A Bayesian Kriging model applied for spatial downscaling of daily rainfall from GCMs

2021 ◽  
Vol 597 ◽  
pp. 126095
Author(s):  
Carlos H.R. Lima ◽  
Hyun-Han Kwon ◽  
Yong-Tak Kim
Keyword(s):  
Daily Rainfall ◽  
Kriging Model ◽  
Bayesian Kriging ◽  
Spatial Downscaling

scholarly journals A Bayesian kriging model for estimating residential exposure to air pollution of children living in a high-risk area in Italy

2013 ◽  
Vol 8 (1) ◽  
pp. 87 ◽  
Author(s):  
Ana M. Vicedo-Cabrera ◽  
Annibale Biggeri ◽  
Laura Grisotto ◽  
Fabio Barbone ◽  
Dolores Catelan
Keyword(s):  
Air Pollution ◽  
High Risk ◽  
Kriging Model ◽  
Risk Area ◽  
Residential Exposure ◽  
High Risk Area ◽  
Bayesian Kriging

scholarly journals Uncertainty Estimations in Different Components of a Hybrid ANN - Fuzzy - Kriging Model for Water Table Level Simulation

10.29007/tx23 ◽  
2018 ◽  
Author(s):  
Evdokia Tapoglou ◽  
Emmanouil A. Varouchakis ◽  
George P. Karatzas
Keyword(s):  
Kriging Model ◽  
Dade County ◽  
Simulation Algorithm ◽  
Training Set ◽  
Bayesian Kriging ◽  
The Third ◽  
The Past ◽  
Final Approach ◽  
Testing Error ◽  
Artificial Neural Network Ann

The purpose of this study is to examine the uncertainty of a combined artificial neural network (ANN), kriging and fuzzy logic methodology, which can be used for spatial and temporal simulation of hydraulic head in an aquifer. This methodology was applied in the past, while the verification of the model was performed by implementing it in a new study area, in Miami – Dade County, FL, USA. The percentile methodology was applied as a first approach in order to define the ANN uncertainty. As a second approach, the uncertainty of the ANN training is tested through a Monte Carlo procedure. The model was executed 300 times using different training set and initial random values each time. The training results constituted a sensitivity analysis of the ANN training to the kriging part of the algorithm. The training and testing error intervals for the ANNs and the kriging prediction intervals calculated through this procedure can be considered narrow compared to the complexity of the study area. For the third and final approach used in this work, the uncertainty of kriging parameter was calculated through the Bayesian kriging methodology. The results derived can prove that the simulation algorithm can provide consistent and accurate results.

A Bayesian Kriging Model to Predict PM10 Annual Average Concentrations for the Lombardy Region (Italy)

Epidemiology ◽  
2011 ◽  
Vol 22 ◽  
pp. S82
Author(s):  
Laura Grisotto ◽  
Dolores Catelan ◽  
Annibale Biggeri ◽  
Paolo Grillo ◽  
Pier Alberto Bertazzi
Keyword(s):  
Kriging Model ◽  
Annual Average ◽  
Bayesian Kriging ◽  
Lombardy Region

Penentuan Metode Intensitas Hujan Berdasarkan Karakteristik Hujan dari Stasiun Pengamat Hujan Disekitar Kecamatan Karawang Timur

2019 ◽  
Vol 5 (1) ◽  
Author(s):  
Melisa Permatasari ◽  
M. Candra Nugraha ◽  
Etih Hartati
Keyword(s):  
Drainage System ◽  
Daily Rainfall ◽  
Intensity Analysis ◽  
Rain Intensity ◽  
Rainy Period ◽  
Discharge Plan ◽  
Calculation Results ◽  
Value Determination ◽  
Maximum Daily Rainfall ◽  
Method Approach

