scholarly journals Development and evaluation of a stochastic daily rainfall model with long-term variability

2017 ◽  
Vol 21 (12) ◽  
pp. 6541-6558 ◽  
Author(s):  
A. F. M. Kamal Chowdhury ◽  
Natalie Lockart ◽  
Garry Willgoose ◽  
George Kuczera ◽  
Anthony S. Kiem ◽  
...  
Keyword(s):  
Gamma Distribution ◽  
Transition Probabilities ◽  
Daily Rainfall ◽  
Primary Objective ◽  
Generation Model ◽  
Gamma Model ◽  
Rainfall Occurrence ◽  
Rainfall Model ◽  
Two Parameters

Abstract. The primary objective of this study is to develop a stochastic rainfall generation model that can match not only the short resolution (daily) variability but also the longer resolution (monthly to multiyear) variability of observed rainfall. This study has developed a Markov chain (MC) model, which uses a two-state MC process with two parameters (wet-to-wet and dry-to-dry transition probabilities) to simulate rainfall occurrence and a gamma distribution with two parameters (mean and standard deviation of wet day rainfall) to simulate wet day rainfall depths. Starting with the traditional MC-gamma model with deterministic parameters, this study has developed and assessed four other variants of the MC-gamma model with different parameterisations. The key finding is that if the parameters of the gamma distribution are randomly sampled each year from fitted distributions rather than fixed parameters with time, the variability of rainfall depths at both short and longer temporal resolutions can be preserved, while the variability of wet periods (i.e. number of wet days and mean length of wet spell) can be preserved by decadally varied MC parameters. This is a straightforward enhancement to the traditional simplest MC model and is both objective and parsimonious.

scholarly journals Development and Evaluation of a Stochastic Daily Rainfall Model with Long Term Variability

2017 ◽  
Author(s):  
A. F. M. Kamal Chowdhury ◽  
Natalie Lockart ◽  
Garry Willgoose ◽  
George Kuczera ◽  
Anthony S. Kiem ◽  
...  
Keyword(s):  
Gamma Distribution ◽  
Transition Probabilities ◽  
Daily Rainfall ◽  
Primary Objective ◽  
Generation Model ◽  
Gamma Model ◽  
Rainfall Occurrence ◽  
Rainfall Model ◽  
Two Parameters

Abstract. The primary objective of this study is to develop a stochastic rainfall generation model that can match not only the short resolution (daily) variability, but also the longer resolution (monthly to multiyear) variability of observed rainfall. This study has developed a Markov Chain (MC) model, which uses a two-state MC process with two parameters (wet-to-wet and dry-to-dry transition probabilities) to simulate rainfall occurrence and a Gamma distribution with two parameters (mean and standard deviation of wet day rainfall) to simulate wet day rainfall depths. Starting with the traditional MC-Gamma model with deterministic parameters, this study has developed and assessed four other variants of the MC-Gamma model with different parameterisations. The key finding is that if the parameters of the Gamma distribution are randomly sampled from fitted distributions prior to simulating the rainfall for each year, the variability of rainfall depths at longer resolutions can be preserved, while the variability of wet periods (i.e. number of wet days and mean length of wet spell) can be preserved by decade-varied MC parameters. This is a straightforward enhancement to the traditional simplest MC model and is both objective and parsimonious.

DEVELOPING AND CALIBRATING A STOCHASTIC RAINFALL GENERATOR MODEL FOR SIMULATING DAILY RAINFALL BY MARKOV CHAIN APPROACH

2015 ◽  
Vol 76 (15) ◽  
Author(s):  
N. S. Dlamini ◽  
M. K. Rowshon ◽  
Ujjwal Sahab ◽  
A. Fikri ◽  
S. H. Lai ◽  
...  
Keyword(s):  
Markov Chain ◽  
Production Systems ◽  
Transition Probabilities ◽  
Daily Rainfall ◽  
Rainfall Series ◽  
Climate Data ◽  
Management Tools ◽  
Stochastic Weather Generator ◽  
Markov Chain Approach

Rainfall is an important parameter in tropical humid regions for which paddy production systems depend. A significant portion of paddy water requirements is supplied by natural rainfall. Several studies have predicted changes in rainfall patterns and in the amount of rain that may be obtainable in future owing to climate change. There is increased concern about future water availability for an important crop such as rice. Need to develop new water management tools for sustainable production is inevitable, but such tools require long-term climate data that is credible and consistent with the time. This study concerns itself with evaluating a stochastic weather generator (WGEN) model for simulating daily rainfall series. The model is assessed using long-term historical rainfall data obtained from a rice growing irrigation schemes in Malaysia. The model is based on a first-order two-state Markov chain approach which uses two transition probabilities and random number to generate rainfall series. Selected statistical properties were computed for each station and compared against those retrieved from the model after model training and testing. The results obtained from these comparisons are quite satisfactory giving confidence about the performance and future outputs from the model. The model has shown good skill in describing the rainfall occurrence process and rainfall amounts for the area. The model will be adapted in a subsequent study for downscaling and simulating effective daily rainfall series corresponding to future climate scenarios.

