Return Period Of Events Research Articles

Copula-based joint modelling of extreme river temperature and low flow characteristics in the risk assessment of aquatic life

Compounding the joint impact of extreme river temperature and low flow characteristics can harm the aquatic habitat of certain organisms (e.g., ecototherm fish) and freshwater ecosystems. Considering only river temperature or low flow via univariate frequency distribution as a stress indicator would be incomplete. Maximum water temperature and low flow series are strongly negatively correlated; thus, their joint probability distribution can be helpful to assess better the risks associated with joint extreme events. This study incorporated the 2-D parametric copulas in the bivariate joint modelling of annual maximum river water temperature and corresponding low flow. This proposed bivariate framework is applied to 5 independent and identically distributed stations in Switzerland. Parametric 1-D probability density functions are employed in modelling the univariate marginal distribution of both variables separately. The efficacy of eighteen different parametric class negatively dependent 2-D copulas is tested. The best-fitted copulas and selected marginals are used to estimate joint return periods for quantiles corresponding to multiple return periods. The joint return periods of annual maximum temperatures conditional to low flows or vice versa are also estimated. Investigation reveals that the occurrence of bivariate events simultaneously is less frequent in the AND-joint case than in the OR-joint event case for all stations. Also, OR-return periods are less (nearly half) the value of univariate return periods. Secondly, higher conditional return periods are observed in annual maximum temperature (or low flow) when increasing the percentile value of the conditioning variable, i.e., low flow (or maximum temperature). Also, when the low flow (or water temperature) conditioning variable is fixed, higher bivariate event return periods are observed at a higher water temperature (or low flow) value. In conclusion, these estimated bivariate statistics can help provide a more complete picture for an adequate assessment of the risks associated with cold-water species.

Climate change effects on sub-daily precipitation in Spain

ABSTRACT Sub-daily extreme precipitation events are responsible for flash floods which generate impacts that cannot be analysed using daily precipitation information. In this study, we assess the effects of climate change in sub-daily rainfall statistics for Spain. We downscale daily records to the hourly scale using machine learning techniques, and then update the downscaling predictors to generate the sub-daily rainfall projections. We use the atmospheric climate change projections of the Coordinated Regional Climate Downscaling Experiment (CORDEX) initiative to update the downscaling predictors. We do not observe important differences between climate change scenarios, except in the average amounts of precipitation. Climate change will intensify the most extreme sub-daily events overall, as well as increase the number of dry spells, although some dry climates concentrate most of the effects. The intensification will increase the 100-year return period event, especially in those regions where large variances are observed, making flash floods more intense in the future.

Climate and Land-Use Change Impacts on Flood Hazards in the Mono River Catchment of Benin and Togo

Flooding is prominent in West Africa, and is expected to be exacerbated, due to global climate and land-use changes. This study assessed the impacts of future climate and land-use changes on flood hazards in the Mono river catchment area of Benin and Togo. Climate scenarios from the representative concentration pathways, RCP 4.5 and RCP 8.5, and land-use projection at the horizon of 2070 were used for runoff simulation at the Athiémé outlet, and flood mapping in the lower Mono river basin. The planned Adjarala dam was also simulated, to evaluate its potential impact. The Soil and Water Assessment Tool (SWAT) was used to investigate the impact of the projected changes on runoff, while the flood-water extent was simulated using the two-dimensional TELEMAC-2D model. TELEMAC-2D was validated with satellite observation and in a participatory way with local stakeholders. SWAT showed good performance during the calibration (KGE = 0.83) and validation (KGE = 0.68) steps. Results show an increase in the magnitude of flood extremes under future climate- and land-use-change scenarios. Events of 10-year return periods during 1987–2010 are expected to become 2-year return-period events under the climate- and land-use-change scenarios considered. The planned Adjarala dam showed potentials for extreme-peak and flood-extent reduction. However, flow-duration curves revealed that the discharge of the river during low-flow periods may also be reduced if the Adjarala dam is built. Adaptation measures as well as sustainable land-use and dam-management options should be identified, to alleviate the impacts of the projected changes.

