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Irrigation is not well represented in land surface, hydrological, and climate models. One way to account for irrigation is by assimilating satellite soil moisture data that contains irrigation signal with land surface models. In this study, the irrigation detection ability of SMAP enhanced 9km and SMAP-Sentinel1 3km and 1km soil moisture products are evaluated using the first moment (mean) and the second moment (variability) of soil moisture data. The SMAP enhanced 9km soil moisture product lacks irrigation signals in an irrigated plain south of Urmia Lake, whereas SMAP-Sentinel1 products record irrigation signal in soil moisture variability. Despite observing higher variability over irrigated areas, there are only small and inconsistent wet biases observed over irrigated pixels relative to nearby non-irrigated pixels during the irrigation season. This is partly attributable to the climatology vegetation water content used in the SMAP-Sentinel 1 soil moisture retrieval algorithm that is not accounting for crop rotation and land management. Thus, in the second part of this study, we updated the retrieval algorithm to use dynamic vegetation water content. The update increased vegetation water content up to 1 kg/m 2, which corresponds with a 0.05 cm 3 /cm 3 increase in soil moisture during irrigation season. The update does not notably change soil moisture retrievals off season. This study shows that irrigation signals are present in both the first and second moment of soil moisture time series, and employing dynamic vegetation water content in the SMAP-Sentinel 1 algorithm can enhance the irrigation signal over agricultural regions.

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Agricultural production is projected to require a 70% expansion by 2050 as a result of population growth, climate change, and dietary shifts toward water-intensive products associated with increasing incomes (Tilman & Clark., 2015). Food production is mainly sustained by irrigation (Jägermeyr et al., 2015), which is by far the largest consumer of freshwater resources globally (Döll & Siebert, 2002). However, the planetary limit for freshwater withdrawal is quickly approaching (or already exceeded) in many parts of the world (Steffen et al., 2015). Moreover, it is expected that 25%-40% of water for irrigation agriculture will be reallocated to higher productivity sectors in the near future (World Bank, 2022). Thus, authorities seek to limit agricultural water consumption globally. However, a key requirement to enforce any regulation is monitoring the water withdrawn by farmers (Foster et al., 2020). Monitoring groundwater storage changes through GRACE (Gravity Recovery and Climate Experiment) satellite observations has revealed significant depletion in major aquifers across the globe (Frappart

The adverse effects of climate change will impact all regions around the world, especially Middle Eastern countries, which have prioritized economic growth over environmental protection. However, these impacts are not evenly distributed spatially, and some locations, namely climate change hotspots, will suffer more from climate change consequences. In this study, we identified climate change hotspots over Iran—a developing country in the Middle East that is facing dire economic situations—in order to suggest pragmatic solutions for vulnerable regions. We used a statistical index as a representative of the differences in climatic parameters for the RCP8.5 and RCP4.5 forcing pathways between historical data (1975–2005), near-future data (2030–2060) and far-future data (2070–2100). More specifically, we used downscaled high-resolution (0.25°) meteorological data from five GCMs of the CMIP5 database to calculate the statistical metric. Results indicate that for the far-future period and RCP4.5, regions stretching from the northwest to southeast of Iran, namely the Hotspot Belt, are the most vulnerable areas, while, for RCP8.5, almost the whole country is vulnerable to climate change. The highest and lowest differences in temperature for RCP8.5 in 2070–2100 are observed during summer in the northwestern and central parts and during winter in the northern and northeastern parts. Moreover, the maximum increase and decrease in precipitation are identified over the western parts of Iran during fall and winter, respectively. Overall, western provinces (e.g., Lorestan and Kermanshah), which are mostly reliant on rainfed agriculture and other climate-dependent sectors, will face the highest change in climate in the future. As these regions have less adaptive capacity, they should be prioritized through upstream policy change and special budget allocation from the government to increase their resiliency against climate change.