Climate change modeling based public health resource planning for Narmada basin, India

Indian Journal of Geo-ine Sciences Vol. 45(5), 2016, pp. 621-638 Climate change modeling based public health resource planning for Narmada basin, India Rahi Jain 1 & Satanand Mishra 2 * 1 Centre for Technology Alternatives for Rural Areas (CTARA), Indian Institute of Technology Bombay (IITB), Mumbai-400 076, India 2 Advanced Materials and Processes Research Institute (AMPRI), Bhopal -462 024, India *[E-mail: snmishra07@gmail.com] Received 27 il 2015; revised 18 ust 2015 Present study is to determine the potential public health risk of climate change parameters (temperature and precipitation) based on its effect on surface drinking water quality of Narmada River Basin. This study was performed by past data collection and multiple linear regression based modeling. The inter-region and inter-district variation was observed in the study. The study found that climate change over time does have an impact on the local climate conditions for the lower regions of the basin. However, the public health risk was more prevalent for the upper region districts based on the linear regression model. Monsoon months especially y and ust are at much greater risk compared to other months. These two months accounted for over 80% of the total risk months in 102 years of data. The study is concluded by providing a framework for evidence-based decision making of resource allocation on the basis of climate parameters. [Keywords: Narmada Water quality; Water borne disease; Climate change; Total Coliform Count] Introduction In lesser developed world like India, communicable diseases have been the major cause of death and water borne diseases like diarrhea, cholera are some of the major diseases 1 5. These nations also suffer from resource limitations which makes resource allocation for different problems an important challenge. The use of evidence based decision making had been proposed 6. The studies are needed which could enable in prioritizing the problems for resource allocation. Climate change studies have been proposed to be important for geographical area planning 7. Climate parameters change have been proposed to have potential impact on the human health 7 10, these health impacts can occur through various routes like change in water quality and quantity, air quality, climatic variations and infection disease ecology 10 12. However, different studies on different geographical regions have showcased different level of impact on the health of the regional human population 7 10, 12, 13. This makes it important to study the climate change impact on the region directly before making any recommendation for regional planning. In resource limited scenario, it is important to identify the months as well as area which are most prone to such diseases. A framework is needed to perform such a study and this paper proposes one such climate parameters based framework for identifying and prioritizing the geographical areas and months for resource allocation. In India, basin level study for estimating impact of different climate parameters on different geographical areas have been performed only for Ganga Basin 10. Multiple water basins and several agro-climactic conditions make it important to study different basins to understand, identify and prioritize the different geographical location and timing for resource allocation. This study uses Narmada River basin as a case study to develop framework for identifying and prioritizing the geographical areas and months with water borne diseases risk based on the climate parameters

622 INDIAN J. MAR. SCI., VOL. 45, NO. 5 MAY 2016 through change in surface water quality. Materials and Methods Narmada is one of the major rivers of western India with 98, 796 sq. km of total basin in three Indian states namely Madhya Pradesh, Maharashtra and Gujarat with river length of 1312 Kms (Figure 1). Narmada basin is divided into five parts namely Upper hilly region, Upper plains, Middle Plains, Lower Hilly s and Lower Plains (Table 1) which encompass 22 districts with majority portion in Madhya Pradesh 23. Upper basins records lower temperature as compared to middle basin. Its origin is in Amarkantak in Shadol district, Madhya Pradesh, flowing through deccan trap in between Vindhya and Satpur hills and meets Arabian Sea at Gulf of Cambay about 10 km north of Bharuch district, Gujarat. Geographically, the basin lies between east longitudes 72 32' to 81 45' and north latitudes 21 20' to 23 45'. It flows through can trap in between Vindhya and Satpura ranges of hills before flowing into the Gulf of Cambay in the Arabian Sea. There are 41 important tributaries to the Narmada River 14, 15. S. No. 1 Table 1: Districts in Narmada River Basin Districts Name State Shahdol 2 Mandla Upper Hilly 3 Balaghat 4 Seoni 5 Jabalpur 6 Narsimhapur 7 Sagar 8 Damoh 9 Upper Chhindwara 10 Plains Hoshangabad 11 Betul 12 Raisen 13 Sehore 14 Middle Plains Khandwa Madhya Pradesh 15 Khargone 16 Dewas 17 Indore 18 Dhar 19 Lower Jhabua 20 Hilly Dhule Maharashtra 21 Lower Baroda 22 Plains Bharuch Gujarat Several major irrigation projects have been completed on this river. The basin contains many important minerals like bauxite, clay, coal, dolomite, graphite, iron ore, manganese, talc and limestone. The river is monitored for its water quality and sediment quality through its 18 and 11 stations respectively. Further, Government of Gujarat maintains 31 gauge and discharge sites in basin. Monthly multi-parametric data of all the 22 districts in Narmada Basin was collected for this study to understand climate parameter change impact on public health through change in water quality. The climate parameters namely average monthly air temperature and monthly precipitation was used for study 10 and water quality parameter namely total coliform count was used as it has been usually considered as the indicator for water borne infectious diseases 7, 10, 13, 16. The effect of various other climatic and non-climatic parameters on the water quality was not considered owing to lack of data availability 17 19. The Graphical Flow Diagram of the data collection and analysis is shown in Figure 2 is similar to our previous analysis 20. The method used to collect the data for climate change parameters was by performing the secondary literature review and data collected was from 1901 to 2002 for each month 21. The multi-parametric climate change data collected was fitted on the modified form of Kay and McDonald multiple linear regression form 22. The model originally developed used around 20 parameters to develop the model. In this study, the coefficients of all parameters other than the water surface temperature and monthly rainfall was added to the