Use of a Bayesian Network for storm-induced flood risk assessment and effectiveness of ecosystem-based risk reduction measures in coastal areas (Port of Sur, Sultanate of Oman)

A Bayesian Network (BN) approach is utilized to leverage synthetic datasets to aid in forecasting onshore hazards and Disaster Risk Reduction (DRR) measures for Port of Sur is assessed. The 3rd generation MIKE21 model was used to simulate flood hazard characteristics (depth and velocity) and the effectiveness of the mangrove forests along the shoreline as an ecosystem-based DRR measure through a reduction in depth and velocity for a 400-year storm return period event. The implementation of the ecosystem-based DRR reduced high damage (more than 64% damage) for residential buildings by 10%. Meanwhile, in the west part of the port, 25% of the population is at extreme risk to life, which can be reduced to 4% by implementing the ecosystem-based DRR. The ecosystem-based DRR reduced the high level of damage (more than 65% damage) for infrastructures by 6% and 9% for the eastern and central parts of the Port of Sur, respectively. The outcomes demonstrate that BN can be used as a simulation tool to conceptualize the potential consequences of a storm in Decision Support System (DSS). For coastal managers to evaluate which options are the best for reducing potential damage to coastal systems.

Risk-Informed Design of RCC Dams under Extreme Seismic Loading

The existing Scoggins Dam and reservoir are in Washington County, Oregon, and the title is held by the U.S. Bureau of Reclamation (Reclamation). Reclamation has previously identified dam safety concerns related to the existing embankment dam. Regional project sponsors, including Clean Water Services, have identified the need for expanded storage capacity in the reservoir to meet growing water demands and address water quality issues in the Tualatin River downstream of the dam. As part of efforts to resolve dam safety issues and increase the water storage in the reservoir, a comprehensive feasibility level design of a new 185-foot-high Roller Compacted Concrete (RCC) dam. Extraordinary seismic hazards have been identified in the region associated with the Cascadia Subduction Zone (CSZ). Further, any dam alternative carried forward for funding, final design, and construction will have to meet the Public Protection Guidelines (PPG) of Reclamation that require a formal quantitative risk analysis. A risk-informed design approach was adopted to configure the layout and cross-section properties of the dam. A multi-phase site characterization program and preliminary RCC mix design program were performed to support the design. In addition, models were developed, and an extensive suite of both (two-dimensional) 2D and (three-dimensional) 3D structural analyses were performed for seismic loadings with total durations of over 200 s, strong shaking of over 140 s, and peak ground accelerations of over 2 gravitational accelerations (g) (up to 50,000-year return period event). This paper describes the feasibility design configuration of the dam, including the seismic hazard characterization, structural analysis models, and seismic response modeling results. The expected performance of the dam relative to the risk-informed design criteria and Reclamation PPGs will be generally described.

Cross-country risk quantification of extreme wildfires in Mediterranean Europe.

We estimate the country-level risk of extreme wildfires defined by burned area (BA) for Mediterranean Europe and carry out a cross-country comparison. To this end, we avail of the European Forest Fire Information System (EFFIS) geospatial data from 2006 to 2019 to perform an extreme value analysis. More specifically, we apply a point process characterization of wildfire extremes using maximum likelihood estimation. By modeling covariates, we also evaluate potential trends and correlations with commonly known factors that drive or affect wildfire occurrence, such as the Fire Weather Index as a proxy for meteorological conditions, population density, land cover type, and seasonality. We find that the highest risk of extreme wildfires is in Portugal (PT), followed by Greece (GR), Spain (ES), and Italy (IT) with a 10-year BA return level of 50'338 ha, 33'242 ha, 25'165 ha, and 8'966 ha, respectively. Coupling our results with existing estimates of the monetary impact of large wildfires suggests expected losses of 162-439 million € (PT), 81-219 million € (ES), 41-290 million € (GR), and 18-78 million € (IT) for such 10-year return period events. SUMMARY: We model the risk of extreme wildfires for Italy, Greece, Portugal, and Spain in form of burned area return levels, compare them, and estimate expectedlosses.