<p>The rain intensity is the high rainfall in unit of time. The length of rain will be reversed by the amount rain intensity. The shorter time the rain lasts, the greater of the intensity and re-period of its rain. The value of rain intensity is required to calculate the flood discharge plan on the drainage system planning area in East Karawang district. Determining the value rain intensity is required the maximum daily rainfall data obtained from the main observer stations in the Plawad station planning area. The method of determination rain intensity analysis can be done with three methods: Van Breen, Bell Tanimoto and Hasper der Weduwen. Selected method is based on the smallest deviation value. Determination deviation value is determined by comparing rain intensity value of Van Breen method, Bell Tanimoto, Hasper der Weduwen. By comparing rain intensity value of the Van Breen method, Bell Tanimoto, Hasper der Weduwen with the results of calculating three methods through the method approach Talbot, Sherman and Ishiguro. Calculation results show that the method of rain has smallest deviation standard is method Van Breen with Talbot approach for rainy period (PUH) 2, 5, 10, 25, 50 and 100 years.</p>

The Influence of Geographical Factors on Extreme Rainfalls in Lampung Province

2019 ◽  
Vol 1 (1) ◽  
pp. 33
Author(s):  
M Welly
Keyword(s):  
Geographical Location ◽  
Daily Rainfall ◽  
Extreme Rainfall ◽  
Design Flood ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
Ungauged Basin ◽  
Flood Discharge ◽  
Design Rainfall ◽  
Geographical Factors

Many people in Indonesia calculate design rainfall before calculating the design flooddischarge. The design rainfall with a certain return period will eventually be convertedinto a design flood discharge by combining it with the characteristics of the watershed.However, the lack of a network of rainfall recording stations makes many areas that arenot hydrologically measured (ungauged basin), so it is quite difficult to know thecharacteristics of rain in the area concerned. This study aims to analyze thecharacteristics of design rainfall in Lampung Province. The focus of the analysis is toinvestigate whether geographical factors influence the design rainfall that occurs in theparticular area. The data used in this study is daily rainfall data from 15 rainfallrecording stations spread in Lampung Province. The method of frequency analysis usedin this study is the Gumbel method. The research shows that the geographical location ofan area does not have significant effect on extreme rainfall events. The effect of risingearth temperatures due to natural exploitation by humans tends to be stronger as a causeof extreme events such as extreme rainfall.Keywords: Influence, geographical, factors, extreme, rainfall.

Efficient Optimization Design Method Using Kriging Model

2005 ◽  
Vol 42 (5) ◽  
pp. 1375-1375 ◽  
Author(s):  
Shinkyu Jeong ◽  
Mitsuhiro Murayama ◽  
Kazuomi Yamamoto
Keyword(s):  
Optimization Design ◽  
Design Method ◽  
Kriging Model

scholarly journals Seasonal Climate Impacts on Vocal Activity in Two Neotropical Nonpasserines

Diversity ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 319
Author(s):  
Cristian Pérez-Granados ◽  
Karl-L. Schuchmann
Keyword(s):  
Air Temperature ◽  
Daily Rainfall ◽  
Climatic Conditions ◽  
Dry Season ◽  
Climate Impacts ◽  
Calling Behavior ◽  
Wet Season ◽  
Focal Species ◽  
Calling Activity ◽  
Vocal Activity

Climatic conditions represent one of the main constraints that influence avian calling behavior. Here, we monitored the daily calling activity of the Undulated Tinamou (Crypturellus undulatus) and the Chaco Chachalaca (Ortalis canicollis) during the dry and wet seasons in the Brazilian Pantanal. We aimed to assess the effects of climate predictors on the vocal activity of these focal species and evaluate whether these effects may vary among seasons. Air temperature was positively associated with the daily calling activity of both species during the dry season. However, the vocal activity of both species was unrelated to air temperature during the wet season, when higher temperatures occur. Daily rainfall was positively related to the daily calling activity of both species during the dry season, when rainfall events are scarce and seem to act as a trigger for breeding phenology of the focal species. Nonetheless, air temperature was negatively associated with the daily calling activity of the Undulated Tinamou during the wet season, when rainfall was abundant. This study improves our understanding of the vocal behavior of tropical birds and their relationships with climate, but further research is needed to elucidate the mechanisms behind the associations found in our study.

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