This presentation aims to set the scene for the session and promote debate about how we as a scientific community can better address the issue of risk in low-income neighbourhoods (also referred to as ‘slums’ and ‘informal settlements’).

Low- and middle-income countries are experiencing rapid urbanisation, often with limited capacity to manage the rapid growth and associated accumulation of risk. One billion people currently live in low-income neighbourhoods. For many cities in the Global South, this means that large proportions (up to 60%) of the urban population live in very small proportions of the urban land which are not formally planned/monitored and are often exposed to a range of single and multi-hazards. This creates challenges in compiling records of hazards, their impacts and feedbacks as many methods are not of sufficient resolution to record high-frequency low-magnitude hazard events that chronically affect these settlements and erode capacity to cope. Additionally, although low-income neighbourhoods often appear as one homogeneous unit on maps, this masks the fact that these areas often act as urban centres within their own right, with high levels of heterogeneity in terms of infrastructure, land use and demographics. This creates challenges in understanding exposure and vulnerability. Overall, these issues mean that our ability to understand single and multi-hazards in low-income neighbourhoods is limited, and resultantly we may miss large proportions of the population in city-wide risk assessments.

Based on my work on a number of projects in low-income neighbourhoods, I will discuss some ways of addressing these challenges and their associated strengths and weaknesses. This includes: (a) Whatsapp focus groups to map social networks of resilience, (b) low-cost smartphone GPS mapping to document the bottom-up response to shocks and stresses within low-income neighbourhoods, (c) exploration of novel ‘big’ and 'medium' data sources. These techniques are participatory and designed to support both residents and city-level stakeholders in better understanding the complexities of risk in slums. The results of this work document that residents of low-income neighbourhoods are not passive but often the first or only responders in dealing with risk in these areas.

How to cite: Taylor, F.: Assessing Risk and Resilience in Urban Low-Income Neighbourhoods, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15196, https://doi.org/10.5194/egusphere-egu23-15196, 2023.

The last IPCC report highlighted that the urban expansion to areas exposed to climate-driven natural hazards might significantly increase disaster risk in the near future. Understanding risk as the combination of hazard, exposure and vulnerability, these increased trends will be more expressive among the economically and socially marginalized urban residents, particularly in informal settlements. A major cause of this process is spatial intraurban inequalities. The urban poor are not only more vulnerable to natural hazards, e.g., due to a lack of resources; they are also often driven towards the less valuable areas, including hazardous locations such as flood plains and steeper hillslopes, which likely increase their exposure. However, disaster risk research still lacks more holistic tools to simulate and better understand the interrelations among urban expansion, intraurban inequalities, and exposure to natural hazards. We aim to develop an urban growth model that incorporates different socioeconomic groups to investigate the impact of these inequalities on their hazard exposure. We developed our model following the multilevel modeling framework for urban growth. First, a demand module quantifies the amount of urban growth that is expected for each socioeconomic group. Then, a spatial allocation module determines where this growth will take place to fulfill these demands. In the allocation, the most probable locations of future urban areas are individually determined for each group as a function of driving forces. These include biophysical and socioeconomic factors such as terrain slope, elevation, and distance to infrastructure. We applied the model in the municipality of Rio de Janeiro, Brazil, while accounting for three different income groups. Preliminary results indicate that the characteristics of new urban areas change significantly as a function of the income class, with a strong pattern of spatial segregation. Low-income households were found to have a higher probability of being located on steeper terrain than middle- and high-income households. In addition, new low-income urban areas were also found to be more distant from the city center, the coastline, and the main city infrastructure including subway and train stations. These results demonstrate the relevance of incorporating existing inequalities into urban growth models, especially in developing spatial planning policies in accordance with existing risks to natural hazards.

How to cite: Bastos Moroz, C., Sieg, T., and Thieken, A.: The inclusion of intraurban inequalities in urban growth models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10235, https://doi.org/10.5194/egusphere-egu23-10235, 2023.

In previous assessments of UK flood risk, flood hazard has often been poorly represented by low skill models. Meanwhile, assessments of future flood risk have lacked a detailed representation of future flood exposure. This study represents a significant step forward in both flood hazard and future exposure representation. Using a state-of-the-art climate-conditioned hydrodynamic model for fluvial, pluvial and coastal flooding paired with the latest UK shared socioeconomic projections of population (UK-SSP), we explore how climate-induced changes in flood hazard interact with increasing exposure due to population changes. Using a detailed exposure dataset, we are also able to explore discrepancies in current and future flood risk between residential property types.

