Entropy principles in the prediction of water quality values at discontinued monitoring stations

A new methodology for predicting water quality values at discontinued water quality monitoring stations is proposed. The method is based upon the Principle of Maximum Entropy (POME) and provides unbiased predictions of water quality levels at upstream tributaries and on the mainstem of a river given observed changes in the distribution of the same water quality parameter at a downstream location. Changes in the values of water quality parameters which are known a priori to have occurred upstream, but which are not sufficiently large to account for all the observed change in the same water quality parameter at the downstream location are able to be incorporated in the method through the introduction of a new term in the basic entropy expression. Application of the procedure to water quality monitoring on the Mackenzie River in Queensland, Australia indicates the method has considerable potential for prediction of water quality at discontinued stations. The method also has potential for identifying the location of causes of observed changes in water quality at a downstream station.     

Computational aspects in use of entropy theory in predicting water quality levels at discontinued stations

The computational aspects of using a new, entropy-based, theory to predict water quality values at discontinued water quality monitoring stations are discussed. The main computational issues addressed are the level of discretization used in converting the continuous probability distribution of water quality values to the discrete levels required for the entropy function, and the choice of the interval of time for which to assign the value of the water quality (period of time averaging) through the entropy function. Unlike most cases of entropy applications involving discretization of continuous functions the results of using entropy theory to predict water quality values at discontinued monitoring stations in this application appear to be insensitive to the choice of the level of discretization even down to the very coarse level discretization associated with only eight intervals. However, depending on the length of record available the choice of the time interval for which the water quality values are assigned (period for time averaging) appear to have a significant impact on the accuracy of the results.     

Simultaneous hydrological prediction at multiple gauging stations using the NARX network for Kemaman catchment,Terengganu, Malaysia

ABSTRACT

This paper presents a neural network model capable of catchment-wide simultaneous prediction of river stages at multiple gauging stations. Thirteen meteorological parameters are considered in the input, which includes rainfall, temperature, mean relative humidity and evaporation. The NARX model is trained with a representative set of hourly data, with optimal time delay for both the input and output. The network trained using 120-day data is able to produce simulations that are in excellent agreement with field observations. We show that for application with one-step-ahead predictions, the loss in network performance is marginal. Inclusion of additional tidal observations does not improve predictions, suggesting that the river stage stations under consideration are not sensitive to tidal backwater effects despite the claim commonly made.

EDITOR D. Koutsoyiannis ASSOCIATE EDITOR F. Pappenberger     

Water quality monitoring at a virtual watershed monitoring station using a modified deep extreme learning machine

ABSTRACT

A new deep extreme learning machine (ELM) model is developed to predict water temperature and conductivity at a virtual monitoring station. Based on previous research, a modified ELM auto-encoder is developed to extract more robust invariance among the water quality data. A weighted ELM that takes seasonal variation as the basis of weighting is used to predict the actual value of water quality parameters at sites which only have historical data and no longer generate new data. The performance of the proposed model is validated against the monthly data from eight monitoring stations on the Zengwen River, Taiwan (2002–2017). Based on root mean square error, mean absolute error, mean absolute percentage error and correlation coefficient, the experimental results show that the new model is better than the other classical spatial interpolation methods.     

浅水湖泊水质系统滤波模型研究

本文建立了浅水湖泊水质系统滤波模型和相应算法,根据滇池流场比较稳定等特点,对湖泊进行单元分区,考虑模型误差扰动和观测误差扰动的存在,建立系统的状态方程和观测方程,然后采用吉尔（Gill)算法求解微分方程,进行系统多点连续模拟和实时预报,用此法对1988年滇池BOD,COD时空变化进行连续预报,取得满意结果。     

Impacts of droughts on low flows and water quality near power stations

ABSTRACT

Power plants often use river waters for cooling purposes and can be sensitive to droughts and low flows. Water quality is also a concern, due to algal blooms and sediment loads that might clog filters. We assessed the impacts of droughts on river flow and water quality from the point of view of power plant operation. The INCA (INtegrated CAtchment) water quality model was coupled with a climate model to create a dataset of flow and water quality time series, using the River Trent (UK) as a case study. The result hints to a significant decrease in flows and an increase in phosphorus concentrations, potentially enhancing algal production. Power plants should expect more stress in the future based on the results of this study, due to reduced cooling water availability and decreasing upstream water quality. This issue might have serious consequences also on the whole national power network.     

