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Spatio-temporal drought assessment in Tel river basin using Standardized Precipitation Index (SPI) and GIS

Spatio-temporal drought assessment in Tel river basin using Standardized Precipitation Index... Geomatics, Natural Hazards and Risk Vol. 2, No. 1, March 2011, 79–93 Spatio-temporal drought assessment in Tel river basin using Standardized Precipitation Index (SPI) and GIS S. SANGITA MISHRA* and R. NAGARAJAN Center of Studies in Resources Engineering, Indian Institute of Technology, Bombay, India (Received 12 April 2010; in final form 18 October 2010) The spatial and temporal characteristics of droughts were investigated to provide a framework for agriculture practices, engineering facilities and sustainable water resources management in the Tel river basin which is about 2756 km and lies 0 0 0 0 between 19817 and 20800 N and 82830 and 82859 E in Kalahandi district of Odisha, India. Using the Standardized Precipitation Index (SPI) as an indicator of drought severity for the period from 1965 to 2008, the characteristics of droughts were examined. The multiple-time scaled SPI values were evaluated for May–October months and point-wise indices were interpolated over the whole area using spatial interpolation technique in Arc GIS 9.2, in order to obtain areal extension of the drought severity. The results indicated that a dramatic and widespread drought event was recorded in the year 2002 at most of the stations and drought categories. However, regardless of the drought severity, an extraordinary dryness was experienced in the years 1966, 1972, 1979, 1987 and 2002 in the entire study area. It was observed that 10 years were successively dry during May at Jayapatna station from 1999 to 2008 in the 6-month drought category. The highest SPI value of 73.06 was observed in July 2002 in the Dharamgarh station in the 9-month drought category. The overall outcome of this study demonstrates that severe and extreme droughts were experienced from time to time across the study area leading to unfavourable results on agricultural practices and water resources in the area. 1. Introduction Drought is one of the most serious natural hazards because of the complexity of the issue, arising from climatic variability which is a function of many factors such as latitude and altitude of a particular place, air mass influences, the location of global high- and low-pressure zones, heat exchange from ocean currents, distribution of mountain barriers, the pattern of prevailing winds and distribution of land and sea, etc. Therefore, the occurrence of drought must be understood and appropriate drought indices should be investigated for different goals such as agriculture practices, engineering practices, water management and fire control. It is also necessary to know some vital features of droughts, such as duration, severity and spatio-temporal extension for the given period for drought vulnerability analysis of a particular area in any climatic region around the world. *Corresponding author. Email: sangita_csre@iitb.ac.in Geomatics, Natural Hazards and Risk ISSN 1947-5705 Print/ISSN 1947-5713 Online ª 2011 Taylor & Francis http://www.tandf.co.uk/journals DOI: 10.1080/19475705.2010.533703 80 S. S. Mishra and R. Nagarajan There are several indices that measure to what extent precipitation for a given period has deviated from historically established norms. Although none of the major indices is inherently superior to the rest in all circumstances, some indices are better suited than others for certain uses. For example, the Palmer drought severity index (PDSI) (Palmer 1965) has been widely used as a means of providing a single measure of meteorological drought severity, for example for the previous 30 years. It is based on a monthly water balance accounting scheme involving precipitation, evapotran- spiration, run-off and soil moisture. The PDSI has been used in making operational water management decisions and planning drought monitoring. Basic drought phenomena and drought preparedness studies are presented by Wilhite and Glantz (1985) and Wilhite (1996). In order to understand whether a deficit of precipitation has different impacts on the groundwater, reservoir storage, soil moisture, snow pack, and stream flow, McKee et al. (1993) developed the standardized precipitation index (SPI). The SPI was designed to quantify the precipitation deficit for multiple time scales, which reflect the impact of drought on the availability of different water resources. Soil moisture conditions respond to precipitation anomalies on a relatively short time scale, while groundwater, stream flow, and reservoir storage reflect the longer-term precipitation anomalies (Tonkaz 2006). For these reasons, McKee et al. (1993) originally calculated the SPI for 3-, 6-, 12-, 24- and 48-month moving average time scales. The