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, 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.
Highlights
Global water consumption has increased at an unprecedented rate during the last century, with many countries turning to groundwater as either an additional or primary source of supply to meet growing agricultural and other sectoral demands (FAO, 2015; Famiglietti, 2014)
A comparison of evaporation estimated by CABLE and two-source energy balance (TSEB) is shown in Fig. S2 in the Supplement
We developed a data-modeling approach to automatically retrieve seasonal irrigation rates for individual center-pivot fields, focusing on fields irrigated by center-pivot infrastructure: consistent with the type of infrastructure that supports the majority of irrigated fields in Saudi Arabia and in other cereal crop production areas worldwide
Summary
Global water consumption has increased at an unprecedented rate during the last century, with many countries turning to groundwater as either an additional or primary source of supply to meet growing agricultural and other sectoral demands (FAO, 2015; Famiglietti, 2014). Global monitoring efforts targeting major aquifer systems around the world have identified strong depletion trends (Wada et al, 2012; Famiglietti, 2014), making the prospect of meeting future water and food security demands even more challenging (Dalin et al, 2017). While these relatively recent estimates of groundwater depletion (Famiglietti et al, 2011; Voss et al, 2013; Rodell et al, 2018) have been obtained through satellite systems such as the Gravity Recovery and Climate Experiment (GRACE; Tapley et al, 2004), their value as a monitoring and management tool is limited due to the coarse observation scale (Alley and Konikow, 2015; Miro and Famiglietti, 2018). In order to provide the granularity of information needed to monitor groundwater abstractions at the field scale (∼ 50 ha), a combination of higher-resolution data and modeling is needed
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