Satellite-based water use dynamics using historical Landsat data (1984–2014) in the southwestern United States

Senay, Gabriel B.; Schauer, Matthew; Friedrichs, M.; Velpuri, Naga Manohar; Singh, R. K.

doi:10.1016/j.rse.2017.05.005

Cited by 80 publications

(87 citation statements)

References 35 publications

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“…The Operational Simplified Surface Energy Balance (SSEBop) model is a thermal remote sensing method that ingests Landsat imagery and generates daily total actual evapotranspiration (ETa) [6]. The primary product of the method is the ET fraction, driven by land surface temperature (Ts) from the Landsat thermal band, which takes a fractional amount of alfalfa-reference potential ET (ETr) derived from GridMET to create actual ET [5,6,23]. GridMET is a gridded dataset at 4-km resolution provided by the University of Idaho that provides daily weather variables for the continental United States from 1979 to present [23].…”

Section: The Ssebop Modeling Approachmentioning

confidence: 99%

“…However, due to advances in thermal remote sensing ET modeling and cloud-based processing, actual evapotranspiration (ETa), from a moderate-resolution satellite such as Landsat, can be used to estimate patterns in water use over large areas like the Central Valley more efficiently. Senay, et al [5] modeled historical ETa for 31 years of Landsat data over several hydrologic sub-basins in the middle and lower Central Valley and demonstrated the reliability in estimating water use over time with remote sensing. In this study, Landsat imagery was processed through the Operational Simplified Surface Energy Balance (SSEBop) model which integrates weather and remotely sensed images to estimate monthly and annual ETa [6].…”

Section: Introductionmentioning

confidence: 99%

“…They argued that diagnostic ETa techniques provided by thermal remote sensing reflects actual conditions and water management behaviors across landscapes [14]. Senay, et al [5] took a similar approach with thermal remote sensing to determine water use estimates and modeled SSEBop monthly and annual ETa for 1984-2014 for selected hydrologic unit code (HUC)-8 sub-basins in the middle and lower Central Valley.…”

Section: Introductionmentioning

confidence: 99%

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Characterizing Crop Water Use Dynamics in the Central Valley of California Using Landsat-Derived Evapotranspiration

Schauer

Senay

2019

Remote Sensing

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Understanding how different crops use water over time is essential for planning and managing water allocation, water rights, and agricultural production. The main objective of this paper is to characterize the spatiotemporal dynamics of crop water use in the Central Valley of California using Landsat-based annual actual evapotranspiration (ETa) from 2008 to 2018 derived from the Operational Simplified Surface Energy Balance (SSEBop) model. Crop water use for 10 crops is characterized at multiple scales. The Mann-Kendall trend analysis revealed a significant increase in area cultivated with almonds and their water use, with an annual rate of change of 16,327 ha in area and 13,488 ha-m in water use. Conversely, alfalfa showed a significant decline with 12,429 ha in area and 13,901 ha-m in water use per year during the same period. A pixel-based Mann-Kendall trend analysis showed the changing crop type and water use at the level of individual fields for all of Kern County in the Central Valley. This study demonstrates the useful application of historical Landsat ET to produce relevant water management information. Similar studies can be conducted at regional and global scales to understand and quantify the relationships between land cover change and its impact on water use.

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Section: The Ssebop Modeling Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Characterizing Crop Water Use Dynamics in the Central Valley of California Using Landsat-Derived Evapotranspiration

Schauer

Senay

2019

Remote Sensing

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“…In this study, however, we consider a seasonally-variable c, as also recommended in Senay et al [14]. Hereby, c is determined as a quotient of T s and maximum air temperature (T max ) for pixels located in mixed wetland and shallow water.…”

Section: Summary Of the Fundamental Principles Of The Ssebop Algorithmmentioning

confidence: 99%

Evapotranspiration Mapping in a Heterogeneous Landscape Using Remote Sensing and Global Weather Datasets: Application to the Mara Basin, East Africa

et al. 2017

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Actual evapotranspiration (ET) is a major water use flux in a basin water balance with crucial significance for water resources management and planning. Mapping ET with good accuracy has been the subject of ongoing research. Such mapping is even more challenging in heterogeneous and data-scarce regions. The main objective of our research is to estimate ET using daily Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature and Global Land Data Assimilation System (GLDAS) weather datasets based on the operational simplified surface energy balance (SSEBop) algorithm at a 1-km spatial scale and 8-day temporal resolution for the Mara Basin (Kenya/Tanzania). Unlike previous studies where the SSEBop algorithm was used, we use a seasonally-varying calibration coefficient for determining the "cold" reference temperature. Our results show that ET is highly variable, with a high inter-quartile range for wetlands and evergreen forest (24% to 29% of the median) and even up to 52% of the median for herbaceous land cover and rainfed agriculture. The basin average ET accounts for about 66% of the rainfall with minimal inter-annual variability. The basin scale validation using nine-years of monthly, gridded global flux tower-based ET (GFET) data reveals that our ET is able to explain 64% of the variance in GFET while the MOD16-NB (Nile Basin) explains 72%. We also observe a percent of bias (PBIAS) of 1.1% and 2.8%, respectively for SSEBop ET and MOD16-NB, indicating a good reliability in the ET estimates. Additionally, the SSEBop ET explains about 52% of the observed variability in the Normalized Difference Vegetation Index (NDVI) for a 16-day temporal resolution and 81% for the annual resolution, pointing to an increased reliability for longer aggregation periods. The annual SSEBop ET estimates are also consistent with the underlying primary (i.e., water and energy) and secondary (i.e., soil, topography, geology, land cover, etc.) controlling factors across the basin. This paper demonstrated how to effectively estimate and evaluate spatially-distributed and temporally-varying ET in data-scarce regions that can be applied elsewhere in the world where observed hydro-meteorological variables are limited.

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“…However, our results indicate that it will be much harder for these methods to estimate accurately patterns of actual applied water use at fieldlevels due to the large unobserved spatial and temporal heterogeneities in farmers individual irrigation behavior. As a result, we suggest that there will be significant uncertainty in model-based estimates of applied irrigation water use, in particular when applying fixed technology-based irrigation efficiency adjustments to remotely sensed estimates of consumptive water use [42,[55][56][57][58]. We suggest that efforts to monitor and enforce agricultural water rights based on remote sensing models therefore should focus on metrics of consumptive rather than applied water use.…”

Section: Implications and Conclusionmentioning

confidence: 99%

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

Foster

Gonçalves²,

Campos³

et al. 2019

Environ. Res. Lett.

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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.

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Satellite-based water use dynamics using historical Landsat data (1984–2014) in the southwestern United States

Cited by 80 publications

References 35 publications

Characterizing Crop Water Use Dynamics in the Central Valley of California Using Landsat-Derived Evapotranspiration

Characterizing Crop Water Use Dynamics in the Central Valley of California Using Landsat-Derived Evapotranspiration

Evapotranspiration Mapping in a Heterogeneous Landscape Using Remote Sensing and Global Weather Datasets: Application to the Mara Basin, East Africa

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

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