Probable Flood Research Articles

A novel multi-step ahead forecasting model for flood based on time residual LSTM

Climate change has significantly impacted hydrology, including extreme precipitation, and changing precipitation patterns that could lead to an increase in flooding. Life and property benefit from accurate and reliable multi-step flood forecasting. Recently, Recurrent Neural Network (RNN) have become increasingly popular among hydrology researchers for their ability to capture historical dependencies, simplify computations by ignoring intermediate hydrological processes, and provide higher prediction accuracy than traditional models. However, RNN-based flood prediction models face two significant challenges. Firstly, due to their strict time-serial, RNN suffer from gradient issues such as vanishing and exploding gradients, which can make training RNN models difficult. To address this issue, we propose a Residual Long Short-Term Memory (ResLSTM) model that incorporates time residual connections into the time connections of LSTM. Secondly, most flood prediction models output a deterministic value, but the natural hydrological characteristics of the basin are a nonlinear and complex system with many influencing factors that have some randomness. This requires the use of probabilistic methods to modeling. Thus, we introduce the probabilistic forecasting model Autoregressive Recurrent Networks (DeepAR) into our flood prediction model, which outputs a prediction interval rather than a deterministic value. Then, we build four flood probability prediction models by combining DeepAR and four enhanced RNN, including ResLSTM (ours), Long Short-Term Memory (LSTM), Gate Recurrent Unit (GRU), and Time Feedforward Connections Simple Gate Recurrent Unit (TFC-SGRU). The performance of these models is evaluated by the long-term hydrologic data of the Passaic and Ramapo River basins in the United States. The results demonstrate that the prediction interval of the four models is more adaptive to flood uncertainties. And the accuracy of peak flow prediction is nearly 100% within a 90% prediction probability interval. The temporal residual-based model is more accurate and robust than the original LSTM and GRU. We believe this study fills a research gap in multi-step-ahead flood probability prediction and improves the accuracy and reliability of flood prediction models.

Assessing Vulnerability in Flood Prone Areas Using Analytic Hierarchy Process—Group Decision Making and Geographic Information System: A Case Study in Portugal

A flood vulnerability index was constructed by coupling Geographic Information System (GIS) mapping capabilities with an Analytic Hierarchy Process (AHP) Group Decision-Making (GDM) resulting from a paired comparison matrix of expert groups to assign weights to each of the standardised criteria. A survey was sent to 25 flood experts from government organisations, universities, research institutes, NGOs, and the private sector (56% academics and 44% non-academics). Respondents made pairwise comparisons for several criteria (population, socio-economic, buildings, and exposed elements) and sub-criteria. The group priorities were obtained by combining the Consistency Ratio (CR) and Euclidean Distance (ED) measures to assess the weight of each expert and obtain a final weight for each criterion and sub-criteria. In Portugal, 23 flood-prone areas were considered, and this work contributes with a tool to assess the flood vulnerability and consequently the flood risk. The flood vulnerability index was calculated, and the relevance of the proposed framework is demonstrated for flood-prone areas, in mainland Portugal. The results showed that in all five hydrographic regions, flood-prone areas with very high vulnerability were found, corresponding to areas with a high probability of flooding. The most vulnerable areas are Ponte de Lima in the North, Coimbra, and Pombal in the Centre; Loures in the Tagus and West Region; Setúbal and Alcácer do Sal in the Alentejo Region and Monchique in the Algarve Region. This methodology has the potential to be successfully applied to other flood-prone areas, combining the opinions of stakeholders validated by a mathematical model, which allows the vulnerability of the site to be assessed.

