Drought Monitoring Based on Standardized Precipitation Evaoptranspiration Index (SPEI) Under the Effect of Climate Change

Document Type : Research Article

Authors

• H. Zareabyaneh ¹
• M. GHobaeisoogh ¹
• Abolfazl Mosaedi ²

¹ Bu-Ali Sina University, Hamedan

² Ferdowsi University of Mashhad

https://doi.org/10.22067/jsw.v0i0.36472

Abstract

Introduction: Drought is a natural and recurrent feature of climate. The characterizations of it may change under the effect of climate change in future periods. During the last few decades a number of different indices have been developed to quantify drought probabilities. Droughts are caused by disruptions to an expected precipitation pattern and can be intensified by unusually high temperature values. Precipitation-based drought indices, including the Standardized precipitation index (SPI), cannot identify the role of temperature increase in drought condition and in addressing the consequences of climate change. Recently, two new standardized drought indices have been proposed for drought variability analysis on multiple time scales, the Reconnaissance Drought Index (RDI, Tsakiris et al., 2007) and the Standardized Precipitation Evapotranspiration Index (SPEI, Vicente-Serrano et al., 2010). The objective of this study is to evaluate the characterization of wet and dry periods under the effect of climate change according to SPEI index in synoptic station of Hamedan for the next thirty years (2011-2040).
Materials and Methods: In this study, the indices of SPEI, SPI and RDI were investigated and the SPEI index as a multiscalar and suitable index was used to detect, monitor, and explore the consequences of global warming on drought conditions in synoptic station of Hamedan (airport). For this purpose, the period of 1981-2010 was chosen as the base period and the simulation of the future climate variables were done based on A1B, A2 and B2 emissions scenarios and performance of multi model ensemble via LARS-WG5 model for the period of 2011-2040. The performance of the multi model ensemble was done by using five global climate models including IPCM4, MPEH5, HADCM3, GFCM21, and NCCCS in the IPCC Fourth Assessment Report (Semenov and Stratonovitch, 2010). By simulating the values of precipitation ,and the values of temperature and the values of estimated evapotranspiration , the values of SPEI, RDI and SPI indices were calculated annually and 1, 3 and 6 months (short- term period) and 12, 18 and 24 months (long- term period) time scales for the base period and the three next decades. Then, the relation among them was computed and investigated via correlation coefficient. Then, by monitoring the humidity condition via SPEI index, the characterization of wet and dry periods including period numbers, longest period, total deficit or surplus, and maximum deficit or surplus were derived based on Run theory and were comprised for the base period and three future decades.
Results and Discussion: Evaluation of LARS-WG5 model for base period showed that the model was able to simulate minimum and maximum temperatures and precipitation data with high accuracy based on statistic error and can be used to generate data for future years according to emission scenario. According to the simulated results of performance of multi model ensemble, the average values of mean temperature and precipitation will increase by 0.820C and 2.5 % for A2 scenario, respectively. In addition, the minimum and maximum temperatures have increased in all of the months according to the three scenarios in comparison with the base period. The correlation results between the investigated indices showed that the maximum and minimum of correlation can be observed between SPI & RDI and SPEI & SPI indices in the base period and future decade for each scenario, respectively. Drought assessment based on the SPEI index in the base period shows that the main drought episodes occurred in the 1999 to 2001 that were consistent with FAO report (2006). Comparison of wet and dry periods in relation to the base period showed that the number of dry periods will increase in time scales of 1 and 3 months and will decrease in other long-term time scales.
Conclusion: Climate change and its effects are among the main challenges of water resources management in the present century. In this study, the effects of this phenomenon on drought monitoring and change of characterizations were investigated. For this purposes, we used daily meteorological variables during thirty years (1981-2010) from Hamedan Synoptic station. The results of drought monitoring were based on SPEI index, and it revealed the high variability of humidity condition in the first decade of simulation in comparison with the second and third decades. This issue indicated that this decade requires more attention and management measurements. Also, according to the results of the derived characterization via Run theory, the number of dry periods will decrease and persistence of the longest dry period and consequently the volume of deficit will increase in the next three decades. In addition, the total volume surplus of wet periods will decrease in relation to the base period that can be interpreted as the increasing of moisture deficit in future decades The SPEI is based on precipitation and temperature data, and it has the advantage of combining multiscalar character with the capacity to include the effects of temperature variability on drought assessment. Thus, we recommend SPEI, as a suitable index for studying and identifying the effect of climate change on drought conditions.