Subgrid variability of snow water equivalent at operational snow stations in the western USA

The spatial distribution of snow water equivalent (SWE) is a key variable in many regional‐scale land surface models. Currently, the assimilation of point‐scale snow sensor data into these models is commonly performed without consideration of the spatial representativeness of the point data with respect to the model grid‐scale SWE. To improve the understanding of the relationship between point‐scale snow measurements and surrounding areas, we characterized the spatial distribution of snow depth and SWE within 1‐, 4‐ and 16‐km² grids surrounding 15 snow stations (snowpack telemetry and California snow sensors) in California, Colorado, Wyoming, Idaho and Oregon during the 2008 and 2009 snow seasons. More than 30 000 field observations of snowpack properties were used with binary regression tree models to relate SWE at the sensor site to the surrounding area SWE to evaluate the sensor representativeness of larger‐scale conditions. Unlike previous research, we did not find consistent high biases in snow sensor depth values as biases over all sites ranged from 74% overestimates to 77% underestimates. Of the 53 assessments, 27 surveys indicated snow station biases of less than 10% of the surrounding mean observed snow depth. Depth biases were largely dictated by the physiographic relationship between the snow sensor locations and the mean characteristics of the surrounding grid, in particular, elevation, solar radiation index and vegetation density. These scaling relationships may improve snow sensor data assimilation; an example application is illustrated for the National Operational Hydrologic Remote Sensing Center National Snow Analysis SWE product. The snow sensor bias information indicated that the assimilation of point data into the National Operational Hydrologic Remote Sensing Center model was often unnecessary and reduced model accuracy. Copyright © 2012 John Wiley & Sons, Ltd.     

Characterization of water quality variation in the Mekong River at Vientiane by frequent observations

To better understand the variation of water quality in the Mekong River, sampling and measurement were scheduled twice a week for about 3 years at Vientiane, followed by basic statistical analysis of the observed data. The frequent measurement revealed detailed characteristics of the water quality variation, which had not been detected by monthly observation in the previous studies. The variation in total ionic content was considered to be governed by dilution of the amount of water. Increases in turbidity could be attributed to physical effects including surface soil erosion and bed material resuspension at the time of discharge increase. Nitrogen concentrations were stably low during the low flow period and abruptly increased in May. After the annual maximum in late May, the nitrate concentration steadily decreased regardless of the remarkable rise in the discharge from mid‐July, whereas the ammonium concentration remained at a similar level until October. It was considered that the first small discernable runoff after the long dry season flushed the accumulated nitrogen in the surface soil and mobilized the nitrogen in the riverbed. The variation in phosphorus concentrations was different from that in nitrogen concentrations. During the high flow period, continual dilutions at discharge peaks and occasional large additions of phosphorus by surface runoff were suggested. Copyright © 2011 John Wiley & Sons, Ltd.     

Optimal operation of reservoirs for downstream water quality control using linked simulation optimization

A methodology is developed for optimal operation of reservoirs to control water quality requirements at downstream locations. The physicochemical processes involved are incorporated using a numerical simulation model. This simulation model is then linked externally with an optimization algorithm. This linked simulation–optimization‐based methodology is used to obtain optimal reservoir operation policy. An elitist genetic algorithm is used as the optimization algorithm. This elitist‐genetic‐algorithm‐based linked simulation–optimization model is capable of evolving short‐term optimal operation strategies for controlling water quality downstream of a reservoir. The performance of the methodology developed is evaluated for an illustrative example problem. Different plausible scenarios of management are considered. The operation policies obtained are tested by simulating the resulting pollutant concentrations downstream of the reservoir. These performance evaluations consider various scenarios of inflow, permissible concentration limits, and a number of management periods. These evaluations establish the potential applicability of the developed methodology for optimal control of water quality downstream of a reservoir. Copyright © 2007 John Wiley & Sons, Ltd.     

鄱阳湖水质现状及变化趋势

根据鄱阳湖1990年监测资料,对水质污染现状进行分析,同时利用1981—1990年资料,分析了水质变化趋势。分析表明:湖体、河口水质均属较清洁水或尚清洁水。但是,随着入湖污染负荷量的增加,湖区水体污染呈上升趋势,宜引起有关部门高度重视。     

Internet-based applications for interrogating 50 years of data from the South African national water quality monitoring network

ABSTRACT

National and regional water quality monitoring networks have been operated in South Africa since the early 1970s. These originally had text-based inventories that were convenient for specialists who were familiar with the national networks and knew the locations of their sites of interest. However, within two decades the networks had expanded in geographical extent and variables monitored to such an extent that users needed spatial context in order to locate sites that fitted their information requirements. Mapping applications running on the Internet, such as Google Earth and Leaflet, form the foundation of a system for providing online inventories and summaries of the data available on the water quality database. The interfaces were constructed using available software, mainly ArcInfo and R. A recent concern is a decrease in the collection of water quality data, which is reducing the value of data summaries for water resource management.     

