Analysis of extreme rainfall trend and mapping of the Wadi pluvial food in the Gaza coastal plain of Palestine

Al-Najjar, Hassan; Purnama, Anton; Özkan, Korhan; Abualtayef, Mazen

doi:10.1007/s11600-022-00890-9

Artykuł - szczegóły

Tytuł artykułu

Analysis of extreme rainfall trend and mapping of the Wadi pluvial food in the Gaza coastal plain of Palestine

Autorzy

Al-Najjar Hassan , Purnama Anton , Özkan Korhan , Abualtayef Mazen

Wybrane pełne teksty z tego czasopisma

https://www.springer.com/journal/11600

Identyfikatory

DOI

10.1007/s11600-022-00890-9

Warianty tytułu

Języki publikacji

Abstrakty

In the eastern Mediterranean region of the Middle East and North Africa, pluvial flooding has become a common and severe climate change consequence event that requires immediate attention. The Wadi-Gaza basin is a critical source of surface water in Palestine; nevertheless, climate change and anthropogenic processes are altering the basin’s hydrological features, resulting in a series of extreme and disastrous flooding events in the coastal plain at the basin downstream. The Mann–Kendall test, Sen’s slope estimator, and the IPTA method that was used to analyze historical rainfall in the basin from 1979 to 2013 refer to a declining trend pattern, which reduces the surge of runoff discharge. Moreover, the future projection for the total monthly rainfall under the ensemble model of CIMP5 for the RCP scenarios of 2.60, 4.50, and 8.50 demonstrate a general decreasing trend in the rainfall with a variation ranging between about − 36 and − 53%. The frequency analysis for the maximum daily rainfall using different computing approaches shows that the theoretical maximum rainfall values for the assessment of flooding events were assigned to 22, 31, 35, 45 52, 59, 66, and 77 mm for the whole basin for the return periods (T) of 2, 5, 10, 25, 50, 100, 200, and 500 years, respectively. The land cover–land use of the basin shows that 51% is barren land while the other portion is divided between urban (~16%), agricultural (~ 13%), and natural (~ 20%) lands. However, the type of soil that covers the basin is classified mainly as clay, loam, and sandy clay. According to the hydraulic analysis of downstream flooding, the volume of surge water that might reach the coastal plain in the event of water storms of 22 and 77 mm is around 6 and 118 million cubic meters, respectively. The area at risk of inundation due to foods in the Wadi-Gaza is between 3 and 17 km2 , covering around 5–29 percent of the Gaza Strip’s middle governorate area, respectively.

Słowa kluczowe

Palestine Wadi-Gaza climate hydrology flooding Mediterranean Sea

Palestyna Wadi-Gaza klimat hydrologia powódź Morze Śródziemne

Wydawca

Instytut Geofizyki PAN
Springer

Czasopismo

Acta Geophysica

Rocznik

2022

Tom

Vol. 70, no 5

Strony

2135--2147

Opis fizyczny

Bibliogr. 61 poz.

Twórcy

autor

Al-Najjar Hassan

alnajjarhsn@gmail.com

Department of Civil and Environmental Engineering, Islamic University of Gaza, Gaza, Palestine

https://orcid.org/0000-0003-2192-4301

autor

Purnama Anton

Department of Mathematics and Statistics, Sultan Qaboos University, Muscat, Oman

autor

Özkan Korhan

Present Address: Institute of Marine Sciences, Middle East Technical University, 33731 Mersin, Turkey

autor

Abualtayef Mazen

Department of Civil and Environmental Engineering, Islamic University of Gaza, Gaza, Palestine

