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Measuring and restructuring the risk in forecasting drought classes: an application of weighted Markov chain based model for standardised precipitation evapotranspiration index (SPEI) at one-month time scale Cover

Measuring and restructuring the risk in forecasting drought classes: an application of weighted Markov chain based model for standardised precipitation evapotranspiration index (SPEI) at one-month time scale

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

Figures & Tables

Table 1.

Summary statistics of the selected stations.

StationJanFebMarAprMayJunRainfallMin tempMax tempRainfallMin tempMax tempRainfallMin tempMax tempRainfallMin tempMax tempRainfallMin tempMax tempRainfallMin tempMax tempAstore37.553.04–7.2545.844.49–5.0470.329.21–0.7377.915.464.0561.1520.317.8525.9524.5211.28Balakot93.1414.312.16149.6315.734.21173.2420.038.04122.9725.7912.6677.5231.2817.4100.734.820.61Kotli73.7417.984.47101.7319.757.17131.4224.4611.5374.7630.4616.5946.6335.621.1688.8937.5923.82Chirat39.739.393.3376.4210.974.2198.7915.737.7562.722.5313.2327.2728.6917.8126.6932.2820.88Chillas10.7412.271.6616.1714.63428.1719.978.6435.6326.1514.0130.9731.4818.9210.9936.8523.88Islamabad55.5717.833.2190.4319.646.07101.0324.3410.6659.1330.4615.8336.0436.0520.6875.7938.4523.89Gupis7.64.75–5.8113.117.09–3.3416.8212.631.743.0919.046.9327.0823.910.8518.7428.714.67Peshawar37.5518.474.3561.3219.996.9582.9124.311.7259.9530.5816.8922.5536.7321.9819.8140.1125.57Saidu Sharif57.529.95–4.83106.6910.8–2.58158.7315.062.32124.920.987.888.6126.5712.3763.433116.63Muzaffarabad95.5416.393.11144.2318.045.43165.9522.619.51106.1828.5914.0577.7933.8518.3120.7637.1421.51Bunji6.1310.190.029.7912.922.8912.1918.567.7325.6524.4112.0726.3628.7415.2111.0233.0418.8DIK1219.764.3922.1422.317.5838.4626.9112.9524.8833.7618.5311.8339.423.4222.9141.1426Drosh48.039.5–0.1877.7111.110.92110.916.474.7791.1323.6610.2557.8629.8915.1420.535.5819.97Garhi–Dupatta104.5414.992.64150.716.454.3171.0920.947.91117.6726.9912.2277.932.5116.68110.3536.2420.04Dir113.6711.86–2.43179.4112.61–0.82246.3316.83166.5322.977.478828.5111.4554.7932.4815.31Gilgit3.6610.18–2.687.1513.020.5411.3118.655.4624.8424.669.1625.4529.411.9110.9134.114.81Kakul66.1212.960.62107.9713.952.23145.5518.096.07113.9623.6710.4270.7628.9214.6197.4232.118.24JulAugSepOctNovDecAstore25.727.2214.4926.5526.7314.3727.1923.2810.1221.0417.864.6119.4811.64–0.2229.795.82–4.38Balakot332.9632.3721.4269.3831.2920.71119.6930.617.1153.8527.2811.5143.6621.976.4660.8216.83.29Kotli273.233.7523.8261.4332.6823.26101.6132.4920.8733.7930.315.7722.7525.299.6844.5420.285.23Chirat95.9629.1419.64104.7726.8818.7735.5526.2917.8719.0522.614.4915.217.0110.0723.4112.225.97Chillas12.6539.1127.0617.6938.1426.1210.4734.6522.156.728.51156.1820.877.6710.6114.063.02Islamabad310.6835.0324.52336.8733.6323.77126.9333.4721.1936.0830.8614.8415.3925.88.5830.9720.384.27Gupis16.1831.817.5126.430.4416.4713.5526.5312.278.8220.516.382.814.20.94.776.99–3.56Peshawar60.6437.4426.6581.4435.7925.8228.6134.9522.9522.2131.2816.6414.2125.8510.1817.0820.725.37Saidu Sharif107.430.4117.81132.4428.6316.5474.2426.6813.6651.8822.668.3931.4917.243.0530.0612.39–1.65Muzaffarabad337.2934.8322.64218.7933.9122.52108.3433.2419.3643.133013.536.9423.937.862.6218.24.09Bunji17.5635.322.4922.8734.6621.4115.2631.3316.286.9125.5410.313.2618.694.545.5112.261.09DIK71.6138.4326.4369.4537.2525.8232.3636.4323.538.2833.3617.563.0727.9110.697.0422.435.67Drosh21.4936.9722.5419.6335.8521.4819.6632.9617.431.2327.0411.2730.9319.245.9434.0712.461.94Garhi–Dupatta250.633.9721.96211.3432.8421.72104.8332.0918.1245.0428.3312.1641.7622.446.668.9617.023.48Dir150.9631.8118.92149.8530.7218.1681.0529.413.5569.5925.397.2762.0720.032.3969.5714.7–0.98Gilgit14.9536.181817.2134.9517.3511.2531.6512.386.3525.956.383.3118.830.665.1212.26–2.22Kakul257.6529.5619.45240.7928.4918.87105.4927.8515.8654.8625.1610.5130.2120.595.8251.0315.812.3
Figure 1.

