Abstract
Abstract. Effective agricultural water management requires accurate and timely information on the availability and use of irrigation water. However, most existing information on irrigation water use (IWU) lacks the objectivity and spatiotemporal representativeness needed for operational water management and meaningful characterization of land–climate interactions. Although optical remote sensing has been used to map the area affected by irrigation, it does not physically allow for the estimation of the actual amount of irrigation water applied. On the other hand, microwave observations of the moisture content in the top soil layer are directly influenced by agricultural irrigation practices and thus potentially allow for the quantitative estimation of IWU. In this study, we combine surface soil moisture (SM) retrievals from the spaceborne SMAP, AMSR2 and ASCAT microwave sensors with modeled soil moisture from MERRA-2 reanalysis to derive monthly IWU dynamics over the contiguous United States (CONUS) for the period 2013–2016. The methodology is driven by the assumption that the hydrology formulation of the MERRA-2 model does not account for irrigation, while the remotely sensed soil moisture retrievals do contain an irrigation signal. For many CONUS irrigation hot spots, the estimated spatial irrigation patterns show good agreement with a reference data set on irrigated areas. Moreover, in intensively irrigated areas, the temporal dynamics of observed IWU is meaningful with respect to ancillary data on local irrigation practices. State-aggregated mean IWU volumes derived from the combination of SMAP and MERRA-2 soil moisture show a good correlation with statistically reported state-level irrigation water withdrawals (IWW) but systematically underestimate them. We argue that this discrepancy can be mainly attributed to the coarse spatial resolution of the employed satellite soil moisture retrievals, which fails to resolve local irrigation practices. Consequently, higher-resolution soil moisture data are needed to further enhance the accuracy of IWU mapping.
Highlights
The agricultural sector uses over 70 % of global freshwater withdrawals for irrigation (Shiklomanov, 2000; Foley et al, 2011)
Kumar et al (2015) first proposed the idea of inferring irrigation from a positive bias between remotely sensed and modeled soil moisture, induced by seasonal water application during the dry season. This idea is based on two key assumptions: first, the satellite soil moisture products are sensitive to large-scale irrigation; and, second, the model does not account for irrigation, neither explicitly nor implicitly through the assimilation of surface humidity or surface temperature observations, which are affected by irrigation (Wei et al, 2013)
To investigate the potential detectability of irrigation water use (IWU), we investigated the correlation between satellite and modeled soil moisture during the growing season (Fig. 3)
Summary
The agricultural sector uses over 70 % of global freshwater withdrawals for irrigation (Shiklomanov, 2000; Foley et al, 2011). As a result of world population increase and rising living standards, water will be a major constraint for agriculture in the coming decades. Climate change will likely have a profound impact on irrigation demand throughout the world. The projected increase in global mean temperature and changing precipitation patterns are expected to decrease natural water availability in already-water-scarce regions of the world (Vörösmarty et al, 2000; Rockström et al, 2012; Kummu et al, 2016). Drought and flood events are expected to occur both more frequently and severely, which further impairs water availability for agriculture (Allan and Soden, 2008)
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