Nội dung được dịch bởi AI, chỉ mang tính chất tham khảo
Nghiên cứu không gian-thời gian về mưa mùa và xu hướng lịch sử cũng như tương lai của nó tại Sudan
Tóm tắt
Việc hiểu các thay đổi đang diễn ra và có thể xảy ra trong lượng mưa mùa là rất quan trọng cho việc lập kế hoạch và phát triển bền vững nông nghiệp Sudan. Nghiên cứu này sử dụng dữ liệu lượng mưa hàng tháng được ghi nhận tại 22 địa điểm trong giai đoạn 1960–2019 và các dự đoán trong tương lai dựa trên một bộ tổng hợp từ 3 mô hình khí hậu toàn cầu (GCMs) cho giai đoạn 2030–2089 để đánh giá xu hướng lượng mưa trong lịch sử và tương lai tại Sudan. Các công cụ ước lượng độ dốc của Sen và kiểm định Mann–Kendall (MK) được sử dụng để ước lượng xu hướng và ý nghĩa của chúng, tương ứng. Phạm vi thời gian được chia đều thành các khoảng 30 năm: khoảng thời gian A (1960–1989), B (1990–2019), sau đó cả hai được gộp lại thành khoảng thời gian C (1960–2019) cho quá khứ, và các khoảng thời gian D (2030–2059), E (2060–2089), và F (2030–2089) cho tương lai. Dự đoán lượng mưa trong tương lai được đánh giá từ hai kịch bản Đường dẫn Kinh tế Xã hội Chia sẻ (SSP) (SSP2-4.5 và SSP5-8.5) từ Dự án So sánh Mô hình Liên kết 6 (CMIP6). Kết quả cho thấy xu hướng giảm lượng mưa trên toàn quốc trong khoảng thời gian A và xu hướng tăng trong khoảng thời gian B. Xu hướng tăng trong khoảng thời gian B dẫn đến một xu hướng tích cực tổng thể cho khoảng thời gian C, ngoại trừ một vài trạm. Xu hướng tương lai dưới kịch bản SSP2-4.5 cho thấy khả năng tiếp tục của mô hình chu kỳ trong khoảng thời gian lịch sử. Ngược lại, SSP5-8.5 cho thấy xu hướng tăng vượt trội về lượng mưa trong giai đoạn 2030–2089.
Từ khóa
#mưa mùa #Sudan #xu hướng khí hậu #mô hình khí hậu toàn cầu #lượng mưaTài liệu tham khảo
Ahmed S (2020) Impacts of drought, food security policy and climate change on performance of irrigation schemes in Sub-saharan Africa: the case of Sudan. Agric Water Manag 232:106064. https://doi.org/10.1016/j.agwat.2020.106064
Almazroui M, Saeed F, Saeed S et al (2020a) Projected change in temperature and precipitation over Africa from CMIP6. Earth Syst Environ 4:455–475. https://doi.org/10.1007/s41748-020-00161-x
Almazroui M, Saeed S, Saeed F et al (2020b) Projections of precipitation and temperature over the south Asian countries in CMIP6. Earth Syst Environ 4:297–320. https://doi.org/10.1007/s41748-020-00157-7
Almazroui M, Islam MN, Saeed F et al (2021) Projected changes in temperature and precipitation over the United States, Central America, and the Caribbean in CMIP6 GCMs. Earth Syst Environ 5:1–24. https://doi.org/10.1007/s41748-021-00199-5
Arshad M, Ma X, Yin J, Ullah W, Ali G, Ullah S, Liu M, Shahzaman M, Ullah I (2020) Evaluation of GPM-IMERG and TRMM-3B42 precipitation products over Pakistan. Atmos Res 249:105341. https://doi.org/10.1016/j.atmosres.2020.105341
Ayugi B, Tan G, Ruoyun N, Babaousmail H, Ojara M, Wido H, Mumo L, Ngoma NH, Nooni IK, Ongoma V (2020) Quantile mapping bias correction on rossby centre regional climate models for precipitation analysis over Kenya, East Africa. Water. https://doi.org/10.3390/w12030801
Babar S, Ramesh H (2014) Analysis of extreme rainfall events over Nethravathi basin. ISH Hydraul Eng 20:212–221. https://doi.org/10.1080/09715010.2013.872353
Beck E, Zimmermann E, McVicar R, Vergopolan N, Berg A, Wood F (2018) Present and future Köppen–Geiger climate classification maps at 1-km resolution. Sci Data 5:1–12. https://doi.org/10.1038/sdata.2018.214
Caloiero T, Caloiero P, Frustaci F (2018) Long-term precipitation trend analysis in Europe and in the Mediterranean basin. Water Environ J 32:433–445. https://doi.org/10.1111/wej.12346
Chen J, Brissette FP, Chaumont D, Braun M (2013) Finding appropriate bias correction methods in downscaling precipitation for hydrologic impact studies over North America. Water Resour Res 49:4187–4205. https://doi.org/10.1002/wrcr.20331
