To quantitatively evaluate and diagnose the carrying capacity of regional water resources under uncertain conditions, an index system and corresponding grade criteria were constructed from the perspective of carrying subsystem. Meanwhile, an improved entropy weight method was used to determine the objective weight of the index. Then, an evaluation model was built by applying set pair analysis, and a set pair potential based on subtraction was proposed to identify the carrying vulnerability factors. Finally, an empirical study was carried out in Anhui Province. The results showed that the consistency among objective weights of each index was considered, and the uncertainty between the index and grade criterion was reasonably dealt with. Furthermore, although the carrying situation in Anhui was severe, the development tended to be improved. The status in Southern Anhui was superior to that in the middle area, and that in the northern part was relatively grim. In addition, for Northern Anhui, the fewer water resources chiefly caused its long-term overloaded status. The improvement of capacity in the middle area was mainly hindered by its deficient ecological water consumption and limited water-saving irrigation area. Moreover, the long-term loadable condition in the southern part was due largely to its relatively abundant water resources and small population size. This evaluation and diagnosis method can be widely applied to carrying issues in other resources and environment fields.

Citation: Wang W S, Jin J L, Ding J, et al. A new approach to water resources system assessment --set pair analysis method.Most traditional assessment methods, which have complicated mathematic formulas, are difficult for calculation and application in water resources system assessment. A new approach to water resources system assessment, the set pair analysis method (SPAM), has been proposed based on the principle of set pair analysis (SPA). The basic ideals and steps of SPAM are discussed. The proposed method can take fuzzy property of threshold values for grade standards into full account and avoid determining the discrepancy uncertainty coefficient i or i 1 , i 2 , i 3 , … in SPA. The presented method is simple in concept, convenient to calculate and feasible for application. Two case studies of water resources assessment have been made. The results show that the proposed method is satisfactory.water resources, assessment, set pair analysis, connection degree Regional water resources system assessment is a complex dynamic system [1] because of the influence of many factors, such as regional physical geography, socioeconomy and so on. For this assessment two key issues must be addressed. One is the reasonable determination of its index systems and standard grades; the other is development of its method or model based on the relationship between the values of various indexes' standard grades and those of sample's indexes. Concerning the current methods of water resources system assessment, there are mainly principal component method, grey method [2] , fuzzy method [3] , projection pursuit method [4] , matter element method [5] and so on. Though these methods can be applied in practice to a certain degree, common defects may exist such as their complexity, a lot of mathematical knowledge required, implementation difficulty and inconvenient application. Based on the point of view in unity and opposition, the Chinese scholar ZHAO KeQin has first put forward set pair analysis (SPA), which has been used widely in many fields of mathematics, physics, information management, economy, resources and environment [6] . The results from our researches have fully indicated that SPA may be considered as a new uncertainty analysis approach and can be used to analyze the internal relationship of a given system from both its whole and part. During recent years, we have introduced SPA into hydrology and water resources field and carried out a systematic study on its application [7] in many aspects such as computation, forecast, assessment and so on, in which the applications to water resources system assessment have made some progress [7][8][9][10][11] . However, so far as the method for assessment based on SPA is concerned, there is not a reasonable, systematic and acceptable framework. Moreover, its simplicity and effectiveness in practice may not be fully exhibited. In this paper we attempt to develop a new approach to water resources system assessment for the framework based on SPA. The proposed approach, namely the set pair...

Abstract:To quantitatively access the effects of drought stress during different growth stages of soybean on development process and yield, a pot-culture experiment was conducted in China's Huaibei Plain with different irrigation treatments over two seasons (2015 and 2016). Two drought stress levels (mild and severe) were applied at four growth stages for the experiment (S: seedling stage; B: branching stage; FPS: flowering and pod-setting stage; and PF: pod-filling stage). The effects of drought stress at different stages on growth and yield were evaluated and compared. Results of this two-year study showed that all growth and yield parameters were significantly affected by the water deficit during the sensitive FPS. Compared to the full irrigation treatment, severe drought stress during FPS caused a 22% loss of final plant height, 61% loss of the leaf area per plant (LAP), and 67% loss of final aboveground dry matter (ADM). Yield components also declined dramatically with water deficits during FPS and PF. Significant seed yield losses of 73-82% per plant were observed in the plants exposed to drought stress during FPS, and were also associated with the highest nonviable pod percentage of 13%. The greatest losses in 100-seed weight (42-48%) were observed under drought stress during PF. A rising trend in response to increasing soil water deficit (SWD) was observed for LAP, yield, and ADM losses. The slope (k) values of these fitting curves varied at different treatments, the highest value of k (7.37 and 8.47 in two years, respectively) was also observed in the sensitive FPS.

