An asynchronous regional regression model for statistical downscaling of daily climate variables

2012 ◽  
Vol 33 (11) ◽  
pp. 2473-2494 ◽  
Author(s):  
Anne M. K. Stoner ◽  
Katharine Hayhoe ◽  
Xiaohui Yang ◽  
Donald J. Wuebbles
Keyword(s):  
Regression Model ◽  
Statistical Downscaling ◽  
Climate Variables ◽  
Regional Regression

scholarly journals Regional regression models of percentile flows for the contiguous US: Expert versus data-driven independent variable selection

2016 ◽  
Author(s):  
Geoffrey Fouad ◽  
André Skupin ◽  
Christina L. Tague
Keyword(s):  
Regression Model ◽  
Regression Models ◽  
Predictive Performance ◽  
Data Driven ◽  
Mean Annual Precipitation ◽  
Expert Assessment ◽  
Independent Variables ◽  
Regional Regression ◽  
Data Driven Approach ◽  
Small Set

Abstract. Percentile flows are statistics derived from the flow duration curve (FDC) that describe the flow equaled or exceeded for a given percent of time. These statistics provide important information for managing rivers, but are often unavailable since most basins are ungauged. A common approach for predicting percentile flows is to deploy regional regression models based on gauged percentile flows and related independent variables derived from physical and climatic data. The first step of this process identifies groups of basins through a cluster analysis of the independent variables, followed by the development of a regression model for each group. This entire process hinges on the independent variables selected to summarize the physical and climatic state of basins. Distributed physical and climatic datasets now exist for the contiguous United States (US). However, it remains unclear how to best represent these data for the development of regional regression models. The study presented here developed regional regression models for the contiguous US, and evaluated the effect of different approaches for selecting the initial set of independent variables on the predictive performance of the regional regression models. An expert assessment of the dominant controls on the FDC was used to identify a small set of independent variables likely related to percentile flows. A data-driven approach was also applied to evaluate two larger sets of variables that consist of either (1) the averages of data for each basin or (2) both the averages and statistical distribution of basin data distributed in space and time. The small set of variables from the expert assessment of the FDC and two larger sets of variables for the data-driven approach were each applied for a regional regression procedure. Differences in predictive performance were evaluated using 184 validation basins withheld from regression model development. The small set of independent variables selected through expert assessment produced similar, if not better, performance than the two larger sets of variables. A parsimonious set of variables only consisted of mean annual precipitation, potential evapotranspiration, and baseflow index. Additional variables in the two larger sets of variables added little to no predictive information. Regional regression models based on the parsimonious set of variables were developed using 734 calibration basins, and were converted into a tool for predicting 13 percentile flows in the contiguous US. Supplementary Material for this paper includes an R graphical user interface for predicting the percentile flows of basins within the range of conditions used to calibrate the regression models. The equations and performance statistics of the models are also supplied in tabular form.

scholarly journals Development of a Regional Regression Model for Estimating Annual Runoff in the Hailar River Basin of China

2010 ◽  
Vol 02 (11) ◽  
pp. 934-943 ◽  
Author(s):  
Limin Duan ◽  
Tingxi Liu ◽  
Xixi Wang ◽  
Yanyun Luo ◽  
Long Wu
Keyword(s):  
Regression Model ◽  
River Basin ◽  
Annual Runoff ◽  
Regional Regression

Cluster based regression model on dengue incidence using dual climate variables

2016 ◽  
Author(s):  
Shermon S. Mathulamuthu ◽  
Vijanth S. Asirvadam ◽  
Sarat C. Dass ◽  
Balvinder S. Gill
Keyword(s):  
Regression Model ◽  
Climate Variables

scholarly journals A Dynamical and Zero-Inflated Negative Binomial Regression Modelling of Malaria Incidence in Limpopo Province, South Africa

2019 ◽  
Vol 16 (11) ◽  
pp. 2000 ◽  
Author(s):  
Gbenga J. Abiodun ◽  
Olusola S. Makinde ◽  
Abiodun M. Adeola ◽  
Kevin Y. Njabo ◽  
Peter J. Witbooi ◽  
...  
Keyword(s):  
South Africa ◽  
Time Series ◽  
Regression Model ◽  
Negative Binomial ◽  
Malaria Incidence ◽  
Negative Binomial Regression ◽  
Limpopo Province ◽  
Negative Binomial Regression Model ◽  
Climate Variables ◽  
Binomial Regression

