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Spatiotemporal variability of future water sustainability using reliability resilience vulnerability framework

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Abstract

Drought projection is used to evaluate the risk of drought in future. Drought projection has been performed using the bias corrected General Circulation Models. However, it is necessary to have a reliable future projection, but the future is uncertain, and the bias correction method can affect the projection. This study evaluated the past and future drought projection over Pakistan and showed spatial distribution of frequency, duration, and severity over a region by using parametric and non-parametric transformation methods for bias correction. Drought was quantified using Standardized Precipitation Evapotranspiration Index (SPEI). Reliability-Resilience-Vulnerability (RRV) approach was used to analyze the drought for the historical (1981–2014), near future (2026–2059, NF) and far future (2066–2099, FF) periods. RRV value represents which region has the most sustainable water resources system. In NF of SSP2-4.5, the severity increases abruptly but duration of drought decreases while both severity and duration decrease in FF. Drought severity for SSP2-4.5 is higher than SSP5-8.5 in NF while the opposite was revealed in FF. The drought frequency didn’t undergo much of changes except for Balochistan which has a frequent drought in NF and FF for both SSP scenarios. NF of SSP2-4.5 showed reduction in RRV values implementing the increase in water availability while the opposite was revealed in FF. RRV values for SSP5-8.5 indicate that droughts become more severe in FF. The parametric transformation method showed more severe droughts for future than the non-parametric transformation. The finding of this study could help water resources managers and farmers to plan and adapt to changes in climate.

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Data availability

The CMIP6 data used in this study can be accessed at no cost from the Earth System Federation portal at https://esgf-node.llnl.gov/search/cmip6. Rainfall data from the Pakistan meteorological department (PMD) Pakistan.

Code availability

The data analysis codes are available from ZH and can be shared on request.

References

  • Aadhar S, Mishra V (2020) On the Projected Decline in Droughts Over South Asia in CMIP6 Multimodel Ensemble. Journal of Geophysical Research: Atmospheres 125. https://doi.org/10.1029/2020JD033587

  • Abbasi A, Khalili K, Behmanesh J, Shirzad A (2019) Drought monitoring and prediction using SPEI index and gene expression programming model in the west of Urmia Lake. Theor Appl Climatol 138:553–567. https://doi.org/10.1007/s00704-019-02825-9

    Article  Google Scholar 

  • Adejuwon JO, Olaniyan SB (2019) Drought occurrence in the sub-humid eco-climatic zone of Nigeria. Theor Appl Climatol 137:1625–1636. https://doi.org/10.1007/s00704-018-2670-7

    Article  Google Scholar 

  • Adnan S, Ullah K, Li S, et al (2018) Comparison of various drought indices to monitor drought status in Pakistan. Clim Dyn 51. https://doi.org/10.1007/s00382-017-3987-0

  • AghaKouchak A, Cheng L, Mazdiyasni O, Farahmand A (2014) Global warming and changes in risk of concurrent climate extremes: Insights from the 2014 California drought. Geophys Res Lett 41:8847–8852. https://doi.org/10.1002/2014GL062308

    Article  Google Scholar 

  • Ahmed K, Shahid S, Wang X et al (2019) Spatiotemporal changes in aridity of Pakistan during 1901–2016. Hydrol Earth Syst Sci 23:3081–3096. https://doi.org/10.5194/hess-23-3081-2019

    Article  Google Scholar 

  • Ahmed M, Dars GH, Ahmed S, Krakauer NY (2023) Analyzing drought trends over Sindh Province, Pakistan. Nat Hazards 119:643–661. https://doi.org/10.1007/s11069-023-06082-y

    Article  Google Scholar 

  • Alamgir M, Mohsenipour M, Homsi R, et al (2019) Parametric assessment of seasonal drought risk to crop production in Bangladesh. Sustainability (Switzerland) 11. https://doi.org/10.3390/su11051442

