Knowledge Resource Center for Ecological Environment in Arid Area
| SRM模型在玛纳斯河流域春季洪水预警中的应用研究 | |
| 其他题名 | Application of SRM to flood forecast and forwarning of Manasi river basin in Spring |
| 张璞 | |
| 出版年 | 2009 |
| 学位类型 | 硕士 |
| 导师 | 王建 |
| 学位授予单位 | 中国科学院寒区旱区环境与工程研究所 |
| 中文摘要 | 新疆深居内陆，远离海洋，是我国较为典型的高纬度干旱、半干旱地区。天山冰雪带像一个天然水库，用源源不断的冰雪融水，滋润着准噶尔盆地广袤的绿洲。另一方面，由于北疆地区冬季严寒、漫长，积雪较多，春季如遇快速升温天气，导致浅山、山前平原以及中低山积雪消融，极易引发融雪性洪水，对当地农牧业生产和人民生命财产造成较大的损失。玛纳斯河是准噶尔盆地最长的内陆河，全长400余公里，发源于天山北坡，是天山北坡40余条大小河流中径流量最大的河流。由于冰雪融水对玛纳斯河川径流的重要贡献，使玛纳斯河流域成为我国第四大灌区，同时也是新疆融雪性洪水高发的区域之一。本论文以玛纳斯河流域为研究区探讨SRM模型在春季融雪性洪水预警系统中的应用，不仅对开展防灾减灾工作具有指导作用，也对合理开发利用水资源、促进新疆区域国民经济可持续发展，具有十分重要的意义。\nSRM模型是具有一定物理性质的半分布式模型，对于地理环境特殊的天山山区来说，模型变量、参数的合理确定和获取将成为本研究攻克的难点。模型变量主要包括借助遥感手段获得的积雪面积，实测的气象、水文数据。模型参数主要包括温度直减率、度日因子、径流系数等。重点要解决的有四个问题：一是如何合理解决流域的分带问题；二是如何获取流域分带积雪覆盖率；三是流域水文特征参数的率定优选；四是模型输入变量的预报。\n（1）制定流域分带体系\n天山山区流域高差大，各高度带水文气象特征不一致，模型参数也相应地具有一定的分带规律性。因此，按照高程分带提取积雪覆盖率可以提高融雪径流模拟和预测精度。根据天山地区自然地理分带情况，将玛纳斯河流域按高程分为4个高程区，流域海拔3600m以上为高山终年积雪区，为带4；海拔1500-3600m为中山区，山峦叠嶂，沟壑纵横，其中海拔2700-3600m地表植被发育较好，为高山高寒草甸，为带3；1500-2700m之间多天山云杉、灌木，是降雨径流主要形成区，为带2；1500m以下至600m为低山丘陵区，为带1。\n（2）流域各分带积雪覆盖率的计算\n积雪覆盖产品是一种栅格数据，使用DEM与遥感图像配准后进行一系列的图像逻辑和代数运算，可以达到提取流域和分高程带雪盖面积的目地。遥感数据和地形数据进行复合分析最为重要的是数据类型和格式的转换与几何配准。由于地形数据来源不同，比例尺和投影也有差异，所以在信息复合进行空间分析之前需将数据转换到一个统一的坐标体系当中去，研究采用的是双标准纬线等积圆锥投影（ALBERS 投影）。MODIS遥感数据在几何校正过程中均按这一投影方式进行变换。对于MODIS原始数据的选择，时间主要集中在融雪期，并尽量选择无云或少云的图像资料。考虑到西部山区，尤其是天山地区云量特别多，采用NASA EOS/MODIS八日合成积雪覆盖产品MOD10A2，这样可以最大程度地降低云层的影响。\n（3）流域水文特征参数的率定优选\nSRM融雪径流模型被认为是一种半理论，半经验的应用模型，其参数表征了特定流域水文特征参数。实验中，首先对玛纳斯河流域的径流特征进行分析，然后分别给出流域温度、雪盖面积及降水间的关系。在此基础上，调试径流系数、度日因子、临界温度、降雨贡献面积、退水系数等模型参数，测试其稳定性。通过模拟和实测的流量过程线的比较，可以比较直观的检验和评价模拟结果的效果。SRM模型采用两个常用的精度分析指标来评价模拟结果。即，无量纲的拟合优度确定系数Nash-Sutcliffe系数 R2 和体积差DV[%](Martinec and Rango, 1989),R2的数值范围是0到1, 值越大表示模拟精度越高。DV可以是任何数值, DV的绝对值越小表明模拟的结果越好。根据世界气象组织对主要融雪模型的评价结果，SRM 在融雪季节模拟中R2平均值为0.811，DV的平均值为5.97% 。本次模拟结果(2004年)的精度指标R2＝0.94和DV＝-1.4%，达到了比较好的模拟效果。\n（4）预报变量获取\n降水、气温等气象数据是融雪径流模型的重要输入参数，由于天山山区流域内水文站、气象站点稀少，且都集中在河流出口断面附近，因此，利用流域分带内的水文、气象站点观测数据插值获取气温、降水数据具有一定的困难，同时缺乏短期预报数据。针对上述问题，尝试引入中国气象局T213数值预报产品（温度格点场、降水格点场数据）输入SRM模型；积雪覆盖率预测参考当前数据和历史平均数据调整得到。本文利用对未来三天的温度、降水和流域分带雪盖百分比预测值循环带入2004年3月～6月融雪期，对未来三天的肯斯瓦特日径流量进行预报，精度指标R2>=0.87和|DV|<=4.9% 。\nSRM模型作为世界气象组织唯一推荐的融雪径流模拟模型，在世界各地很多流域进行了成功的模拟，具有简单易用，精度较高的特点。本文尝试使用中国气象局T213数值产品来进行流域分带温度和降水的预报，应用SRM 模型对玛纳斯河肯斯瓦特水文站日径流量进行预报，取得了较为满意的结果。下一步研究的有两个关键问题，一是提高山区积雪制图的精度；二是利用计算机技术，将优化数学算法应用到流域水文模型参数的率定优选上。这将进一步提高模型模拟、预报的精度。 |
| 英文摘要 | Located in the hiding inland far from ocean, Xinjiang is one of the typical arid and semi-arid regions of China. Like a natural reservoir, the ice and snow zone of Tianshan irrigates the grand oasis around Junggar basin with its endless snowmelt water. Meanwhile, due to the cold and long winter with thick snow in the northern Xinjiang, snowmelt flood is prone to be induced by rapid warming weather that contributes to the snowmelt in the shallow mountains and piedmont plains as well as low mountains, which caused great loss upon the local agricultural production and people's lives and property. As the longest inland river in the Junggar basin, Manas river is more than 400 kilometers long and posses the largest runoff among the 40 or more rivers originated from the north slope of Tianshan. The snowmelt water contributes greatly to the runoff of Manas basin, which make it the forth-largest irrigation district all around China and meanwhile one of those regions where snowmelt flood most likely to break out. Adopting the Manas basin as the researching field, this paper discussed the application of SRM to the forewarning system of springtime snowmelt flood, which would not only play a great guiding role in disaster prevention and mitigation, but also benefit the reasonable utilization of water source and promotion of the sustainable development of the national economy of Xinjiang.