JAIN et al.: CLIMATE CHANGE MODELING BASED PUBLIC HEALTH RESOURCE PLANNING 623 constant. The final equation is as given below: =4.09091+( 6716 )+ (1088 ) (1) Where, Y stands for the Log10 value of total coliform count per 100 ml, ST stands surface water temperature in degree Celsius and R stands for the total monthly rainfall in mm. Surface water temperature was calculated from air temperature data using the average slope and y-intercept values i.e. 0.71 and 2.56 respectively obtained by Morill et al for 43 rivers across the globe 23. The public health risk to water borne disease is considered when the log10 value of total coliform count is found more than 3.3. This calculation for the Hoshangabad district was skipped was directly used from our other analysis 20. Fig. 1 Map showing Narmada River basin catchment

624 INDIAN J. MAR. SCI., VOL. 45, NO. 5 MAY 2016 Results The study found tropical climate conditions with temperature and precipitation range for the entire basin was 15.0-37.2 C and 0-927.8 mm respectively (Table 2). In case of temperature parameter at regional level, it was observed that minimum and maximum reported temperature for a region increased and decreased respectively from Upper hills to Lower plains. Further, the variation in the monthly air and precipitation values for all the districts in the basin for all years had been found as shown in Table 3 and Table 4. Monsoon season had lower variation for both temperature and precipitation for all districts. The maximum variation in temperature was consistently for winter season namely ember, ember, uary and ruary. Further, districts in lower plains region have relatively lesser temperature variation of up to % as compared to other regions that have much higher variation. In case of precipitation, much higher variation was observed as compared to temperature with maximum variation being observed in the months of ember and ember. Further, an increasing trend in precipitation variation from higher region districts to lower region districts was observed. The correlation coefficients of different months over the years indicated that temperature has much stronger correlation as compared to precipitation (Table 5). In case of temperature, significant positive correlation with correlation coefficient in range of 0.4-0.6 was observed for all districts in the month of ember and ember. This indicates higher susceptibility of winter months towards climate change and also explains the potential cause behind the higher temperature variation for the winter months. Further, slightly positive correlation with correlation coefficient in the range of -0.4 was observed for tember, ober and ruary for almost all districts. In addition, the total number of months with correlation with time was increasing from the districts of higher region to lower region districts. In case of precipitation, only lower regions namely Middle plains, Lower hilly and Lower plains seems to have some positive or negative correlation with correlation coefficient in the range -0.4 and - and -0.4 respectively. Further, only one month per district was found correlated with time. Monsoon season months namely y and ust showed correlation in most of the districts except in lower plains regions in which ruary was found to be negatively correlated. This indicates the climate change lower impact on the rainfall as compared to temperature. The study found variation in the total coliform count with change in temperature and precipitation value for all districts. The district wise variation was observed in total number of months under water borne disease risk out of 1224 months (102 years) as shown in Table 6. These districts were categorized into low risk, medium risk and highrisk districts based on the average number of public health risk months/year. The low risk districts are those with less than or equal to one public health risk month/year. Medium risk districts are those with greater than one public health risk month/year and less than or equal to two months/year. High-risk districts are those with greater than two public health risk months/year. The most of the high-risk districts are present in upper hilly and upper plains, while all the low risk districts are present in middle plains region. This indicates districts namely Shahdol, Mandla, Balaghat, Seoni, Jabalpur, Narsimhapur, Chhindwara, Raisen and Dhule require maximum focus to minimize the water borne disease health risk. Further, at the regional level, Upper hilly region is the most risk prone region that requires maximum focus for water borne disease risk minimization followed by the upper plains region and lower hilly region.

JAIN et al.: CLIMATE CHANGE MODELING BASED PUBLIC HEALTH RESOURCE PLANNING 625 Fig. 2 Graphical Flow Diagram of data collection and analysis In all the districts, it was observed that the monsoon season months namely e, y, ust and tember showed consistently high coliform risk, but one month of ober in Seoni District in year 1985 is an exception (Table 6). This unexpected risk in Seoni district could be attributed to the unexpectedly very heavy rainfall of 2 mm mean precipitation; it was also the highest recorded mean precipitation for ober month for Seoni District. The study results are consistent with the findings of Moors et al regarding monsoon season to be water disease prone owing to high precipitation which lead to surface run-off from nearby areas into surface water bodies 10. These surface run-off causes water quality deterioration of surface water bodies. The years (1901-2002) for each district were segregated based on the number of months/ per year in which public health risk was found (Table 7). The years with zero or one month with public health risk were designated low risk years. The years with two months of public health risk were designated medium risk years. The years with 3 or more months of public health risk were designated high-risk years. The segregations showed that maximum four months/year have been found to be of public health risk. Further, a strong positive correlation of 0.95 was found between total number of months under water borne disease risk out of 1224 months (102 years) and high-risk years. This further indicates that the identified risk prone districts and region based on total number of months under water borne disease risk out of 1224 months (102 years) will also be the areas with greater incidence of high public health risk years. The further analysis of the monsoon months showed that among the four months namely e, y, ust and tember, y and ust are most risky months as they together account for around 80% of the months with water borne disease risk (Table 6). Further, most of districts have y as the most risky month, but 50% of districts in Upper regions (Hills and Plains) namely Shahdol, Seoni, Jabalpur Narsimhapur, Sagar, Damoh and Betul that have ust with either higher or equal risk as compared to y. This indicates the resources spent on controlling water borne diseases should focus on the monsoon months especially y and ust.

