Analyzing trend and forecast of rainfall and temperature in Valmiki Tiger Reserve, India, using non parametric test and random forest machine learning algorithm

• Autorzy: Roshani , Sajjad Haroon , Saha Tamal Kanti , Rahaman Md Hibjur , Masroor Md , Sharma Yatendra , Pal Swades

Identyfikatory

DOI

10.1007/s11600-022-00978-2

• Języki publikacji: EN

Abstrakty

EN

Assessment of spatiotemporal dynamics of meteorological variables and their forecast is essential in the context of climate change. Such analysis can help suggest possible solutions for flora and fauna in protected areas and adaptation strategies to make forests and communities more resilient. The present study attempts to analyze climate variability, trend and forecast of temperature and rainfall in the Valmiki Tiger Reserve, India. We utilized rainfall and temperature gridded data obtained from the Indian Meteorological Department during 1981–2020. The Mann–Kendall test and Sen’s slope estimator were employed to examine the time series trend and magnitude of change at the annual, monthly and seasonal levels. Random forest machine learning algorithm was used to estimate seasonal prediction and forecasting of rainfall and temperature trend for the next ten years (2021–2030). The predictive capacity of the model was evaluated by statistical performance assessors of coefficient of correlation, mean absolute error, mean absolute percentage error and root mean squared error. The findings revealed a significant decreasing trend in rainfall and an increasing trend in temperature. However, a declining trend for maximum temperature has been observed for winter and post-monsoon seasons. The results of seasonal forecasting exhibited a considerable decrease in rainfall and temperature across the Reserve during all the seasons. However, the temperature will increase during the summer season. The random forest machine learning algorithm has shown its effectiveness in forecasting the temperature and rainfall variables. The findings suggest that these approaches may be used at various spatial scales in different geographical locations.

Słowa kluczowe

EN

climate variability; trend analysis; forecasting; nonparametric tests; random forest; Valmiki Tiger reserve

PL

zmienność klimatu; analiza trendów; prognozowanie; testy nieparametryczne; las losowy; rezerwat Valmiki Tiger

• Wydawca: Instytut Geofizyki PAN ; Springer
• Czasopismo: Acta Geophysica
• Rocznik: 2023
• Tom: Vol. 71, no. 1
• Strony: 531--552
• Opis fizyczny: Bibliogr. 112 poz.

