Development of a new agro-meteorological drought index (SPAEI-Agro) in a data-scarce region

References

• Aadhar, S. and Mishra, V., 2017. High-resolution near real-time drought monitoring in South Asia. Scientific Data, 4 (1), 170145. doi:10.1038/sdata.2017.145.
   PubMedGoogle Scholar
• Abbaspour, K.C., et al., 2007. Modelling hydrology and water quality in the pre-alpine/alpine Thur watershed using SWAT. Journal of Hydrology, 333 (2–4), 413–430. doi:10.1016/j.jhydrol.2006.09.014.
   Web of Science ®Google Scholar
• Abbaspour, K.C., et al., 2015. A continental-scale hydrology and water quality model for Europe: calibration and uncertainty of a high-resolution large-scale SWAT model. Journal of Hydrology, 524, 733–752. doi:10.1016/j.jhydrol.2015.03.027
   Web of Science ®Google Scholar
• Abbaspour, K.C., Vaghefi, S.A., and Srinivasan, R., 2018. A guideline for successful calibration and uncertainty analysis for soil and water assessment: a review of papers from the 2016 international SWAT conference. Water, 10 (1), 6. doi:10.3390/w10010006.
   Web of Science ®Google Scholar
• Abdulkareem, J.H., et al., 2018. Review of studies on hydrological modelling in Malaysia. Modeling Earth Systems and Environment, 4 (4), 1577–1605. doi:10.1007/s40808-018-0509-y.
   Web of Science ®Google Scholar
• Agnew, C., 1993. World atlas of desertification: United Nations environment programme. London: Edward Arnold. 1992, 69.
   Google Scholar
• Ahmad, M.I., Sinclair, C.D., and Werritty, A., 1988. Log-logistic flood frequency analysis. Journal of Hydrology, 98 (3–4), 205–224. doi:10.1016/0022-1694(88)90015-7.
   Web of Science ®Google Scholar
• Akaike, H., 1974. A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19 (6), 716–723. doi:10.1109/TAC.1974.1100705.
   Web of Science ®Google Scholar
• Allen, R.G., et al., 1998. Crop evapotranspiration-guidelines for computing crop water requirements-FAO irrigation and drainage paper 56. FAO, Rome, 300 (9), D05109.
   Google Scholar
• Arnold, J.G., et al., 1998. Large area hydrologic modeling and assessment part I: model development1. Journal of the American Water Resources Association, 34 (1), 73–89. doi:10.1111/j.1752-1688.1998.tb05961.x.
   Web of Science ®Google Scholar
• Arnold, J.G. et al., 2012. SWAT: Model use, calibration, and validation. Transactions of the ASABE, 55 (4), 1491–1508. doi:10.13031/2013.42256.
   Web of Science ®Google Scholar
• Asokan, S.M., Jarsjö, J., and Destouni, G., 2010. Vapor flux by evapotranspiration: effects of changes in climate, land use, and water use. Journal of Geophysical Research: Atmospheres, 115 (D24). doi:10.1029/2010JD014417.
   Google Scholar
• Beguería, S., et al., 2014. Standardized precipitation evapotranspiration index (SPEI) revisited: parameter fitting, evapotranspiration models, tools, datasets and drought monitoring. International Journal of Climatology, 34 (10), 3001–3023. doi:10.1002/joc.3887.
   Web of Science ®Google Scholar
• Bhardwaj, K., et al., 2020. Propagation of meteorological to hydrological droughts in India. Journal of Geophysical Research: Atmospheres, 125 (22), e2020JD033455. doi:10.1029/2020JD033455.
   Web of Science ®Google Scholar
• Branisavljević, N., et al., 2009. Hydro meteorological data quality assurance and improvement. Journal of Serbian Society for Computational Mechanics, 3 (1), 228–249. Available from: https://grafar.grf.bg.ac.rs/handle/123456789/223.
   Google Scholar
• Budyko, M.I., 1974. Climate and life. Academic Press.
   Google Scholar
• Chang, T.J., 1991. Investigation of precipitation droughts by use of Kriging method. Journal of Irrigation and Drainage Engineering, 117 (6), 935–943. doi:10.1061/(ASCE)07339437(1991)117:6(935).
