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Abstract. 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 andfuture availability of fossil groundwater resources, which have been used to drive thisexpansion. Large-scale studies using satellite gravimetric data show a declining trend over thisregion. However, water management agencies require much more detailed information on both thespatial distribution of agricultural fields and their varying levels of water exploitation through time than coarse gravimetric data can provide. Relying on self-reporting from farmoperators or sporadic data collection campaigns to obtain needed information are not feasibleoptions, nor do they allow for retrospective assessments. In this work, a water accountingframework that combines satellite data, meteorological output from weather prediction models, anda 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 amulti-temporal statistical analysis of Landsat 8 data was developed. Next, actual crop evaporationrates 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. TheCommunity Atmosphere Biosphere Land Exchange (CABLE) model was then adapted to use satellite-basedvegetation 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 inferredby 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: asmall-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 abstractionfor 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 acomprehensive 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 informedwater accounting decisions across multiple spatial and temporal scales.

Mapping groundwater abstractions from irrigated agriculture: big data, inverse modeling, and a satellite–model fusion approach