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Predicting future water demand for Long Xuyen Quadrangle under the impact of climate variability

Research Article - Hydrology
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Abstract

Long Xuyen Quadrangle is one of the important agricultural areas of the Mekong Delta of Vietnam accounting for 25% of rice production. In recent years, the area faces drought and salinization problems, as part of climate change impact and sea level rise. These are the main causes that led to the crop water deficits for agricultural production. Therefore, this work was conducted to predict crop water requirement (CWR) based on consideration of other related meteorological factors and further redefine the crop planting calendar (CPC) for three main cropping seasons including winter–spring (WS), summer–autumn (SA) and autumn–spring (AS) using the Cropwat crop model based on the current climate conditions and future climate change scenarios. Meteorological data for the baseline period (2006–2016) and future corresponding to timescales 2020s, 2055s and 2090s of Representative Concentration Pathways (RCP)4.5 and RCP8.5 scenarios are used to predict CWR and CPC for the study area. The results showed that WS and SA crops needed more irrigation water than AS crop and the highest irrigation water requirement of the WS and SA crops occurred on developmental stage, while the AW crop appeared on growth, developmental and late stage for the baseline and timescales of RCP4.5 and RCP8.5 scenarios. Calculation results of the shift of CPC indicated that the CWR of the AW crop decreased lowest approximately 6.6–20.6% for timescales of RCP4.5 scenario and 20.6–25.5% for RCP8.5 scenario compared with the baseline.

Keywords

Cropwat Irrigation water Effective rainfall Crop calendar Climate change 

Notes

Acknowledgements

The author would like to thank the Southern Regional Hydro-meteorological Center of Vietnam (SRHCV) for providing database.