Conceptualizing the effectiveness of flood risk information with a socio-hydrological model: A case study in Lower Kelani River Basin, Sri Lanka

In rapidly changing human-flood systems, dynamic models can assist with system thinking, policy-making, and response. Previous research has shown that flood memory is important for better responses during disasters. To date, socio-hydrological research has primarily focused on memory accumulation after flood events and the related dynamics and feedback mechanisms. Notably, the impact of risk information has not yet been considered in any modeling exercise. Accordingly, this study improved upon the socio-hydrological model (SHM) by incorporating the impact of risk information on collective memory and associated dynamics. Probable flood maps were used to assess the response of a floodplain community from the Lower Kelani River Basin, Sri Lanka, via two interview surveys conducted at a 6-month interval. The surveys were conducted under two categories: A—after distributing maps and conducting awareness sessions, and B—after showing and distributing flood maps. The results showed that the flood maps helped to improve the risk perception of floodplain communities. Of the two categories, the memory decay process was slower for Category A. Furthermore, the SHM application showed that flood damage in the study area could be reduced by 10–30% when flood maps were distributed, and awareness sessions were conducted at 1–5-yr intervals.

Probabilistic mapping and sensitivity assessment of dam-break flood hazard

ABSTRACT Quantitative assessment of dam-break flood hazard is central in dam emergency action planning and is typically performed deterministically. In structural failure of concrete or masonry dams the collapse is assumed to be total and instantaneous, with the reservoir level at the spillway crest level. A probabilistic method is here proposed based on a set of dam-break scenarios characterized by different breach widths and reservoir levels in order to provide an appraisal of uncertainties in flood hazard indicators. Each scenario is attributed a weight, defined as a conditional probability given a dam-break event. Probabilistic flood hazard and inundation maps are produced for the case study of the hypothetical collapse of the Mignano dam (River Arda, northern Italy), and a sampling-based global sensitivity analysis is performed. Dam-break flooding was simulated using a two-dimensional hydrodynamic model on a high-resolution mesh. The probabilistic maps inherently provide quantitative information on the uncertainty of dam-break flood hazard.

The development of the Raster-based Probability Flood Inundation Model (RProFIM) approach for flood modelling in the upstream Citarum Watershed, West Java, Indonesia

The development of the Raster-based Probability Flood Inundation Model (RProFIM) approach for flood modelling in the upstream Citarum Watershed, West Java, Indonesia

Assessing the Implication of Climate Change to Forecast Future Flood Using SWAT and HEC-RAS Model under CMIP5 Climate Projection in Upper Nan Watershed, Thailand

Climate change will affect Southeast Asian countries, particularly Thailand. There are still insufficient studies on rainfall, streamflow, and future floods in the Upper Nan Watershed, northern Thailand. This study examined how future climate change will affect the rainfall, streamflow, and flooding in the Upper Nan Watershed. SWAT and HEC-RAS models were utilized to assess the future streamflow and flooding in this area. The models used data from 1980–2020, which were taken from seven Upper Nan meteorological stations and two discharge stations. In this study, the impact of future climate change was predicted using three GCMs, under RCP4.5 and RCP8.5 scenarios. The historical data analyzed in this study indicated that rainfall in the study area has a positive trend. Climate change will increase further, from 18% to 19%, which will cause more fluctuations and lead to wetter conditions, both in the wet and dry seasons. Climate change delayed the hydrograph peak and the SWAT-modelled streamflow in the N1 and N64 stations by between 0.3% and 5.1%. RCP8.5 inundated all of the stations more than RCP4.5. Our models showed that in the medium future (2041–2060), the inundated area will be similar to that during the 100-year flood probability. Thus, monitoring and preparation are necessary to avoid repeating the considerable 2011 flood losses in Thailand.

Lime stabilised road batters: A laboratory simulation of site flood conditions using a customised erosion apparatus and sample digitisation

The structural benefits of lime stabilisation on clayey soil are widely appreciated. However, its potential in surface erosion contexts, particularly for road batters, has had less consideration. Compared to conventional concrete or vegetative batter protection, lime stabilisation could be a cost-efficient option in Western Queensland; where large road distances experience severe and variable flooding, funding is limited, vegetation is seasonal, and clayey soils are prevalent. The present study assesses the feasibility of trialling 6.7 km of lime stabilised road batters in the Flinders Highway in Western Queensland. Soils sampled from the site were treated with varying percentages of Quicklime and tested in a customised erosion apparatus, labelled the Surface Erosion Apparatus (SEA). With the aid of an ANSYS model, the SEA was designed to simulate shear stress acting along the batter during the 1 % Annual Exceedance Probability (AEP) flood at the project location (as estimated with a TULOW model). The SEA preserves the eroded surface, which facilitated a time-lapse observation of erosion and a post-erosion sample digitisation method to plot the Erosion Function. All levels of lime stabilisation recorded approximately ten times less erosion than untreated samples under 1 % AEP conditions, with an estimated Erosion Category of Low or Very Low Erodibility. Conclusive trends from the laboratory simulation suggested that lime stabilised road batters would be suitable for the trial project.