Twórcy

autor

Roshani

• Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, India

• https://orcid.org/0000-0002-0330-4232

autor

Sajjad Haroon

• Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, India

• https://orcid.org/0000-0002-2007-1266

autor

Saha Tamal Kanti

• Department of Geography, University of Gour Banga, Malda, West Bengal, India

autor

Rahaman Md Hibjur

• Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, India

• https://orcid.org/0000-0002-8999-7497

autor

Masroor Md

• Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, India

• https://orcid.org/0000-0002-2483-3088

autor

Sharma Yatendra

• Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, India

• https://orcid.org/0000-0003-4153-8803

autor

Pal Swades

• Department of Geography, University of Gour Banga, Malda, West Bengal, India

Bibliografia

• 1. Abiy AZ, Melesse AM, Abtew W, Whitman D (2019) Rainfall trend and variability in Southeast Florida: Implications for freshwater availability in the Everglades. PLoS ONE 14:e0212008. https://doi.org/10.1371/journal.pone.0212008
• 2. Aguasca-Colomo R, Castellanos-Nieves D, Méndez M (2019) Comparative analysis of rainfall prediction models using machine learning in islands with complex orography: Tenerife Island. Appl Sci 9:4931. https://doi.org/10.3390/app9224931
• 3. Ahmed IA, Salam R, Naikoo MW, Rahman A, Praveen B, Hoai PN, Pham QB, Anh DT, Tri DQ, Elkhrachy I (2022) Evaluating the variability in long-term rainfall over India with advanced statistical techniques. Acta Geophys 70:801–818. https://doi.org/10.1007/s11600-022-00735-5
• 4. Ahokpossi Y (2018) Analysis of the rainfall variability and change in the Republic of Benin (West Africa). Hydrol Sci J 63:2097–2123. https://doi.org/10.1080/02626667.2018.1554286
• 5. Alahacoon N, Edirisinghe M, Simwanda M, Perera E, Nyirenda VR, Ranagalage M (2021) Rainfall variability and trends over the African continent using TAMSAT data (1983–2020): towards climate change resilience and adaptation. Remote Sens 14:96. https://doi.org/10.3390/rs14010096
• 6. Alemu ZA, Dioha MO (2020) Climate change and trend analysis of temperature: the case of Addis Ababa. Ethiopia Environ Syst Res 9:27. https://doi.org/10.1186/s40068-020-00190-5
• 7. Ali K, Abubaker K (2019) Long-term trends and seasonality detection of the observed flow in Yangtze river using Mann-kendall and Sen’s innovative trend method. Water 11:1855. https://doi.org/10.3390/w11091855
• 8. Alifujiang Y, Abuduwaili J, Groll M, Issanova G, Maihemuti B (2021) Changes in intra-annual runoff and its response to climate variability and anthropogenic activity in the Lake Issyk-Kul Basin. Kyrgyzstan CATENA 198:104974. https://doi.org/10.1016/j.catena.2020.104974
• 9. Anochi JA, de Almeida VA, de Campos Velho HF (2021) Machine learning for climate precipitation prediction modeling over South America. Remote Sens 13:2468. https://doi.org/10.3390/rs13132468
• 10. Asare-Nuamah P, Botchway E (2019) Understanding climate variability and change: analysis of temperature and rainfall across agroecological zones in Ghana. Heliyon 5:e02654. https://doi.org/10.1016/j.heliyon.2019.e02654
• 11. Asfaw A, Simane B, Hassen A, Bantider A (2018) Variability and time series trend analysis of rainfall and temperature in northcentral Ethiopia: a case study in Woleka sub-basin. Weather Clim Extrem 19:29–41. https://doi.org/10.1016/j.wace.2017.12.002
• 12. Baig MRI, Shahfahad NMW, Ansari AH, Ahmad S, Rahman A (2022) Spatio-temporal analysis of precipitation pattern and trend using standardized precipitation index and Mann-Kendall test in coastal Andhra Pradesh. Model Earth Syst Environ 8:2733–2752. https://doi.org/10.1007/s40808-021-01262-w
