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A novel spatially weighted accumulative procedure for regional drought monitoring Cover

A novel spatially weighted accumulative procedure for regional drought monitoring

Tellus
Volume 72 (2020): Issue 1
By: ,  ,  ,  ,  ,  ,   and    
Open Access
|Jan 2020
Figures & tables

Figures & Tables

Fig. 1.

Flow chart of the proposed procedure.

Fig. 2.

Geographical locations of the six selected stations of the Northern area of Pakistan.

Table 2.

An initial classification of drought categories based on the values of the SDI.

SDIMajor drought classesSDI < = −2Extremely Dry (ED)SDI > −2 & SDI < = −1.5Severely Dry (SD)SDI > −1.5 & SDI < = −1Median Dry (MD)SDI > −1 & SDI < = 1Normal Dry (ND)SDI > 1 & SDI < = 1.5Median Wet (MW)SDI > 1.5 & SDI < = 2Severely Wet (SW)SDI > 2Extremely Wet (EW)
Table 4.

Steady-state probabilities for seven drought classes at scale-1 on varying indices (SPI, SPEI and SPTI) for selected stations.

EDEWMDMWNDSDSWSUMSPINA0.02310.09250.09580.67650.06950.04251AstoreScale-1SPEINA0.03010.16720.08660.66110.02140.03361SPTINA0.01950.08000.11870.67890.05340.04951SPINA0.01420.20120.08700.6480NA0.04961BunjiScale-1SPEINA0.00710.18530.17130.59370.02670.01601SPTINA0.01420.20470.07270.6604NA0.04791SPINA0.02130.28420.07460.5755NA0.04441GupisScale-1SPEINA0.00530.16200.08850.68360.04630.01421SPTINA0.01240.28420.09410.5577NA0.05151ChilasSPINA0.02480.19400.09940.6426NA0.03911Scale-1SPEINA0.01590.18520.10790.64130.02490.02481SPTINA0.02660.17050.09240.6590NA0.05151SPINA0.01240.20300.07980.6569NA0.04791GilgitScale-1SPEINA0.00180.19260.18000.57600.02500.02471SPTINA0.01060.19940.07450.6587NA0.05681SPINA0.02310.15660.08700.6836NA0.04971SkarduScale-1SPEINA0.01590.18870.09890.63780.02670.03201SPTINA0.00180.03200.07460.8401NA0.05151
Table 1.

The climatology characteristics during the period 1971–2017 of six selected stations.

VariableStationMean1st QuartileMedian3rd QuartileKurtosisSt.DevPrecipitationAstore40.9110.8025.7052.633.0141.93Bunji14.341.307.1017.107.6718.90Gupis16.850.005.7019.3814.0530.21Chilas16.760.957.0019.339.0223.53Gilgit12.421.106.0514.7310.0816.57Skardu20.632.309.1026.755.7025.90Maximum temperatureAstore16.667.3816.7023.86−1.368.65Bunji25.1915.7824.9532.03−1.328.98Gupis19.9510.3319.7027.40−1.309.46Chilas27.9217.6827.3535.63−1.389.66Gilgit25.4615.6025.1532.80−1.339.21Skardu19.929.9520.0527.90−1.249.82Minimum temperatureAstore4.34−2.434.3010.70−1.227.48Bunji11.863.7811.5017.70−1.247.80Gupis6.74−1.106.9013.33−1.268.06Chilas15.145.6814.3023.20−1.419.08Gilgit8.070.607.7513.53−1.247.30Skardu5.06−2.735.5511.80−1.178.36
Table 3.

BIC of selected probability distributions for SPI, SPEI and SPTI on six stations at scale-1.

AstoreBunjiGupisDistributionSPISPEISPTISPISPEISPTISPISPEISPTI3P Weibull−1036.51−700.54−483.52−1030.98−1178.06−188.45−735.14−910.74−370.40Trapezoidal−940.42−710.05−354.12−712.78−1224.00−151.71−531.57−946.94−318.904P Beta−1031.38−700.28−473.37−1020.69−1210.97−74.24−788.07−823.15−374.23Johnson SB−945.13−685.96−358.76−664.59−1248.43−148.56−535.96−977.69−273.65ChilasGilgitSkarduDistributionSPISPEISPTISPISPEISPTISPISPEISPTIJohnson SU−399.83−489.81−217.79−720.24−1015.98−107.79−499.83−528.99−590.053P Weibull−800.23−488.05−275.42−1097.48−1016.34−164.62−735.12−640.78−516.574P Beta−805.61−574.97213.41−1085.12−1162.23−73.37−700.23−523.68−506.86Trapezoidal−720.38−579.72−72.94−922.26−1185.66−150.09−720.38−644.67−418.48Johnson SB−761.68−594.79−103.55−971.01−1213.27−75.88−711.68−656.24−399.98
Table 5.

Correlation coefficients among different stations at scale-1.

SPISPEISPTIStationsrp Valuerp Valuerp ValueAstore0.5083.00000.6272.00000.5400.0000Bunji0.5020.00000.5822.00000.4959.0000Gupis0.3914.00000.7170.00000.4309.0000Chilas0.7689.00000.6091.00000.5299.0000Gilgit0.5221.00000.5734.00000.5210.0000Skardu0.7524.00000.6028.00000.5229.0000
Fig. 3.

Theoretical vs. empirical histograms of selected distributions for six stations.

Fig. 4.

Temporal plots of SPI index for six stations and SWADI at scale-1.

Fig. 5.

Count-plot for drought categories vs steady-state probabilities weights for Astore at scale-1.

Fig. 6.

Count-plot for drought categories vs steady-state weights for Bunji at scale-1.

DOI: https://doi.org/10.1080/16000870.2020.1838194 | Journal eISSN: 3035-9554
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Language: English
Page range: 1838194 - 1838194
Published on: Jan 1, 2020
Published by: Stockholm University Press
In partnership with: Paradigm Publishing Services
Keywords:
meteorological stations,
Standardized Drought Indices (SDI),
Markov chain,
steady-state probabilities

© 2020 Rizwan Niaz, Ijaz Hussain, Zulfiqar Ali, Muhammad Faisal, Elsayed Elsherbini Elashkar, Alaa Mohamd Shoukry, Showkat Gani, Fares Fawzi Al-Deek, published by Stockholm University Press
This work is licensed under the Creative Commons Attribution 4.0 License.

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