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Copyright of Journal of Irrigation & Drainage is the property of Journal of Irrigation & Drainage Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Irrigation & Drainage is the property of Journal of Irrigation & Drainage Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Irrigation & Drainage is the property of Journal of Irrigation & Drainage Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to accurately estimate the reference crop evapotranspiration (ETc) of wheat using incomplete meteorological elements, three verification and analysis processes in estimation of some meteorological elements were carried out by using daily forecast weather data and the corresponding daily measured meteorological data of Xinxiang during winter wheat growing seasons from 2014 to 2016, as well as modified solar radiation parameters and adjustment coefficients. First, the accuracy of forecast air temperature was verified. Then, the actual water vapor pressure and solar radiation were estimated based on forecast air temperature. Finally, the forecast air temperature and averaged wind speed were adopted to estimate the reference ETc based on the Penman-Monteith formula. The results showed that the daily forecast air temperature could replace the daily observed air temperature, water vapor pressure and solar radiation using the forecast maximum and minimum temperature, which met the requirements of Penman-Monteith formula, and the accuracy of the radiation estimated using weather forecast data was higher than that of the aerodynamics. Generally, the accuracy of the radiation in the daily reference ETc estimated by weather forecast data was higher than that of the aerodynamics, the estimated values by weather forecast was relatively lower, and the underestimate rate was below 7%. Through statistical analysis, there was a high correlation between daily ETc estimated by weather forecast and that estimated by Penman-Monteith equation (R²=0.77). Therefore, it was feasible to use the daily meteorological data from weather forecast to estimate the reference ETc, and it was suggested to use the radiation method to estimate ETc in arid and semi-arid areas, which provided theoretical and methodological guarantee for agricultural irrigation forecast and had reference significance for guiding the optimal allocation of local agricultural water resources. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Applied Ecology / Yingyong Shengtai Xuebao is the property of Chinese Journal of Applied Ecology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Crop water requirement is an important basic data for planning, design and management of irrigation engineering. Obtaining high-precision regional crop water requirement prediction using multi-source data and the priori knowledge has great significance for optimal allocation of regional water resources. In the paper, multisource data was integrated using the Bayesian maximum entropy (BME) method for crop water requirement prediction. A long series of crop water requirement measured and calculated by using the crop coefficients adjusted for actual measurement, were taken as the hard data. The soft data included the missing data in partial years for some stations, literature data, the Kriging interpolation data considering the main influence elements of crop water requirement, the crop water requirement data based on the principal component analysis (PCA) and geographically weighted regression (GWR) method, and the remote sensing data. For the soft data from different sources, the expression method of probability density function was put forward and the crop water requirement information from different sources was well integrated using the BME method. Hard data for the period of 1954-2013were collected from measurements from the irrigation stations in North China. Soft data for winter wheat in North China were also collected by searching literatures and the others. The results showed that spatial distribution of crop water requirement in North China was almost consistent for the hard data, hard data + Kriging soft data, hard data + GWR soft data and hard data + the soft data except for literature data. In the southern Henan had smaller crop water requirement, but the middle part (northern part of the Yellow River) of the North China was relatively high. The crop water requirement was relatively high in Shandong but low in the northeast of Hebei such as Leting, Tangshan. The results from hard data + literature soft data were slightly different from the others and the difference might be because the time periods used were different. In general, the integration accuracy of hard data + literature soft data was 9.41% lower than that based on hard data only. Hard data integrated with the other soft data could improve the integration effect. In particular, the integration accuracy of hard data + Kriging soft data, hard data + GWR soft data and hard data + the soft data except for literature data increased by 85.33%, 85.75% and 91.69%, respectively. The integration of multi-source data through considering the terrain, meteorological factors and etc, can could better reflect the spatial distribution of crop water requirements for winter wheat, and significantly improve the estimation accuracy of crop water requirement for winter wheat. The presented method provided the most important basic data for the precise management and optimization of water and soil resources in the region with sparse monitoring stations. In the paper, we need pay attention to some questions in the soft data processing. The partially missing data of some stations need amend the variance calculation results. In order to avoid the agglomeration phenomenon, the selection of interpolation data need adopt the method of random sampling, and at the same time, the distance between adjacent samplings must be limited, should not be too far or near, and 20 km was advisable. In order to avoid too big error and uncertainty, the literature data must be screened and pretreatment, otherwise, the integration effect may be affected. [ABSTRACT FROM AUTHOR]

Soil and water resources are important factors impacting the productivity of agro-ecosystem in arid and semi-arid areas worldwide. Most studies focused on the aspects of soil and water resources potentials under natural status based on a provincial or larger scale. It is still unknown about the potential's distributions and matching patterns of soil and water resources in the entire northwest areas of China. Furthermore, with the ever-growing population and urbanization, the contradiction between the supply and the demand in terms of soil and water resources has become much more serious in this region than ever, and has become the bottle-neck factor constraining regional agricultural productivity. To alleviate the contradiction, optimal matching of soil and water potentials and resources plays an important role in the sustainable development of local economy, ecology, and society. To well address the above-mentioned issues, an innovative methodology for calculating the matching index of soil and water resources was developed to analyze the current matching status of soil and water resources in northwest arid area of China. Additionally, a calculation method was also developed to estimate the soil and water resources utilization potential under different climatic scenarios, including the RCP 8.5 and RCP 4.5 scenarios in 2020 and 2030, respectively, in which an accurate and precise equation was developed for visualizing the resource utilization potential. During the base year of 2010, the matching index exhibited a gradual ascending trend from the northwest to the southeast. Total amounts of water resources and planting areas will significantly reduce under the RCP 8.5 and RCP 4.5 scenarios in 2020 and 2030, compared to those in 2010. The integrated economical profits and entire potential values are positive (desirable) under both scenarios in 2020 and 2030, indicating planting structure of the six provinces in northwest arid area can be optimized. The potential values of soil and water resources fall in the range of -0.10×104 to 0.83×104 yuan/hm2 and -1.20×104 to 0.97×104 yuan/hm2 under RCP 8.5 and RCP 4.5 scenarios in 2020, whereas they fall in the range of -0.39×104 to 2.17×104 yuan/hm2 and -0.36×104 to 1.66×104 yuan/hm2 under RCP 8.5 and RCP 4.5 scenarios in 2030, respectively. The calculation method in this study provides a new approach for estimating the potential utilization efficiency of soil and water resources in northwest arid area of China, and this methodology can be extended to a wide range of the world with the same environment to capture the great benefit of food security while minimizing the negative impact on soil and water. Our results clearly demonstrated that the soil and water resources can be well matched in a desirable manner in the near future in northwest China if planting structure and areas are well optimized and cropping patterns are well managed. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)