\nSnowmelt Runoff Model (SRM) is a semi-distributed model that posses some certain physical features. The Tianshan mountain has special geophysical environment, proper selection and obtaining of the model variables and parameters will thus be the difficulty for this researching work. The model variables majorly contain the snow cover area inversed by remote sensing methods and the hydro-meteorological observations, while the model parameters mainly consist of temperature lapse rate, degree-day factor and runoff coefficient. There are four key issues for further research, including properly settlement the zoning issues of the basins, obtaining the snow coverage over different elevation zones, optimization the hydrological characteristic parameters, and prediction the model input variables.\n(1)Establishment of basin zoning system\nThe elevation difference of Tianshan basin is significant and its hydro-meteorological characteristics differ as well, the model parameters accordingly obey some zoning laws. Therefore, calculation of the snow coverage in terms of elevation zones will improve the snowmelt runoff simulation and prediction accuracy. According to the geophysical zoning condition of Tianshan mountain, the Manas basin can be divided into four elevation zones, the region with altitude over 3600m is the alpine nival zone and is regarded as the zone 4; the zonal region with elevation between 1500-3600m is the middle-mountain zone which featured with overlapping mountain peaks and ravines, while the earth surface vegetations grow well in the zone between 2700-3600m, thus called alpine meadow, which is referred to as the zone 3; Tianshan spruce and shrubs are flourish between 1500-2700m altitude, which is the main forming region of precipitation runoff and is the zone 2; the region with elevation lower than 1500m and higher than 600m is the hilly area where is regarded as the zone 1.\n(2) The calculation of the snow coverage of individual basin zone\nSnow cover product is a kind of grid data, conduction of a series of image logic and algebraic operations after correction of DEM with remote sensing figures will serve the purpose of calculating the snow coverage of basin and elevation zones. The most important points in complex analyzing of remote sensing data and topography data are transformation and geometric registration of data type and format. Due to different topographical data source and scale as well as projection, all the data used need to be transferred into a unified coordinate system before combined information are spatially analyzed. The ALBERS projection is adopted in this research and is also used for the transformation of MODIS remote sensing data in geometric correcting process. The time period for MODIS raw data selection mainly concentrates at snowmelt season and the no-cloud and less-cloud image information should be mostly chose. Considering the particularly large cloud cover in the western mountain, especially in Tianshan, adoption of MODIS/Terra Snow Cover 8-Day L3 Global 500m Grid (MOD10A2) data would minimize the impact from cloud.\n(3) Optimization of the basin hydrological characteristic parameters.