Meteorological drought is a natural hazard that can occur under all climatic regimes. Monitoring the drought is a vital and important part of predicting and analyzing drought impacts. Because no single index can represent all facets of meteorological drought, we took a multi-index approach for drought monitoring in this study. We assessed the ability of eight precipitation-based drought indices (SPI (Standardized Precipitation Index), PNI (Percent of Normal Index), DI (Deciles index), EDI (Effective drought index), CZI (China-Z index), MCZI (Modified CZI), RAI (Rainfall Anomaly Index), and ZSI (Z-score Index)) calculated from the station-observed precipitation data and the AgMERRA gridded precipitation data to assess historical drought events during the period 1987-2010 for the Kashafrood Basin of Iran. We also presented the Degree of Dryness Index (DDI) for comparing the intensities of different drought categories in each year of the study period (1987-2010). In general, the correlations among drought indices calculated from the AgMERRA precipitation data were higher than those derived from the station-observed precipitation data. All indices indicated the most severe droughts for the study period occurred in 2001 and 2008. Regardless of data input source, SPI, PNI, and DI were highly inter-correlated (R 2 =0.99). Furthermore, the higher correlations (R 2 =0.99) were also found between CZI and MCZI, and between ZSI and RAI. All indices were able to track drought intensity, but EDI and RAI showed higher DDI values compared with the other indices. Based on the strong correlation among drought indices derived from the AgMERRA precipitation data and from the station-observed precipitation data, we suggest that the AgMERRA precipitation data can be accepted to fill the gaps existed in the station-observed precipitation data in future studies in Iran. In addition, if tested by station-observed precipitation data, the AgMERRA precipitation data may be used for the data-lacking areas.

A b s t r a c t. Quantifying soil quality is important for assessing soil management practices effects on spatial and temporal variability of soil quality at the field scale. We studied the possibility of defining a simple and practical fuzzy soil quality index based on biological, chemical and physical indicators for assessing quality variations of soil irrigated with well water and treated urban wastewater during two experimental years. In this study 6 properties considered as minimum data set were selected out of 18 soil properties as total data set using the principal component analysis. Treated urban wastewater use had greater impact on biological and chemical quality. The results showed that the studied minimum data set could be a suitable representative of total data set. Significant correlation between the fuzzy soil quality index and crop yield (R 2 = 0.72) indicated the index had high biological significance for studied area. Fuzzy soil quality index approach (R 2 = 0.99) could be effectively utilized as a tool leading to better understanding soil quality changes. This is a first trial of creation of a universal index of soil quality undertaken.K e y w o r d s: fuzzy membership functions, principal component analysis, soil quality, treated urban wastewater

Climate change is expected to lead to declining crop yields in semi‐arid regions due to higher temperatures and more severe droughts, which calls for adaptations in crop management. We used the WOFOST and AquaCrop crop simulation models to examine the response of crop yield in winter wheat and maize to a set of climate change scenarios up to 2040 in the semi‐arid climate of Mashhad in north‐east Iran. Modelled climate change from six AOGCMs including GFCM21, HADCM3, INCM3, IPCM4, MPEH5 and NCCCSM under IPCC SRES A2 and B1 emission scenarios was used. The crop models were calibrated and validated against 7 years of observed crop yield data, confirming that the models adequately simulated yields of wheat and maize in the study area. The bootstrap method was used to estimate the uncertainty of crop yield projections. The results showed a mean yield decrease of 10–34% for winter wheat and 8–18% for maize, depending on the crop model and climate change scenario. The period of flowering to maturity of winter wheat and maize would be shortened on average by 9 and 5 days, respectively. Changes in crop management were considered for adaptation to climate change. Simulation results indicated that early sowing of winter wheat and late sowing of maize enhanced yield and water productivity across all climate change scenarios and that late‐maturity cultivars of winter wheat and early‐maturity hybrids of maize generally have higher productivity than standard cultivars. Increasing heat tolerance of the crops and changing irrigation management of winter wheat were also found to be beneficial adaptation options. © 2019 John Wiley & Sons, Ltd. © 2019 John Wiley & Sons, Ltd.

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