The impacts of climatic and land surface characteristics on the storm-flood relationship in a tropical monsoon basin of Thailand

Storms and the resultant floods have always been catastrophic disasters and raised increasing global concerns in the context of climate change. However, the relationships between storms and floods remain largely elusive. Here we examine the storm-flood relationship and its variations in the Upper Chao Phraya River Basin (UCPRB), a typical tropical monsoon basin in southeast Asia. The distributions of storms and floods are characterized by statistical models with the aid of climatic and land surface covariates. The storm-flood relationship is depicted by the concept of storm-flood elasticity, which represents the corresponding changes in flood peaks in response to changes in the storm peaks with the same return period. The storm-flood elasticity coefficients for 100-year return period events range from 0.61 to 1.20, and the values of storm-flood elasticity coefficients tend to be smaller for long-return period events than for short-return-period events, under high-typhoon-precipitation (high-TP), high-non-typhoon-precipitation (high-nTP), and low-forest (low-F) conditions, and in humid regions than in arid regions. The climatic covariates are shown to have stronger effects on the storm-flood elasticity coefficient than the land surface covariate in most basins. In the basins where deforestation shows strong impacts on the storm-flood relationship, afforestation can be an effective approach for flood control. In most basins in the UCPRB, the variation of typhoon precipitation has larger impacts than those of non-typhoon precipitation, indicating that typhoon precipitation should be paid more attention to when considering the future changes in floods. The findings help develop a better understanding of storm-flood relationships in tropical monsoon regions, and the methods of this study can also be applied in other climate regions.

Deaggregation of Wind Speeds for Hurricane Scenarios Used in Risk-Informed Resilience Assessment of Coastal Communities

Coastal cities in the east and southeast regions of the US have seen significant population growth and economic development in the last 2 decades. As a result, urban infrastructure, populations, and economies are becoming increasingly vulnerable to hurricane-driven hazards. Criteria for wind design in national standards are intended specifically for the design and performance assessment of individual buildings and other facilities for life safety. They are not adequate for assessing community resilience because hurricane winds are spatially nonuniform. Although scenario-based approaches to representing hurricane demands are more useful in community performance assessment because the distinct features of various hurricane scenarios and their impact on a community can be captured, they are not tied to a specific hazard level. This study introduces a new method for systematically identifying a set of hurricane scenarios corresponding to a stipulated return period (RP) for resilience assessment of coastal communities using a deaggregation approach, which establishes a connection that has not existed previously between hurricane scenarios and the building regulatory process. A community patterned after Miami, Florida, was used to demonstrate the proposed hazard deaggregation and damage analysis. Hurricane scenario events that are dominant contributors to the stipulated RP events, coupled with fragility models of engineered buildings, were used to identify building damage patterns and form an improved basis for risk-informed decision making.

Multi‐Level Monte Carlo Models for Flood Inundation Uncertainty Quantification

AbstractFlood events are the most commonly occurring natural disaster, with over 5 million properties at risk in the UK alone. Changes in the global climate are expected to increase the frequency and magnitude of flood events. Flood hazard assessments, using climate projections as input, guide policy decisions and engineering projects to reduce the impact of large return period events. Probabilistic flood modeling is required to take into account uncertainties in climate model projections. However, the dichotomous relationship between probabilistic modeling, computational cost and model resolution limits the applicability of such techniques. This paper examines improvements to traditional Monte Carlo methods using Latin hypercube sampling (LHS) and Multi‐level Monte Carlo (MLMC) to quantify the uncertainty in flood extent resulting from input hydrograph uncertainty. The results demonstrate that MLMC is a more efficient modeling strategy than current methods (i.e., traditional Monte Carlo) with high resolution outputs produced in less time than previously possible. The novel application of MLMC technique to three Scottish case studies, demonstrating a variety of river characteristics, domain sizes and computational costs, using a high resolution 5 m grid resulted in a 99.2% reduction in computational cost compared to traditional Monte Carlo methods and up to 2.3 times speedup over Latin Hypercube Sampling.

