A novel evapotranspiration based irrigation quantification method using the hydrological similar pixels algorithm

Brombacher, Joost; Silva, Isadora Rezende de Oliveira; Degen, Jelle; Pelgrum, H.

doi:10.1016/j.agwat.2022.107602

Cited by 27 publications

(21 citation statements)

References 40 publications

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“…Remote sensing technology offers unprecedented opportunities for answering the following research question: how much water is used for irrigation? More in details, remotely sensed observations of soil moisture and evapotranspiration are particularly suitable for the development of irrigation quantification techniques, as demonstrated by a number of recent studies implementing evapotranspiration-based (see, e.g., Romaguera et al, 2014;Van Eekelen et al, 2015;Peña-Arancibia et al, 2016;Brombacher et al, 2022) and soil-moisture-based (see, e.g., Brocca et al, 2018;Jalilvand et al, 2019;Zaussinger et al, 2019;Dari et al, 2020Dari et al, , 2022bZappa et al, 2021;Zhang et al, 2022) approaches. Brocca et al (2018) first proposed an irrigation quantification methodology relying on the inversion of the satellite soil moisture signal, currently known as the SM-based (Soil-Moisture-based) inversion approach.…”

Section: Introductionmentioning

confidence: 99%

Regional data sets of high-resolution (1 and 6 km) irrigation estimates from space

Dari

Brocca²,

Modanesi³

et al. 2022

Preprint

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Abstract. Irrigation water use represents the primary source of freshwater consumption by humans. The amount of water withdrawals for agricultural purposes is expected to further increase in the upcoming years to face the rising world population and higher living standards. Hence, effective plans for enacting a rational management of agricultural water use are urgent, but they are limited by knowledge gaps about irrigation. Detailed information on irrigation dynamics (i.e., extents, timing, and amounts) is generally lacking worldwide, but satellite observations can be used to fill this gap. This paper describes the first regional-scale and high-resolution (1 and 6 km) irrigation water data sets obtained from satellite observations. The products are developed over three major river basins characterized by varying irrigation extents and methodologies, as well as by different climatic conditions. The data sets are an outcome of the European Space Agency (ESA) Irrigation+ project. The irrigation amounts have been estimated through the SM-based (Soil-Moisture-based) inversion approach over the Ebro river basin (North-eastern Spain), the Po valley (Northern Italy), and the Murray-Darling basin (South-eastern Australia). The satellite-derived irrigation products referring to the case studies in Europe have a spatial resolution of 1 km, and they are retrieved by exploiting Sentinel-1 soil moisture data obtained through the RT1 (first-order Radiative Transfer) model. A spatial sampling of 6 km is instead used for the Australian pilot area, since in this case the soil moisture information comes from CYGNSS (Cyclone Global Navigation Satellite System) observations. All the irrigation products are delivered with a weekly temporal aggregation. The 1 km data sets over the two European regions cover a period ranging from January 2016 to July 2020, while the irrigation estimates over the Murray-Darling basin are available for the time span April 2017 – July 2020. The retrieved irrigation amounts have been compared with benchmark rates collected over selected agricultural districts. Results highlight satisfactory performances over the major part of the pilot sites falling within the two regions characterized by a semi-arid climate, namely the Ebro and the Murray-Darling basins, quantified by median values of RMSE, Pearson correlation, r, and BIAS equal to 12.4 mm/14-day, 0.66, and -4.62 mm/14-day, respectively, for the Ebro basin and to 10.54 mm/month, 0.77, and -3.07 mm/month, respectively, for the Murray-Darling basin. The assessment of the performances over the Po valley is affected by the limited availability of in situ reference data for irrigation.

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Section: Introductionmentioning

confidence: 99%

Regional data sets of high-resolution (1 and 6 km) irrigation estimates from space

Dari

Brocca²,

Modanesi³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Brombacher et al. (2022) used a hydrological similarity concept (van Eekelen et al., 2015) to compare actual evapotranspiration (

\mathrm{E}{\mathrm{T}}_{\mathrm{a}\mathrm{c}\mathrm{t}}

) of an IR pixel with an average

\mathrm{E}{\mathrm{T}}_{\mathrm{a}\mathrm{c}\mathrm{t}}

of natural (nIR) pixels to calculate the

\mathrm{E}{\mathrm{T}}_{\mathrm{a}\mathrm{c}\mathrm{t}}

caused by the irrigation (incremental ET). They define hydrological similarity based on a set of features derived from soil texture, DEM, reference ET, and precipitation datasets.…”

Section: Discussionmentioning

confidence: 99%

Is It Possible to Quantify Irrigation Water‐Use by Assimilating a High‐Resolution Satellite Soil Moisture Product?

