共查询到20条相似文献，搜索用时 140 毫秒
1.
2.
3.
采用太湖湖泊生态系统研究站太湖梅梁湾富营养化四种主要驱动因子叶绿素a(Chl.a)、水温、总磷(TP)、总氮浓度1992-2010年的监测数据,利用小波变换方法,分析了四种因子不同时间尺度上的时间格局特征.结果表明,四种驱动因子在年际和年代际尺度上都具有清晰的多时间尺度特征,主周期各不相同,但Chl.a和TP浓度的小波实部变化趋势具有较高的一致性.本文分析了各因子在不同时间尺度上的强弱分布以及高低交替,并在此基础上从时间尺度的角度预测出在未来3~5年内,太湖梅梁湾湖区的富营养化程度将保持在一个比较平稳的水平,为太湖富营养化的认识和治理提供更加可靠的科学依据. 相似文献
4.
湖泊藻型富营养化控制——技术、理论及应用 总被引：73，自引：7，他引：66
湖泊富营养化防治走过了从控制营养盐、直接除藻、到生物调控、生态工程及生态恢复等艰难历程,各国为此投入了巨额资金、然而收效甚微,富营养化依然是全球性重大水环境问题。回顾和分析富营养化湖泊治理研究与实践的成功经验与失败教训,无疑将有助于采取更切实可行的技术有效控制湖泊富营养化。综观全球营养养化治理研究成果,不难看出,富营养化是一个典型的生态问题,生态问题只有用生态学方法解决。在全湖性富营养化难以快速根治的情况下,如何集中技术优势和有限财力,优先解决对人类生活影响较大的局部水域富营养化问题,逐步修复受损的湖泊生态系统,提高水体自净能力、改善水体环境质量并建立湖泊健康生态系统。 相似文献
5.
湖泊氮素生物地球化学循环及微生物的作用 总被引：21，自引：2，他引：19
氮素是影响湖泊富营养化的关键元素之一,对湖泊中氮素生物地球化学循环整个过程进行全面的了解,有利于对湖泊富营养化进行控制和治理.本文综述了湖泊生态系统(特别是富营养化湖泊)中氮素的输入、输出及其在沉积物-水界面的迁移转化规律,着重分析和比较了藻型湖泊和草型湖泊的不同食物链中的氮素营养循环过程,重点讨论了微生物参与的硝化作用、反硝化作用、生物固氮和厌氧氨氧化等过程的最新研究进展,并对氮循环相关的研究方法和技术进行了小结.最后指出当前国内外研究中亟待解决的问题,并对湖泊氮循环今后的研究方向提出了建议. 相似文献
6.
7.
三十年来长江中下游湖泊富营养化状况变迁及其影响因素 总被引：9，自引：5，他引：4
为弄清长江中下游通江/历史通江湖泊富营养化现状、成因及修复策略,对该区域27个大型湖泊和水库开展了4个季度的水质调查,并结合部分湖泊1988-1992年及2008年两个时段富营养化调查成果,分析近30年来长江中下游地区大型湖泊富营养化关键指标变化的特征及其驱动因素.结果表明,目前该区域绝大多数湖泊处于富营养水平,较1980s有明显加重,浮游植物叶绿素a及总磷是最主要的营养状态指数贡献因子;湖泊的富营养化状况与湖泊的江湖连通状况、换水周期等流动性状况、渔业养殖及管理、流域纳污、治理强度等人类活动方式和强度密切相关;与历史调查结果相比,氮、磷的增幅相对较小,而有机质污染程度明显加重、浮游植物叶绿素a浓度大幅增高,表明营养盐之外的其他因素,如水文节律的变化、江湖阻隔、不合理的渔业养殖活动等,对该区域湖泊的富营养化问题加剧、浮游植物生产力增高起到更为重要的作用.因此,从治理途径和策略上来看,增加湖泊的流通性、恢复部分湖泊的自然水文波动节律、优化湖泊渔业管理、提升湖泊流域营养盐的有效截留能力、实施湖泊生态修复工程是控制长江中下游湖泊富营养化、提升区域湖泊生态质量的关键. 相似文献
8.