   Web of Science ®Google Scholar
• Clark, M.P., et al., 2015. A unified approach for process-based hydrologic modeling: 1. Modeling concept. Water Resources Research, 51 (4), 2498–2514. doi:10.1002/2015WR017198.
   Web of Science ®Google Scholar
• Crausbay, S.D. et al., 2017. Defining ecological drought for the twenty-first century. Bulletin of the American Meteorological Society, 98 (12), 2543–2550. doi:10.1175/BAMS-D-16-0292.1.
   Web of Science ®Google Scholar
• Dai, A., 2011. Drought under global warming: a review. WIREs Climate Change, 2 (1), 45–65. doi:10.1002/wcc.81.
   Web of Science ®Google Scholar
• Djebou, S. and Clement, D., 2019. Streamflow drought interpreted using SWAT model simulations of past and future hydrologic scenarios: application to Neches and Trinity River Basins, Texas. Journal of Hydrologic Engineering, 24 (9), 05019024. doi:10.1061/(ASCE)HE.1943-5584.0001827.
   Web of Science ®Google Scholar
• Eckhardt, K. and Arnold, J.G., 2001. Automatic calibration of a distributed catchment model. Journal of Hydrology, 251 (1–2), 103–109. doi:10.1016/S0022-1694(01)00429-2.
   Web of Science ®Google Scholar
• Eltahir, E.A., 1992. Drought frequency analysis of annual rainfall series in central and western Sudan. Hydrological Sciences Journal, 37 (3), 185–199. doi:10.1080/02626669209492581.
   Web of Science ®Google Scholar
• Esmaeili-Gisavandani, H., et al., 2021. Improving the performance of rainfall-runoff models using the gene expression programming approach. Journal of Water and Climate Change, 12 (7), 3308–3329. doi:10.2166/wcc.2021.064.
   Web of Science ®Google Scholar
• Eum, H.-I., Gupta, A., and Dibike, Y., 2020. Effects of univariate and multivariate statistical downscaling methods on climatic and hydrologic indicators for Alberta, Canada. Journal of Hydrology, 588, 125065. doi:10.1016/j.jhydrol.2020.125065
   Web of Science ®Google Scholar
• Faramarzi, M., et al., 2009. Modelling blue and green water resources availability in Iran. Hydrological Processes, 23 (3), 486–501. doi:10.1002/hyp.7160.
   Web of Science ®Google Scholar
• Gassman, P.W., Sadeghi, A.M., and Srinivasan, R., 2014. Applications of the SWAT model special section: overview and insights. Journal of Environmental Quality, 43 (1), 1–8. doi:10.2134/jeq2013.11.0466.
   PubMed Web of Science ®Google Scholar
• Gaur, A., et al., 2007. Implications of drought and water regulation in the Krishna basin, India. Water Resources Development, 23 (4), 583–594. doi:10.1080/07900620701488513.
   Web of Science ®Google Scholar
• Gong, Y., et al., 2012. A comparison of single- and multi-gauge based calibrations for hydrological modeling of the Upper Daning River Watershed in China’s Three Gorges Reservoir Region. Hydrology Research, 43 (6), 822–832. doi:10.2166/nh.2012.021.
   Web of Science ®Google Scholar
• Goyal, M.K., et al., 2018. Comparative assessment of SWAT model performance in two distinct catchments under various DEM scenarios of varying resolution, sources and resampling methods. Water Resources Management, 32 (2), 805–825. doi:10.1007/s11269-017-1840-1.
   Web of Science ®Google Scholar
• Ha, L.T., et al., 2017. SWAT-CUP for calibration of spatially distributed hydrological processes and ecosystem services in a Vietnamese River Basin using remote sensing. Hydrology and Earth System Sciences Discuss, 1–35. doi:10.5194/hess-2017-251.
   Google Scholar
• Haan, C.T., 1989. Parametric uncertainty in hydrologic modeling. Transactions of the ASAE, 32 (1), 0137–0146. doi:10.13031/2013.30973.
   Google Scholar
• Haan, C.T., et al., 1995. Statistical procedure for evaluating hydrologic/water quality models. Transactions of the ASAE, 38 (3), 725–733. doi:10.13031/2013.27886.