• 13. Balehegn M, Balehey S, Fu C, Liang W (2019) Indigenous weather and climate forecasting knowledge among Afar pastoralists of north eastern Ethiopia: role in adaptation to weather and climate variability. Pastoralism. https://doi.org/10.1186/s13570-019-0143-y
• 14. Bao Z, Zhang J, Wang G, Chen Q, Guan T, Yan X, Liu C, Liu J, Wang J (2019) The impact of climate variability and land use/cover change on the water balance in the Middle Yellow River Basin. China J Hydrol 577:123942. https://doi.org/10.1016/j.jhydrol.2019.123942
• 15. Behailu G, Ayal DY, Zeleke TT, Ture K, Bantider A (2021) Comparative analysis of meteorological records of climate variability and farmers’ perceptions in Sekota Woreda. Ethiopia Clim Serv 23:100239. https://doi.org/10.1016/j.cliser.2021.100239
• 16. Belay A, Demissie T, Recha JW, Oludhe C, Osano PM, Olaka LA, Solomon D, Berhane Z (2021) Analysis of climate variability and trends in Southern Ethiopia. Climate 9:96. https://doi.org/10.3390/cli9060096
• 17. Bista RB (2019) Trend and forecasting analysis on climate variability: a case of Nepal, pp 1–10
• 18. Breiman L (2001) Random forests. Mach Learn 45:5–32. https://doi.org/10.1023/A:1010933404324
• 19. Cai J, Xu K, Zhu Y, Hu F, Li L (2020) Prediction and analysis of net ecosystem carbon exchange based on gradient boosting regression and random forest. Appl Energy 262:114566. https://doi.org/10.1016/j.apenergy.2020.114566
• 20. Canchala T, Alfonso-Morales W, Carvajal-Escobar Y, Cerón WL, Caicedo-Bravo E (2020) Monthly rainfall anomalies forecasting for southwestern Colombia using artificial neural networks approaches. Water 12:2628. https://doi.org/10.3390/w12092628
• 21. Cramer S, Kampouridis M, Freitas AA, Alexandridis AK (2017) An extensive evaluation of seven machine learning methods for rainfall prediction in weather derivatives. Expert Syst Appl 85:169–181. https://doi.org/10.1016/j.eswa.2017.05.029
• 22. Dad JM, Muslim M, Rashid I, Reshi ZA (2021) Time series analysis of climate variability and trends in Kashmir Himalaya. Ecol Indic 126:107690. https://doi.org/10.1016/j.ecolind.2021.107690
• 23. Dankwa P, Cudjoe E, Amuah EEY, Kazapoe RW, Agyemang EP (2021) Analyzing and forecasting rainfall patterns in the Manga-Bawku area, northeastern Ghana: Possible implication of climate change. Environ Challenges 5:100354. https://doi.org/10.1016/j.envc.2021.100354
• 24. Das S, Narula P, Sarkar K (2020) Design of intermittent rainfall-pattern for structures with gridded data: Validation and implementation. J Build Eng 27:100939. https://doi.org/10.1016/j.jobe.2019.100939
• 25. Devi RM, Bhaskar Sinha AP, Dimri SS (2017) Application of regional climate models in assessing climate change impact on forest of Kanha tiger reserve. Indian for 143:921–927
• 26. Devi RM, Patasaraiya MK, Sinha B, Bisaria J, Dimri AP (2020) Analyzing precipitation and temperature trends of Kanha and Satpura Tiger Reserve, Central India. Theor Appl Climatol 140:1435–1450. https://doi.org/10.1007/s00704-020-03134-2
• 27. Dimri T, Ahmad S, Sharif M (2020) Time series analysis of climate variables using seasonal ARIMA approach. J Earth Syst Sci 129:149. https://doi.org/10.1007/s12040-020-01408-x
• 28. Dubey SK, Ranjan RK, Misra AK, Wanjari N, Vishwakarma S (2022) Variability of precipitation extremes and drought intensity over the Sikkim State, India, during 1950–2018. Theor Appl Climatol 148:1–14. https://doi.org/10.1007/s00704-022-03931-x
• 29. El Bilali A, Taleb A, Brouziyne Y (2021) Groundwater quality forecasting using machine learning algorithms for irrigation purposes. Agric Water Manag 245:106625. https://doi.org/10.1016/j.agwat.2020.106625