综合营养状态指数(TLI)在夏季长江中下游湖库评价中的局限及改进意见 总被引：1，自引：0，他引：1
综合营养状态指数(TLI)在中国湖库富营养化评价中应用非常广泛.对于该指数的各分项指标,基于叶绿素a的评估结果是富营养化风险的直接体现,是最终指示;而基于理化指标(总氮、总磷、透明度和高锰酸盐指数)的评估结果是间接指示.如果两者TLI评估结果存在显著差异,则说明基于理化参数的TLI评估结果低估或者高估了实际富营养化水平和相关风险.本文针对长江中下游湖库的基于水质理化指标和基于叶绿素a的TLI结果是否匹配的问题开展了调查分析.结果表明,对于非通江浅水湖泊而言,基于总氮、总磷、高锰酸盐指数的TLI评估结果均低估了富营养化水平和相关风险;对于通江浅水湖泊而言,基于总氮、总磷和透明度的TLI评估结果高估了富营养化水平和相关风险,而基于高锰酸盐指数的结果低估了富营养化水平;对于深水水库,基于总氮的TLI指数评估结果高估了富营养化水平,而基于总磷、透明度和高锰酸盐指数的结果低估了富营养化水平.上述水质理化指标和叶绿素a评估结果不匹配的原因为以下两点:第一,部分物理化学指标失去了对富营养化风险(叶绿素a)的指示意义,如通江浅水湖泊的总氮、总磷、透明度和高锰酸盐指数以及深水湖泊的总氮;第二,部分富营养化理化指标和叶绿素a原有关系发生错位,比如对于深水湖泊总磷对叶绿素a的响应比TLI指数构建所采用的关系更加敏感.针对TLI理化指标评估在长江中下游湖库应用中存在的问题提出如下改进建议:1)结合长时间序列历史数据,基于分位数回归等方法构建特定湖泊的叶绿素a和理化参数的响应关系,开发“一湖一策”的评估公式;2)根据换水周期和湖泊面积水深比对进行湖泊分类,建立特定湖泊类型的叶绿素a和理化参数的响应关系,构建“一类一策”的评估公式;3)在富营养化评估结果中应分别量化富营养化状态参数(营养盐水平)和富营养化风险参数(叶绿素a)以及两者比值,但生物指标是富营养化评估的最终指示.现阶段我国富营养化评价和管理多为“全国一策”,可能很难满足经济高效的管理需求.因此,本研究所建议和综述的“一类一策”和“一湖一策”的湖泊富营养化评估方法对未来的湖泊生态管理可能具有重要意义. 相似文献
9.
10.
环太湖河流进出湖水量及污染负荷(2000-2002年) 总被引：27，自引：5，他引：22
天然水域,尤其是富营养的浅水湖泊,沉积物中磷的释放是蓝藻水华发生、形成和持续生长的重要因素．分析沉积物中磷的赋存形态转化及其潜在生态效应,有助于理解沉积物中磷的迁移转化过程及其与湖泊富营养化之间的关系．本文综述国内湖泊水域中磷的主要形态、来源和转化过程以及其生物有效性的研究进展．重点讨论了近5年来中国东部浅水湖泊沉积物磷的形态分析、转化和生物有效性评估的现状,以及沉积物中磷形态与浅水湖泊富营养化之间的潜在联系． 相似文献
11.
