A Sensitivity Analysis of a Fao-56 Dual Crop Coefficient-Based Model Under Various Field Conditions

Laluet, Pierre; Olivera-Guerra, Luis; Rivalland, Vincent; Simonneaux, Vincent; Inglada, Jordi; Bellvert, Joaquim; Er‐Raki, Salah; Merlin, Olivier

doi:10.2139/ssrn.4170062

Cited by 2 publications

(3 citation statements)

References 68 publications

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“…For the observed SSM errors, a standard deviation of 20% of the observed SSM value is set for both S1-derived SSM and in situ SSM, this value being in line with the errors of the existing SSM satellite products (Das et al, 2019;Kerr et al, 2010). For the model error, a standard deviation of 10% of the parameter value is applied to the three most sensitive SAMIR parameters (two parameters related to the NDVI-Kcb relationship and Zrmax; Laluet et al, 2023a), as well as to the surface and root compartment depletions (De and Dr). To avoid as much as possible the degeneracy of particles, their weights are reset whenever the effective sample size Neff reaches a threshold of one third of the initial particle size number (more details in Moradkhani et al, 2005;van Leeuwen et al, 2019).…”

Section: Inverting Smthreshold and Idose Parametersmentioning

confidence: 99%

“…The remaining 6% are represented by the average of the five main crop types. Averaging the plots by crop type is justified by the fact that the soil texture is fairly homogeneous over the area (in addition to having a low sensitivity in SAMIR; Laluet et al, 2023a). Figure 5 shows the mean and standard deviation of the S1-derived SSM and the S2-derived NDVI time series for the five average plots.…”

Section: Aggregation At the Crop Type Scalementioning

confidence: 99%

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Retrieving the irrigation actually applied at district scale: assimilating high-resolution Sentinel-1-derived soil moisture data into a FAO-56-based model

Laluet¹,

Olivera-Guerra²,

Altés³

et al. 2023

Preprint

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Irrigation is the most water consuming activity in the world. Knowing the timing andamount of irrigation that is actually applied is therefore fundamental for watermanagers. However, this information is rarely available at all scales and is subject tolarge uncertainties due to the great diversity of existing agricultural practices andassociated irrigation regimes (full irrigation, deficit irrigation, or over irrigation). To fillthis gap, we propose a two-step approach based on 15 m resolution Sentinel-1 (S1)surface soil moisture (SSM) data to retrieve the actual irrigation at the weekly scaleover an entire irrigation district. As a first step, the S1-derived SSM is assimilated into aFAO-56-based crop water balance model (SAMIR) to retrieve for each crop type boththe irrigation amount (Idose) and the soil moisture threshold (SMthreshold) at whichirrigation is triggered. For this, a particle filter method is implemented, with particlesreset each month to provide time varying SMthreshold and Idose. As a second step,the retrieved SMthreshold and Idose values are used as input to SAMIR for estimatingthe weekly irrigation and its uncertainty. The assimilation approach (SSM-ASSIM) istested over the 8000 hectare Algerri-Balaguer irrigation district located in north-easternSpain, where in situ irrigation data integrating the whole district are available at theweekly scale during 2019. For assessment, the performance of SSM-ASSIM iscompared against that of the default FAO-56 irrigation module (called FAO56-DEF),which sets SMthreshold to the critical soil moisture value and systematically fills thesoil reservoir for each irrigation event. During 2019, with a yearly observed irrigation of687 mm, SSM-ASSIM (FAO56-DEF) shows a Root Mean Square Difference betweenretrieved and in situ irrigation of 6.7 (17) mm week-1, and a Pearson correlationcoefficient of 0.88 (0.47). The SSM-ASSIM approach shows great potential forretrieving the weekly water use over extended areas for any irrigation regime, includingover irrigation.

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Section: Inverting Smthreshold and Idose Parametersmentioning

confidence: 99%

Section: Aggregation At the Crop Type Scalementioning

confidence: 99%

Retrieving the irrigation actually applied at district scale: assimilating high-resolution Sentinel-1-derived soil moisture data into a FAO-56-based model

Laluet¹,

Olivera-Guerra²,

Altés³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…In order to represent the modelling uncertainty, the most sensitive parameters of FAO-2Kc model are perturbed according to a normal distribution with a varying standard deviation equal to 10% of their corresponding values. Based on the comprehensive sensitivity analysis of the FAO-2Kc model byLaluet et al (2022), four main parameters are selected: the maximum rooting depth (Zr max ), slope and offset of the linear relationship between Kcb and NDVI as well as the maximum possible Kcb (Kcb max ). In addition, the surface and root-zone depletions, including initial states, are perturbed by 10% of their values without considering spatial or temporal autocorrelation.…”

mentioning

confidence: 99%

Modeling actual irrigation water use regimes at district scale: application to the FAO-56 dual crop coefficient method

Olivera-Guerra

Laluet

Altés

et al. 2022

Preprint

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The modeling of irrigation in land surface models are generally based on two soil moisture parameters SMthreshold and SMtarget at which irrigation automatically starts and stops respectively. Typically, both parameters are usually set to optimal values allowing to fill the soil water reservoir with just the estimated right amount and to avoid crop water excess at all times. The point is that agricultural practices greatly vary according to many factors (climatological, crop, soil, technical, human, etc.). To fill the gap, we propose a new calibration method of SMthreshold and SMtarget to represent the irrigation water use in any (optimal, deficit or even over) irrigation regime. The approach is tested using the dual-crop coefficient FAO-56 model implemented at the field scale over an 8,100 ha irrigation district in northeastern Spain where the irrigation water use is precisely monitored at the district scale. Both irrigation parameters are first retrieved at monthly scale from the irrigation observations of year 2019. The irrigation simulated by the FAO-56 model is then evaluated against observations at district and weekly scale over 5 years (2017-2021) separately. The performance of the newly calibrated irrigation module is also assessed by comparing it against three other modules with varying configurations including default estimates for SMthreshold and SMtarget. The proposed irrigation module obtains systematically the best performance for each of the 5 years with an overall correlation coefficient of 0.95±0.02 and root-mean square error of 0.27±0.07 hm3/week (0.64±0.17 mm/day). Unlike the three irrigation modules used as benchmark, the new irrigation module is able to reproduce the farmers’ practices throughout the year, and especially, to simulate the actual water use in the deficit and excess irrigation regimes occurring in the study area in spring and summer respectively.

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A Sensitivity Analysis of a Fao-56 Dual Crop Coefficient-Based Model Under Various Field Conditions

Cited by 2 publications

References 68 publications

Retrieving the irrigation actually applied at district scale: assimilating high-resolution Sentinel-1-derived soil moisture data into a FAO-56-based model

Retrieving the irrigation actually applied at district scale: assimilating high-resolution Sentinel-1-derived soil moisture data into a FAO-56-based model

Modeling actual irrigation water use regimes at district scale: application to the FAO-56 dual crop coefficient method

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