南水北调中线水源区水质生态监测(2005年)

在生态调查基础上从微生物学、理化及叶绿素a指标等对南水北调中线水源区进行了生态监测．结果表明,水源区为寡污型水体,水体基本处于贫-中营养状态,水源区指标除TN为Ⅲ类水质外,其它监测指标均符合Ⅰ类水质标准．是理想的水源地．本研究为建立中线水源区长期生态研究数据信息库及库区生态环境保护政策制定提供科学依据．     

太湖流域上游平原河网区水质空间差异与季节变化特征

在太湖流域上游的宜溧—洮滆水系主要河道设置67个监测点,分别于2014年1月(冬季)、4月(春季)、8月(夏季)、11月(秋季)进行水质监测,采用多元统计方法分析了水质的空间差异性和季节性变化,并利用水质标识指数法对水环境质量进行评价.结果表明,宜溧—洮滆水系污染程度较严重,总氮(TN)、总磷(TP)和高锰酸盐指数(CODMn)浓度年均值分别为4.93、0.26和7.63 mg/L;单因素多元方差分析和聚类分析显示污染物浓度具有显著时空差异性,时间上冬、春季污染程度较高而夏、秋季较低,空间上无锡和常州氮、磷污染较为严重,宜兴和溧阳市有机污染程度较高;水质标识评价结果显示流域内水质基本为IV类或V类,其中TN、TP及CODMn是关键污染指标.     

程海的水质状况及咸化趋势

程海又名黑坞海,位于东经100°38—41′,北纬26°27—38′之间,即今云南省永胜县城西南20km处,是横断山区最大的一个内流高山湖泊。长期以来,程海的水量出湖略大于入湖,湖水量逐年减少,水质向咸化方向发展。本文根据1981年5月27日—6月5日对程海的综合调查资料,重点分析了1972年至1981年水质演变情况,对水质状况及成化趋势进行了初步研究。     

Seasonal and inter-annual variability of water quality in the Uruguay River,Argentina

Abstract

Water quality of the Uruguay River was evaluated with multi-parametric (temperature, turbidity, conductivity, pH, dissolved oxygen) and sediment trap data (particle flux, total organic carbon and nitrogen contents) and correlated to precipitation, river discharge and El Niño Southern Oscillation (ENSO) indices for the period 2006–2011. Hydro-meteorological parameters averaged 24–85% variability with coincident precipitation (200–400 mm month^-1), discharge (7000–28 000 m³ s^-1) and turbidity peaks (50–80 NTU) in the austral spring, and absolute maxima during the El Niño 2009 episode. Spectral analysis of discharge and sea-surface temperature anomaly (SSTA) showed consistent variance maxima at approx. 3 and 1.5 years. Deseasonalized discharge was significantly correlated to SSTA. During river floods, pH decreased (from 7.5 to 6.6) and particle dynamics peaked (turbidity: 15–80 NTU; vertical fluxes: 20–200 g m^-2 d^-1; total solid load: <1000 to 100 000 t d^-1),whereas TOC remained stable (3.2 ± 0.8%) and C/N ratios increased (10–12) due to the higher contribution of terrestrial detritus.     

邢台地震台数字化水位监测效能分析

对邢台地震台地下流体数字化水位观测资料的内在质量及年、月、日动态特征等进行分析.结果表明:所辖流体观测井水位资料连续、可靠,内在质量较高,4个测点均是较理想的井水位观测点,其中,衡水井、辛集井水位对远场大震的同震效应明显;宁晋井因受井孔条件所限,同震效应不明显.井水位年变均为趋势性下降变化,但下降幅度均大于5 m,不利...     