INDIAN J. MAR. SCI., VOL. 45, NO. 5 MAY 2016 626 Table 2: imum, imum and Average surface air temperature and monthly rainfall for all the regions over 102 years Parameter Monthly Mean Surface Air Temperature ( C) Monthly Rainfall (mm) Upper Hills 15.3 16.0 29.2 27.3 24.5 22.5 15.0 15.00 7 9 7 0 0 26 79.27 81.42 32.37 4 0 5 0 30.5 34.2 37.2 35.4 28.5 27.6 37.16 115.1 114.5 94.3 50.6 67.7 452.3 813.6 716.4 550.6 2 128.6 136.1 813.6 18.8 21.2 30.6 33.8 31.2 25.1 21.1 18.5 19.4 23.6 19.7 9.8 14.0 169.2 386.1 375.5 209.6 48.6 13.3 11.5 108.4 Upper Plains 15.7 16.3 2 29.3 23.7 16.0 15.7 0 0 0 0 0 9.19 54.14 85.15 7.77 3 3 1 0 25.2 3 33.7 36.0 34.8 29.8 27.8 28.5 2 36.0 103.1 109.9 96.1 43.0 57.6 456.1 927.8 908.5 688.0 188.8 175.2 13 927.8 18.7 21.0 30.6 33.7 31.3 26.8 25.2 21.3 12.4 11.5 11.7 5.0 9.3 132.5 35 344.1 205.9 32.2 21.1 9.9 95.7 Middle Plains 17.4 17.9 3 24.1 19.2 17.4 0 0 0 0 0 2.29 24.32 37.30 7.61 3 0 0 0 22.9 29.9 34.1 35.8 3 29.9 28.3 23.0 35.8 39.6 35.6 4 16.5 45.7 386.0 732.7 598.4 55 14 164.8 110.4 732.7 2 2 26.8 31.2 33.6 31.3 27.6 26.6 22.7 2 3.9 4.2 7.9 1 256.6 209.5 16 27.0 19.7 6.4 68.9 Lower Hills 18.4 23.4 28.8 2 24.3 24.0 19.8 18.5 0 0 0 0 0 2.31 45.16 34.26 10.64 0 5 0 0 22.4 24.2 28.8 32.5 34.1 33.3 28.8 29.1 22.9 34.1 17.0 3.7 41.0 60.8 82.9 429.3 697.8 609.8 596.1 215.5 143.9 43.9 697.8 2 21.7 31.6 29.8 26.8 22.9 20.6 1.6 3.0 2.9 8.9 152.7 351.4 252.0 205.5 32.4 19.0 4.1 86.2 Lower Plains 18.9 19.8 24.1 22.5 20.4 18.9 0 0 0 0 0 1.62 62 23.46 6 1 6 0 0 23.4 32.3 33.9 33.4 30.5 29.5 3 30.4 23.9 33.9 18.0 14.5 17.3 9.6 81.3 601.9 899.0 515.5 568.5 215.7 194.0 19.5 899.0 21.1 22.7 26.8 29.9 31.6 30.6 28.2 27.5 27.8 28.1 2 26.8 0.8 1.0 0.7 0.6 6.0 17 378.7 223.6 174.1 24.4 13.2 1.3 83.3

JAIN et al.: CLIMATE CHANGE MODELING BASED PUBLIC HEALTH RESOURCE PLANNING 627 Table 3: imum, imum and Average surface air temperature and monthly rainfall for all the districts over 102 years District Parameter Monthly Mean Surface Air Temperature ( C) Monthly Mean Rainfall (mm) Upper Hilly Shahdol 15.3 16.0 29.2 24.5 22.5 15.0 15.0 28.5 79.3 166.1 68.6 19.1 21.6 27.8 31.0 35.1 33.6 26.6 25.9 19.0 35.1 108.3 114.5 9 45.7 54.0 338.0 566.2 618.5 386.3 145.6 61.7 100.6 618.5 17.0 19.5 2 29.2 32.7 30.6 24.1 19.9 16.9 24.3 23.4 20.4 8.9 14.2 135.6 329.9 377.2 188.1 4 9.7 10.4 98.7 Mandla 17.1 18.0 24.0 27.8 3 23.7 19.1 16.8 16.8 31.2 119.0 185.3 65.2 0.4 21.1 2 32.4 36.2 3 29.1 27.0 25.1 20.8 36.2 115.1 112.4 93.2 44.3 55.9 452.3 785.4 675.5 474.8 202.0 98.0 136.1 785.4 18.9 21.3 30.7 33.9 31.4 27.1 26.6 21.2 18.5 22.0 22.3 9.1 13.4 182.3 4 422.7 215.7 50.8 14.5 13.0 1 Balaghat 18.3 19.7 31.0 25.9 24.5 19.9 38.5 171.6 14 7 0.4 30.5 34.2 37.2 35.4 28.5 27.6 37.2 87.3 87.8 79.9 50.6 48.8 41 813.6 716.4 550.6 2 65.7 1 813.6 2 22.7 27.5 31.7 34.8 31.9 27.6 27.1 2 19.6 15.9 20.4 16.3 11.1 12.7 184.2 428.5 375.4 229.2 54.4 1 11.1 114.1 Seoni 17.3 18.2 24.0 29.8 27.3 23.4 19.0 16.9 16.9 108.0 81.4 32.4 0.4 21.1 23.7 33.0 35.9 34.4 27.6 21.3 35.9 88.4 99.6 94.3 5 67.7 413.4 70 59 455.5 2 128.6 121.0 70 19.2 21.5 30.6 33.7 30.9 21.2 18.7 16.2 21.2 2 1 15.8 174.9 361.0 3 205.4 49.1 18.7 11.5 10 Upper Plains Jabalpur 16.3 16.8 23.3 27.1 29.7 27.1 24.9 24.4 25.1 23.2 18.4 16.1 16.1 23.7 95.6 154.8 16.3 2 2 28.6 31.8 35.4 34.3 26.6 2 2 35.4 96.9 109.9 85.1 41.4 57.6 456.1 679.0 638.0 383.8 162.2 1 111.2 679.0