A climate-conditioned catastrophe risk model for UK flooding

Abstract. We present a transparent and validated climate-conditioned catastrophe flood model for the UK, that simulates pluvial, fluvial and coastal flood risks at 1 arcsec spatial resolution (∼ 20–25 m). Hazard layers for 10 different return periods are produced over the whole UK for historic, 2020, 2030, 2050 and 2070 conditions using the UK Climate Projections 2018 (UKCP18) climate simulations. From these, monetary losses are computed for five specific global warming levels above pre-industrial values (0.6, 1.1, 1.8, 2.5 and 3.3 ∘C). The analysis contains a greater level of detail and nuance compared to previous work, and represents our current best understanding of the UK's changing flood risk landscape. Validation against historical national return period flood maps yielded critical success index values of 0.65 and 0.76 for England and Wales, respectively, and maximum water levels for the Carlisle 2005 flood were replicated to a root mean square error (RMSE) of 0.41 m without calibration. This level of skill is similar to local modelling with site-specific data. Expected annual damage in 2020 was GBP 730 million, which compares favourably to the observed value of GBP 714 million reported by the Association of British Insurers. Previous UK flood loss estimates based on government data are ∼ 3× higher, and lie well outside our modelled loss distribution, which is plausibly centred on the observations. We estimate that UK 1 % annual probability flood losses were ∼ 6 % greater for the average climate conditions of 2020 (∼ 1.1 ∘C of warming) compared to those of 1990 (∼ 0.6 ∘C of warming), and this increase can be kept to around ∼ 8 % if all countries' COP26 2030 carbon emission reduction pledges and “net zero” commitments are implemented in full. Implementing only the COP26 pledges increases UK 1 % annual probability flood losses by 23 % above average 1990 values, and potentially 37 % in a “worst case” scenario where carbon reduction targets are missed and climate sensitivity is high.

Adaptation strategies for future coastal flooding: Performance evaluation of green and grey infrastructure in South Korea

Climate change is contributing to an increasing frequency and intensity of floods in Korea. This study predicts areas with a high probability of flooding in coastal areas of South Korea owing to future climate change, which is likely to cause extreme rainfall and sea-level rise, using a spatiotemporal downscaled future climate change scenario with random forest, artificial neural network, and k-nearest neighbor techniques. In addition, the change in coastal flooding risk probability according to the application of different adaptation strategies (green spaces and seawalls) was identified. The results showed a clear difference in the risk probability distribution in the absence and presence of either adaptation strategy. Their effectiveness in moderating future flooding risks is subject to change owing to strategy type, geographic region, and urbanization intensity and the results show that green spaces are slightly more effective than seawalls when forecasting for 2050. This demonstrates the importance of a nature-based strategy. Moreover, this study highlights the need to prepare adaptation measures according to regional characteristics to mitigate the impact of climate change. Korea is surrounded by seas on three sides that have independent geophysical and climate characteristics. The south coast has a higher risk of coastal flooding than the east and west coasts. In addition, a higher urbanization rate is associated with a higher risk probability. This implies that climate change response strategies for coastal cities are necessary as the population and socioeconomic activities of coastal urban areas are likely to increase in the future.

