Meteorological influence on forecasting urban pollutants: Long-term predictability versus extreme events in a spatially heterogeneous urban ecosystem

2021 ◽  
pp. 152537
Author(s):  
Giulia Ulpiani ◽  
Patrick Nzivugira Duhirwe ◽  
Geun Young Yun ◽  
Mathew J. Lipson
Keyword(s):  
Extreme Events ◽  
Urban Ecosystem ◽  
Urban Pollutants

Research Status and Tendency of Urban Soil Pollution

2011 ◽  
Vol 414 ◽  
pp. 226-231
Author(s):  
Huai Li Zheng ◽  
Zhen Zhen Jiang ◽  
Wei Fan ◽  
Jun Ren Zhu ◽  
Zhi Zhang ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Soil Pollution ◽  
Urban Soil ◽  
Urban Ecosystem ◽  
Vital Role ◽  
Ecological Environment ◽  
Mental Contamination ◽  
Urban Sustainable Development ◽  
Urban Pollutants ◽  
Pollution Research

Urban soil is a compositional part of urban ecosystem playing a vital role in urban sustainable development for it functions importantly in ecological, environmental and economical area and it is urban pollutants’ source and concourse. This paper systematically concludes and expounds a series of research achievements about soil heavy mental contamination, micro-organic material pollution, the environmental and healthy risk assessment of urban soil pollution of domestic and foreign research in recent 10 years. The development focus and tendency of urban soil pollution research are provided on this base for supplying vital scientific clues for improving urban ecological environment.

scholarly journals Identifikasi perubahan suhu udara dan curah hujan di Bogor

2020 ◽  
Vol 10 (4) ◽  
pp. 616-626
Author(s):  
Rahmat Hidayat ◽  
Alfi Wardah Farihah
Keyword(s):  
Air Temperature ◽  
Extreme Events ◽  
Climatic Parameters ◽  
West Java ◽  
Climate Parameters ◽  
Increased Temperature ◽  
Temperature Indices ◽  
Consecutive Dry Days

Climate datasets were analyzed to identify the changing climatic parameters and extreme events in Bogor, West Java. This study aims to analyze the characteristic of observational datasets in Baranangsiang and Dramaga, namely, air temperature and rainfall, and to indentify the changing structure of those climate parameters. The analysis has been conducted using RClimdex to understand the long-term changing air temperature and rainfall based on 10 indices for air temperature and 8 indices for rainfall. Results show that the rainfall in Baranangsiang has the daily mean of 10 mm/day and in Dramaga of 8 mm/day. The daily mean of air temperature in Baranangsiang and Dramaga is 27˚C and 25.5˚C, respectively. Generally, the declined slopes of the temperature indices in Barangsiang, namely, TN90p, TNx, TX10p, TNn, TXn, TR20, and SU25, indicate cooler temperature. In Dramaga, the increased temperature indices, namely, TN90p, TX90p, TXx, SU25, and TXn, indicate the warmer temperature. The rainfall indices generally decline, except for CDD, which indicate the increased consecutive dry days in Baranangsiang.   

On the Occurence of Extreme Events in Long-term Correlated and Multifractal Data Sets

2008 ◽  
pp. 1195-1207 ◽  
Author(s):  
Mikhail I. Bogachev ◽  
Jan F. Eichner ◽  
Armin Bunde
Keyword(s):  
Extreme Events ◽  
Data Sets

scholarly journals A Framework to Advance the Understanding of the Ecological Effects of Extreme Climate Events

2019 ◽  
Vol 11 (21) ◽  
pp. 5954 ◽  
Author(s):  
Carlos Sanz-Lazaro
Keyword(s):  
Climate Change ◽  
Extreme Events ◽  
Ecological Effects ◽  
Rocky Shores ◽  
Long Term Effects ◽  
Starting Point ◽  
Experimental Approaches ◽  
Key Aspects ◽  
Climate Events

Climate change is modifying disturbance regimes, affecting the severity and occurrence of extreme events. Current experiments investigating extreme events have a large diversity of experimental approaches and key aspects such as the interaction with other disturbances, the timing, and long-term effects are not usually incorporated in a standardized way. This lack of comparability among studies limits advances in this field of research. This study presents a framework that is comprised of two experimental approaches designed to test expected changes on disturbance regime due to climate change. These approaches test the effects of disturbances becoming more clustered and more extreme. They use common descriptor variables regardless of the type of disturbance and ecosystem. This framework is completed with a compilation of procedures that increase the realism of experiments in the aforementioned key aspects. The proposed framework favours comparability among studies and increases our understanding of extreme events. Examples to implement this framework are given using rocky shores as a case study. Far from being perfect, the purpose of this framework is to act as a starting point that triggers the comparability and refinement of these types of experiments needed to advance our understanding of the ecological effects of extreme events.

scholarly journals Assessing the impacts of extreme weather events on potable water quality: the value to managers of a highly participatory, integrated modelling approach

2018 ◽  
Vol 2 (1) ◽  
pp. 9-24
Author(s):  
Edoardo Bertone ◽  
Oz Sahin ◽  
Russell Richards ◽  
Anne Roiko
Keyword(s):  
Water Quality ◽  
Decision Support ◽  
Extreme Events ◽  
Drinking Water Treatment ◽  
Extreme Weather ◽  
Integrated Modelling ◽  
Extreme Weather Events ◽  
Weather Events ◽  
Sd Model

