CN104615885A - Short-term forecasting method for eutrophication shallow lake algae source lake flooding - Google Patents
Short-term forecasting method for eutrophication shallow lake algae source lake flooding Download PDFInfo
- Publication number
- CN104615885A CN104615885A CN201510059526.1A CN201510059526A CN104615885A CN 104615885 A CN104615885 A CN 104615885A CN 201510059526 A CN201510059526 A CN 201510059526A CN 104615885 A CN104615885 A CN 104615885A
- Authority
- CN
- China
- Prior art keywords
- lake
- partiald
- general
- flooding
- probability
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 241000195493 Cryptophyta Species 0.000 title claims abstract description 25
- 238000012851 eutrophication Methods 0.000 title description 3
- 238000013277 forecasting method Methods 0.000 title description 2
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 99
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims abstract description 49
- 229910052760 oxygen Inorganic materials 0.000 claims abstract description 49
- 239000001301 oxygen Substances 0.000 claims abstract description 49
- 238000000034 method Methods 0.000 claims abstract description 39
- 238000009826 distribution Methods 0.000 claims abstract description 37
- ATNHDLDRLWWWCB-AENOIHSZSA-M chlorophyll a Chemical compound C1([C@@H](C(=O)OC)C(=O)C2=C3C)=C2N2C3=CC(C(CC)=C3C)=[N+]4C3=CC3=C(C=C)C(C)=C5N3[Mg-2]42[N+]2=C1[C@@H](CCC(=O)OC\C=C(/C)CCC[C@H](C)CCC[C@H](C)CCCC(C)C)[C@H](C)C2=C5 ATNHDLDRLWWWCB-AENOIHSZSA-M 0.000 claims abstract description 35
- 238000012544 monitoring process Methods 0.000 claims abstract description 31
- 229930002868 chlorophyll a Natural products 0.000 claims abstract description 29
- 239000003643 water by type Substances 0.000 claims abstract description 21
- 230000002123 temporal effect Effects 0.000 claims abstract description 19
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 9
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 claims description 10
- 230000000694 effects Effects 0.000 claims description 7
- 229930002875 chlorophyll Natural products 0.000 claims description 6
- 235000019804 chlorophyll Nutrition 0.000 claims description 6
- OAICVXFJPJFONN-UHFFFAOYSA-N Phosphorus Chemical compound [P] OAICVXFJPJFONN-UHFFFAOYSA-N 0.000 claims description 5
- 229910052757 nitrogen Inorganic materials 0.000 claims description 5
- 229910052698 phosphorus Inorganic materials 0.000 claims description 5
- 239000011574 phosphorus Substances 0.000 claims description 5
- 238000004458 analytical method Methods 0.000 claims description 4
- 238000011438 discrete method Methods 0.000 claims description 3
- 238000000926 separation method Methods 0.000 claims description 3
- 235000016709 nutrition Nutrition 0.000 claims 6
- 230000035764 nutrition Effects 0.000 claims 6
- 238000006424 Flood reaction Methods 0.000 claims 3
- 239000000463 material Substances 0.000 claims 1
- 238000004064 recycling Methods 0.000 claims 1
- 230000006355 external stress Effects 0.000 abstract 1
- 238000004364 calculation method Methods 0.000 description 25
- 238000006243 chemical reaction Methods 0.000 description 13
- 238000007726 management method Methods 0.000 description 10
- 238000005516 engineering process Methods 0.000 description 8
- 235000015097 nutrients Nutrition 0.000 description 8
- 238000005259 measurement Methods 0.000 description 7
- 230000008569 process Effects 0.000 description 7
- 230000008878 coupling Effects 0.000 description 6
- 238000010168 coupling process Methods 0.000 description 6
- 238000005859 coupling reaction Methods 0.000 description 6
- 241000192700 Cyanobacteria Species 0.000 description 5
- 238000007689 inspection Methods 0.000 description 5
- 230000036541 health Effects 0.000 description 4
- 238000011160 research Methods 0.000 description 4
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 3
- 229910052799 carbon Inorganic materials 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 230000002349 favourable effect Effects 0.000 description 3
- 238000005070 sampling Methods 0.000 description 3
- 239000013049 sediment Substances 0.000 description 3