Chiew S, Teng J, Vaze J, Kirono DGC (2009) Influence of global climate model selection on runoff impact assessment. J Hydrol 379:172–180. https://doi.org/10.1016/j.jhydrol.2009.10.004
Ehret U, Zehe E, Wulfmeyer V et al (2012) HESS Opinions “should we apply bias correction to global and regional climate model data?”. Hydrol Earth Syst Sci 16:3391–3404. https://doi.org/10.5194/hess-16-3391-2012
Endris HS, Omondi P, Jain S et al (2013) Assessment of the performance of CORDEX regional climate models insimulating East African rainfall. J Clim 26:8453–8475. https://doi.org/10.1175/JCLI-D-12-00708.1
Fontaine B, Louvet S (2006) Sudan-Sahel rainfall onset: definition of an objectives index, types of years, and experimental hindcasts. J Geophys Res Atmos 111:1–14. https://doi.org/10.1029/2005JD007019
Gajbhiye S, Meshram C, Singh SK, Srivastava PK, Islam T (2016) Precipitation trend analysis of Sindh River basin, India, from 102-year record (1901–2002). Atmos Sci Lett 17:71–77. https://doi.org/10.1002/asl.602
Hagemann S, Chen C, Haerter JO et al (2011) Impact of a statistical bias correction on the projected hydrological changes obtained from three GCMs and two hydrology models. J Hydrometeorol 12:556–578. https://doi.org/10.1175/2011JHM1336.1
Harris I, Osborn TJ, Jones P, Lister D (2020) Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci Data 7:1–18. https://doi.org/10.1038/s41597-020-0453-3
Hulme M, Tosdevin N (1989) The tropical easterly Jet and Sudan rainfall: a review. Theor Appl Climatol 39:179–187. https://doi.org/10.1007/BF00867945
IPCC. Climate Change 2014: Synthesis Report; Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; Core Writing Team, Pachauri, R.K., Meyer, L.A. Geneva, Switzerland
Johnson F, Sharma A (2012) A nesting model for bias correction of variability at multiple time scales in general circulation model precipitation simulations. Water Resour Res 48:1–16. https://doi.org/10.1029/2011WR010464
Kendall MG (1963) The advanced theory of statistics. Technometrics 5:525–528. https://doi.org/10.1080/00401706.1963.10490133
Klutse NAB, Quagraine KA, Nkrumah F et al (2021) The climatic analysis of summer monsoon extreme precipitation events over West Africa in CMIP6 simulations. Earth Syst Environ 5:25–41. https://doi.org/10.1007/s41748-021-00203-y
Kumar V, Jain K, Singh Y (2010) Analysis of long-term rainfall trends in India. Hydrol Sci 55:484–496. https://doi.org/10.1080/02626667.2010.481373
Lafon T, Dadson S, Buys G, Prudhomme C (2013) Bias correction of daily precipitation simulated by a regional climate model: a comparison of methods. Int J Climatol 33:1367–1381. https://doi.org/10.1002/joc.3518
Mann B (1945) Non-parametric test against trend. Econometrica 13:245–259
Maraun D (2013) Bias correction, quantile mapping, and downscaling: revisiting the inflation issue. J Clim 26:2137–2143. https://doi.org/10.1175/JCLI-D-12-00821.1
Maraun D, Wetterhall F, Ireson AM, Chandler RE, Kendon EJ et al (2010) Precipitation downscaling under climate change: recent developments to bridge the gap between dynamical models and the end user. Rev Geophys 48:1–34. https://doi.org/10.1029/2009RG000314
Maurer EP, Pierce DW (2014) Bias correction can modify climate model simulated precipitation changes without adverse effect on the ensemble mean. Hydrol Earth Syst Sci 18:915–925. https://doi.org/10.5194/hess-18-915-2014
Mauritsen T, Bader J, Becker T, Behrens J, Bittner M, Brokopf R, Brovkin V et al (2019) Developments in the MPI-M Earth System Model version 1.2 (MPI-ESM1.2) and its response to increasing CO2. J Adv Model Earth Syst 11:998–1038. https://doi.org/10.1029/2018MS001400
Mehrotra R, Sharma A (2019) A resampling approach for correcting systematic spatiotemporal biases for multiple variables in a changing climate. Water Resour Res 55:754–770. https://doi.org/10.1029/2018WR023270
Mumo L, Yu J, Fang K (2018) Assessing impacts of seasonal climate variability on maize yield in Kenya. Int J Plant Prod 12:297–307. https://doi.org/10.1007/s42106-018-0027-x
