气候变化领域集成服务门户(CCPortal): Mapping groundwater abstractions from irrigated agriculture: Big data; inverse modeling; and a satellite-model fusion approach

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DOI	10.5194/hess-24-5251-2020
	Mapping groundwater abstractions from irrigated agriculture: Big data; inverse modeling; and a satellite-model fusion approach
	Valencia O.M.L.; Johansen K.; Solorio B.J.L.A.; Li T.; Houborg R.; Malbeteau Y.; Almashharawi S.; Altaf M.U.; Fallatah E.M.; Dasari H.P.; Hoteit I.; McCabe M.F.
发表日期	2020
ISSN	1027-5606
起始页码	5251
结束页码	5277
卷号	24 期号:11
英文摘要	The agricultural sector in Saudi Arabia has witnessed rapid growth in both production and area under cultivation over the last few decades. This has prompted some concern over the state and future availability of fossil groundwater resources, which have been used to drive this expansion. Large-scale studies using satellite gravimetric data show a declining trend over this region. However, water management agencies require much more detailed information on both the spatial distribution of agricultural fields and their varying levels of water exploitation through time than coarse gravimetric data can provide. Relying on self-reporting from farm operators or sporadic data collection campaigns to obtain needed information are not feasible options, nor do they allow for retrospective assessments. In this work, a water accounting framework that combines satellite data, meteorological output from weather prediction models, and a modified land surface hydrology model was developed to provide information on both irrigated crop water use and groundwater abstraction rates. Results from the local scale, comprising several thousand individual center-pivot fields, were then used to quantify the regional-scale response. To do this, a semi-automated approach for the delineation of center-pivot fields using a multioral statistical analysis of Landsat 8 data was developed. Next, actual crop evaporation rates were estimated using a two-source energy balance (TSEB) model driven by leaf area index, land surface temperature, and albedo, all of which were derived from Landsat 8. The Community Atmosphere Biosphere Land Exchange (CABLE) model was then adapted to use satellite-based vegetation and related surface variables and forced with a 3 km reanalysis dataset from the Weather Research and Forecasting (WRF) model. Groundwater abstraction rates were then inferred by estimating the irrigation supplied to each individual center pivot, which was determined via an optimization approach that considered CABLE-based estimates of evaporation and TSEB-based satellite estimates. The framework was applied over two study regions in Saudi Arabia: a small-scale experimental facility of around 40 center pivots in Al Kharj that was used for an initial evaluation and a much larger agricultural region in Al Jawf province comprising more than 5000 individual fields across an area exceeding 2500 km2. Total groundwater abstraction for the year 2015 in Al Jawf was estimated at approximately 5.5 billion cubic meters, far exceeding any recharge to the groundwater system and further highlighting the need for a comprehensive water management strategy. Overall, this novel data-model fusion approach facilitates the compilation of national-scale groundwater abstractions while also detailing field-scale information that allows both farmers and water management agencies to make informed water accounting decisions across multiple spatial and temporal scales. © 2020 Author(s).
语种	英语
scopus关键词	Agricultural robots; Aluminum alloys; Big data; Cables; Crops; Cultivation; Digital storage; Evaporation; Groundwater resources; Inverse problems; Land surface temperature; Predictive analytics; Satellites; Surface measurement; Water management; Weather forecasting; Experimental facilities; Groundwater abstraction; Land surface hydrology; Retrospective assessments; Spatial and temporal scale; Water management strategies; Weather prediction model; Weather research and forecasting models; Recharging (underground waters); exploitation; groundwater abstraction; inverse analysis; irrigation system; mapping; satellite data; spatial resolution
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/159265
作者单位	Valencia, O.M.L., Water Desalination and Reuse Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia; Johansen, K., Water Desalination and Reuse Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia; Solorio, B.J.L.A., Water Desalination and Reuse Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia; Li, T., Water Desalination and Reuse Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia; Houborg, R., Planet Analytics Engineering, San Francisco, CA 94107, United States; Malbeteau, Y., Water Desalination and Reuse Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia; Almashharawi, S., Water Desalination and Reuse Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia; Altaf, M.U., Water Desalination and Reuse Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia; Fallatah, E.M., National Center for Water Research and...
推荐引用方式 GB/T 7714	Valencia O.M.L.,Johansen K.,Solorio B.J.L.A.,et al. Mapping groundwater abstractions from irrigated agriculture: Big data; inverse modeling; and a satellite-model fusion approach[J],2020,24(11).
APA	Valencia O.M.L..,Johansen K..,Solorio B.J.L.A..,Li T..,Houborg R..,...&McCabe M.F..(2020).Mapping groundwater abstractions from irrigated agriculture: Big data; inverse modeling; and a satellite-model fusion approach.Hydrology and Earth System Sciences,24(11).
MLA	Valencia O.M.L.,et al."Mapping groundwater abstractions from irrigated agriculture: Big data; inverse modeling; and a satellite-model fusion approach".Hydrology and Earth System Sciences 24.11(2020).