Abstract
This paper proposes a new approach to predicting dry season periods by using annual cumulative rainfall for the past 35 years to determine dry season parameters (onset, end, and duration) in Agam District, West Sumatra, Indonesia. After determining the dry season parameters, the trend of each parameter was determined by using the Mann–Kendall test and Sen’s slope estimator. Finally, the parameters for future dry seasons were predicted by using the autoregressive integrated moving average (ARIMA) model. The results showed that (1) on average, the dry season period in Agam District starts on April 28 and ends on September 17 with a duration of 142 days, (2) dry season periods would be delayed in the future, as indicated by positive trends in the past 35 years for both the onset and end with probabilities of 78.83 and 84.42%, respectively, (3) the ARIMA model successfully predicted the dry season with good performance for the onset (NSE = 0.747) and very good performance for the end (NSE = 0.769), and (4) the dry season period in the next 5 years would start between April 29 and May 5 and end between September 19 and October 4. This study suggested that relevant stakeholders should reassess the schedule for water use and distribution of agricultural water, postpone transplanting to the first week of July in future dry seasons, and prepare additional irrigation water to prevent water deficit.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Aldrian E, Susanto RD (2003) Identification of three dominant rainfall regions within Indonesia and their relationship to sea surface temperature. Int J Climatol 23:1435–1452. https://doi.org/10.1002/joc.950
Alotaibi K, Ghumman AR, Haider H, Ghazaw YM, Shafiquzzaman M (2018) Future predictions of rainfall and temperature using GCM and ANN for arid regions: a case study for the Qassim Region. Saudi Arabia Water 10:1260. https://doi.org/10.3390/w10091260
Avia LQ (2019) Change in rainfall per-decades over Java Island. IOP Conference Series: Earth Environ Sci 374:012037. https://doi.org/10.1088/1755-1315/374/1/012037
Box GEP, Jenkins G (1976) Time series analysis, forecasting and control. Holden-Day, San Francisco, Holden-Day, pp 21–84
Box GEP, Jenkins GM, Reinsel GC, Ljung GM (2016) Time series analysis: forecasting and control, 5th edn. Wiley, New Jersey, pp 325–329
BPS S of AR (Central Bureau of Statistics of Agam Regency) (2020) Agam Regency in figures 2019 (BPS-Statistics of Agam Regency), pp 281.
BPS S of WS (Central Bureau of Statistics of West Sumatera Province) (2020) West Sumatera Province in figures 2019 (BPS-Statistics of West Sumatera Province), pp. 379.
Brouwer C, Heibloem M (1986) Irrigation Water Management: Irrigation Water Needs. Training Manual, Food and Agriculture Organization (FAO) of the United Nations, Rome. http://www.fao.org/3/s2022e/s2022e08.htm. Accessed July 1 2021.
Caraka RE, Tahmid M, Putra RM, Iskandar A, Mauludin MA, Hermansah GNE, Rohayani H, Pardamean B (2018) Analysis of plant pattern using water balance and cimogram based on oldeman climate type. IOP Conference Series: Earth Environ Sci 195:012001. https://doi.org/10.1088/1755-1315/195/1/012001
Chen FW, Liu CW (2012) Estimation of the spatial rainfall distribution using inverse distance weighting (IDW) in the middle of Taiwan. Paddy Water Environ, 10:209–222. https://doi.org/10.1007/s10333-012-0319-1
Farajzadeh J, Fard AF, Lotfi S (2014) Modeling of monthly rainfall and runoff of Urmia lake basin using “feed-forward neural network” and “time series analysis” model. Water Resources Industry 7(8):38–48. https://doi.org/10.1016/j.wri.2014.10.003
Feng G, Cobb S, Abdo Z, Fisher DK, Ouyang Y, Adeli A, Jenkins JN (2016) Trend analysis and forecast of precipitation, reference evapotranspiration, and rainfall deficit in the Blackland Prairie of Eastern Mississippi. J Appl Meteorol Climatol 55(7):1425–1439. https://doi.org/10.1175/JAMC-D-15-0265.1
Filder TN, Muraya MM, Mutwiri RM (2019) Application of seasonal autoregressive moving average models to analysis and forecasting of time series monthly rainfall patterns in Embu County, Kenya. Asian J Prob Stat 4(4):1–15. https://doi.org/10.9734/AJPAS/2019/v4i430123
Gedefaw M, Yan D, Wang H, Qin T, Girma A, Abiyu A, Batsuren D (2018) Innovative trend analysis of annual and seasonal rainfall variability in Amhara Regional State. Ethiopia Atmosphere 9:326. https://doi.org/10.3390/atmos9090326
Gilbert RO (1987) Statistical methods for environmental pollution monitoring. Van Nostrand Reinhold Company, New York, pp 204–223
Hamada J, Yamanaka MD, Matsumoto J, Fukao S, Winarso PA, Sribimawati T (2002) Spatial and temporal variations of the Rainy Season over Indonesia and their link to ENSO. J Meteorol Soc Jpn 80(2):285–310. https://doi.org/10.2151/jmsj.80.285
Han P, Wang PX, Zhang SY, Zhu DH (2010) Drought forecasting based on the remote sensing data using ARIMA models. Math Comput Model 51:1398–1403. https://doi.org/10.1016/j.mcm.2009.10.031
Hao Z, Singh VP, Xia Y (2018) Seasonal drought prediction: advances, challenges, and future prospects. Rev Geophys 56:108–141. https://doi.org/10.1002/2016RG000549
Hargreaves GH and Samani ZA (1985) Reference crop evapotranspiration from temperature. Appl Eng Agric 1(2): 96–99. https://doi.org/10.13031/2013.26773
Indonesian Agency for Agricultural Research and Development (2020) Integrated Planting Calendar [Online]. Ministry of Agriculture Indonesia. Available at: http://katam.litbang.pertanian.go.id. Accessed November 24 2020.