Keywords

• Climate change
• drought
• Multi Model Ensemble
• Run Theory
• Standardized Precipitation Evapotranspiration Index (SPEI)

20.1001.1.20084757.1394.29.2.10.5

References

Abramowitz M., and Stegun I.A. 1965. Handbook of mathematical functions, with formulas, graphs, and mathematical tables. Dover Publications.

2- Ashraf B., Mousavi Baygi M., Kamali G.A., and Davari K. 2011. Prediction of water requirement of sugar beet during 2011-2030 by using simulated weather data with LARS-WG downscaling model. Journal of Water and Soil, 25 (5): 1184–1196 (In Persian with English abstract).

3- Ashraf B., Alizadeh A., Mousavi Baygi M., and Bannayan Awal M. 2014. Verification of temperature and precipitation simulated data by individual and ensemble performance of five AOGCM models for north east of Iran. Journal of Water and Soil. 28 (20): 253-266 (in Persian with English abstract).

4- Blekinsop B., and Fowler H.J. 2007. Changes in drought frequency, severity and duration for the British Isles projected by the PRUDENCE regional climate models. Journal of Hydrology, 342: 50–71.

5- Bryant E.A. 1991. Natural Hazards. Cambridge, New York and Melbourne: Cambridge University Press.

6- Burke E.J., and Brown S.J. 2008. Evaluating uncertainties in the projection of future drought. Journal of Hydrometeor, 9: 292–299.

7- Burke E.J., Brown S.J., and Christidis N. 2006. Modeling the recent evolution of global drought and projections for the twenty-first century with the Hadley Centre Climate Model. Journal of Hydrometeorology, 7: 1113–1125.

8- Dubrovsky M., Svoboda M.D., Trnka, M., Hayes M.J., Wilhite D.A., Zalud Z., and Hlavinka P. 2008. Application of relative drought indices in assessing climate-change impacts on drought conditions in Czechia. Theoretical Applied Climatology, 96: 155–171.

9- Edwards D.C., and McKee T.B. 1997. Characteristics of 20th century drought in the United States at multiple time scales. Climatology Report, 97, 2: Colorado State University, Fort Collins, Colorado.

10- Food and Agriculture Organization of the United Nations. 2006. National strategy and action plan on drought Preparedness, management and mitigation in the agricultural sector prepared with the assistance of the through the TCP Project No. 3003 /IRA, V 1, P 60.

11- Golmohammadi M., and Massah Bavani A. 2011. The perusal of climate change impact on drought intensity and duration Journal of Water and Soil, 25 (2): 315–326 (in Persian with English abstract)

12- Intergovernmental Panel on Climate Change (IPCC). 2007. Synthesis Report 2007, AR4, Cambridge University Press, Cambridge, United Kingdomand New York, USA.

13- Jones P.D., and Moberg A. 2003. Hemispheric and large-scale surface air temperature variations: An extensive revision and an update to 2001. Journal Climate, 16: 206–223.

14- Keyantash J., and Dracup J.A. 2002. The Quantification of Drought Indices, American Metrological Society, 83(8): 1167–1180.

15- Khazanedari L., Zabol Abasi F., Ghandhari Sh., Kouhi, M., and Malbousi, Sh. 2010. Drought conditions in the next thirty years in Iran. Journal of Geography and Regional Development Research, 12: 83-98 (In Persian with English abstract).