- 239000002352 surface water Substances 0.000 description 3
- 238000012876 topography Methods 0.000 description 3
- 239000002028 Biomass Substances 0.000 description 2
- 230000001133 acceleration Effects 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 230000015556 catabolic process Effects 0.000 description 2
- 238000006731 degradation reaction Methods 0.000 description 2
- 238000009792 diffusion process Methods 0.000 description 2
- 239000003651 drinking water Substances 0.000 description 2
- 235000020188 drinking water Nutrition 0.000 description 2
- 230000004907 flux Effects 0.000 description 2
- 230000005484 gravity Effects 0.000 description 2
- 238000011065 in-situ storage Methods 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 238000012821 model calculation Methods 0.000 description 2
- 230000036284 oxygen consumption Effects 0.000 description 2
- 230000029553 photosynthesis Effects 0.000 description 2
- 238000010672 photosynthesis Methods 0.000 description 2
- 230000009467 reduction Effects 0.000 description 2
- 230000029058 respiratory gaseous exchange Effects 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 150000003839 salts Chemical class 0.000 description 2
- 238000004062 sedimentation Methods 0.000 description 2
- 239000000126 substance Substances 0.000 description 2
- 150000003568 thioethers Chemical class 0.000 description 2
- 230000009466 transformation Effects 0.000 description 2
- 230000035508 accumulation Effects 0.000 description 1
- 238000009825 accumulation Methods 0.000 description 1
- 238000005842 biochemical reaction Methods 0.000 description 1
- 239000010866 blackwater Substances 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 230000006866 deterioration Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 239000005416 organic matter Substances 0.000 description 1
- -1 sulfides Chemical class 0.000 description 1
- 230000003442 weekly effect Effects 0.000 description 1
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A10/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE at coastal zones; at river basins
- Y02A10/40—Controlling or monitoring, e.g. of flood or hurricane; Forecasting, e.g. risk assessment or mapping
Landscapes
- Aeration Devices For Treatment Of Activated Polluted Sludge (AREA)
Abstract
本发明提供一种富营养化浅水湖泊藻源性湖泛的短期预报方法，包括：在浅水湖泊湖泛易发水域设定若干监测点，对气象参数和水环境参数进行测定；利用空间网格划分和插值算法，将监测点的水环境参数插值到全湖，其空间分布作为水质数值模型的初始条件；以未来三天风场时空分布作为模型外部应力，驱动三维水动力—水质数值模型进行运算，得到未来三天浅水湖泊叶绿素a和溶解氧浓度的时空分布；利用叶绿素a和溶解氧浓度的时空分布，结合气象参数建立概率经验公式，计算未来三天湖泛易发水域发生湖泛的概率；对于发生湖泛概率较大的区域，进一步确定发生湖泛的位置和面积。
The invention provides a short-term forecast method for algae-derived lake flooding in eutrophic shallow lakes, including: setting a number of monitoring points in shallow lake lake flood-prone waters to measure meteorological parameters and water environment parameters; using spatial grids The division and interpolation algorithm interpolates the water environment parameters of the monitoring points to the whole lake, and its spatial distribution is used as the initial condition of the water quality numerical model; the spatial and temporal distribution of the wind field in the next three days is used as the external stress of the model to drive the three-dimensional hydrodynamic-water quality numerical model. Calculate the temporal and spatial distribution of chlorophyll a and dissolved oxygen concentration in shallow lakes in the next three days; use the temporal and spatial distribution of chlorophyll a and dissolved oxygen concentration, combined with meteorological parameters to establish a probability empirical formula, and calculate the probability of lake flooding in lake flooding-prone waters in the next three days Probability; For areas with a high probability of lake flooding, further determine the location and area of lake flooding.
Description
技术领域technical field
本发明涉及一种富营养化浅水湖泊藻源性湖泛的短期预报方法，属于环境科学与工程学科基础理论和应用基础研究技术领域。The invention relates to a short-term forecast method for algae-derived lake flooding of eutrophic shallow lakes, and belongs to the technical field of basic theory and applied basic research of environmental science and engineering disciplines.
