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Sensitivity of evapotranspiration to climate change using DSSAT model in sub humid climate region of Eastern Uttar Pradesh

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Abstract

Climate variability impacts the components of hydrological cycle especially evapotranspiration (ET) and soil moisture, that plays a crucial role in determining water flux of an agriculture system and is thus, essential to study the response of ET to climate change. The present study is an attempt to understand the trend in observed ET (1978–2003) and variation in projected ET RegCM4.0, RCP 4.5 scenario during 2040–2060. Observed ET is compared with simulated ET using NCEP, NASA Power, RegCM4.0 and agriculture field data. Apart from studying the effect of relative humidity (RH), solar radiation (SLR), minimum and maximum temperature and wind speed (WS) on ET, the FAO Penman–Monteith and Priestly–Taylor methods in CERES Rice and CERES Wheat crop model were used to simulate ET. Further, the cumulative impact of rainfall and ET on agriculture drought has been estimated based on standardized Reconnaissance Drought Index (RDIst). The result shows a declining trend of ET during 1978–2003, but an increase during 2040s (2040–2061) for both wheat and rice. Overall, the ET simulated using weather data input from agriculture field shows highest concordance with observed ET, followed by NASA/RegCM4 and NCEP. Moreover, the FAO Penman–Monteith gives more accurate result in comparison to the Priestley–Taylor method. Environmental modification suggests that RH is the most influential parameter for ET followed by temperature, SLR and WS. Based on RDIst it was observed that rainfall is negatively associated with ET and their cumulative effect on water availability can be efficiently estimated using drought index.

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References

  • Abtew W, Melesse A (2013) Evaporation and Evapotranspiration: Measurements and Estimations. Springer Science Business Media, Dordrecht, pp 197–202

    Book  Google Scholar 

  • Aggarwal PK, Mall RK (2002) Climate Change and Rice Yields in Diverse Agro-Environments of India. II. Effect of uncertainties in scenarios and crop models on impact assessment. Clim Change 52:331–343

    Article  Google Scholar 

  • Babatunde OA, Abiye OE, Sunmonu LA, Olufemi AP, Ayoola MA, Akinola OE, Ogolo EO (2017) A comparative evaluation of four evapotranspiration models based on Eddy Covariance measurement over a grass-covered surface in Ile-Ife, Southwestern Nigeria. Model Earth Syst Environ 3(4):1273–1283

    Article  Google Scholar 

  • Bai H, Tao F (2017) Sustainable intensification options to improve yield potential and eco-efficiency for rice-wheat rotation system in China. Field Crops Res 211: 59(4):89–105

    Article  Google Scholar 

  • Banerjee S, Chatterjee S, Sarkar S, Jena S (2016) Projecting future crop evapotranspiration and irrigation requirement of potato in lower gangetic plains of India using the CROPWAT 8.0 model. Am J Potato Res 59(4):313–327

    Article  Google Scholar 

  • Blaney HF, Criddle WD (1950) Determining water requirements in irrigated areas from climatological and irrigation data. USDA(SCS) 96:48

    Google Scholar 

  • Doorenbos J, WO Pruitt (1975) Pruitt WO (1975) Guidelines for predicting crop water requirements irrigation and drainage paper 24. FAO, Rome, pp 1–144

    Google Scholar 

  • Espadafor M, Lorite IJ, Gavilan P, Berengena J (2011) An analysis of the tendency of reference evapotranspiration estimates and other climate variables during the last 45 years in Southern Spain. Agric Water Manag 98:1045–1061

    Article  Google Scholar 

  • Federer CA, Vorosmarty C, Fekete B (1996) Intercomparison of methods for calculating potential evaporation in regional and global water balance model. Water Resour Res 32:2315–2321

    Article  Google Scholar 

  • Gao Z, He J, Dong K, Li X (2017) Trends in reference evapotranspiration and their causative factors in the West Liao River basin (China). Agric For Meteorol 232:106–117

    Article  Google Scholar 

  • Giorgi F (2012) RegCM4: model description and preliminary tests over multiple CORDEX domains. Clim Res 52:7–29

    Article  Google Scholar 

  • Grace B, Quick B (2013) A comparison of methods for the calculation of potential evapotranspiration under the windy semi-arid conditions of southern Alberta. Can Water Resour J 13:9–19

    Article  Google Scholar 

  • Guo D, Westra S, Maier HR (2017) Sensitivity of potential evapotranspiration to changes in climate variables for different Australian climatic zones. Hydrol Earth Syst Sci 21:2107–2126

    Article  Google Scholar 

  • Hamed KH (2008) Trend detection in hydrologic data: the Mann–Kendall trend test under the scaling hypothesis. J Hydrol 349:350–363

    Article  Google Scholar 

  • Hargreaves GH, Samani ZA (1985) Reference crop evapotranspiration from temperature. Appl Eng Agric 1:96–99

