黄河流域生态文明建设是关系中华民族永续发展的根本大计，要做到黄河水资源节约集约利用，以及生态环境保护，必须要实现黄河水文要素的实时监测以及黄河流域水生态系统稳定性的定量评价。本研究以黄河流域为研究区，结合无人机、卫星数据，首先提出了能够反演水文要素的方法；其次基于食物网模型确定了水生态系统中的关键物种，通过分析水文、水质生态位初步筛选影响生态系统稳定性的关键环境因子；最后利用关键环境因子和水生物的变化情况，定量分析了水生态系统的稳定性。

（1）利用遥感手段反演河流断面、流速与流量：利用无地面实测信息断面反演方法（Riba-zero）提出了遥感反演河流过水断面的方法，其主要优点在于能够不依赖于任何地面资料，仅根据遥感数据获取的水上断面信息即可实现过水断面反演，填补了遥感手段难获取水下地形的缺陷；改进了利用反射率计算河流流速的方式，提出了一种跨空间C/M流速计算方法（Transcaled C/M-V）的，克服了原方法赖多个站点资料以及忽略空间上河流的形状分异性、光谱异质性的不足；发展了VHR-AMHG河流流量计算方法将设有水文站点河流的资料推广到流域内未设站河流，估算缺资料区河流流量。河流断面的反演的结果表明：综合汛期非汛期，统计黄河中游段所有站点的水下断面面积的反演误差，总体均方根误差（RMSE）为69.59 m²，相对误差为27.76%，相对均方根误差rRMSE为0.49；汛期RMSE为102.52 m²，相对误差为34.12%，rRMSE为0.57；非汛期RMSE为59.06 m²，相对误差为24.00%，rRMSE为0.40；汛期的水下地形反演精度要差于非汛期。对于河流流速的反演，总体RMSE为0.12m/s，平均相对误差为19.00%，rRMSE为0.36；汛期RMSE为0.12m/s，平均相对误差为14.56%，rRMSE为0.15；非汛期RMSE为0.12m/s，平均相对误差为20.88%，rRMSE为0.25。相比较于原方法，本章提出方法的rRMSE为0.36，而原方法的rRMSE为1.33，几乎是本章提出方法的4倍。对于河流流量的反演，VHR-AMHG计算精度更高，估算流量与实际流量的RMSE为32.15 m³/s，相对误差为0.61，rRMSE为0.71，利用本文提出的VHR-AMHG可用于缺资料区的河流流量估算，为缺资料区可持续水资源管理提供了新的技术方法，也为河流生态稳定性评价奠定了水文数据基础。

（2）以食物网模型为基础筛选关键环境因子：基于食物网模型（Ecopath）筛选水生态系统浮游植物群落关键物种，通过生态学生态位宽度经典模型，分析了关键物种水文、水质生态位，筛选了影响生态系统稳定性的关键环境因子。研究区筛选出浮游植物的关键物种有舟形藻、菱形藻、色球藻、肘状针杆藻、四尾栅藻共5种。在水文因子梯度下，浮游植物关键物种中，菱形藻生态位宽度最大，色球藻生态位宽度最小，5类关键物种整体上沿流速、水深的生态位宽度较大，表明在该两类水文因子梯度下各关键物种可以互补式生长，因而在稳定性分析中需要考虑；采样时期为枯水年，虽然流量的空间分异较小，但流速、水深的空间分异较大，为避免枯水年采样对关键环境因子筛选的影响，直接将流速、水深、流量共同纳入作为环境因子的初筛结果提供给后文水生态系统稳定性评估；在水质物理因子梯度下，舟形藻具有最大的生态位宽度值，色球藻的生态位宽度值最小，5类关键物种沿电导率、水温和浑浊度的梯度有较大的生态位宽度，纳入环境因子初筛结果；在水质化学因子梯度下，舟形藻同样具有最大的生态位宽度值，而肘状针杆藻的生态位宽度值最小， 5个关键物种在TN、TP、NO₂-N、DO、NO₃-N以及NH₄-N的生态位宽度较大，需要在稳定性分析作为重点环境因子来考虑。

