharge and Flood Inundation in Ungauged Urban Watersheds Using Remotely Sensed Precipitation Data: A Case Study in Freetown, Sierra Leone

As the human population increases, land cover is converted from vegetation to urban development, causing increased runoff from precipitation events. Additional runoff leads to more frequent and more intense floods. In urban areas, these flood events are often catastrophic due to infrastructure built along the riverbank and within the floodplains. Sufficient data allow for flood modeling used to implement proper warning signals and evacuation plans, however, in least developed countries (LDC), the lack of field data for precipitation and river flows makes hydrologic and hydraulic modeling difficult. Within the most recent data revolution, the availability of remotely sensed data for land use/land cover (LULC), flood mapping, and precipitation estimates has increased, however, flood mapping in urban areas of LDC is still limited due to low resolution of remotely sensed data (LULC, soil properties, and terrain), cloud cover, and the lack of field data for model calibration. This study utilizes remotely sensed precipitation, LULC, soil properties, and digital elevation model data to estimate peak discharge and map simulated flood extents of urban rivers in ungauged watersheds for current and future LULC scenarios. A normalized difference vegetation index (NDVI) analysis was proposed to predict a future LULC. Additionally, return period precipitation events were calculated using the theoretical extreme value distribution approach with two remotely sensed precipitation datasets. Three calculation methods for peak discharge (curve number and lag method, curve number and graphical TR-55 method, and the rational equation) were performed and compared to a separate Soil and Water Assessment Tool (SWAT) analysis to determine the method that best represents urban rivers. HEC-RAS was then used to map the simulated flood extents from the peak discharges and ArcGIS helped to determine infrastructure and population affected by the floods. Finally, the simulated flood extents from HEC-RAS were compared to historic flood event points, images of flood events, and global surface water maximum water extent data. This analysis indicates that where field data are absent, remotely sensed monthly precipitation data from Integrated Multi-satellitE Retrievals for GPM (IMERG) where GPM is the Global Precipitation Mission can be used with the curve number and lag method to approximate peak discharges and input into HEC-RAS to represent the simulated flood extents experienced. This work contains a case study for seven urban rivers in Freetown, Sierra Leone.

A Model-Based Tool for Assessing the Impact of Land Use Change Scenarios on Flood Risk in Small-Scale River Systems—Part 2: Scenario-Based Flood Characteristics for the Planned State of Land Use

Land use changes can significantly influence the water balance, and thus especially the development of flood-triggering runoff peaks. Hence, it is advisable to assess possible changes already at the level of municipal planning. Since many different actors are usually involved in spatial planning, it is useful to provide a shared platform where stakeholders can access the same information to analyze and evaluate flood hazards. Therefore, a GIS routine for the prediction of soil sealing induced runoff peaks and resulting potential flooding in the watercourse was developed, which is embedded in a GIS based decision support system (GIS-DSS). The so-called storm water routine (SWR) is founded on preprocessed flood characteristics, calculated by means of hydrological/hydraulic models (described in part 1). The potential impact of land use change is assessed purely in GIS as flow difference which is routed through the river system. To validate this simplified method, a process model was set up with an exemplary land use change and its results were compared with the GIS-based results. For 16 of the 18 rainfall scenarios tested, the SWR provided very good to good agreement with the detailed model. For short and highly dynamic rain events the SWR approach is less reliable. Several supplements like the integration of LID are conceivable.

A multi-hazard map-based flooding, gully erosion, forest fires, and earthquakes in Iran

We used three state-of-the-art machine learning techniques (boosted regression tree, random forest, and support vector machine) to produce a multi-hazard (MHR) map illustrating areas susceptible to flooding, gully erosion, forest fires, and earthquakes in Kohgiluyeh and Boyer-Ahmad Province, Iran. The earthquake hazard map was derived from a probabilistic seismic hazard analysis. The mean decrease Gini (MDG) method was implemented to determine the relative importance of effective factors on the spatial occurrence of each of the four hazards. Area under the curve (AUC) plots, based on a validation dataset, were created for the maps generated using the three algorithms to compare the results. The random forest model had the highest predictive accuracy, with AUC values of 0.994, 0.982, and 0.885 for gully erosion, flooding, and forest fires, respectively. Approximately 41%, 40%, 28%, and 3% of the study area are at risk of forest fires, earthquakes, floods, and gully erosion, respectively.

