Assessing landscape scale heterogeneity in irrigation water use with remote sensing andin situmonitoring

Abstract

Understanding how irrigation is used across agricultural landscapes is essential to support efforts to grow more food while reducing pressures on limited freshwater resources. However, to date, few studies have analyzed the underlying spatial and temporal variability in farmers' individual water use decisions at a landscape scale. We compare estimates of irrigation water requirements derived using state-of-the-art remote sensing models with metered abstraction records for 1400 fields over a 13 year period in the US state of Nebraska, one of the world's most intensively irrigated agricultural regions. We show that farmers' observed water use decisions often diverge significantly from biophysical estimates of crop irrigation requirements. In particular, our findings are consistent with widespread use of water conservation practices by farmers in drought years as an adaptive response to rising irrigation costs and regulatory water supply constraints in these years. We also demonstrate that, in any individual year, farmers observed water use exhibits large field-to-field variability, which cannot be explained fully by differences in weather, soil type, crop choice, or technology. Our results highlight the value of using both in situ monitoring and remote sensing to evaluate farmers' individual water use behavior and understand likely responses to future changes in climate or water policy. Moreover, our findings also demonstrate potential challenges for current efforts in developed and developing countries to apply model-based approaches for field-level water use accounting and enforcement of irrigation water rights.

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

“…Given these findings, we argue that while a fixed technological efficiency may be able to predict reasonably water use average over multiple years and fields, such assumptions are likely to mask important spatial and temporal heterogeneity in irrigation water application or abstraction rates at plot scales. Indeed, studies in our sample show that accuracy of water use estimates in general improves when estimates are averaged over a large number of plots and years (Foster et al, 2019), suggesting that, in some cases, errors in satellite‐based estimates may cancel out at larger spatial and temporal scales. However, as highlighted in section 2.3, notable errors can still exist even after such averaging is conducted.…”

“…Crop coefficients are then inputted, along with meteorological data, to soil water balance models to estimate rates of irrigation given assumptions about the level of soil moisture depletion at which irrigation will be triggered and expected application and conveyance efficiencies of water use. A key difference between crop coefficient and thermal‐infrared or soil moisture‐based models is that reflectance‐based crop coefficient models are most commonly used to provide estimates of crop irrigation requirements rather than actual abstraction rates (Abuzar et al, 2017; Campos et al, 2017; Foster et al, 2019; Gonçalves et al, 2020; Santos et al, 2010; Segovia‐Cardozo et al, 2019; Vuolo et al, 2015). This is because reflectance‐based crop coefficients capture reductions in crop ET caused by suboptimal crop development over the growing season but do not provide direct information about additional reductions in crop ET as a result of water stress limiting plant transpiration.…”

“…Similar patterns are also observed when considering the number of years of in situ data used for validation, which ranges from 1–17 years (median of 4 years) for studies that performed some comparison with observation data. Validation of satellite‐derived estimates is commonly only conducted for a limited range of hydrometeorological conditions, which may limit insights about model performance given that irrigation efficiency (Foster et al, 2019) and meteorological data uncertainties (Gibson et al, 2017; Mourtzinis et al, 2017) have been shown to be strongly dependent on hydroclimatic conditions. Studies by Brocca et al (2018) and Abolafia‐Rosenzweig et al (2019) provide notable exceptions to the short temporal length of most validation exercises.…”

Simulation of Evapotranspiration at a 3-Minute Time Interval Based on Remote Sensing Data and SEBAL Model

“…Remote sensing evapotranspiration model is of great significance for improving irrigation water use efficiency and simulating crop yield [14,15]. At present, remote sensing evapotranspiration model is mainly combined with meteorological data to evaluate irrigation water demand, irrigation efficiency and crop water monitoring in water resources management in irrigation areas [16][17][18]. In the above research, the input data with high temporal resolution played a very important role in the fine description of the above process.…”

Water shortage risks from perennial crop expansion in California’s Central Valley

“…While multiple datasets exist to support this effort, they face notable limitations, mainly in the scale mismatch between the agriculture and water sectors. For example, planting and irrigation decisions are made at the scale of individual landowners (Foster et al 2019), and water allocations might be determined at the river basin scale, while the water resources impacts of agricultural trade are felt globally (D'Odorico et al 2019). Food-energywater systems are perhaps best analyzed at the mesoscale (Lant et al 2019), i.e.…”