Comparison between quantile regression technique and generalised additive model for regional flood frequency analysis : a case study for Victoria, Australia

For design flood estimation in ungauged catchments, Regional Flood Frequency Analysis (RFFA) is commonly used. Most of the RFFA methods are primarily based on linear modelling approaches, which do not account for the inherent nonlinearity of rainfall-runoff processes. Using data from 114 catchments in Victoria, Australia, this study employs the Generalised Additive Model (GAM) in RFFA and compares the results with linear method known as Quantile Regression Technique (QRT). The GAM model performance is found to be better for smaller return periods (i.e., 2, 5 and 10 years) with a median relative error ranging 16–41%. For higher return periods (i.e., 20, 50 and 100 years), log-log linear regression model (QRT) outperforms the GAM model with a median relative error ranging 31–59%

Flood frequency analysis at ungauged catchments with the GAM and MARS approaches in the Montreal region, Canada

Regional frequency analysis (RFA) aims to estimate quantiles of extreme hydrological variables (e.g. floods or low-flows) at sites where little or no hydrological data is available. This information is of interest for the optimal planning and management of water resources. A number of regional estimation models are evaluated and compared in this study and then used for regional estimation of flood quantiles at ungauged catchments located in the Montreal region in southern Quebec, Canada. In this study, two neighborhood approaches using canonical correlation analysis (CCA) and the region of influence (ROI) method are applied to delineate homogenous regions. Three regression methods namely log-linear regression model (LLRM), generalized additive models (GAM), and multivariate adaptive regression splines (MARS), recently introduced in the RFA context, are considered for regional estimation. These models are also applied considering all stations (ALL). The considered models, especially MARS, have never been used previously in a concrete application. Results indicate that MARS and GAM have comparable predictive performances, especially when applied with the whole dataset. Results also show that MARS used in combination with the CCA approach provide improved performances compared to all considered regional approaches. This may reflect the flexibility of the combination of these two approaches, their robustness, and their ability to better reproduce the hydrological phenomena, especially in real-world conditions when limited data are available

A cold-health watch and warning system, applied to the province of Quebec (Canada).

CONTEXT: A number of studies have shown that cold has an important impact on human health. However, almost no studies focused on cold warning systems to prevent those health effects. For Nordic regions, like the province of Quebec in Canada, winter is long and usually very cold with an observed increase in mortality and hospitalizations throughout the season. However, there is no existing system specifically designed to follow in real-time this mortality increase throughout the season and to alert public health authorities prior to cold waves. OBJECTIVE: The aim is to establish a watch and warning system specifically for health impacts of cold, applied to different climatic regions of the province of Quebec. METHODOLOGY: A methodology previously used to establish the health-heat warning system in Quebec is adapted to cold. The approach identifies cold weather indicators and establishes thresholds related to extreme over-mortality or over-hospitalization events in the province of Quebec, Canada. RESULTS AND CONCLUSION: The final health-related thresholds proposed are between (-15 °C, -23 °C) and (-20 °C, -29 °C) according to the climatic region for excesses of mortality, and between (-13 °C, -23 °C) and (-17 °C, -30 °C) for excesses of hospitalization. These results suggest that the system model has a high sensitivity and an acceptable number of false alarms. This could lead to the establishment of a cold-health watch and warning system with valid indicators and thresholds for each climatic region of Quebec. It can be seen as a complementary system to the existing one for heat warnings, in order to help the public health authorities to be well prepared during an extreme cold event

Regional Frequency Analysis at Ungauged Sites with Multivariate Adaptive Regression Splines.

Hydrological systems are naturally complex and nonlinear. A large number of variables, many of which not yet well considered in regional frequency analysis (RFA), have a significant impact on hydrological dynamics and consequently on flood quantile estimates. Despite the increasing number of statistical tools used to estimate flood quantiles at ungauged sites, little attention has been dedicated to the development of new regional estimation (RE) models accounting for both nonlinear links and interactions between hydrological and physio-meteorological variables. The aim of this paper is to simultaneously take into account nonlinearity and interactions between variables by introducing the multivariate adaptive regression splines (MARS) approach in RFA. The predictive performances of MARS are compared with those obtained by one of the most robust RE models: the generalized additive model (GAM). Both approaches are applied to two datasets covering 151 hydrometric stations in the province of Quebec (Canada): a standard dataset (STA) containing commonly used variables and an extended dataset (EXTD) combining STA with additional variables dealing with drainage network characteristics. Results indicate that RE models using MARS with the EXTD outperform slightly RE models using GAM. Thus, MARS seems to allow for a better representation of the hydrological process and an increased predictive power in RFA