背景技术Background technique
近20年来，我国长江中下游地区浅水湖泊水环境问题日趋严重，在夏秋季由湖水富营养化引发的蓝藻水华暴发时有发生，严重影响水质景观，甚至危及供水安全。由蓝藻水华引发的“湖泛”是湖泊富营养化水体在藻类大量暴发、积聚和死亡后，在适宜的气象、水文条件下，与底泥中的有机物在缺氧和厌氧条件下产生生化反应，释放硫化物等硫醚类物质，形成褐黑色伴有恶臭的“黑水团”，从而导致水体水质迅速恶化，生态系统受到严重破坏。以太湖为例，2007年5月底，太湖贡湖湾无锡市南泉水厂附近发生湖泛，使无锡太湖饮用水水源地水质急剧恶化并发臭，造成无锡供水危机，使无锡市近200万人口生活受到严重影响。2008年至2014年春夏间，由蓝藻水华延伸出的湖泛现象从未曾中断过，持续时间1-6天不等，其中最大湖泛面积可达17km2，严重影响了太湖水生态系统。In the past 20 years, the water environment problems of shallow lakes in the middle and lower reaches of the Yangtze River in my country have become increasingly serious. In summer and autumn, cyanobacteria blooms caused by lake eutrophication have occurred frequently, seriously affecting the water quality landscape, and even endangering the safety of water supply. "Lake flooding" caused by cyanobacterial blooms is the occurrence of eutrophic water bodies in lakes under suitable meteorological and hydrological conditions after a large number of algae outbreaks, accumulations and death, and organic matter in the sediment under anoxic and anaerobic conditions. The biochemical reaction releases sulfides such as sulfides, forming a brown-black "black water mass" with a foul smell, which leads to the rapid deterioration of the water quality and the serious damage to the ecosystem. Taking Taihu Lake as an example, at the end of May 2007, lake flooding occurred near the Nanquan Water Plant in Wuxi City in the Gonghu Bay of Taihu Lake, which caused the water quality of the drinking water source of Wuxi Taihu Lake to deteriorate sharply and stink, resulting in a water supply crisis in Wuxi. severely affected. From 2008 to spring and summer of 2014, the lake flooding phenomenon extended by cyanobacteria blooms has never been interrupted, and the duration ranges from 1 to 6 days. The largest lake flooding area can reach 17km 2 , seriously affecting the water ecosystem of Taihu Lake .
可以预见，无论是湖泊富营养化问题，还是湖泛问题，在短期内无法得到根本解决，都将一直存在并威胁着浅水湖泊水环境和供水安全。为了避免水危机事件的再次发生，保障富营养化湖泊饮用水的安全，有必要发展蓝藻水华和湖泛的预测预警技术，在一定范围内，对蓝藻水华和湖泛发生、发展的时间、空间分布特征进行模拟和分析，为政府部门的快速决策和应急措施提供理论依据，提高政府的应对治理能力，最大程度降低其生态危害和健康风险。It is foreseeable that whether it is the problem of lake eutrophication or lake flooding, it cannot be fundamentally solved in the short term, and will always exist and threaten the water environment and water supply security of shallow lakes. In order to avoid the recurrence of water crisis events and ensure the safety of drinking water in eutrophic lakes, it is necessary to develop prediction and early warning technologies for cyanobacterial blooms and lake flooding. , spatial distribution characteristics to simulate and analyze, provide a theoretical basis for government departments to make rapid decisions and emergency measures, improve the government's ability to deal with governance, and minimize its ecological hazards and health risks.
目前对于蓝藻水华的预测预警技术研究较多，但是对于湖泛的研究，多停留在跟踪性观测和机理研究方面，真正在灾害发生之前，应用于湖泛预测预警实践，并定期发布湖泛预报的较少。我国的浅水湖泊多是重要的水源地和旅游区，大量的蓝藻是否会在取水口或旅游区堆积形成灾害性水华，影响水质和景观，是否形成湖泛，引发供水危机，成为困惑地方管理部门的问题。基于上述技术背景，有必要建立一个可操作性强、时效性强的预测预警方法，在夏秋季节实现对湖泛发生概率，发生区域及面积的短期动态预测，从而为浅水湖泊的日常管理工作提供有利的科学依据。At present, there are many researches on the prediction and early warning technology of cyanobacterial blooms, but the research on lake flooding mostly stays in the aspects of follow-up observation and mechanism research. Less predicted. Shallow lakes in my country are mostly important water sources and tourist areas. Whether a large number of blue-green algae will accumulate in water intakes or tourist areas will form disastrous algal blooms, affecting water quality and landscape, and whether lake flooding will cause water supply crises, which has become a puzzle for local management. departmental issues. Based on the above technical background, it is necessary to establish a predictive and early warning method with strong operability and timeliness to realize the short-term dynamic prediction of lake flooding occurrence probability, occurrence area and area in summer and autumn, so as to provide the daily management work of shallow lakes. Favorable scientific basis.