Bibliografia

1. Abd Rabou AN, Maged M, Yassin MM, Mohammed R, AlAgha MR, Hamad DM, Ali AK (2007) The herpetofauna of the Gaza strip with particular emphasis on the vicinity of Wadi Gaza. Islam Univ J (series of Natural Studies and Engineering) 15(1):P111-135
2. Abdel-Fattah M, Saber M et al (2017) A Hydrological and geomorphometric approach to understanding the generation of Wadi flash floods. Water 9(7):553
3. Ahmed K, Shahid S, Sachindra DA, Nawaz N, Chung ES (2019) Fidelity assessment of general circulation model simulated precipitation and temperature over Pakistan using a feature selection method. J Hydrol 573:281–298. https://doi.org/10.1016/j.jhydrol.2019.03.092
4. Akinsanola AA, Ajayi VO, Adejare AT, Adeyeri OE, Gbode IE, Ogunjobi KO, Nikulin G, Abolude AT (2017) Evaluation of rainfall simulations over West Africa in dynamically downscaled CMIP5 global circulation models. Theoret Appl Climatol 132:437–450. https://doi.org/10.1007/s00704-017-2087-8
5. Alexandrov Y, Laronne JB, Reid I (2003) Suspended sediment transport in flash floods of the semiarid Northern Negev, Israel. In: Savat E, Najem W, Leduc C, Shakeel A (eds) Hydrology of the Mediterranean and semiarid regions. International Association of Hydrological Sciences, Publication, Wallingford, pp 346–352
6. Alexandrov Y, Balaban N, Bergman N, Chocron M, Laronne JB, Powell DM, Reid I, Tagger S, Wener-Frank I (2008) Differentiated suspended sediment transport in headwater basins of the Besor catchment, northern Negev. Isr J Earth Sci 57:177–188
7. Ali RO, Abubaker SR (2019) Trend analysis using mann-kendall, sen’s slope estimator test and innovative trend analysis method in Yangtze River basin, China: review. Int J Eng Technol 8:110–119
8. Allaire M (2018) Socio-economic impacts of flooding: a review of the empirical literature. Water Secur 3:18–26. https://doi.org/10.1016/j.wasec.2018.09.002
9. Al-Najjar H, Ceribasi G, Dogan E, Abualtayef M, Qahman K, Shaqfa A (2020) Stochastic time-series models for drought assessment in the Gaza Strip (Palestine). J Water Clim Change 11(S1):85–114. https://doi.org/10.2166/wcc.2020.330
10. Al-Najjar H, Ceribasi G, Dogan E, Ceyhunlu AI (2022) Simulation-based assessment for the response of Mediterranean Gaza coastal aquifer to climate induced changes using statistical and global climate models (GCMs). J Water Clim Change 1:5. https://doi.org/10.2166/wcc.2022.339
11. Apurv T, Mehrotra R, Sharma A, Goyal MK, Dutta S (2015) Impact of climate change on floods in the Brahmaputra basin using CMIP5 decadal predictions. J Hydrol 527:281–291. https://doi.org/10.1016/j.jhydrol.2015.04.056
12. Bai Y, Zhang Z, Zhao W (2019) Assessing the impact of climate change on flood events using HEC-HMS and CMIP5. Water Air Soil Pollut 230(6):119. https://doi.org/10.1007/s11270-019-4159-0
13. Bedient PB, Huber WC (2002) Hydrology and floodplain analysis. Prentice-Hall, Virginia
14. Bevacqua E, Maraun D, Vousdoukas MI, Voukouvalas E, Vrac M, Mentaschi L, Widmann M (2019) Higher probability of compound flooding from precipitation and storm surge in Europe under anthropogenic climate change. Sci Adv 5(9):aaw5531. https://doi.org/10.1126/sciadv.aaw5531
15. Brunner MI, Swain DL, Wood RR, Willkofer F, Done JM, Dilleland E, Ludwing R (2021) An extremeness threshold determines the regional response of floods to changes in rainfall extremes. Commun Earth Environ 2:173. https://doi.org/10.1038/s43247-021-00248-x
16. Bulti DT, Abebe BG (2020) A review of flood modeling methods for urban pluvial flood application. Model Earth Syst Environ 6:1293–1302. https://doi.org/10.1007/s40808-020-00803-z
17. Das B, Pal SC, Malik S, Chakrabortty R (2018) Modeling groundwater potential zones of Puruliya district, West Bengal, India using remote sensing and GIS techniques. Geol Ecol Landscapes. https://doi.org/10.1080/24749508.2018.1555740
18. De Vries AJ, Ouwersloot HG et al (2018) Identification of tropical-extratropical interactions and extreme precipitation events in the Middle East based on potential vorticity and moisture transport. J Geophys Res Atmos 123(2):861–881
19. Dewan A (2013) Floods in a megacity: geospatial techniques in assessing hazards, risk and vulnerability. Springer, Berlin
