Fishery Assessment And Sustainable Development In The Xiliao River Of Inner Mongolia

XiLiao River of Inner Mongolia is the geographical position is mainly in Tongliao city and Chifeng city of the eastern Inner Mongolia, there are12banners, counties and districts in Chifeng city, there are8banners, counties and districts in Tongliao city. Most of the water is in Inner Mongolia autonomous region, except for parts in the northeastern of Hebei province. Upstream is in mountains in Chifeng City, and the downstream is in plain in Tongliao City. In the XiLiao River, there are7large reservoirs with a total capacity of3352million m3, irrigation capacity790million m3; there are23medium-sized reservoirs, with a total capacity of437million m3, irrigation capacity of242million m3; there84small reservoirs with a total capacity of156million m3, irrigation capacity of94billion m3.The stock assessment of fresh-water fishery resources of the two cities of XiLiao River, can indicate the development potential and sustainable utilization capacity of the fishery resources in the region. This will provide some scientific evidences for the management, protection and sustainable utilization of the fishery resource in this region. This is also the purpose of this thesis.The gray system theory is put forward by the Chinese scholar professor Deng Julong in1982, it deals with the incomplete information. The basic theory is that all the random variables are regarded as gray variables which changed within a certain range. It transforms the original random data vector into a data vector which has relatively strong orders. Thus we could build continuous differential equation model, weaken uncertainties, strength the orderliness. Grey system may be used for few data and in the grey information condition. In comparison with the traditional methods, grey modeling has no error accumulation problem, and can be used for the long-term and high precision prediction, make up for the deficiency of conventional statistic methods. Fishery system is a multi-factor, multi-layer and multi-object system, which is consisted of many intricate relationships, thus fishery system is a typical grey system and the grey system theory can be used it it. However, there is few reports of the application of grey model in fresh-water fisheries. Therefore, the thesis applied the grey forecasting model to predict the fresh-water culture production and aquaculture area in ChiFeng city and Tongliao city for2009-2013.Using gray prediction model, this thesis predicted fishery production and fishery area in TongLiao city, ChiFeng city and the related reservoirs. In the analysis results of the16data sets, when using5year data for prediction, there are7data sets with prediction accuracy of grade1, there are7data sets with prediction accuracy of grade2, there is1data set with prediction accuracy of grade3, there is1data set with prediction accuracy of grade4, the percentage of accuracy above grade2is87.5%. When using10year data for prediction, there are4data sets with prediction accuracy of grade1, there are5data sets with prediction accuracy of grade2, there are4data sets with prediction accuracy of grade3, there are3data sets with poor prediction accuracy which do not meet the prediction requirement, the percentage of accuracy above grade2is56.3%. When using15year data for prediction (there are12year data in TongLiao city), there are2data sets with prediction accuracy of grade1, there are5data sets with prediction accuracy of grade2, there are6data sets with prediction accuracy of grade3, there is1data set with prediction accuracy of grade4, there are2data sets with poor prediction accuracy which do not meet the prediction requirement, the percentage of accuracy above grade2is43.7%. When using20year data for prediction, there are5data sets with prediction accuracy of grade2, there are3data sets with prediction accuracy of grade3, there are2data sets with prediction accuracy of grade4, there is no data set with prediction accuracy of grade1. When using23year data to predict the fishery production of ChiFeng city, the prediction accuracy is grade2. Therefore, when using short time series data sets the prediction accuracy was high, the long time series data sets have low prediction accuracy, and even do not meet the prediction accuracy requirements. When using of GM(1,1) Gray prediction model to predict the sub-model, the accuracy of change suggested the stability of fish production systems. However, GM(1,1) Gray prediction model can not rule out the affect of extreme weather or policy impact on fishery production system, therefore, this prediction depends on the stability of system. If we use the data based on1998-2001, even the model prediction values back to the generation of relative error which is also very small, but it can not predict the crash of fishery production accurately in2002. Therefore, the key to accurate prediction in fishery system is stability. To make sure how to determine the stability of this system is the focus of study. At present, we can use sub prediction method combining statistical to predict, when we clear some