A new drought index and its application based on geographically weighted regression (GWR) model and multi-source remote sensing data

Wei, Wei; Zhang, Xing; Xie, Binbin; Zhou, Junju; Zhang, Haoyan

doi:10.1007/s11356-022-23200-8

“…In order to facilitate the study of precipitation differences under complex terrain conditions in the Tianshan Mountains, the K-nearest neighbor method (Zhengyong et al, 2015) was adopted in this study to divide the Tianshan Mountains into four subregions, as shown in Figure 1B. According to Gu (Yang et al, 2015), dry and wet conditions in the Tianshan Mountains were roughly divided (Figure 1B): the whole Tianshan Mountains are dominated by arid areas (52%) and semi-arid areas (31%), and some extreme dry and dry areas (15%) are mainly distributed in the low rainfall area of Turpan Hami basin in the Eastern Tianshan Mountains (203 mm/a), a few semihumid areas (2%) are mainly distributed in the northern part of Tianshan Mountains (424 mm/a), including the Ili Valley and the main peak area of precipitation in the middle of Tianshan Mountains, which is basically consistent with the zoning results of Wei et al(2022).…”

Section: Study Areasupporting

confidence: 84%

Construction of high-resolution precipitation dataset and its implication to drought over the Tianshan Mountains, China

Li

¹

,

He²,

Li³

et al. 2023

Front. Earth Sci.

1

0

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Understanding the drought characteristics of mountainous areas in northwest China with sparse rainfall stations requires high precision, as well as high-resolution precipitation data. Considering the spatial relationship of precipitation and environmental factors, this study downscales Global Precipitation Measurement (GPM) and Multi-Source Weighted-Ensemble Precipitation (MSWEP) based on the geographically weighted regression (GWR) and multi-scale geographically weighted regression (MGWR) models integrated with interpolation. A high-resolution (1 km×1 km) precipitation dataset during 1979–2020 is reconstructed in the Tianshan Mountains, and the drought characteristics are analyzed by using the optimal dataset. The results show that: 1) Compared with GWR, MGWR model has higher downscaling accuracy; 2) The optimal MSWEP downscaling dataset (CC = 0.93, |BIAS| = 0.48%) compared to GPM (CC = 0.81, |BIAS| = 1.87%) is closer to the observed precipitation; 3) In the past 40 years, 71% and 9% of the Tianshan Mountains show significant wetting and drying trends respectively, and 16 drought events are identified. 4) The West subregion of the Tianshan Mountains is characterized by low frequency, long duration and high severity of drought events. The characteristics of the East are opposite to those of the West. Occasional extreme drought events occur in the North and South. This paper provides data support and method reference for the study of water-vapor balance and regional ecohydrological process in the arid area of Northwest China.

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“…In the model with local spatial effect represented by Geographically Weighted Regression (GWR), the interest is to regionalize the study area by obtaining subregions with their own pattern (Fotheringham et al, 2002;Fotheringham et al, 2017;Li et al, 2020;Bergs, 2021;Kedron et al, 2021). Wei et al (2022) found that the GWR was advantageous and effective when analyzing climatic conditions in regions of China from 2001 to 2019.…”

mentioning

confidence: 99%

An Analysis of the Gross Domestic Product of Municipalities: a Spatial Glance into the State of Paraná-Brazil

Cima

¹

,

Rocha-Junior

²

,

Uribe-Opazo

³

et al. 2023

AOL

0

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The vast relevance of applications of spatial regression models has recently captured the interest of Economics and Agriculture, in the sense of better understanding the spatial behavior of the region under study, in the different forms of approaches. It is interesting to understand why some regions show greater variability than others, and why some forms of regional development are better explained. It is up to the researcher to understand, explore, and organize a series of observations, so that it is possible to make predictions, diagnoses, and recommendations to public policy managers and regional development agents. The municipalities’ Gross Domestic Product (Gdp) has driven studies involving spatial information. The objective of this study was to analyze the Gdp of the municipalities in Paraná-Brazil, in 2018, regarding soybean yield, corn yield, pig production, and the tax on the circulation of goods, through different approaches of spatial regression models. SAR and CAR models are global models, while the GWR model is considered a local one. Three spatial analysis models were used to perform this study: Spatial Autoregressive (SAR), Conditional Autoregressive (CAR), and Geographically Weighted Regression (GWR). The results were compared using the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Cross-Validation Criterion (CVC), and the descriptive graphic of residual diagnoses-Worm Plot. The best result obtained was for the GWR model, which best explained the GDP of the state of Paraná-Brazil in terms of its covariates.

show abstract

“…In the model with local spatial effect represented by Geographically Weighted Regression (GWR), the interest is to regionalize the study area by obtaining subregions with their own pattern (Fotheringham et al, 2002;Fotheringham et al, 2017;Li et al, 2020;Bergs, 2021;Kedron et al, 2021). Wei et al (2022) found that the GWR was advantageous and effective when analyzing climatic conditions in regions of China from 2001 to 2019.…”

mentioning

confidence: 99%

An Analysis of the Gross Domestic Product of Municipalities: A Spatial Glance into the State of Paraná-Brazil

Cima

¹

,

Rocha-Junior

²

,

Uribe-Opazo

³

et al. 2023

AOL

0

View full text Add to dashboard Cite

The vast relevance of applications of spatial regression models has recently captured the interest of Economics and Agriculture, in the sense of better understanding the spatial behavior of the region under study, in the different forms of approaches. It is interesting to understand why some regions show greater variability than others, and why some forms of regional development are better explained. It is up to the researcher to understand, explore, and organize a series of observations, so that it is possible to make predictions, diagnoses, and recommendations to public policy managers and regional development agents. The municipalities’ Gross Domestic Product (Gdp) has driven studies involving spatial information. The objective of this study was to analyze the Gdp of the municipalities in Paraná-Brazil, in 2018, regarding soybean yield, corn yield, pig production, and the tax on the circulation of goods, through different approaches of spatial regression models. SAR and CAR models are global models, while the GWR model is considered a local one. Three spatial analysis models were used to perform this study: Spatial Autoregressive (SAR), Conditional Autoregressive (CAR), and Geographically Weighted Regression (GWR). The results were compared using the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Cross-Validation Criterion (CVC), and the descriptive graphic of residual diagnoses-Worm Plot. The best result obtained was for the GWR model, which best explained the GDP of the state of Paraná-Brazil in terms of its covariates.

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A new drought index and its application based on geographically weighted regression (GWR) model and multi-source remote sensing data

Cited by 11 publications

References 82 publications

Construction of high-resolution precipitation dataset and its implication to drought over the Tianshan Mountains, China

Construction of high-resolution precipitation dataset and its implication to drought over the Tianshan Mountains, China

An Analysis of the Gross Domestic Product of Municipalities: a Spatial Glance into the State of Paraná-Brazil

An Analysis of the Gross Domestic Product of Municipalities: A Spatial Glance into the State of Paraná-Brazil

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