Musonda B, Jing Y, Iyakaremye V, Ojara M (2020) Analysis of long-term variations of drought characteristics using standardized precipitation index over Zambia. Atmosphere 11:1268. https://doi.org/10.3390/atmos11121268
Onyutha C (2018) Trends and variability in African long-term precipitation. Stoch Environ Res Risk Assess 32:2721–2739. https://doi.org/10.1007/s00477-018-1587-0
Ongoma V, Chen H, Gao C (2019) Evaluation of CMIP5 twentieth century rainfall simulation over the equatorialEast Africa. Theor Appl Climatol 135:893–910. https://doi.org/10.1007/s00704-018-2392-x
Osman Z, Shamseldin Y (2002) Qualitative rainfall prediction models for central and southern Sudan using El Nino-southern oscillation and Indian Ocean sea surface temperature indices. Int J Climatol 22:1861–1878. https://doi.org/10.1002/joc.860
Partal T, Kahya E (2006) Trend analysis in Turkish precipitation data. Hydrol Process 20:2011–2026. https://doi.org/10.1002/hyp.5993
Piani C, Weedon GP, Best M et al (2010) Statistical bias correction of global simulated daily precipitation and temperature for the application of hydrological models. J Hydrol 395:199–215. https://doi.org/10.1016/j.jhydrol.2010.10.024
Salih A, Elagib N, Tjernström M, Zhang Q (2018) Characterization of the Sahelian-Sudan rainfall based on observations and regional climate models. Atmos Res 202:205–218. https://doi.org/10.1016/j.atmosres.2017.12.001
Sayemuzzaman M, Jha K (2014) Seasonal and annual precipitation time series trend analysis in North Carolina, United States. Atmos Res 137:183–194. https://doi.org/10.1016/j.atmosres.2013.10.012
Sen PK (1968) Estimates of the regression coefficient based on Kendall’s Tau. J Am Stat Assoc 63:1379–1389. https://doi.org/10.1080/01621459.1968.10480934
Siddig K, Stepanyan D, Wiebelt M, Grethe H, Zhu T (2020) Climate change and agriculture in the Sudan: Impact pathways beyond changes in mean rainfall and temperature. Ecol Econ 169:106566. https://doi.org/10.1016/j.ecolecon.2019.106566
Stocker F, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley M (2013) Climate Change 2013: the physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge
Teklu T, Von Braum J, Zaki E (1991) Drought and famine relationships in Sudan: policy implications. Washington, D.C., USA Institute
Teutschbein C, Seibert J (2012) Bias correction of regional climate model simulations for hydrological climate-change impact studies: review and evaluation of different. J Hydrol 456–457:12–29. https://doi.org/10.1016/j.jhydrol.2012.05.052
Volodin M, Mortikov V, Kostrykin V, Galin Y, Lykossov N, Gritsun S, Diansky A, Gusev V, Iakovlev G, Shestakova A, Emelina V (2018) Simulation of the modern climate using the INM-CM48 climate model. Russ J Numer Anal Math Model 33:367–374. https://doi.org/10.1515/rnam-2018-0032
Wu T, Lu Y, Fang Y, Xin X, Li L, Li W, Jie W, Zhang J, Liu Y, Zhang L, Zhang F, Zhang Y, Wu F, Li J, Chu M, Wang Z, Shi X, Liu X, Wei M, Huang A, Zhang Y, Liu X (2019) The Beijing Climate Center Climate System Model (BCC-CSM): the main progress from CMIP5 to CMIP6. Geosci Model Dev 12:1573–1600. https://doi.org/10.5194/gmd-12-1573-2019
WWRP-1 (2010) 4th WMO international verification methods workshop
Yaduvanshi A, Srivastava PK, Pandey AC (2015) Integrating TRMM and MODIS satellite with socio-economicvulnerability for monitoring drought risk over a tropical region of India. Phys Chem. Earth 83–84:14–27. https://doi.org/10.1016/j.pce.2015.01.006
Zebaze S, Jain S, Salunke P et al (2019) Assessment of CMIP5 multimodel mean for the historical climate of Africa. Atmos Sci Lett 20:1–12. https://doi.org/10.1002/asl.926
Zhang Z, Xu CY, El-Tahir H, Cao J, Singh P (2012) Spatial and temporal variation of precipitation in Sudan and their possible causes during 1948–2005. Stoch Environ Res Risk Assess 26:429–441. https://doi.org/10.1007/s00477-011-0512-6