发明内容Contents of the invention
本发明的目的在于提供一种富营养化浅水湖泊藻源性湖泛的短期预报方法。首先以天气预报中的风场为驱动力，求解浅水湖泊三维水动力—水质耦合数值模型，计算未来三天浅水湖泊叶绿素a和溶解氧浓度的时空分布，然后结合未来三天的气象因子(风速、降雨等)信息建立经验公式，计算湖泛易发水域发生湖泛的概率，并进一步确定湖泛发生位置和面积。The purpose of the present invention is to provide a short-term forecasting method for algae-derived lake flooding of eutrophic shallow lakes. First, the wind field in the weather forecast is used as the driving force to solve the three-dimensional hydrodynamic-water quality coupling numerical model of shallow lakes, and calculate the temporal and spatial distributions of chlorophyll a and dissolved oxygen concentrations in shallow lakes in the next three days, and then combine the meteorological factors (wind speed) in the next three days , rainfall, etc.) information to establish empirical formulas to calculate the probability of lake flooding in waters prone to lake flooding, and further determine the location and area of lake flooding.
本发明的上述目的通过独立权利要求的技术特征实现，从属权利要求以另选或有利的方式发展独立权利要求的技术特征。The above objects of the invention are achieved by the technical features of the independent claims, which the dependent claims develop in an alternative or advantageous manner.
为达成上述目的，本发明所采用的技术方案如下：In order to achieve the above object, the technical scheme adopted in the present invention is as follows:
一种富营养化浅水湖泊藻源性湖泛的短期预报方法，包括：在浅水湖泊湖泛易发水域设定监测点，对气象参数(如气温、风速、风向)和水环境参数(如水温、透明度、叶绿素a浓度、溶解氧溶度和营养盐浓度)进行测定；利用空间网格划分和插值技术，将监测点的水环境参数插值到全湖，其空间分布作为水质数值模型的初始条件；以未来三天风场时空分布作为模型驱动力，驱动三维水动力—水质数值模型进行运算，得到未来三天浅水湖泊叶绿素a和溶解氧浓度的时空分布；利用叶绿素a和溶解氧浓度的时空分布，结合气象参数建立概率经验公式，计算未来三天湖泛易发水域发生湖泛的概率；对于发生湖泛概率较大的区域，进一步确定发生湖泛的位置和面积。A short-term forecast method for algae-derived lake flooding in eutrophic shallow lakes, comprising: setting monitoring points in shallow lake lake flood-prone waters, and monitoring meteorological parameters (such as air temperature, wind speed, wind direction) and water environment parameters (such as water temperature , transparency, chlorophyll a concentration, dissolved oxygen solubility and nutrient concentration) were measured; using spatial grid division and interpolation technology, the water environment parameters of the monitoring points were interpolated to the whole lake, and their spatial distribution was used as the initial condition of the water quality numerical model ;Using the temporal and spatial distribution of the wind field in the next three days as the driving force of the model, drive the three-dimensional hydrodynamic-water quality numerical model to perform calculations, and obtain the spatial and temporal distribution of chlorophyll a and dissolved oxygen concentrations in shallow lakes in the next three days; use the temporal and spatial distributions of chlorophyll a and dissolved oxygen concentrations Based on the meteorological parameters, the probability empirical formula is established to calculate the probability of flooding in the waters prone to flooding in the next three days; for areas with a high probability of flooding, the location and area of flooding will be further determined.
