Novel approach of upscaling the FAO AquaCrop model into regional scale by using distributed crop parameters derived from remote sensing data

doi:10.1016/j.agwat.2020.106288

Agricultural Water Management

Volume 240, 1 October 2020, 106288

https://doi.org/10.1016/j.agwat.2020.106288 Get rights and content

Highlights

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A new distributed crop model-AquaCrop-RS model was developed.
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The precision of the AquaCrop-RS model has improved.
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Sensitivity analysis have been conducted for the crop parameters of CCx* and Brel*.

Abstract

Distributed crop models were considered a useful tool for allocating rational water resources. However, how to upscale the crop model into the regional scale is a key issue because the associated crop parameters tend to have spatial heterogeneity according to their feedback to the environment. The remote sensing may provide a possible method for generating the spatial distribution of crop parameters, thereby improving the upscaling approach. On this basis, this study aims to achieve the spatial distribution of crop parameters and establish the distributed AquaCrop-RS model by means of remote sensing. The results showed that the distribution of two crop parameters, namely, the maximum canopy coverage CC_x* and the relative biomass B_rel* was significantly heterogeneous with the spatial variability coefficient of 8% and 8.5% for CC_x* in 2012 and 2013, respectively, and that of 6.3% and 4.6% for B_rel*. Compared with the AquaCrop-GIS model without introducing the spatial parameters, the simulation accuracy of AquaCrop-RS model was improved by 20% in regional crop water consumption and by 26–36% in regional yield. The new upscaling approach could be an effective and promising method of crop model upscaling.

Introduction

The Heihe River Basin (HRB), which is located in Northwest China, is the second largest inland river basin in China. The middle oasis of the HRB between Yingluo Gorge and Zhengyi Gorge is an important agricultural production base in Gansu Province (Li et al., 2001). In the basin, the crop water requirement is mainly supplied by irrigation (Kang et al., 2004). However, the water resources of the oasis are unevenly distributed because of the lack of reasonable measures for irrigation and irrigation distribution. Increasing the water utilization level in the middle reaches of the Heihe River is of considerable significance to understand the spatiotemporal distribution of the crop demand for water in the oasis, thus ensuring the accurate estimation of the water consumption, yield, and water use efficiency based on the spatial distribution of crops.

Crop growth models are considered as very useful methods of estimating crop water consumption, yield, and water use efficiency. At present, the most commonly used crop growth models are DSSAT (Jones et al., 2003), EPIC (Williams et al., 1989), WOFOST (Diepen et al., 1989). Although these models can precisely simulate the growth status and water consumption of the crops at the field scale, the widespread application of such models is limited to a large extent because of their complexity, high requirements on the adjustment of the model parameters, and preparation of the input data for the end users of the models (Vanuytrecht et al., 2014a). To solve this problem, the Food and Agriculture Organization (FAO) developed the water-driven AquaCrop model (Raes et al., 2009; Steduto et al., 2009). The AquaCrop model requires relatively few input parameters, and the input data are intuitive, clear, and easy to obtain. The AquaCrop model can also be applied to a variety of crops (Geerts et al., 2009; Mabhaudhi et al., 2014; Bello and Walker, 2017), leading to its rapid development and application worldwide. Many experiments and results have been reported using the AquaCrop model in various applications (Heng et al., 2009; Tsegay et al., 2012; Iqbal et al., 2014), including optimizing irrigation strategies (Andarzian et al., 2011), and management measures (Abrha et al., 2012; Zinyengere et al., 2011; Shrestha et al., 2013) and predicting the impact of climate change on cereal production (Vanuytrecht et al., 2014b). Research on this model has been relatively mature in the fields of water consumption and crop yield.

Meanwhile, several scholars have applied the AquaCrop model to the regional scale. Iqbal et al. (2014) conducted a field experiment in Luancheng, and their model was calibrated and validated with the field data. Then, their calibrated model was again verified in Shijiazhuang to obtain the results of the two areas to reflect the agricultural production in the North China Plain, similar to the method employed by Paredes et al. (2015). However, when large spatial variations exist among crops and their growth environment, directly applying the results of the model at the field scale to the regional scale would lead to numerous errors (Mo et al., 2005).

