在地表模型 ORCHIDEE v2.2 中验证新的全球灌溉计划

IF 4 3区 地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY Geoscientific Model Development Pub Date : 2024-03-15 DOI:10.5194/gmd-17-2141-2024
Pedro F. Arboleda-Obando, A. Ducharne, Z. Yin, P. Ciais
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引用次数: 0

摘要

摘要灌溉活动对维持粮食生产非常重要,占全球总取水量的 70%。此外,由于蒸散量(ET)的增加和叶面积指数(LAI)的变化,这些活动对水文和气候产生了影响。在本文中,我们在地表模型 ORCHIDEE(ORganising Carbon and Hydrology in Dynamic EcosystEms)中提出了一种新的灌溉方案。)它通过简单的环境限制和使用取决于当地基础设施的分配规则,根据可用淡水限制实际灌溉。我们进行了简单的敏感性分析和参数调整,以设定参数值,并将观测到的灌溉量与报告值进行匹配,同时假设土地上的参数值是统一的。我们的方案与全球范围内的灌溉取水量相匹配,但我们发现在印度、中国和美国(全球灌溉最密集的地区)的一些地区,灌溉量被低估了。在所有灌溉地区,该方案都减少了蒸散发的负偏差。它还加剧了叶面积指数(LAI)的正偏差,但灌溉非常密集的地区除外,在这些地区,灌溉减少了叶面积指数的负偏差。蒸散发的增加降低了河流的排泄值,在某些情况下降幅很大,但这并不一定能更好地反映排泄动态。然而,灌溉对模拟的总蓄水量异常值(TWSAs)及其变化趋势的影响并不大。部分原因可能是由于没有使用不可再生的地下水,而将其包括在内可能会通过增加供水量来提高干旱和半干旱地区的灌溉估算值。灌溉偏差与地貌描述因子的相关性表明,纳入灌溉水稻和水坝管理也可以改善灌溉估算。尽管存在这种复杂性,但我们的研究结果表明,新的灌溉方案有助于模拟灌溉区可接受的地表条件和通量,这对于探索气候、水资源和灌溉活动的共同演变非常重要。
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Validation of a new global irrigation scheme in the land surface model ORCHIDEE v2.2
Abstract. Irrigation activities are important for sustaining food production and account for 70 % of total global water withdrawals. In addition, due to increased evapotranspiration (ET) and changes in the leaf area index (LAI), these activities have an impact on hydrology and climate. In this paper, we present a new irrigation scheme within the land surface model ORCHIDEE (ORganising Carbon and Hydrology in Dynamic EcosystEms)). It restrains actual irrigation according to available freshwater by including a simple environmental limit and using allocation rules that depend on local infrastructure. We perform a simple sensitivity analysis and parameter tuning to set the parameter values and match the observed irrigation amounts against reported values, assuming uniform parameter values over land. Our scheme matches irrigation withdrawals amounts at global scale, but we identify some areas in India, China, and the USA (some of the most intensively irrigated regions worldwide), where irrigation is underestimated. In all irrigated areas, the scheme reduces the negative bias of ET. It also exacerbates the positive bias of the leaf area index (LAI), except for the very intensively irrigated areas, where irrigation reduces a negative LAI bias. The increase in the ET decreases river discharge values, in some cases significantly, although this does not necessarily lead to a better representation of discharge dynamics. Irrigation, however, does not have a large impact on the simulated total water storage anomalies (TWSAs) and its trends. This may be partly explained by the absence of nonrenewable groundwater use, and its inclusion could increase irrigation estimates in arid and semiarid regions by increasing the supply. Correlation of irrigation biases with landscape descriptors suggests that the inclusion of irrigated rice and dam management could improve the irrigation estimates as well. Regardless of this complexity, our results show that the new irrigation scheme helps simulate acceptable land surface conditions and fluxes in irrigated areas, which is important to explore the joint evolution of climate, water resources, and irrigation activities.
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来源期刊
Geoscientific Model Development
Geoscientific Model Development GEOSCIENCES, MULTIDISCIPLINARY-
CiteScore
8.60
自引率
9.80%
发文量
352
审稿时长
6-12 weeks
期刊介绍: Geoscientific Model Development (GMD) is an international scientific journal dedicated to the publication and public discussion of the description, development, and evaluation of numerical models of the Earth system and its components. The following manuscript types can be considered for peer-reviewed publication: * geoscientific model descriptions, from statistical models to box models to GCMs; * development and technical papers, describing developments such as new parameterizations or technical aspects of running models such as the reproducibility of results; * new methods for assessment of models, including work on developing new metrics for assessing model performance and novel ways of comparing model results with observational data; * papers describing new standard experiments for assessing model performance or novel ways of comparing model results with observational data; * model experiment descriptions, including experimental details and project protocols; * full evaluations of previously published models.
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