\nSRM is thought to be a semi-theoretical and semi-experiential operational model with its parameters indicating the hydrological featuring parameters for certain basins. In the experiments we conducted, the runoff characteristics of Manas basin are firstly analyzed, and the relationship between precipitation and basin temperature as well as snow cover area are then respectively given. Based on which, the runoff coefficient, degree-day factor, critical temperature, rainfall contributing area and recession coefficient of the model are adjusted to test its stability. Through comparing between simulated and observed runoff process curve, the modeling output can be directly checked and evaluated. \nSRM additionally uses two well established accuracy criteria, namely, the coefficient of determination, R2, and the volume difference, DV[%](Martinec and Rango, 1989), the value range of R2 is from 0 to 1. The larger the value of R2 is, the better the simulation accuracy is. DV could be any value, and the smaller its absolute value is, the preferable the modeling outcomes are. According to the assessment results of WMO upon major snowmelt models, the mean of R2 is 0.811 and the mean of DV is 5.97% for springtime simulation by SRM. The accuracy criteria of this simulation outputs (2004), R2＝0.94 and DV＝-1.4%, which has reached a relatively good simulation effect. \n(4) Obtain prediction variables\nPrecipitation and temperature as well as some other such meteorological observations are all important input parameters for SRM. Due to the fact that the hydrological and meteorological stations in Tianshan mountain are rare and what’s worse, they mostly concentrate at the river export section, it is somewhat difficult to obtain the temperature and rainfall data through interpolating the observations from those meteorological and hydrological stations distributed inside the basin zone, meanwhile, it is also short of short-term forecasting data. Aiming at the above issues, the China Meteorological Administration (CMA) T213 numerical prediction products including temperature grid field and precipitation grid field observations are tried to be introduced into SRM and the snow coverage prediction are gained through referring to the current observations and historical mean. Through introducing the prediction of the coming three days’ temperature, rainfall and snow coverage of elevation zones into the snowmelt season from March to June in 2004, this paper forecasted the future three days’ daily runoff of Kensiwate hydrological station with its accuracy indicator R2>=0.87 and |DV|<=4.9%.\nAs the only snowmelt runoff simulation model recommended by the World Meteorological Organization (WMO), SRM has conducted successful simulations in various basin all around the world, which is quiet easy to use with high accuracy. Through trying using the T213 numerical products from CMA, the temperature and precipitation of elevation zones are predicted in this paper, from which satisfying outcomes were obtained. Further research would contains two key issues, one is the improvement of the high-mountain snow cover mapping accuracy, the other is the application of optimized mathematic arithmetic to the optimization of basin hydrological model parameters, which will further improve the simulation and prediction accuracy. |
| 中文关键词 | SRM模型 ; 融雪径流 ; 玛纳斯河 ; T213 ; 气象数值预报 |
| 英文关键词 | SRM snow-melting runoff Manasi river T213 Numerical Forecast Products |
| 语种 | 中文 |
| 国家 | 中国 |
| 来源学科分类 | 地图学与地理信息系统 |
| 来源机构 | 中国科学院西北生态环境资源研究院 |
| 资源类型 | 学位论文 |
| 条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/286768 |
| 推荐引用方式 GB/T 7714 | 张璞. SRM模型在玛纳斯河流域春季洪水预警中的应用研究[D]. 中国科学院寒区旱区环境与工程研究所,2009. |
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