SPI is probability-based (Chung and Salas 2000) and was designed to be a spatially invariant indicator of drought (Bordi et al. 2001, Livada and Assimako- poulos 2007), which recognizes the importance of time scales in the analysis of water availability and water use. It is essentially a standardizing transform of the probability of the observed precipitation (Guttman 1999). It can be calculated for a precipitation total observed over any duration desired by a user. It is well known in practice that short-term durations (weeks or months) are important to agricultural activities, whereas long-term durations (seasons, years, etc.) are significant in water supply management. An attempt was made to carry out drought vulnerability analysis in Tel river basin, located in the Kalahandi district of Odisha which is one of the worst drought- affected districts of India (see figure 1). The Tel river basin covers an area of 0 0 0 0 2756 km and lies between 19817 and 20800 N and 82830 and 82859 E near Bhawanipatna region of Kalahandi district. The study area experiences tropical wet and dry climate where the wet season (June–September) is much shorter and receives lower precipitation from the southwest monsoon than normal and the other months of the year are generally dry because it does not receive any precipitation from the northeast monsoon. The long breaks of monsoon (dry spells) during the crop growing season and wide variation in the quantum of rainfall from year to year result in frequent failure of crops and consequently the entire area is drought-prone. Over the past decades, many studies have been done on the reasons for drought occurrences in Orissa (Gulati et al. 2009) and its impact on crop yield and livelihood (Ota 2001) using rainfall analysis (Subudhi et al. 1996, Sudhishri et al. 2004). However, there is a need to analyse the spatial and temporal variation of drought in this area so that water users can formulate drought mitigation plans and make decisions based on the future horizon to reduce the harmful impacts of drought. In this study, the aim was to investigate the drought occurrences at multiple time scales using SPI for the May–October months in the study area at each station over several years. Maps showing the spatial extent of drought severity were produced Spatio-temporal drought assessment 81 Figure 1. Location map of Tel watershed. using the inverse distance square method for different time scales for each month. Point-wise and spatial extent of drought was evaluated accordingly. 2. Methodology The monthly rainfall data of seven meteorological stations over a period from 1965 to 2008, which are well distributed in the Tel watershed area was used for drought analysis. The identification and assessment of drought severity were done using the SPI. The SPI is calculated by taking the difference of the precipitation from the mean for a particular time scale, then dividing it by the standard deviation. SPI ¼ðXik  XiÞ=oi  ð1Þ where o´i ¼ standardized deviation for the ith station, Xik ¼ Precipitation for the ith station and kth observation, Xi ¼ mean precipitation for the ith station. The SPI calculation for any location is based on a series of accumulated precipitation for a fixed time scale of interest (e.g. 1, 3, 6, 9, 12 . . . months). Such a series is fitted to a probability distribution, which is then transformed into a normal distribution so that the mean SPI for the location and desired period is zero (Edwards and McKee 1997). Positive SPI values indicate greater than median precipitation, and negative values indicate less than median precipitation. Because the SPI is normalized, wetter and drier climates can be represented in the same way 82 S. S. Mishra and R. Nagarajan (Bordi et al. 2001), and wet periods can also be monitored using the SPI (Bordi et al. 2004). The calculations become more complicated when the SPI is normalized to reflect the variable behaviour of the precipitation for time scales shorter than 12 months. McKee et al. (1994) defined the criteria for a drought event for all of the time scales and classified the SPI to define various drought intensities (see table 1). 3. Results and discussion The SPI indices were determined using monthly total precipitation series at the seven meteorological stations in the Tel river basin area. Multi-time scales (1, 2, 3, 6, and 9 months) were used in order to achieve different goals in the study. Calculated SPI indices were mapped in order to determine spatial distribution of drought magnitude over the area using the inverse distance weighted method for the given time scales. 