An Efficient Modeling Approach for Probabilistic Assessments of Present-Day and Future Fluvial Flooding

Risk-informed flood risk management requires a comprehensive and quantitative risk assessment, which often demands multiple (thousands of) river and flood model simulations. Performing such a large number of model simulations is a challenge, especially for large, complex river systems (e.g., Mekong) due to the associated computational and resource demands. This article presents an efficient probabilistic modeling approach that combines a simplified 1D hydrodynamic model for the entire Mekong Delta with a detailed 1D/2D coupled model and demonstrates its application at Can Tho city in the Mekong Delta. Probabilistic flood-hazard maps, ranging from 0.5 to 100 year return period events, are obtained for the urban center of Can Tho city under different future scenarios taking into account the impact of climate change forcing (river flow, sea-level rise, storm surge) and land subsidence. Results obtained under present conditions show that more than 12% of the study area is inundated by the present-day 100 year return period of water level. Future projections show that, if the present rate of land subsidence continues, by 2050 (under both RCP 4.5 and RCP 8.5 climate scenarios), the 0.5 and 100 year return period flood extents will increase by around 15- and 8-fold, respectively, relative to the present-day flood extent. However, without land subsidence, the projected increases in the 0.5 and 100 year return period flood extents by 2050 (under RCP 4.5 and RCP 8.5) are limited to between a doubling to tripling of the present-day flood extent. Therefore, adaptation measures that can reduce the rate of land subsidence (e.g., limiting groundwater extraction), would substantially mitigate future flood hazards in the study area. A combination of restricted groundwater extraction and the construction of a new and more efficient urban drainage network would facilitate even further reductions in the flood hazard. The projected 15-fold increase in flood extent projected by 2050 for the twice per year (0.5 year return period) flood event implies that the “do nothing” management approach is not a feasible option for Can Tho.

A Comprehensive Approach for Floodplain Mapping through Identification of Hazard Using Publicly Available Data Sets over Canada

Quantifying flood inundation and hazards over large regions is paramount for gaining critical information on flood risk over the vulnerable population and environment. Readily available global data and enhancement in computational simulations have made it easier to simulate flooding at a large scale. This study explores the usability of publicly available datasets in flood inundation and hazard mapping, and ensures the flood-related information reaches the end-users efficiently. Runoff from the North American Regional Reanalysis and other relevant inputs are fed to the CaMa-Flood model to generate flooding patterns for 1 in 100 and 1 in 200-year return period events over Canada. The simulated floodplain maps are overlaid on the property footprints of 34 cities (falling within the top 100 populated cities of Canada) to determine the degree of exposure during 1991, 2001 and 2011. Lastly, Flood Map Viewer—a web-based public tool, is developed to disseminate extensive flood-related information. The development of the tool is motivated by the commitment of the Canadian government to contribute $63 M over the next three years for the development of flood maps, especially in high-flood risk areas. The results from the study indicate that around 80 percent of inundated spots belong to high and very-high hazard classes in a 200-year event, which is roughly 4 percent more than observed during the 100-year event. We notice an increase in the properties exposed to flooding during the last three decades, with a signature rise in Toronto, Montreal and Edmonton. The flood-related information derived from the study can be used along with vulnerability and exposure components to quantify flood risk. This will help develop appropriate pathways for resilience building for long-term sustainable benefits.

Numerical Comparison of the Hydrological Response of Different Permeable Pavements in Urban Area

Floods are becoming more frequent, especially in urban environments where most of the surface is waterproofed. Permeable pavement (PP) can be applied as low impact development (LID) systems for runoff mitigation in urban areas. Their effectiveness can be assessed, case by case, by numerical simulations. In this study, the effectiveness of mitigating runoff of different permeable pavements has been evaluated. In particular, porous asphalt (PA), pervious concrete (PC), permeable interlocking concrete pavement (PICP) and grid pavement (GP) have been investigated using EPA Storm Water Management Model (SWMM) software. To this aim, a car parking area located in the University Campus of Palermo (Italy) has been taken as a case study, considering several scenarios, each having a different percentage and planimetric layout of a PP type combined with an impermeable pavement. All the scenarios were tested assuming four synthetic rainfall events, referring to return periods of 5, 10, 50 and 100 years, and a real high return period event that occurred in Palermo in 2020. The results showed that amongst the different PPs considered, only the PA, bounded at the bottom by an impermeable layer, was practically ineffective. The other three PPs, proved to be effective in a noticeable way and furthermore for each scenario studied, they proved to bear almost the same mitigated runoff. The results proved appreciable differences in runoff as a function of the location of the PP over the study area.