• 30. Fang X, Li X, Zhang Y, Zhao Y, Qian J, Hao C, Zhou J, Wu Y (2021) Random forest-based understanding and predicting of the impacts of anthropogenic nutrient inputs on the water quality of a tropical lagoon. Environ Res Lett 16:055003. https://doi.org/10.1088/1748-9326/abf395
• 31. Feng P, Wang B, Liu DL, Ji F, Niu X, Ruan H, Shi L, Yu Q (2020) Machine learning-based integration of large-scale climate drivers can improve the forecast of seasonal rainfall probability in Australia. Environ Res Lett 15:084051. https://doi.org/10.1088/1748-9326/ab9e98
• 32. Franzke CLE, Barbosa S, Blender R, Fredriksen H, Laepple T, Lambert F, Nilsen T, Rypdal K, Rypdal M, Scotto MG, Vannitsem S, Watkins NW, Yang L, Yuan N (2020) The structure of climate variability across scales. Rev Geophys. https://doi.org/10.1029/2019RG000657
• 33. Ganeshkumar B, Gopala Krishna GVT (2022) Spatial assessment of climate variability effects on coconut crops in Tamil Nadu State — a case study. Theor Appl Climatol 148:121–129. https://doi.org/10.1007/s00704-022-03941-9
• 34. Gao F, Chen X, Yang W, Wang W, Shi L, Zhang X, Liu Y, Tian Y (2022) Statistical characteristics, trends, and variability of rainfall in Shanxi province, China, during the period 1957–2019. Theor Appl Climatol 148:955–966. https://doi.org/10.1007/s00704-022-03924-w
• 35. Gibson PB, Chapman WE, Altinok A, Delle Monache L, DeFlorio MJ, Waliser DE (2021) Training machine learning models on climate model output yields skillful interpretable seasonal precipitation forecasts. Commun Earth Environ 2:159. https://doi.org/10.1038/s43247-021-00225-4
• 36. Girma A, Qin T, Wang H, Yan D, Gedefaw M, Abiyu A, Batsuren D (2020) Study on recent trends of climate variability using innovative trend analysis: the case of the upper Huai River Basin. Polish J Environ Stud 29:2199–2210. https://doi.org/10.15244/pjoes/103448
• 37. Government of Bihar (2018) Official website of Valmiki Tiger Reserve. https://www.valmikitigerreserve.com/. Accessed 12 Mar 2022
• 38. Gul S, Ren J (2022) Application of non-parametric innovative trend analysis of different time scale precipitation during (1951–2016) in Khyber Pakhtunkhwa, Pakistan. Acta Geophys. https://doi.org/10.1007/s11600-021-00703-5
• 39. Gumus V, Avsaroglu Y, Simsek O (2022) Streamflow trends in the Tigris river basin using Mann−Kendall and innovative trend analysis methods. J Earth Syst Sci. https://doi.org/10.1007/s12040-021-01770-4
• 40. Guo Y, Fu Y, Hao F, Zhang X, Wu W, Jin X, Robin Bryant C, Senthilnath J (2021) Integrated phenology and climate in rice yields prediction using machine learning methods. Ecol Indic 120:106935. https://doi.org/10.1016/j.ecolind.2020.106935
• 41. Haefner N, Wincent J, Parida V, Gassmann O (2021) Artificial intelligence and innovation management: A review, framework, and research agenda✰. Technol Forecast Soc Change 162:120392. https://doi.org/10.1016/j.techfore.2020.120392
• 42. Haidar A, Verma B (2018) A novel approach for optimizing climate features and network parameters in rainfall forecasting. Soft Comput 22:8119–8130. https://doi.org/10.1007/s00500-017-2756-7
• 43. Hanoon MS, Ahmed AN, Zaini N, Razzaq A, Kumar P, Sherif M, Sefelnasr A, El-Shafie A (2021) Developing machine learning algorithms for meteorological temperature and humidity forecasting at Terengganu state in Malaysia. Sci Rep 11:18935. https://doi.org/10.1038/s41598-021-96872-w
• 44. Hessebo MT, Woldeamanuel T, Tadesse M (2021) Spatial and temporal climate variability and change in the bilate catchment, central Rift Valley lakes region, Ethiopia. Phys Geogr 42:199–225. https://doi.org/10.1080/02723646.2019.1698094