The present study comprehensively analyzes error characteristics and performance of the two latest GPM-era satellite precipitation products over eastern China from April 2014 to March 2016. Analysis results indicate that the two products have totally different spatial distributions of total bias. Many of the underestimations for the GSMap-gauged could be traced to significant hit bias, with a secondary contribution from missed precipitation. For IMERG, total bias illustrates significant overestimation over most of the eastern part of China, except upper reaches of Yangtze and Yellow River basins. GSMap-gauged tends to overestimate light precipitation (<16 mm/day) and underestimate precipitation with rain rate larger than 16 mm/day; however, IMERG underestimates precipitation at rain rate between 8 and 64 mm/day and overestimates precipitation at rain rate more than 64 mm/day. IMERG overestimates extreme precipitation indices (RR99P and R20TOT), with relative bias values of 17.9% and 11.5%, respectively. But GSMap-gauged shows significant underestimation of these indices. In addition, both products performed well in the Huaihe, Liaohe, and Yangtze River basins for extreme precipitation detection. At basin scale comparisons, the GSMap-gauged data has a relatively higher accuracy than IMERG, especially at the Haihe, Huaihe, Liaohe, and Yellow River basins.

Abstract:The Global Precipitation Mission (GPM) Core Observatory that was launched on 27 February 2014 ushered in a new era for estimating precipitation from satellites. Based on their high spatial-temporal resolution and near global coverage, satellite-based precipitation products have been applied in many research fields. The goal of this study was to quantitatively compare two of the latest GPM-era satellite precipitation products (GPM IMERG and GSMap-Gauge Ver. 6) with a network of 840 precipitation gauges over the Chinese mainland. Direct comparisons of satellite-based precipitation products with rain gauge observations over a 20 month period from April 2014 to November 2015 at 0.1 • and daily/monthly resolutions showed the following results: Both of the products were capable of capturing the overall spatial pattern of the 20 month mean daily precipitation, which was characterized by a decreasing trend from the southeast to the northwest. GPM IMERG overestimated precipitation by approximately 0.09 mm/day while GSMap-Gauge Ver. 6 underestimated precipitation by −0.04 mm/day. The two satellite-based precipitation products performed better over wet southern regions than over dry northern regions. They also showed better performance in summer than in winter. In terms of mean error, root mean square error, correlation coefficient, and probability of detection, GSMap-Gauge was better able to estimate precipitation and had more stable quality results than GPM IMERG on both daily and monthly scales. GPM IMERG was more sensitive to conditions of no rain or light rainfall and demonstrated good capability of capturing the behavior of extreme precipitation events. Overall, the results revealed some limitations of these two latest satellite-based precipitation products when used over the Chinese mainland, helping to characterize some of the error features in these datasets for potential users.

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Abstract:As a new development form for evaluating the regional water resources carrying capacity, forewarning regional water resources of their carrying capacities is an important adjustment and control measure for regional water security management. Up to now, most research on this issue have been qualitative analyses, with a lack of quantitative research. For this reason, an index system for forewarning regional water resources of their carrying capacities and grade standards, has been established in Anhui Province, China, in this paper. Subjective weights of forewarning indices can be calculated using a fuzzy analytic hierarchy process, based on an accelerating genetic algorithm, while objective weights of forewarning indices can be calculated by using a projection pursuit method, based on an accelerating genetic algorithm. These two kinds of weights can be combined into combination weights of forewarning indices, by using the minimum relative information entropy principle. Furthermore, a forewarning model of regional water resources carrying capacity, based on entropy combination weight, is put forward. The model can fully integrate subjective and objective information in the process of forewarning. The results show that the calculation results of the model are reasonable and the method has high adaptability. Therefore, this model is worth studying and popularizing.

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