outside interfering factors of gray system, and this method is based on an underlying assumption that the system dose not exist severe mutation in a short time. So we can think that it is accurate for the gray prediction model predicting the production of relative stable short-term, it is not appropriate for a long term extrapolation application. Because of the Xiliao He River Basin drought in a dry environment perennial, the gray prediction model showed that there is still room for the development of fish farming area in Chifeng, and Tongliao fish farming area could not improve under the adverse conditions of natural environment. Even though both of this regional fisheries production has increased year by year, the upgrade potential is really limited. Although sometimes the prediction accuracy of using long and short time series data sets were the same, but the prediction results are far from those in actual production. Then it may be concluded that the grey prediction model is able to make accurate short-term prediction, but may not be used for long-term extrapolation.Because the fishery area in XiLiao River generally declined due to frequent droughts, the fishery production has been limited by the environment carrying capacity. Although the gray model predicted that the fishery production is still increasing year by year, but the potential of increase is very limited.In fisheries management, maximum sustainable yield (MSY) is still one of the management goal. Surplus production models are the classic methods to predict MSY, and are among the main theories and models in modern fish stock assessment and management. It treat fish population or resource stock as a study unit, analyze the relationship between sustainable yield, optimum fishing effort and population size. The required data are a time series of fishery statistics of yield and fishing effort or catch per unit effort, it does not require the biological information of the fish population. Because of simplicity and less data demanding, the surplus production model have been widely used in fish stock assessment. This model is based on the theory of S style population growth, it combines recruitment, growth, and natural mortality together as an one variable function, then derives the appropriate math equations for the surplus production model. In China in1973the Schaefer (1973) model was used to assess the round scad resource in the Pearl river estuary in Guangdong. Since then this model has become one of the familiar models in the fisheries department in China, and had been widely used in the fish stock assessment of marine and large inland fresh water fisheries in China. This thesis applied two classical surplus production models, four non-equilibrium surplus production model, and two software packages developed by European and US scientists, to estimate the maximum sustainable yield (MSY) and fishing effort at MSY (power of fishing boats) of the fisheries of ChiFeng cityFor the fisheries statistics of ChiFeng city, the results of this dissertation showed that different assessment models produced different parameter estimates. On the basis of the results and the original data, I tend to suggest that the MSY of ChiFeng city fisheries may be about8000t. Among the production models used in this dissertation, the non-equilibrium surplus production model (Schnute, W-H, D-Fox, I-Fox) had better performances, in particular the relative estimation errors (REE) of W-H and D-Fox were less than10%. For the Schaefer and Fox type simulated data, the Schaefer and Fox type production models were not sensitive to the simulated data, they achieved relatively good parameter estimates. The results also showed that the moving average and white noises could not significantly influence the results. On the basis of the surplus production model results, the fisheries resource of ChiFeng city have been basically fully utilized, it can be a difficult task if further expansion of the fishery is sought.The implementation of sustainable development strategy of fisheries in the region has very important significance for the economic prosperity and social development. This thesis has put forward the following suggestions:1, to strengthen the regional ecological management and to increase the renewable ability of water resources;2, to strengthen the regional exploitation management to increase the utilization ability of water resources;3, to strengthen the regional water consumption management to increase the saving ability of water resources;4, to strengthen the regional environment protection management to increase anti-pollution ability of water resources;5, to adjust the fisheries industry structure to increase the fishery production ability of the region;6, to increasing the investment in science and technology to ensure steady growth of fishery production;7, to strengthen the construction of infra-structure to safe-guard the fishery production;8, to improve fish stock assessment to provide scientific basis for the fishery production;9, to strengthen the fishery law enforcement management to improve the fishery production management level.