Spatial interpolation of McArthur's Forest Fire Danger Index across Australia: Observational study

Sanabria, L. A.; Qin, Xuerong; Li, J.; Cechet, R. P.; Lucas, Chris

doi:10.1016/j.envsoft.2013.08.012

Cited by 49 publications

(28 citation statements)

References 20 publications

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“…We first interpolate the AQI surface through the values of those stations by Ordinary Kriging (OK) [40], which is a geostatistical method where the weights for interpolation are computed by the neighboring values called "semivariances" (γ). In Equation (2), n is the number of pairs of sample points z separated by distance h, andγ phq is the semivariogram which is a function of distance [41].…”

Section: Spatial Characteristicsmentioning

confidence: 99%

A Visualization Approach to Air Pollution Data Exploration—A Case Study of Air Quality Index (PM2.5) in Beijing, China

Fan

Mao

2016

Atmosphere

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Abstract:In recent years, frequent occurrences of significant air pollution events in China have routinely caused panic and are a major topic of discussion by the public and air pollution experts in government and academia. Therefore, this study proposed an efficient visualization method to represent directly, quickly, and clearly the spatio-temporal information contained in air pollution data. Data quality check and cleansing during a preliminary visual analysis is presented in tabular form, heat matrix, or line chart, upon which hypotheses can be deduced. Further visualizations were designed to verify the hypotheses and obtain useful findings. This method was tested and validated in a year-long case study of the air quality index (AQI of PM 2.5 ) in Beijing, China. We found that PM 2.5 , PM 10 , and NO 2 may be emitted by the same sources, and strong winds may accelerate the spread of pollutants. The average concentration of PM 2.5 in Beijing was greater than the AQI value of 50 over the six-year study period. Furthermore, arable lands exhibited considerably higher concentrations of air pollutants than vegetation-covered areas. The findings of this study showed that our visualization method is intuitive and reliable through data quality checking and information sharing with multi-perspective air pollution graphs. This method allows the data to be easily understood by the public and inspire or aid further studies in other fields.

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Section: Spatial Characteristicsmentioning

confidence: 99%

A Visualization Approach to Air Pollution Data Exploration—A Case Study of Air Quality Index (PM2.5) in Beijing, China

Fan

Mao

2016

Atmosphere

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“…The same phenomenon was for instance in Sanabria et al (2013), Yao et al (2013), Ünal and Özcakal (2011). The underestimation of velocities by IDW was another disadvantage in this case.…”

Section: Discussionmentioning

confidence: 73%

Spatial interpolation of point velocities in stream cross-section

Hasníková

Pavlásek

Vach

2015

Journal of Hydrology and Hydromechanics

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Abstract:The most frequently used instrument for measuring velocity distribution in the cross-section of small rivers is the propeller-type current meter. Output of measuring using this instrument is point data of a tiny bulk. Spatial interpolation of measured data should produce a dense velocity profile, which is not available from the measuring itself. This paper describes the preparation of interpolation models.Measuring campaign was realized to obtain operational data. It took place on real streams with different velocity distributions. Seven data sets were obtained from four cross-sections varying in the number of measuring points, 24-82. Following methods of interpolation of the data were used in the same context: methods of geometric interpolation arithmetic mean and inverse distance weighted, the method of fitting the trend to the data thin-plate spline and the geostatistical method of ordinary kriging. Calibration of interpolation models carried out in the computational program Scilab is presented. The models were tested with error criteria by cross-validation. Ordinary kriging was proposed to be the most suitable interpolation method, giving the lowest values of used error criteria among the rest of the interpolation methods.

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“…Numerous studies have established different climatic geospatial datasets such as precipitation and surface temperature, etc., using interpolation algorithms based on a certain number of recorded locations directed by the other auxiliary variables (i.e. : DEM) to improve the results (Ahmed et al, 2014;Huang and Hu, 2009;Sanabria et al, 2013). In this sense, the present study developed a hybrid interpolation method to estimate the Rc in ungauged basins of Africa using the Inverse Distance Weighting (IDW) interpolation algorithm directed by major runoff controlling factors (Potential runoff coefficient, surface temperature and precipitation).…”

Section: Runoff Coefficient Estimation In Ungauged Basinsmentioning

confidence: 99%

Spatial Relationship between Precipitation and Runoff in Africa

Karamage

Fan

Justine

et al. 2018

Preprint

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Abstract. Lack of sufficient and reliable hydrological information is a key hindrance to water resource planning and management in Africa. Hence, the objective of this research is to examine the relationship between precipitation and runoff at three spatial scales, including the whole continent, 25 major basins and 55 countries. For this purpose, the long-term monthly runoff coefficient (Rc) was estimated using the long-term monthly runoff data (R) calculated from the Global Runoff Data Centre (GRDC) streamflow records and Global Precipitation Climatology Centre (GPCC) precipitation datasets for the period of time spanning from 1901 to 2017. Subsequently, the observed Rc data were interpolated in order to estimate Rc over the ungauged basins under guidance of key runoff controlling factors, including the land-surface temperature (T), precipitation (P) and potential runoff coefficient (Co) inferred from the land use and land cover, slope and soil texture information. The results show that 16 % of the annual mean precipitation (672.52 mm) becomes runoff (105.72 mm), with a runoff coefficient of 0.16, and the remaining 84 % (566.80 mm) evapotranspirates over the continent during 1901–2017. Spatial analysis reveals that the precipitation–runoff relationship varies significantly among different basins and countries, mainly dependent on climatic conditions and its inter-annual variability. Generally, high runoff depths and runoff coefficients are observed over humid tropical basins and countries with high precipitation intensity compared to those located in subtropical and temperate drylands.

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Spatial interpolation of McArthur's Forest Fire Danger Index across Australia: Observational study

Cited by 49 publications

References 20 publications

A Visualization Approach to Air Pollution Data Exploration—A Case Study of Air Quality Index (PM2.5) in Beijing, China

A Visualization Approach to Air Pollution Data Exploration—A Case Study of Air Quality Index (PM2.5) in Beijing, China

Spatial interpolation of point velocities in stream cross-section

Spatial Relationship between Precipitation and Runoff in Africa

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