IRRI (International Rice Research Institute) (1974) An agroclimatic classification for evaluating cropping systems potentials in Southeast Asian rice growing regions. Los Baños, Philippines, pp. 10.
Irsyad F, Saptomo SK, Setiawan BI (2014) Determination of dry season onset and duration using polynomial function with visual basic for applications (VBA). Indonesian J Agricultural Meteorol 28(1):40–46. (in Bahasa). https://doi.org/10.29244/j.agromet.28.1.40-46
Kendall MG (1975) Rank correlation methods, 4th edn. Charles Griffin, London, UK, pp 70–89.
Laux P, Kunstmann H, Bárdossy A (2008) Predicting the regional onset of the rainy season in West Africa. Int J Climatol 28(3):329–342. https://doi.org/10.1002/joc.1542
Li W, Fu R (2006) Influence of cold air intrusions on the wet season onset over Amazonia. J Clim 19:257–275. https://doi.org/10.1175/JCLI3614.1
Magee L (1998) Nonlocal behavior in polynomial regressions. Am Stat 52(1):20–22 https://doi.org/10.1080/00031305.1998.10480531
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 Scientific Reports 9(1):6317. https://doi.org/10.1038/s41598-019-42811-9
Mann HB (1945) Nonparametric tests against trend. Econometrica 13:245–259. https://doi.org/10.2307/1907187
Marengo JA, Liebmann B, Kousky VE, Filizola NP, Wainer IC (2001) Onset and end of the rainy season in the Brazilian Amazon Basin. J Clim 14:833–852. https://doi.org/10.1175/1520-0442(2001)014%3C0833:OAEOTR%3E2.0.CO;2
Moeletsi ME, Shabalala ZP, Nysschen GD, Walker S (2016) Evaluation of an inverse distance weighting method for patching daily and decadal rainfall over the Free State Province. South Africa Water SA 42(3):466–474. https://doi.org/10.4314/wsa.v42i3.12
Moran PAP (1950) Notes on continuous stochastic phenomena. Biometrika 37(1–2):17–23. https://doi.org/10.1093/biomet/37.1-2.17
Moriasi DN, Arnold JG, Van Liew MW, Bingner RL, Harmel RD, Veith TL (2007) Model evaluation guidelines for systemic quantification of accuracy in watershed simulations. Am Soc Agricultural Biol Eng 50(3):885–900. https://doi.org/10.13031/2013.23153
Munandar TA, Sumiati (2017) The classification of cropping patterns based on regional climate classification using decision tree approach. J Comput Sci 13(9):408–415. https://doi.org/10.3844/jcssp.2017.408.415
Murat M, Malinowska I, Gos M, Krzyszczak J (2018) Forecasting daily meteorological time series using ARIMA and regression models. International Agrophysics 32:253–364. https://doi.org/10.1515/intag-2017-0007
Narula SC (1979) Orthogonal polynomial regression. Int Stat Rev 47(1):31–36. https://doi.org/10.2307/1403204
Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I—a discussion of principles. J Hydrol 10(3):282–290. https://doi.org/10.1016/0022-1694(70)90255-6
Naylor RL, Battisti DS, Vimont DJ, Falcon WP, Burke MB (2007) Assessing the risks of climate variability and climate change for Indonesian rice agriculture. Proc Natl Acad Sci USA 104(19):7752–7757. https://doi.org/10.1073/pnas.0701825104
Nega W, Hailu BT, Fetene A (2019) An assessment of the vegetation cover change impact on rainfall and land surface temperature using remote sensing in a subtropical climate, Ethiopia. Remote Sensing Appl Soc Environ 16:100266. https://doi.org/10.1016/j.rsase.2019.100266
Nugroho BDA, Nuraini L (2016) Cropping pattern scenario based on global climate indices and rainfall in Banyumas District, Central Java, Indonesia. Agric Agricultural Sci Proc 9:54–63. https://doi.org/10.1016/j.aaspro.2016.02.124
Nyatuame M, Agodzo SK (2018) Stochastic ARIMA model for annual rainfall and maximum temperature forecasting over Tordzie watershed in Ghana. J Water Land Dev 37:127–140. https://doi.org/10.2478/jwld-2018-0032
Oldeman LR, Suardi D (1977) Climatic determinants in relation to cropping patterns. Cropping systems research and development for the Asian rice farmer, the International Rice Research Institute. 21–24 September 1976, Los Baṅos, Laguna, Philippines, pp 61–81.