Our model values current yearly residential economic floods losses at £894 million, with fluvial flooding accounting for approximately 75% of these. This flood risk is not borne equally between the devolved British nations, however. In Scotland and Wales approximately 30% of local authorities have over £100 of annual flood losses per property, whereas only 4% of local authorities in England experience such levels. There are also significant discrepancies in flood risk by property type. Flats and terraced housing experience almost twice the rate of yearly inundations than detached and semi-detached housing. Under a high emissions climate scenario (RCP 8.5), by 2070 we estimate flood risk will increase by 29% whilst under a low emissions scenario (RCP 4.5) flood risk will increase by 10%. Similarly to current flood risk, these increases too are not borne equally across regions and property types.

Whilst climate change is set to drive an increase in flood risk over the next 50 years, our study suggests that population dynamics may have potential for offsetting relative flood risk rises. In all SSP projections studied here (SSP1, SSP2 and SSP5), an increase in population drives an increase in the yearly rate of property inundations. As such, the highest increases in flood risk due to population change are seen in SSP5 (+50%). However, due to population growth predominantly occurring in low flood risk areas, relative flood risk decreases most in SSP5 (-10%). Similar changes, although smaller in magnitude, are seen under SSP1 and SSP2. Despite these decreases in relative flood risk due to population change, when paired to climate change, we still see significant increase in flood risk over the next 50 years. Consequently, low emissions scenarios, such as SSP1/RCP2.6, see the lowest increases in flood risk by 2070 (+5%). These results show a clear need for climate change mitigation actions in order to limit flood risk increases. However, this study presents a promising signal where population growth may limit the burden of increased flood risk, due to climate change.

How to cite: Lamb, C., Pregnolato, M., Pianosi, F., and Bates, P.: Drivers of present and future residential flood risk in Britain, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1072, https://doi.org/10.5194/egusphere-egu23-1072, 2023.

Chennai is the fourth largest metropolitan city in India and being a coastal city, this region receives extreme precipitation events, especially during the North-East monsoon season. Rapid urbanization has led to profound changes in the city's land-use land cover, which has the potential to impact the local microclimate. The 2015 December Chennai flood resulted in the loss of lives and an economy of 2.5 billion $. Hence a reliable forecast system for the city helps for better preparedness. It is well acknowledged that urbanization affects rainfall distribution, but several limitations exist in past studies. The discrepancies between climatological and numerical investigations have not been addressed. To understand the impact of urbanization on the rainfall space-time distribution, it is also essential to choose rainfall events that originated from distinct synoptic conditions. Hence in this study, the Weather Research Forecast (WRF) model and advanced statistical analysis are used to examine the effects of urbanization on rainfall modification over Chennai city. The present investigation considers satellite estimations and observed station data due to the absence of a dense rain-gauge network. From ECMWF reanalysis ERA5 data, large-scale weather predictors are selected to create weather patterns using a fuzzy clustering method, with Chennai as the domain center. Subsequently dividing the observational rainfall data into two equal periods, the changes in the rainfall quantiles (80th to 99.9th) are obtained for each cluster.  Since large-scale circulation patterns are similar, these shifts show the likely impact of urbanization on rainfall.  Extreme value theory combined with regional frequency analysis is implemented to understand the changing rainfall statistics in the past 50 years. Numerous WRF simulations are performed at convection-permitting scales with varied domain and physics configurations for three extreme precipitation events from different synoptic conditions. The study also investigates the ability of scale-aware convective schemes to describe precipitation processes in high-resolution simulations for the study domain. Then, the WRF simulations are conducted with optimal physics combinations with default USGS land cover data (1991-1992) and high-resolution land-use data (2017) with Local Climatic Zone (LCZ) classifications, that represents the present urbanization. The simulations with recent land cover data coupling with Building Energy Parameterization (BEP) in the WRF model significantly improved the rainfall prediction skill in the spatial-temporal domain minimizing the bias. Interestingly, an intense convective rainfall event which was neither detected by the regional meteorological department nor WRF simulations with the default LULC map has been forecasted by representing the current urbanization scenario in the model. Further, this study also explains the dynamic and thermodynamic responses influencing rainfall distribution due to urbanization. The study advances the role of urbanization in a coastal city and provides pathways for better urban planning and design. The improved ensemble forecasts help for better preparedness during extreme rainfall events.

How to cite: Yaswanth, P., Narasimhan, B., and Balaji, C.: Urban effects on the Extreme Precipitation: Advanced Statistical Analysis and Numerical Weather Prediction Model at Convective Scales, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-60, https://doi.org/10.5194/egusphere-egu23-60, 2023.