20. Du S, Wang C, Shen J, Wen J, Gao J, Wu J, Lin W, Xu H (2018) Mapping the capacity of concave green land in mitigating urban pluvial floods and its beneficiaries. Sustain Cities Soc. https://doi.org/10.1016/j.scs.2018.11.003
21. Ferreira S, Hamilton K, Vincent JR (2011) Nature, socioeconomics and adaptation to natural disasters: new evidence from foods. Policy research working paper. The World Bank, Washington, DC
22. Gao C, Booij MJ, Xu YP (2019) Impacts of climate change on characteristics of daily-scale rainfall events based on nine selected GCMs under four CMIP5 RCP scenarios in Qu River basin, East China. Int J Climatol. https://doi.org/10.1002/joc.6246
23. Guo L, Zhang XL, Liu R, Liu YS, Liu Q (2017) Achievements and preliminary analysis on China national flash flood disasters investigation and evaluation. Geo-Inf Science 19(12):1548–1556
24. Hall J et al (2014) Understanding flood regime changes in Europe: a state-of-the-art assessment. Hydrol Earth Syst Sci 18(7):2735–2772. https://doi.org/10.5194/hess-18-2735-2014
25. Homsi R, Shiru MS, Shahid S, Ismail T, Harun SB, Al-Ansari N, Chau KW, Yaseen ZM (2020) Precipitation projection using a CMIP5 GCM ensemble model: a regional investigation of Syria. Eng Appl Comput Fluid Mech 14(1):90–106. https://doi.org/10.1080/19942060.2019.1683076
26. Ishida K, Gorguner M, Ercan A, Trinh T, Kavvas ML (2017) Trend analysis of watershed-scale precipitation over Northern California by means of dynamically-downscaled CMIP5 future climate projections. Sci Total Environ 592:12–24. https://doi.org/10.1016/j.scitotenv.2017.03.086
27. Kendall MG (1975) Rank correlation methods. Griffin, Oxford, UK
28. Khan N, Shahid S, Ahmed K, Ismail T, Nawaz N, Son M (2018) Performance assessment of general circulation model in simulating daily precipitation and temperature using multiple gridded datasets. Water 10(12):1793. https://doi.org/10.3390/w10121793
29. Khan I, Lei H, Shah AA, Khan I, Muhammad I (2021) Climate change impact assessment, flood management, and mitigation strategies in Pakistan for sustainable future. Environ Sci Pollut Res 28:29720–32973. https://doi.org/10.1007/s11356-021-12801-4
30. Laronne JB (1989) Probability distribution of event sediment yields in the Northern Negev, Israel. In: Boardman J, Foster I, Dearing J (eds) Soil Erosion in Agricultural Land. Wiley, London, pp 481–492
31. Maghsood FF, Moradi H, Massah Bavani AR, Panahi M, Berndtsson R, Hashemi H (2019) Climate change impact on flood frequency and source area in Northern Iran under CMIP5 scenarios. Water 11(2):273. https://doi.org/10.3390/w11020273
32. Malik S, Pal SC (2021) Potential flood frequency analysis and susceptibility mapping using CMIP5 of MIROC5 and HEC-RAS model: a case study of lower Dwarkeswar River. East India SN Appl Sci 3:31. https://doi.org/10.1007/s42452-020-04104-z
33. Malik S, Pal SC, Sattar A, Singh SK, Das B, Chakrabortty R, Mohammad P (2020) Trend of extreme rainfall events using suitable global circulation model to combat the water logging condition in Kolkata Metropolitan Area. Urban Climate 32:100599. https://doi.org/10.1016/j.uclim.2020.100599
34. Mallakpour I, Villarini G (2016) Investigating the relationship between the frequency of flooding over the central United States and large-scale climate. Adv Water Resour 92:159–171. https://doi.org/10.1016/j.advwatres.2016.04.008
35. Mann HB (1945) Nonparametric tests against trend. Econometrica 13(245–259):42
36. Massoud E, Massoud T, Guan B, Sengupta A, Espinoza V, De Luna M, Raymond C, Waliser D (2020) Atmospheric rivers and precipitation in the middle East and North Africa (MENA). Water 12(10):2863. https://doi.org/10.3390/w12102863
37. Merz B, Blöschl G, Vorogushyn S, Dottori F, Aerts JCJH, Bates P, Bertola M, Kemter M, Kreibich H, Lall U, Macdonald E (2021) Causes, impacts and patterns of disastrous river floods. Nat Rev Earth Environ 2:592–609. https://doi.org/10.1038/s43017-021-00195-3
38. Moss RH, Edmonds JA, Hibbard KA, Manning MR, Rose SK, Van Vuuren DP et al (2010) The next generation of scenarios for climate change research and assessment. Nature 463:747–756
39. Netzel LM, Heldt S, Engler S, Denecke M (2021) The importance of public risk perception for the effective management of pluvial floods in urban areas: a case study from Germany. J Flood Risk Manag. https://doi.org/10.1111/jfr3.12688