A probabilistic assessment of urban flood risk and impacts of future climate change

Climate change has resulted in the increased frequency of extreme storms and associated flood risk uncertainties in urban areas, which brings more challenges to future urban flood prevention. Assessing future trends in probabilistic urban flood risk can improve uncertainty estimation of urban floods under climate change, and thus it is becoming increasingly urgent and important. In this study, a probabilistic risk assessment approach of urban flooding was developed to quantify the probabilistic flood risk in urban areas and assess the impacts of future climate change on flood risk. Results showed probabilistic urban flood risk under the historical condition ranged from 0 to 0.508 with a mean value of 0.104, indicating probabilistic urban flood risk was at the medium risk level. The areas where probabilistic flood risk reached the medium risk level under historical condition accounted for 34.3 % of the study area. The ranges of probabilistic flood risk in the study area were 0–0.588 and 0–0.601 with mean values of 0.126 and 0.131 under SSP2-4.5 and SSP5-8.5 climate change scenarios, respectively. The areas where probabilistic flood risk reached the high risk level respectively accounted for 17.1 % and 32.7 % of the study area for SSP2-4.5 and SSP5-8.5 scenarios. Compared with the historical period, probabilistic risk of urban flooding in the study area was projected to increase by 51.3 % and 67.4 % on average for SSP2-4.5 and SSP5-8.5 scenarios, respectively. The largest increase of probabilistic flood risk was approximately 200 % and 250 % for the SSP2-4.5 and SSP5-8.5 scenarios, respectively, which mainly occurred for forestlands and grasslands. These results can effectively improve the uncertainty estimation of risk trends for urban floods under future climate change, and thus better-informed decision making for urban flood prevention.

Assessing the effects of increased impervious surface on the aquifer recharge through river flow network, case study of Jackson, Tennessee, USA

Understanding pathways connecting urbanization to the recharge process across the land surface and river environment is of great significance in achieving low-impact development. Accordingly, the contribution of an urbanized region with a low and high development rate, along with the expected overflow into the river network resulting from increased impervious surfaces, was assessed in the recharge rate at Jackson, Tennessee. To this end, first, the losses were calculated using the standard and modified SCS-CN methods for the maximum probable flood condition. Then, TUFLOW was applied to simulate the two-dimensional flood for a historic 24-h probable maximum precipitation event with a 100-year return period. The results of TUFLOW were later calibrated using the results of standard and modified SCS-CN methods. A calibrated MODFLOW was employed to assess the effects of urbanization and, consequently, the plausible extended river network on the recharge rate. Results revealed that the West Wood contribution in groundwater recharge was 19 % less than the Musa Street, while it supplies approximately 2.7 % more flow than Musa Street. The performance evaluation results of TUFLOW showed 0.4916 and 0.689 as Nash–Sutcliffe, respectively, for the standard and modified SCS-CN methods. Although the flow velocity and depth were respectively increased by 3.3 % and 8.3 % under modified SCS-CN compared to the standard one, the soil water storage capacity remained constant at equal to 0.16 mm. Results revealed that the maximum soil water storage capacity was fulfilled soon through the modified SCS-CN than the standard method leading to higher flood volume and discharge. To this end, the discharge resulting from modified SCS-CN was approximately 1.5 times higher than that in the standard method under the same precipitation condition. Our findings suggest that designing any construction, mainly dams downstream, based on the modified SCS-CN estimations will provide more safety, particularly in crowded regions. Also, overflowing the excess surface runoff into the river network resulted from the increased impervious surface amplifying the flow volume, depth, and velocity across the river networks, finally leaving the area without increasing the aquifer's recharge rate. The results provide insights into possible sustainable development options and flood management in the built-up area.

Uncertainty Analysis and Quantification in Flood Insurance Rate Maps Using Bayesian Model Averaging and Hierarchical BMA