  • Ali Z, Hamed MM, Muhammad MKI et al (2023) Performance evaluation of CMIP6 GCMs for the projections of precipitation extremes in Pakistan. Clim Dyn 61:4717–4732. https://doi.org/10.1007/s00382-023-06831-6

    Article  Google Scholar 

  • Ayugi B, Tan G, Rouyun N, et al (2020) Evaluation of meteorological drought and flood scenarios over Kenya, East Africa. Atmosphere (Basel) 11. https://doi.org/10.3390/atmos11030307

  • Blench R, Marriage Z, Overseas Development Institute (London E (1999) Drought and livestock in semi-arid Africa and Southwest Asia. Overseas Development Institute

  • Cannon AJ, Sobie SR, Murdock TQ (2015) Bias correction of GCM precipitation by quantile mapping: How well do methods preserve changes in quantiles and extremes? J Clim 28:6938–6959. https://doi.org/10.1175/JCLI-D-14-00754.1

    Article  Google Scholar 

  • Conforti P, Ahmed S, Markova G (2018) The impact of disasters and crises on agriculture and food security 2017. Food and Agriculture Organization of the United Nations (FAO)

  • Eckstein D, Hutfils M-L, Winges M, Germanwatch Körperschaft Global Climate Risk Index (2019) Who Suffers Most From Extreme Weather Events? Weather-related Loss Events in 2017 and 1998 to 2017

  • Edossa DC, Woyessa YE, Welderufael WA (2016) Spatiotemporal analysis of droughts using self-calibrating Palmer’s Drought Severity Index in the central region of South Africa. Theor Appl Climatol 126:643–657. https://doi.org/10.1007/s00704-015-1604-x

    Article  Google Scholar 

  • Enayati M, Bozorg-Haddad O, Bazrafshan J et al (2021) Bias correction capabilities of quantile mapping methods for rainfall and temperature variables. J Water Climate Change 12:401–419. https://doi.org/10.2166/wcc.2020.261

    Article  Google Scholar 

  • Evers M, Almoradie A, de BMM (2018) Enhancing Flood Resilience Through Collaborative Modelling and Multi-criteria Decision Analysis (MCDA). In: Fekete Alexander and Fiedrich F (ed) Urban Disaster Resilience and Security: Addressing Risks in Societies. Springer International Publishing, Cham, pp 221–236

  • Eyring V, Bony S, Meehl GA et al (2016) Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci Model Dev 9:1937–1958. https://doi.org/10.5194/gmd-9-1937-2016

    Article  Google Scholar 

  • Eyring V, Gillett NP, Achutarao K, et al (2021) Human influence on the climate system. In climate change 2021: the physical science basis. Contribution of working group i to the sixth assessment report of the intergovernmental panel on climate change. IPCC Sixth Assessment Report

  • Gudmundsson L, Bremnes JB, Haugen JE, Engen-Skaugen T (2012) Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations – A comparison of methods. Hydrol Earth Syst Sci 16:3383–3390. https://doi.org/10.5194/hess-16-3383-2012

    Article  Google Scholar 

  • Hargreaves GH, Samani ZA (1985) Reference Crop Evapotranspiration from Temperature. Appl Eng Agric 1:96–99. https://doi.org/10.13031/2013.26773

  • Hwang C-L, Yoon K (1981) Methods for Multiple Attribute Decision Making. In: Hwang C-L, Yoon K (eds) Multiple Attribute Decision Making: Methods and Applications A State-of-the-Art Survey. Springer, Berlin, pp 58–191

    Chapter  Google Scholar 

  • Intergovernmental Panel on Climate Change (IPCC) (2013) Climate Change 2013 – The Physical Science Basis: Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge

    Google Scholar 

  • Karim R, Tan G, Ayugi B, et al (2020) Evaluation of Historical CMIP6 Model Simulations of Seasonal Mean Temperature over Pakistan during 1970–2014. Atmosphere (Basel) 11. https://doi.org/10.3390/atmos11091005