40. Nyaupane N, Thakur B, Kalra A, Ahmad S (2018) Evaluating future flood scenarios using CMIP5 climate projections. Water 10(12):1866. https://doi.org/10.3390/w10121866
41. Paprotny D, Sebastian A, Morales-Nápoles O, Jonkman SN (2018) Trends in flood losses in Europe over the past 150 years. Nat Commun 9(1):1985. https://doi.org/10.1038/s41467-018-04253-1
42. Riahi K, Rao S, Krey V, Cho C, Chirkov V, Fischer G et al (2011) RCP 8.5 — a scenario of comparatively high greenhouse gas emissions. Clim Change 109:33–57
43. Salazar-Galán S, García-Bartual R, LuisSalinas J, FélixFrancésa F (2021) A process-based flood frequency analysis within a trivariate statistical framework. Appl Semi-Arid Mediterr Case Study. https://doi.org/10.1016/j.jhydrol.2021.127081
44. Santos PP, Reis E (2018) Assessment of stream flood susceptibility: a cross-analysis between model results and flood losses. J Flood Risk Manag. https://doi.org/10.1111/jfr3.12290
45. Sen PK (1968) Estimates of the regression coefficient based on Kendall’s Tau. J Am Stat Assoc 63:1379–1389
46. Sen Z (2014) Trend identification simulation and application. J Hydrol Eng 19:635–642
47. Sen Z (2017) Innovative trend significance test, and applications. Theor Appl Climatol 127:939–947
48. Sen Z, Sisman E, Dabanli I (2019) Innovative polygon trend analysis (IPTA) and applications. J Hydrol 575:202–210
49. Slater LJ, Villarini G (2016) Recent trends in U.S. flood risk. Geophys Res Lett 43(24):12–428. https://doi.org/10.1002/2016GL071199
50. Sonkoué D, Monkam D, Fotso-Nguemo TC, Yepdo ZD, Vondou DA (2019) Evaluation and projected changes in daily rainfall characteristics over Central Africa based on a multimodel ensemble mean of CMIP5 simulations. Theoret Appl Climatol 137(3–4):2167–2186. https://doi.org/10.1007/s00704-018-2729-5
51. Supharatid S, Aribarg T, Supratid S (2015) Assessing potential flood vulnerability to climate change by CMIP3 and CMIP5 models: case study of the 2011 Thailand great flood. J Water Clim Change. https://doi.org/10.2166/wcc.2015.116
52. Tabari H (2020) Climate change impact on flood and extreme precipitation increases with water availability. Sci Rep 10:13768. https://doi.org/10.1038/s41598-020-70816-2
53. Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Am Meteorol Soc 93(4):485–498
54. Ubeid KF (2016) Quaternary alluvial deposits of Wadi Gaza in the middle of the Gaza Strip (Palestine): facies, granulometric characteristics, and their paleoflow direction. J African Earth Sci 118:274–283. https://doi.org/10.1016/j.jafrearsci.2016.03.012
55. Van Vuuren DP, Edmonds J, Kainuma M, Riahi K, Thomson A, Hibbard K et al (2011) The representative concentration pathways: an overview. Clim Change 109:5–31
56. Wing OEJ, Bates PD, Smith AM, Sampson CC, Johnson KA, Fargione J, Morefield P (2018) Estimates of present and future flood risk in the conterminous United States. Environ Res Lett 13(3):034023. https://doi.org/10.1088/1748-9326/aaac65
57. WMO (2018) State of the Climate in 2018 shows accelerating climate change impacts. World Meteorological Organization
58. Xin X, Wu T, Zhang J, Yao J, Fang Y (2020) Comparison of CMIP6 and CMIP5 simulations of precipitation in China and the East Asian summer monsoon. Int J Climatol. https://doi.org/10.1002/joc.6590
59. Yuan Z, Xu J, Wang Y (2018) Projection of future extreme precipitation and flood changes of the Jinsha River Basin in China based on CMIP5 climate models. Int J Environ Res Public Health 15(11):2491. https://doi.org/10.3390/ijerph15112491
60. Zhai X, Zhang Y, Zhang Y, Guo L, Liu R (2020) Simulating flash flood hydrographs and behavior metrics across China: implications for flash flood management. Sci Total Environ. https://doi.org/10.1016/j.scitotenv.2020.142977
61. Zhao Y, Weng Z, Chen H, Yang J (2020) Analysis of the evolution of drought, flood, and drought-flood abrupt alternation events under climate change using the daily SWAP index. Water 12(7):1969. https://doi.org/10.3390/w12071969

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).

Typ dokumentu

Bibliografia

Identyfikator YADDA

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