Land surface changes resulting from human activities have emerged as a factor of equal, if not greater importance than climate change in affecting landslide occurrence. Gisborne is a remote city on the northeast North Island of New Zealand, close to the Hikurangi Subduction Zone, and is vulnerable to natural hazards, such as tsunami, earthquakes, coastal erosion, flooding, and particularly landslides. Vulnerability is also characterised by a suite of social, economic, and infrastructural issues which uniquely culminate in Gisborne. Gisborne has the highest percentage of indigenous Māori people for a New Zealand city and experiences a high degree of socio-economic disadvantage. The city is remote from other urban centres, taking >3 hours to reach the nearest adjacent city via rural roads that are vulnerable to closure during and following natural hazard events.  In particular, landslide risk in recent years has been exacerbated by urban and suburban expansion of residential development into sub-optimal terrain, on steep hill slopes surrounding the city. The hills are underlain by weak Neogene sediments and uplifted Pleistocene estuarine deposits. Gisborne District Council has previously attempted to delineate landslide risk areas but has been hampered by the lack of detailed empirical data. Based on observational data from Sentinel-1 imagery, this study used interferometric synthetic aperture radar (InSAR) to reveal the pattern of slope deformation across Gisborne’s steepland periphery from January 2016 to December 2021. Velocities in the line of sight were obtained from the stack of interferograms and projected along the direction of maximum slope, to extract the true displacement on the slopes. The ascending and descending data sets were combined to reveal the vertical and horizontal components of the deformation. The results were combined with a regional LiDAR dataset, aerial imagery and field observations to delineate areas of slope deformation. Finally, slope deformation time series data was compared with rainfall records to identify seasonal changes, as well as shrink-swell of expansive soils. Results identified 132 unstable slopes within the study area, affected by soil creep, slumping and earthflows. Despite clear evidence of the effects of tree removal, loading of slopes by construction activity, and installation of unconsented, inadequate retaining walls contributing to slope failure, such practises unfortunately continue.

How to cite: Brook, M., Cook, M., and Cave, M.: City expansion, land-use change and slope failures in Gisborne, New Zealand: déjà vu all over again, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-153, https://doi.org/10.5194/egusphere-egu23-153, 2023.

This study applies a belief-based probabilistic approach to find the relative influence of environmental conditions and human actions on the probability of occurrence of landslides in the Darjeeling Himalayas, India. Subjective (belief-based) probabilistic methods are an approach to model complex relationships in regions with low or no data. Here, we use two subjective probabilistic methods: expert elicitation and Bayesian belief network. Expert elicitation (EE) is a technique to quantify the knowledge of experts based on their theoretical or practical experience on a topic of interest. A Bayesian belief network (BBN) takes into consideration and represents the experts’ knowledge (and other data, if available) to give a probabilistic, rational outcome under the influence of uncertainty. BBNs are represented as a graph consisting of a set of random variables (called ‘nodes’) that are interconnected via edges (called ‘arcs’). BBN follow the Bayes’ theorem.

We first use expert judgement and secondary literature to determine 20 prominent variables that influence landsliding, divided into 2 triggering variables (earthquake, rainfall), 11 geomorphic variables (e.g., soil type, flood depth, elevation), and 6 anthropogenic variables (e.g., infrastructure development, machinery vibration). We then conduct EE with ten landslide experts, to find the prior and conditional probabilities of each of these 20 landslide-related variables. Prior probability is the probability of occurrence of the variables (A) that influence or trigger landslides (P(A)) and conditional probability is the probability of occurrence of landslide(s) given P(A)). Using BBN modelling, we then provide a comparison of answers across all ten experts per variable and across all variables per expert. Finally, we examine single and multiple combinations of variables and their relative influence on landsliding in the study area. We finally list suggestions, challenges faced, and limitations on designing and carrying out belief-based probabilistic procedures.

How to cite: Choudhury, S., Malamud, B. D., and Donovan, A.: Bayesian belief network modelling for landslide hazard assessment using probabilistic estimates from experts, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16148, https://doi.org/10.5194/egusphere-egu23-16148, 2023.

The Himalayas are at the forefront of hazards related to climate change as well as other global change drivers such as land-use and land-cover change including complex interactions between diverse drivers.

Scientists, civil society organizations and local communities have identified key issues related to how specific Himalayan ecosystems are responding to these drivers including episodes of greening and browning of vegetation as well spread of Pine and the retreat of Oak forests, higher incidence of forest fire due to changes in winter rainfall and summer heat.

Using evidence from multi-decadal time-series of remotely sensed data on state of vegetation combined with climate data and measurements and observations from instrumented catchments we now have insights on the greening and browning of vegetation at larger scales and the comparative ecohydrological response of Pine and Oak dominated catchments at finer scales.   The Himalayas show elevation specific patterns of greening and browning trends that differ across the Himalayas from west to east.  Pine dominated catchments seem to have lower levels and temporally homogeneous soil moisture profiles as well as higher evapotranspiration and lower discharge regimes compared to Oak dominated catchments.  We discuss the implications of the evidence for biodiversity and ecosystem services under emerging and future climate change.

How to cite: Krishnaswamy, J., Daniel, D., Sen, S., and Khanna, J.: Emerging ecological and environmental hazards in the Himalayas, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11871, https://doi.org/10.5194/egusphere-egu23-11871, 2023.