根据洪湖2014—2019年水质及藻类监测数据,运用综合营养状态指数法评价了丰、平、枯3个时期的营养状态.在此基础上运用逐步回归分析法确定影响藻类生长的显著因子,并根据不同水量不同营养状态细分9种情形对藻类生长做回归预测分析,同时运用BP神经网络模型对回归预测的结果进行比较验证.结果表明:洪湖丰、平水期以蓝藻门为主,枯水期以硅藻门为主;湖泊的营养状态处于中度富营养与轻度富营养之间.分析各时期藻种生物量与影响因子的相关性,发现丰水期控制因子有水温、CODMn和透明度;平水期和枯水期控制因子有水温、总氮、总磷.以2014—2018年数据逐步回归分析得出枯水期+中营养和枯水期+轻度富营养决定系数较低,其余7种时期决定系数均在0.5以上,说明逐步回归并不适用于所有时期.使用2014—2018年的数据进行神经网络训练和验证,2019年的数据进行预测,比较BP神经网络与逐步回归的均方根误差发现全年预测时BP神经网络效果更好;枯水期+中营养和枯水期+轻度富营养逐步回归效果较好,逐步回归的均方根误差仅为1600～4000;丰水期和平水期2种方法预测效果相当.合理地选择预测模型能为湖泊水华做出预警,控制显著变量可以达到防治水华污染的效果. 相似文献
12.
Rainfall prediction is of vital importance in water resources management. Accurate long-term rainfall prediction remains an open and challenging problem. Machine learning techniques, as an increasingly popular approach, provide an attractive alternative to traditional methods. The main objective of this study was to improve the prediction accuracy of machine learning-based methods for monthly rainfall, and to improve the understanding of the role of large-scale climatic variables and local meteorological variables in rainfall prediction. One regression model autoregressive integrated moving average model (ARIMA) and five state-of-the-art machine learning algorithms, including artificial neural networks, support vector machine, random forest (RF), gradient boosting regression, and dual-stage attention-based recurrent neural network, were implemented for monthly rainfall prediction over 25 stations in the East China region. The results showed that the ML models outperformed ARIMA model, and RF relatively outperformed other models. Local meteorological variables, humidity, and sunshine duration, were the most important predictors in improving prediction accuracy. 4-month lagged Western North Pacific Monsoon had higher importance than other large-scale climatic variables. The overall output of rainfall prediction was scalable and could be readily generalized to other regions. 相似文献
13.
Turgay Partal 《水文研究》2009,23(25):3545-3555
This study combines wavelet transforms and feed‐forward neural network methods for reference evapotranspiration estimation. The climatic data (air temperature, solar radiation, wind speed, relative humidity) from two stations in the United States was evaluated for estimating models. For wavelet and neural network (WNN) model, the input data was decomposed into wavelet sub‐time series by wavelet transformation. Later, the new series (reconstructed series) are produced by adding the available wavelet components and these reconstructed series are used as the input of the WNN model. This phase is pre‐processing of raw data and the main different of the WNN model. The performance of the WNN model was compared with classical neural networks approach [artificial neural network (ANN)], multi‐linear regression and Hargreaves empirical method. This study shows that the wavelet transforms and neural network methods could be applied successfully for evapotranspiration modelling from climatic data. Copyright © 2009 John Wiley & Sons, Ltd. 相似文献
14.
Özgür Kişi 《水文研究》2009,23(14):2081-2092
This paper proposes the application of a conjunction model (neuro‐wavelet) for forecasting monthly lake levels. The neuro‐wavelet (NW) conjunction model is improved combining two methods, discrete wavelet transform and artificial neural networks. The application of the methodology is presented for the Lake Van, which is the biggest lake in Turkey, and Lake Egirdir. The accuracy of the NW model is investigated for 1‐ and 6‐month‐ahead lake level forecasting. The root mean square errors, mean absolute relative errors and determination coefficient statistics are used for evaluating the accuracy of NW models. The results of the proposed models are compared with those of the neural networks. In the 1‐month‐ahead lake level forecasting, the NW conjunction model reduced the root mean square errors and mean absolute relative errors by 87–34% and 86–31% for the Van and Egirdir lakes, respectively. In the 6‐month‐ahead lake level forecasting, the NW conjunction model reduced the root mean square errors and mean absolute relative errors by 34–48% and 30‐46% for the Van and Egirdir lakes, respectively. The comparison results indicate that the suggested model could significantly increase the short‐ and long‐term forecast accuracy. Copyright © 2009 John Wiley & Sons, Ltd. 相似文献
15.