INDIAN J. MAR. SCI., VOL. 45, NO. 5 MAY 2016 628 Table 3: imum, imum and Average surface air temperature and monthly rainfall for all the districts over 102 years District Parameter Monthly Mean Surface Air Temperature ( C) Monthly Mean Rainfall (mm) 18.0 20.4 3 33.3 31.1 20.7 17.9 19.3 22.7 7.4 12.3 162.5 353.8 380.6 193.0 39.7 17.4 11.8 103.3 Narsimhapur 16.1 16.5 2 29.3 23.7 16.0 16.0 31.5 99.0 150.7 12.8 2 22.3 28.2 3 35.0 3 26.8 23.6 20.5 35.0 68.3 67.5 72.9 34.5 44.5 401.1 690.4 649.7 476.8 15 138.6 102.5 690.4 17.8 2 32.8 3 25.2 24.1 2 17.6 24.4 13.9 14.9 15.5 6.7 1 137.8 352.3 353.4 212.9 34.9 21.8 9.4 98.6 Sagar 15.7 16.3 26.8 30.5 24.5 24.9 18.5 16.0 15.7 9.2 58.6 156.2 7.8 20.4 2 28.9 33.1 35.6 34.8 24.9 21.1 35.6 103.1 74.3 38.4 409.2 787.3 908.5 585.6 138.1 143.3 103.9 908.5 17.8 2 3 33.7 31.8 27.1 21.3 18.2 15.2 11.5 7.4 7.0 119.3 375.0 393.7 203.2 18.7 1 99.2 Damoh 16.0 16.4 23.3 30.4 2 23.4 18.7 16.4 16.0 9.2 58.6 156.2 7.8 2 22.5 28.8 32.4 35.6 3 29.7 27.5 20.6 35.6 103.1 74.3 38.4 409.2 787.3 908.5 585.6 138.1 143.3 103.9 908.5 17.9 2 3 33.7 31.7 27.2 21.2 15.2 11.5 7.4 7.0 119.3 375.0 393.7 203.2 18.7 1 99.2 Chhindwara 17.2 18.4 24.1 3 26.8 2 24.4 24.2 18.9 17.2 17.2 30.7 92.8 95.2 19.3 0.4 21.4 24.0 33.4 35.5 33.9 28.2 27.3 24.4 21.7 35.5 6 66.8 96.1 43.0 51.7 349.3 646.6 5 473.1 188.8 149.9 114.0 646.6 19.4 21.6 30.6 3 30.6 24.9 21.3 18.9 1 14.3 8.1 11.8 152.3 321.5 310.8 201.0 44.0 22.4 1 93.8 Hoshangabad 17.2 17.8 24.3 30.9 27.3 25.2 2 24.5 17.1 17.1 69.1 112.9 10.5 21.3 29.3 33.3 35.5 34.0 28.6 27.0 27.5 24.2 21.6 35.5 62.0 5 68.4 19.6 42.3 339.2 753.2 627.5 56 136.0 165.3 12 753.2

JAIN et al.: CLIMATE CHANGE MODELING BASED PUBLIC HEALTH RESOURCE PLANNING 629 Table 3: imum, imum and Average surface air temperature and monthly rainfall for all the districts over 102 years District Parameter Monthly Mean Surface Air Temperature ( C) Monthly Mean Rainfall (mm) 19.1 21.4 30.9 33.8 31.2 25.1 21.4 18.9 8.8 7.8 12.0 4.0 8.9 1 355.5 331.9 218.0 3 23.6 9.4 9 Betul 18.2 19.3 28.6 31.3 27.3 24.9 24.9 24.0 19.5 16.3 54.1 85.1 11.5 0.4 25.2 3 33.7 35.6 34.0 28.6 27.2 25.1 2 35.6 67.3 43.7 85.5 34.8 42.3 364.3 623.3 574.9 448.0 186.1 175.2 13 623.3 20.4 2 27.3 31.5 34.1 31.1 25.9 22.3 2 9.4 7.3 14.3 5.6 8.1 135.1 284.4 261.1 171.7 43.1 24.4 9.8 81.2 Raisen 16.5 17.2 23.7 31.0 16.5 16.5 19.7 68.6 136.0 12.4 21.3 23.3 29.3 33.6 35.9 34.8 28.1 28.3 24.2 21.6 35.9 75.5 65.3 35.8 20.9 42.2 354.2 927.8 822.0 688.0 114.5 146.8 112.9 927.8 18.5 21.0 30.8 34.0 31.8 27.1 21.4 10.5 8.8 6.6 3.7 9.7 11 408.4 371.5 232.5 22.3 2 9.4 101.5 Sehore 17.2 17.6 24.3 28.2 31.6 28.2 25.2 19.1 17.4 17.2 17.6 57.0 108.4 11.9 21.6 23.9 29.5 33.6 36.0 34.8 29.8 27.8 28.5 28.2 2 36.0 39.6 35.6 3 16.5 41.3 334.0 732.7 598.4 55 107.3 133.9 110.4 732.7 19.2 21.5 31.2 34.3 32.0 27.6 25.9 22.0 19.3 7.0 4.3 5.6 2.9 8.8 1 347.7 30 217.9 9.0 89.6 Middle Plains Khandwa 19.5 29.3 31.7 24.5 19.8 21.8 31.6 60.7 7.6 22.7 29.9 34.1 35.8 34.4 29.2 28.5 22.8 35.8 36.3 17.5 4 11.7 39.9 386.0 453.2 485.1 376.5 138.4 164.8 98.1 485.1 20.8 22.9 27.5 31.8 34.1 31.4 27.6 22.8 20.6 26.6 5.5 2.9 7.1 2.7 7.6 141.2 235.9 201.5 132.5 33.7 23.9 8.7 66.9 Khargone 18.4 19.3 29.0 31.3 2 2 18.7 18.4 14.9 24.3 38.4 11.7 22.9 25.1 33.8 35.4 34.2 28.1 28.5 28.6 23.0 35.4 15.3 9.1 1 45.7 348.1 438.3 407.6 372.0 117.9 136.3 56.0 438.3

