Publikationsdatum:
2022-06-09
Beschreibung:
In this paper we have put forward a Bayesian framework for the analysis and testing of possible non-stationarities in extreme events. We use the extreme value theory to
model temperature and precipitation data in the Dar es Salaam region, Tanzania. Temporal
trends are modeled writing the location parameter of the generalized extreme value distribution in terms of deterministic functions of explanatory covariates. The analyses are performed using synthetic time series derived from a Regional Climate Model. The simulations, performed in an area around the Dar es Salaam city, Tanzania, take into account two Representative Concentration Pathways scenarios from the Intergovernmental Panel on Climate Change. Our main interest is to analyze extremes with high spatial and temporal resolution and to pursue this requirement we have adopted an individual grid box analysis approach. The approach presented in this paper is composed of the following key
elements: (1) an advanced Bayesian method for the estimation of model parameters, (2) a
rigorous procedure for model selection, and (3) uncertainty assessment and propagation.
The results of our analyses are intended to be used for quantitative hazard and risk
assessment and are presented in terms of hazard curves and probabilistic hazard maps. In the case study we found that for both the temperature and precipitation data, a linear trend in the location parameter was the only model performing better than the stationary one in the areas where evidence against the stationary model exists.
Beschreibung:
This research has been developed in the framework of the FP7 European project CLUVA (Climate change and Urban Vulnerability in Africa), Grant No. 265137. This research has been funded by the FP7 European project CLUVA (Climate change and Urban ulnerability in Africa).
Beschreibung:
Published
Beschreibung:
289-320
Beschreibung:
4A. Clima e Oceani
Beschreibung:
JCR Journal
Beschreibung:
open
Schlagwort(e):
Non-stationary extreme events
;
Climate change
;
Multi-hazard
;
Bayesian inference
;
Extreme precipitation
;
Extreme temperature
;
Dar es Salaam
;
Tanzania
;
01. Atmosphere::01.01. Atmosphere::01.01.02. Climate
;
03. Hydrosphere::03.02. Hydrology::03.02.05. Models and Forecasts
;
03. Hydrosphere::03.03. Physical::03.03.02. General circulation
;
05. General::05.08. Risk::05.08.99. General or miscellaneous
Repository-Name:
Istituto Nazionale di Geofisica e Vulcanologia (INGV)
Materialart:
article
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