Eight data-driven models and five data pre-processing methods were summarized; the multiple linear regression (MLR), artificial neural network (ANN) and wavelet decomposition (WD) models were then used in short-term streamflow forecasting at four stations in the East River basin, China. The wavelet–artificial neural network (W-ANN) method was used to predict 1-month-ahead monthly streamflow at Longchuan station (LS). The results indicate better performance of MLR and wavelet–multiple linear regression (W-MLR) in analysing the stationary trained dataset. Four models showed similar performance in 1-day-ahead streamflow forecasting, while W-MLR and W-ANN performed better in 5-day-ahead forecasting. Three reservoirs were shown to have more influence on downstream than upstream streamflow and models had the worst performance at Boluo station. Furthermore, the W-ANN model performed well for 1-month-ahead streamflow forecasting at LS with consideration of a deterministic component. 相似文献
16.
基于遥感藻总量和气象因子的巢湖不同湖区藻华预测 总被引：1，自引：0，他引：1
湖泊能为人类提供不可或缺的资源,而全球普遍存在的湖泊富营养化导致的藻华频繁暴发正不断损害湖泊生态环境服务功能.为合理保护湖泊环境和防治藻华危害,需预测藻华暴发.以我国富营养巢湖为研究区,本文构建了一种基于遥感藻总量和气象因子的不同湖区藻华暴发概率预测方法.基于MODIS/Aqua数据,研究首先反演了2003—2019年日尺度的藻华分布和考虑垂向结构的水柱藻总量.然后,统计了西、中和东巢湖的藻华面积,判别了藻华/非藻华日,并匹配日平均藻总量和气象因子.最后,筛选出藻华形成的关键影响因子——藻总量、气温和水汽压,并构建了不同湖区日藻华暴发概率的Logistic预测模型.不同湖区月平均藻总量基本一致,但藻华暴发日占比呈“西高东低”特征.对西、中和东巢湖的藻华/非藻华检验样本,模型精度分别为90%、85%和89.5%,模型也适用于2020年夏秋季和冬春季藻华预测.湖泊藻华暴发是藻类大量增殖并在一定气象条件下的产物,故基于遥感藻总量和气象因子的藻华暴发概率预测科学合理,可推广应用于太湖等其他富营养湖泊. 相似文献
17.
A data analysis method is proposed to cluster and explore spatio-temporal characteristics of the 22 years of precipitation data (1982–2003) for Taiwan. The wavelet transform self-organizing map (WTSOM) framework combines the wavelet transform (WT) and a self-organizing map (SOM) neural network. WT is used to extract dynamic and multiscale features of the non-stationary precipitation time-series, and SOM is applied to objectively identify spatially homogeneous clusters on the high-dimensional wavelet-transformed feature space. Haar and Morlet wavelets are applied in the data preprocessing stage to preserve the desired characteristics of the precipitation data. A two-level SOM neural network is applied to identify clusters in the wavelet space in the clustering stage. The performance of clustering is evaluated using silhouette coefficients. The results indicate that singularities or sharp transitions are more significant than changes in the periodicity or data structure in the spatial–temporal precipitation data. The WTSOM results show that six clusters are optimal for both Haar and Morlet wavelet functions, but their corresponding geographic locations are different. The geographic locations of clusters based on the Haar wavelet, which captures the occurrence of extreme hydrological events, appear in blocks while those classified by the Morlet wavelet, which indicates periodicity changes and describes fine structures, appear in strips that cross the island of Taiwan. Principal component analysis is applied to the precipitation data of each cluster. The first principal components explain 62–90% of the total variation of data. Characteristics of precipitation data for each cluster are explored using scalogram analysis. The results show that both extreme hydrological events and periodicity changes appear in the spatial and temporal precipitation data but with different characteristics for each cluster. Recognizing homogeneous hydrologic regions and identifying the associated precipitation characteristics improves the efficiency of water resources management in adapting to climate change, preventing the degradation of the water environment, and reducing the impact of climate-induced disasters. Measures for countering the stress of precipitation variation for water resources management are provided. 相似文献
18.