   Google Scholar
• Hamel, P. and Guswa, A.J., 2015. Uncertainty analysis of a spatially explicit annual water-balance model: case study of the Cape Fear basin, North Carolina. Hydrology and Earth System Sciences, 19 (2), 839–853. doi:10.5194/hess-19-839-2015.
   Web of Science ®Google Scholar
• Hao, Z., AghaKouchak, A., and Phillips, T.J., Aug 1 2013. Changes in concurrent monthly precipitation and temperature extremes. Environmental Research Letters, 8 (3), 034014. doi:10.1088/1748-9326/8/3/034014.
   Web of Science ®Google Scholar
• Hargreaves, G.H. and Samani, Z.A., 1982. Estimating potential evapotranspiration. Journal of the Irrigation and Drainage Division, 108 (3), 225–230. doi:10.1061/JRCEA4.0001390.
   Web of Science ®Google Scholar
• Hargreaves, G.H. and Samani, Z.A., 1985. Reference crop evapotranspiration from temperature. Applied Engineering in Agriculture, 1 (2), 96–99. doi:10.13031/2013.26773.
   Google Scholar
• Harmel, R.D., Smith, P.K., and Migliaccio, K.W., 2010. Modifying goodness-of-fit indicators to incorporate both measurement and model uncertainty in model calibration and validation. Transactions of the ASABE, 53 (1), 55–63. doi:10.13031/2013.29502.
   Web of Science ®Google Scholar
• Hellwig, J., et al., 2020. Large-scale assessment of delayed groundwater responses to drought. Water Resources Research, 56 (2), e2019WR025441. doi:10.1029/2019WR025441.
   Web of Science ®Google Scholar
• Homdee, T., Pongput, K., and Kanae, S., 2016. A comparative performance analysis of three standardized climatic drought indices in the Chi River basin, Thailand. Agriculture and Natural Resources, 50, 211–219. doi:10.1016/j.anres.2016.02.002
   Google Scholar
• Hughes, J.D., Petrone, K.C., and Silberstein, R.P., 2012. Drought, groundwater storage and stream flow decline in southwestern Australia. Geophysical Research Letters, 39 (3). doi:10.1029/2011GL050797.
   PubMedGoogle Scholar
• Jacobsen, S.E., Jensen, C.R., and Liu, F., 2012. Improving crop production in the arid mediterranean climate. Field Crops Research, 128, 34–47. doi:10.1016/j.fcr.2011.12.001
   Web of Science ®Google Scholar
• Kaffas, K., Hrissanthou, V., and Sevastas, S., 2018. Modeling hydromorphological processes in a mountainous basin using a composite mathematical model and ArcSWAT. CATENA, 162, 108–129. doi:10.1016/j.catena.2017.11.017
   Web of Science ®Google Scholar
• Kendall, M.G., 1948. Rank correlation methods. Oxford, England: Rank correlation methods. Griffin.
   Google Scholar
• Kumari, P., et al., 2022. Assessment of meteorological drought using standardized precipitation evapotranspiration index—hyderabad case study. In: S. Kolathayar, A. Mondal, and S.C. Chian, eds. Climate change and water security, lecture notes in civil engineering. Singapore: Springer, 243–252. doi:10.1007/978-981-16-5501-2_20.
   Google Scholar
• Li, Z., Xu, Z., and Li, Z., 2011. Performance of WASMOD and SWAT on hydrological simulation in Yingluoxia watershed in northwest of China. Hydrological Processes, 25 (13), 2001–2008. doi:10.1002/hyp.7944.
   Web of Science ®Google Scholar
• Liu, M., et al., 2017. A new drought index that considers the joint effects of climate and land surface change. Water Resources Research, 53 (4), 3262–3278. doi:10.1002/2016WR020178.
   Web of Science ®Google Scholar
• Mallya, G., et al., 2016. Trends and variability of droughts over the Indian monsoon region. Weather and Climate Extremes, 12, 43–68. doi:10.1016/j.wace.2016.01.002
   Web of Science ®Google Scholar
• McKee, T.B., Doesken, N.J., and Kleist, J., 1993. The relationship of drought frequency and duration to time scales. In Proceedings of thehaan 8th Conference on Applied Climatology (Vol. 17, No. 22, pp. 179–183). 17-22 January 1993, Anaheim, California.