A GIS-Based Hydrological Modeling Approach for Rapid Urban Flood Hazard Assessment

Urban floods are detrimental to societies, and flood mapping techniques provide essential support for decision-making on the better management of flood risks. This study presents a GIS-based flood characterization methodology for the rapid and efficient identification of urban flood-prone areas, which is especially relevant for large-scale flood hazards and emergency assessments for data-scarce studies. The results suggested that optimal flood mapping was achieved by adopting the median values of the thresholds for local depression extraction, the topographic wetness index (TWI) and aggregation analyses. This study showed the constraints of the depression extraction and TWI methods and proposed a methodology to improve the performance. A new performance indicator was further introduced to improve the evaluation ability of hazard mapping. It was shown that the developed methodology has a much lower demand on the data and computation efforts in comparison to the traditional two-dimensional models and, meanwhile, provides relatively accurate and robust assessments of flood hazards.

Associations between floods and bacillary dysentery cases in main urban areas of Chongqing, China, 2005-2016: a retrospective study

BACKGROUND

Understanding the association between floods and bacillary dysentery (BD) incidence is necessary for us to assess the health risk of extreme weather events. This study aims at exploring the association between floods and daily bacillary dysentery cases in main urban areas of Chongqing between 2005 and 2016 as well as evaluating the attributable risk from floods.

METHODS

The association between floods and daily bacillary dysentery cases was evaluated by using distributed lag non-linear model, controlling for meteorological factors, long-term trend, seasonality, and day of week. The fraction and number of bacillary dysentery cases attributable to floods was calculated. Subgroup analyses were conducted to explore the association across age, gender, and occupation.

RESULTS

After controlling the impact of temperature, precipitation, relative humidity, long-term trend, and seasonality, a significant lag effect of floods on bacillary dysentery cases was found at 0-day, 3-day, and 4-day lag, and the cumulative relative risk (CRR) over a 7-lag day period was 1.393 (95%CI 1.216-1.596). Male had higher risk than female. People under 5 years old and people aged 15-64 years old had significantly higher risk. Students, workers, and children had significantly higher risk. During the study period, based on 7-lag days, the attributable fraction of bacillary dysentery cases due to floods was 1.10% and the attributable number was 497 persons.

CONCLUSIONS

This study confirms that floods can increase the risk of bacillary dysentery incidence in main urban areas of Chongqing within an accurate time scale, the risk of bacillary dysentery caused by floods is still serious. The key population includes male, people under 5 years old, students, workers, and children. Considering the lag effect of floods on bacillary dysentery, the government and public health emergency departments should advance to the emergency health response in order to minimize the potential risk of floods on public.

Volumetric Quantification of Flash Flood Using Microwave Data on a Watershed Scale in Arid Environments, Saudi Arabia

Actual flood mapping and quantification in an area provide valuable information for the stakeholder to prevent future losses. This study presents the actual flash flood quantification in Al-Lith Watershed, Saudi Arabia. The study is divided into two steps: first is actual flood mapping using remote sensing data, and the second is the flood volume calculation. Two Sentinel-1 images are processed to map the actual flood, i.e., image from 25 May 2018 (dry condition), and 24 November 2018 (peak flood condition). SNAP software is used for the flood mapping step. During SNAP processing, selecting the backscatter data representing the actual flood in an arid region is challenging. The dB range value from 7.23–14.22 is believed to represent the flood. In GIS software, the flood map result is converted into polygon to define the flood boundary. The flood boundary that is overlaid with Digital Elevation Map (DEM) is filled with the same elevation value. The Focal Statistics neighborhood method with three iterations is used to generate the flood surface elevation inside the flood boundary. The raster contains depth information is derived by subtraction of the flood surface elevation with DEM. Several steps are carried out to minimize the overcalculation outside the flood boundary. The flood volume can be derived by the multiplication of flood depth points with each cell size area. The flash flood volume in Al-Lith Watershed on 24 November 2018 is 155,507,439 m3. Validity checks are performed by comparing it with other studies, and the result shows that the number is reliable.