Recent studies have considered the connections between malaria incidence and climate variables using mathematical and statistical models. Some of the statistical models focused on time series approach based on Box–Jenkins methodology or on dynamic model. The latter approach allows for covariates different from its original lagged values, while the Box–Jenkins does not. In real situations, malaria incidence counts may turn up with many zero terms in the time series. Fitting time series model based on the Box–Jenkins approach and ARIMA may be spurious. In this study, a zero-inflated negative binomial regression model was formulated for fitting malaria incidence in Mopani and Vhembe―two of the epidemic district municipalities in Limpopo, South Africa. In particular, a zero-inflated negative binomial regression model was formulated for daily malaria counts as a function of some climate variables, with the aim of identifying the model that best predicts reported malaria cases. Results from this study show that daily rainfall amount and the average temperature at various lags have a significant influence on malaria incidence in the study areas. The significance of zero inflation on the malaria count was examined using the Vuong test and the result shows that zero-inflated negative binomial regression model fits the data better. A dynamical climate-based model was further used to investigate the population dynamics of mosquitoes over the two regions. Findings highlight the significant roles of Anopheles arabiensis on malaria transmission over the regions and suggest that vector control activities should be intense to eradicate malaria in Mopani and Vhembe districts. Although An. arabiensis has been identified as the major vector over these regions, our findings further suggest the presence of additional vectors transmitting malaria in the study regions. The findings from this study offer insight into climate-malaria incidence linkages over Limpopo province of South Africa.

Remote Sensing-based House Value Estimation Using an Optimized Regional Regression Model

2013 ◽  
Vol 79 (9) ◽  
pp. 809-820 ◽  
Author(s):  
Zhenyu Lu ◽  
Jungho Im ◽  
Lindi J. Quackenbush ◽  
Sanglim Yoo
Keyword(s):  
Remote Sensing ◽  
Regression Model ◽  
Regional Regression ◽  
Value Estimation ◽  
House Value

scholarly journals Evaluation of Empirical Statistical Downscaling Models’ Skill in Predicting Tanzanian Rainfall and Their Application in Providing Future Downscaled Scenarios

2016 ◽  
Vol 29 (9) ◽  
pp. 3231-3252 ◽  
Author(s):  
Habiba I. Mtongori ◽  
Frode Stordal ◽  
Rasmus E. Benestad
Keyword(s):  
Statistical Downscaling ◽  
Large Scale ◽  
Domain Size ◽  
Seasonal Rainfall ◽  
Total Precipitation ◽  
Climate Variables ◽  
Skill Scores ◽  
The Common ◽  
Crop Agriculture ◽  
Near Term

Abstract Projections of three important seasonal rainfall parameters—total precipitation (), wet-day mean () and wet-day frequency ()—considered to be relevant to crop agriculture were performed. Links between large-scale climate variables and local precipitation in Tanzania were investigated during the March–May (MAM), October–December (OND), and December–April (DJFMA) rainfall seasons. Variables found to have strong links were used to downscale the local precipitation for three future periods; near term, midcentury, and end of century. Downscaling models for , and were calibrated using observed large-scale seasonal rainfall and projected downscaled parameters were obtained based on rainfall simulations from ensembles of GCMs. The models’ skill scores were found to be sensitive to the domain size and number of leading EOFs used. The common EOF method employed in the downscaling modulated the skills depending on the GCMs used. The spread in the rainfall projections was found to be larger in OND and moderate in MAM and DJFMA. The multimodel mean projections in response to two RCPs (RCP4.5 and RCP8.5) suggest a shift toward wetter (drier) conditions () for OND (DJFMA) for all three periods. There is no uniform projection for MAM; some stations are projected to feature wetter and some drier conditions. In the midcentury and end of century, there is an increase of precipitation to about 40% for some areas getting OND rainfall and a decrease of precipitation up to about 10% for some areas getting MAM or DJFMA rainfall. Generally, the magnitude of change strongly differs across the areas.

scholarly journals Non-parametric Bayesian mixture of sparse regressions with application towards feature selection for statistical downscaling

2014 ◽  
Vol 21 (6) ◽  
pp. 1145-1157 ◽  
Author(s):  
D. Das ◽  
J. Dy ◽  
J. Ross ◽  
Z. Obradovic ◽  
A. R. Ganguly
Keyword(s):  
Climate Change ◽  
Feature Selection ◽  
Transfer Function ◽  
Regression Model ◽  
Statistical Downscaling ◽  
Regional Climate ◽  
Function Approach ◽  
Climate Projections ◽  
Bayesian Mixture ◽  
Selection For

Abstract. Climate projections simulated by Global Climate Models (GCMs) are often used for assessing the impacts of climate change. However, the relatively coarse resolutions of GCM outputs often preclude their application to accurately assessing the effects of climate change on finer regional-scale phenomena. Downscaling of climate variables from coarser to finer regional scales using statistical methods is often performed for regional climate projections. Statistical downscaling (SD) is based on the understanding that the regional climate is influenced by two factors – the large-scale climatic state and the regional or local features. A transfer function approach of SD involves learning a regression model that relates these features (predictors) to a climatic variable of interest (predictand) based on the past observations. However, often a single regression model is not sufficient to describe complex dynamic relationships between the predictors and predictand. We focus on the covariate selection part of the transfer function approach and propose a nonparametric Bayesian mixture of sparse regression models based on Dirichlet process (DP) for simultaneous clustering and discovery of covariates within the clusters while automatically finding the number of clusters. Sparse linear models are parsimonious and hence more generalizable than non-sparse alternatives, and lend themselves to domain relevant interpretation. Applications to synthetic data demonstrate the value of the new approach and preliminary results related to feature selection for statistical downscaling show that our method can lead to new insights.

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