   Google Scholar
• McMillan, H.K., Westerberg, I.K., and Krueger, T., 2018. Hydrological data uncertainty and its implications. Wiley Interdisciplinary Reviews: Water, 5 (6), e1319. doi:10.1002/wat2.1319.
   Web of Science ®Google Scholar
• Mondal, A. and Mujumdar, P.P., 2012. On the basin-scale detection and attribution of human-induced climate change in monsoon precipitation and streamflow. Water Resources Research, 48 (10). doi:10.1029/2011WR011468.
   Google Scholar
• Monish, N.T. and Rehana, S., 2019. Suitability of distributions for standard precipitation and evapotranspiration index over meteorologically homogeneous zones of India. Journal of Earth System Science, 129 (1), 25. doi:10.1007/s12040-019-1271-x.
   Web of Science ®Google Scholar
• Monteith, J.L., 1965. Evaporation and environment. Symposia of the Society for Experimental Biology, 19, 205–234.
   PubMedGoogle Scholar
• Moriasi, D.N., et al., 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 50 (3), 885–900. doi:10.13031/2013.23153.
   Web of Science ®Google Scholar
• Mueller, B., et al., 2011. Evaluation of global observations-based evapotranspiration datasets and IPCC AR4 simulations. Geophysical Research Letters, 38 (6). doi:10.1029/2010GL046230.
   Web of Science ®Google Scholar
• Nalbantis, I. and Tsakiris, G., 2009. Assessment of hydrological drought revisited. Water Resources Management, 23 (5), 881–897. doi:10.1007/s11269-008-9305-1.
   Web of Science ®Google Scholar
• Nam, W.-H., et al., 2012. A decision support system for agricultural drought management using risk assessment. Paddy and Water Environment, 10 (3), 197–207. doi:10.1007/s10333-012-0329-z.
   Web of Science ®Google Scholar
• Narsimlu, B., et al., 2015. SWAT model calibration and uncertainty analysis for streamflow prediction in the Kunwari River Basin, India, using sequential uncertainty fitting. Environmental Processes, 2 (1), 79–95. doi:10.1007/s40710-015-0064-8.
   Google Scholar
• Nash, J.E. and Sutcliffe, J.V., 1970. River flow forecasting through conceptual models part I — a discussion of principles. Journal of Hydrology, 10 (3), 282–290. doi:10.1016/0022-1694(70)90255-6.
   Google Scholar
• Neitsch, S.L., et al., 2011. Soil and water assessment tool theoretical documentation version 2009. Texas Water Resources Institute.
   Google Scholar
• Odusanya, A.E., et al., 2019. Multi-site calibration and validation of SWAT with satellite-based evapotranspiration in a data-sparse catchment in southwestern Nigeria. Hydrology and Earth System Sciences, 23 (2), 1113–1144. doi:10.5194/hess-23-1113-2019.
   Web of Science ®Google Scholar
• Oloruntade, A.J., et al., 2017. Analysis of meteorological and hydrological droughts in the Niger-South Basin, Nigeria. Global and Planetary Change, 155, 225–233. doi:10.1016/j.gloplacha.2017.05.002
   Web of Science ®Google Scholar
• Pathak, A.A., Dodamani, B.M., and Dodamani, B.M., 2016. Comparison of two hydrological drought indices. Perspectives in Science, 8, 626–628. doi:10.1016/j.pisc.2016.06.039
   Google Scholar
• Perrone, D., 2020. Groundwater overreliance leaves farmers and households high and dry. One Earth, 2 (3), 214–217. doi:10.1016/j.oneear.2020.03.001.
   Google Scholar
• Priestley, C.H.B. and Taylor, R.J., 1972. On the assessment of surface heat flux and evaporation using large-scale parameters. Monthly Weather Review, 100 (2), 81–92. doi:10.1175/1520-0493(1972)100<0081:OTAOSH>2.3.CO;2OTAOSH>2.3.CO;2.
