scholarly journals Effectiveness of Nature-Based Solutions in Mitigating Flood Hazard in a Mediterranean Peri-Urban Catchment

Water ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2893
Author(s):  
Carla S.Ferreira ◽  
Sandra Mourato ◽  
Milica Kasanin-Grubin ◽  
António J.D. Ferreira ◽  
Georgia Destouni ◽  
...  
Keyword(s):  
Water Depth ◽  
Model Simulation ◽  
Flood Hazard ◽  
Coupled Model ◽  
Urban Catchment ◽  
Rainfall Events ◽  
Flood Depth ◽  
Mitigation And Adaptation ◽  
Flood Magnitude ◽  
Central Portugal

Urbanization alters natural hydrological processes and enhances runoff, which affects flood hazard. Interest in nature-based solutions (NBS) for sustainable mitigation and adaptation to urban floods is growing, but the magnitudes of NBS effects are still poorly investigated. This study explores the potential of NBS for flood hazard mitigation in a small peri-urban catchment in central Portugal, prone to flash floods driven by urbanization and short but intense rainfall events typical of the Mediterranean region. Flood extent and flood depth are assessed by manually coupling the hydrologic HEC-HMS and hydraulic HEC-RAS models. The coupled model was run for single rainfall events with recurrence periods of 10–, 20–, 50–, and 100–years, considering four simulation scenarios: current conditions (without NBS), and with an upslope NBS, a downslope NBS, and a combination of both. The model-simulation approach provides good estimates of flood magnitude (NSE = 0.91, RMSE = 0.08, MAE = 0.07, R2 = 0.93), and shows that diverting streamflow into abandoned fields has positive impacts in mitigating downslope flood hazard. The implementation of an upslope NBS can decrease the water depth at the catchment outlet by 0.02 m, whereas a downslope NBS can reduce it from 0.10 m to 0.23 m for increasing return periods. Combined upslope and downslope NBS have a marginal additional impact in reducing water depth, ranging from 0.11 m to 0.24 m for 10– and 100–year floods. Decreases in water depth provided by NBS are useful in flood mitigation and adaptation within the peri-urban catchment. A network of NBS, rather than small isolated strategies, needs to be created for efficient flood-risk management at a larger scale.

scholarly journals River Flood Hazard Modeling: Forecasting Flood Hazard for Disaster Risk Reduction Planning

2019 ◽  
Vol 5 (11) ◽  
pp. 2309-2317 ◽  
Author(s):  
Murphy Ponce Mohammed
Keyword(s):  
Model Simulation ◽  
Flood Hazard ◽  
Extreme Rainfall ◽  
Base Flow ◽  
Extreme Rainfall Event ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
River Reach ◽  
Analysis System ◽  
Initial Base

The objective of the study is to create a flood hazard model of Tarlac River and to calibrate the model based on data gathered from the Philippine Atmospheric Geophysical and Astronomical Services Administration. The study employed analytical method wherein the 1D flood modeling was utilized. GIS, DEM data, rainfall data, river analysis system, HEC-GeoRAS, hydrologic modeling system, and HEC-GeoHMS were utilized. The different flood models revealed that Tarlac River is not expected to be overtopped by flood water as regards the different extreme rainfall events considered in the present study. The RAS model simulation was based on the concept that there is no base flow observed within the river reach before the occurrence of any extreme rainfall event. Henceforth, there is still no 100 percent assurance that the river reach will not be overtopped with the occurrence of initial base flow in combination with the occurrence of higher extreme rainfall events. Further studies or investigations should be delved into such combination of events. Possible levee breach of the Tarlac River as well as the possible incorporation of flood mitigating interventions in future modeling scenarios can be likewise considered.