INDIAN J. MAR. SCI., VOL. 45, NO. 5 MAY 2016 630 Table 3: imum, imum and Average surface air temperature and monthly rainfall for all the districts over 102 years District Parameter Monthly Mean Surface Air Temperature ( C) Monthly Mean Rainfall (mm) 20.7 2 27.2 31.3 33.7 31.3 27.0 26.6 23.0 20.7 1.6 3.7 2.2 8.1 1 205.1 164.9 127.2 6.5 57.9 Dewas 17.5 17.9 24.4 28.6 31.4 24.1 19.3 17.5 17.6 57.0 108.4 11.9 22.0 24.2 33.8 35.7 3 29.9 28.6 25.2 2 35.7 39.6 35.6 3 16.5 41.3 334.0 732.7 598.4 55 107.3 133.9 110.4 732.7 19.6 21.7 31.3 34.1 31.8 27.8 26.6 22.4 19.8 7.0 4.3 5.6 2.9 8.8 1 347.7 30 217.9 9.0 89.6 Indore 17.5 24.2 31.0 24.4 19.2 17.9 17.5 9.1 28.1 41.7 8.7 22.3 24.3 29.2 33.6 35.4 34.5 29.9 28.3 22.9 35.4 2 6.8 13.1 7.4 42.8 353.7 488.9 464.5 462.2 14 1 5 488.9 19.8 21.8 26.6 31.0 33.6 31.6 27.8 27.0 22.7 2 3.1 1.0 1.9 1.1 8.2 1 246.4 194.4 161.8 17.8 66.1 Dhar 17.4 3 24.3 19.4 17.4 2.3 37.3 11.5 24.0 32.8 34.3 3 29.3 28.3 28.6 22.7 34.3 8.5 3.7 19.3 11.4 36.5 343.0 537.8 465.2 501.7 12 110.8 537.8 19.7 21.6 30.4 3 30.7 27.3 22.7 2 25.9 1.4 0.6 2.5 1.6 6.7 119.1 247.8 186.7 160.4 24.4 15.2 3.0 64.1 Lower Hilly Jhabua 18.4 24.1 28.1 3 25.9 24.9 2 18.7 2.3 45.2 58.1 10.6 22.4 24.2 28.8 32.5 34.1 33.3 28.8 29.1 22.9 34.1 12.2 3.7 22.3 10.5 38.9 38 682.5 609.8 596.1 115.3 119.5 22.4 682.5 2 21.8 3 32.5 30.8 26.8 27.1 27.0 23.4 20.7 1.5 0.4 2.4 1.5 5.6 14 332.8 249.6 181.9 21.5 15.3 2.4 79.6 Dhule 18.9 23.4 28.8 2 24.3 24.0 19.8 18.5 7.0 55.9 34.3 17.2 0.4 23.7 31.0 32.3 31.1 27.5 27.1 27.1 22.3 32.3 17.0 2.4 41.0 60.8 82.9 429.3 697.8 607.9 573.0 215.5 143.9 43.9 697.8

JAIN et al.: CLIMATE CHANGE MODELING BASED PUBLIC HEALTH RESOURCE PLANNING 631 Table 3: imum, imum and Average surface air temperature and monthly rainfall for all the districts over 102 years Monthly Mean Surface Air Temperature ( C) Monthly Mean Rainfall (mm) 21.0 22.5 29.2 30.9 29.9 27.0 27.3 27.8 24.9 2 0.9 0.9 6.6 189.5 392.4 217.9 175.6 28.3 11.9 1.3 85.5 Bharuch 30.7 32.2 31.8 28.5 29.3 23.7 32.2 4.2 14.5 17.3 2.8 81.3 601.9 899.0 512.4 54 215.7 194.0 19.5 899.0 Lower Plains 18.9 21.2 19.8 22.9 24.1 27.2 District Parameter 30.6 32.4 31.3 28.2 22.5 25.1 20.4 18.9 27.2 1.3 1.1 0.6 1.1 5.4 2.0 159.8 76.1 365.1 2 229.3 17 20.6 1 1.3 81.1 Baroda 23.4 32.3 33.9 33.4 30.5 29.5 3 30.4 23.9 33.9 18.0 14.4 7.4 9.6 61.6 453.0 700.9 515.5 568.5 137.7 176.3 14.8 700.9 19.1 19.9 30.7 29.0 26.6 22.5 20.4 19.1 1.6 6 31.5 1 2 21.6 29.0 30.7 22.5 20.5 25.1 1.7 4.4 12.3 165.1 37 254.4 229.1 43.4 22.7 5.7 92.7 Table 4: Percentage Variation of Temperature and Precipitation with respect to change in year for each month over 102 years (Darker shade of green represents low variation and higher shades of red represents high variation) Monthly Mean Surface Air Temperature ( C) Monthly Mean Rainfall (mm) District Seoni 4.3 4.2 3.8 3.6 5.0 2.5 1.7 2.9 5.4 126 106 108 118 109 45 33 28 45 104 162 186 Upper Hilly Balaghat Mandla 4.4 4.4 4.5 4.8 3.8 4.1 3.6 3.7 3.6 3.6 5.0 4.9 2.3 2.4 1.6 1.6 1.9 1.8 2.7 5.2 5.3 117 104 106 97 104 99 109 113 102 103 42 45 31 31 26 25 41 40 94 94 152 154 190 173 Shahdol 4.9 5.2 4.4 3.9 3.8 4.9 2.8 1.5 1.7 2.8 5.6 5.2 93 91 97 106 95 50 29 24 35 84 147 161