  • Khan F, Pilz J, Ali S (2021) Evaluation of CMIP5 models and ensemble climate projections using a Bayesian approach: a case study of the Upper Indus Basin, Pakistan. Environ Ecol Stat 28:383–404. https://doi.org/10.1007/s10651-021-00490-8

    Article  Google Scholar 

  • Khan N, Sachindra DA, Shahid S, et al (2020) Prediction of droughts over Pakistan using machine learning algorithms. Adv Water Resour 139. https://doi.org/10.1016/j.advwatres.2020.103562

  • Knutti R, Furrer R, Tebaldi C et al (2010) Challenges in Combining Projections from Multiple Climate Models. J Climate 23:2739–2758. https://doi.org/10.1175/2009JCLI3361.1

    Article  Google Scholar 

  • Komaragiri SR, Kumar DN (2010) Multicriterion Analysis in Engineering and Management

  • Kouhestani S, Eslamian SS, Abedi-Koupai J, Besalatpour AA (2016) Projection of climate change impacts on precipitation using soft-computing techniques: A case study in Zayandeh-rud Basin. Iran Glob Planet Change 144:158–170. https://doi.org/10.1016/j.gloplacha.2016.07.013

    Article  Google Scholar 

  • Longley P (2005) Geographic information systems and science. John Wiley & Sons

    Google Scholar 

  • Loucks P, Beek E, Stedinger J, et al (2005) Water Resources Systems Planning and Management: An Introduction to Methods, Models And Applications

  • Moeletsi ME, Walker S, Hamandawana H (2013) Comparison of the Hargreaves and Samani equation and the Thornthwaite equation for estimating dekadal evapotranspiration in the Free State Province, South Africa. Phys Chem Earth 66:4–15. https://doi.org/10.1016/j.pce.2013.08.003

    Article  Google Scholar 

  • Naz F, Dars GH, Ansari K, et al (2020) Drought trends in Balochistan. Water (Switzerland) 12. https://doi.org/10.3390/w12020470

  • O’Neill BC, Tebaldi C, van Vuuren DP et al (2016) The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geosci Model Dev 9:3461–3482. https://doi.org/10.5194/gmd-9-3461-2016

    Article  Google Scholar 

  • Pakistan Labour Force Survey (2020-21) Ministry of planning, development & special initiatives Pakistan bureau of statistics

  • Salman SA, Shahid S, Afan HA, et al (2020) Changes in climatic water availability and crop water demand for Iraq region. Sustainability (Switzerland) 12. https://doi.org/10.3390/SU12083437

  • Sandoval-Solis S, Mckinney D, Loucks P (2011) Sustainability index for water resources planning and management. J Water Resour Plan Manag Am Soc Civil Eng. J Water Resour Plan Manag 137:381–390. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000134

    Article  Google Scholar 

  • Shiru MS, Shahid S, Chung E-S et al (2019) A MCDM-based framework for selection of general circulation models and projection of spatio-temporal rainfall changes: A case study of Nigeria. Atmos Res 225:1–16. https://doi.org/10.1016/j.atmosres.2019.03.033

    Article  Google Scholar 

  • Shiru MS, Shahid S, Dewan A, et al (2020) Projection of meteorological droughts in Nigeria during growing seasons under climate change scenarios. Sci Rep 10. https://doi.org/10.1038/s41598-020-67146-8

  • Singh RM, Shukla P (2020) Drought Characterization Using Drought Indices and El Niño Effects. Natl Acad Sci Lett 43:339–342. https://doi.org/10.1007/s40009-019-00870-6

    Article  Google Scholar 

  • Song YH, Chung ES, Shahid S (2021) Spatiotemporal differences and uncertainties in projections of precipitation and temperature in South Korea from CMIP6 and CMIP5 general circulation models. Int J Climatol 41:5899–5919. https://doi.org/10.1002/joc.7159

    Article  Google Scholar 

  • Song YH, Chung ES, Shiru MS (2020) Uncertainty analysis of monthly precipitation in GCMs using multiple bias correction methods under different RCPs. Sustainability (Switzerland) 12. https://doi.org/10.3390/su12187508

