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• Exploring the urban inundation response law based on designed rainstorms by coupled hydrodynamic and rainfall-tracking model. • Source tracking analysis method is novelty proposed to excavate the response law of the connectivity among source areas to characteristics of rainstorms. • The analysis of source areas contribution further illustrates the reason why the study area would undergo inundation damage when the precipitation exceeds 100 mm. • The study is helpful to further understand the response law of inundation and relationship among source areas to characteristics of rainstorms, provide novel insights for constructing integrated flooding intervention practices. Extreme stormwater and high-impervious area, caused by climate change and high-urbanization, are widely considered as the essential factors for causing urban flood. Understanding the response law of urban inundation to characteristics of rainstorms is helpful to implement integrated flood mitigation measures. Therefore, this paper is focused on exploring the urban inundation response law based on designed rainstorms by coupled hydrodynamic and rainfall-tracking model. Pilot area of Guyuan sponge city, which is divided into 5 source areas, is adopted to conduct 36 types of rainfall scenario simulations with the combination of different rainfall amounts and durations as well as peak coefficients. Inundation analysis methods is used to obtain the relationship between maximum inundation maps and characteristics of rainstorms, and source tracking analysis method is novelty proposed to excavate the response law of the connectivity among source areas to characteristics of rainstorms. First, Results of inundation analysis reveal that rainfall amount, duration and peak value are the first, second and third critical rainstorm characteristics that determine the severity of disasters, the variation of inundation volume are 140 %, 30 % and 4.2 % when precipitation and duration as well as peak value double. Second, the inundation area that water depth more than 0.5 m increases quickly is the major reason that inundation volume varies with 2 times as the increasing of precipitation; the transferring runoff volume between source areas and land use reaches 0–6 % and 0–10 % of inundation volume. Third, SA 1 and 2 are the critical sub-areas to manage the runoff, when precipitation equals to 200 mm, SA 1 contributes 19.5–33.7 % of inundation volume in SA 2, and SA 2 contributes 28–30 % of inundation volume in SA 3. The study is helpful to further understand the response law of inundation and relationship among source areas to characteristics of rainstorms, provide novel insights for constructing integrated flooding intervention practices. [ABSTRACT FROM AUTHOR]

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

• Adverse impacts of sea-level rises are not limited to a landward shift in shoreline. • Shallow groundwater can impact inland infrastructure kilometers from the coastline. • About 2/3 of the stormdrain length is subject to seawater & groundwater intrusion. • Coastal stressors can increase flood magnitude, frequency, and duration. • Compound flooding volume increases up to 6 times by a 2 m sea-level rise. Low-lying coastal areas are susceptible to multiple types of flooding from marine, subsurface, and surface sources. The co-occurrence of a rainfall event with high sea level, which raises coastal groundwater tables conjointly, may result in a compound flooding event, which could be much more widespread and dangerous than that of an individual flooding source. Focusing on Imperial Beach as a low-elevation coastal community in California, this paper provides a generalized methodology to determine the vulnerability of coastal stormdrain systems to compound seawater, groundwater, and stormwater flooding under a changing climate. Although marine inundation by itself is expected to flood only 7% of the study area by 2100 under the most pessimistic scenario (i.e. 2 m rise), our results show that more than 20% of the study area's subterranean stormdrain system is already at the risk of subsurface flooding at current sea levels. While marine inundation is a concern near the coastline as sea level rises, the results show that seawater intrusion into the stormdrain system can impact areas kilometers away from the coastline. The consequences of sea-level rise (SLR) can be exacerbated by an under-resourced stormdrain system, caused by groundwater infiltration through system defects. As such, by a 2 m rise in current sea-level, the flooding volume may double in an ideal system (i.e., no defects) while this ratio can increase six fold in a slightly defective stormdrain system (with 0.25 % porosity systemwide). The continuous simulations of the stormdrain system performance indicate that flood events will be more destructive and frequent under these circumstances. [ABSTRACT FROM AUTHOR]