Forecasting monthly precipitation using sequential modelling 总被引：1，自引：1，他引：0
In the hydrological cycle, rainfall is a major component and plays a vital role in planning and managing water resources. In this study, new generation deep learning models, recurrent neural network (RNN) and long short-term memory (LSTM), were applied for forecasting monthly rainfall, using long sequential raw data for time series analysis. “All-India” monthly average precipitation data for the period 1871–2016 were taken to build the models and they were tested on different homogeneous regions of India to check their robustness. From the results, it is evident that both the trained models (RNN and LSTM) performed well for different homogeneous regions of India based on the raw data. The study shows that a deep learning network can be applied successfully for time series analysis in the field of hydrology and allied fields to mitigate the risks of climatic extremes. 相似文献
19.
为探究“十三五”期间武汉市湖泊水质变化特征及规律,分析当前武汉市湖泊水环境的主要问题及成因,为武汉市水生态环境保护“十四五”规划提供科学支撑,以武汉市166个湖泊为研究对象,根据武汉市环境监测中心2015-2019年对各个湖泊的监测数据,采用水质综合污染指数、富营养化状态评价、动态度分析等方法,对武汉市湖泊水环境进行综合评价.结果表明：1）2015-2019年武汉市湖泊水质总体好转,2019年全市湖泊综合污染指数较2015年下降7.74%,多数湖泊营养状态呈现稳中向好趋势,但湖泊水质较难实现持续改善.2）武汉市湖泊主要超标污染物总磷、氨氮、化学需氧量和高锰酸盐指数平均质量浓度在2015-2019年间均呈下降趋势,但2019年有47%湖泊总磷浓度劣于Ⅳ类评价标准,磷元素是制约湖泊水质的主要因素.3）2015-2019年武汉市中心城区湖泊综合污染指数下降,湖泊富营养化状态好转,但青山北湖、南湖等湖泊综合污染指数较高,排口排污、底泥内源污染是影响中心城区湖泊水质的主要因素.新城区湖泊水质改善仍面临一定压力,东西湖水系重度富营养化湖泊数量增多,后湖水系湖泊综合污染指数上升,农业面源、工业生产对新城区湖泊水质影响较大,湖泊破碎化导致的自净能力降低也是影响湖泊水质的重要因素. 相似文献
20.
云南星云湖水质变化及其人文因素驱动力分析 总被引：1，自引：0，他引：1
星云湖目前存在水污染加重、富营养化进程加快、水体功能受损等问题.以星云湖为研究对象,根据星云湖2005-2015年的水质数据、社会经济统计数据和遥感影像图,运用目视解译、叠加分析、污染足迹模型及主成分分析法,分析了星云湖流域近10年以来水质变化趋势、入湖河流污染物污染足迹及其人文因素驱动力.结果表明:(1)水质数据趋势表明,从月变化看,3月份水质最好,9月份水质最差;从年变化看,2005-2015年间,2008年水质状况最好,2014年的水质状况最差,从2008-2014年水质持续变差,到2015年好转.(2)2015年有机物、氮和磷的污染足迹分别为583.26、705.88和494.11 km~2.污染足迹前4位的入湖河流依次为:大街河东西大河东河渔村河东西大河西河,占星云湖流域总污染足迹的66.21%.污染程度大的大街河、东西大河和渔村河周边土地利用类型为水田、旱地和村庄.(3)星云湖水质影响因素第1主成分(总人口、播种面积、农村人口、化肥使用量、农膜使用量、大牲畜存栏量)与农村生活和农业面源污染有关;第2主成分(人均GDP、第一产业产值、第二产业产值、第三产业产值)与社会经济发展有关.因此,星云湖流域水质变化的人文因素驱动力为农村生活和农业面源污染类和社会经济发展类,其中第1主成分的贡献率是84.389%,农村生活和农业面源污染是水质变化的主要驱动力. 相似文献