3.1 The SPI on 1-month time scale The 1-month SPI results showed that in June, the study area experienced mild to moderate drought conditions in almost all the years under observation, whereas the no-drought condition was observed in June for the years 1974, 1975, 1980, 1984, 1985, 1990, 1991, 1995, 2001, 2004, 2007 and 2008. The year-wise variation of drought severity is shown in figure 2. Extreme drought was experienced in Junagarh with SPI value of 72.5 (see figure 3) in the month of July in 2007. Regarding the spatial distribution of drought magnitude, July was the most critical month experiencing extreme and severe droughts in most of the years under observation. May followed July in experiencing severe drought and moderate drought in 15 and 30 years out of 44 years, respectively. All the meteorological stations were under moderate and severe drought in this month and the variation of drought intensity in the study area is shown in figure 4. While September exhibits only 13.27% of the drought event in 44 years, October was the next driest month to May, with drought events covering 21.97% of the total 44 years. So the winter crops may be in water stress unless an additional source of water is provided by different irrigation methods. The month of July is essential for agricultural practices, so drought in this month may affect crop production. 3.2 The SPI on 2-month time scale Drought magnitude is increased with a 2-month scale in comparison to the 1-month scale. The highest magnitude observed was 72.74 in the month of July in Table 1. Drought intensities using SPI. SPI values Drought category 0to 70.99 Mild drought 71.00 to 71.49 Moderate drought 71.50 to 71.99 Severe drought 72.0 or more Extreme drought Spatio-temporal drought assessment 83 Figure 2. Variation of drought severity on the Tel river basin on 1-month scale in June. Dharamgarh during 2002 (see figure 5). It was observed that almost the whole area was suffering from severe drought conditions during the same month, which is not at all suitable for kharif crop production. It was observed that the 2002 rainy season was affected by drought with all the months having negative SPI values and variation of SPI in all the seven stations under 1-, 2-, 3-, 6- and 9-month time scales are shown in figures 6a–e. Successive drought events were considerably decreased and only 22.72% drought was observed in May, which was the highest value among the months during the examination period in comparison to the 1-month scale. These results may indicate that 2 successive months of drought in the study area were as 84 S. S. Mishra and R. Nagarajan Figure 2. (Continued). common as in the 1-month scale and it may affect the production of Rabi crops. It may also result in lesser water availability for domestic uses. 3.3 The SPI on 3-month time scale Regarding the 3-month events, the lowest extreme drought event was experienced during May with the value of 72.02 in Junagarh (see figure 7). At the same time, a 3-month drought had mild conditions for the whole area as compared to the other time scales. May was found to be the most critical month with 43.15% of severe drought and 25% of moderate drought of the total area (figure 8a), while October was the least Spatio-temporal drought assessment 85 Figure 2. (Continued). critical month with 25% of moderate drought, 11.36% severe drought and only 2.27% extreme drought (figure 8b) during the investigation period. The maximum number of observed drought events occurred in May followed by July and August months. 3.4 The SPI on 6-month time scale August was the most critical month considering both number of dry years and the extending area of extreme drought events on a 6-month time scale. On the other hand, drought persistence was observed in Jayapatna, where 10 years were successively dry during May from 1999 to 2008 (figure 9). Numbers of dry months were found to be 124 (23.48%) with the highest value in August among other months 86 S. S. Mishra and R. Nagarajan Figure 2. (Continued). during the examination period. By the end of June 2002, extreme drought covered an area of 78.5% of the total area and the remaining areas stayed under severe drought conditions with a negligible moderate drought (see figure 10). Extreme drought was experienced in Dharamgarh with the highest SPI value (72.92) in July 2002. In the 2002 rainy season, another dramatic prolonged event happened; all the stations in the study area experienced drought conditions for all investigated months. During this period, the drought event started with an SPI value of 71.05 as the highest in May, reached a peak value (72.92) in July at the Dharamgarh station and ended in August with a value of 70.95. The 2002 drought was of extreme nature and resulted in the lowest rice yield in the entire state. For example, the rice yield was decreased to 690 kg/ha as compared to 1554 kg/ha in 2001 as per the reports of Odisha Agricultural Statistics Department, 2006–07. The report also states that almost all villages in the study area suffered from more than 50% crop loss in the same year. Analysis shows that the years 1966, 1972, 1979, 1987 and 2002 are the most drought-affected years during the investigation period in the 6-month time scale. Spatio-temporal drought assessment 87 Figure 3. 