Omotosho JB (1992) Long-range prediction of the onset and end of the rainy season in the West African Sahel. Int J Climatol 12:369–382. https://doi.org/10.1002/joc.3370120405
Oue H, Laban S (2020) Water use of rice and mung bean cultivations in a downstream area of an irrigation system in South Sulawesi in the 2nd dry season. Paddy Water Environ 18:87–98. https://doi.org/10.1007/s10333-019-00766-7
Pereira LS, Allen RG (1999) Crop Water Requirements. In: van Lier HN, Pereira LS, Steiner FR (ed) CIGR Handbook of agricultural engineering, volume I Land and Water Engineering. the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan, US, pp, 213–262 https://doi.org/10.13031/2013.36302
Robertson A, Moron V, Qian J, Chang CP, Tangang F, Aldrian E, Koh TY, Juneng L (2011) The Maritime Continent Monsoon. In: Chang et al. (ed) The global monsoon system: Reseasrch and forecast, 2nd edn. World Scientific Publication Company, Singapore, pp. 85–98. https://doi.org/10.1142/9789814343411_0006
Scott LM, Janikas MV (2010) Spatial Statistics in ArcGIS. In: Fischer MM, Getis A (ed) Handbook of applied spatial analysis: software tools, methods and applications. Springer, German, pp, 27–40. https://doi.org/10.1007/978-3-642-03647-7
Sen PK (1968) Estimates of the regression coefficient based on Kendall’s Tau. J Am Stat Assoc 63:1379–1389. https://doi.org/10.2307/2285891
Setiawan BI (2020) A simple method to determine patterns of wet and dry seasons. IOP Conference Series: Earth and Environmental Science. https://doi.org/10.1088/1755-1315/542/1/012055
Sofia DA, Wibowo H, Nursila N (2019) Water balance analysis and shifting planting schedules in the Cimandiri irrigation area. Jurnal Teknologi Rekayasa 4(2):193–202. (in Bahasa with English abstract) https://doi.org/10.31544/jtera.v4.i2.2019.193-202
Soltani S, Modarres R, Eslamian SS (2007) The use of time series modeling for the determination of rainfall climates of Iran. Int J Climatol 27:819–829. https://doi.org/10.1002/joc.1427
Supari TF, Salimun E, Aldrian E, Sopaheluwakan A, Juneng L (2018) ENSO modulation of seasonal rainfall and extremes in Indonesia. Clim Dyn 51:2559–2580. https://doi.org/10.1007/s00382-017-4028-8
Wang S, Feng J, Liu G (2013) Application of seasonal time series model in the precipitation forecast. Math Comput Model 58:677–683. https://doi.org/10.1016/j.mcm.2011.10.034
Wei WWS (2006) Time series: univariate and multivariate methods -, 2nd edn. Pearson Addison Wesley, New York, pp 68–105
Xu D, Zhang Q, Ding Y, Huang H (2020) Application of a hybrid ARIMA–SVR model based on the SPI for the forecast of drought-a case study in Henan Province, China. J Appl Meteorol Climatol 59(7):1239–1259. https://doi.org/10.1175/JAMC-D-19-0270.1
Yoosefdoost A, Sadeghian MS, Farahani MAN, Rasekhi A (2017) Comparison between performance of statistical and low cost ARIMA model with GFDL, CM2.1 and CGM 3 atmosphere-ocean general circulation models in assessment of the effects of climate change on temperature and precipitation in Taleghan Basin. Am J Water Resources 5(4):92–99. https://doi.org/10.12691/ajwr-5-4-1
Acknowledgements
We thank the Water Resources Management Agency (Pengelolaan Sumber Daya Air (PSDA)) West Sumatra, and the Meteorology, Climatology and Geophysical Agency (Badan Meteorologi, Klimatologi, dan Geofisika (BMKG)) West Sumatra, Indonesia, for providing the daily rainfall data in this study.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Irsyad, F., Oue, H. Predicting future dry season periods for irrigation management in West Sumatra, Indonesia. Paddy Water Environ 19, 683–697 (2021). https://doi.org/10.1007/s10333-021-00867-2
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10333-021-00867-2