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DOI	10.5194/hess-23-2351-2019
	Can global precipitation datasets benefit the estimation of the area to be cropped in irrigated agriculture?
	Kaune A.; Werner M.; López López P.; Rodríguez E.; Karimi P.; De Fraiture C.
发表日期	2019
ISSN	1027-5606
起始页码	2351
结束页码	2368
卷号	23 期号:5
英文摘要	The area to be cropped in irrigation districts needs to be planned according to the available water resources to avoid agricultural production loss. However, the period of record of local hydro-meteorological data may be short, leading to an incomplete understanding of climate variability and consequent uncertainty in estimating surface water availability for irrigation area planning. In this study we assess the benefit of using global precipitation datasets to improve surface water availability estimates. A reference area that can be irrigated is established using a complete record of 30 years of observed river discharge data. Areas are then determined using simulated river discharges from six local hydrological models forced with in situ and global precipitation datasets (CHIRPS and MSWEP), each calibrated independently with a sample of 5 years extracted from the full 30-year record. The utility of establishing the irrigated area based on simulated river discharge simulations is compared against the reference area through a pooled relative utility value (PRUV). Results show that for all river discharge simulations the benefit of choosing the irrigated area based on the 30 years of simulated data is higher compared to using only 5 years of observed discharge data, as the statistical spread of PRUV using 30 years is smaller. Hence, it is more beneficial to calibrate a hydrological model using 5 years of observed river discharge and then to extend it with global precipitation data of 30 years as this weighs up against the model uncertainty of the model calibration. © Author(s) 2019.
语种	英语
scopus关键词	Hydrology; Irrigation; Precipitation (meteorology); Regional planning; Rivers; Agricultural productions; Climate variability; Global precipitation; Hydrological modeling; Hydrological models; Irrigated agriculture; Irrigation districts; Meteorological data; Uncertainty analysis; agricultural production; agrometeorology; calibration; hydrological modeling; irrigation system; precipitation (climatology); river discharge; surface water; water availability; yield response
来源期刊	Hydrology and Earth System Sciences
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/159679
作者单位	Kaune, A., Water Science and Engineering Department, IHE Delft Institute for Water Education, Delft, Netherlands, Wageningen Institute for Environment and Climate Research, Wageningen University and Research, Netherlands; Werner, M., Water Science and Engineering Department, IHE Delft Institute for Water Education, Delft, Netherlands, Division of Inland Water Systems, Deltares, Delft, Netherlands; López López, P., Division of Inland Water Systems, Deltares, Delft, Netherlands, Division Department of Physical Geography, Faculty of Geosciences, Utrecht University, Utrecht, Netherlands; Rodríguez, E., Grupo de Investigación en Ingeniería de Recursos Hídricos (GIREH), Universidad Nacional de Colombia, Bogotá, Colombia; Karimi, P., Water Science and Engineering Department, IHE Delft Institute for Water Education, Delft, Netherlands; De Fraiture, C., Water Science and Engineering Department, IHE Delft Institute for Water Education, Delft, Netherlands, Wageningen Institute for Environment and Climate Research, W...
推荐引用方式 GB/T 7714	Kaune A.,Werner M.,López López P.,et al. Can global precipitation datasets benefit the estimation of the area to be cropped in irrigated agriculture?[J],2019,23(5).
APA	Kaune A.,Werner M.,López López P.,Rodríguez E.,Karimi P.,&De Fraiture C..(2019).Can global precipitation datasets benefit the estimation of the area to be cropped in irrigated agriculture?.Hydrology and Earth System Sciences,23(5).
MLA	Kaune A.,et al."Can global precipitation datasets benefit the estimation of the area to be cropped in irrigated agriculture?".Hydrology and Earth System Sciences 23.5(2019).