INDIAN J. MAR. SCI., VOL. 45, NO. 5 MAY 2016 632 Upper Plains Jabalpur 5.0 4.5 3.8 4.9 2.8 1.8 3.0 5.5 110 98 102 117 104 50 33 27 43 101 161 160 Narsimhapur 5.0 5.4 4.8 4.1 4.9 3.1 2.0 2.4 3.8 5.8 5.0 125 108 110 118 100 54 37 31 51 110 161 178 Sagar 5.8 5.7 5.0 4.1 3.4 3.6 2.2 4.2 5.9 5.1 120 123 127 132 107 66 43 40 61 129 180 178 Damoh 5.2 5.3 4.8 3.9 3.4 3.3 2.0 2.3 5.7 120 123 127 132 107 66 43 40 61 129 180 178 Chhindwara 4.5 4.8 4.2 3.8 3.4 4.8 2.7 1.8 2.4 3.4 5.4 137 114 115 117 105 45 33 29 49 103 163 191 Hoshangabad 5.0 5.1 4.4 3.7 3.1 4.4 3.0 2.0 4.0 5.6 4.9 137 136 126 121 102 52 39 35 58 107 164 200 Betul 4.4 4.0 3.4 2.8 4.3 2.7 2.0 2.5 5.3 150 142 134 133 113 48 39 36 59 105 167 210 Raisen 5.7 5.5 4.9 4.0 3.3 4.5 3.3 2.7 4.3 5.8 5.2 131 137 133 131 105 62 42 40 62 127 178 199 Sehore 5.3 5.3 4.5 3.6 2.9 4.1 3.1 3.9 5.6 4.8 127 148 128 121 103 51 35 35 56 109 159 198 Middle Plains Khandwa 4.5 4.8 3.9 3.2 2.3 3.9 2.0 2.5 3.4 5.5 132 146 141 116 112 48 36 39 58 101 160 190 Khargone 5.0 4.0 3.3 2.3 3.8 2.8 2.0 2.5 5.7 136 148 153 132 114 54 38 42 61 105 160 179 Dewas 5.1 5.3 4.3 3.9 3.0 2.0 3.8 5.7 127 148 128 121 103 51 35 35 56 109 159 198 Indore 5.2 5.5 4.4 2.5 3.8 3.1 2.7 3.7 5.9 4.8 136 159 144 146 114 56 37 42 60 112 158 201

JAIN et al.: CLIMATE CHANGE MODELING BASED PUBLIC HEALTH RESOURCE PLANNING 633 Lower Plains Bharuch Baroda 4.1 4.3 4.4 3.7 2.7 2.8 1.7 1.8 2.5 2.7 2.4 2.5 1.8 1.8 2.2 2.8 3.0 4.2 3.6 231 217 250 198 284 224 242 203 216 192 68 64 44 41 56 52 77 77 163 151 230 212 274 236 District Lower Hilly Dhule Jhabua 4.2 4.9 5.3 3.9 4.1 3.2 3.2 2.2 3.4 3.3 2.9 1.8 2.0 2.3 2.5 3.2 5.2 5.4 4.3 4.2 200 150 192 150 195 178 211 165 145 135 54 59 36 37 49 46 58 70 112 123 154 174 172 209 Dhar 5.1 5.4 4.3 3.4 2.3 3.6 3.0 3.6 5.8 131 153 169 161 120 60 39 44 66 116 168 202 Table 5: Correlation coefficient of Temperature and Precipitation with respect to change in year for each month over 102 years Monthly Mean Surface Air Temperature Monthly Mean Rainfall Upper Plains Narsimhapur Jabalpur 8 6 8 7 3 4 3 5 - -9-3 -6-2 -3 4 7 0 2 0.51 0.49 1 3 3-5 1-3 -5-4 0-1 4 6-3 -5 3 6-1 -8 1-1 -4-2 1 1 Seoni 0 0.40 7 1 6-0 -4 0 2 2 0.43 2-7 2-6 -6-8 -1 7-7 0-1 Upper Hilly Balaghat Mandla 1 0.40 8 5 5 0 8 6-7 -7-3 -5 4-1 6 3 4 5 0.46 0.51 0.43 0.48 5 4-1 -1 5 0-8 -6-8 -6-6 -5-1 - -1 2-0 -7 3 1 2 0 4 Shahdol 3 7 3 5-6 -8 1 1 6 0.55 0.53 9-7 -5-6 -1-1 -5-1 -4 2 0 4