scholarly journals Intensity-Duration-Frequency relationships: stochastic modeling and disaggregation of daily rainfall in the lagoa Mirim watershed, Rio Grande do Sul, Brazil

2016 ◽  
Vol 36 (3) ◽  
pp. 492-502 ◽  
Author(s):  
Rita de C. F Damé ◽  
Claudia F. A. Teixeira-Gandra ◽  
Hugo A. S. Guedes ◽  
Gisele M. da Silva ◽  
Suélen C. R. da Silveira
Keyword(s):  
Stochastic Modeling ◽  
Mean Squared Error ◽  
Transition Probabilities ◽  
Information Gain ◽  
Daily Rainfall ◽  
Gumbel Distribution ◽  
Rain Gauge ◽  
Rainfall Occurrence ◽  
Intensity Duration Frequency ◽  
Annual Maximum Daily Rainfall

ABSTRACT This study aimed to investigate information gain by using rainfall intensity-duration-frequency (IDF) relationships, with data gathered within N+M years from seven rain gauge stations located in the Lagoa Mirim Watershed (South Atlantic basin). After N years of daily rainfall, the transition probabilities of a time homogeneous two-state Markov chain were defined to simulate rainfall occurrence, as well as gamma distribution to measure it; for that, daily rainfall series were composed of N+M years, with M being the generated series. The series were adjusted to Gumbel distribution, being used in annual maximum daily rainfall disaggregation for durations of 10, 20, 30, 40, 50, 60, 120, 360, 720 and 1440 min. Daily rainfall disaggregation was validated through IDF relationships taken from pluviograph records of N years and from N+M years, using the “t” test of relative mean squared error. We can infer that there was information gain using IDF relationships of rainfall occurrence when using N years of observed data and M years of generated data by stochastic modeling compared to those obtained from a composed series of N years.

scholarly journals Distribution of rainfall probability in the Tapajos River Basin, Amazonia, Brazil

2019 ◽  
Vol 14 (3) ◽  
pp. 1
Author(s):  
Vanessa Conceição dos Santos ◽  
Claudio Blanco ◽  
José Francisco de Oliveira Júnior
Keyword(s):  
River Basin ◽  
Transition Probabilities ◽  
Daily Rainfall ◽  
Probability Of Occurrence ◽  
Rainfall Occurrence ◽  
Order Markov Chain ◽  
Historical Series ◽  
The Matrix ◽  
Geographical Position ◽  
Homogeneous Regions

Studies on the probability of rainfall and its spatiotemporal variations are important for the planning of water resources and optimization of the calendar of agricultural activities. This study identifies the occurrence of rain by first-order Markov Chain (MC) and by two states in the Tapajos River Basin (TRB), Amazon, Brazil. Cluster analysis (CA), based on the Ward method, was used to classify homogeneous regions and select samples for checking the probability of rainfall occurrence by season. The historical series of daily rainfall data of 80 stations were used for the period 1990-2014. The CA technique identified 8 homogeneous regions and their probability of occurrence of rainfall, helping to determine which regions and periods have greater need of irrigation. Results of the probability of occurrence of dry and rainy periods in the TRB were used to define the dry (May thru September) and rainy seasons (October thru April). Elements of the matrix transition probabilities showed variability in relation to time and, in addition, the influence of geographical position of seasonal rainfall in determining dry and rainy periods at specific sites in the TRB.

TROPICAL DAILY RAINFALL AMOUNT MODELLING USING MARKOV CHAIN-MIXED EXPONENTIAL (MCME)

2015 ◽  
Vol 74 (11) ◽  
Author(s):  
Fadhilah Yusof ◽  
Lee Mee Yung ◽  
Zulkifli Yusop
Keyword(s):  
Markov Chain ◽  
Daily Rainfall ◽  
Predictive Ability ◽  
First Order ◽  
Rainfall Occurrence ◽  
Rainfall Process ◽  
Proposed Model ◽  
Rainfall Model ◽  
Rainfall Occurrences ◽  
Mixed Exponential Distribution

This study is concerned with the development of a stochastic rainfall model that can generate many sequences of synthetic daily rainfall series with the similar properties as those of the observed. The proposed model is Markov chain-mixed exponential (MCME). This model is based on a combination of rainfall occurrence (represented by the first-order two-state Markov chain) and the distribution of rainfall amounts on wet days (described by the mixed exponential distribution). The feasibility of the MCME model is assessed using daily rainfall data from four rainfall stations (station S02, S05, S07 and S11) in Johor, Malaysia. For all the rainfall stations, it was found that the proposed MCME model was able to describe adequately rainfall occurrences and amounts. Various statistical and physical properties of the daily rainfall processes also considered. However, the validation results show that the models’ predictive ability was not as accurate as their descriptive ability. The model was found to have fairly well ability in predicting the daily rainfall process at station S02, S05 and S07. Nonetheless, it was able to predict the daily rainfall process at station S11 accurately. 