A stochastic methodology for pluvial flood mapping in urban areas with a fast-processing DEM-based flooding algorithm 

2021 ◽  
Author(s):  
Luis Mediero ◽  
Enrique Soriano ◽  
Peio Oria ◽  
Stefano Bagli ◽  
Attilio Castellarin ◽  
...  
Keyword(s):  
Water Depth ◽  
Urban Areas ◽  
Flood Hazard ◽  
Stochastic Simulations ◽  
Regional Government ◽  
Flood Hazards ◽  
Hydrodynamic Models ◽  
Rainfall Events ◽  
The Real ◽  
Water Depths

<p>High-intensity and short-duration storms can generate pluvial floods in urban areas. Currently, 2D hydrodynamic models are recognised to be the best tool to simulate pluvial floods. The T-year synthetic design storm is usually assumed to generate the T-year pluvial flood. However, synthetic design storms cannot represent the variability in duration, precipitation and intensity temporal distribution of real storms that should be considered to account for their influence on water depths in pluvial floods. A more sound approach consists in estimating the T-year water depth in a given location from the frequency curve of water depths generated by a long series of possible rainfall events similar to the real storms.</p><p>However, 2D hydrodynamic models require high computation times that are not well suited with stochastic simulations. The Safer_RAIN tool is a rapid hydrostatic flood model based on a filling-and-spilling technique that has been developed within the SAFERPLACES project funded by the EIT Climate-KIC (Samela et al., 2020). Depressions and links between them are identified from a digital terrain model. The continuity equation is used to simulate how depressions are filled and spill to downstream depressions. Infiltration is simulated by using a distributed implementation of the Green and Ampt model that accounts for ponding time.</p><p>In this study, a stochastic methodology to delineate pluvial flood hazards is proposed in the Pamplona metropolitan area in Spain. First, the Safer_RAIN tool has been benchmarked by using spatially distributed high-resolution quantitative precipitation estimates (QPE) at time steps of 10 minutes for three real pluvial flood events. QPEs were obtained merging the data recorded at a set of automatic weather stations from the Spanish State Meteorological Agency (AEMET), the Regional Government of Navarre and crowdsourced networks, with continuous fields of radar observations. The Safer_RAIN tool has been benchmarked with the 2D hydrodynamic IBER model. In Barañáin, the results show a bias of -0.17–0.18 m and a RMSE of 0.22–0.49 m between water depths, as well as an accuracy correlation coefficient (ACC) of 0.87–0.99. In Zizur Mayor, the bias is -0.19–0.20 m, the RMSE is 0.29–0.55 m and the ACC is between 0.88 and 0.98.</p><p>Second, a long set of 10 000 synthetic storms has been generated by using a stochastic rainfall generator based on a bivariate copula approach fitted to data recorded at four rainfall-gauging stations located close to the case study. The 10 000 synthetic storms generated with a Gumbel copula fitted to the real rainfall events have been used as input data of the Safer_RAIN tool. Safer_RAIN preprocessing was done in 112 seconds and each simulation lasted around 45 seconds. A Generalized Pareto distribution function was fitted to the 10 000 water depth values in each cell of the grid. Pluvial flood hazard maps were obtained by estimating the T-year water depth in each cell of the grid.</p><p><strong> </strong></p><p>Samela et al. (2020). Safer_RAIN: A DEM-Based Hierarchical Filling-&-Spilling Algorithm for Pluvial Flood Hazard Assessment and Mapping across Large Urban Areas, Water, 12, 1514.</p>

scholarly journals Flood Control by Weir Design Using HEC-RAS Model: The Case of Al-Musandaq Escape

2021 ◽  
Vol 877 (1) ◽  
pp. 012025
Author(s):  
Tabarak W. Mahdi ◽  
Ali N. Hillo
Keyword(s):  
Input Data ◽  
Flood Control ◽  
Model Simulation ◽  
Flood Hazard ◽  
Environmental Safety ◽  
Optimal Level ◽  
Clear Understanding ◽  
Optimum Level ◽  
Flood Depth ◽  
Elevation Model

Abstract Proper flood control plays an important part in designing hydraulic structures and environmental safety measures. However, in Iraq, a clear understanding either of the estimation or management of the magnitude of flooding is yet to reach a higher level. This has resulted in grave and frequent damage to much property and life in Maysan town. Therefore, this study was focused on the Flood Hazard contro; in the River Tigris at the downstream of Al-Kut Barrage to the Al-Musandaq Escape, by adopting the HEC RAS model. Here, information related to the hydrological and topographical Digital Elevation Model (DEM) data were used as the input data. The hydrological data enabled the estimation of the flood depth of the river, for April 2019. All the geometric data were prepared by the HEC-RAS model. The unsteady-state model simulation was performed employing the input data. In this study, the best method for flood control and management is to design a weir having an optimal level, the optimum level that does not permit the passage of a flow that exceeds 700 m3/s to the city of Maysan during the flood season and a flow that is not below 250 m3/s during the dry season, is 9.406 m.