作为进一步的示例，前述方法的具体实现包括：As a further example, the specific implementation of the aforementioned method includes:
1、在湖泛易发水域设定监测点位，对气象参数和水环境参数进行测定1. Set monitoring points in lake-prone waters to measure meteorological parameters and water environment parameters
基于对浅水湖泊易发水域的认识，分别在重点关注湖区设置若干个监测点，并可进行人工原位巡查。在每一点位处，采用多参数水质仪(YSI6600-V2)(YellowSpring Instruments，USA)测定水质参数，如叶绿素a浓度、溶解氧浓度、溶解氧饱和度等，采用塞氏盘法测定水体透明度，并即时测定气温、风速、风向、水温等指标。采集表层水样(10-30cm深度)1升，样品灌装于采样瓶中立即放入带有冰盒的保温箱内，上岸后用于实验室内氮、磷营养盐和溶解性有机碳等指标的测定，样品的分析按照湖泊生态系统观测方法进行。Based on the understanding of the prone waters of shallow lakes, several monitoring points were set up in key lake areas, and manual in-situ inspections can be carried out. At each point, a multi-parameter water quality instrument (YSI6600-V2) (YellowSpring Instruments, USA) was used to measure water quality parameters, such as chlorophyll a concentration, dissolved oxygen concentration, dissolved oxygen saturation, etc., and the transparency of the water body was measured by the Sephardt disc method. And real-time measurement of air temperature, wind speed, wind direction, water temperature and other indicators. Collect 1 liter of surface water sample (10-30cm depth), fill the sample in a sampling bottle and immediately put it in an incubator with an ice box, and use it for nitrogen, phosphorus nutrients and dissolved organic carbon in the laboratory after landing The determination of indicators and the analysis of samples are carried out according to the lake ecosystem observation method.
2、利用空间网格划分和插值技术，将在监测点测定的水环境参数插值到全湖2. Using spatial grid division and interpolation technology, the water environment parameters measured at the monitoring points are interpolated to the whole lake
根据对湖泛易发水域地形的现场测定，采用三角形网格对全湖区域进行划分，网格分辨率可依照湖区关注程度和岸线弯曲程度，设置为几百米至1千米不等。采用反距离权重插值算法将监测点位的水环境参数值插值到全湖每一个计算网格上，得到全湖水环境参数的初始分布情况。According to the on-site measurement of the topography of the flood-prone waters of the lake, the entire lake area is divided by a triangular grid. The grid resolution can be set from several hundred meters to 1 kilometer according to the degree of attention in the lake area and the curvature of the shoreline. The inverse distance weight interpolation algorithm is used to interpolate the water environment parameter values of the monitoring points to each calculation grid of the whole lake to obtain the initial distribution of the water environment parameters of the whole lake.
3、采用三维水动力—水质数值模型计算未来三天浅水湖泊叶绿素a和溶解氧浓度的时空分布。3. Using a three-dimensional hydrodynamic-water quality numerical model to calculate the spatial and temporal distribution of chlorophyll a and dissolved oxygen concentrations in shallow lakes in the next three days.
1)水动力模型1) Hydrodynamic model
描述浅水湖泊水流运动的三维水动力守恒型方程组为：The three-dimensional hydrodynamic conservation equations describing the water flow movement in shallow lakes are:
式中：u、v、w为沿x、y、σ方向的流速分量；ζ为水位；D＝h+ζ为全水深；ρ为密度；g为重力加速度；f为柯氏系数；Am、Km为水平、垂向紊动粘性系数。In the formula: u, v, w are flow velocity components along x, y, σ directions; ζ is water level; D=h+ζ is full water depth; ρ is density; g is gravity acceleration; f is Korotkoff coefficient; A m , K m is the horizontal and vertical turbulent viscosity coefficient.