16- Kirono D.G.C., Kent D.M., Hennessy K.J., and Mpelasoka F. 2011. Characteristics of Australian droughts under enhanced greenhouse conditions: Results from 14 global climate models. Journal of Arid Environments, 75: 566-575.

17- Lloyd–Hughes B., and Saunders M.A. 2002. A drought climatology for Europe. International. Journal Climatology, 22: 1571–1592.

18- McKee T.B., Doesken N.J., and Kleist J. 1993. The relationship of drought frequency and duration to time scales. Proceedings of the Eighth Conference on applied Climatology. American Meteorological Society, Boston, 179–184.

19- Mishra A.K., and Singh V.P. 2010. A review of drought concepts. Journal of Hydrology, 391: 202–216.

20- Mishra A.k., Singh V.P., and Desai V.R. 2009. Drought characterization: a probabilistic approach. Stochastic Environ Research Risk Assess, 23: 41–55.

21- Nicholls N. 2004. The changing nature of Australian droughts. Climatic Change, 63: 323–336.

22- Pereira A.R., and Pruitt W.O. 2004. Adaptation of the thornthwaite scheme for estimating daily reference evapotranspiration. Agricultural Water Management, 66: 251–257.

23- Racsko P., Szeidl L., and Semenov M. 1991. A serial approach to local stochastic weather models. Ecological Modeling, 57: 27–41.

24- Rebetez M., Mayer H., Dupont, O., Schindler D., Gartner K., Kropp J.P., and Menzel A. 2006. Heat and drought 2003 in Europe: A climate synthesis. Ann. For. Sci., 63: 569–577.

25- Salehnia N., Mossavi Baygi M., and Ansari H. 2013. Drought prediction with PDSI, Lars–WG5 and HadCM3 (case study Neyshabour basin).Iranian Journal of lrrigation and Drainage, 7(1): 93–103 (in Persian with English abstract)

26- Semenov M.A., and Barrow E.M. 1997. Use of a stochastic weather generator in the development of climate change scenarios. Climate Change, 35: 397–414.

27- Semenov M., and Stratonovitch P. 2010. Use of multi–model ensembles from global climate models for assessment of climate change impacts. Climate Research, 41: 1-14.

28- Sheffield J., and Wood E.F. 2008. Projected changes in drought occurrence under future global warming from multi–model, multi-scenario, IPCC AR4 simulations. Climate Dyn., 31: 79–105.

29- Solomon S.D., Qin M., Manning M., Marquis K., Averyt M.M.B., Tignor H.L., Miller Jr., and Z. Chen Eds. 2007. Climate Change 2007.The Physical Science Basis. Cambridge University Press, 996 pp.

30- Tigkas D. 2008. Drought characterisation and monitoring in regions of Greece. European Water 23/24, 29–39.

31- Tsakiris G., Nalbantis I., Pangalou D., Tigkas D., and Vangelis H. 2008. Drought meteorological monitoring network design for the reconnaissance drought index (RDI). In: Franco Lopez, A. (Ed.), Proceedings of the 1st International Conference “Drought Management: scientific and Technological Innovations”, Option Mediterraneennes, Series A, No. 80, Zaragoza, Spain, 12–14 June 2008, 57–62.

32 - Tsakiris G., Pangalou D., and Vangelis, H. 2007. Regional drought assessment based on the Reconnaissance Drought Index (RDI). Water Resources Management, 21: 821–833.

33- Vicente-Serrano S.M., Begueria S., and Lopez-Moreno J.I. 2010. A Multi–scalar drought index sensitive to global warming: The Standardized Precipitation Evapotranspiration Index – SPEI. Journal of Climate, 23(7): 1696– 1718.

34- Wilhite D.A. 2000. Drought as a natural hazard: concepts and definitions. In: Wilhite D.A. (Ed.), Drought: A Global Assessment, vol. 1. Routledge, New York, 1–18.