The FAO developed the software of AquaData and AquaGIS (Lorite et al., 2013), which achieved the division of the spatial differentiation of the inputs and outputs, to improve the effectiveness of the AquaCrop model at the regional scale. Lorite et al. (2013) used this software to simulate the impact of climate change over the past and next 30 years on wheat production in Andalusia, southern Spain, which significantly improved the efficiency of regional simulation. However, upscaling the spatial scale of the model is not simply applying the field model to the regional scale. The spatial heterogeneity of the crop growth environment leads to the spatial variability of the crop parameters of the model to the environment. Therefore, considering the spatial variation of the model parameters when applying the AquaCrop model to the regional scale is necessary. A large number of studies (Geerts et al., 2009; Vanuytrecht et al., 2014c) showed that two crop parameters, i.e., maximum canopy coverage (CC_x*) and relative biomass (B_rel*), have a significant effect on the simulation results. However, these two factors have not been considered sufficiently in the present regional studies (Li et al., 2016) and the description of the spatial heterogeneity of the underlying surface has not been provided adequately in detail.

With the development of remote sensing, the spatial and refined representations of regional parameters have been achieved (Xing and Zhang, 2003), which can provide the spatially continuous input data, such as the green canopy coverage and Normalized Difference Vegetation Index, for the regional application of the model. This technology effectively addresses the problem of spatially discrete data and improves the accuracy of the model in the regional application (Dorigo et al., 2007). Coupling of remote sensing with crop growth models is a hotspot in current research. Scholars have initialized the model parameters to increase the simulation accuracy by inversion. For example, Battude et al. (2016, 2017) coupled the Simple Algorithm For Yield (SAFY) to a water balance model based on the high spatial and temporal resolution remote sensing data and modeled water needs and total irrigation depths of maize in the south west of France.

In order to solve the problem of upscaling the AquaCrop model into regional scale, this study selected Yingke Irrigation District (YID)—a typical irrigation district in the middle oasis of the Heihe River—as the study area and seed maize as the research subject. Based on the calibration and validation of the basic crop parameters of the model, the spatial inversion methods for the key crop parameters of CC_x* and B_rel*, were explored using remote sensing data (i.e., MOD13Q1,250 m, 16days, and MOD17A2H, 500 m, 8days), and the spatiotemporal variation of the two parameters was analyzed. On this basis, a distributed AquaCrop-RS model based on the spatially heterogeneous parameters was built with the help of AquaCrop-GIS to improve the regional simulation accuracy, and to realize the refined simulation of the yield and water use efficiency of seed maize in YID.

Section snippets

Study area

This study was conducted in YID, a typical irrigation district in the middle oasis of HRB, which is located in Ganzhou District, Zhangye City, with the geographic position of 38°50′N–38°58′N, 100°17′E–100°34′E (see Fig. 1), with annual average temperature of 6.5–8.5 °C, multiyear average precipitation of approximately 133 mm, and annual evapotranspiration of reference crops (ET₀) of approximately 1200 mm. The groundwater depth in YID is relatively deep, ranging from 40 m in the southwest to

Calibration and validation of the AquaCrop model at the field scale

The model was initially calibrated with the experimental data of 2012 and subsequently validated with the data of 2013 in this study. Table 1 shows the crop parameters used for calibration. Results show that the simulated and observed CC, biomass, and SWC are in well agreement at all observation points as shown in Fig. 5. The evaluation indices for the performance of the model are shown in Table 2. The calibration results show that indices of agreement (d) of CC, Biomass, SWC, and yield in 2012

Conclusion

In this study, two key parameters, i.e., CC_x* and B_rel*, in the AquaCrop model are spatially derived by remote sensing, and on this basis, a regional AquaCrop-RS model is established. Affected by variety characteristics, management, and soil texture, the spatial distribution of CC_x* of seed maize in the irrigated area is significantly different with CV of 8% and 8.5% in 2012 and 2013, respectively; the same as the spatial distribution of B_rel* for human activities and soil texture with CV of

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

The research grants from the Chinese National Natural Science Fund (51822907), the Fund of China Institute of Water Resources and Hydropower Research (ID0145B742017 and ID0145B492017).