采用无结构网格下的有限体积模型对控制方程进行求解。求解区域平面采用无结构网格(三角形)剖分，使计算边界更加精确地拟合岸界；垂直方向采用σ坐标转换，使整个水域具有相同的垂向分层数。控制方程为时均化的N-S方程与封闭它的湍流闭合子模型，离散方法采用有限体积思想，所有物理变量设置在三角单元的中心。采用内、外模分离技术对控制方程组的在每一个三角单元上的积分形式进行数值求解，先求解二维外模式获得自由表面和垂向平均流速，其中外模式的时间离散采用牛顿向前差分格式，空间离散采用二阶精度的Roe型数值通量和一阶精度的分片常数逼近重构方式；再求解三维内模式获得三维流场，其中时间离散采用牛顿向前差分格式，空间对流项采用二阶精度的迎风格式，垂直扩散项采用显式格式。由于内外模时间步长不同，在计算过程中必须在每一个内模步长中进行外、内模流场的一致性检验。The governing equations are solved using a finite volume model on an unstructured grid. The plane of the solution area is subdivided by unstructured grid (triangle), so that the calculation boundary can fit the shore boundary more accurately; the vertical direction adopts σ coordinate transformation, so that the whole water area has the same vertical layer number. The governing equation is the time-averaged N-S equation and the turbulence closed submodel that closes it. The discrete method adopts the idea of finite volume, and all physical variables are set at the center of the triangular unit. Using the internal and external model separation technology to numerically solve the integral form of the governing equations on each triangular unit, first solve the two-dimensional external model to obtain the free surface and vertical average flow velocity, and the time discretization of the external model adopts Newton forward The difference format, the spatial discretization adopts the second-order precision Roe type numerical flux and the first-order precision slice constant approximation reconstruction method; and then solves the three-dimensional inner model to obtain the three-dimensional flow field, in which the time discretization adopts the Newton forward difference scheme, and the spatial convection The term is in an upwind form with second-order precision, and the vertical diffusion term is in an explicit form. Since the time steps of the inner and outer models are different, the consistency check of the flow fields of the outer and inner models must be carried out in each step of the inner model during the calculation process.
2)水质模型2) Water quality model
在水动力要素影响下，水质参数浓度的输运方程为：Under the influence of hydrodynamic factors, the transport equation of water quality parameter concentration is:
其中C代表物质浓度。当C为叶绿素a浓度时，动力反应项SK分别考虑藻类的生长、死亡和沉降作用。当C为溶解氧浓度时，动力反应项SK分别考虑大气富氧、水体中光合作用对溶解氧的增加以及水生植物呼吸作用、BOD降解、硝化反应耗氧和底泥需氧量对溶解氧的减少作用。水质参数输运模型的求解过程采取与水动力模型内模式相同的离散方式。where C represents the concentration of the substance. When C is the concentration of chlorophyll a, the dynamic response term S K considers the growth, death and sedimentation of algae, respectively. When C is the concentration of dissolved oxygen, the kinetic reaction term S K considers the effect of oxygen enrichment in the atmosphere, the increase of dissolved oxygen by photosynthesis in the water body, and the effect of aquatic plant respiration, BOD degradation, oxygen consumption on nitrification reaction, and sediment oxygen demand on dissolved oxygen. reduction effect. The solution process of the water quality parameter transport model adopts the same discretization method as that of the internal model of the hydrodynamic model.
3)模型驱动力3) Model driving force
驱动水动力—水质耦合模型运行的外力作用主要为未来三天的风应力(Vx，Vy)，Vx，Vy分别为x、y方向上的风速值，单位为m/s。The external force driving the operation of the hydrodynamic-water quality coupling model is mainly the wind stress (V x , V y ) in the next three days, where V x and V y are the wind speed values in the x and y directions, respectively, in m/s.
风应力获取方式：在中国天气网How to obtain wind stress: on China Weather Network
(http://www.weather.com.cn/html/weather/101190201.shtml)上查询未来三天风力的蒲福风级和风向，根据表1的转换规则，将未来三天天气预报中每天的风力蒲福风级转换成合成风速值V，根据表2的转换规则，将未来三天天气预报中每天的风向转换成角度θ，则Vx＝-Vcosθ，Vy＝-Vsinθ。(http://www.weather.com.cn/html/weather/101190201.shtml) to query the Beaufort wind scale and wind direction of the wind force in the next three days, according to the conversion rules in Table 1, the wind force of each day in the weather forecast for the next three days The Beaufort wind scale is converted into a synthetic wind speed value V. According to the conversion rules in Table 2, the wind direction in the weather forecast for the next three days is converted into an angle θ, then V x = -Vcosθ, V y = -Vsinθ.
