- 无标题文档
查看论文信息

中文题名:

 区域水循环驱动的河流健康趋势预测    

姓名:

 张远    

保密级别:

 公开    

论文语种:

 中文    

学科代码:

 070503    

学科专业:

 地图学与地理信息系统    

学生类型:

 硕士    

学位:

 理学硕士    

学位类型:

 学术学位    

学位年度:

 2018    

校区:

 北京校区培养    

学院:

 地理科学学部    

研究方向:

 生态水文    

第一导师姓名:

 赵长森    

第一导师单位:

 北京师范大学水科学研究院    

提交日期:

 2018-06-04    

答辩日期:

 2018-05-31    

外文题名:

 RIVER HEALTH TREND PREDICTION DRIVEN BY REGIONAL WATER CYCLE    

中文关键词:

 河流健康 ; 指标体系 ; 驱动机制 ; 水循环 ; 趋势预测    

中文摘要:
河流是湿地生态系统、森林生态系统、海洋生态系统等多种生态系统联结的纽带,是人类最重要的水资源获取途径之一,河流通过其永不停息的运动影响到自然界的方方面面,对人类社会的影响尤为严重。对河流健康的研究已有二十多年的历史,但以往对河流健康的研究定量化不足、主观性强,同时对深层次生物驱动机制研究欠缺,难以耦合河流生态系统内部生物因子与外部影响因素,无法定量预测环境变化影响下的河流健康发展趋势,导致生态修复政策方针前瞻性不足。为此本研究建立了一套定量的河流健康评价体系,提高了指标的定量化程度,降低了主观因素的影响,开展了河流健康现状评价及气候变化影响下的趋势预测研究,克服了以往研究中存在的不足,完善了河流健康生物驱动机制。本文以全国第一个水生态文明建设城市—山东省济南市为例,取得的主要成果如下: (1)构建了河流健康定量评价指标体系。在总结前人河流健康相关研究成果的基础上,统计河流健康评价方法中选用的评价指标,建立候选指标集,然后通过相关分析、机理讨论剔除冗余指标,得到了河流健康评价指标体系,并利用遥感、无人机及地面监测数据对指标进行量化。所构建的河流健康评价体系由两级指标构成:一级指标包括河流水质、河流水文、河流生物与河流生境,每个一级指标包括多个二级指标,其中河流水质包括阴离子表面活性剂、总氮、总磷、硫酸盐等12个水质指标;河流水文包括流速与生态需水供需比;河流生物包括鱼类、藻类及底栖动物三类生物的多样性;河流生境包括堤岸稳定性、河道弯曲程度、河岸植被覆盖率等9个指标。二级指标共26个,每个指标的权重由客观定权法计算得到,然后逐级指标加权,计算河流健康得分。 (2)评价了济南市河流健康现状。从河流水文、水质、生物、生境四个方面共26个二级评价指标入手,评价济南市河流健康状况。结果发现在2014-2016年9次采样中,随着时间推移,典型站点河流健康得分具有波动上升/下降的趋势,总体来讲春夏季河流健康状态较秋冬季好,8、9月份往往成为河流健康最好的月份,三年中河流健康状况最好的时间为2015年9月,其次分别为2014年8月与2016年9月。济南市河流健康得分自南向北逐渐减小,位于南部山区的点位河流健康状况较好,且健康状况较为稳定,城区及北部平原上的点位受人类活动影响河流健康状况较差,且年际间河流健康状况波动大。 (3)完善了河流健康生物驱动机制。利用偏最小二乘法分析了河流水生物的关键水文水质驱动因子;然后利用基于食物链的Ecosim模型模拟无外力状态下的水生物演化趋势,与实测水生物多样性对比后得到外界因子变化(如水质变化)引起的水生物波动ΔH;进而根据水文因子变化ΔHY、水质因子变化ΔWQ与生物多样性变化ΔH间的关系,结合偏最小二乘回归建立水文-水质-水生物多维响应模型。结果发现鱼类关键驱动因子包括碳酸盐、溶解氧、重碳酸盐、氨氮、亚硝酸氮、总碱度、总氮、总磷、水深与流速;底栖动物关键驱动因子包括碳酸盐、钙离子、电导率、氨氮、总硬度、总磷、气温、水深与河宽;藻类关键驱动因子包括碳酸盐、高锰酸盐指数、钙离子、氯化物、硝酸氮、总氮、气温、透明度、流量与流速。所建立的以生物多样性为因变量,关键驱动因子为自变量的水文-水质-水生物多维响应模型,经过levene’s检验与t检验发现预测值与实测值在统计学上无显著差异,模拟结果较好。 (4)预测了气候变化影响下的河流健康发展趋势。以济南市行政区内完整的玉符河流域为例,利用IPCC5三种情景(RCP2.6、4.5、8.5)预测数据,驱动RS-DTVGM模型对未来气候变化下径流量进行预测;水量的变化引起水质的联动变化,基于此探究水量与水体污染物浓度的关系,在水文、水质因子变异研究的基础上,结合构建的水文-水质-水生物多维响应模型预测未来河流水生物状况,从而预测河流健康发展趋势,与河流健康现状对比,明确气候变化对河流健康的影响。结果发现相对于现状年,2030年的河流健康状况总体好转,气候变化对水循环的影响传递到了河流生态系统中,改善了河流健康状况,在RCP2.5、RCP4.5以及RCP8.5情景下的河流健康得分均大于现状年,且河流健康得分RCP2.5
外文摘要:
Rivers are the link between wetland ecosystems, forest ecosystems, and marine ecosystems. They are one of the most important ways for humans to obtain water resources. Rivers affect all aspects of nature through their never-ending movements and the impact to society is particularly serious. The research on river health has been more than twenty years, however, in the past, research on river health has been quantified and subjective. At the same time, research on deep-level biological driving mechanisms is lacking, and it is difficult to couple internal biological factors and external influences of river ecosystems. Factors that cannot quantitatively predict the health development trend of rivers under the influence of environmental changes have led to a lack of forward-looking policy for ecological restoration policies. To this end, the study established a quantitative river health evaluation system, which improved the quantification of indicators, reduced the impact of subjective factors, carried out river health status assessment and trend prediction research under the influence of climate change, overcoming previous studies. There are deficiencies in the river, improving the river's healthy biological driving mechanism. This article takes Jinan City, the first city of water ecological civilization in China, as an example. The main achievements are as follows: (1) Build a satellite- unmanned aerial vehicle-ground site integrated river health quantitative evaluation index system. On the basis of summarizing the research results of predecessor river health, the evaluation indicators selected in river health assessment methods were used to establish candidate index sets. Then, redundant indicators were removed through correlation analysis and mechanism discussion, and a river health evaluation index system was obtained. The indicators were quantified using satellite- unmanned aerial vehicle-ground site multi-source data. The constructed river health assessment system consists of two levels of indicators. The first-level indicators include four indicators of river water quality, river hydrology, river biology, and river habitat. Each first-level indicator includes multiple secondary indicators. The river water quality includes anionic surfaces. Twelve water quality indicators such as active agents, total nitrogen, total phosphorus, and sulfate; river hydrology including the ratio of supply and demand for flow rate and ecological water