Flood Insurance Rate Maps (FIRMs) managed by FEMA have been providing ongoing flood information to most communities in the United States over the past half-century. However, the uncertainty associated with the modeling of FIRMs, some of which are created by using a single Hydrologic Engineering Center River Analysis System (HEC-RAS) one-dimensional (1D) steady-flow model, may have adverse effects on the reliability of flood stage and inundation extent predictions. Therefore, a systematic understanding of the uncertainty in the modeling process of FIRMs is necessary. Bayesian model averaging (BMA), which is a statistical approach that can combine estimations from multiple models and produce reliable probabilistic predictions, was applied to evaluating the uncertainty associated with FIRMs. In this study, both the BMA and hierarchical BMA (HBMA) approaches were used to quantify the uncertainty within the detailed FEMA models of the Deep River and the Saint Marys River in the state of Indiana based on water stage predictions from 150 HEC-RAS 1D unsteady-flow model configurations that incorporate four uncertainty sources including bridges, channel roughness, floodplain roughness, and upstream flow input. Given the ensemble predictions and the observed water stage data in the training period, the BMA weight and the variance for each model member were obtained, and then the BMA prediction ability was validated for the observed data from the later period. The results indicate that the BMA prediction is more robust than both the original FEMA model and the ensemble mean. Furthermore, the HBMA framework explicitly shows the propagation of various uncertainty sources, and both the channel roughness and the upstream flow input have a larger impact on prediction variance than bridges. Hence, it provides insights for modelers into the relative impact of individual uncertainty sources in the flood modeling process. The results show that the probabilistic flood maps developed based on the BMA analysis could provide more reliable predictions than the deterministic FIRMs.

SOME ASPECTS OF THE LATEST FLOODING IN THE KHERSON REGION

Problem Description and Purpose of the Study. This article examines the problem of flooding of settlements in the Kherson region. The purpose of the study is to identify the latest risks of flooding of populated areas in the Kherson region and to formulate a list of measures aimed at reducing the threats and consequences of flooding. Data & Methods. The research used data from the Kherson Regional Center for Hydrometeorology; Main Department of the State Emergency Service of Ukraine in the Kherson Region; The Department of Civil Protection of the Kherson Regional State Administration, the Office of the Kakhovskaya HPP. Among the research methods, the comparative method, cartographic and geo-informational methods, correlational, expert method were used. Results. Kherson region has the largest share of irrigated land among all regions of Ukraine. This fact, as well as the presence of significant waterless areas, reductions in the relief with the release of groundwater and water loss along the main irrigation canals, leads to constant flooding of certain areas. The most affected areas arelocated in the paludal depressions and coastal lowlands. Due to the strengthening of the continentality of the climate, the precipitation regime is also changing. The territory is characterised by summer-autumn rains with daily precipitation above 30 mm, which causes sudden flooding of downgrades and rise of groundwater in these periods. In order to monitor the potential risk of flooding, it is proposed to monitor interdependence: the water level of the Kakhovka Water Reservoir – the level of groundwater. Although this dependence has a deferred character, thus it is confirmed every year. There are four irrigation systems in the region: North Crimean, Kakhovka, Inhulets and Pravoberezhia (Right-Bank). Each year, the area of irrigated land increases from 0.1 to 1.0 thousand hectares, which significantly increases the risks of flooding in combination with the frequency of heavy rainfall. In a number of settlements with a high probability of flooding, a system of vertical and horizontal drainage has been built, but it requires constant maintenance and usually does not cope with its tasks.

Climate-mine life cycle interactions for northern Canadian regions

This study quantifies the impacts of climate change on the mine life cycle (development, operation and closure phases) of 30 mines located in the northern regions of Canada. To this end, climate projections based on a five-member transient climate change simulation ensemble, performed using a state-of-the art regional climate model, spanning the 1991–2050 period, corresponding to the Representative Concentration Pathway 8.5 emission scenario are used. A reanalysis-driven simulation for the 1991–2010 period compared against available observations confirm suitability of the model for application in climate change simulations. Assessment of projected changes to mine-relevant climate variables that are important from structural integrity and operation perspectives reveal potential vulnerabilities and opportunities. Active layer thickness increases in the 0.3–2 m range in permafrost regions, coupled with increases in flood probability, as reflected in snow-melt rate increases in the 0.14–6.77% range and increases in the 100-year return levels of daily maximum rainfall in the 5–50% range, suggest potential impacts on the structural integrity of mine infrastructure, such as slope instability and foundation settlement of tailings dams, and supporting infrastructure such as ice/all-season roads. Increases in soil moisture, projected in the 0–11% range, at a few mines, suggest potential impacts on material handling systems, such as increases in the traction factor of the muck-haul and tire rolling resistance, that can lead to low productivity. Projected increases to wind speeds in the 5–10% range for the northernmost regions suggest potential impacts on the tailings management facility in terms of increases in tailings resuspension. Overall, this study identified northernmost and northeastern mines to be more vulnerable, with air/soil temperature, precipitation and wind speed being the most influential climate variables. This systematic study, for the first time, has identified potential vulnerabilities of northern Canadian mines, which can inform future high-resolution climate modelling and detailed at-site climate-mine interaction studies that is required for climate-change adaptation related decision-making.