Study area: meteorological stations of Northern Area and KPK (Pakistan).

Table 2.

Classification criteria SPEI.

SPEI valuesClass≥2Extremely wet (EV)1.50 to 1.99Severe wet (SW)1.00 to 1.49Moderate wet (MW).99 to −.99Near normal (NN)–1 to −1.49Moderate drought (MD)–1.5 to 1.99Severe drought (SD)≤ −2Extreme drought (ED)
Table 3.

One step transition probability matrix.

AstoreBalakotMDMWNNSDSWMDMWNNSDSWMD0.1360.0450.6360.0450.136MD0.1250.0630.81300MW0.0680.1690.6610.0850.017MW0.0250.10.80.0250.05P1ijNN0.0380.1180.6980.1210.025P1ijNN0.0280.0720.7470.1130.041SD000.4530.5380.009SD0.0320.1130.50.290.065SW00.0670.93300SW0.0450.0910.7270.0910.045
Table 4.

Percentage frequencies of drought class.

StationsPercentages of drought classesMDMWNNSDSWAstore0.090.030.710.150.02Balakot0.090.040.750.090.03Kotli0.120.030.640.170.03Chirat0.070.030.690.150.05Chilas0.080.020.670.170.06Islamabad0.110.020.660.150.07Gupis0.110.020.670.160.04Peshawar0.10.020.670.180.04Saidu-Shareef0.090.040.690.150.03Muzafarabad0.080.040.710.140.02Bunji0.10.030.680.170.04DIKhan0.090.030.660.160.05Drosh0.080.030.70.140.04Gari-Dupata0.070.040.70.160.03Dir0.070.030.710.160.03Gilgit0.080.030.670.160.06Kakul0.120.020.630.170.05
Table 5.

Standardised weights of transition from one state to another stat.

LagsStationsw1w2w3w4w5Astore0.2490.2260.2010.1750.149Balakot0.2300.2150.2000.1860.170Kotli0.2470.2220.1990.1770.154Chirat0.2360.2170.2000.1830.164Chilas0.2370.2210.2020.1810.159Islamabad0.2360.2160.1980.1820.167Gupis0.2400.2220.2010.1790.159Peshawar0.2290.2130.1990.1860.173Saidu-Shareef0.2400.2210.2010.1790.159Muzafarabad0.2480.2250.1990.1750.153Bunji0.2290.2160.2010.1850.170DIKhan0.2150.2070.2000.1930.185Drosh0.2280.2130.2000.1870.172Gari-Dupata0.2330.2150.1980.1840.170Dir0.2260.2130.2000.1870.173Gilgit0.2570.2300.2000.1710.143Kakul0.2390.2160.1960.1830.167
Table 6.

Predicted probabilities for the month of December 2017 at Astore.

Initial monthDrought State of December 2017StepWeightsMDMWNNSDSWJuly 2017NNP(5)0.1490.0230.0840.7830.0940.016August 2017NNP(4)0.1750.0200.0800.8040.0830.014September 2017NNP(3)0.2010.0150.0730.8320.0680.012October 2017SDP(2)0.2260.0010.0060.2540.7390.001November 2017NNP(1)0.2490.0050.0400.9230.0280.005December 2017Pi0.0110.0520.7120.2160.009
Table 7.

One month ahead forecast probabilities of various drought classes.

Drought classes probabilitiesStationsInitial statesMDMWNNSDSWAstoreNN0.0110.0520.7120.2160.009BalakotNN0.0090.0330.8950.0550.008KotliNN0.0180.0750.7540.1390.013ChiratNN0.0210.1150.7550.0640.045ChilasMD0.0830.1110.6840.0740.048IslamabadNN0.0110.0180.6330.3350.003GupisNN0.0140.0420.8700.0630.010PeshawarMW0.2130.2920.2250.0110.259Saidu-ShareefNN0.0070.0350.5730.3760.009MuzafarabadMD0.0130.0600.8040.1020.021BunjiNN0.0050.0260.4270.5330.009DIKhanNN0.0250.0560.6170.2170.084DroshMD0.1500.3210.3950.0220.113Gari-DupataNN0.0100.0670.8210.0880.013DirNN0.0090.0620.8410.0730.016GilgitMW0.0100.0430.7980.1300.019KakulNN0.0130.0560.6620.2590.010
DOI: https://doi.org/10.1080/16000870.2020.1840209 | Journal eISSN: 3035-9554
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Language: English
Page range: 1840209 - 1840209
Published on: Jan 1, 2020
Published by: Stockholm University Press
In partnership with: Paradigm Publishing Services
Keywords:
Drought,
Markov chain,
standardised precipitation evapotranspiration index (SPEI),
autocorrelation

© 2020 Zulfiqar Ali, Ijaz Hussain, Amna Nazeer, Muhammad Faisal, Muhammad Ismail, Sadia Qamar, Marco Grzegorczyk, Faisal Maqbool Zahid, Guangheng Ni, published by Stockholm University Press
This work is licensed under the Creative Commons Attribution 4.0 License.

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