1-month July SPI (1965–2008) time series for Junagarh station. Figure 4. Spatial distribution of 1-month scaled droughts in May of 2007. 3.5 The SPI on 9-month time scale Analysis demonstrated that in the 9-month time scale, every drought category was observed in the study area, even if its magnitude is small. Severe and extreme droughts covered an area of 66.1% in June 1973 (figure 11a), and 61.4% in May 88 S. S. Mishra and R. Nagarajan Figure 5. Spatial distribution of 2-month scaled droughts in July of 2002. 2002 (figure 11b), respectively. Those are the highest areal values considering spatial distributions of the experienced droughts. October was found to be the most vulnerable month to drought where 55.45% of years were drought affected during the investigation period. Similar to the 6-month droughts, the year 2002 was found to be the driest year with an SPI value of 73.05 in July in the Dharamgarh station (figure 12). The SPI values were negative for all the analysed months. 4. Conclusion The present study concerned the spatiotemporal behaviours of multiple time drought indices of SPI in the dry humid regions of Odisha, India. An analysis of the variation of drought scenario in different places of the watershed on different time scales show that the study area was facing severe and extreme drought conditions in July month with the SPI values of 72.5, 72.74, 72.92 and 73.05 on 1-, 2-, 6- and 9-month Spatio-temporal drought assessment 89 Figure 6. (a)–(e) SPI values in all meteorological stations in the year 2002 in 1-month, 2-month, 3-month, 6-month and 9-month time scales. 90 S. S. Mishra and R. Nagarajan Figure 7. 3-month SPI values 1965–2008 time series for Junagarh station. Figure 8. (a) Spatial distribution of 3-month scaled droughts in May in the study area. (b) Spatial distribution of 3-month scaled droughts in October in the study area. time scales, respectively, in almost all the years under observation. Dharamgarh was the worst affected among all meteorological stations having highest SPI values while other stations were facing extreme droughts of lower magnitude. August was the most vulnerable month in 6-month time scale in all stations except Jayaptna. It was observed from the study that 10 years (1999–2008) were successively dry in May on the 6-month time scale in the Jayaptna station. The maximum number of observed drought events occurred in May, July and August months under the 3-month category. The years 1966, 1972, 1979, 1987 and 2002 were the most drought-affected years during the investigation period. Spatio-temporal drought assessment 91 Figure 9. 6-month SPI values 1965–2008 time series for Jayapatna station. Figure 10. Spatial distribution of 6-month scaled droughts by the end of June 2002. 92 S. S. Mishra and R. Nagarajan Figure 11. (a) Spatial distribution of 9-month scaled droughts in June in the study area. (b) Spatial distribution of 9-month scaled droughts in May in the study area. Figure 12. 9-month SPI values 1965–2008 time series for Dharamgarh station. Acknowledgements The authors are grateful to two anonymous referees for their comments and suggestions that helped us to improve the earlier version of the manuscript. References BORDI, I., FRAEDRICH, K., JIANG, J.M. and SUTERA, A., 2004, Spatio-temporal variability of dry and wet periods in eastern China. Theoretical and Applied Climatology, 79, pp. 81–91. Spatio-temporal drought assessment 93 BORDI, I., FRIGIO, S., PARENTI, P., SPERANZA, A. and SUTERA, A., 2001, The analysis of the Standardized Precipitation Index in the Mediterranean area: regional patterns. Annali di Geofisica, 44, pp. 979–993. CHUNG, C.H. and SALAS, J.D., 2000, Drought occurrence probabilities and risks of dependent hydrologic processes. ASCE Journal of Hydrologic Engineering, 5, pp. 259–268. EDWARDS, D.C. and MCKEE, T.B., 1997, Characteristics of 20th century drought in the United States at multiple time scales. Climatology Rep 97–2 (Fort Collins, CO: Colorado State University Department of Atmospheric Science), pp. 155. GULATI, A., GUPTA, P., JHA, M., SARATHI, P.P. and VISHAL, K., 2009, Impact of climate change, variability, and extreme rainfall events on agricultural production and food insecurity in Orissa. In Proceedings of ISPRS Archives XXXVIII-8/W3 Workshop on Impact of Climate Change on Agriculture (Enschede, The Netherlands: ISPRS), pp. 372–375. GUTTMAN, N.B., 1999, Accepting the Standardized Precipitation Index: a calculation algorithm. Journal of American Water Resources Association, 35, pp. 311–322. LIVADA, I. and ASSIMAKOPOULOS, V.D., 2007, Spatial and temporal analysis of drought in Greece using the Standardized Precipitation Index (SPI). Theoretical and Applied Climatology, 89, pp. 143–153. MCKEE, B., DOESKEN, N.J. and