Effects of Nonstationarity in Urban Land Cover and Rainfall on Historical Flooding Intensity in a Semiarid Catchment

Nonstationarity of rainfall extremes and urban land cover pose uncertainties for local stormwater managers attempting to address urban flooding. Due to the recent attention focused on adapting urban drainage design to rainfall intensity modification from climate change, impacts from land cover changes due to infill and redevelopment have not been addressed. This study fills this gap by quantitatively investigating the impacts of historical changes in imperviousness of existing urban areas and rainfall on urban drainage network flooding. The study applies the US Environmental Protection Agency (EPA) Storm Water Management Model (SWMM) to simulate the changes in rainfall and impervious land cover in an urban drainage system located in Salt Lake City, Utah, US, over two 15-year periods (1971–1985 and 2001–2015). Impacts on event flooding intensity are compared among four SWMM continuous simulation scenarios based on flooding events with different frequencies. Results show that impervious land cover yields higher percent changes up to 240%, 35%, and 85%, respectively, than rainfall changes for the minimum, mean, and maximum event flooding intensity. Notably, an 18% increase in imperviousness percentage is more impactful than a 25% increase in rainfall intensity on low-frequency (over a 5-year return period) flooding events. The combined impacts of increased imperviousness and intensity are the most significant, increasing average flooding intensity from 35% to 145% as the event return period increased from 5-year to 100-year. These findings affirm the need to account for changes in urban land cover from infill development and redevelopment along with rainfall changes in planning and design of stormwater urban drainage networks.

Collided with COVID-19 Pandemic, the 2020 Yangtze Flood Is Exceptionally Severe

During June to July, 2020, persistent heavy precipitation in the Yangtze River Basin (YRB) is resulting in extensive flooding, with over 158 fatalities and tremendous economic losses. This year’s disastrous flooding extreme is exceptionally different from those of other years. It contains over 1000-year return period events (for 30-day cumulative precipitation) as observed in Anhui, Guizhou and Sichuan Provinces. The mean precipitation is 308 mm in July 2020, being 54 mm higher than that of July 1998, when serious floods affected the entire Basin causing tremendous socio-economic consequences. Compared with 1998, the short-term (e.g., 1 day) precipitation in YRB did not show significant increases, while the long-term (e.g., 30 days) cumulative precipitation increases significantly. The highest observed 30-day cumulative precipitation is 1221 mm (in Anhui Province) in 2020, while the highest one in 1998 was 1028 mm (in Jiangxi Province). We thus find that this persistent heavy precipitation is the main cause of flooding in 2020. At the same time, TGR may mitigate up 43% of upstream flood, although the main contributors to this year’s YRB flood are in the middle and lower reaches. Affected by COVID-19, the number of people at risk in the threatened area are increased, and their capacities to mitigate the dual impacts of COVID-19 pandemic and flooding are hindered since (a) the flooding-caused mitigations may limit people’s ability to prevent from virus spreading, and (b) the pandemic is retaining a large amount of migrant workers being within YRB and subject to flooding impacts. Overall, our main discovery is that, although the short-term precipitation in YRB did not increase significantly in 2020, the cumulative one increased significantly in 2020.

An Impacts-Based Flood Decision Support System for a Tropical Pacific Island Catchment with Short Warnings Lead Time

