Center for Agricultural Water Research in China, China Agricultural University, Beijing 100083, China; Department of Biological and Agricultural Engineering, University of California Davis, California 95616, USA.
Center for Agricultural Water Research in China, China Agricultural University, Beijing 100083, China; Planning and Construction section, Wujiang District Water Affairs Bureau, Suzhou 215000, China.
Sci Total Environ. 2020 Jun 10;720:137569. doi: 10.1016/j.scitotenv.2020.137569. Epub 2020 Feb 26.
Globally, agriculture is by far the largest water consuming sector and in areas where water is scarce, the spatial optimization of crop water consumption used to improve irrigation benefits becomes critical for regional water management. The spatial heterogeneity of environmental parameters brings great challenge to spatial optimization. Therefore, cellular automaton (CA), crop suitability (CS), spatial distributed crop water consumption model and optimization model were integrated and applied on the middle reaches of Heihe River basin, northwest of China. The cellular automata based Water Consumption Optimization (CA-WCSO) model is not only a spatial dynamic optimization model for crop water consumption, but also a decision support tool that reflects the interaction between water consumption at field level and management regulations at regional level. Six optimization paths: i) forward progressive (FP), ii) forward interlacing (F-IL), iii) forward interpolation (F-IP), iv) reverse progressive (R-P), v) reverse interlacing (R-IL) and vi) reverse interpolation (R-IP) of crop water consumption for the baseline year and the planning year were applied on the study site. Results for baseline year (2015) demonstrate that the six optimization paths can slightly reduce the water consumption (>1.4%) but significantly improve the irrigation benefits of the region by 20.56%. Using CA-WCSO model, decision makers can modify model's constraints and select appropriate optimization path to get the optimized crop planting patterns and make future regional water allocation plans.
在全球范围内，农业是迄今为止用水量最大的部门，在水资源匮乏的地区，优化作物耗水量的空间分布对于区域水资源管理至关重要。环境参数的空间异质性给空间优化带来了巨大挑战。因此，本研究将元胞自动机（CA）、作物适宜性（CS）、空间分布式作物耗水模型和优化模型集成应用于中国西北黑河流域中游地区。基于元胞自动机的耗水优化（CA-WCSO）模型不仅是一种作物耗水的空间动态优化模型，也是一种决策支持工具，反映了田间耗水与区域管理法规之间的相互作用。针对基准年和规划年，研究应用了六种作物耗水优化路径：i）正向渐进（FP）、ii）正向交错（F-IL）、iii）正向插值（F-IP）、iv）反向渐进（R-P）、v）反向交错（R-IL）和 vi）反向插值（R-IP）。基准年（2015 年）的结果表明，这六种优化路径可以略微减少用水量（>1.4%），但显著提高该地区 20.56%的灌溉效益。决策者可以通过 CA-WCSO 模型修改模型的约束条件并选择适当的优化路径，以获得优化的作物种植模式并制定未来的区域水资源分配计划。