Urban Overheating - Progress on Mitigation Science and Engineering Applications

The combination of global warming and urban sprawl is the origin of the most hazardous climate change effect detected at urban level: Urban Heat Island, representing the urban overheating respect to the countryside surrounding the city. This book includes 18 papers representing the state of the art of detection, assessment mitigation and adaption to urban overheating. Advanced methods, strategies and technologies are here analyzed including relevant issues as: the role of urban materials and fabrics on urban climate and their potential mitigation, the impact of greenery and vegetation to reduce urban temperatures and improve the thermal comfort, the role the urban geometry in the air temperature rise, the use of satellite and ground data to assess and quantify the urban overheating and develop mitigation solutions, calculation methods and application to predict and assess mitigation scenarios. The outcomes of the book are thus relevant for a wide multidisciplinary audience, including: environmental scientists and engineers, architect and urban planners, policy makers and students

Comparison between quantile regression technique and generalised additive model for regional flood frequency analysis

Design flood estimates are needed for the planning and design of hydraulic structures, and in many other water and environmental management tasks. Design flood estimation is a challenging task, in particular for poorly gauged and ungauged catchments. In Australia, there are numerous ungauged catchments; for these catchments Regional Flood Frequency Analysis (RFFA) techniques are generally adopted to estimate design floods. Most of the RFFA techniques previously adopted in Australia are based on rational method and/or linear modelling approaches. However, with the recent advancements in statistical computation methods, there are several other techniques becoming popular gradually in hydrological applications which can account for non-linearity in the rainfall-runoff processes. Generalized additive model (GAM) is one of the recently developed techniques which can deal with the non-linearity, which has not been widely explored in hydrological research, in particular for the RFFA problems. Therefore, this research is devoted to examining the applicability of GAM in RFFA and compare its performances with one of the most widely used linear RFFA technique (log-log linear model). This study is carried out using data from 114 small to medium sized gauged catchments of Victoria, Australia. This data has primarily been sourced from Australia Rainfall Runoff (ARR), Project 5 Regional Flood Methods. This study is based on a number of alternative groups, e.g. a combined group consisting of all the 114 catchments and sub-groups formed based on cluster analysis. Four regions are formed using hierarchical and k-means clustering techniques. All the five groups are used for developing log-log linear models and GAM based models. The predictor variables for each of these models are selected based on the statistical significance of the predictor variables, i.e. p-statistics. For validation of the developed prediction models, a 10-fold cross validation method is adopted. The performances of the prediction models for the alternative models are assessed using a number of statistical measures including coefficient of determination (R2), median relative error (RE) and median Qpred/Qobs ratio values. It is found that, none of the models from the combined group and clustering groups perform equally well for the six average recurrence intervals (ARIs) (2, 5, 10, 2, 50 and 100 years) with respect to the selected statistical measures. Overall, log-log linear model from clustering group A1 is found to be the best performing model. GAM based RFFA models perform better for smaller ARIs (i.e., 2, 5 and 10 years); which is as expected since the hydrological behaviour of catchments for smaller ARIs is generally more non-linear, e.g. higher loss and hence rainfall produces lower runoff for more frequent events. Some predictor variables (e.g., evap), which were not adopted in the previous RFFA models, in Australia are found to be significant in the GAM based RFFA models. Overall, it is found that consideration of non-linearity via GAM can add new dimensions in RFFA modelling for selecting appropriate predictor variables and to deal with non-linearity. Overall, the results of this study demonstrate that GAM has a strong potential to enhance the accuracy of RFFA models in Australia; however, additional predictor variables are needed (than what are included in this study) to capture the non-linearity more explicitly between runoff and flood producing variables

Indoor air Quality and Its Effects on Health in Urban Houses of Indonesia: A case study of Surabaya

There is a possibility that the sick building syndrome has already spread widely among the newly constructed apartments in major cities of Indonesia. This study investigates the current conditions of indoor air quality, focusing especially on formaldehyde and TVOC, and their effects on health among occupants in the urban houses located in the city of Surabaya. A total of 471 respondents were interviewed and 82 rooms were measured from September 2017 to January 2018. The results indicated that around 50% of the respondents in the apartments showed some degrees of chemical sensitivity risk. More than 60% of the measured formaldehyde levels in the apartments exceeded the WHO standard, 0.08 ppm. The respondents living in rooms with higher mean formaldehyde values tended to have higher multiple chemical sensitivity risk scores. KEYWORDS: Indoor air quality, Sick building syndrome, QEESI, Formaldehyde, Developing countrie