Early warnings decision support systems are recognized as effective soft adaptation tools to prepare for the impacts of imminent flooding and minimize potential injuries and/or loss of life in flood-prone regions. This paper presents a case study of a pilot project that aimed to establish an impacts-based flood monitoring, early warnings, and decision support system for the Vaisigano River which flows through Apia, the capital of Samoa. This river is located in a characteristic short and steep catchment with rapid critical flood peak durations following periods of intense rainfall. The developed system integrates numerical weather prediction rainfall forecasts, real-time rainfall, river level and flow monitoring data, precomputed rainfall-runoff simulations, and flood inundation estimates of exposure levels and threat to human safety at buildings and on roads for different return period events. Information is ingested into a centralized real-time, web-based, flood decision support system portal that enables hydrometeorological officers to monitor, forecast and alert relevant emergency or humanitarian responders of imminent flooding with adequate lead time. This includes nowcasts and forecasts of estimated flood peak time, magnitude and likely impacts of inundation. The occurrence of three distinct extreme rainfall and flood events over the 2020/2021 tropical cyclone season provided a means to operationally test the system. In each case, the system proved adequate in alerting duty officers of imminent flooding in the Vaisigano catchment with up to 24 h warnings and response lead time. Gaps for improvement of system capabilities and performance are discussed, with recommendations for future work suggested.

Evaluation of Debris Flows for Flood Plain Estimation in a Small Ungauged Tropical Watershed for Hurricane Otto

The variability of climate, increase in population, and lack of territorial plans in Costa Rica have caused intense disasters with human and economic losses. In 2016, Hurricane Otto hit the country’s northern area, leaving substantial damages, including landslides, debris flows, and flooding. The present study evaluated different scenarios to estimate flooded areas for Newtonian (clean water), and non-Newtonian flows with volumetric sediment concentrations (Cv) of 0.3, 0.45, 0.55, and 0.65 using Hydro-Estimator (HE), rain gauge station, and the 100-year return period event. HEC–HMS modeled the rainfall products, and FLO-2D modeled the hydrographs and Cv combinations. The simulation results were evaluated with continuous statistics, contingency table, Nash Sutcliffe Efficiency, measure of fit (F), and mean absolute differences (E) in the floodplains. Flow depths, velocities, and hazard intensities were obtained in the floodplain. The debris flood was validated with field data and classified with a Cv of 0.45, presenting lower MAE and RMSE. Results indicated no significant differences in flood depths between hydrological scenarios with clean-water simulations with a difference of 8.38% in the peak flow. The flood plain generated with HE rainfall and clear-water condition presented similar results compared to the rain gauge input source. Additionally, hydraulic results with HE and Cv of 0.45 presented E and F values similar to the simulation of Cv of 0.3, demonstrating that the HE bias did not influence the determination of the floodplain depth and extent. A mean bias factor can be applied to a sub-daily temporal resolution to enhance HE rain rate quantifications and floodplain determination.

Testing the impacts of wildfire on hydrological and sediment response using the OpenLISEM model. Part 2: Analyzing the effects of storm return period and extreme events

Wildfires can have strong negative effects on soil and water resources, especially in headwater areas. The spatially explicit OpenLISEM model was applied to a burned catchment in southern Portugal to quantify the individual and combined impacts of wildfire and rainfall on hydrological and erosion processes. The companion paper has calibrated and assessed model performance in this area before and after a fire. In this study, the model was applied with design storms of six different return periods (0.2, 0.5, 1, 2, 5, and 10 years) to simulate and evaluate pre- and post-wildfire hydrological and erosion responses at the catchment scale. Our results show that rainfall amount and intensity played a more important role than fire occurrence in the catchment discharge and sediment yields. Fire occurrence was found to be an important factor for peak discharge, indicating that high post-fire hydro-sedimentary responses are frequently related to extreme rainfall events. The results also suggest a partial shift from runoff to splash erosion after fire, especially for higher return periods. This can be explained by increased splash erosion in burned upstream areas saturating the sediment transport capacity of surface runoff, limiting runoff erosion in downstream areas. Therefore, the pre-fire erosion risk in the croplands of this catchment was partly shifted to a post-fire erosion risk in upper slope forest and natural areas, especially for storms with lower return periods, although erosion risks in croplands were important both before and after fires. These findings have significant implications to identify areas for post-wildfire stabilization and rehabilitation, which is particularly important given the predicted increase in the occurrence of fires and extreme rainfall events with climate change.