Origins and characteristics of urban wet weather pollution in combined sewer systems: the experimental urban catchment “Le Marais” in Paris

1998 ◽  
Vol 37 (1) ◽  
pp. 35-43 ◽  
Author(s):  
Marie-Christine Gromaire-Mertz ◽  
Ghassan Chebbo ◽  
Mohamed Saad
Keyword(s):  
Waste Water ◽  
Sewer System ◽  
Urban Catchment ◽  
Rainfall Events ◽  
Sources Of Pollution ◽  
Sewer Systems ◽  
Combined Sewer ◽  
Wet Weather ◽  
Wet Weather Flow ◽  
Different Sources

An experimental urban catchment has been created in the centre of Paris, in order to obtain a description of the pollution of urban wet weather flows at different levels of the combined sewer system, and to estimate the contribution of runoff, waste water and sewer sediments to this pollution. Twenty-two rainfall events were studied from May to October 1996. Dry weather flow was monitored for one week. Roof, street and yard runoff, total flow at the catchment outlet and waste water were analysed for SS, VSS, COD and BOD5, on both total and dissolved fraction. Results show an evolution in the characteristics of wet weather flow from up to downstream: concentrations increase from the catchment entry to the outlet, as well as the proportion of particle-bound pollutants and the part of organic matter. A first evaluation of the different sources of pollution establishes that a major part of wet weather flow pollution originates from inside the combined sewer, probably through erosion of sewer sediments.

scholarly journals A New Tool to Estimate Inundation Depths by Spatial Interpolation (RAPIDE): Design, Application and Impact on Quantitative Assessment of Flood Damages

Water ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 1805 ◽  
Author(s):  
Anna Scorzini ◽  
Alessio Radice ◽  
Daniela Molinari
Keyword(s):  
River Basin ◽  
Water Depth ◽  
Spatial Interpolation ◽  
Large Scale ◽  
Quantitative Estimation ◽  
Digital Terrain Model ◽  
Water Levels ◽  
Flood Depth ◽  
Inundation Depth ◽  
Basin Scale

Rapid tools for the prediction of the spatial distribution of flood depths within inundated areas are necessary when the implementation of complex hydrodynamic models is not possible due to time constraints or lack of data. For example, similar tools may be extremely useful to obtain first estimates of flood losses in the aftermath of an event, or for large-scale river basin planning. This paper presents RAPIDE, a new GIS-based tool for the estimation of the water depth distribution that relies only on the perimeter of the inundation and a digital terrain model. RAPIDE is based on a spatial interpolation of water levels, starting from the hypothesis that the perimeter of the flooded area is the locus of points having null water depth. The interpolation is improved by (i) the use of auxiliary lines, perpendicular to the river reach, along which additional control points are placed and (ii) the possibility to introduce a mask for filtering interpolation points near critical areas. The reliability of RAPIDE is tested for the 2002 flood in Lodi (northern Italy), by comparing the inundation depth maps obtained by the rapid tool to those from 2D hydraulic modelling. The change of the results, related to the use of either method, affects the quantitative estimation of direct damages very limitedly. The results, therefore, show that RAPIDE can provide accurate flood depth predictions, with errors that are fully compatible with its use for river-basin scale flood risk assessments and civil protection purposes.

scholarly journals A metric-based assessment of flood risk and vulnerability of rural communities in the Lower Shire Valley, Malawi

2015 ◽  
Vol 370 ◽  
pp. 139-145 ◽  
Author(s):  
A. J. Adeloye ◽  
F. D. Mwale ◽  
Z. Dulanya
Keyword(s):  
Rural Communities ◽  
Spatial Variability ◽  
Flood Risk ◽  
Flood Hazard ◽  
Sub Saharan Africa ◽  
Structured Interviews ◽  
Adaptation Measures ◽  
Flood Depth ◽  
Inundation Area ◽  
Sub Saharan