KLEIST, N., 1993, The relationship of drought frequency and duration to time scales. In Proceedings of 8th Applied Meteorology, 17–22 January, Anaheim, CA (Boston, MA: American Meteorological Society), pp. 179–184. MCKEE, B., DOESKEN, N.J. and KLEIST, N., 1994, Drought monitoring with multiple time scales. In Proceedings of the Ninth Conference on Applied Climatology (Boston, MA: American Meteorological Society), pp. 233–236. OTA, A.B., 2001, Orissa drought 2001: its magnitude and consequences. In Proceedings of Conference Livelihoods and Poverty Reduction: Lessons from Eastern India. Available online at: http://www.anthrobase.com/Txt/O/Ota_A_01.htm [accessed 5 October 2010]. PALMER, W.C., 1965, Meteorological drought. Research Paper No. 45, U.S. Weather Bureau, Washington, D.C., pp. 58. SUBUDHI, C.R., PRADHAN, P.C., BEHARA, B., SENAPATI, P.C. and SINGH, G.S., 1996, Rainfall characteristics at Phulbani. Indian Journal of Soil Conservation, 24, pp. 41–43. SUDHISHRI, S., PANDA, R.K. and PATNAIK, U.S., 2004, Models for agricultural drought investigation at Koraput (Orissa). Journal of Soil and Water Conservation, India, 3, pp. 157–168. TONKAZ, T., 2006, Spatio-temporal assessment of historical droughts using SPI with GIS in GAP region, Turkey. Journal of Applied Sciences, 6, pp. 2565–2571. WILHITE, D.A., 1996, A methodology for drought preparedness. Natural Hazards, 13, pp. 229–252. WILHITE, D.A. and GLANTZ, M.H., 1985, Understanding the drought phenomenon: the role of definitions. Water International, 10, pp. 111–120. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png "Geomatics, Natural Hazards and Risk" Taylor & Francis

Spatio-temporal drought assessment in Tel river basin using Standardized Precipitation Index (SPI) and GIS

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Abstract

Geomatics, Natural Hazards and Risk Vol. 2, No. 1, March 2011, 79–93 Spatio-temporal drought assessment in Tel river basin using Standardized Precipitation Index (SPI) and GIS S. SANGITA MISHRA* and R. NAGARAJAN Center of Studies in Resources Engineering, Indian Institute of Technology, Bombay, India (Received 12 April 2010; in final form 18 October 2010) The spatial and temporal characteristics of droughts were investigated to provide a framework for agriculture practices, engineering facilities and sustainable water resources management in the Tel river basin which is about 2756 km and lies 0 0 0 0 between 19817 and 20800 N and 82830 and 82859 E in Kalahandi district of Odisha, India. Using the Standardized Precipitation Index (SPI) as an indicator of drought severity for the period from 1965 to 2008, the characteristics of droughts were examined. The multiple-time scaled SPI values were evaluated for May–October months and point-wise indices were interpolated over the whole area using spatial interpolation technique in Arc GIS 9.2, in order to obtain areal extension of the drought severity. The results indicated that a dramatic and widespread drought event was recorded in the year 2002 at most of the stations and drought categories. However, regardless of the drought severity, an extraordinary dryness was experienced in the years 1966, 1972, 1979, 1987 and 2002 in the entire study area. It was observed that 10 years were successively dry during May at Jayapatna station from 1999 to 2008 in the 6-month drought category. The highest SPI value of 73.06 was observed in July 2002 in the Dharamgarh station in the 9-month drought category. The overall outcome of this study demonstrates that severe and extreme droughts were experienced from time to time across the study area leading to unfavourable results on agricultural practices and water resources in the area. 1. Introduction Drought is one of the most serious natural hazards because of the complexity of the issue, arising from climatic variability which is a function of many factors such as latitude and altitude of a particular place, air mass influences, the location of global high- and low-pressure zones, heat exchange from ocean currents, distribution of mountain barriers, the pattern of prevailing winds and distribution of land and sea, etc. Therefore, the occurrence of drought must be understood and appropriate drought indices should be investigated for different goals such as agriculture practices, engineering practices, water management and fire control. It is also necessary to know some vital features of droughts, such as duration, severity and spatio-temporal extension for the given period for drought vulnerability analysis of a particular area in any climatic region around the world. *Corresponding author. Email: sangita_csre@iitb.ac.in Geomatics, Natural Hazards and Risk ISSN 1947-5705 Print/ISSN 1947-5713 Online ª 2011 Taylor & Francis http://www.tandf.co.uk/journals DOI: 10.1080/19475705.2010.533703 80 S. S. Mishra and R. Nagarajan There are several indices that