References (43)

U. Akumaga et al.
Validation and testing of the FAO AquaCrop model under different levels of nitrogen fertilizer on rainfed maize in Nigeria, West Africa
Agric. For. Meteorol.
(2017)
B. Andarzian et al.
Validation and testing of the AquaCrop model under full and deficit irrigated wheat production in Iran
Agric. Water Manage.
(2011)
M. Battude et al.
Modeling water needs and total irrigation depths of maize crop in the south west of France using high spatial and temporal resolution satellite imagery
Agric. Water Manage.
(2017)
Z.A. Bello et al.
Evaluating AquaCrop model for simulating production of amaranthus (Amaranthuscruentus) a leafy vegetable, under irrigation and rainfed conditions
Agric. Forest. Meteorol.
(2017)
W.A. Dorigo et al.
A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling
Int J Appl Earth Obs.
(2007)
M.A. Iqbal et al.
Evaluation of the faoaquacrop model for winter wheat on the north china plain under deficit irrigation from field experiment to regional yield simulation
Agric. Water Manage.
(2014)
P.D. Jamieson et al.
A test of the computer simulation model ARCWHEAT1 on wheat crops grown in New Zealand
Field Crop Res.
(1991)
Y. Jiang et al.
Assessment of irrigation performance and water productivity in irrigated areas of the middle Heihe River basin using a distributed agro-hydrological model
Agric. Water Manage.
(2015)
J.W. Jones et al.
The DSSAT cropping system model
Eur. J. Agron.
(2003)
X. Li et al.
Quantifying landscape structure of the Heihe River Basin, north-west China using FRAGSTATS
J. Arid Environ.
(2001)

J. Li et al.

Modeling crop water consumption and water productivity in the middle reaches of Heihe River Basin

Comput. Electron. Agric.

(2016)

I.J. Lorite et al.

AquaData and AquaGIS: two computer utilities for temporal and spatial simulations of water-limited yield with AquaCrop

Comput. Electron. Agric.

(2013)

T. Mabhaudhi et al.

Parameterisation and evaluation of the FAO-AquaCrop model for a South African taro (Colocasiaesculenta L. Schott) landrace

Agric. Forest Meteorol.

(2014)

X. Mo et al.

Prediction of crop yield, water consumption and water use efficiency with a SVAT-crop growth model using remotely sensed data on the North China Plain

Ecol Model.

(2005)

P. Paredes et al.

Performance assessment of the FAO AquaCrop model for soil water, soil evaporation, biomass and yield of soybeans in North China Plain

Agric Water Manage.

(2015)

N. Shrestha et al.

Cereal yield stabilization in Terai (Nepal) by water and soil fertility management modeling

Agric. Water Manage.

(2013)

E. Vanuytrecht et al.

AquaCrop: FAO’s crop water productivity and yield response model

Environ. Model. Softw.

(2014)

E. Vanuytrecht et al.

Comparing climate change impacts on cereals based on CMIP3 and EU-ENSEMBLES climate scenarios

Agric. For. Meteorol.

(2014)

E. Vanuytrecht et al.

Global sensitivity analysis of yield output from the water productivity model

Environ. Model. Softw.

(2014)

B. Wu et al.

Validation of ETWatch using field measurements at diverse landscapes: a case study in Hai Basin of China

J. Hydrol.

(2012)

N. Zinyengere et al.

Using seasonal climate forecasts to improve maize production decision support in Zimbabwe

Agric. For. Meteorol.

(2011)

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Novel approach of upscaling the FAO AquaCrop model into regional scale by using distributed crop parameters derived from remote sensing data

Highlights

Abstract

Introduction

Section snippets

Study area

Calibration and validation of the AquaCrop model at the field scale

Conclusion

Declaration of Competing Interest

Acknowledgments

Agric. For. Meteorol.

Agric. Water Manage.

Agric. Water Manage.

Agric. Forest. Meteorol.

Int J Appl Earth Obs.

Agric. Water Manage.

Field Crop Res.

Agric. Water Manage.

Eur. J. Agron.

J. Arid Environ.

Comput. Electron. Agric.

Comput. Electron. Agric.

Agric. Forest Meteorol.

Ecol Model.

Agric Water Manage.

Agric. Water Manage.

Environ. Model. Softw.

Agric. For. Meteorol.

Environ. Model. Softw.

J. Hydrol.

Agric. For. Meteorol.