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Confidential Manuscript Submitted to Geophysical Research Letters 1 Confidential manuscript submitted to Geophysical Research Letters 1 Drought and famine in India , 1870 - 2016 2 3 Vimal Mishra 1 , Amar Deep Tiwari 1 , Reepal Shah 1 , Mu Xiao 2 , D.S. Pai 3 , Dennis Lettenmaier 2 4 5 1. Civil Engineering, Indian Institute of Technology, Gandhinagar, India 6 2. Department of Geography, University of California, Los Angeles, USA 7 3. India Meteorological Department (IMD), Pune 8 9 Abstract 10 11 Millions of people died due to famines caused by droughts and crop failures in India in the 19 th and 12 20 th centuries . However, the relationship of historical famines with drought is complic ated and not 13 well understood in the context of the 19 th and 20 th - century events. Using station - based observations 14 and simulations from a hydrological model, we reconstruct soil moisture ( agricultural ) drought in 15 India for the period 1870 - 2016. We show that over this century and a half period , India experienced 16 seven major drought periods (1876 - 1882, 1895 - 1900, 1908 - 1924, 1937 - 1945, 1982 - 1990, 1997 - 17 2004, and 2011 - 2015) based on severity - area - duration (SAD) analysis of reconstructed soil 18 moisture. Out of six major famines (1873 - 74, 1876, 1877, 1896 - 97, 1899, and 1943) that occurred 19 during 1870 - 2016 , five are linked to soil moisture drought , and one (1943) was not. On the other 20 hand, five major droughts were not linked with famine, and three of tho se five non - famine droughts 21 occurred after Indian Independence in 1947. Famine deaths due to droughts have been significantly 22 reduced in modern India , h owever, ongoing groundwater storage depletion has the potential to 23 cause a shift back to recurrent famines . 24 25 1.0 Introduction 26 27 Famine is defined as "food shortage accompanied by a significant number of deaths” [ Dyson , 28 1991] . India has a long history of famines that led to the starvation of millions of people 29 [ Passmore , 1951] . During the era of British rule in India ( 1765 - 1947 ) , twelve major famine s 30 occurred ( in 1769 - 70, 17 83 - 84, 1791 - 92, 1837 - 38, 1860 - 61, 1865 - 67, 1868 - 70, 1873 - 74, 1876 - 78, 31 1896 - 97, 1899 - 1900, and 1943 - 44 ) which lead to the deaths of million s people [ Maharatna , 1996] . 32 Many of these famines were caused by the failure of the summer monsoon, which led to 33 widespread droughts and crop failures [ Cook et al. , 2010] . Although no major famines have 34 occurred since Indian independence in 194 7 , l arge - scale droughts in the second half of the 20 th and 35 early 21 st centuries have continued to have devastating effects on India [ Bhalme et al. , 1983; 36 Parthasarathy et al. , 1987; Gadgil and Gadgil , 2006; Mishra et al. , 2016] . D roughts in the late 18 th 37 and early 19 th centuries had progressively more severe effects due to a rising population, low crop 38 yields , and lack of irrigation [ FAO , 2014] . Hence, an understanding of historical famine and 39 drought in India relates both to physical factors associated with drought, and agricultural 40 productivity and management. 41 42 Soil moisture drought affects crop production and food security in India especially in the abse nce 43 of irrigation [ Mishra et al. , 2014, 2018] . Soil moisture droughts doubtle ss affected food production 44 and famines in India before the widespread advent of irrigation in the mid - 20 th century . However, 45 the crucial role of soil moisture in famines in India has received little attention, perhaps due to the 46 general absence of long - term observations. Most previous attempts to study 18 th and 19 th - century 47 drought s in India have been limited to meteorological [ Bhalme et al. , 1983; Mool ey and 48 Parthasarathy , 1984] or paleoclimate reconstruction s [ Cook et al. , 2010 ] and mainly facilitate 49 stud ies of the role of large - scale climate variability. Here , we provide the first reconstruction of 50 droughts based on soil moisture (their proximate link to dryland agriculture) for the last century and 1 Confidential manuscript submitted to Geophysical Research Letters 51 a half ( 1870 - 2016 ) and the i r relationship to famines . We use the Variable Infiltration Capacity 52 (VIC) model to reconstruct soil moisture using methods similar to those demonstrated prev iously 53 for the conterminous U.S. [ Andreadis and Lettenmaier , 2006] and China [ Wang et al. , 2011] . 