measure to what extent precipitation for a given period has deviated from historically established norms. Although none of the major indices is inherently superior to the rest in all circumstances, some indices are better suited than others for certain uses. For example, the Palmer drought severity index (PDSI) (Palmer 1965) has been widely used as a means of providing a single measure of meteorological drought severity, for example for the previous 30 years. It is based on a monthly water balance accounting scheme involving precipitation, evapotran- spiration, run-off and soil moisture. The PDSI has been used in making operational water management decisions and planning drought monitoring. Basic drought phenomena and drought preparedness studies are presented by Wilhite and Glantz (1985) and Wilhite (1996). In order to understand whether a deficit of precipitation has different impacts on the groundwater, reservoir storage, soil moisture, snow pack, and stream flow, McKee et al. (1993) developed the standardized precipitation index (SPI). The SPI was designed to quantify the precipitation deficit for multiple time scales, which reflect the impact of drought on the availability of different water resources. Soil moisture conditions respond to precipitation anomalies on a relatively short time scale, while groundwater, stream flow, and reservoir storage reflect the longer-term precipitation anomalies (Tonkaz 2006). For these reasons, McKee et al. (1993) originally calculated the SPI for 3-, 6-, 12-, 24- and 48-month moving average time scales. The SPI is probability-based (Chung and Salas 2000) and was designed to be a spatially invariant indicator of drought (Bordi et al. 2001, Livada and Assimako- poulos 2007), which recognizes the importance of time scales in the analysis of water availability and water use. It is essentially a standardizing transform of the probability of the observed precipitation (Guttman 1999). It can be calculated for a precipitation total observed over any duration desired by a user. It is well known in practice that short-term durations (weeks or months) are important to agricultural activities, whereas long-term durations (seasons, years, etc.) are significant in water supply management. An attempt was made to carry out drought vulnerability analysis in Tel river basin, located in the Kalahandi district of Odisha which is one of the worst drought- affected districts of India (see figure 1). The Tel river basin covers an area of 0 0 0 0 2756 km and lies between 19817 and 20800 N and 82830 and 82859 E near Bhawanipatna region of Kalahandi district. The study area experiences tropical wet and dry climate where the wet season (June–September) is much shorter and receives lower precipitation from the southwest monsoon than normal and the other months of the year are generally dry because it does not receive any precipitation from the northeast monsoon. The long breaks of monsoon (dry spells) during the crop growing season and wide variation in the quantum of rainfall from year to year result in frequent failure of crops and consequently the entire area is drought-prone. Over the past decades, many studies have been done on the reasons for drought occurrences in Orissa (Gulati et al. 2009) and its impact on crop yield and livelihood (Ota 2001) using rainfall analysis (Subudhi et al. 1996, Sudhishri et al. 2004). However, there is a need to analyse the spatial and temporal variation of drought in this area so that water users can formulate drought mitigation plans and make decisions based on the future horizon to reduce the harmful impacts of drought. In this study, the aim was to investigate the drought occurrences at multiple time scales using SPI for the May–October months in the study area at each station over several years. Maps showing the spatial extent of drought severity were produced Spatio-temporal drought assessment 81 Figure 1. Location map of Tel watershed. using the inverse distance square method for different time scales for each month. Point-wise and spatial extent of drought was evaluated accordingly. 2. Methodology The monthly rainfall data of seven meteorological stations over a period from 1965 to 2008, which are well distributed in the Tel watershed area was used for drought analysis. The identification and assessment of drought severity were done using the SPI. The SPI is calculated by taking the difference of the precipitation from the mean for a particular time scale, then dividing it by the standard deviation. SPI ¼ðXik  XiÞ=oi  ð1Þ where o´i ¼ standardized deviation for the ith station, Xik ¼ Precipitation for the ith station and kth observation, Xi ¼ mean precipitation for the ith station. The SPI calculation for any location is based on a series of accumulated precipitation for a fixed time scale of interest (e.g. 1, 3, 6, 9, 12 . . . months). Such a series is fitted to a