Irrigation scheduling of paddy rice using short-term weather forecast data

2019 ◽  
Vol 213 ◽  
pp. 714-723 ◽  
Author(s):  
Jingjing Cao ◽  
Junwei Tan ◽  
Yuanlai Cui ◽  
Yufeng Luo
2021 ◽  
Author(s):  
Danlu Guo ◽  
Andrew Western ◽  
Quan Wang ◽  
Dongryeol Ryu ◽  
Peter Moller ◽  
...  

<p>Irrigation water is an expensive and limited resource. Previous studies show that irrigation scheduling can boost efficiency by 20-60%, while improving water productivity by at least 10%. In practice, scheduling decisions are often needed several days prior to an irrigation event, so a key aspect of irrigation scheduling is the accurate prediction of crop water use and soil water status ahead of time. This prediction relies on several key inputs such as soil water, weather and crop conditions. Since each input can be subject to its own uncertainty, it is important to understand how these uncertainties impact soil water prediction and subsequent irrigation scheduling decisions.</p><p>This study aims to evaluate the outcomes of alternative irrigation scheduling decisions under uncertainty, with a focus on the uncertainties arising from short-term weather forecast. To achieve this, we performed a model-based study to simulate crop root-zone soil water content, in which we comprehensively explored different combinations of ensemble short-term rainfall forecast and alternative decisions of irrigation scheduling. This modelling produced an ensemble of soil water contents to enable quantification of risks of over- and under-irrigation; these ensemble estimates were summarized to inform optimal timing of next irrigation event to minimize both the risks of stressing crop and wasting water. With inclusion of other sources of uncertainty (e.g. soil water observation, crop factor), this approach shows good potential to be extended to a comprehensive framework to support practical irrigation decision-making for farmers.</p>


1969 ◽  
Vol 50 (12) ◽  
pp. 947-956
Author(s):  
Carl W. Kreitzberg

Effective reasoning, analysis and communication regarding natural phenomena require the use of models to render tractable the complexities of nature. This paper attempts to put into perspective the proper roles of different types of models to maximize the effectiveness of their utilization. The advances in short term forecasting envisioned for the 1970's from full implementation of new knowledge, models and technology will materialize only if the managers and researchers join in an interagency effort to provide the operational meteorologists with the education, techniques, tools and, particularly, the challenging working environment needed to fully develop man's role in forecasting. A program to meet these requirements is outlined. The types of models discussed include: descriptive or synoptic, dynamic or analytic, numerical or physical, statistical or optimized. The uses of models discussed include: education (basic concepts), research (experimental), operations (customized). Since the operational meteorologist is responsible for the intelligent use of these types of models, he must continually update his training and properly understand the potential contributions of the models. It is anticipated that during the 1970's routine computer models will become more refined and specialized data such as trajectories and probabilities will become more common. Highly specialized products will be available from special purpose models on a special request basis as field forecasters gain access to remote terminals. Also, forecasters will have access to specialized consultants when unusual events or unusual forecast requirements arise. Background materials will be provided to the applied meteorologists so that he may gain physical understanding from educational and research models including systematic numerical experiments. Communication advances will provide for dynamic (motion picture) displays of radar, synchronous satellite, weather map and weather forecast data. Only if the operational forecasters do receive the necessary management and scientific support, will their jobs be challenging and attractive to highly motivated and qualified students; only then will the customers of specialized short term forecasts receive the benefits made feasible by science and technology.


2017 ◽  
Vol 149 ◽  
pp. 192-203 ◽  
Author(s):  
Junghun Lee ◽  
Sungjin Lee ◽  
Jonghun Kim ◽  
Doosam Song ◽  
Hakgeun Jeong

2015 ◽  
Vol 33 (6) ◽  
pp. 411-427 ◽  
Author(s):  
I. J. Lorite ◽  
J. M. Ramírez-Cuesta ◽  
M. Cruz-Blanco ◽  
C. Santos

2021 ◽  
Vol 13 (2) ◽  
pp. 164
Author(s):  
Chuyao Luo ◽  
Xutao Li ◽  
Yongliang Wen ◽  
Yunming Ye ◽  
Xiaofeng Zhang

The task of precipitation nowcasting is significant in the operational weather forecast. The radar echo map extrapolation plays a vital role in this task. Recently, deep learning techniques such as Convolutional Recurrent Neural Network (ConvRNN) models have been designed to solve the task. These models, albeit performing much better than conventional optical flow based approaches, suffer from a common problem of underestimating the high echo value parts. The drawback is fatal to precipitation nowcasting, as the parts often lead to heavy rains that may cause natural disasters. In this paper, we propose a novel interaction dual attention long short-term memory (IDA-LSTM) model to address the drawback. In the method, an interaction framework is developed for the ConvRNN unit to fully exploit the short-term context information by constructing a serial of coupled convolutions on the input and hidden states. Moreover, a dual attention mechanism on channels and positions is developed to recall the forgotten information in the long term. Comprehensive experiments have been conducted on CIKM AnalytiCup 2017 data sets, and the results show the effectiveness of the IDA-LSTM in addressing the underestimation drawback. The extrapolation performance of IDA-LSTM is superior to that of the state-of-the-art methods.


2013 ◽  
Vol 341-342 ◽  
pp. 1303-1307 ◽  
Author(s):  
Jian Dong Mao ◽  
Xiao Jing Zhang ◽  
Juan Li

Accurate short-term wind power forecasting has important significance to safety, stability and economy of power system dispatching and also it is a difficult problem in practical engineering application. In this paper, by use of the data of numerical weather forecast, such as wind speed, wind direction, temperature, relative humidity and pressure of atmosphere, a short-term wind power forecasting system based on BP neural network has been developed. For verifying the feasibility of the system, some experiments have been were carried out. The results show that the system is capable of predicting accurately the wind power of future 24 hours and the forecasting accuracy of 85.6% is obtained. The work of this paper has important engineering directive significance to the similar wind power forecasting system.


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