634 INDIAN J. MAR. SCI., VOL. 45, NO. 5 MAY 2016 Table 5: Correlation coefficient of Temperature and Precipitation with respect to change in year for each month over 102 years Monthly Mean Surface Air Temperature Monthly Mean Rainfall Indore 6 6 5 1 1-2 1 3 4 4 0.48 0.48 3 8 0 0-1 9-5 6-0 9-9 3 Middle Plains Dewas Khargone 4 4 6 6 1 5 7 0 9 0-6 -2 1 4 0 0 5 6 3 5 0.48 0.48 0 1 6 4 8 5 0 2 2 8 6-1 -1 0 6-5 -5 5 3-9 -6 8-3 Khandwa 0 5 2 5 8-4 6-3 4 2 0.46 0.46 3 9 3-2 3 0-9 1-6 5-8 -2 Sehore 6 6 3 7-9 -1-1 4 0 0 1 4 5 2 8-1 0-5 5-9 8 District Raisen 1 6 1 6 3 - -2-2 2 8 0.46 0.46 7 0 1-3 5 9-6 3-3 7-6 0 Betul 5 5 3 6 5-0 4-5 3 8 0.44 0.45 5 9-2 -5 5-8 -9-4 -1-8 2 Hoshangaba d 9 6 2 7 5-1 0-2 5 9 0.46 5 0 0-5 6 1-8 4-9 3-7 6 Chhindwara 8 9 9 6-0 0 2 0 0.45 0.43 2 0 2-7 -2-0 -5 0-7 0-4 8 Damoh 1 4 7 6 3-1 -5-2 9 0.50 0.48 6 7-1 -3 7-4 9-1 8-4 0 Sagar 1 4 8 7 3 - -5-3 4 7 0.45 0.46 6 7-1 -3 7-4 9-1 8-4 0

JAIN et al.: CLIMATE CHANGE MODELING BASED PUBLIC HEALTH RESOURCE PLANNING 635 Table 5: Correlation coefficient of Temperature and Precipitation with respect to change in year for each month over 102 years Monthly Mean Surface Air Temperature Monthly Mean Rainfall Lower Plains Bharuch Baroda 6 6 4 4 2 4 4 3 3 3-1 -1-1 -9-2 -2 4 1 2 2 0.42 0.43 0.49-7 -0-0 -0-1 -5-8 -7 3-1 7 4-3 -9 1 1-4 -6 3 7 9 8 1 District Lower Hilly Dhule Jhabua 6 6 7 5 3 6 1 3 3 0-1 2-4 0 1 8 3 5 3 0.45 0.46 0.50 0-6 -1-0 4-6 -1 5-5 7-1 -8 8 5-9 -4 8 1-7 0-4 7 Dhar 6 6 7 3 0 0 3 5 5 0.48 5 3 9 5 0 9-1 3-4 4-4 1 Table 6: Total number of months under water borne disease risk out of 1224 months (102 years) # Districts Name Months with Coliform Risk % contribution of each month to total risk Total 1 Shahdol 4 87 94 22 0 207 1.9 42.0 45.4 10.6 2 Mandla 9 96 95 38 0 238 3.8 4 39.9 16.0 Upper Hilly 3 Balaghat 9 97 94 41 0 241 3.7 4 39.0 17.0 4 Seoni 9 85 85 36 1 216 4.2 39.4 39.4 16.7 0.5 5 Jabalpur 8 83 91 29 0 211 3.8 39.3 43.1 13.7 6 Narsimhapur 6 82 88 42 0 218 2.8 37.6 40.4 19.3 7 Sagar 5 75 84 32 0 196 38.3 42.9 16.3 8 Damoh 5 76 83 30 0 194 39.2 42.8 15.5 9 Upper Plains Chhindwara 4 82 79 37 0 202 2.0 40.6 39.1 18.3 10 Hoshangabad 4 79 77 37 0 197 2.0 4 39.1 18.8 11 Betul 3 57 63 22 0 145 39.3 43.4 15.2 12 Raisen 4 83 79 40 0 206 1.9 4 38.3 19.4 13 Sehore 2 82 70 35 0 189 1.1 43.4 37.0 18.5 14 Khandwa 2 38 28 8 0 76 5 36.8 10.5 15 Middle Plains Khargone 3 24 11 8 0 46 6.5 52.2 23.9 17.4 16 Dewas 2 82 70 35 0 189 1.1 43.4 37.0 18.5

636 INDIAN J. MAR. SCI., VOL. 45, NO. 5 MAY 2016 17 Indore 5 44 27 20 0 96 5.2 45.8 28.1 20.8 18 Dhar 3 45 27 22 0 97 3.1 46.4 27.8 22.7 19 Jhabua 6 78 48 30 0 162 3.7 48.1 18.5 Lower Hilly 20 Dhule 19 81 58 46 0 204 9.3 39.7 22.5 21 Baroda 13 73 39 20 0 145 9.0 5 13.8 Lower Plains 22 Bharuch 24 78 39 27 0 168 14.3 46.4 23.2 16.1 Overall 149 1607 1429 657 1 3843 3.9 41.8 37.2 17.1 ~ S.No. Regio n Table 7: The number of months/ per year with public health risk for all districts # of years with different risk potential from 1901-2002 (# of risk months/yr) Districts Name Low Risk Medium Risk High Risk 0/yr 1/yr 2/yr 3/yr 4/yr High Risk Year s Total Total Numbe r of Risk Months 1 Shahdol 1 18 60 23 0 23 207 2 Upper Mandla 0 9 53 37 3 40 238 3 Hilly Balaghat 0 8 51 41 2 43 241 4 Seoni 1 21 46 33 1 34 216 5 Jabalpur 2 20 49 31 0 31 211 6 Narsimhapur 1 21 45 33 2 35 218 7 Sagar 4 28 43 26 1 27 196 8 Damoh 4 29 43 25 1 26 194 9 Upper Plains Chhindwara 4 24 44 30 0 30 202 10 Hoshangabad 4 25 48 24 1 25 197 11 Betul 16 37 39 10 0 10 145 12 Raisen 3 20 52 26 1 27 206 13 Sehore 2 31 49 20 0 20 189 14 Khandwa 43 43 15 1 0 1 76 15 Middl Khargone 65 29 7 1 0 1 46 16 e Dewas 2 31 49 20 0 20 189 17 Plains Indore 33 45 21 3 0 3 96 18 Dhar 31 47 22 2 0 2 97 19 Lower Jhabua 9 38 41 14 0 14 162 20 Hilly Dhule 4 23 48 23 4 27 204 21 Lower Baroda 12 46 33 11 0 11 145 22 Plains Bharuch 9 37 39 15 2 17 168