probability distribution, which is then transformed into a normal distribution so that the mean SPI for the location and desired period is zero (Edwards and McKee 1997). Positive SPI values indicate greater than median precipitation, and negative values indicate less than median precipitation. Because the SPI is normalized, wetter and drier climates can be represented in the same way 82 S. S. Mishra and R. Nagarajan (Bordi et al. 2001), and wet periods can also be monitored using the SPI (Bordi et al. 2004). The calculations become more complicated when the SPI is normalized to reflect the variable behaviour of the precipitation for time scales shorter than 12 months. McKee et al. (1994) defined the criteria for a drought event for all of the time scales and classified the SPI to define various drought intensities (see table 1). 3. Results and discussion The SPI indices were determined using monthly total precipitation series at the seven meteorological stations in the Tel river basin area. Multi-time scales (1, 2, 3, 6, and 9 months) were used in order to achieve different goals in the study. Calculated SPI indices were mapped in order to determine spatial distribution of drought magnitude over the area using the inverse distance weighted method for the given time scales. 3.1 The SPI on 1-month time scale The 1-month SPI results showed that in June, the study area experienced mild to moderate drought conditions in almost all the years under observation, whereas the no-drought condition was observed in June for the years 1974, 1975, 1980, 1984, 1985, 1990, 1991, 1995, 2001, 2004, 2007 and 2008. The year-wise variation of drought severity is shown in figure 2. Extreme drought was experienced in Junagarh with SPI value of 72.5 (see figure 3) in the month of July in 2007. Regarding the spatial distribution of drought magnitude, July was the most critical month experiencing extreme and severe droughts in most of the years under observation. May followed July in experiencing severe drought and moderate drought in 15 and 30 years out of 44 years, respectively. All the meteorological stations were under moderate and severe drought in this month and the variation of drought intensity in the study area is shown in figure 4. While September exhibits only 13.27% of the drought event in 44 years, October was the next driest month to May, with drought events covering 21.97% of the total 44 years. So the winter crops may be in water stress unless an additional source of water is provided by different irrigation methods. The month of July is essential for agricultural practices, so drought in this month may affect crop production. 3.2 The SPI on 2-month time scale Drought magnitude is increased with a 2-month scale in comparison to the 1-month scale. The highest magnitude observed was 72.74 in the month of July in Table 1. Drought intensities using SPI. SPI values Drought category 0to 70.99 Mild drought 71.00 to 71.49 Moderate drought 71.50 to 71.99 Severe drought 72.0 or more Extreme drought Spatio-temporal drought assessment 83 Figure 2. Variation of drought severity on the Tel river basin on 1-month scale in June. Dharamgarh during 2002 (see figure 5). It was observed that almost the whole area was suffering from severe drought conditions during the same month, which is not at all suitable for kharif crop production. It was observed that the 2002 rainy season was affected by drought with all the months having negative SPI values and variation of SPI in all the seven stations under 1-, 2-, 3-, 6- and 9-month time scales are shown in figures 6a–e. Successive drought events were considerably decreased and only 22.72% drought was observed in May, which was the highest value among the months during the examination period in comparison to the 1-month scale. These results may indicate that 2 successive months of drought in the study area were as 84 S. S. Mishra and R. Nagarajan Figure 2. (Continued). common as in the 1-month scale and it may affect the production of Rabi crops. It may also result in lesser water availability for domestic uses. 3.3 The SPI on 3-month time scale Regarding the 3-month events, the lowest extreme drought event was experienced during May with the value of 72.02 in Junagarh (see figure 7). At the same time, a 3-month drought had mild conditions for the whole area as compared to the other time scales. May was found to be the most critical month with 43.15% of severe drought and 25% of moderate drought of the total area (figure 8a), while October was the least Spatio-temporal drought assessment 85 Figure 2. (Continued). critical month with 25% of moderate drought, 11.36% severe drought and only 2.27% extreme drought (figure 8b) during the investigation period. The maximum number of observed drought events occurred in May followed by July and August months. 