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• High-resolution pluvial flood hazard maps are developed at a municipality scale. • The methodology uses the Safer_RAIN fast-processing DEM-based algorithm. • Empirical probability distributions with 10,000 water depths are obtained in each cell. • Bivariate return-period curves show that several storms can generate the T -year flood. • The stochastic methodology improves the standard approach based on design storms. Climate change and rapid expansion of urban areas are expected to increase pluvial flood hazard and risk in the near future, and particularly so in large developed areas and cities. Therefore, large-scale and high-resolution pluvial flood hazard mapping is required to identify hotspots where mitigation measures may be applied to reduce flood risk. Depressions or low points in urban areas where runoff volumes can be stored are prone to pluvial flooding. The standard approach based on estimating synthetic design hyetographs assumes, in a given depression, that the T -year design storm generates the T -year pluvial flood. In addition, urban areas usually include several depressions even linked or nested that would require distinct design hyetographs instead of using a unique synthetic design storm. In this paper, a stochastic methodology is proposed to address the limitations of this standard approach, developing large-scale ∼ 2 m-resolution pluvial flood hazard maps in urban areas with multiple depressions. The authors present an application of the proposed approach to the city of Pamplona in Spain (68.26 km2). The Safer_RAIN fast-processing algorithm based on digital elevation models (DEMs) is compared with the IBER 2D hydrodynamic model in four real storms by using 10-min precipitation fields. Precipitation recorded at rainfall-gauging stations was merged with continuous fields obtained from a meteorological radar station. Given the hydrostatic limitations of Safer_RAIN, the benchmarking results are adequate in terms of water depths in depressions. A long set of 10 000 synthetic storms that maintain the statistical properties of observations in Pamplona is generated. Safer_RAIN is used to simulate runoff response, and filling and spilling processes, in depressions for the 10 000 synthetic storms, obtaining the probability distribution of water depths in each cell. Maps of pluvial flood hazards are developed in the Pamplona metropolitan area for 10 return periods in the range from two to 500 years from such pixel-based series of simulated water depths. Bivariate return-period curves are estimated in a set of cells, showing that several storms can generate a given T -year pluvial flood with an increasing precipitation with storm duration that depends on the draining catchment soil characteristics. The methodology proposed is useful to develop maps of pluvial flood hazards in large multi-depression urban areas in reasonable computation times, identifying the main pluvial flood hotspots. [ABSTRACT FROM AUTHOR]

• Long-term hybrid DInSAR enables quick assessment and characterisation of subsidence. • City centre, dairy industry, emerging urban clusters and oilfields were evaluated. • Spatially extended accelerating land subsidence was observed in the downtown area. • Rapid spatially-extended subsidence was observed in the emerging urban clusters. • Both oil extraction and groundwater withdrawal led to subsidence in the oilfields. We present a framework for studying reconnaissance to characterisation of land subsidence in the slowly subsiding city of Mehsana, Gujarat, India, due to groundwater overdraft and oil extraction. Considering interferometric synthetic aperture radar (InSAR) data availability constraints, we first conducted a long-term conventional Differential InSAR (DInSAR) investigation followed by time-series advanced DInSAR analyses by Persistent Scatterer InSAR (PSInSAR). We identified land subsidence in different parts of the study area, with subsidence rates ranging from ≤ -1 cm/yr to ≥ -3 cm/yr. During the entire observation period (2004–2019), the core urban area has been consistently subsiding with differential subsidence rates of −1.7 to −2.1 cm/yr during 2004–2006, −1.6 to −1.7 cm/yr during 2007–2011 and −2.6 to −2.9 cm/yr during 2016–2018 in conventional DInSAR and −2.5 to −3.2 cm/yr in advanced DInSAR. The subsidence measurements by collateral DInSAR observations in multifrequency and/or multi-imaging modes during 2016–2017 agree well in terms of the spatial extent of subsiding areas and the rates of subsidence. We characterised land subsidence in four type areas due to the diverse causes based on the temporal behaviour of land subsidence: (a) densely-populated city centre, (b) dairy plants and other industries in urban/ peri- urban areas, (c) emerging urban clusters in peri -urban/rural areas, and (d) oilfields with agri-horticulture farms in the rural areas. We found a consistently increasing land subsidence trend in 'a', reducing land subsidence in 'b', newly emerging and rapidly growing land subsidence in 'c', and spatially-extensive subtle land subsidence in 'd'. From the temporal nature of groundwater level (GWL) decline and land subsidence, we inferred that the land subsidence has been occurring primarily due to groundwater overdraft associated with urban growth, industry and agri-horticultural farms and secondarily due to oil extraction in the southeastern part of the study area. A comparison of annual rainfall difference from the long-term average vs GWL change vs land subsidence shows that the rainfall deficit during 2–3 consecutive years and groundwater overdraft are primarily related to land subsidence in the study area. [ABSTRACT FROM AUTHOR]

• Crowd-sourced data was analyzed to detect explanatory variables for New York City's street flooding. • The adverse contributions of the drainage network towards street flooding occurrence in New York City were revealed. • In some boroughs with a low frequency of reports, street flooding complaints were strongly influenced by small increases in the predictors. Street flooding is problematic in urban areas, where impervious surfaces, such as concrete, brick, and asphalt prevail, impeding the infiltration of water into the ground. During rain events, water ponds and rise to levels that cause considerable economic damage and physical harm. Previous urban flood studies and models have evaluated the factors contributing to street flooding, such as precipitation, slope, elevation, and the drainage network. Yet, due to the complexity of the interconnectedness of these factors and lack of available data, difficulty remains in ascertaining the localized areas prone to and experiencing street flooding. Thus, residents and city management of problem areas are unaware and unable to prepare for street flooding events. This study presents an evaluation of New York City's 311 street flooding reports, via an inference model, as a way to detect the zip codes where street flooding is prevalent. The potential explanatory variables for street flooding complaints were precipitation amounts and 311 sewer back up (water arising from home drains as a result of rainfall), manhole overflow (water arising from manhole covers on the street) and catch basin (a clogged basin preventing rainwater from entering storm drains) complaints. Using Stage IV radar precipitation data and 311 sewer reports, spanning a 10-year period, a Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, with an embedded Zero-Inflation model is used to detect the variables statistically significant as predictors of flood complaint counts, specific to each zip code. The model is also tested using an Out-of-Sample prediction scheme by training it with the detected explanatory variables. Precipitation was found to be a predictor in 81% of the zip codes. For the infrastructural variables, manhole overflow complaints were significant to street flood complaints in 21% of the zip codes, back up complaints were significant in 41% of the zip codes, and catch basin complaints were significant in 47% of the zip codes. Thus, for an appreciable number of zip codes, infrastructural complaints were found to be predictors of street flooding complaints. This is the first study of its kind to investigate the infrastructural contributions of street flooding by 311 analysis, thereby identifying factors of street flooding, aside from precipitation. Leading contributions of the study include the demonstration of infrastructural impact towards the occurrence of street flooding and also the circumscription to the zip code and borough levels, allowing for tailored preventative actions in critical areas. [ABSTRACT FROM AUTHOR]