Economic conditions and perceptions of immigrants as an economic threat in Europe: Temporal dynamics and mediating processes

2021 ◽  
pp. 002071522199352
Author(s):  
Boris Heizmann ◽  
Nora Huth
Keyword(s):  
Temporal Dynamics ◽  
European Social Survey ◽  
Primary Objective ◽  
Conflict Theory ◽  
Economic Conditions ◽  
Political Climate ◽  
Short Term ◽  
Economic Threat ◽  
Party Positions

This article addresses the extent to which economic downturns influence the perception of immigrants as an economic threat and through which channels this occurs. Our primary objective is an investigation of the specific mechanisms that connect economic conditions to the perception of immigrants as a threat. We therefore also contribute to theoretical discussions based on group threat and realistic group conflict theory by exposing the dominant source of competition relevant to these relationships. Furthermore, we investigate whether people react more sensitive to short-term economic dynamics within countries than to the long-term economic circumstances. Our database comprises all waves of the European Social Survey from 2002 to 2017. The macro-economic indicators we use include GDP per capita, unemployment, and national debt levels, covering the most salient economic dimensions. We furthermore control for the country’s migration situation and aggregate party positions toward cultural diversity. Our results show that the dynamic short-term developments of the economy and migration within countries are of greater relevance for perceived immigrant threat than the long-term situation. In contrast, the long-term political climate appears to be more important than short-term changes in the aggregate party positions. Further mediation analyses show that objective economic conditions influence anti-immigrant attitudes primarily through individual perceptions of the country’s economic performance and that unemployment rates are of primary importance.

scholarly journals Cardiovascular and musculoskeletal health disorders associate with greater decreases in physical capability in older women

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Juopperi Samuli ◽  
Sund Reijo ◽  
Rikkonen Toni ◽  
Kröger Heikki ◽  
Sirola Joonas
Keyword(s):  
Chronic Diseases ◽  
Musculoskeletal Disorders ◽  
Population Sample ◽  
The Elderly ◽  
Primary Objective ◽  
Baseline Body Mass Index ◽  
Functional Tests ◽  
Physical Capability ◽  
Study Population

Abstract Background Good physical capability is an important part of healthy biological ageing. Several factors influencing physical capability have previously been reported. Long-term reports on physical capability and the onset of clinical disorders and chronic diseases are lacking. Decrease in physical capacity has been shown to increase mortality. This study focuses on the prevalence of chronic diseases. The primary objective of the study was to reveal the association between physical capability and morbidity. Secondary objectives included the validity of self-reported physical capability and the association between baseline physical capability and mortality. Methods The OSTPRE (Kuopio Osteoporosis Risk Factor and Prevention Study) prospective cohort involved all women aged 47–56 years residing in the Kuopio Province, Finland in 1989. Follow-up questionnaires were mailed at five-year intervals. Physical capability questions were first presented in 1994. From these women, we included only completely physically capable subjects at our baseline, in 1994. Physical capability was evaluated with five scale self-reports at baseline and in 2014 as follows: completely physically capable, able to walk but not run, can walk up to 1000 m, can walk up to 100 m and temporarily severely incapable. The prevalences of selected chronic diseases, with a minimum prevalence of 10% in 2014, were compared with the change in self-reported physical capability. Additionally, associations between long-term mortality and baseline physical capability of the whole 1994 study population sample were examined with logistic regression. The correlation of self-reported physical capability with functional tests was studied cross-sectionally at the baseline for a random subsample. Results Our study population consisted of 6219 Finnish women with a mean baseline age of 57.0 years. Self-reported physical capability showed statistically significant correlation with functional tests. Cardiovascular diseases and musculoskeletal disorders show the greatest correlation with decrease of physical capability. Prevalence of hypertension increased from 48.7% in the full physical capability group to 74.5% in the “able to walk up to 100 metres” group (p < 0.001). Rheumatoid arthritis showed a similar increase from 2.1 to 7.4% between these groups. Higher baseline body mass index (BMI) decreases long-term capability (P < 0.001). Women reporting full physical capability at baseline had a mortality rate of 15.1%, in comparison to 48.5% in women within the “able to walk up to 100 m” group (p = 0.357). Mortality increased steadily with worsening baseline physical capability. Conclusions The results of this study show that chronic diseases, particularly cardiovascular and musculoskeletal disorders, correlate with faster degradation of physical capability in the elderly. Similar results are shown for increase in BMI. We also demonstrate that the risk of mortality over a 20-year period is higher in individuals with poor baseline physical capability.