Annual and intra-annual climate variability and change of the Volta Delta, Ghana

Deltas are geographically and socio-ecologically distinct systems, with a unique climate and contextually high vulnerabilities to climate dynamics. Hence, they require specific climate change adaptation and policy responses, informed by delta-scale analysis. However, available climate knowledge on deltas is based mainly on broad-scale analysis that masks information unique to deltas. This applies to the Volta Delta system of Ghana. This study presents annual and intra-annual climate variability and trend analysis carried out across the Volta Delta, using the coefficient of variation (CV), anomaly, Mann-Kendall and Pettit statistics. There were time and space differences in climate change and variability. Statistically significant (P < 0.05) positive trends were observed for the major wet season and the mean annual rainfall for Ada and highly significant (P < 0.01) positive trends for Akatsi. These contrasted with the observations in Adidome, which experienced a statistically highly significant (P < 0.01) decreasing trend in rainfall. There were significant (P< 0.05) increases in annual minimum, maximum, and mean temperatures over time in both coastal and inland delta stations. The annual rate of change of mean temperature ranged from 0.03 to 0.05. Ada, the more coastal location, has experienced a narrow range of temperature change, most probably due to the buffering capacity of the ocean. Point changes were observed in the climate data series in four (4) localities. We recommend that adaption and policy actions should include, the provision of small-scale irrigation, encouraging adoption of drought-resistant crop varieties and crop diversification, and also be made responsive to the existing spatiotemporal climate variability and change within the Volta Delta.

Urbanization and climate change impacts on future flood risk in the Pearl River Delta under shared socioeconomic pathways

Climate change and urbanization are converging to challenge the flood control in the Pearl River Delta (PRD) due to their adverse impacts on precipitation extremes and the urban areas environment. Previous studies have investigated temporal changes in flood risk with various single factor, few have considered the joint effects of climate change, urbanization and socio-economic development. Here, based on the representative concentration pathway (RCP) scenarios, we conducted a comprehensive assessment of future (2030-2050) flood risk over the PRD combined with a thorough investigation of climate change, urbanization and socio-economic development. Precipitation extremes were projected using the regional climate model RegCM4.6, and urbanization growth was projected based on the CA-Markov model. The economic and population development was estimated by the shared socio-economic pathways (SSPs). Flood risk mapping with different RCPs-urbanization-SSPs scenarios was developed for the PRD based on the set pair analyze theory. The results show that climate change and urbanization are expected to exacerbate flood risk in most parts of the PRD during the next few decades, concurrently with more intense extreme precipitation events. The high flood risk areas are projected mainly in the urban regions with unfavorable terrain and dense population. The highest flood risk areas are expected to increase by 8.72% and 19.80% under RCP4.5 and RCP8.5 scenarios, respectively. Reducing greenhouse gas emissions may effectively mitigate the flood risk over the PRD. This study highlight the links between flood risk and changing environment, suggesting that flood risk management and preventative actions should be included in regional adaptation strategies.

Multidecadal variability in the Nile River basin hydroclimate controlled by ENSO and Indian Ocean dipole