• 45. Hu A, Bates SC (2018) Internal climate variability and projected future regional steric and dynamic sea level rise. Nat Commun 9:1068. https://doi.org/10.1038/s41467-018-03474-8
• 46. IPCC 2021 (2021) IPCC report-the physical science basis summary for policymakers
• 47. IPCC 2022 (2022) Climate change 2022: impacts, adaptation and vulnerability summary for policymakers
• 48. Jönsson AM, Lagergren F (2017) Potential use of seasonal forecasts for operational planning of north European forest management. Agric for Meteorol 244–245:122–135. https://doi.org/10.1016/j.agrformet.2017.06.001
• 49. Kendall MG (1975) Rank correlation methods. Charles Griffin, London, p 4
• 50. Khan N, Shahid S, Chung ES, Behlil F, Darwish MSJ (2020) Spatiotemporal changes in precipitation extremes in the arid province of Pakistan with removal of the influence of natural climate variability. Theor Appl Climatol 142:1447–1462. https://doi.org/10.1007/s00704-020-03389-9
• 51. Khaniya B, Jayanayaka I, Jayasanka P, Rathnayake U (2019) Rainfall trend analysis in Uma Oya Basin, Sri Lanka, and future water scarcity problems in perspective of climate variability. Adv Meteorol. https://doi.org/10.1155/2019/3636158
• 52. Kingra PK, Setia R, Kaur S, Kaur J, Singh S, Singh SP, Kukal SS, Pateriya B (2018) Analysis and mapping of spatio-temporal climate variability in punjab using classical statistics and geostatistics. Mausam 69:147–155. https://doi.org/10.54302/mausam.v69i1.246
• 53. Lee J, Kim C-G, Lee J, Kim N, Kim H (2018) Application of artificial neural networks to rainfall forecasting in the Geum River Basin. Korea Water 10:1448. https://doi.org/10.3390/w10101448
• 54. Li B, Shi X, Lian L, Chen Y, Chen Z, Sun X (2020) Quantifying the effects of climate variability, direct and indirect land use change, and human activities on runoff. J Hydrol 584:124684. https://doi.org/10.1016/j.jhydrol.2020.124684
• 55. Mahmood R, Jia S, Zhu W (2019) Analysis of climate variability, trends, and prediction in the most active parts of the Lake Chad basin. Africa Sci Rep 9:6317. https://doi.org/10.1038/s41598-019-42811-9
• 56. Mahmoodi N, Osati K, Salajegheh A, Mohseni Saravi M (2022) Assessing the trends of streamflow and its linkages with climate variables in the Dez river basin. Iran Int J Environ Sci Technol 19:107–120. https://doi.org/10.1007/s13762-021-03432-w
• 57. Mallick J, Talukdar S, Alsubih M, Salam R, Ahmed M, Ben KN, Shamimuzzaman M (2021) Analysing the trend of rainfall in Asir region of Saudi Arabia using the family of Mann-Kendall tests, innovative trend analysis, and detrended fluctuation analysis. Theor Appl Climatol 143:823–841. https://doi.org/10.1007/s00704-020-03448-1
• 58. Mandala UK (2015) Climate variability and soil moisture status in the Khadokholawatershed in eastern Terai region of Nepal. Econ J Nepal 35
• 59. Mann HB (1945) Nonparametric tests against trend. Econometrica 13:245. https://doi.org/10.2307/1907187
• 60. Martel JL, Mailhot A, Brissette F, Caya D (2018) Role of natural climate variability in the detection of anthropogenic climate change signal for mean and extreme precipitation at local and regional scales. J Clim 31:4241–4263. https://doi.org/10.1175/JCLI-D-17-0282.1
• 61. Masroor M, Rehman S, Avtar R, Sahana M, Ahmed R, Sajjad H (2020) Exploring climate variability and its impact on drought occurrence: evidence from Godavari Middle sub-basin. India Weather Clim Extrem 30:100277. https://doi.org/10.1016/j.wace.2020.100277
• 62. Masroor M, Avtar R, Sajjad H, Choudhari P, Kulimushi LC, Khedher KM, Komolafe AA, Yunus AP, Sahu N (2022) Assessing the influence of land use/land cover alteration on climate variability: an analysis in the Aurangabad district of Maharashtra state. India Sustain. https://doi.org/10.3390/su14020642