   Web of Science ®Google Scholar
• Raffensperger, J.P., et al., 2017. Optimal hydrograph separation using a recursive digital filter constrained by chemical mass balance, with application to selected Chesapeake Bay watersheds (No. 2017–5034), scientific investigations report. U.S. Geological Survey. doi:10.3133/sir20175034.
   Google Scholar
• Rehana, S. and Monish, N.T., 2020. Characterization of regional drought over water and energy limited zones of India using potential and actual evapotranspiration. Earth and Space Science, 7 (10), e2020EA001264. doi:10.1029/2020EA001264.
   Web of Science ®Google Scholar
• Rehana, S., Naidu, G.S., and Monish, N.T., 2020. Spatiotemporal variations of precipitation and temperatures under CORDEX climate change projections: a case study of Krishna River Basin, India. In: P. Singh, R.P. Singh, and V. Srivastava, eds. Contemporary environmental issues and challenges in Era of climate change. Singapore: Springer, 157–170. doi:10.1007/978-981-32-9595-7_8.
   Google Scholar
• Rehana, S. and Sireesha Naidu, G., 2021. Development of hydro-meteorological drought index under climate change – semi-arid river basin of Peninsular India. Journal of Hydrology, 594, 125973. doi:10.1016/j.jhydrol.2021.125973
   Web of Science ®Google Scholar
• Ruan, H., et al., 2017. Runoff simulation by SWAT model using high-resolution gridded precipitation in the Upper Heihe River Basin, Northeastern Tibetan Plateau. Water, 9 (11), 866. doi:10.3390/w9110866.
   Web of Science ®Google Scholar
• Sepulcre-Canto, G., Horion, S.M.A.F., Singleton, A., Carrao, H. and Vogt, J., 2012. Development of a Combined Drought Indicator to detect agricultural drought in Europe. Natural Hazards and Earth System Sciences, 12(11), 3519–3531. doi: 10.5194/nhess-12-3519-2012.
   Web of Science ®Google Scholar
• Seshasai, M., et al., 2016. Agricultural drought: assessment & monitoirng. Mausam, 67. doi:10.54302/mausam.v67i1.1155.
   Web of Science ®Google Scholar
• Sheng, Y. and Hashino, M., 2007. Probability distribution of annual, seasonal and monthly precipitation in Japan. Hydrological Sciences Journal, 52 (5), 863–877. doi:10.1623/hysj.52.5.863.
   Web of Science ®Google Scholar
• Shokouhifar, Y., et al., 2022. Evaluation of climate change effects on flood frequency in arid and semi-arid basins. Water Supply, 22 (8), 6740–6755. doi:10.2166/ws.2022.271.
   Google Scholar
• Shukla, S. and Wood, A.W., 2008. Use of a standardized runoff index for characterizing hydrologic drought. Geophysical Research Letters, 35 (2). doi:10.1029/2007GL032487.
   PubMed Web of Science ®Google Scholar
• Singh, S.K., et al., 2019b. Towards baseflow index characterisation at national scale in New Zealand. Journal of Hydrology, 568, 646–657. doi:10.1016/j.jhydrol.2018.11.025
   Web of Science ®Google Scholar
• Singh, S.K., 2020. Hydrological model parameters space during calibration. International Journal of Hydrology Science and Technology, 10 (1), 52–71. doi:10.1504/IJHST.2020.104987.
   Google Scholar
• Singh, V., et al., 2013. Hydrological stream flow modelling on Tungabhadra catchment: parameterization and uncertainty analysis using SWAT CUP. Current Science, 104, 1187–1199.
   Web of Science ®Google Scholar
• Singh, G.R., Dhanya, C.T., and Chakravorty, A., 2021. A Robust drought index accounting changing precipitation characteristics. Water Resources Research, 57 (7), e2020WR029496. doi:10.1029/2020WR029496.
   Web of Science ®Google Scholar
• Srivastava, A., et al., 2017. Evaluation of variable-infiltration capacity model and MODIS-terra satellite-derived grid-scale evapotranspiration estimates in a River Basin with tropical monsoon-type climatology. Journal of Irrigation and Drainage Engineering, 143 (8), 04017028. doi:10.1061/(ASCE)IR.1943-4774.0001199.
