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Irrigation in agricultural and urban settings is responsible for nearly 80% of the water use in the Phoenix Metropolitan Area. Over the last three decades, there has been a continuous decrease in cropland area and its water consumption. Meanwhile, urbanization has increased outdoor irrigation to maintain residential areas and parks. Given these trends, irrigation water use (IWU) is subject to large uncertainties which challenge land and water management. In this work, we used a land surface model with an irrigation module to quantify urban and agricultural IWU under the individualized and combined effects of future urban growth and anticipated climate change. A large set of scenario combinations (96 in total) allowed us to bracket plausible pathways of IWU change in the 21st Century. We found that land use change reduced IWU by −4.6% to −0.1% due to savings from crop‐urban conversion, while climate change effects led to increases in IWU by +3.8% to +8.6%. When combined, total IWU changed from +2.5% to +5.8% in the intermediate future (2041–2070) and from −0.5% to 6.8% in the far future (2071–2100). These outcomes suggest that water savings from land use change will likely not be able to compensate for the increasing demand from urban irrigation when considering climate change, under current irrigation practices. Our approach to model the interconnections between land and water under climate change can be used to support sustainable water planning in cities in other arid regions.

 

Channel feedback is essential in frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems. Unfortunately, prior work on multiuser MIMO has shown that the feedback overhead scales linearly with the number of base station (BS) antennas, which is large in massive MIMO systems. To reduce the feedback overhead, we propose an angle-of-departure (AoD) adaptive subspace codebook for channel feedback in FDD massive MIMO systems. Our key insight is to leverage the observation that path AoDs vary more slowly than the path gains. Within the angle coherence time, by utilizing the constant AoD information, the proposed AoDadaptive subspace codebook is able to quantize the channel vector in a more accurate way. From the performance analysis, we show that the feedback overhead of the proposed codebook only scales linearly with a small number of dominant (path) AoDs instead of the large number of BS antennas. Moreover, we compare the proposed quantized feedback technique using the AoD-adaptive subspace codebook with a comparable analog feedback method. Extensive simulations show that the proposed AoD-adaptive subspace codebook achieves good channel feedback quality, while requiring low overhead.