Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Cause, conseguenze e strategie di mitigazione Proponiamo il primo di una serie di articoli in cui affronteremo l’attuale problema dei mutamenti climatici. Presentiamo il documento redatto, votato e pubblicato dall’Ipcc - Comitato intergovernativo sui cambiamenti climatici - che illustra la sintesi delle ricerche svolte su questo tema rilevante.

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Climate Change 2007: The Physical Science Basis.

Meteorology today : An introduction to weather, Climate, and the environment

As urban space continues to expand to accommodate a growing global population, there remains a real need to quantify and qualify the impacts of urban space on natural processes. The expansion of global urban areas has resulted in marked alterations to natural processes, environmental quality and natural resource consumption. The urban landscape influences infiltration and evapotranspiration, complicating our capacity to quantify their dynamics across a heterogeneous landscape at contrasting scales. Impervious surfaces exacerbate runoff processes, whereas runoff from pervious areas remains uncertain owing to variable infiltration dynamics. Increasingly, the link between the natural hydrological cycle and engineered water cycle has been made, realising the contributions from leaky infrastructure to recharge and runoff rates. Urban landscapes are host to a suite of contaminants that impact on water quality, where novel contaminants continue to pose new challenges to monitoring and treatment regimes. This review seeks to assess the major advances and remaining challenges that remain within the growing field of urban hydrology.

https://www.tandfonline.com/doi/pdf/10.1080/02626667.2015.1128084

Impacts of urbanisation on hydrological and water quality dynamics, and urban water management: a review

Flooding can cause major disruptions in cities, and lead to significant impacts on people, the economy and on the environment. These impacts may be exacerbated by climate and socio-economic changes. Resilience thinking has become an important way for city planners and decision makers to manage flood risks.Despite different definitions of resilience, a consistent theme is that flood resilient cities are impacted less by extreme flood events. Therefore, flood risk professionals and planners need to understand flood impacts to build flood resilient cities. This paper presents a state-of-the-art literature review on flood impact assessment in urban areas, detailing their application, and their limitations. It describes both techniques for dealing with individual categories of impacts, as well as methodologies for integrating them. The paper will also identify future avenues for progress in improving the techniques.

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Urban flood impact assessment: A state-of-the-art review

Microbial risks associated with exposure to pathogens in contaminated urban flood water

Cities are increasingly vulnerable to floods generated by intense rainfall, because of their high degree of imperviousness, implementation of infrastructures, and changes in precipitation patterns due to climate change. Accurate information of convective storm characteristics at high spatial and temporal resolution is a crucial input for urban hydrological models to be able to simulate fast runoff processes and enhance flood prediction. In this paper, a detailed study of the sensitivity of urban hydrological response to high resolution radar rainfall was conducted. Rainfall rates derived from X-band dual polarimetric weather radar for four rainstorms were used as input into a detailed hydrodynamic sewer model for an urban catchment in Rotterdam, the Netherlands. Dimensionless parameters were derived to compare results between different storm conditions and to describe the effect of rainfall spatial resolution in relation to storm and hydrodynamic model properties: rainfall sampling number (rainfall resolution vs. storm size), catchment sampling number (rainfall resolution vs. catchment size), runoff and sewer sampling number (rainfall resolution vs. runoff and sewer model resolution respectively). Results show catchment smearing effect for rainfall resolution approaching half the catchment size, i.e. for catchments sampling numbers greater than 0.5 averaged rainfall volumes decrease about 20%. Moreover, deviations in maximum water depths, form 10 to 30% depending on the storm, occur for rainfall resolution close to storm size, describing storm smearing effect due to rainfall coarsening. Model results also show the sensitivity of modelled runoff peaks and maximum water depths to the resolution of the runoff areas and sewer density respectively. Sensitivity to temporal resolution of rainfall input seems low compared to spatial resolution, for the storms analysed in this study. Findings are in agreement with previous studies on natural catchments, thus the sampling numbers seem to be promising as an approach to describe sensitivity of hydrological response to rainfall variability for intra-urban catchments and local convective storms. More storms and different urban catchments of varying characteristics need to be analysed in order to validate these findings.

