【作者】 刘超；

【导师】 秦毅；

【作者基本信息】 西安理工大学 ， 水文学及水资源， 2004， 硕士

【摘要】 针对西北电网水调部门提出的开发黄河上游径流中长期预报软件的任务，根据WMO和葛守西的总结，本论文利用四个系统模型和一个概念模型对黄河上游的龙、刘两库的汛期入库径流量进行旬径流预报尝试，并对所用的模型进行了泛化性、稳健性、模型结构、对水文信息反映和实用性及作中长期预报时何种系统模型的输入最好作了比较研究，为龙、刘两库的径流中期预报模型的采用提出了建议。得出的研究结论和主要成果如下： (1)在资料条件较好时，多元回归模型、概化坦克模型、神经网络模型和具有成因概念的系统模型都具有一定的实用性。 (2)不论资料是否短缺，神经网络模型在率定期、在检验期的结果都很接近，这说明神经网络模型稳定，且泛化性和实用性都比较好。但由于它无法做实时修正，从而影响了它的信息性。 (3)SMG模型不但实用而且可以进行实时的修正，使其预报精度能够得到较好的保证，且不论在率定期、还是在检验期、不论对特丰径流预报，还是对特枯径流预报，他们的精度都比较接近，说明模型具有一定的泛化性。它成功的原因是模型的结构由有物理成因概念的水文要素表达式经推导而得的，具有一定的成因概念基础。西安理工大学硕士学位论文 (4)多元回归模型和概化的水箱模型，在建模时精度较高，检验时下滑，而且对资料系列长度的要求比其它模型要多得多，在资料不足12年的情况，建议不要使用这两个模型。 (5)实时修正对预报模型很重要，但在没有新资料信息输入时，用神经网络作实时修正，反而会影响预报的效果。 (6)不论何种模型，进行合理的分区和准确的信息输入都是提高模型预报精度的一个有效的方法。 (7)通过对比分析，认为加衰减递推实时修正的 SMG模型是最适合黄河上游的中长期水文预报模型，神经网络次之。 (8)新安江(三水源)模型在红旗、唐乃亥流域的中期预报建模时失败。分析原应，可能有三，一是模型本身不适用;二是资料不足，参数难以合理率定;三是参数率定的经验不足。就红旗、唐乃亥流域的水文特征分析而言，取得如下结果。 (9)挑河流域和唐乃亥流域内不论是降水还是下垫面条件，上下游都存在着明显的差异，这种差异在挑河流域以崛县站为分界，确定崛县站以上流域是挑河的主要产流区;在唐乃亥流域以玛曲站为分界，确定玛曲站以上流域是唐乃亥的主要产流区。更多还原

【Abstract】 Following the requirement of the reservoir operation section of North-west electricity management bureau in developing a 10 day runoff forecasting scheme and considering the situation of no suitable forecasting model to serve the purpose, this thesis, from Ge shouxi’s and WMO’s point, defined a group of standards which are practicability, information ability which means the ability of a model to adopt information contend in observed data, extension capacity and robustness, being used for evaluating performance of a forecast model and with them makes a comparison among systematic models as multi-regression model, generalized tank model, neural network method and system model of genesis (in short SMG), and one conceptual model, the Xinganjiang model. After this, a suitable model was proposed to be used to forecast 10 day runoff towards Liujiaxia and Longyangxia reservoirs both of them being settled on the upstream of the Yellow river. The conclusion drawn from the studies are as follows.(1) All systematic models mentioned above are of practicability when there is adequate observed data in rainfall and runoff.(2) No matter what is the data condition, being in plentiful or shortage, the performance of neural network model showed being more or less the same both in calibration and verification stages,which means that neural network model is good at robustness and extension capacity. The forecast result showed that the model has no information ability because it fails to use updating model.(3) SMG model is not only good at practicability but also information ability due to real time updating, which guarantees the precision of the model forecasts. And no matter what stage is, calibration or verification, and what kinds of runoff situation, most plentiful runoff or most shortage, very close forecast precision in each case is obtained. It is suggest that the SMG model be as neural network model in extension capacity. This may be because it comes from deduction of formula with genesis base.(4) Both multi regression model and generalized tank model have high precision in calibration stage, but sharply decrease in verification. Beside that, the quantity of observed data is vital for these two models. So it is suggest that the models be not adopted when data sequence is less than 12 years.(5) Real time updating is quite important for good forecast. However, if there is no new information joining the input of updating model, the neural network updating will not only lose its effective force but also bring even worse results.(6) Dividing catchment into subbasin according to physiographic characteristics of the catchment and inputting information which well represents current situation is a very effective method in increasing forecast precision for all the systematic models mentioned.(7) From analysis, the SMG model with decaying recursion updating is the most suitable model for reservoir 10 day runoff forecast at Liujiaxia and Longyangxia. And neural network model is the next.(8) The Xinanjiang model has been failed to be calibrated in both catchment. There are perhaps three reasons for that. The one is the model being not suitable to the region; the tow is inadequate observed data for calibrating parameters of the model and the three is lack of the experience of the user in use of the model.As to the hydrological characteristics of the basin, the last conclusion comes from that no matter which catchment, Tao or Tangnihai basin, there are significant differences in rainfall and runoff between upstream and downsteam. For Tao river basin, the key runoff area located at Mianxian and upward region, and Maque and upwards for Tangnihai basin.更多还原