The evaluation of Effect of Climate Change on Agricultural Drought Using ETDI and SPI Indexes

Background and objectives: Due to absolute dependence of agriculture on water, determine of drought condition in each region is very useful in planning the food sourcing. Unfortunately, there is no same definition about the “drought condition”, so there are some indexes to determine it. Standardized Precipitation Index (SPI) is one the meteorological indexes, which widely used to determine agricultural drought conditions. Evapotranspiration Deficit Index (ETDI) was also designed for this purpose. This index is used to determine agricultural drought conditions in arid and semi-arid region. Although there were most research about other drought indexes such as SPI, but there is few studies in oversea countries about use of ETDI index. Thus in this study tried to determine drought by ETDI and SPI indexes in Neyshabur plain by used of climate change models

Materials and methods: This research was conducted to determine drought condition in Neyshabur plain located at longitude between 58˚ 13’-59˚ 30’ N and latitude between 35˚ 40’-36˚ 39’ E, Iran. Evapotranspiration Deficit Index (ETDI) was developed based on weekly evapotranspiration deficit to determine drought condition in this region. In order to comparison of the ETDI results to other drought indices, we used Standardized Precipitation Index (SPI) as one the most common drought index. The data were collected from Neyshabur meteorological station for irrigated farms (wheat in Soleymani and Faroub farms, barley and corn) and rain-fed farms (rain-fed wheat) during 1992-2011. In order to estimate weather data for each index in the irrigated farms during two future periods (2020-2039 and 2080-2099), HADCM3, ECHOAM and CGCM3 T47 models were used based on A2, B1 and A1B scenarios and the climate model that has been used in rain-fed farm is the HADCM3 based on A2 and B1 scenarios. Root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R2) were used to comparison of the ETDI and SPI results.

Results: Results showed that average ETDI were in initial wet condition for Faroub farm during base period (1992-2011) while it will be in drought condition during future periods (2020-2039 and 2080-2099). ETDI index was in normal condition for Soleymani farm during base period. Average ETDI indexes for these farms were in normal and initial dry condition during 2020-2039 and 2080-2099 periods, respectively. For barley and corn, ETDI indexes were in normal and initial dry condition during base period, respectively. This index was in normal statute for both of them during future periods. The ETDI value for rain-fed wheat was less compared to irrigated wheat during base period, although, this index will be increased during future periods. In most of scenarios, ETDI indexes showed negative values. It means that high drought condition will be happened during future periods due to deficit evapotranspiration. Results according to SPI index revealed that this region was in moderately drought condition and this situation will not change.

Conclusion: High differences were obtained between ETDI and SPI results. Since agricultural drought depends on evapotranspiration deficits, ETDI is better index compared to SPI. The value of RMSE revealed poor adaptation between two indexes during future periods. In addition, ETDI were not correlated with SPI for all the scenarios in all scenarios. These differences are reasonable because SPI index only uses precipitation data and ETDI uses evapotranspiration. According to the results, it seems that SPI cannot be suggested as a good index in agricultural studies.