   Web of Science ®Google Scholar
• Svoboda, M., et al., 2002. The drought monitor. Bulletin of the American Meteorological Society, 83 (8), 1181–1190. doi:10.1175/1520-0477-83.8.1181.
   Web of Science ®Google Scholar
• Tang, F.F., Xu, H.S., and Xu, Z.X., 2012. Model calibration and uncertainty analysis for runoff in the Chao River Basin using sequential uncertainty fitting. Procedia Environmental Sciences, 18th Biennial ISEM Conference on Ecological Modelling for Global Change and Coupled Human and Natural System, 13, 1760–1770. doi:10.1016/j.proenv.2012.01.170.
   Google Scholar
• Thiemig, V., et al., 2013. Hydrological evaluation of satellite-based rainfall estimates over the Volta and Baro-Akobo Basin. Journal of Hydrology, 499, 324–338. doi:10.1016/j.jhydrol.2013.07.012
   Web of Science ®Google Scholar
• Thornthwaite, C.W., 1948. An approach toward a rational classification of climate. Geographical Review, 38 (1), 55–94. doi:10.2307/210739.
   Web of Science ®Google Scholar
• Tirivarombo, S., Osupile, D., and Eliasson, P., 2018. Drought monitoring and analysis: Standardised Precipitation Evapotranspiration Index (SPEI) and Standardised Precipitation Index (SPI). Physics and Chemistry of the Earth, Parts A/B/C, 106, 1–10. doi:10.1016/j.pce.2018.07.001
   Web of Science ®Google Scholar
• Van Lanen, H.A.J., et al., 2013. Hydrological drought across the world: impact of climate and physical catchment structure. Hydrology and Earth System Sciences, 17 (5), 1715–1732. doi:10.5194/hess-17-1715-2013.
   Web of Science ®Google Scholar
• Vicente-Serrano, S.M., et al., 2015. Spatio-temporal variability of droughts in Bolivia: 1955–2012. International Journal of Climatology, 35 (10), 3024–3040. doi:10.1002/joc.4190.
   Web of Science ®Google Scholar
• Vicente-Serrano, S.M., Beguería, S., and López-Moreno, J.I., 2010. A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. Journal of Climate, 23 (7), 1696–1718. doi:10.1175/2009JCLI2909.1.
   Web of Science ®Google Scholar
• Vrugt, J.A., et al., 2008. Treatment of input uncertainty in hydrologic modeling: doing hydrology backward with Markov chain Monte Carlo simulation. Water Resources Research, 44 (12). doi: 10.1029/2007WR006720.
   Google Scholar
• Wilhite, D.A. and Glantz, M.H., 1985. Understanding: the drought phenomenon: the role of definitions. Water International, 10 (3), 111–120. doi:10.1080/02508068508686328.
   Google Scholar
• Xu, K., et al., 2018. Comparative analysis of meteorological and hydrological drought over the Pearl River basin in southern China. Hydrology Research, 50 (1), 301–318. doi:10.2166/nh.2018.178.
   Web of Science ®Google Scholar
• Yang, Y., et al., 2017. Lags in hydrologic recovery following an extreme drought: assessing the roles of climate and catchment characteristics. Water Resources Research, 53 (6), 4821–4837. doi:10.1002/2017WR020683.
   Web of Science ®Google Scholar
• Yevjevich, 1967. An objective approach to definitions and investigations of continental hydrologic droughts. Journal of Hydrology, 7, 353. doi:10.1016/0022-1694(69)90110-3
   Google Scholar
• Yu, M., et al., 2018. Analysis of drought propagation using hydrometeorological data: from meteorological drought to agricultural drought. Journal of Korea Water Resources Association, 51, 195–205. doi:10.3741/JKWRA.2018.51.3.195
   Google Scholar
• Zhang, G., Gan, T.Y., and Su, X., 2021. Twenty-1st century drought analysis across China under climate change. Climate Dynamics. doi:10.1007/s00382-021-06064-5.
   Web of Science ®Google Scholar
• Zhang, L., et al., 2004. A rational function approach for estimating mean annual evapotranspiration. Water Resources Research, 40 (2). doi:10.1029/2003WR002710.
   Web of Science ®Google Scholar