（3）定量化构建水生态系统稳定性指标：利用典型相关分析（CCA）进一步筛选了关键环境因子，并耦合浮游植物群落生物量构建了浮游植物-环境变化响应模型，定量分析水生态系统浮游植物群落的稳定性、恢复力和抵抗力，基于浮游植物群落稳定性与生物多样性建立了一套评价水生态系统健康状况的标准，解决了水生态系统状况定量化评价难的问题：济南市浮游植物群落的水文关键驱动因子空间分异大，流速是主要水文关键驱动因子；水质化学关键驱动因子为总氮(TN)和总磷(TP)；水质物理关键驱动因子包括水温、浑浊度和电导率，其中，电导率出现的频率最高；研究区浮游植物平均稳定性为0.35，群落稳定性的最高值出现在2016年6月 (0.94)，群落稳定性的最低值出现在2016年7月 (0.00)。浮游植物群落平均恢复力为0.51，而最高值与次高值分别为1.00 (2016年6月)和0.83(2015年6月)，均出现在6月, 在2016年7月达到最小值 (0.00)。 综合抵抗力的最高值出现在2016年6月 (0.88)，次高值出现在2015年6月 (0.76)。浮游植物群落恢复力处在最高值时期，水文因子、水质物理和水质化学因子的抵抗力均为五个时期中的最高或次高；浮游植物群落恢复力处在最低值时期，沿水文因子、水质物理和水质化学因子的抵抗力均为五个时期中的最低，且群落的稳定性、群落恢复力、综合抵抗力的最高值与最低值出现时期与群落的生物多样性吻合, 变化趋势与生物多样性基本一致；基于稳定性和生物多样性的关系并对照生物多样性评价生态系统健康标准，得出当群落稳定性处于[0.35,0.40]之间时，生态系统健康处于一个临界点状态，而当群落稳定性大于0.40时，生态系统健康处于健康状态，当群落稳定性小于0.35时，生态系统健康处于不健康状态。

The construction of ecological civilization in the Yellow River Basin is a fundamental plan that concerns the sustainable development of the China. To achieve the better use of the Yellow River's water resources it is necessary to propose a real-time monitoring method of the Yellow River's hydrological parameters and quantitatively assess of the stability of the Yellow River Basin's river ecosystem. Combining unmanned aerial vehicles and satellite data, a method for inverting hydrological parameters was first proposed. Secondly, the key species of phytoplankton in the riverine ecosystem were determined based on the food web model, and the hydrological and water quality niches of those species were analyzed to initially select the important environmental factors for assessing the stability of the rivering ecosystem. Finally, by quantifying the relationship between environmental factors and the biomass of phytoplankton communities, and stability of rivering system is quantitatively assessed.

(1) Using remote sensing technology to inverse cross-section, river velocity and river discharge: a newly proposed method was proposed to to estimates river bathymetry with zero ground measurement at ungauged rivers (the Riba-zero method), and can achieve that by only using remote sensing data, thus filling the gap that the remote sensing method is difficult to obtain underwater cross-section; a newly developed C/M method (transcaled spatial C/M-V method) proposed to improve the original method, breaking through the bottleneck of the original method that requires a high number of hydrological stations, and taking the spatial heterogeneity and spectral heterogeneity of rivers into account; the VHR-AMHG method was developed to apply the priori knowledge of one hydrological station to those ungauged rivers for estimating the river discharge in data-scarce regions. As for river cross-section inversion, the RMSE of the estimated underwater cross-section area of all stations in the middle Yellow River regardless of season is 69.59 m², the relative error is 27.76%, the rRMSE is 0.49; in the flood season, the RMSE is 102.52 m², the relative error is 34.12%, the rRMSE is 0.57; in the non-flood season, the RMSE is 59.06 m², the relative error is 24.00%, the rRMSE is 0.40. The precision of estimating the underwater cross-section area in the flood season is worse than that in the non-flood season. As for river velocity inversion, the overall RMSE is 0.12m/s, the relative error is 19.00% and the rRMSE is 0.36; in flood season, the overall RMSE is 0.12m/s, the relative error is 14.56% and the rRMSE is 0.15; in non-flood season, the overall RMSE is 0.12m/s, the relative error is 20.88% and the rRMSE is 0.25, and our transcaled spatial C/M-V method performs 4 times better than original method. As for river discharge inversion, the proposed VHR-AMHG method was able to calculate river discharge more precisely, with RMSE of 32.15 m³/s, relative error of 0.61 and rRMSE of 0.71, indicating that VHR-AMHG method could be used to calculate discharge in data-scarce rivers in the study area, and provides new technical methods for sustainable water resources management in data-scarce regions, and also lays a hydrological data foundation for river ecological stability evaluation.