Climate change impacts on the hydroclimate of the Nile River Basin (NRB) tend to be analyzed mostly based on short-term data and confined to a specific hydroclimate variable at sub-basin level. This study provides a better understanding of the hydrological cycle and the hydroclimate variability of NRB and aim to find the origin of the driving forces. Firstly, eight change point detection methods were used to investigate the abrupt changes in the NRB hydroclimate. Next, we used wavelet transform coherence (WTC), spatial correlation, and detrended cross-correlation (DCCA) to analyze the inter-annual to multidecadal variabilities of the hydroclimate of NRB because they are effective in capturing the temporal variability at multiple scales. Our results show significant hydroclimatic changes and trends attributed to climate change impact after the 1970s. For instance, precipitation and relative humidity (RH) decreasing at 16.2 mm/decade and 0.3 5%/decade, respectively. In contrast, geopotential height (GPH), climate warming, wind speed and zonal wind stress increasing at 3.1 m/decade, 0.19 °C/decade, 0.02 m/decade and 1.51 m²/s²/decade, respectively. These observed changes are strongly linked to El Niño and Indian Ocean Dipole (IOD). Our results also indicate that the largely strengthened IOD and El Niño amplitudes since the 1970s controlled the multidecadal variability of NRB's hydroclimate. In addition to ENSO-induced warming in NRB, El Niño exhibited a strong negative (positive) influence on precipitation and RH (GPH, surface temperature, wind speed, AET) over lowlands of Ethiopia, Kenya, Uganda, Sudan, Eritrea, Rwanda, and Burundi over the past 70 years. Our analysis revealed that IOD can either intensify or decrease the impacts of El Niño on the NRB's hydroclimate. For instance, IOD have a greater negative influence on the precipitation variability over Sudan, Ethiopia, Congo, Egypt, and Eritrea. These results were further confirmed by the changes in atmospheric circulation patterns in NRB during active El Niño and La Niño episodes. The increase in GPH anomalies associated with El Niño warming indicates a greater saturation vapor pressure, which at lower levels cause a lower dew point and a higher surface temperature. In addition, El Niño-driven changes to vector and meridional wind patterns created a strong anti-cyclonic wave of dry air that keeps moving dry air into the NRB. These waves propagate southward towards the NRB, controlling the circulation of air mass, heat, and moisture fluxes and affect the surface weather patterns of NRB.

A clustering classification of catchment anthropogenic modification and relationships with floods

Anthropogenic modifications at catchments scale may be reconducted primarily at soil sealing and streams culverting, even if important consequences result from roads density and, more in general, infrastructures as they cause landscape fragmentation, and agricultural areas extension. Their most important outcomes in terms of hydrologic balance are the decreasing time of concentration and the increasing flood risk at catchment scale. The research introduces a methodological approach to classify the degree of anthropogenic modifications at catchment scale: clustering techniques have been applied to 508 catchments in a high-risk flooding sector of the Mediterranean region. Then, flood data recorded in the study area in the 1900-2018 period have been compared to clustering classification, pointing out the relationships with soil sealing and hydrographical network culverting in the catchment. The analysis has been performed considering fourteen subsets of 8 descriptive parameters each that differ in the evaluation of culverting in the terminal part of the hydrographical network; the analysis has been conducted identifying the optimal number of descriptive parameters and the corresponding best number of clusters on quantitative basis. The results show that three classes clustering is the more appropriate from a computational point of view. That division looks coherent with the features of the studied basins and is well correlated with floods occurrence in the last 100 years. Finally, the proposed methodology of anthropogenic disturbance classification at catchment scale may be applied to other areas even adapting and implementing other descriptive parameters. Then, it may be used to support the planning of mitigation strategies in term of flood risk.

Novel hybrid models between bivariate statistics, artificial neural networks and boosting algorithms for flood susceptibility assessment

Across the world, the flood magnitude is expected to increase as well as the damage caused by their occurrence. In this case, the prediction of areas which are highly susceptible to these phenomena becomes very important for the authorities. The present study is focused on the evaluation of flood potential within Trotuș river basin in Romania using six ensemble models created by the combination of Analytical Hierarchy Process (AHP), Certainty Factor (CF) and Weights of Evidence (WOE) on one hand, and Gradient Boosting Trees (GBT) and Multilayer Perceptron (MLP) on the other hand. A number of 12 flood predictors, 172 flood locations and 172 non-flood locations were used. A percentage of 70% of flood and non-flood locations were used as input in models. From the input data, 70% were used as training sample and 30% as validating sample. The highest accuracy was obtained by the MLP-CF model in terms of both training (0.899) and testing (0.889) samples. A percentage between 21.88% and 36.33% of study area is covered with high and very high flood potential. The results validation, performed through the ROC Curve method, highlights that the MLP-CF model provided the most accurate results.