• 63. Meenal R, Michael PA, Pamela D, Rajasekaran E (2021) Weather prediction using random forest machine learning model. Indones J Electr Eng Comput Sci 22:1208. https://doi.org/10.11591/ijeecs.v22.i2.pp1208-1215
• 64. Mohamed MA, El Afandi GS, El-Mahdy ME-S (2022) Impact of climate change on rainfall variability in the Blue Nile basin. Alexandria Eng J 61:3265–3275. https://doi.org/10.1016/j.aej.2021.08.056
• 65. Mohammed UD, Legesse SA, Berlie AB, Ehsan MA (2022) Climate change repercussions on meteorological drought frequency and intensity in South Wollo, Ethiopia. Earth Syst Environ. https://doi.org/10.1007/s41748-022-00293-2
• 66. Mohsenzadeh Karimi S, Kisi O, Porrajabali M, Rouhani-Nia F, Shiri J (2020) Evaluation of the support vector machine, random forest and geo-statistical methodologies for predicting long-term air temperature. ISH J Hydraul Eng 26:376–386. https://doi.org/10.1080/09715010.2018.1495583
• 67. Mokhtar A, He H, Alsafadi K, Li Y, Zhao H, Keo S, Bai C, Abuarab M, Zhang C, Elbagoury K, Wang J, He Q (2020) Evapotranspiration as a response to climate variability and ecosystem changes in southwest. China Environ Earth Sci. https://doi.org/10.1007/s12665-020-09007-1
• 68. Mondal A, Lakshmi V, Hashemi H (2018) Intercomparison of trend analysis of Multisatellite monthly precipitation products and gauge measurements for River Basins of India. J Hydrol 565:779–790. https://doi.org/10.1016/j.jhydrol.2018.08.083
• 69. Murthy KK, Sinha SK, Kaul R, Vaidyanathan S (2019) A fine-scale state-space model to understand drivers of forest fires in the Himalayan foothills. For Ecol Manage 432:902–911. https://doi.org/10.1016/j.foreco.2018.10.009
• 70. Musie M, Sen S, Chaubey I (2020) Hydrologic responses to climate variability and human activities in lake Ziway Basin. Ethiopia Water 12:164. https://doi.org/10.3390/w12010164
• 71. Naing WYN, Htike ZZ (2015) Forecasting of monthly temperature variations using random forests. ARPN J Eng Appl Sci 10:10109–10112
• 72. Nazim Osman Bushara AA (2014) Weather forecasting in Sudan using machine learning schemes. J Netw Innov Comput 2:309–317
• 73. Ndlovu M, Clulow AD, Savage MJ, Nhamo L, Magidi J, Mabhaudhi T (2021) An assessment of the impacts of climate variability and change in KwaZulu-Natal province. South Africa Atmosphere (basel) 12:427. https://doi.org/10.3390/atmos12040427
• 74. Norbu S, Basnet JB (2022) Seasonal rainfall trend and annual rainfall variability analysis for selected meteorological stations in Bhutan. SSRN Electron J. https://doi.org/10.2139/ssrn.4027295
• 75. Nyembo LO, Larbi I, Rwiza MJ (2021) Analysis of spatio-temporal climate variability of a shallow lake catchment in Tanzania. J Water Clim Chang 12:469–483. https://doi.org/10.2166/wcc.2020.197
• 76. Onyutha C, Turyahabwe C, Kaweesa P (2021) Impacts of climate variability and changing land use/land cover on River Mpanga flows in Uganda. East Africa Environ Challenges 5:100273. https://doi.org/10.1016/j.envc.2021.100273
• 77. Owoicho AM, Emmanuel E, Godwin O (2022) Investigation of trends in climate extreme over Makurdi, Nigeria using climate indices. Acta Sci Appl Phys, 2(1).
• 78. Panda A, Sahu N (2019) Trend analysis of seasonal rainfall and temperature pattern in Kalahandi, Bolangir and Koraput districts of Odisha. India Atmos Sci Lett. https://doi.org/10.1002/asl.932
• 79. Pawar U, Rathnayake U (2022) Spatiotemporal rainfall variability and trend analysis over Mahaweli Basin, Sri Lanka. Arab J Geosci 15:1–16. https://doi.org/10.1007/s12517-022-09646-7