Abstract A decision support tool was created to estimate the treatment efficiency of an Australian drinking water treatment system based on different combinations of extreme weather events and long-term changes. To deal with uncertainties, missing data, and nonlinear behaviours, a Bayesian network (BN) was coupled with a system dynamics (SD) model. The preliminary conceptual structures of these models were developed through stakeholders' consultation. The BN model could rank extreme events, and combinations of them, based on the severity of their impact on health-related water quality. The SD model, in turn, was used to run a long-term estimation of extreme events' impacts by including temporal factors such as increased water demand and customer feedback. The integration of the two models was performed through a combined Monte Carlo–fuzzy logic approach which allowed to take the BN's outputs as inputs for the SD model. The final product is a participatory, multidisciplinary decision support system allowing for robust, sustainable long-term water resources management under uncertain conditions for a specific location.

scholarly journals An Investigation of Stretched Exponential Function in Quantifying Long-Term Memory of Extreme Events Based on Artificial Data following Lévy Stable Distribution

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-7
Author(s):  
HongGuang Sun ◽  
Lin Yuan ◽  
Yong Zhang ◽  
Nicholas Privitera
Keyword(s):  
Exponential Distribution ◽  
Exponential Function ◽  
Extreme Events ◽  
Stable Distribution ◽  
Wide Spectrum ◽  
Long Term Memory ◽  
Term Memory ◽  
Stretched Exponential ◽  
Stretched Exponential Distribution

Extreme events, which are usually characterized by generalized extreme value (GEV) models, can exhibit long-term memory, whose impact needs to be quantified. It was known that extreme recurrence intervals can better characterize the significant influence of long-term memory than using the GEV model. Our statistical analyses based on time series datasets following the Lévy stable distribution confirm that the stretched exponential distribution can describe a wide spectrum of memory behavior transition from exponentially distributed intervals (without memory) to power-law distributed ones (with strong memory or fractal scaling property), extending the previous evaluation of the stretched exponential function using Gaussian/exponential distributed random data. Further deviation and discussion of a historical paradox (i.e., the residual waiting time tends to increase with an increasing elapsed time under long-term memory) are also provided, based on the theoretical analysis of the Bayesian law and the stretched exponential distribution.

scholarly journals To what extent does variability of historical rainfall series influence extreme event statistics of sewer system surcharge and overflows?

2009 ◽  
Vol 60 (1) ◽  
pp. 87-95 ◽  
Author(s):  
K. Schaarup-Jensen ◽  
M. R. Rasmussen ◽  
S. Thorndahl
Keyword(s):  
Extreme Events ◽  
Extreme Event ◽  
Rain Gauge ◽  
Water Levels ◽  
Rainfall Series ◽  
Rainfall Time Series ◽  
Extreme Statistics ◽  
Maximum Water ◽  
Present Case Study

In urban drainage modelling long term extreme statistics has become an important basis for decision-making e.g. in connection with renovation projects. Therefore it is of great importance to minimize the uncertainties with regards to long term prediction of maximum water levels and combined sewer overflow (CSO) in drainage systems. These uncertainties originate from large uncertainties regarding rainfall inputs, parameters, and assessment of return periods. This paper investigates how the choice of rainfall time series influences the extreme events statistics of max water levels in manholes and CSO volumes. Traditionally, long term rainfall series, from a local rain gauge, are unavailable. In the present case study, however, long and local rain series are available. 2 rainfall gauges have recorded events for approximately 9 years at 2 locations within the catchment. Beside these 2 gauges another 7 gauges are located at a distance of max 20 kilometers from the catchment. All gauges are included in the Danish national rain gauge system which was launched in 1976. The paper describes to what extent the extreme events statistics based on these 9 series diverge from each other and how this diversity can be handled, e.g. by introducing an “averaging procedure” based on the variability within the set of statistics. All simulations are performed by means of the MOUSE LTS model.

Relationships of Rainy Season Precipitation and Temperature to Climate Indices in California: Long-Term Variability and Extreme Events

2018 ◽  
Vol 31 (5) ◽  
pp. 1921-1942 ◽  
Author(s):  
Yi-Chin Liu ◽  
Pingkuan Di ◽  
Shu-Hua Chen ◽  
John DaMassa
Keyword(s):  
Extreme Events ◽  
North Pacific ◽  
Minimum Temperature ◽  
Rainy Season ◽  
Time Lag ◽  
Precipitation Variability ◽  
Climate Indices ◽  
Zero Time ◽  
Enso Indices

To better understand the change in California’s climate over the past century, the long-term variability and extreme events of precipitation as well as minimum, mean, and maximum temperatures during the rainy season (from November to March) are investigated using observations. Their relationships to 28 rainy season average climate indices with and without time lags are also studied. The precipitation variability is found to be highly correlated with the tropical/Northern Hemisphere pattern (TNH) index at zero time lag with the highest correlation in Northern California and the Sierra and the correlation decreasing southward. This is an important finding because there have been no conclusive studies on the dominant climate modes that modulate precipitation variability in Northern California. It is found that the TNH modulates California precipitation variability through the development of a positive (negative) height anomaly and its associated low-level moisture fluxes over the northeast Pacific Ocean during the positive (negative) TNH phase. Temperature fields, especially minimum temperature, are found to be primarily modulated by the east Pacific/North Pacific pattern, Pacific decadal oscillation, North Pacific pattern, and Pacific–North American pattern at zero time lag via changes in the lower-tropospheric temperature advections. Regression analysis suggests a combination of important climate indices would improve predictability for precipitation and minimum temperature statewide and subregionally compared to the use of a single climate index. While California’s precipitation currently is primarily projected by ENSO, this study suggests that using the combination of the TNH and ENSO indices results in better predictability than using ENSO indices only.

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