气象条件对淀山湖水质的影响

根据2007-2014年淀山湖湖体每月高锰酸盐指数(CODMn)、氨氮(NH3-N)、总氮(TN)和总磷(TP)等水质资料和青浦区气象局月平均气温、降水量和日照时数等气象资料,运用数理统计方法和特征值比较法分析淀山湖湖体CODMn、NH3-N、TN和TP等水质资料变化规律及温度、降水和日照时数等对水质的影响.结果表明:(1)气象条件影响淀山湖湖体水质.平均气温、日照时数影响NH3-N、TN和TP浓度,表现在平均气温高、日照时数多,NH3-N、TN浓度降低,相反平均气温低、日照时数少,NH3-N、TN浓度升高;平均气温高,也会使TP浓度上升,平均气温低,TP浓度降低.降水对水体中CODMn、NH3-N和TN等浓度有稀释作用,降水量多的月份其浓度偏低,相反降水量少的月份其浓度偏高.(2)受气象条件影响,CODMn、NH3-N、TN和TP有季节变化.CODMn浓度4-9月较高,10月翌年3月较低;NH3-N、TN浓度最高值出现在2-4月前后,最低值出现在7-10月;TP浓度最高值出现在7-8月,最低值出现在10月翌年5月.(3)淀山湖CODMn、NH3-N、TN和TP浓度呈下降趋势.     

梁子湖水质时空格局分析

基于大样本监测数据,从点位、湖区、全局3个层次,对梁子湖水质的季节变化和空间差异进行综合分析,发现梁子湖水质存在显著的季节差异和较大的空间差异:(1)季节上,夏、秋、冬季污染相对较重,春季污染相对较轻;夏、秋季主要污染指标为高锰酸盐指数(COD_(Mn)),冬季为总氮(TN);(2)空间上,东梁子湖和牛山湖污染相对较重,西梁子湖污染相对较轻;东梁子湖主要污染指标为COD_(Mn)、总磷(TP)和TN,牛山湖为COD_(Mn)、TN和氨氮,西梁子湖为COD_(Mn)和TN.研究结果有助于更好地了解梁子湖水质的时空变化特点,进而制定有针对性的保护措施和管理对策.     

基于水质-水位二元响应关系推求过水型湖泊适宜生态水位研究

适宜的生态水位能够反映湖区生态系统的多种需求,是湖泊长期稳定健康运转的基本保障.目前湖泊适宜生态水位的推求大多侧重恢复天然水位情势.然而过水型湖泊承担着防洪、供水、航运等多种功能,频繁的人类活动导致湖泊水位情势异常复杂.同时随着社会经济的快速发展,水质恶化对过水型湖泊生态系统造成了较大的负面影响,仅恢复天然水位情势难以反映过水型湖泊的生态需求.因此,在IHA-RVA法的基础上,本文针对过水型湖泊吞吐性强的特点,利用水质-水位二元响应关系系统地提出了一套逐月修正过水型湖泊适宜生态水位阈值,并确定适宜水位变动率的方法.以洪泽湖为应用实例,结果表明:1)根据湖泊水文情势和入湖污染物变化情况,湖泊调度周期可以划分为平水期(1-4月)、泄水期(5-6月)、蓄水前期(7-9月)和蓄水后期(10-12月);2)各时期内,洪泽湖水位和水质呈现较强的相关性,其中平水期、泄水期和蓄水后期水质均随着水位上升而下降,平均Pearson系数达-0.77,仅在蓄水前期水质随水位上升而改善;3)现阶段洪泽湖的自净能力和污染物滞留比例竞争关系激烈,逐月适宜生态水位阈值为:12.92～12.99、12.79～12.99...     

Spatial and temporal patterns of the water quality in the Danjiangkou Reservoir,China

Abstract

The three-route South-to-North Water Diversion Project (SNWDP), transferring water from the water-rich Yangtze River and its tributaries to the much drier area of North China for irrigation, industrial and domestic use, has been implemented in China since 2002. Thus, water quality in the Danjiangkou Reservoir, the water source area of the SNWDP's Middle Route, is of great concern. We investigate its water quality from 2004 to 2006 by monitoring some important physical (T, turbidity and SPM) and chemical (DO, pH, alkalinity, TDS, SpCond, ORP, COD_Mn and BOD) parameters and nutrient (nitrogen and phosphorus) contents. Consequently, their spatial and temporal patterns in the reservoir were examined. The results indicate that the water of the reservoir is of a Ca and HCO₃ type, and the major pollutants are nitrogen and COD_Mn. Comparisons among the sampling sites show that water quality increases downstream, implying the self-purification capacity of the reservoir. The reservoir in general has better water quality in the dry season than in the wet season. Integrated basin management would be critical of the water quality in the Danjingkou Reservoir for the interbasin water transfer project.