• 80. Praveen B, Talukdar S, Shahfahad MS, Mondal J, Sharma P, Islam ARMT, Rahman A (2020) Analyzing trend and forecasting of rainfall changes in India using non-parametrical and machine learning approaches. Sci Rep 10:10342. https://doi.org/10.1038/s41598-020-67228-7
• 81. Rahman MN, Azim SA (2021) Spatiotemporal evaluation of rainfall trend during 1979–2019 in seven climatic zones of Bangladesh. Geol Ecol Landscapes. https://doi.org/10.1080/24749508.2021.2022425
• 82. Rani A, Sharma D, Babel MS, Sharma A (2022) Spatio-temporal assessment of agro climatic indices and the monsoon pattern in the Banas River Basin. India Environ Challenges 7:100483. https://doi.org/10.1016/j.envc.2022.100483
• 83. Rehman S, Sajjad H, Masroor M, Rahaman MH, Roshani AR, Sahana M (2022) Assessment of evidence-based climate variability in Bhagirathi sub-basin of India: a geostatistical analysis. Acta Geophys. https://doi.org/10.1007/s11600-022-00726-6
• 84. Roshani SH, Rahaman MH, Masroor M, Ahmed R (2022) Assessing forest health using remote sensing-based indicators and fuzzy analytic hierarchy process in Valmiki Tiger Reserve. India Int J Environ Sci Technol. https://doi.org/10.1007/s13762-022-04512-1
• 85. Sa’adi Z, Shahid S, Ismail T, Chung E-S, Wang X-J (2019) Trends analysis of rainfall and rainfall extremes in Sarawak, Malaysia using modified Mann-Kendall test. Meteorol Atmos Phys 131:263–277. https://doi.org/10.1007/s00703-017-0564-3
• 86. Sah S, Singh R, Chaturvedi G, Das B (2021) Trends, variability, and teleconnections of long-term rainfall in the Terai region of India. Theor Appl Climatol 143:291–307. https://doi.org/10.1007/s00704-020-03421-y
• 87. Saha TK, Pal S (2019) Exploring physical wetland vulnerability of Atreyee river basin in India and Bangladesh using logistic regression and fuzzy logic approaches. Ecol Indic 98:251–265. https://doi.org/10.1016/j.ecolind.2018.11.009
• 88. Sahana M, Rehman S, Ahmed R, Sajjad H (2021) Analyzing climate variability and its effects in Sundarban Biosphere Reserve, India: reaffirmation from local communities. Environ Dev Sustain 23:2465–2492. https://doi.org/10.1007/s10668-020-00682-5
• 89. Sahu N, Panda A, Nayak S, Saini A, Mishra M, Sayama T, Sahu L, Duan W, Avtar R, Behera S (2020) Impact of indo-pacific climate variability on high streamflow events in Mahanadi river Basin. India Water 12:1952. https://doi.org/10.3390/w12071952
• 90. Sam MG, Nwaogazie IL, Ikebude C (2022) Climate change and trend analysis of 24-hourly annual maximum series using Mann-Kendall and Sen slope methods for rainfall IDF modeling. Int J Environ Clim Chang 12:44–60. https://doi.org/10.9734/ijecc/2022/v12i230628
• 91. Sarkar D, Sarkar T, Saha S, Mondal P (2021a) Compiling non-parametric tests along with CA-ANN model for precipitation trends and variability analysis: A case study of Eastern India. Water Cycle 2:71–84. https://doi.org/10.1016/j.watcyc.2021.11.002
• 92. Sarkar A, Saha S, Sarkar D, Mondal P (2021b) Variability and trend analysis of the rainfall of the past 119 (1901–2019) years using statistical techniques: a case study of Uttar Dinajpur. India J Clim Chang 7:49–61. https://doi.org/10.3233/JCC210011
• 93. Sasikala V, Venkatramaphanikumar S, Babu PA, Kumar ME, Krishna NG (2020) Weather predictive system using machine learning algorithms. J Xi’an Univ Archit Technol 12:444–452
• 94. 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
• 95. Shah SA, Jehanzaib M, Kim MJ, Kwak D-Y, Kim T-W (2022) Spatial and temporal variation of annual and categorized precipitation in the Han River Basin, South Korea. KSCE J Civ Eng 26:1990–2001. https://doi.org/10.1007/s12205-022-1194-y