Abstract. In response to the increasing frequency and economic damages of natural disasters globally, disaster risk management has evolved to incorporate risk assessments that are multi-dimensional, integrated and metric-based. This is to support knowledge-based decision making and hence sustainable risk reduction. In Malawi and most of Sub-Saharan Africa (SSA), however, flood risk studies remain focussed on understanding causation, impacts, perceptions and coping and adaptation measures. Using the IPCC Framework, this study has quantified and profiled risk to flooding of rural, subsistent communities in the Lower Shire Valley, Malawi. Flood risk was obtained by integrating hazard and vulnerability. Flood hazard was characterised in terms of flood depth and inundation area obtained through hydraulic modelling in the valley with Lisflood-FP, while the vulnerability was indexed through analysis of exposure, susceptibility and capacity that were linked to social, economic, environmental and physical perspectives. Data on these were collected through structured interviews of the communities. The implementation of the entire analysis within GIS enabled the visualisation of spatial variability in flood risk in the valley. The results show predominantly medium levels in hazardousness, vulnerability and risk. The vulnerability is dominated by a high to very high susceptibility. Economic and physical capacities tend to be predominantly low but social capacity is significantly high, resulting in overall medium levels of capacity-induced vulnerability. Exposure manifests as medium. The vulnerability and risk showed marginal spatial variability. The paper concludes with recommendations on how these outcomes could inform policy interventions in the Valley.

scholarly journals Projected changes in temperature and precipitation over mainland Southeast Asia by CMIP6 models

2021 ◽  
Author(s):  
S. Supharatid ◽  
J. Nafung ◽  
T. Aribarg
Keyword(s):  
Climate Change ◽  
Coupled Model ◽  
Southwest Monsoon ◽  
Future Climate Change ◽  
Boreal Summer ◽  
Northeast Monsoon ◽  
Adaptation Measures ◽  
Mitigation And Adaptation ◽  
Temperature And Precipitation ◽  
Projected Changes

Abstract Five mainland SEA countries (Cambodia, Laos, Myanmar, Vietnam, and Thailand) are threatened by climate change. Here, the latest 18 Coupled Model Intercomparison Project Phase 6 (CMIP6) is employed to examine future climate change in this region under two SSP-RCP (shared socioeconomic pathway-representative concentration pathway) scenarios (SSP2-4.5 and SSP5-8.5). The bias-corrected multi-model ensemble (MME) projects a warming (wetting) over Cambodia, Laos, Myanmar, Vietnam, and Thailand by 1.88–3.89, 2.04–4.22, 1.88–4.09, 2.03–4.25, and 1.90–3.96 °C (8.76–20.47, 12.69–21.10, 9.54–21.10, 13.47–22.12, and 7.03–15.17%) in the 21st century with larger values found under SSP5-8.5 than SSP2-4.5. The MME model displays approximately triple the current rainfall during the boreal summer. Overall, there are robust increases in rainfall during the Southwest Monsoon (3.41–3.44, 8.44–9.53, and 10.89–17.59%) and the Northeast Monsoon (−2.58 to 0.78, −0.43 to 2.81, and 2.32 to 5.45%). The effectiveness of anticipated climate change mitigation and adaptation strategies under SSP2-4.5 results in slowing down the warming trends and decreasing precipitation trends after 2050. All these findings imply that member countries of mainland SEA need to prepare for appropriate adaptation measures in response to the changing climate.

Influence of Ocean and Atmosphere Components on Simulated Climate Sensitivities

2013 ◽  
Vol 26 (1) ◽  
pp. 231-245 ◽  
Author(s):  
Michael Winton ◽  
Alistair Adcroft ◽  
Stephen M. Griffies ◽  
Robert W. Hallberg ◽  
Larry W. Horowitz ◽  
...  
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Climate Model ◽  
Model Simulation ◽  
Coupled Model ◽  
Ocean Model ◽  
Historical Simulation ◽  
Model Version ◽  
Salinity Response ◽  
The Impact