Author’s contribution

In this study, TAD proposed the research project, outlined the study project and designed the proposal. SKL has simulated the crop model. Both TAD and SKL jointly analyzed the results, edited and wrote the manuscript.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. Adnan AA, Jibrin JM, Kamara AY, Abdulrahman BL, Shaibu AS, Garba II (2017) CERES–Maize model for determining the optimum planting dates of early maturing maize varieties in Northern Nigeria. Front Plant Sci 8:1118.  https://doi.org/10.3389/fpls.2017.01118 CrossRefGoogle Scholar
  2. Aggarwal PK, Singh AK (2010) Implications of global climatic change on water and food security. Water Resources Dev Manag 1:49–63CrossRefGoogle Scholar
  3. Archontoulis SV, Miguez FE, Moore KJ (2014) A methodology and an optimization tool to calibrate phenology of short-day species included in the APSIM PLANT model: application to soybean. Environ Model Softw 62:465–477CrossRefGoogle Scholar
  4. Arku AY, Musa SM, Mofoke ALE (2012) Determination of water requirements for irrigating Hibiscus (Rosa Sinensis) in Maiduguri Metropolis. J Appl Hyto-Technol Environ Sanit 1(1):37–42Google Scholar
  5. Asia Pacific Network (APN) (2010) Climate change in Southeast Asia and assessment on impact, vulnerability and adaptation on rice production and water resource. Project Reference Number: CRP2008-03CMY-JintrawetGoogle Scholar
  6. Babel MS, Turyatunga E (2014) Evaluation of climate change impacts and adaptation measures for maize cultivation in the western Uganda agro-ecological zone. Theor Appl Climatol.  https://doi.org/10.1007/s00704-014-1097-z Google Scholar
  7. Banik P, Tiwari NK, Ranjan S (2014) Crop water assessment of plain and hilly region using Cropwat model. Int J Sustain Mater 1(3):168–180Google Scholar
  8. Bhat NR, Lekha VS, Suleiman MK, Ali SI, George P, AlMulla L (2012) Estimation of water requirements for young date palms under arid climatic conditions of Kuwait. World J Agric Sci 8(5):448–452Google Scholar
  9. Bouraima AK, Zhang WH, Wei CF (2015) Irrigation water requirements of rice using Cropwat model in Northern Benin. Int J Agric Biol Eng 8(2):58–64Google Scholar
  10. Chatterjee SK, Banerjee S, Bose MM (2012) Climate change impact on crop water requirement in ganga river basin, West Bengal, India. In: 2012 3rd international conference on biology, environment and chemistry, vol 46.  https://doi.org/10.7763/ipcbee
  11. Cheng ZQ, Meng JH, Qiao YY, Wang YM, Dong WQ, Han YX (2018) Preliminary study of soil available nutrient simulation using a modified WOFOST model and time-series remote sensing observations. Remote Sens 10:64.  https://doi.org/10.3390/rs10010064 CrossRefGoogle Scholar
  12. Chowdhury S, Al-Zahrani M, Abbas A (2013) Implications of climate change on crop water requirements in arid region: an example of Al-Jouf, Saudi Arabia. J King Saud Univ Engg Sci 28:21–30Google Scholar
  13. Deb P, Kiem AS, Babel MS, Chu ST, Chakma B (2015) Evaluation of climate change impacts and adaptation strategies for maize cultivation in the Himalayan foothills of India. J Water Clim Change.  https://doi.org/10.2166/wcc.2015.070 Google Scholar
  14. Deb P, Tran DA, Udmale PD (2016) Assessment of the impacts of climate change and brackish irrigation water on rice productivity and evaluation of adaptation measures in Ca Mau province, Vietnam. Theor Appl Climatol 125(3–4):641–656CrossRefGoogle Scholar
  15. Dharmarathna WRSS, Herath S, Weerakoon SB (2014) Changing the planting date as a climate change adaptation strategy for rice production in Kurunegala district. Sri Lanka Sustain Sci 9(1):103–111CrossRefGoogle Scholar
  16. Dinh Q, Balica S, Popescu I, Jonoski A (2012) Climate change impact on flood hazard, vulnerability and risk of the Long Xuyen Quadrangle in the Mekong Delta. Int J River Basin Manag 10(1):103–120CrossRefGoogle Scholar
  17. Duchemin B, Maisongrande P, Boulet G, Benhadj I (2008) A simple algorithm for yield estimates: evaluation for semi-arid irrigated winter wheat monitored with green leaf area index. Environ Model Softw 23(7):876–892CrossRefGoogle Scholar
  18. Food and Agriculture Organization (FAO) (1998) Crop evapotranspiration: Guidelines for computing crop water requirements. FAO irrigation and drainage paper 56. Rome, ItalyGoogle Scholar
  19. Food and Agriculture Organization (FAO) (2016) El Niño event in Viet Nam: Agriculture, food security and livelihood need assessment in response to drought and salt water intrusion. Assessment Report, p 75Google Scholar
  20. Kawasaki J, Herath S (2011) Impact assessment of climate change on rice production in Khon Kaen province, Thailand. J Int Soc SE Asian Agric Sci 2:14–28Google Scholar
  21. Keating BA, Carberry PS, Hammer GL, Probert ME, Robertson MJ, Holzworth D, Hutha NI, Hargreavesa JNG, Meinke H, Hochman Z, McLean G, Verburg K, Snow V, Dimes JP, Silburn M, Wang E, Brown S, Bristow KL, Smith CJ (2003) An overview of APSIM, a model designed for farming systems simulation. Eur J Agron 18:267–288.  https://doi.org/10.1016/S1161-0301(02)00108-9 CrossRefGoogle Scholar
  22. Khoshravesh M, Mostafazadeh-Fard B, Heidarpour M, Kiani AR (2013) AquaCrop model simulation under different irrigation water and nitrogen strategies. Water Sci Tech 67(1):232–238CrossRefGoogle Scholar
  23. Kim HY, Ko J, Kang S, Tenhunen J (2013) Impacts of climate change on paddy rice yield in a temperate climate. Glob Change Biol 19:548–562CrossRefGoogle Scholar
  24. Lee TS, Najim MM, Aminul MH (2004) Estimating evapotranspiration of irrigated rice at the west coast of the peninsular of Malaysia. J Appl Irrig Sci 39(1):103–117Google Scholar
  25. Liu G, Xie Y, Gao XF, Feng YJ (2008) Sensitivity analysis on parameters of ALMANAC crop model. Chin J Agrometeorol 29(3):259–263Google Scholar
  26. Mainuddin M, Kirby M, Hoanh CT (2013) Impact of climate change on rainfed rice and options for adaptation in the lower Mekong Basin. Nat Hazards 66(2):905–938CrossRefGoogle Scholar
