Drought monitoring is an essential component of drought risk management. It is normally performed using various drought indices that are effectively continuous functions of rainfall and other hydrometeorological variables. A number of drought indices have been introduced and applied in different countries to date. This paper compares the performance of seven indices for drought monitoring in the Tehran province of Iran. The indices used include deciles index (DI), percent of normal (PN), standard precipitation index (SPI), China-Z index (CZI), modified CZI (MCZI), Z-Score and effective drought index (EDI). The comparison of indices is based on drought cases and classes that were detected in the province over the 32 years of data, as well as over the latest 1998-2001 drought spell. The results show that SPI, CZI and Z-Score perform similarly with regard to drought identification and respond slowly to drought onset. DI appears to be very responsive to rainfall events of a particular year, but it has inconsistent spatial and temporal variation. The SPI and EDI were found to be able to detect the onset of drought, its spatial and temporal variation consistently, and it may be recommended for operational drought monitoring in the Province. However, the EDI was found to be more responsive to the emerging drought and performed better.

Drought is common to Iran and the agricultural sector is its main victim. Reducing demand for irrigation water is considered the best management practice to alleviate losses. An equitable water reduction approach has been traditionally applied in the management of irrigation systems. This research work examines and compares this approach with that based on the optimization method to manage agricultural water demand during drought to minimize damage. To evaluate these methodologies, the 1999 drought in the Zayandeh Rud irrigation system was selected and the required models developed. In the optimization method, crop growth stages and their sensitivity to water stress at different stages are embedded in the calculations. The results show that the optimization method resulted in 42% more income for the agricultural sector using the same amount of water allocated in the 1999 drought. This difference emphasizes the importance of water allocation with respect to growth stages rather than simply cutting allocations on an equitable basis to combat water scarcity. However, managing the system using the optimization method is more complex and requires a new framework and planning to make it operational.

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This study aims to investigate the effect of climate change on the probability of drought occurrence in central Iran. To this end, a new drought index called Multivariate Standardized Drought Index (MSDI) was developed, which is composed of the Standardized Precipitation Evapotranspiration Index (SPEI) and the Standardized Soil Moisture Index (SSI). The required data included precipitation, temperature (from CRU TS), and soil moisture (from the ESA CCA SM product) on a monthly time scale for the 1980-2016 period. Moreover, future climate data were downloaded from CMIP6 models under the latest SSPs-RCPs emission scenarios (SSP1-2.6 and SSP5-8.5) for the 2020-2056 period. Based on the NRMSE, Sn, and NS evaluation criteria, the Galambos and Clayton functions were selected to derive copula-based joint distribution functions in both periods. The results showed that more severe droughts and longer will occur in the future compared to the historical period and in particular under the SSP5-8.5 scenario. From the derived joint return period, a drought event with defined severity or duration will happen in a shorter return period as compared with the historical period. In other words, joint return period indicated a higher probability of drought occurrence in the future period. Moreover, the joint return period analysis revealed that the return period of mild droughts will remain the same, while it decresed over extreme droughts in the future.

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The increasing severity of drought worldwide and recent environmental efforts to restore wetlands have put more pressure on management of water resources.The Lake Urmia Basin is an excellent example of this; the lake has been shrinking for the last two decades, agricultural water demand is growing and frequent drought has affected the basin negatively. The present study is part of the Lake Urmia drought risk management (DRM) that suggests a drought warning system with measures to reduce agricultural water consumption and allocate a reasonable amount of water for the lake at each drought level. The approaches to reduce agricultural demand include reductions in cropped area and deficit irrigation; these have been accepted by stakeholders and are calculated by means of optimization models. Strategies to allocate water to Lake Urmia include full allocation of 3100 MCM yr−1 (the new water allocation of the lake) or up to 65% partial allocation of this amount in accordance with the severity of drought. These policies were simulated for the agricultural and environmental sectors throughout the historical and generated time series for streamflow. The results of simulation show that the new water allocations put increasing pressure on the agricultural sector and the basin will remain under water scarcity for more than 50% of the time after applying the DRM. It is a unique way to save the lake, which will otherwise dry up within a decade. The methodology can be recommended to model components of similar drought plans which will benefit from its applicability and objectivity. Copyright © 2017 John Wiley & Sons, Ltd.

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