demand; river life including the diversity of fish, algae, and benthic animals; river habitats including embankments Stability, river bend, river bank vegetation coverage and other 9 indicators. There are 26 secondary indicators, and the weight of each indicator is calculated by the objective weighting method, and then weighted by step by step indicators to calculate the river health score. (2) Evaluated the current status of river health in Jinan City. A total of 26 secondary evaluation indicators from four aspects of river hydrology, water quality, biology, and habitat were used to evaluate the health status of rivers in Jinan City. The results showed that during the nine sampling periods from 2014 to 2016, the river health score at a typical point had a tendency of fluctuations to rise/decline over time. Overall, the health status of rivers in spring and summer was better than that in autumn and winter, and August/September was often It is the best month for river health; J1 in the mountainous area has better health status than other rivers, and the health condition is relatively stable. In the rest of the year, the health status of rivers fluctuates greatly. The river health score of J1 in 2014-2016 has been at a relatively high level. The best time for river health in three years was September 2015, followed by August 2014 and September 2016 respectively. The health scores of rivers in Jinan gradually decreased from south to north. The health scores of J1 and J5 in the southern mountainous areas were high, and the interannual changes were not significant. The health conditions were relatively stable; while the northern plains were affected by human activities, the health status of the rivers was relatively low. (3) Improve the biological health driving mechanism of rivers. The key hydrological and water driving factors of river aquatic organisms were analyzed using partial least squares (PLS). Then the Ecosim model based on the food chain was used to simulate the evolution trend of aquatic organisms without external force. The external factors were compared with the actual aquatic biodiversity. (such as water quality) caused by aquatic biological fluctuations ΔH. Then, according to the relationship between hydrological factor change ΔHY, water quality factor change ΔWQ and ΔH, combined with partial least-squares regression (PLSR) to establish a hydrology-water quality-water ecological multidimensional response model. The results found that the key drivers of fish diversity include carbonate, dissolved oxygen, bicarbonate, ammonia nitrogen, nitrite nitrogen, and total alkalinity, Total Nitrogen, Total Phosphorus, Depth of Water and Velocity; Key Drivers of Zoobenthos Diversity include Carbonate, Calcium, Conductivity, NH3-N, TH, TP, Tema, Dep, and Wid; key drivers of algal diversity include carbonate, Permanganate index, calcium ion, chloride, nitrate nitrogen, total nitrogen, temperature, transparency, flow, and velocity The multi-dimensional response model of hydrology-water quality-water biology with biodiversity as the dependent variable and the key driver factor as independent variables was established. There was no statistically significant difference between the predicted value and the measured value by the leven's test and t-test. (4) Predicted the trend of river health development under the influence of climate change. Taking the Yufu River basin in the administrative area of Jinan City as an example, the prediction data of three scenarios (RCP2.6, 4.5, 8.5) of IPCC5 are used to drive the RS-DTVGM model to predict the future climate change downflow; the change in water volume causes The linkage change of water quality is based on the relationship between the water quantity and the concentration of pollutants in the water. Based on the study of hydrology and water quality factor variation, combined with the constructed hydrology-water quality-water biological multi-dimensional response model, the river biological status in the future is predicted to predict rivers. The trend of health development is compared with the current state of river health and the effects of climate change on river health are clarified. The results show that compared to the current year, the health status of rivers generally improved in 2030, the impact of climate change on the water cycle was transmitted to the ecosystem, and the health status of the river was improved. The rivers in the context of RCP2.5, RCP4.5, and RCP8.5 Health scores are greater than the current year, and river health scores RCP2.5
参考文献总数:

 107    

馆藏号:

 硕070503/18016    

开放日期:

 2019-07-09    

无标题文档

   建议浏览器: 谷歌 360请用极速模式,双核浏览器请用极速模式