Jalilvand

Abolafia‐Rosenzweig

Tajrishy

et al. 2023

Water Resources Research

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Agricultural production is projected to require a 70% expansion by 2050 as a result of population growth, climate change, and dietary shifts toward water-intensive products associated with increasing incomes (Tilman & Clark., 2015). Food production is mainly sustained by irrigation (Jägermeyr et al., 2015), which is by far the largest consumer of freshwater resources globally (Döll & Siebert, 2002). However, the planetary limit for freshwater withdrawal is quickly approaching (or already exceeded) in many parts of the world (Steffen et al., 2015). Moreover, it is expected that 25%-40% of water for irrigation agriculture will be reallocated to higher productivity sectors in the near future (World Bank, 2022). Thus, authorities seek to limit agricultural water consumption globally. However, a key requirement to enforce any regulation is monitoring the water withdrawn by farmers (Foster et al., 2020). Monitoring groundwater storage changes through GRACE (Gravity Recovery and Climate Experiment) satellite observations has revealed significant depletion in major aquifers across the globe (Frappart

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“…Jalilvand et al (2019) argued that choosing an adjacent nIR pixel for the posterior bias correction can maximize the climate similarity as the estimated irrigation at the nIR pixel may exclusively cause by the model bias rather than a different precipitation pattern. Brombacher et al (2022) used a hydrological similarity concept (van Eekelen et al, 2015) to compare actual evapotranspiration (𝐸𝑇 act ) of an IR pixel with an average 𝐸𝑇 act of natural (nIR) pixels to calculate the 𝐸𝑇 act caused by the irrigation (incremental ET). They define hydrological similarity based on a set of features derived from soil texture, DEM, reference ET, and precipitation datasets.…”

Section: Posterior Bias Correctionmentioning

confidence: 99%

Is it Possible to Quantify Irrigation Water-Use by Assimilating a High-Resolution Soil Moisture Product?

Das

Jalilvand

Abolafia‐Rosenzweig

et al. 2022

Preprint

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Irrigation is the largest human intervention in the water cycle that can modulate climate extremes, yet global irrigation water use (IWU) remains largely unknown. Microwave remote sensing offers a practical way to quantify IWU by monitoring changes in soil moisture caused by irrigation. This study evaluates the ability to quantify IWU by assimilating high-resolution (1km) SMAP-Sentinel 1 (SMAP-S1) remotely sensed soil moisture with a physically-based land surface model (LSM) using a particle batch smoother (PBS). A suite of synthetic experiments is devised to evaluate different error sources. Results from the synthetic experimentation show that unbiased simulations with known irrigation timing can produce an accurate irrigation estimate with a mean annual bias of 0.45% and the mean R 2 of 96.5%, relative to observed IWU. Unknown irrigation timing can significantly deteriorate the model performance by increasing the mean annual bias to 23% and decreasing the mean R 2 to 36%. In realworld experiments, the PBS data assimilation approach provides a mean bias of -18.6% when the timing of irrigation water use is known. This underestimation is possibly attributable to missing part of the irrigation signal. Yet, significantly higher irrigation was estimated over the irrigated pixels compared to the non-irrigated pixels, indicating that data assimilation can skillfully convey irrigation signals to LSMs. LSM calibration provides a 10% improvement to soil moistrue RMSE relative to the open-loop simulation. PBS data assimilation provides an additional 50% improvement to simulated soil moisture RMSE by correcting the model state and superimposing the optimal (unmodeled) irrigation on precipitation forcing.

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A novel evapotranspiration based irrigation quantification method using the hydrological similar pixels algorithm

Cited by 27 publications

References 40 publications

Regional data sets of high-resolution (1 and 6 km) irrigation estimates from space

Regional data sets of high-resolution (1 and 6 km) irrigation estimates from space

Is It Possible to Quantify Irrigation Water‐Use by Assimilating a High‐Resolution Satellite Soil Moisture Product?

Is it Possible to Quantify Irrigation Water-Use by Assimilating a High-Resolution Soil Moisture Product?

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