    Article  Google Scholar 

  • Irmak S, Kabenge I, Skaggs KE, Mutiibwa D (2012) Trend and magnitude of changes in climate variables and reference evapotranspiration over 116-yr period in the Platte River Basin, central Nebraska–USA. J Hydrol 421:228–244

    Article  Google Scholar 

  • IPCC (2013) Working group 1 fifth assessment report on climate change 2013: the physical science basis Geneva, Switzerland

  • Jepsen SM, Harmon TC, Ficklin DL, Molotch NP, Guan B (2018) Evapotranspiration sensitivity to air temperature across a snow-influenced watershed: Space-for-time substitution versus. J Hydrol 556:645–659

    Article  Google Scholar 

  • Jhajharia D, kumar R, Dabral PP, Singh VP, Choudhary RR, Dinpashoh Y (2015) Reference evapotranspiration under changing climate over the Thar Desert in India. Meteorol Appl 22(3):425–435

    Article  Google Scholar 

  • Jones JW, Hoogenboom G, Porter CH, Boote KJ, Batchelor WD, Hunt LA, Wilkens PW, Singh U, Gijsman AJ, Ritchie JT (2003) The DSSAT cropping system model. Eur J Agron 18:235–265

    Article  Google Scholar 

  • Kendall MG (1975) Rank correlation methods, 4th edn. Charles Griffin, London

    Google Scholar 

  • Kumar M, Denis DM, Suryavanshi S (2016) Long-term climatic trend analysis of Giridih district, Jharkhand (India) using statistical approach. Model Earth Syst Environ 2:116. https://doi.org/10.1007/s40808-016-0162-2

    Article  Google Scholar 

  • Kundu S, Khare D, Mondal A (2017) Interrelationship of rainfall, temperature and reference evapotranspiration trends and their net response to the climate change in Central India. Theor Appl Climatol 130(4):879–900

    Article  Google Scholar 

  • Lal R, Stewart BA, Uphoff N, Hansen DO (2005) Climate change, soil carbon dynamics and global food. CRC Press, Boca Raton, pp 113–143

    Google Scholar 

  • Lei H, Gong T, Zhang Y, Yang D (2018) Biological factors dominate the interannual variability of evapotranspiration in an irrigated cropland in the North China Plain. Agric For Meteorol 251:262–276

    Article  Google Scholar 

  • Lv Z, Liu X, Cao W, Zhu Y (2017) A model-based estimate of regional wheat yield gaps and water use efficiency in main winter wheat production regions of china. Sci Rep 7(1):6081

    Article  Google Scholar 

  • Madhu S, Kumar TVL, Barbosa H, Rao KK, Bhaskar VV (2015) Trend Analysis of evapotranspiration and its response to drought over India. Theor Appl Climatol 121(2):41–51

    Article  Google Scholar 

  • Mall RK, Aggarwal PK (2002) Climate change and rice yields in diverse agro-environments India. Evaluation of impact assessment models. Clim Change 52:315–330

    Article  Google Scholar 

  • Mall RK, Gupta BRD (2000) Wheat yield forecast models based on meteorological parameters. J Agrometerol 2(1):83–87

    Google Scholar 

  • Mall RK, Gupta BRD (2002) Comparison of evapotranspiration models. Mausam 53(2):119–126

    Google Scholar 

  • Mall RK, Sonkar G, Bhatt D, Sharma NK, Singh KK (2016) Managing impact of extreme weather events in sugarcane. Mausam 67(1):233–250

    Google Scholar 

  • Mall RK, Gupta A, Sonkar G (2017) Effect of climate change on agricultural crops. Cur Dev Biotechnol Bioeng 1:23–46

    Google Scholar 

  • Mall RK, Singh N, Singh KK, Sonkar G, Gupta A (2018) Evaluating the performance of RegCM4.0 climate model for climate change impact assessment on wheat and rice crop in diverse agro-climatic zones of Uttar Pradesh, India. Clim Change 149(3):503–515

    Article  Google Scholar 

  • Mann HB (1945) Non-parametric tests against trend. Econometrica 13:245–259

    Article  Google Scholar 

  • Meshram S, Kant S, Sahu KC (2014) Identification of meterological drought year for Varanasi. Recent Res Sci Technol 6(1):245–247

    Google Scholar 

  • Milly PC, Dunne KA (2016) Potential evapotranspiration and continental drying. Nat Clim Change 6:946–949

    Article  Google Scholar 

  • Mishra A, Singh R, Raghuwanshi NS, Chatterjee C, Froebrich J (2013) Spatial variability of climate change impacts on yield of rice and wheat in the Indian Ganga Basin. Sci Total Environ 469:132–138

    Article  Google Scholar 

  • Pawar GS, Kale MU, Lokhande JN (2017) Response of AquaCrop model to different irrigation schedules for irrigated cabbage. Agric Res 6(1):73–81

    Article  Google Scholar 

  • Prabnakorn S, Maskey S, Suryadi FX, Fraiture CD (2018) Rice yield in response to climate trends and drought Index in the Mun River Basin, Thailand. Sci Total Environ 621:108–119