(2) The foodweb model (Ecopath) was firstly used to select the keyspecies of phytoplankton communities in the river ecosystem; then using classical niche breadth model to analyze the hydrological and water quality niches of those keyspecies for selecting the key environment factors that influences the stability of river ecosystem. Among the five keystone species of phytoplankton, Nitzschia showed the largest mean niche breadth (1.861), while Chroococcus showed the smallest mean niche breadth (1.473). The niche breadth of five species are quite large in the gradience of river velocity and river depth, indicating a compensatory growth of each species, therefore, river velocity and river depth are supposed to be taken into account when assessing riverine stability. But considering that the sampling of hydrological information were during dry year, the river discharge shows small spatial differentiation, while the river velocity and river depth show significant spatial differentiation. Therefore, overlooking the effect of river discharge might cause bias in stability assessment, and river velocity, river depth and river discharge were selected as the initial result of hydrological factors. In gradience of water physical factors, Navicula sp. had the largest niche breadth and the niche breadth of Chroococcus sp. was the lowest. The niche breadth in the gradience of conductivity, water temperature and turbidity are large, so the conductivity, water temperature and turbidity were selected for stability assessment. In gradience of water chemical factors, Navicula sp. had the largest niche breadth and the niche breadth of Synedra ulna was the lowest. The niche breadth in the gradience of TN, TP, NO₂-N, DO, NO₃-N and NH₄-N are large, and they are selected for stability assessment.

(3) Key driving factors were further selected by CCA, then combining with biomass of phytoplankton communities, the phytoplankton–environment response model was established, which can quantatively analyze the stability, resilience and resistance of the river ecosystem. The results show that, the hydrological key driving factors of phytoplankton communities are different within study area, and velocity is the main key driving factor. The water quality chemical key driving factors the stations have in common are TP and TN, whereas water quality physical key driving factors include WT, Turb, and Cond with Cond being the most common. The average stability of phytoplankton communities was 0.35, ranging from 0.94 in June 2016 to 0.00 in July 2016. The average resilience was 0.51, the highest and second highest values being 1.00 (June 2016) and 0.83 (June. 2015), respectively. Both occurred in June. The lowest at 0.00 was observed in July 2016. The maximum value of IR was in June 2016 (0.88), the second highest value was also in June 2015 (0.76). IR in May. 2016 (0.40) is smaller than IR in November 2016 (0.57). IR of phytoplankton communities showed different trends in different periods, which is consistent with the trend of resilience and revealing positive relationship. Resistance to hydrological and water quality chemical and water quality physical factors are at the highest or second highest in the five periods with BA, during which the resilience of the phytoplankton communities reached the highest. Resistance to the said factors are the lowest when the resilience of the community is at its lowest. Trends of resilience, resistance, and stability are synchronized with that of biodiversity of phytoplankton communities. Based on stability and biodiversity, we developed a set of criteria for quantitatively assessing the health status of ecosystems. When stability is between 0.35 and 0.40, health of Jinan riverine ecosystem is at a tipping point; above 0.4, healthy status; below 0.4, poor status.