  • Stevens B, Bony S (2013) What are climate models missing? Science  340:1053–1054. https://doi.org/10.1126/science.1237554

    Article  CAS  Google Scholar 

  • Sung JH, Chung ES, Shahid S (2018) Reliability-Resiliency-Vulnerability approach for drought analysis in South Korea using 28 GCMs. Sustainability (Switzerland) 10. https://doi.org/10.3390/su10093043

  • Tallaksen LM, Madsen H, Clausen B (1997) Définition et modélisation de la durée et du volume déficitaire des étiages. Hydrol Sci J 42:15–33. https://doi.org/10.1080/02626669709492003

    Article  Google Scholar 

  • Taylor KE (2001) Summarizing multiple aspects of model performance in a single diagram. J Geophys Res 106:7183–7192. https://doi.org/10.1029/2000JD900719

    Article  Google Scholar 

  • Teutschbein C, Seibert J (2012) Bias correction of regional climate model simulations for hydrological climate-change impact studies: review and evaluation of different methods. J Hydrol (amst) 456–457:12–29. https://doi.org/10.1016/j.jhydrol.2012.05.052

    Article  Google Scholar 

  • Thornthwaite CW (1948) An Approach toward a Rational Classification of Climate

  • Tobler WR (1970) A Computer movie simulating urban growth in the Detroit region. Econ Geogr 46:234–240

    Article  Google Scholar 

  • Trenberth KE, Dai A, Van Der Schrier G et al (2014) Global warming and changes in drought. Nat Clim Chang 4:17–22

    Article  Google Scholar 

  • Vicente-Serrano SM, Beguería S, López-Moreno JI (2010) A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. J Clim 23:1696–1718. https://doi.org/10.1175/2009JCLI2909.1

    Article  Google Scholar 

  • Wehner MF, Reed KA, Li F et al (2014) The effect of horizontal resolution on simulation quality in the community atmospheric model, CAM5.1. J Adv Model Earth Syst 6:980–997. https://doi.org/10.1002/2013MS000276

    Article  Google Scholar 

  • Wells C, Saggioro E, Pettyu C, Cornforth R (2023) Pakistan climate change impact storylines based on existing vulnerability literature

  • WMO (2019)- WMO statement on the state of the global climate in 2018

  • Woldemeskel FM, Sharma A, Sivakumar B, Mehrotra R (2014) A framework to quantify GCM uncertainties for use in impact assessment studies. J Hydrol (amst) 519:1453–1465. https://doi.org/10.1016/j.jhydrol.2014.09.025

    Article  Google Scholar 

  • World Bank (2022a) Agriculture, forestry, and fishing, value added - Pakistan. https://data.worldbank.org/indicator/NV.AGR.TOTL.CD?locations=PK

  • World Bank (2022b) World development indicators: structure of value added. https://wdi.worldbank.org/table/4.2

  • Yilmaz B, Harmancioglu N (2010) Multi-criteria decision making for water resource management: a case study of the Gediz River Basin, Turkey. Water SA 36:563–576

    Article  Google Scholar 

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Acknowledgements

This study was supported by the National Research Foundation of Korea (2021R1A2C2005699).

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  1. Eun-Sung Chung

    Present address: Department of Civil Engineering, Seoul National University of Science and Technology, 232 Gongneung-Ro, Seoul, 01811, Nowon-Gu, Korea

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  1. Department of Civil Engineering, Seoul National University of Science and Technology, 232 Gongneung-Ro, Seoul, 01811, Nowon-Gu, Korea

    Zulfiqar Hammad

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ZH: Manuscript writing, results verification, data analyses and manuscript correction. ESC: conceptualization, results verification, and manuscript correction.

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Correspondence to Eun-Sung Chung.

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Hammad, Z., Chung, ES. Spatiotemporal variability of future water sustainability using reliability resilience vulnerability framework. Theor Appl Climatol (2024). https://doi.org/10.1007/s00704-024-04949-z

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