  27. Manasa HG, Shivapur AV (2016) Implications of climate change on crop water requirements in Hukkeri Taluk of Belagavi District, Karnataka, India. Int J Res Eng Technol 5(6):236–241CrossRefGoogle Scholar
  28. Ministry of Natural Resources and Environment (MNRE) (2016) Climate change scenarios and sea level rise for Vietnam. Publishers resources, environment and map of VietnamGoogle Scholar
  29. Nazeer M (2009) Simulation of maize crop under irrigated and rainfed conditions with Cropwat model. ARPN J Agric Biol Sci 4(2):68–73Google Scholar
  30. Oyeogbe IA, Oluwasemire KO (2013) Evaluation of So model for predicting soil water characteristics in south-western Nigeria. Int J Soil Sci 8(2):8–67CrossRefGoogle Scholar
  31. Poudel S, Shaw R (2016) The relationships between climate variability and crop yield in a mountainous environment-A case study in Lamjung District-Nepal. Climate 4:13CrossRefGoogle Scholar
  32. Research Centers in Southeast Asia (RCSA) (2016) The drought and salinity intrusion in the Mekong River Delta of Vietnam. Assessment Report, p 55Google Scholar
  33. Rong L, Zhang CL, Zhang XX, Wu SN, Wang ZJ (2015) Wheat Production Simulation Based on the ALMANAC Model of North China Region. Sustainable Agriculture Research 2(3):148.  https://doi.org/10.5539/sar.v2n3p148 CrossRefGoogle Scholar
  34. Rosenthal WD, Vanderlip RL (2004) Simulation of individual leaf areas in grain sorghum. Agronomie 24(8):493–501CrossRefGoogle Scholar
  35. Shah PV, Mistry RN, Amin JB, Parmar AM, Shaikh RA (2015) Irrigation scheduling using Cropwat. Int J Adv Res Eng Sci Technol 2(4):1–10Google Scholar
  36. Shrestha S, Thin NMM, Deb P (2014) Assessment of climate change impacts on irrigation water requirement and rice yield for Ngamoeyeik irrigation project in Myanmar. J Water Clim Change 5(3):427–442CrossRefGoogle Scholar
  37. Shrestha S, Deb P, Bui TTT (2016) Adaptation strategies for rice cultivation under climate change in Central Vietnam. Mitig Adapt Strat Glob Change 21(1):15–37CrossRefGoogle Scholar
  38. Silvestro PC, Pignatti S, Yang H, Yang G, Pascucci S, Castaldi F, Casa R (2017) Sensitivity analysis of the Aquacrop and SAFYE crop models for the assessment of water limited winter wheat yield in regional scale applications. PLoS ONE 12(11):e0187485.  https://doi.org/10.1371/journal.pone.0187485 CrossRefGoogle Scholar
  39. Soora NK, Aggarwal PK, Saxena R, Rani S, Jain S, Chauhan N (2013) An assessment of regional vulnerability of rice to climate change in India. Clim Chan 118(3–4):683–699CrossRefGoogle Scholar
  40. Surendran U, Sushanth CM, Mammen G, Joseph EJ (2014) Modelling the impacts of increase in temperature on irrigation water requirements in Palakkad district: a case study in humid tropical Kerala. J Water Clim Change 5:472–485CrossRefGoogle Scholar
  41. Tan MH, Zheng LQ (2017) Different irrigation water requirements of seed corn and field corn in the Heihe River Basin. Water 9:606.  https://doi.org/10.3390/w9080606 CrossRefGoogle Scholar
  42. Todorovic M, Albrizio R, Zivotic L, Saab MA, Stöckle C, Steduto P (2009) Assessment of AquaCrop, CropSyst, and WOFOST models in the simulation of sunflower growth under different water regimes all rights reserved. Agron J 101:509–521.  https://doi.org/10.2134/agronj2008.0166s CrossRefGoogle Scholar
  43. Trinh LT, Duong CC, Steen PVD, Lens PNL (2013) Exploring the potential for wastewater reuse in agriculture as a climate change adaptation measure for Can Tho city, Vietnam. Agric Water Manag 128:43–54CrossRefGoogle Scholar
  44. Umair M, Shen Y, Qi Y, Zhang Y, Ahmad A, Pei H, Liu M (2017) Evaluation of the CropSyst model during wheat-maize rotations on the North China Plain for identifying soil evaporation losses. Front Plant Sci 8:1667.  https://doi.org/10.3389/fpls.2017.01667 CrossRefGoogle Scholar
  45. Wassmann R, Jagadish SVK, Sumfleth K, Pathak H, Howell G, Ismail A, Serraj R, Redona E, Singh RK, Heuer S (2009) Regional vulnerability of climate change impacts on Asian rice production and scope for adaptation. In: Advances in agronomy. Elsevier, San Diego, pp 91–103Google Scholar
  46. Worou ON, Gaiser T, Oikeh S (2015) Sensitive parameters for EPIC model evaluation and validity under soil water and nutrients limited conditions with NERICA cropping in West Africa. Afr J Agric Res 10(22):2286–2299CrossRefGoogle Scholar
  47. Xie Y, Kiniry JR, Nedbalek V, Rosenthal WD (2001) Maize and sorghum simulations with CERES-Maize, SORKAM, and ALMANAC under water-limiting conditions. Agron J 93:1148–1155CrossRefGoogle Scholar
  48. Yadav S, Deb P, Kumar S, Pandey V, Pandey PK (2016) Trends in major and minor meteorological variables and their influence on reference evapotranspiration for mid Himalayan region at East Sikkim, India. J Mt Sci 13(2):302–315CrossRefGoogle Scholar
  49. Yue Y, Li J, Ye X, Wang ZQ, Zhu AX, Wang JA (2015) An EPIC model-based vulnerability assessment of wheat subject to drought. Nat Hazards 78(3):1629–1652CrossRefGoogle Scholar
  50. Zhiming F, Dengwei L, Yuehong Z (2007) Water requirements and irrigation scheduling of spring maize using GIS and Cropwat model in Beijing-Tianjin-Hebei region. Chin Geogr Sci 17(1):056–063CrossRefGoogle Scholar
  51. Zhong S, Zhang W, Lu J, Wei C (2014) Temporal variation of soil water and its influencing factors in hilly area of Chongqing, China. Int J Agric Biol Eng 7(4):47–59Google Scholar
  52. Zoidou M, Tsakmakis ID, Gikas GD, Sylaios G (2017) Water Footprint for cotton irrigation scenarios utilizing Cropwat and AquaCrop models. Eur Water 59(285–290):2017Google Scholar

Copyright information

© Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences 2018

Authors and Affiliations

  1. 1.Sustainable Management of Natural Resources and Environment Research Group, Faculty of Environment and Labour SafetyTon Duc Thang UniversityHo Chi Minh CityVietnam
  2. 2.VNUHCM-University of ScienceHo Chi Minh CityVietnam

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