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Projection of irrigation water demand based on the simulation of synthetic crop coefficients and climate change

机译：基于合成作物系数的模拟和气候变化的灌溉水需求投影

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In the context of major changes (climate, demography, economy, etc.), the southern Mediterranean area faces serious challenges with intrinsically low, irregular, and continuously decreasing water resources. In some regions, the proper growth both in terms of cropping density and surface area of irrigated areas is so significant that it needs to be included in future scenarios. A method for estimating the future evolution of irrigation water requirements is proposed and tested in the Tensift watershed, Morocco. Monthly synthetic crop coefficients ( K c ) of the different irrigated areas were obtained from a time series of remote sensing observations. An empirical model using the synthetic K c and rainfall was developed and fitted to the actual data for each of the different irrigated areas within the study area. The model consists of a system of equations that takes into account the monthly trend of K c , the impact of yearly rainfall, and the saturation of K c due to the presence of tree crops. The impact of precipitation change is included in the K c estimate and the water budget. The anthropogenic impact is included in the equations for K c . The impact of temperature change is only included in the reference evapotranspiration, with no impact on the K c cycle. The model appears to be reliable with an average r 2 of 0.69 for the observation period (2000–2016). However, different subsampling tests of the number of calibration years showed that the performance is degraded when the size of the training dataset is reduced. When subsampling the training dataset to one-third of the 16 available years, r 2 was reduced to 0.45. This score has been interpreted as the level of reliability that could be expected for two time periods after the full training years (thus near to 2050). The model has been used to reinterpret a local water management plan and to incorporate two downscaled climate change scenarios (RCP4.5 and RCP8.5). The examination of irrigation water requirements until 2050 revealed that the difference between the two climate scenarios was very small ( ?2 ?%), while the two agricultural scenarios were strongly contrasted both spatially and in terms of their impact on water resources. The approach is generic and can be refined by incorporating irrigation efficiencies.
机译：在重大变化（气候，人口，经济等）的背景下，南地中海地区面临严重挑战，内在低，不规则，不断降低的水资源。在一些地区，在灌溉区域的种植密度和表面积方面的适当增长是如此重要的，即它需要包括在未来的情况下。提出了一种估算灌溉水需求的未来演化的方法，并在摩洛哥张力分水岭中进行了测试。不同灌溉区域的每月合成作物系数（k c）是从遥感观察的时间序列获得的。使用合成K C和降雨的实证模型开发并安装了研究区域内各种灌溉区域的实际数据。该模型由一个方程式组成，该系统考虑了每月趋势的K C，年降雨量的影响，并且由于树木作物的存在而对K C的饱和度。降水变化的影响包括在K C估计和水预算中。人为的影响包括在K C的等式中。温度变化的影响仅包括在参考蒸散中，对K C循环没有影响。该模型似乎可靠，平均R 2为0.69，用于观察期（2000-2016）。然而，校准年数的不同的数据采样测试表明，当训练数据集的大小降低时，性能降低。将培训数据集将训练数据集其分布为16个可用年的三分之一时，R 2降至0.45。该得分被解释为可靠性，在完整培训年后可以预期的两次（因此靠近2050）。该模型已被用于重新诠释本地水管理计划，并纳入两个较低的气候变化情景（RCP4.5和RCP8.5）。对灌溉用水要求的检查，直到2050年透露，两种气候情景之间的差异非常小（？2？％），而两种农业情景在空间上以及对水资源的影响方面非常鲜明对比。该方法是通用的，可以通过纳入灌溉效率来改进。

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