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

Abstract

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 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 multi-temporal 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 inputs, 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 5,000 individual fields across an area exceeding 2,500 km2. Total groundwater abstraction for the year 2015 in Al Jawf were 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.

Detection and Quantification of Irrigation Water Amounts at 500 m Using Sentinel-1 Surface Soil Moisture

“…Indeed, the F-score increased from 0.34 (0.53) to 0.75 from considering the IP NDVI (IP default ) to IP actual . In line with López Valencia et al (2020) [69], we conclude that a correct definition of the start and end of season is critical to higher detection accuracy.…”

Satellite‐Based Monitoring of Irrigation Water Use: Assessing Measurement Errors and Their Implications for Agricultural Water Management Policy

“…Variations on this approach involve calculating the difference between estimated ET fluxes for irrigated locations with those for neighboring rainfed pixels (Romaguera et al, 2014;Van Eekelen et al, 2015;Vogels et al, 2020) or, alternatively, with ET fluxes simulated by land surface hydrology models. The latter typically do not include representations of irrigation practices, meaning that the additional ET "observed" in reality through satellite remote sensing can be attributed to irrigation water consumption (Anderson et al, 2015;Droogers et al, 2010;Lopez Valencia et al, 2020). In a subset of studies (n = 11/20), estimates of consumptive irrigation water use are converted to applied or abstracted water by applying efficiency adjustment factors to account for nonconsumptive losses such as deep percolation or runoff.…”