• A novel method for assessing prediction of extremal hydrological events. • Bayesian causal modelling tool for predictive assessment. • Hybridization of Classical and new Hydrology based on Artificial Intelligence. • HydroPredicT_Extreme as a new tool for the prediction of potential flooding events. • Applicability to different time scales of extreme and sudden events. Disastrous losses related to high-flow events have increased dramatically over the past decades largely due to an increase in flood-prone regions settlements and shift in hydrological trends largely due to Climate Change. To mitigate the societal impact of hydrological and hydraulic extremes, knowledge of the processes leading to these extreme events is vital. Hydrological modelling is one of the main tools in this quest for knowledge but comes with uncertainties. For that it is necessary to deeply study the impact of hydrological models' structure on the magnitude and timing of extreme rainfall-runoff events. This paper is mainly aimed to show the development of a method called "HydroPredicT_Extreme" based on Bayesian Causal Modelling (BCM), a technique within Artificial Intelligence (AI). This method may enhance predictive capacity of extreme rainfall-runoff events. "HydroPredicT_Extreme" follows an iterative methodology that comprise 2 main stages. First one comprises a mixed graphical/analytical method from Hydrograph. This stage is conditioned by two initial constraints which are, a) pluviometry station is representative of hydrograph downstream flow behaviour; b) there must be independence of events. This first stage comprises sub-phases such as: 1.1. Calculation of Response Time (RT) through a mixed graphical/analytical approach, 1.2 Subtraction of RT from the flow series to remove the Rainfall-Flow delay; 1.3 Calculation base flow rate; 1.4 Subtraction base-flow from flow series to work on absolute inputs. Second man stage is called Bayesian Causal Modelling Translation (BCMT) that comprises the 2.1 Learning, 2.2 Training, 2.3 Simulation through BCM modelling, 2.4 Sensitivity Analysis-Validation. This whole methodology will become a digital application and software that could be extrapolated to several similar case studies. This may be coupled with posterior devices for the prevention of catastrophic flood consequences in the form of MultiHazard-Early Warning System (MH-EWS) or others. [ABSTRACT FROM AUTHOR]

• Use of distributed storage with optimised layout and control reduces peak flows. • Two-step optimisation identifies contribution of layout/RTC to peak flow reduction. • Peak flow reductions due to distributed storage range from 5% to 40% • RTC achieves higher relative peak flow reduction for a smaller storage volume. • Flood hydrograph analysis provides insights on peak flow reduction mechanisms. The impact of urban flooding is increasing due to the effects of increasing urbanisation and climate change. The use of storages is a relatively well-established approach to reduce peak flows and therefore reduce the need for costly upgrades to stormwater conveyance infrastructure. Recently, real-time control (RTC) has been considered as a means of increasing the performance of these storages. However, previous RTC studies have generally focussed on evaluating RTC with pre-determined storage layouts, i.e., storage locations and volumes, and have ignored the impact of optimising the storage layout. Hence, the objectives of this paper are (i) to introduce a two-step approach to minimising peak flows at the catchment scale by firstly optimising the layout of distributed storages and then optimising their RTC strategies, and (ii) to test the effectiveness of this approach on a real catchment in Adelaide, South Australia. Results show that distributed storage with optimised layouts can achieve significantly higher peak flow reductions than more commonly used end-of-system storage. These range from 5% for 100 m3 of system storage up to 40% for 700 m3 of system storage – whereas end-of-system storage achieved no peak flow reduction for up to 700 m3 of storage. The addition of optimised RTC to distributed storages is able to achieve an additional 10% in peak flow reduction. This increase in performance due to RTC is more significant for smaller system storage volumes. Analysis of the flood hydrographs shows the system peak flow reductions are achieved by attenuating hydrographs at individual storages, as well as delaying hydrographs to reduce their coincidence at the confluence of sub-catchments. These results highlight the potential for using optimised distributed storage layout and RTC as an alternative approach to end-of-system storage to reduce flood peaks. [ABSTRACT FROM AUTHOR]