54 55 56 2. Data and Methods: 57 58 We obtained 0.25° daily gridded precipitation data from the India Meteorology Department (IMD) 59 for the period 1901 - 2016 [ Pai et al. , 2015] , which we regridded to 0.5 ° spatial resolution by using 60 synergic mapping (SYMAP) algorithm as described in Maurer et al. (2002) . Pai e t al. , [2015] 61 developed the IMD gridded precipitation product using data from 6995 observational stations 62 across India using inverse distance weighting [ Shepard , 1984] . Orographic features and spatial 63 variability in the Indian summer monsoon precipitation are well captured by the gridded 64 precipitatio n [ Pai et al. , 2015] . 65 66 Because the IM D gridded precipitation product is available only for the post - 1900 period, we 67 developed a compatible product at 0.5 degree using station observations for 1870 - 1900. Data 68 availability and the number of stations varied during this period; however , we were a ble to obtain 69 reasonably complete precipitation data from 1690 stations spread across India for most of the pre - 70 1900 period . We carefully checked the quality of data for each station as follows. We used the daily 71 precipitation observations, and first deter mined, for each station, the number of months with 72 complete records. We dropped (treated as missing) months with more than ten missing 73 precipitation days. If precipitation data were available for 20 or more days in a month, the total 74 (cumulative) precipita tion for that month was considered for the analysis. We found that the 75 number of stations after the quality check varied from about 517 in 187 0 to more than 16 0 0 in 1900 76 (Fig. S 1 ). After the initial quality control, we estimated time (fraction of the 31 ye ars from 1870 77 through 1900) with available data for each station (Fig. S 2 ). We found that most of the stations 78 have monthly precipitation data for more than 50% of the 1870 - 1900 period (Fig. S 2 ). Therefore, 79 we decided not to drop stations with missing data but rather to exclude months with large data 80 gaps. 81 82 The station density in the pre - 1900 period varied substantially over India. For instance, northern 83 and peninsular India have higher station densities than western and east - central India (Fig. S 2 ). 84 Moreover, stations located in eastern India (except a few stations with long - term data) have 85 relatively shorter records in comparison to the stations located in the north and southern India (Fig. 86 S 2 ). We also compared monthly precipitation over the 1870 - 19 00 period with the long - term mean 87 (1870 - 2016) to identify spurious values and outliers. Finally, using the monthly precipitation data 88 for 1870 - 1900, we developed a gridded (0.5 deg) precipitation product for the entire country using 89 the methods described i n Maurer et al. , [2002] . 90 91 We extracted daily precipitation, maximum and minimum temperatures, and wind speed from the 92 20 th - century reanalysis (20CR, NOAA - CIRES 20th Century Reanalysis version 2c) from NOAA's 93 Physical Science Division (PSD: https://www.esrl.noaa.gov/psd/dat a/20thC_Rean/ ) for 1851 - 2014. 94 The 20CR data are available globally at 2.0º spatial and 6 - hour, daily, and monthly temporal 95 resolutions. All the variables from 20CR and regridded to 0.5º spatial resolution using the synergic 96 mapping (SYMAP) algorithm as des cribed in Maurer et al. (2002). Additional information on 97 20CR dataset can be found in Compo et al. , [2006] . More detailed information on data prepration 98 from 20CR and Berkeley Earth can be obtained from supplemental section S1. 99 100 2 Confidential manuscript submitted to Geophysical Research Letters 101 We compared mean precipitation for the monsoon period (June through September) as well as the 102 water year (June through May) for 1870 - 1900 against the post - 1900 (1901 - 1930) period (Fig S 3 ). 103 We f ou nd that the key features regarding the mean and spatial variability of precipitation are well 104 captured in our gridded dataset for 1870 - 1900. Additionally, we compared precipitation and Palmer 105 Drought Severity Index (PDSI: http://www.cgd.uc ar.edu/cas/catalog/climind/pdsi.html) from Dai et 106 al. (2004). All - India monthly precipitation anomalies from IMD and Dai are well correlated 107 (correlation = 0.87, Fig. S 4 ). Moreover, we f ou nd that spatial patterns of PDSI from our gridded 108 precipitation and temperature products are consistent with PDSI from [ Dai et al. , 2004] for the ten 109 most severe (based on annual precipitation deficit) drought events in India during 1 870 - 201 4 (Fig. 110 S 5 ). Furthermore, air temperature from Berkeley Earth and IMD compare well for the overlapping 111 period 1951 - 201 4 (Fig. S 6 ). The high - bias in temperature from IMD is mainly due to bias in the 112 Himalayan region (Fig. S 6 ) , which is attributable to low gauge density in the region [Shah and 113 Mishra, 2014; Mishra, 2015] . We note that the most severe drought and famines largely are to the 114 south of this region. 115 116 We us ed the Variable Infiltration Capacity [ Liang et al.
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