JAIN et al.: CLIMATE CHANGE MODELING BASED PUBLIC HEALTH RESOURCE PLANNING 637 Conclusion A The predictive modeling based framework had been proposed to identify and prioritize the regions, districts and months for the limited resource allocation to maximize the reduction in the public health risk due to the water borne disease. The study was performed using the Narmada river basin as a case study for the framework development. It was found that lower regions of the basin are more susceptible to climate change. However, it was identified from the study that the Upper regions of the Narmada basin require more focus. District wise, Shahdol, Mandla, Balaghat, Seoni, Jabalpur, Narsimhapur, Chhindwara, Raisen and Dhule have higher water borne disease based public health risk as compared to other 13 districts in the Narmada Basin. Monsoon months require more focus especially y and ust months. In addition, this study shows that despite the higher susceptibility of lower regions to climate change, it is the upper regions that are at greater public health risk. This indicates the possibility of lesser relevance of global climate change in certain geographical areas for certain human disease especially for planning. Finally, this study stresses upon the relevance of the local level modeling as it showed inter-district variation in the disease risk susceptibility. The major limitation of the study is the limited data availability. This prevented study validation, which could be done by comparing with the number of patients reported with water-borne diseases in hospitals of the different districts. Further, the coefficients used for predictive modeling in the study were developed for the other system as in this system lack of data on the total coliform count in surface waters prevented development the more customized model. References 1 Department of Measurement and Health Information, Mortality and Burden of Disease estimates for WHO member states in 2002. World Health Organization, 2004. [Online]. Available: http://www.who.int/healthinfo/statistics/bodgbddeat hdalyestimates.xls. [Accessed: 08--2014]. 2 Department of Measurement and Health Information, Mortality and Burden of Disease estimates for WHO member states in 2008. World Health Organization, 2011. [Online]. Available:www.who.int/gho/mortality_burden_dise ase/global_burden_disease_death_estimates_sex_20 08.xls. [Accessed: 08--2014]. 3 Department of Measurement and Health Information, Mortality and Burden of Disease estimates for WHO member states in 2012. World Health Organization, 2014. [Online].Available:http://www.who.int/entity/health info/global_burden_disease/ghe_deaths_2012_co untry.xls?ua=1. [Accessed: 08--2014]. 4 Department of Measurement and Health Information, Mortality and Burden of Disease estimates for WHO member states in 2000. World Health Organization, 2014. [Online]. Available:http://www.who.int/entity/healthinfo/glob al_burden_disease/ghe_deaths_2000_country.xls? ua=1. [Accessed: 08--2014]. 5 Department of Measurement and Health Information, Mortality and Burden of Disease estimates for WHO member states in 2004. World Health Organization, 2009. [Online]. Available:www.who.int/healthinfo/global_burden_d isease/gbddeathdalycountryestimates2004.xls. [Accessed: 08--2014]. 6 World Health Organization, Changing dsets: Strategy on Health Policy and Systems Research. Geneva, 2012. 7 El-Fadel, M., Ghanimeh, S., oun, R., & Alameddine, I., Climate change and temperature rise: implications on food- and water-borne diseases. Sci. Total Environ, 437(2012), 15 21. 8 Funari, E., Manganelli, M. & Sinisi, L., Impact of climate change on waterborne diseases. Ann Ist Super Sanita, 48(4) (2012), 473 487. 9 Harper, S. L., Edge, V. L., Schuster-Wallace, C. J., Berke, O., & McEwen, S., Weather, water quality and infectious gastrointestinal illness in two Inuit communities in Nunatsiavut, Canada: potential implications for climate change. Ecohealth,. 8(2011), 93 108. 10 Moors, E., Singh, T., Siderius, C., Balakrishnan, S., & Mishra, A., Climate change and waterborne diarrhoea in northern India: impacts and adaptation strategies. Sci. Total Environ., 468 469(2013), 139 151. 11 Haines, A., Kovats, R. S., Campbell-Lendrum, D., & Corvalan, C., Climate change and human health: impacts, vulnerability and public health. Public Health, 120(2006), 585 596. 12 Patz, J. A., Campbell-Lendrum D., Holloway T., & a Foley J., Impact of regional climate change on human health. Nature, 438(2005), 310 317. 13 Hofstra, N., Quantifying the impact of climate change on enteric waterborne pathogen concentrations in surface water. Curr. Opin. Environ. Sustain, 3(6), 471 479. 14 Malviya, A., Diwakar, S. K. & Choubey, O. N., Chemical assessment of narmada river water at

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