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On the sensitivity of urban hydrodynamic modelling to rainfall spatial and temporal resolution

In this paper, a database of water-related insurance damage claims related to private properties and content was analysed. The aim was to investigate whether the probability of occurrence of rainfall-related damage was associated with the intensity of rainfall. Rainfall data were used for the period of 2003–2009 in the Netherlands based on a network of 33 automatic rain gauges operated by the Royal Netherlands Meteorological Institute. Insurance damage data were aggregated to areas within 10-km range of the rain gauges. Through a logistic regression model, high claim numbers were linked to maximum rainfall intensities, with rainfall intensity based on 10-min to 4-h time windows. Rainfall intensity proved to be a significant damage predictor; however, the explained variance, approximated by a pseudo-R2 statistic, was at most 34% for property damage and at most 30% for content damage. When directly comparing predicted and observed values, the model was able to predict 5–17% more cases correctly compared to a random prediction. No important differences were found between relations with property and content damage data. A considerable fraction of the variance is left unexplained, which emphasizes the need to study damage generating mechanisms and additional explanatory variables.

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A statistical analysis of insurance damage claims related to rainfall extremes

. Flood-damage prediction models are essential building blocks in flood risk assessments. So far, little research has been dedicated to damage from small-scale urban floods caused by heavy rainfall, while there is a need for reliable damage models for this flood type among insurers and water authorities. The aim of this paper is to investigate a wide range of damage-influencing factors and their relationships with rainfall-related damage, using decision-tree analysis. For this, district-aggregated claim data from private property insurance companies in the Netherlands were analysed, for the period 1998–2011. The databases include claims of water-related damage (for example, damages related to rainwater intrusion through roofs and pluvial flood water entering buildings at ground floor). Response variables being modelled are average claim size and claim frequency, per district, per day. The set of predictors include rainfall-related variables derived from weather radar images, topographic variables from a digital terrain model, building-related variables and socioeconomic indicators of households. Analyses were made separately for property and content damage claim data. Results of decision-tree analysis show that claim frequency is most strongly associated with maximum hourly rainfall intensity, followed by real estate value, ground floor area, household income, season (property data only), buildings age (property data only), a fraction of homeowners (content data only), a and fraction of low-rise buildings (content data only). It was not possible to develop statistically acceptable trees for average claim size. It is recommended to investigate explanations for the failure to derive models. These require the inclusion of other explanatory factors that were not used in the present study, an investigation of the variability in average claim size at different spatial scales, and the collection of more detailed insurance data that allows one to distinguish between the effects of various damage mechanisms to claim size. Cross-validation results show that decision trees were able to predict 22–26% of variance in claim frequency, which is considerably better compared to results from global multiple regression models (11–18% of variance explained). Still, a large part of the variance in claim frequency is left unexplained, which is likely to be caused by variations in data at subdistrict scale and missing explanatory variables.

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Decision-tree analysis of factors influencing rainfall-related building structure and content damage

This study presents a first attempt to quantify tangible and intangible flood damage according to two different damage metrics: monetary values and number of people affected by flooding. Tangible damage includes material damage to buildings and infrastructure; intangible damage includes damages that are difficult to quantify exactly, such as stress and inconvenience. The data used are representative of lowland flooding incidents with return periods up to 10 years. The results show that monetarisation of damage prioritises damage to buildings in comparison with roads, cycle paths and footpaths. When, on the other hand, damage is expressed in terms of numbers of people affected by a flood, road flooding is the main contributor to total flood damage. The results also show that the cumulative damage of 10 years of successive flood events is almost equal to the damage of a singular event with a T = 125 years return period. Differentiation between urban functions and the use of different kinds of damage metrics to quantify flood risk provide the opportunity to weigh tangible and intangible damages from an economic and societal perspective.

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J.A.E. Ten Veldhuis

Papers

Microbial risks associated with exposure to pathogens in contaminated urban flood water

On the sensitivity of urban hydrodynamic modelling to rainfall spatial and temporal resolution

A statistical analysis of insurance damage claims related to rainfall extremes

Decision-tree analysis of factors influencing rainfall-related building structure and content damage

How the choice of flood damage metrics influences urban flood risk assessment