scholarly journals Long-Term Care Financing for an Aging Population: The Experience of Singapore

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 31-31
Author(s):  
Ngee Choon Chia ◽  
Huijun Cynthia Chen
Keyword(s):  
Long Term Care ◽  
Transition Probabilities ◽  
Aging Population ◽  
Elderly Male ◽  
Term Care ◽  
Cash Benefit ◽  
Opt Out ◽  
Insurance Rate ◽  
Long Term Care Insurance

Abstract Singapore has a rapidly aging population. Long-term care (LTC) is one of the largest financial risks facing elderly in Singapore. Singapore implemented Eldershield, a long-term care insurance scheme which provided defined cash benefit payouts in the event of severe disability; but capped at a maximum of six years. Eldershield enrolled people at age 40, but offered an opt-out option. As of 2015, 65% of those aged 40 to 83 opted to be covered by Eldershield, making Singapore as having the highest voluntary LTC insurance rate in the world. This paper uses an actuarial multi-state disability model and calibrates the transition probabilities and duration-of-stay at various health (disability) states to assess the adequacy and comprehensiveness of Eldershield. The time-limited cash benefit design in Eldershield helped defray about 13% of LTC costs. Removing the time cap will help defray 23% and 26% of the LTC costs for elderly male and female respectively. Furthermore, the simulation results demonstrate that relaxing the trigger benefit and having staggered payouts will improve the adequacy of long-term care insurance. The experience of Singapore’s LTC insurance offers insights into the challenges of designing an insurance that tends to occur at higher age and insuring against a cost that could range from zero to a significantly large sum over a long period. Even with the enhanced Careshield Life, which provides cash payouts for life, other policy designs, for example caregiver grants, may be needed to ensure more adequate financing of long-term care.

scholarly journals A 16-year prospective cohort study to evaluate effects of long-term fluctuations in obesity indices of prediabetics on the incidence of future diabetes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shahla Safari ◽  
Maryam Abdoli ◽  
Masoud Amini ◽  
Ashraf Aminorroaya ◽  
Awat Feizi
Keyword(s):  
High Risk ◽  
Transition Probabilities ◽  
Clinical Settings ◽  
High Tendency ◽  
Mean Values ◽  
Latent Markov Model ◽  
Term Evaluation ◽  
Obesity Indices

AbstractThis study aimed to evaluate the patterns of changes in obesity indices over time in prediabetic subjects and to classify these subjects as either having a low, moderate, and high risk for developing diabetes in the future. This study was conducted among 1228 prediabetics. The patterns of changes in obesity indices based on three measurements including first, mean values during the follow-up period, and last visit from these indices were evaluated by using the latent Markov model (LMM). The mean (standard deviation) age of subjects was 44.0 (6.8) years and 73.6% of them were female. LMM identified three latent states of subjects in terms of change in all anthropometric indices: a low, moderate, and high tendency to progress diabetes with the state sizes (29%, 45%, and 26%), respectively. LMM showed that the probability of transitioning from a low to a moderate tendency to progress diabetes was higher than the other transition probabilities. Based on a long-term evaluation of patterns of changes in obesity indices, our results reemphasized the values of all five obesity indices in clinical settings for identifying high-risk prediabetic subjects for developing diabetes in future and the need for more effective obesity prevention strategies.

scholarly journals Calibration of Transition Intensities for a Multistate Model: Application to Long-Term Care

Risks ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 37
Author(s):  
Manuel L. Esquível ◽  
Gracinda R. Guerreiro ◽  
Matilde C. Oliveira ◽  
Pedro Corte Real
Keyword(s):  
Markov Chain ◽  
Continuous Time ◽  
Long Term Care ◽  
Transition Probabilities ◽  
Markov Chain Model ◽  
Chain Model ◽  
Chain Process ◽  
Term Care ◽  
National Network

We consider a non-homogeneous continuous time Markov chain model for Long-Term Care with five states: the autonomous state, three dependent states of light, moderate and severe dependence levels and the death state. For a general approach, we allow for non null intensities for all the returns from higher dependence levels to all lesser dependencies in the multi-state model. Using data from the 2015 Portuguese National Network of Continuous Care database, as the main research contribution of this paper, we propose a method to calibrate transition intensities with the one step transition probabilities estimated from data. This allows us to use non-homogeneous continuous time Markov chains for modeling Long-Term Care. We solve numerically the Kolmogorov forward differential equations in order to obtain continuous time transition probabilities. We assess the quality of the calibration using the Portuguese life expectancies. Based on reasonable monthly costs for each dependence state we compute, by Monte Carlo simulation, trajectories of the Markov chain process and derive relevant information for model validation and premium calculation.

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