Global mean frequency increases of daily and sub-daily heavy precipitation in ERA5

Changes in heavy precipitation frequency can be viewed as a change in the event return period, a common metric of risk. Compared with intensity, frequency changes are less well-studied and past work has largely been constrained to analysis of well-instrumented regions. We exploit the latest ERA5 reanalysis and its global hourly accumulations at 1/4° spatial resolution and apply a metric that captures frequency changes across both wet and dry regions. According to ERA5 and in a global average sense, during 1989–2018, hourly events that occurred once per year in 1979–1988 increased in frequency by 71 (53–93, 95% range) %, while the one day per year heavy event frequency increased by 44 (37–54) %. Thus, hourly events that occurred once per year in the baseline decade are on track to double in frequency by 2021–2030, and the daily events by 2047–2056. Furthermore, our results replicate prior findings that relative frequency increases are larger for increasingly rare events, and for the first time we quantify that mean frequency increases have been greater over ocean than land. Ocean increases are larger by factors of 3.0 and 2.1 for the hourly and daily events that occurred once per year in 1979–1988, respectively.

The Web-GIS TRIG Eau Platform to Assess Urban Flood Mitigation by Domestic Rainwater Harvesting Systems in Two Residential Settlements in Italy

Urban flooding has become one of the most frequent natural disasters in recent years, and the low-impact development (LID) approach is currently recognised as an alternative to traditional grey infrastructure to mitigate the negative impact of urbanisation on hydrological processes. The main objective of the present research was to develop a web-GIS platform in order to assess the impact of LID systems on mitigating urban flooding and to support their implementation at the urban catchment scale. The TRIG Eau platform, developed in the framework of the homonymous INTERREG MARITTIMO IT-FR project, is configured as a web-GIS application of the stormwater management model (SWMM). Urban flood conditions were examined for two case studies in Liguria and Tuscany (IT), where DRWH systems are proposed as a mitigation strategy. The presented results and their visualisation showcase the potential of the TRIG Eau platform to better support the implementation of LIDs. Findings from the flood analysis confirm that even for the 10-year return period event, DRWHs are effective in reducing network stress by more than 70% in cases of empty tanks, thus underlining the need for RTC technology to pre-empty the system.

Assessing geomorphic floodplain models for large scale coarse resolution 2D flood modelling in data scarce regions

River valleys are dynamic living ecosystems of utmost importance for flood attenuation that are shaped by inundation dynamics. Topographic and bathymetric surveys represent pivotal information for accurate and up-to-date floodplain studies. Both floodplain morphology as well as fluvial river cross section and thalweg profiles are required for inundation modelling and mapping. However, economic and technical limitations hinder their availability in some regions, resulting in challenges to build two dimensional (2D) flood wave routing simulations at proper accuracy and resolution. In this study, we assess the effectiveness of characterizing the fluvial morphology by means of geomorphic methods (GMs). Different GMs, used as surrogates of fluvial bathymetry, are tested and compared to a dataset of surveyed natural cross sections available for the Tiber River basin (Italy). Quantitative flood modelling performances are developed using a validated 150 m flood model, providing inundation extent and floodplain flow depths for the 200 years return period event. The ability (or inefficiency) of surrogating the lack of surveyed fluvial bathymetric data with GMs for supporting large scale hydraulic inundation modelling studies is assessed by testing the following 5 floodplain modelling configurations: (1) rectangular shaped cross section; (2) floodplain-based; (3) global river database; (4) linear regression bathymetric relationship; and (5) and a very coarse 700 grid resolution. In addition, two Global Flood Hazard Mapping (GFHM) products (hydrological, and hydrogeomorphic) were used as part of the large scale floodplain modelling evaluation framework. Results demonstrate that, once the fluvial channel flow area is preserved, all tested GM models produce consistent simulation of inundation depths and extents (Fit index ≥ 0.90). This work provides a quantitative assessment of the validity of the hypothesis stressing that the floodplain conveyance capacity is the driving principle of flood inundation dynamics under extreme flooding scenarios. Understanding the role of geomorphology during extreme magnitude floods may support the idea that under specific conditions, high resolution models and detailed topography/bathymetry are surplus to requirements. This work supports the application of geomorphic approaches over large riparian domains as a parsimonious solution for flood hazard mapping in data scarce regions for applications beyond flood mitigation and forecasting.