    Article  Google Scholar 

  • Priestly CHB, Taylor RJ (1972) On the assessment of surface heat flux and evaporation using large-scale parameters. Mon Weather Rev 100:81–92

    Article  Google Scholar 

  • Priya A, Islam A, Nema AK, Sikka AK (2015) Assessing sensitivity of reference evapotranspiration to changes in climate variables: a case study of Akola. India Mausam 66(4):777–784

    Google Scholar 

  • Qian B, Jong RD, Huffman T, Wang H, Yang J (2016) Projecting yield changes of spring wheat under future climate scenarios on the Canadian Prairies. Theor Appl Climatol 123:651–669

    Article  Google Scholar 

  • Sentelhas PC, Gillespie TJ, Santos EA (2010) Evaluation of Penman–Monteith and alternative methods for estimating reference evapotranspiration with missing data in Southern Ontario, Canada. Agric Water Manag 975:635–644

    Article  Google Scholar 

  • Shelia V, Simunek J, Boote K, Hoogenboom G (2018) Coupling DSSAT and HYDRUS-1D for simulation of soil water dynamics in the soil-plant-atmosphere system. J Hydrol Hydromech 66(2):232–245

    Article  Google Scholar 

  • Shrivastava S, Kar SC, Sharma AR (2018) The DSSAT model simulations of soil moisture and evapotranspiration over central India and comparison with remotely-sensed data. Model Earth Syst Environ 4(1):27–37

    Article  Google Scholar 

  • Shweta, Krishna AP (2015) Selection of the best method of ETo estimation other than Penman–Monteith and their application for the humid subtropical region. Agric Res 4(2):215–219

    Article  Google Scholar 

  • Singh KK, Mall RK, Singh RS, Srivastava AK (2010) Evaluation of CANEGRO sugarcane model in East Uttar Pradesh, India. J Agrometerol 12(2):181–186

    Google Scholar 

  • Singh PK, Singh KK, Rathore LS, Baxla AK, Bhan SC, Gupta A, G. B., et al (2016) Rice (Oryza sativa L.) yield gap using the CERES-rice model of climate variability for different agroclimatic zones of India. Curr Sci 110:405–413

    Article  Google Scholar 

  • Singh N, Mall RK, Sonkar G, Singh KK, Gupta A (2018)) Evaluation of RegCM4 climate model for assessment of climate change impact on crop production. Mausam 69(3):389–400

    Google Scholar 

  • Tasakiris G, Vangelis H (2005) Establishing a drought Index incorporating evapotranspiration. Eur Water 9(10):3–11

    Google Scholar 

  • Taxak AK, Murumkar AR, Arya DS (2014) Long-term spatial and temporal rainfall trends and homogeneity analysis in Wainganga basin, Central India. Weather Clim Extreme 4:50–61

    Article  Google Scholar 

  • Thornthwaite CW (1948) An approach toward a rational classification of climate. Geogr Rev 38:55–94

    Article  Google Scholar 

  • Wang W, Li C, Xing W, Fu (2017) Projecting the potential evapotranspiration by coupling different formulations and input data reliabilities: the possible uncertainty source for climate change impacts on hydrological regime. J Hydrol 555:298–313

    Article  Google Scholar 

  • White JW, Hoogenboom G, Kimball BA, Wall GW (2011) Methodologies for simulating impacts of climate change on crop production. Field Crop Res 124:357–368

    Article  Google Scholar 

  • Xu Z, Liu Z, Fu G, Chen Y (2010) Trends of major hydroclimatic variables in the Tarim River basin during the past 50 years. J Arid Environ 74(2):256–267

    Article  Google Scholar 

  • Zhao H, Xu Z, Zhao J, Huang W (2017) A drought rarity and evapotranspiration-based index as a suitable agricultural drought indicator. Ecol Indic 82:530–538

    Article  Google Scholar 

Download references

Acknowledgements

Authors thank the Climate Change Programme, Department of Science and Technology-New Delhi (Grant no: DST/CCP/CoE/80/2017(G)), for financial support. The authors wish to express their gratitude to India Meteorological Department, New Delhi for providing observed Evapotranspiration data. We are thankful to Department of Agronomy, Institute of Agricultural Sciences, B.H.U. for providing observed temperature, solar radiation, rainfall and wind speed data. We are grateful to CCCR- IITM for RegCM outputs from the domain CORDEX- South Asia. All contributors are thankfully acknowledged.

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Correspondence to R. K. Mall.

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Tyagi, S., Singh, N., Sonkar, G. et al. Sensitivity of evapotranspiration to climate change using DSSAT model in sub humid climate region of Eastern Uttar Pradesh. Model. Earth Syst. Environ. 5, 1–11 (2019). https://doi.org/10.1007/s40808-018-0513-2

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  • DOI: https://doi.org/10.1007/s40808-018-0513-2

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