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首页> 外文期刊>The Science of the Total Environment >Assessing irrigated agriculture's surface water and groundwater consumption by combining satellite remote sensing and hydrologic modelling
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Assessing irrigated agriculture's surface water and groundwater consumption by combining satellite remote sensing and hydrologic modelling

机译:通过结合卫星遥感和水文模型评估灌溉农业的地表水和地下水消耗

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摘要

Globally, irrigation accounts for more than two thirds of freshwater demand. Recent regional and global assessments indicate that groundwater extraction (GWE) for irrigation has increased more rapidly than surface water extraction (SWE), potentially resulting in groundwater depletion. Irrigated agriculture in semi-arid and arid regions is usually from a combination of stored surface water and groundwater. This paper assesses the usefulness of remotely-sensed (RS) derived information on both irrigation dynamics and rates of actual evapotranspiration which are both input to a river-reach water balance model in order to quantify irrigation water use and water provenance (either surface water or groundwater). The assessment is implemented for the water-years 2004/ 05-2010/11 in five reaches of the Murray-Darling Basin (Australia); a heavily regulated basin with large irrigated areas and periodic droughts and floods. Irrigated area and water use are identified each water-year (from July to June) through a Random Forest model which uses RS vegetation phenology and actual evapotranspiration as predicting variables. Both irrigated areas and actual evapotranspiration from irrigated areas were compared against published estimates of irrigated areas and total water extraction (SWE + GWE).The river-reach model determines the irrigated area that can be serviced with stored surface water (SWE), and the remainder area (as determined by the Random Forest Model) is assumed to be supplemented by groundwater (GWE). Model results were evaluated against observed SWE and GWE The modelled SWE generally captures the observed inter-annual patterns and to some extent the magnitudes, with Pearson's correlation coefficients > 0.8 and normalised root-mean-square-error < 30%. In terms of magnitude, the results were as accurate as or better than those of more traditional (i.e., using areas that fluctuate based on water resource availability and prescribed crop factors) irrigation modelling. The RS irrigated areas and actual evapotranspiration can be used to: (ⅰ) understand irrigation dynamics, (ⅱ) constrain irrigation models in data scarce regions, as well as (ⅲ) pinpointing areas that require better ground-based monitoring.
机译:在全球范围内,灌溉占淡水需求的三分之二以上。最近的区域和全球评估表明,用于灌溉的地下水提取(GWE)的增长速度比地表水提取(SWE)的增长快,有可能导致地下水枯竭。半干旱和干旱地区的灌溉农业通常来自地表水和地下水的结合。本文评估了遥感(RS)得出的有关灌溉动态和实际蒸散速率的信息的有用性,这些信息都输入到河水平衡模型中,以便量化灌溉用水和水源(地表水或水源)。地下水)。评估是在墨累达令盆地(澳大利亚)的五个河段进行的2004 / 05-2010 / 11水年评估;受严格管制的流域,有大面积的灌溉区,并定期发生干旱和洪水。通过随机森林模型确定每个水年(7月至6月)的灌溉面积和用水量,该模型使用RS植被物候和实际蒸散量作为预测变量。将灌溉面积和灌溉面积的实际蒸散量与已公布的灌溉面积和总取水量估算值(SWE + GWE)进行了比较。河道模型确定了可以利用存储的地表水(SWE)服务的灌溉面积。剩余面积(由随机森林模型确定)假定由地下水(GWE)补充。针对观察到的SWE和GWE对模型结果进行了评估。建模的SWE通常捕获观察到的年际模式和一定程度的幅度,Pearson相关系数> 0.8,归一化均方根误差<30%。就规模而言,灌溉模型的结果与更传统的灌溉模型一样准确或更好(即,使用的区域根据水资源的可获得性和规定的作物因素而波动)。 RS灌溉区和实际蒸散量可用于:(ⅰ)了解灌溉动态,(ⅱ)限制数据稀缺地区的灌溉模型,以及(ⅲ)指出需要更好的地面监控的区域。

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