Abstract The influence of alternative ocean and atmosphere subcomponents on climate model simulation of transient sensitivities is examined by comparing three GFDL climate models used for phase 5 of the Coupled Model Intercomparison Project (CMIP5). The base model ESM2M is closely related to GFDL’s CMIP3 climate model version 2.1 (CM2.1), and makes use of a depth coordinate ocean component. The second model, ESM2G, is identical to ESM2M but makes use of an isopycnal coordinate ocean model. The authors compare the impact of this “ocean swap” with an “atmosphere swap” that produces the GFDL Climate Model version 3 (CM3) by replacing the AM2 atmospheric component with AM3 while retaining a depth coordinate ocean model. The atmosphere swap is found to have much larger influence on sensitivities of global surface temperature and Northern Hemisphere sea ice cover. The atmosphere swap also introduces a multidecadal response time scale through its indirect influence on heat uptake. Despite significant differences in their interior ocean mean states, the ESM2M and ESM2G simulations of these metrics of climate change are very similar, except for an enhanced high-latitude salinity response accompanied by temporarily advancing sea ice in ESM2G. In the ESM2G historical simulation this behavior results in the establishment of a strong halocline in the subpolar North Atlantic during the early twentieth century and an associated cooling, which are counter to observations in that region. The Atlantic meridional overturning declines comparably in all three models.

Combination of geomorphic classifiers through Machine Learning-based techniques for flood hazard assessment 

2021 ◽  
Author(s):  
Andrea Magnini ◽  
Michele Lombardi ◽  
Simone Persiano ◽  
Antonio Tirri ◽  
Francesco Lo Conti ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hazard Assessment ◽  
Water Depth ◽  
Flood Hazard ◽  
Research Centre ◽  
Hazard Mapping ◽  
Hazard Maps ◽  
Flood Hazard Assessment ◽  
Flood Prone Areas ◽  
Flood Hazard Maps

<p><span xml:lang="EN-US" data-contrast="auto"><span>Every year flood events cause worldwide vast economic losses, as well as heavy social and environmental impacts, which have been steadily increasing for the last five decades due to the complex interaction between climate change and anthropogenic pressure (</span></span><span xml:lang="EN-US" data-contrast="auto"><span>i.e.</span></span><span xml:lang="EN-US" data-contrast="auto"><span> land-use and land-cover modifications). As a result, the body of literature on flood risk assessment is constantly and rapidly expanding, aiming at developing faster, computationally lighter and more efficient methods relative to the traditional and resource</span></span><span xml:lang="EN-US" data-contrast="auto"><span>-</span></span><span xml:lang="EN-US" data-contrast="auto"><span>intensive hydrodynamic numerical models. Recent and reliable fast-processing techniques for flood hazard assessment and mapping consider binary geomorphic classifiers retrieved from the analysis of Digital Elevation Models (DEMs). These procedures (termed herein “DEM-based methods”) produce binary maps distinguishing between floodable and non-floodable areas based on the comparison between the local value of the considered geomorphic classifier and a threshold, which in turn is calibrated against existing flood hazard maps. Previous studies have shown the reliability of DEM-based methods using a single binary classifier, they also highlighted that different classifiers are associated with different performance, depending on the geomorphological, climatic and hydrological characteristics of the study area. The present study maps flood-prone areas and predicts water depth associated with a given non-exceedance probability by combining several geomorphic classifiers and terrain features through regression trees and random forests. We focus on Northern Italy (c.a. 100000 km</span></span><sup><span xml:lang="EN-US" data-contrast="auto"><span>2</span></span></sup><span xml:lang="EN-US" data-contrast="auto"><span>, including Po, Adige, Brenta, Bacchiglione and Reno watersheds), and we consider the recently compiled MERIT (Multi-Error Removed Improved-Terrain) DEM, with 3sec-resolution (~90m at the Equator). We select the flood hazard maps provided by (</span></span><span xml:lang="EN-US" data-contrast="auto"><span>i</span></span><span xml:lang="EN-US" data-contrast="auto"><span>) the Italian Institute for Environmental Protection and Research (ISPRA), and (ii) the Joint Research Centre (JRC) of the European Commission as reference maps. Our findings (a) confirm the usefulness of machine learning techniques for improving univariate DEM-based flood hazard mapping, (b) enable a discussion on potential and limitations of the approach and (c) suggest promising pathways for further exploring DEM-based approaches for predicting a likely water depth distribution with flood-prone areas.</span></span><span> </span></p>

Sign in / Sign up

Export Citation Format

Share Document