Simulation of water temperature in paddy fields by a heat balance model using plant growth status parameter with interpolated weather data from weather stations

Xie, Wenpeng; Kimura, Masaomi; Iida, Toshiaki; Kubo, Naritaka

doi:10.1007/s10333-020-00818-3

Cited by 6 publications

(13 citation statements)

References 32 publications

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“…In addition, the ATMOS41 is part of the Montana Mesonet monitoring stations in the Upper Missouri River Basin, where collected data are used for drought detection and natural resource management, amongst others [ 29 ]. Further applications include the investigation of crop water stress in apple orchards at Washington State University [ 30 ] and the estimation of the plant growth status of paddy rice in Japan [ 31 ].…”

Section: Introductionmentioning

confidence: 99%

“…Considering the wide use of the ATMOS41 weather station for small- and large-scale weather monitoring in sub-Saharan Africa [ 24 , 25 , 26 ] as well as industrialized countries [ 27 , 28 , 29 , 30 , 31 ], independent testing under “out of the lab” conditions can provide further insight and eventually identify possible limitations of the ATMOS41 station. In this way, a thorough performance assessment can inform private costumers and research organizations regarding the potential fields of application and provide impulses for further hardware or software developments.…”

Section: Introductionmentioning

confidence: 99%

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Performance of the ATMOS41 All-in-One Weather Station for Weather Monitoring

Dombrowski

Franssen

Brogi

et al. 2021

Sensors

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Affordable and accurate weather monitoring systems are essential in low-income and developing countries and, more recently, are needed in small-scale research such as precision agriculture and urban climate studies. A variety of low-cost solutions are available on the market, but the use of non-standard technologies raises concerns for data quality. Research-grade all-in-one weather stations could present a reliable, cost effective solution while being robust and easy to use. This study evaluates the performance of the commercially available ATMOS41 all-in-one weather station. Three stations were deployed next to a high-performance reference station over a three-month period. The ATMOS41 stations showed good performance compared to the reference, and close agreement among the three stations for most standard weather variables. However, measured atmospheric pressure showed uncertainties >0.6 hPa and solar radiation was underestimated by 3%, which could be corrected with a locally obtained linear regression function. Furthermore, precipitation measurements showed considerable variability, with observed differences of ±7.5% compared to the reference gauge, which suggests relatively high susceptibility to wind-induced errors. Overall, the station is well suited for private user applications such as farming, while the use in research should consider the limitations of the station, especially regarding precise precipitation measurements.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance of the ATMOS41 All-in-One Weather Station for Weather Monitoring

Dombrowski

Franssen

Brogi

et al. 2021

Sensors

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“…Irrigation and drainage discharge were inferred from water depth and irrigation schedules. For vegetation growth conditions, we used the method of Xie et al (2021) to infer vegetation growth-status parameters (KL) from the transient relationship between water temperature changes and energy balance. We added them to the data set as one of the features, as described in Text S2 of Supporting Information S1.…”

Section: Data Sourcesmentioning

confidence: 99%

“…The framework comprises two main components, a physical process model and a neural network, as shown in Figure 1. Xie et al (2021Xie et al ( , 2022 developed a two-layer heat balance model for calculating physical processes for paddy field analysis. It simulates the heat transfer in the paddy field and computes the water temperature by calculating the net inflow of heat to the paddy water while accounting for the influence of the vegetation layer on the temperature.…”

Section: Framework Outlinementioning

confidence: 99%

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Interpretable Framework of Physics‐Guided Neural Network With Attention Mechanism: Simulating Paddy Field Water Temperature Variations

Xie

Kimura

Takaki

et al. 2022

Water Resources Research

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With the development of large‐scale rice cultivation management initiatives in East Asia, there is concern that a reduction in the number of human cultivators per unit area may lead to poor water management, which could result in decreased land productivity, owing to abnormally high‐ and low‐temperature damage to crops. Accurate simulation of paddy field water temperature is important for studying its impact on crops and providing timely information to aid in decision‐making for more efficient management under limited resources. We propose a neural‐network framework that considers the heat transfer by the vegetation canopy and applies physical‐theory constraints in its training. A novel tuning method is proposed to cope with the trade‐off between water temperature accuracy and physical consistency during training to ensure that the calculated water temperature variations in a paddy field enjoy high accuracy and physical consistency. In the experiments, the proposed framework outperforms physical process models and pure neural network models while maintaining high accuracy in the case of sparse data sets. Furthermore, an attention‐mechanism input layer is integrated into the model to rank feature importance, providing global interpretation to the proposed framework. We also perform sensitivity analysis on the physical process and propose models to compare their different strategies of feature ranking. The results show that the two methods have different sensitivities to different feature patterns, but they complement each other. In summary, the proposed model is credible and stable for practical applications and has the potential to guide more efficient paddy management.

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The development of a hybrid model to forecast paddy water temperature as an alert system for high‐temperature damage

Xie

Kimura

Asada

et al. 2022

Irrigation and Drainage

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Climate change has led to increasing global air temperatures. In the field of crop cultivation, long-term high temperatures (heatwaves) during the ricegrowing season might increase the risk of high-temperature damage to rice, which might result in reductions to the yield and quality of rice. In this study, a hybrid forecast model consisting of a combined paddy field heat balance model and a meteorological forecast model is proposed for predicting 1-dayahead water temperatures as an alert system for high-temperature damage to paddy fields, with resolution in terms of hours. The results show close agreement between the measured and predicted water temperatures, and the hightemperature alert accuracy was 88.5%. Additionally, the climate resilience of paddy fields was investigated by using the rising annual temperatures due to climate change. The observations indicate that while paddy fields are sensitive to the climate, their climate resilience can be improved through artificial measures. Farmers and managers of paddy fields can thus be made aware of the water temperatures of the paddy fields in advance to enable reasonable management of water resources and avoid high-temperature damages caused by extreme weather conditions.

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Simulation of water temperature in paddy fields by a heat balance model using plant growth status parameter with interpolated weather data from weather stations

Cited by 6 publications

References 32 publications

Performance of the ATMOS41 All-in-One Weather Station for Weather Monitoring

Performance of the ATMOS41 All-in-One Weather Station for Weather Monitoring

Interpretable Framework of Physics‐Guided Neural Network With Attention Mechanism: Simulating Paddy Field Water Temperature Variations

The development of a hybrid model to forecast paddy water temperature as an alert system for high‐temperature damage

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