A Proposed Simultaneous Calculation Method for Flood by River Water, Inland Flood, and Storm Surge at Tidal Rivers of Metropolitan Cities: A Case Study of Katabira River in Japan

All metropolitan cities in Japan are located in low-lying areas that surround ports. Accordingly, significant floods that occur in these cities will trigger the simultaneous occurrence of flooding by river water and inland flooding. However, existing studies have focused on the impact of flooding by river water, inland flooding, and high tide in tidal rivers, and disaster mitigation measures focused on detailed flooding processes in such flooding areas have not been conducted thus far. This study focused on a tidal river, i.e., Katabira River of Yokohama city, one of Japan’s metropolitan cities, to construct a simultaneous occurrence model of flooding by river water and inland flooding, including the impact of a high tide. Numerical analysis was conducted using this model, and the results show that the flooded area significantly changed from 0.004 to 0.149 km2 according to the tide level of the estuary. Moreover, by simultaneously solving the calculation of flooding by river water and inland flooding, we found that there was a difference of 50 min between the occurrences of these floods. Therefore, we found that there is a possibility that, if evacuation is not conducted at the time of occurrence of inland flooding, evacuation during subsequent river-water flooding may not be possible. Based on these results, our proposed method was found to be useful for tidal rivers of metropolitan cities.

System dynamics modelling of urbanization under energy constraints in China

The rapid urbanization in China has been associated with a growing hunger for energy consumption and steadily-increasing CO2 emissions. In this paper, an integrated system dynamics model composed of four sub-models is developed to simulate the urbanization and energy consumption in China from 1998 to 2050. Three scenarios are provided: accelerated economic development, emission reduction constraint, and low-carbon oriented. The result reveals that rapid economic growth and sufficient energy supply will foster China's urbanization in all three scenarios. Under the low carbon transition scenario, China's urbanization rate is expected to reach 76.41% in 2050, both reducing carbon emissions and promoting eco-friendly development. All three scenarios witness a dramatic growth of residential energy consumption and a steady increase of industrial energy consumption. China still has a long way to achieve the low-carbon transition goal. China should promote renewable resources and energy, pursue a low-carbon lifestyle, and reduce energy intensity over the next few decades.

Spatiotemporal Evolution of Urban Expansion Using Landsat Time Series Data and Assessment of Its Influences on Forests

Analysis of urban land use dynamics is essential for assessing ecosystem functionalities and climate change impacts. The focus of this study is on monitoring the characteristics of urban expansion in Hang-Jia-Hu and evaluating its influences on forests by applying 30-m multispectral Landsat data and a machine learning algorithm. Firstly, remote sensed images were preprocessed with radiation calibration, atmospheric correction and topographic correction. Then, the C5.0 decision tree was used to establish classification trees and then applied to make land use maps. Finally, spatiotemporal changes were analyzed through dynamic degree and land use transfer matrix. In addition, average land use transfer probability matrix (ATPM) was utilized for the prediction of land use area in the next 20 years. The results show that: (1) C5.0 decision tree performed with precise accuracy in land use classification, with an average total accuracy and kappa coefficient of more than 90.04% and 0.87. (2) During the last 20 years, land use in Hang-Jia-Hu has changed extensively. Urban area expanded from 5.84% in 1995 to 21.32% in 2015, which has brought about enormous impacts on cultivated land, with 198,854 hectares becoming urban, followed by forests with 19,823 hectares. (3) Land use area prediction based on the ATPM revealed that urbanization will continue to expand at the expense of cultivated land, but the impact on the forests will be greater than the past two decades. Rationality of urban land structure distribution is important for economic and social development. Therefore, remotely sensed technology combined with machine learning algorithms is of great significance to the dynamic detection of resources in the process of urbanization.

Choosing the LID for Urban Storm Management in the South of Taiyuan Basin by Comparing the Storm Water Reduction Efficiency

Low impact development (LID) is a storm management philosophy. This paper aims at choosing the LID for urban storm management by comparing the efficiency in the south urban district in the Taiyuan Basin. Firstly, we set up a 1D–2D model to simulate the hydrological and hydraulic process of the area. Then the efficiency of different LID scenarios was analyzed by ratio of surcharging pipeline, percentage of ponding road, external outflow, infiltration, surface runoff, facility storage, and LID area ratio. It was found that the continuous porous pavement and rain garden are beneficial for use in residential and commercial settings in urban areas, and the rain garden performs more effectively and efficiently than the continuous porous pavement. The area occupied by LID might be under 20% of the impervious building area, because the LID performance was not improved significantly with the LID area when the ratio exceeded 20%. The LIDs could be more useful for small return periods and short duration storms, and could not replace conventional runoff management practices and drainage systems.