Shoreline modelling on timescales of days to decades

AbstractClimate change is resulting in global changes to sea level and wave climates, which in many locations significantly increase the probability of erosion, flooding and damage to coastal infrastructure and ecosystems. Therefore, there is a pressing societal need to be able to forecast the morphological evolution of our coastlines over a broad range of timescales, spanning days-to-decades, facilitating more focused, appropriate and cost-effective management interventions and data-informed planning to support the development of coastal environments. A wide range of modelling approaches have been used with varying degrees of success to assess both the detailed morphological evolution and/or simplified indicators of coastal erosion/accretion. This paper presents an overview of these modelling approaches, covering the full range of the complexity spectrum and summarising the advantages and disadvantages of each method. A focus is given to reduced-complexity modelling approaches, including models based on equilibrium concepts, which have emerged as a particularly promising methodology for the prediction of coastal change over multi-decadal timescales. The advantages of stable, computationally-efficient, reduced-complexity models must be balanced against the requirement for good generality and skill in diverse and complex coastal settings. Significant obstacles are also identified, limiting the generic application of models at regional and global scales. Challenges include the accurate long-term prediction of model forcing time-series in a changing climate, and accounting for processes that can largely be ignored in the shorter term but increase in importance in the long term. Further complications include coastal complexities, such as the accurate assessment of the impacts of headland bypassing. Additional complexities include complex structures and geology, mixed grain size, limited sediment supply, sources and sinks. It is concluded that with present computational resources, data availability limitations and process knowledge gaps, reduced-complexity modelling approaches currently offer the most promising solution to modelling shoreline evolution on daily-to-decadal timescales.

Identification of the relationship between rainfall intensity and the probability of urban flooding in Nganjuk District

Floods are hydrometeorological disasters that often occur in various regions, including in Nganjuk Regency. From 2019 to 2022, there were urban floods that inundated highways, markets and hospitals and disrupted community and government activities. Heavy rain is one of the causes of flooding. Rain intensity, duration and frequency (IDF) curves were constructed using rainfall data from six rain gauge stations. This curve is used to determine the 10-year and 25-year probabilities of PUH intensity and the probability of causing flooding in Nganjuk District. Rain intensity in Nganjuk Regency is PUH 10 and 25 with an average rain duration of 15-60 minutes which is included in the moderate rain category, namely 20-50 mm/day. The results of this analysis will become the basis for consideration in formulating policies and strategies for urban flood disaster prevention and management in Nganjuk Regency. Therefore, coordination between the city government and the government is needed to reduce the risk of urban flooding in Nganjuk Regency.

Uncertainty of internal climate variability in probabilistic flood simulations using d4PDF

Many studies have proved that hydrological extreme values estimated from decadal observation data and river inundation simulations are associated with various uncertainties; however, few studies have evaluated the uncertainties associated with internal climate variability. We used large-ensemble river inundation simulations to quantitatively evaluate uncertainties in river depth at the Takahama monitoring station and flood extent in the Yodo River basin. Using a single 60-year ensemble, the river depth for a 1,000-year return period (RP) flood scale have uncertainty between –11.7% and +9.2% in a 3,000-year flood simulation. Thus, the RP of the simulated river depth ranges from 207–3,441 years. To maintain the RP uncertainty within ±300 years would require a simulation of ≥1,200 years. The flood extent uncertainty with an RP of 1,000 years was found to be –8.4% and +7.6% based on a 3,000-year simulation for the lower Yodo River basin. According to this result, the RP of the simulated flood extent ranges from 340–3,060 years. These results suggest that the decadal data used in conventional flood risk analyses potentially contain large uncertainty related to internal climate variability in the RP for water depth and flood extent by approximately 0.3–3-fold.

