scholarly journals Bayesian trend analysis for daily rainfall series of Barcelona

2010 ◽  
Vol 26 ◽  
pp. 71-76 ◽  
Author(s):  
M. I. Ortego ◽  
J. Gibergans-Báguena ◽  
R. Tolosana-Delgado ◽  
J. J. Egozcue ◽  
M. C. Llasat
Keyword(s):  
Climate Change ◽  
Climate Change Scenario ◽  
Pareto Distribution ◽  
Generalized Pareto Distribution ◽  
Daily Rainfall ◽  
Change Scenario ◽  
Generalized Pareto ◽  
Abrupt Changes ◽  
Physical Features ◽  
Threshold Approach

Abstract. A Point-Over-Threshold approach using a reparameterization of the Generalized Pareto Distribution (GPD) has been used to assess changes in the daily rainfall Barcelona series (1854–2006). A Bayesian approach, considering the suitable scale and physical features of the phenomenon, has been used to look for these alterations. Two different models have been assessed: existence of abrupt changes in the new GPD parameters due to modifications of the observatory locations and trends in these GPD parameters, pointing to a climate change scenario.

scholarly journals A multiple threshold method for fitting the generalized Pareto distribution and a simple representation of the rainfall process

2010 ◽  
Vol 7 (4) ◽  
pp. 4957-4994 ◽  
Author(s):  
R. Deidda
Keyword(s):  
Time Series ◽  
Distribution Function ◽  
Pareto Distribution ◽  
Generalized Pareto Distribution ◽  
Daily Rainfall ◽  
Rainfall Time Series ◽  
Threshold Method ◽  
Generalized Pareto ◽  
Multiple Threshold ◽  
Optimum Threshold

Abstract. Previous studies indicate the generalized Pareto distribution (GPD) as a suitable distribution function to reliably describe the exceedances of daily rainfall records above a proper optimum threshold, which should be selected as small as possible to retain the largest sample while assuring an acceptable fitting. Such an optimum threshold may differ from site to site, affecting consequently not only the GPD scale parameter, but also the probability of threshold exceedance. Thus a first objective of this paper is to derive some expressions to parameterize a simple threshold-invariant three-parameter distribution function which is able to describe zero and non zero values of rainfall time series by assuring a perfect overlapping with the GPD fitted on the exceedances of any threshold larger than the optimum one. Since the proposed distribution does not depend on the local thresholds adopted for fitting the GPD, it will only reflect the on-site climatic signature and thus appears particularly suitable for hydrological applications and regional analyses. A second objective is to develop and test the Multiple Threshold Method (MTM) to infer the parameters of interest on the exceedances of a wide range of thresholds using again the concept of parameters threshold-invariance. We show the ability of the MTM in fitting historical daily rainfall time series recorded with different resolutions. Finally, we prove the supremacy of the MTM fit against the standard single threshold fit, often adopted for partial duration series, by evaluating and comparing the performances on Monte Carlo samples drawn by GPDs with different shape and scale parameters and different discretizations.

scholarly journals A multiple threshold method for fitting the generalized Pareto distribution to rainfall time series

2010 ◽  
Vol 14 (12) ◽  
pp. 2559-2575 ◽  
Author(s):  
R. Deidda
Keyword(s):  
Time Series ◽  
Distribution Function ◽  
Pareto Distribution ◽  
Generalized Pareto Distribution ◽  
Daily Rainfall ◽  
Rainfall Time Series ◽  
Threshold Method ◽  
Generalized Pareto ◽  
Multiple Threshold ◽  
Optimum Threshold

Abstract. Previous studies indicate the generalized Pareto distribution (GPD) as a suitable distribution function to reliably describe the exceedances of daily rainfall records above a proper optimum threshold, which should be selected as small as possible to retain the largest sample while assuring an acceptable fitting. Such an optimum threshold may differ from site to site, affecting consequently not only the GPD scale parameter, but also the probability of threshold exceedance. Thus a first objective of this paper is to derive some expressions to parameterize a simple threshold-invariant three-parameter distribution function which assures a perfect overlapping with the GPD fitted on the exceedances over any threshold larger than the optimum one. Since the proposed distribution does not depend on the local thresholds adopted for fitting the GPD, it is expected to reflect the on-site climatic signature and thus appears particularly suitable for hydrological applications and regional analyses. A second objective is to develop and test the Multiple Threshold Method (MTM) to infer the parameters of interest by using exceedances over a wide range of thresholds applying again the concept of parameters threshold-invariance. We show the ability of the MTM in fitting historical daily rainfall time series recorded with different resolutions and with a significative percentage of heavily quantized data. Finally, we prove the supremacy of the MTM fit against the standard single threshold fit, often adopted for partial duration series, by evaluating and comparing the performances on Monte Carlo samples drawn by GPDs with different shape and scale parameters and different discretizations.