Performance assessment of extensive green roof runoff flow and quality control capacity based on pilot experiments

Green roof is an important measure in "Sponge Cities" to reduce the runoff and improve the runoff quality. The runoff quantity and quality control capacity of five types of extensive green roofs (EGRs) were analyzed in Beijing for 51 nature rainfall events and 6 simulated events from July 2017 to October 2018. Different module scales (sizes) and substrate depths were examined to study their correlation to runoff retention, peak flow reduction, pollutant event mean concentration (EMC) and load reduction performance of EGRs. In general, both the single-field rainfall events and the long-term monitoring showed that as the module scale and substrate thickness increased, the retention capacity of the EGRs increased. As the module scale increased, the peak flow reduction rate (P_frr) of the EGR modules increased, while the thickness of the substrate appeared to have less of an effect on P_frr. When water quality effect was considered, compared with module scale, the substrate thickness had a more obvious effect on the average EMC of different pollutants. As the substrate thickness increased, the EMC of pollutants decreased. Under six simulated design rainfalls, EMC reduction rate of suspended solid (SS) of all types of EGRs ranged from 64.3%-73.1% while no reduction was found in the EMC of chemical oxygen demand (COD). The EMC trends of ammonia nitrogen (NH₄⁺-N), nitrate nitrogen (NO₃^--N), total nitrogen (TN) and total phosphorus (TP) were almost the same, and their EMCs decreased with increasing total rainfall depth. When the pollutant load was considered, the EGRs in this study were a sink of NH₄⁺-N, NO₃^--N, TN, and TP but a source of COD.

Comparison of urbanization and climate change impacts on urban flood volumes: Importance of urban planning and drainage adaptation

Understanding the drivers behind urban floods is critical for reducing its devastating impacts to human and society. This study investigates the impacts of recent urban development on hydrological runoff and urban flood volumes in a major city located in northern China, and compares the urbanization impacts with the effects induced by climate change under two representative concentration pathways (RCPs 2.6 and 8.5). We then quantify the role of urban drainage system in mitigating flood volumes to inform future adaptation strategies. A geo-spatial database on landuse types, surface imperviousness and drainage systems is developed and used as inputs into the SWMM urban drainage model to estimate the flood volumes and related risks under various urbanization and climate change scenarios. It is found that urbanization has led to an increase in annual surface runoff by 208 to 413%, but the changes in urban flood volumes can vary greatly depending on performance of drainage system along the development. Specifically, changes caused by urbanization in expected annual flood volumes are within a range of 194 to 942%, which are much higher than the effects induced by climate change under the RCP 2.6 scenario (64 to 200%). Through comparing the impacts of urbanization and climate change on urban runoff and flood volumes, this study highlights the importance for re-assessment of current and future urban drainage in coping with the changing urban floods induced by local and large-scale changes.

Analyzing Mega City-Regions through Integrating Urbanization and Eco-Environment Systems: A Case Study of the Beijing-Tianjin-Hebei Region

The high-speed economic growth of mega city-regions in China has been characterized by rapid urbanization accompanied by a series of environmental issues ranging from widespread soil contamination to groundwater depletion. This article begins with an analysis of the interaction between urbanization and the ecological system and reviews existing frameworks for analyzing urban and ecological systems. By taking the Beijing-Tianjin-Hebei region as an example, the article introduces a conceptual framework to analyze mega city-regions and forecast possible interactions between urbanization and eco-environment by applying simulation model. The proposed framework and its components can provide guidance to identify the impacts of urbanization and external forces such as globalization on eco-environment by integrating the internal and external factors, synthesize the complex components of mega city-regions in databases, understand and diagnose the casual relationship between urban policies and ecological consequences.