3.4 The SPI on 6-month time scale August was the most critical month considering both number of dry years and the extending area of extreme drought events on a 6-month time scale. On the other hand, drought persistence was observed in Jayapatna, where 10 years were successively dry during May from 1999 to 2008 (figure 9). Numbers of dry months were found to be 124 (23.48%) with the highest value in August among other months 86 S. S. Mishra and R. Nagarajan Figure 2. (Continued). during the examination period. By the end of June 2002, extreme drought covered an area of 78.5% of the total area and the remaining areas stayed under severe drought conditions with a negligible moderate drought (see figure 10). Extreme drought was experienced in Dharamgarh with the highest SPI value (72.92) in July 2002. In the 2002 rainy season, another dramatic prolonged event happened; all the stations in the study area experienced drought conditions for all investigated months. During this period, the drought event started with an SPI value of 71.05 as the highest in May, reached a peak value (72.92) in July at the Dharamgarh station and ended in August with a value of 70.95. The 2002 drought was of extreme nature and resulted in the lowest rice yield in the entire state. For example, the rice yield was decreased to 690 kg/ha as compared to 1554 kg/ha in 2001 as per the reports of Odisha Agricultural Statistics Department, 2006–07. The report also states that almost all villages in the study area suffered from more than 50% crop loss in the same year. Analysis shows that the years 1966, 1972, 1979, 1987 and 2002 are the most drought-affected years during the investigation period in the 6-month time scale. Spatio-temporal drought assessment 87 Figure 3. 1-month July SPI (1965–2008) time series for Junagarh station. Figure 4. Spatial distribution of 1-month scaled droughts in May of 2007. 3.5 The SPI on 9-month time scale Analysis demonstrated that in the 9-month time scale, every drought category was observed in the study area, even if its magnitude is small. Severe and extreme droughts covered an area of 66.1% in June 1973 (figure 11a), and 61.4% in May 88 S. S. Mishra and R. Nagarajan Figure 5. Spatial distribution of 2-month scaled droughts in July of 2002. 2002 (figure 11b), respectively. Those are the highest areal values considering spatial distributions of the experienced droughts. October was found to be the most vulnerable month to drought where 55.45% of years were drought affected during the investigation period. Similar to the 6-month droughts, the year 2002 was found to be the driest year with an SPI value of 73.05 in July in the Dharamgarh station (figure 12). The SPI values were negative for all the analysed months. 4. Conclusion The present study concerned the spatiotemporal behaviours of multiple time drought indices of SPI in the dry humid regions of Odisha, India. An analysis of the variation of drought scenario in different places of the watershed on different time scales show that the study area was facing severe and extreme drought conditions in July month with the SPI values of 72.5, 72.74, 72.92 and 73.05 on 1-, 2-, 6- and 9-month Spatio-temporal drought assessment 89 Figure 6. (a)–(e) SPI values in all meteorological stations in the year 2002 in 1-month, 2-month, 3-month, 6-month and 9-month time scales. 90 S. S. Mishra and R. Nagarajan Figure 7. 3-month SPI values 1965–2008 time series for Junagarh station. Figure 8. (a) Spatial distribution of 3-month scaled droughts in May in the study area. (b) Spatial distribution of 3-month scaled droughts in October in the study area. time scales, respectively, in almost all the years under observation. Dharamgarh was the worst affected among all meteorological stations having highest SPI values while other stations were facing extreme droughts of lower magnitude. August was the most vulnerable month in 6-month time scale in all stations except Jayaptna. It was observed from the study that 10 years (1999–2008) were successively dry in May on the 6-month time scale in the Jayaptna station. The maximum number of observed drought events occurred in May, July and August months under the 3-month category. The years 1966, 1972, 1979, 1987 and 2002 were the most drought-affected years during the investigation period. Spatio-temporal drought assessment 91 Figure 9. 6-month SPI values 1965–2008 time series for Jayapatna station. Figure 10. Spatial distribution of 6-month scaled droughts by the end of June 2002. 92 S. S. Mishra and R. Nagarajan Figure 11. (a) Spatial distribution of 9-month scaled droughts in June in the study area. (b) Spatial distribution of 9-month scaled droughts in May in the study area. Figure 12. 9-month SPI values 1965–2008 time series for Dharamgarh station. 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"Geomatics, Natural Hazards and Risk" – Taylor & Francis

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