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Drought is a major global environmental challenge. It affects the livelihoods of many people, mainly in developing countries. Iran is one of the most affected and vulnerable countries in the Middle East to drought. In this paper, we present a microlevel analysis by employing the Tobit multiplicative heteroscedasticity regression to examine the effects of drought on small farm household education expenditures in rural Iran. We collected primary data from a sample of 300 smallholders in Marvdasht County in Fars Province of Iran. The results revealed a negative relationship between the farm income and education expenditures. This means that the farm households in rural Iran that were affected by the drought increased their expenditures on education for their children. The analysis of income elasticity indicated that a one percent decrease in farm income led to a 0.86% increase in education expenditures, which indicated that the education expenditures were necessary. Furthermore, we observed that in drought-affected families, girls were more likely to be pulled out of university education than were boys; however, for school education, there were no significant differences between the boys and girls. Our findings revealed the need to provide improved facilities and further finances for education expenditures, especially for female university students, and to formulate environmental management policies that include the provision of education facilities by the government of Iran in drought-affected villages. Our findings also shed light on the presence of positive externalities and the important role of education in helping rural households better cope with the negative repercussions of drought on their livelihoods. • This paper presents a micro-level analysis of the effects of drought on small farm household education expenditures in rural Iran. • Importantly, households suffering from severe drought increase their per capita education expenditures and appear to be spending more on their children education. • The farm income elasticity of education expenditures is inelastic. • Underscores the policy importance of subsidized rural education and employment generation to sustain and boost rural livelihoods in the wake of drought risk in Iran. [ABSTRACT FROM AUTHOR]

In this paper, we present a new methodology for improving the results of seasonal drought forecasting by developing a Bayesian Maximum Entropy-based fusion (BMEF) model. The BMEF model combines the forecasts done by four individual (single-source) data-driven models to achieve better outcomes. Regional drought indices of Effective Drought Index (EDI) and Multiple Standard Precipitation Index (MSPI) are forecasted using the individual forecasting models of Artificial Neural Network (ANN), Adaptive Neuro Fuzzy Inference System (ANFIS), Support Vector Regression (SVR), and M5tree. The outputs of the individual models with the best performances are selected to be fused using the BMEF model and the results are analyzed and compared. The effect of different large-scale climate signals on rainfall and drought forecasting is analyzed and the most effective climate variables are selected as predictors in the forecasting models. Next, the uncertainty analysis on the results of the individual models as well as those of the BMEF model is carried out by deriving the probability mass functions of the drought indices using a resampling technique and Monte Carlo analysis. Finally, the results of the uncertainty analysis are evaluated to compare the performance of individual models and the BME-based fusion model in decreasing the uncertainty of seasonal drought forecasting. The performance of the proposed methodology is evaluated by using it to forecast seasonal drought conditions in the southwest of Iran. Based on the results of the uncertainty analysis, the BMEF model provides more reliable forecasts particularly for severe drought events than the individual models. It is also inferred that adding the SST to the predictors, decreases the uncertainty of drought forecasts. • A new methodology is proposed to improve seasonal drought forecasting. • A BME-based fusion (BMEF) model is developed for combining the individual forecasts. • Drought indices of EDI and MSPI are forecasted under uncertainty. • Favorable effect of sea surface temperature (SST) on drought forecasting is shown. • The BMEF model reduces the uncertainty of the drought forecasts. [ABSTRACT FROM AUTHOR]

Drought is a complex natural hazard. It occurs due to a prolonged period of deficient in rainfall amount in a certain region. Unlike other natural hazards, drought hazard has a recurrent occurrence. Therefore, comprehensive drought monitoring is essential for regional climate control and water management authorities. In this paper, we have proposed a new drought indicator: the Seasonally Combinative Regional Drought Indicator (SCRDI). The SCRDI integrates Bayesian networking theory with Standardized Precipitation Temperature Index (SPTI) at varying gauge stations in various month/seasons. Application of SCRDI is based on five gauging stations of Northern Area of Pakistan. We have found that the proposed indicator accounts the effect of climate variation within a specified territory, accurately characterizes drought by capturing seasonal dependencies in geospatial variation scenario, and reduces the large/complex data for future drought monitoring. In summary, the proposed indicator can be used for comprehensive characterization and assessment of drought at a certain region. • Comprehensive drought monitoring. • Save time and resources in the forecasting of future drought. • Systematic way to combine SDI by using a Bayesian network approach. • Easy in the calculation, save time and resources. [ABSTRACT FROM AUTHOR]