• 96. Shao G, Guan Y, Zhang D, Yu B, Zhu J (2018) The impacts of climate variability and land use change on streamflow in the Hailiutu River Basin. Water (switzerland). https://doi.org/10.3390/w10060814
• 97. Shawul AA, Chakma S (2020) Trend of extreme precipitation indices and analysis of long-term climate variability in the Upper Awash basin, Ethiopia. Theor Appl Climatol 140:635–652. https://doi.org/10.1007/s00704-020-03112-8
• 98. Somboonsuke B, Phitthayaphinant P, Sdoodee S, Kongmanee C (2018) Farmers’ perceptions of impacts of climate variability on agriculture and adaptation strategies in Songkhla Lake basin. Kasetsart J Soc Sci 39:277–283. https://doi.org/10.1016/j.kjss.2018.05.006
• 99. Swain S, Mishra SK, Pandey A, Dayal D (2022) Spatiotemporal assessment of precipitation variability, seasonality, and extreme characteristics over a Himalayan catchment. Theor Appl Climatol 147:817–833. https://doi.org/10.1007/s00704-021-03861-0
• 100. Swe TT, Phyu P, Thein SPP (2019) Weather prediction model using random forest algorithm and apache spark. Int J Trend Sci Res Dev 3:549–552
• 101. Syed A, Liu X, Moniruzzaman M, Rousta I, Syed W, Zhang J, Olafsson H (2021) Assessment of climate variability among seasonal trends using in situ measurements: a case study of Punjab. Pakistan Atmosphere (basel) 12:939. https://doi.org/10.3390/atmos12080939
• 102. Thapa P (2019) Observed and perceived climate change analysis in the Terai region, Nepal. Glob Sci journals 7
• 103. Uddin MA, Kamal ASMM, Shahid S (2022) Vegetation response to climate and climatic extremes in northwest Bangladesh: a quantile regression approach. Theor Appl Climatol 148:985–1003. https://doi.org/10.1007/s00704-022-03968-y
• 104. Umar DA, Ramli MF, Aris AZ, Jamil NR, Aderemi AA (2019) Evidence of climate variability from rainfall and temperature fluctuations in semi-arid region of the tropics. Atmos Res 224:52–64. https://doi.org/10.1016/j.atmosres.2019.03.023
• 105. Upadhayaya RP, Baral MP (2020) Trends of climate change in some selected districts of Western Nepal. Janapriya J Interdiscip Stud 9(1):148–158
• 106. Vu Duy V, Ouillon S, Nguyen Minh H (2022) Sea surface temperature trend analysis by Mann-Kendall test and sen’s slope estimator: a study of the Hai Phong coastal area (Vietnam) for the period 1995–2020. Vietnam J Earth Sci. https://doi.org/10.15625/2615-9783/16874
• 107. Wang C (2019) Three-ocean interactions and climate variability: a review and perspective. Clim Dyn 53:5119–5136. https://doi.org/10.1007/s00382-019-04930-x
• 108. Wudineh FA, Moges SA, Kidanewold BB (2022) Detecting hydrological variability in precipitation extremes: application of reanalysis climate product in data-scarce Wabi Shebele Basin of Ethiopia. J Hydrol Eng. https://doi.org/10.1061/(ASCE)HE.1943-5584.0002156
• 109. Yang R, Xing B (2022) Spatio-temporal variability in hydroclimate over the upper Yangtze River Basin, China. Atmosphere (basel) 13:1–34. https://doi.org/10.3390/atmos13020317
• 110. Yaseen ZM, Ali M, Sharafati A, Al-Ansari N, Shahid S (2021) Forecasting standardized precipitation index using data intelligence models: regional investigation of Bangladesh. Sci Rep 11:3435. https://doi.org/10.1038/s41598-021-82977-9
• 111. Yürekli K (2015) Impact of climate variability on precipitation in the upper Euphrates-Tigris rivers Basin of Southeast Turkey. Atmos Res 154:25–38. https://doi.org/10.1016/j.atmosres.2014.11.002
• 112. Zhou Z, Ren J, He X, Liu S (2021) A comparative study of extensive machine learning models for predicting long-term monthly rainfall with an ensemble of climatic and meteorological predictors. Hydrol Process. https://doi.org/10.1002/hyp.14424

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).