Evaluating the impact of proximity to reported toxic release facilities and flood events on chronic health outcomes in the city of Galena Park, Texas

Evidence has conclusively revealed that environmental justice communities experience poor environmental conditions compared to more affluent majority communities. However, there has been little research evaluating the health impacts of immediate proximity to industrial pursuits and flood events on a population compared to others living within the same community who are only marginally removed from these locations. This cross-sectional study (N = 130) utilized three approaches to assess health outcomes (1) the 12 item Short Form Health Survey, which creates a general physical component score, (2) self-reported noncancerous chronic conditions, and (3) self-reported diagnosis of twelve different cancers. Three risk levels were spatially created using a 5-scale ordinal score for each residential parcel based on the corresponding flood probability level and proximity to facilities which report to the United States Environmental Protection Agencies Toxic Release Inventory. Analysis revealed that general physical health scores were significantly lower (P-value < 0.001) in the medium and high-risk locations, Similarly chronic conditions witnessed a non-significant twofold increased risk in the highest-risk locations compared to the lowest (POR 1.91; 95 % CI 0.82–4.39) and a non-significant increased risk of cancer diagnosis (POR 1.51; 95 % CI 0.38–5.99). This research underscores the importance of place and health outcomes even within relatively geographically compact communities. Public health and urban planning interventions and designs should take into account fine grain approaches to respond to community needs while still being mindful of limited resources.

Decision-support for land reclamation location and design choices in the Maldives

Land reclamation in the Maldives is widespread. Current land reclamation practices, however, lack a systematic approach to anticipate sea-level rise and do not account for local flood risk differences to inform location and design choices. To address these limitations, this paper applies two decision-support tools: a hazard threshold analysis, and a cost-benefit analysis. Both tools produce site-specific estimates of land elevations or flood defence heights but do so for different goals. The hazard threshold analysis identifies hazard-based solutions that meet an acceptable flood probability for an intended lifespan without follow-up actions by reliability optimisation. The cost-benefit analysis identifies risk-based solutions using dynamic programming. We apply both tools to two land reclamation sites, a newly reclaimed airport island and a land extension of an inhabited island, in the Maldives. We find that total hazard-based heights for long-term planning horizons are highly uncertain, with local height differences of up to 1.9 m across sea-level rise scenarios by 2100. Risk-based Island elevations, in contrast, differ much less across scenarios, offering a practical advantage for decision-making. However, land reclamation choices on location, land elevation and investment in flood protection are not only driven by hazard-related aspects, such as reef characteristics, swell exposure, and sea-level rise, but also by estimates of exposed assets, reclamation, and flood protection costs. Taken together, the two decision-support tools are helpful for improving adaptation decisions and are also applicable in other small island regions.

Hydrological Analysis of Batu Dam, Malaysia in the Urban Area: Flood and Failure Analysis Preparing for Climate Change

Extensive hydrological analysis is carried out to estimate floods for the Batu Dam, a hydropower dam located in the urban area upstream of Kuala Lumpur, Malaysia. The study demonstrates the operational state and reliability of the dam structure based on hydrologic assessment of the dam. The surrounding area is affected by heavy rainfall and climate change every year, which increases the probability of flooding and threatens a dense population downstream of the dam. This study evaluates the adequacy of dam spillways by considering the latest Probable Maximum Precipitation (PMP) and Probable Maximum Flood (PMF) values of the concerned dams. In this study, the PMP estimations are applied using comparison of both statistical method by Hershfield and National Hydraulic Research Institute of Malaysia (NAHRIM) Envelope Curve as input for PMF establishments. Since the PMF is derived from the PMP values, the highest design flood standard can be applied to any dam, ensuring inflow into the reservoirs and limiting the risk of dam structural failure. Hydrologic modeling using HEC-HMS provides PMF values for the Batu dam. Based on the results, Batu Dam is found to have 200.6 m3/s spillway discharge capacities. Under PMF conditions, the Batu dam will not face overtopping since the peak outflow of the reservoir level is still below the crest level of the dam.