scholarly journals Climate change in a Point-Over-Threshold model: an example on ocean-wave-storm hazard in NE Spain

2010 ◽  
Vol 26 ◽  
pp. 113-117 ◽  
Author(s):  
R. Tolosana-Delgado ◽  
M. I. Ortego ◽  
J. J. Egozcue ◽  
A. Sánchez-Arcilla
Keyword(s):  
Climate Change ◽  
Pareto Distribution ◽  
Threshold Model ◽  
Generalized Pareto Distribution ◽  
Ocean Wave ◽  
Data Set ◽  
Climate Conditions ◽  
Generalized Pareto ◽  
Distribution Parameters ◽  
Ne Spain

Abstract. A reparametrization of the Generalized Pareto Distribution is here proposed. It is suitable to parsimoniously check trend assumptions within a Point-Over-Threshold model of hazardous events. This is based on considerations about the scale of both the excesses of the event magnitudes and the distribution parameters. The usefulness of this approach is illustrated with a data set from two buoys, where hypotheses about the homogeneity of climate conditions and lack of trends are assessed.

Improving daily rainfall extremes simulation using the generalized Pareto distribution: a case study in Western Iran

2022 ◽  
Vol 85 ◽  
pp. 193-204
Author(s):  
N Shahraki ◽  
S Marofi ◽  
S Ghazanfari
Keyword(s):  
Spatial Correlation ◽  
Water Resource ◽  
Pareto Distribution ◽  
Generalized Pareto Distribution ◽  
Daily Rainfall ◽  
Information Criterion ◽  
Rainfall Simulation ◽  
Model Order ◽  
Generalized Pareto ◽  
Agricultural Planning

Prediction of the occurrence or non-occurrence of daily rainfall plays a significant role in agricultural planning and water resource management projects. In this study, gamma distribution function (GDF), kernel, and exponential (EXP) distributions were coupled (piecewise) with a generalized Pareto distribution. Thus, the gamma-generalized Pareto (GGP), kernel-generalized Pareto (KGP), and exponential-generalized Pareto (EGP) models were used. The aim of the present study was to introduce new methods to modify the simulated generation of extreme rainfall amounts of rainy seasons based on the preserved spatial correlation. The best approach was identified using the normalized root mean square error (NRMSE) criterion. For this purpose, the 30-yr daily rainfall datasets of 21 synoptic weather stations located in different climates of West Iran were analyzed. The first, second, and third-order Markov chain (MC) models were used to describe rainfall time series frequencies. The best MC model order was detected using the Akaike information criterion and Bayesian information criterion. Based on the best identified MC model order, the best piecewise distribution models, and the Wilks approach, rainfall events were modeled with regard to the spatial correlation among the study stations. The performance of the Wilks approach was verified using the coefficient of determination. The daily rainfall simulation resulted in a good agreement between the observed and the generated rainfall data. Hence, the proposed approach is capable of helping water resource managers in different contexts of agricultural planning.

scholarly journals Simulating Agroforestry Adoption in Rural Indonesia: The Potential of Trees on Farms for Livelihoods and Environment

Land ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 385
Author(s):  
Beatrice Nöldeke ◽  
Etti Winter ◽  
Yves Laumonier ◽  
Trifosa Simamora
Keyword(s):  
Climate Change ◽  
Climate Change Scenario ◽  
Agroforestry System ◽  
Environmental Benefits ◽  
Small Scale ◽  
Change Scenario ◽  
Trees On Farms ◽  
Agroforestry Adoption ◽  
Mitigate Climate Change ◽  
Small Scale Farmers

In recent years, agroforestry has gained increasing attention as an option to simultaneously alleviate poverty, provide ecological benefits, and mitigate climate change. The present study simulates small-scale farmers’ agroforestry adoption decisions to investigate the consequences for livelihoods and the environment over time. To explore the interdependencies between agroforestry adoption, livelihoods, and the environment, an agent-based model adjusted to a case study area in rural Indonesia was implemented. Thereby, the model compares different scenarios, including a climate change scenario. The agroforestry system under investigation consists of an illipe (Shorea stenoptera) rubber (Hevea brasiliensis) mix, which are both locally valued tree species. The simulations reveal that farmers who adopt agroforestry diversify their livelihood portfolio while increasing income. Additionally, the model predicts environmental benefits: enhanced biodiversity and higher carbon sequestration in the landscape. The benefits of agroforestry for livelihoods and nature gain particular importance in the climate change scenario. The results therefore provide policy-makers and practitioners with insights into the dynamic economic and environmental advantages of promoting agroforestry.

Sign in / Sign up

Export Citation Format

Share Document