表1 蒲福风级与风速的转换Table 1 Conversion of Beaufort wind scale and wind speed
表2 风向与度数的转换Table 2 Conversion of wind direction and degree
4)模型的运算流程4) The calculation process of the model
通过模型运算，得到未来三天全湖每个计算网格内叶绿素a和溶解氧浓度随时间变化的数值，进而得到全湖的时空分布。Through model calculation, the values of chlorophyll a and dissolved oxygen concentration in each calculation grid of the whole lake in the next three days are obtained over time, and then the temporal and spatial distribution of the whole lake is obtained.
4、建立概率经验公式，计算未来三天湖泛易发水域发生湖泛的概率4. Establish a probability empirical formula to calculate the probability of lake flooding in lake flood-prone waters in the next three days
湖泛的发生与很多因素相关，但是在短期预报中，可以不考虑在较长时间尺度上影响湖泛发生的水体营养盐等因素，只筛选出在几天内快速影响湖泛发生的水体中藻类浓度、溶解氧浓度及风速、降雨等气象因子的作用，因此藻源性湖泛的概率预报模型可表示为The occurrence of lake flooding is related to many factors, but in the short-term forecast, factors such as nutrients and salts that affect the occurrence of lake flooding on a longer time scale can be ignored, and only those water bodies that rapidly affect the occurrence of lake flooding within a few days can be screened out. Algae concentration, dissolved oxygen concentration, wind speed, rainfall and other meteorological factors, so the probability prediction model of algal flooding can be expressed as
F＝f1(N1t)*f2(V)*f3(R)*f4(N2t)F＝f 1 (N1 t )*f 2 (V)*f 3 (R)*f 4 (N2 t )
其中f1(N1t)为由t时刻藻类数量引起的概率，f2(V)为由风速条件引起的概率，f3(R)为由降雨条件引起的概率，f4(N2t)为由t时刻溶解氧溶度引起的概率。四种概率分别按表3进行赋值。在全湖每个计算网格上利用前述概率预报模型，可以得到全湖湖泛发生的概率分布。where f 1 (N1 t ) is the probability caused by the algae quantity at time t, f 2 (V) is the probability caused by the wind speed condition, f 3 (R) is the probability caused by the rainfall condition, f 4 (N2 t ) is Probability due to dissolved oxygen solubility at time t. The four probabilities are assigned according to Table 3. Using the aforementioned probability forecast model on each calculation grid of the whole lake, the probability distribution of flooding in the whole lake can be obtained.
表3 叶绿素、溶解氧含量、风速、降雨与湖泛发生概率的对应表Table 3 Correspondence between chlorophyll, dissolved oxygen content, wind speed, rainfall and probability of lake flooding
5、对于发生湖泛概率较大的区域，进一步确定发生湖泛的位置和面积5. For areas with a high probability of lake flooding, further determine the location and area of lake flooding
对于所关注的湖泛易发湖区，可事先划分成一些区段，每个区段包含若干计算网格。分别利用前述概率预报模型计算每区段水域内所覆盖的计算网格上湖泛发生的概率，如果该区段存在概率大于50％的网格，则预报该段水域发生湖泛；该区段内概率大于50％的网格面积之和，即发生湖泛的面积。For the lake prone lake area concerned, it can be divided into some sections in advance, and each section contains several calculation grids. Use the aforementioned probability forecasting model to calculate the probability of lake flooding on the calculation grid covered in each section of water area, if there is a grid with a probability greater than 50% in this section, it is predicted that lake flooding will occur in this section of water area; The sum of grid areas with inner probability greater than 50% is the area where flooding occurs.
由以上本发明的技术方案可知，本发明的富营养化浅水湖泊藻源性湖泛的短期预报方法，基于确定性模型原理，依据藻类、溶解氧浓度与各种物理因子、生化因子的相互作用关系，建立湖泊水动力学—水质数值耦合模型，并在适当的初始条件和边界条件下，结合气象参数，利用数值方法求解耦合模型的数学基本方程组这一核心，得到不同时间和空间尺度上藻类生物量和溶解氧浓度的分布，从而实现湖泛的预测。It can be seen from the above technical solutions of the present invention that the short-term prediction method of algae-derived lake flooding in shallow eutrophic lakes of the present invention is based on the principle of deterministic models and based on the interaction between algae, dissolved oxygen concentration and various physical factors and biochemical factors. relationship, establish a numerical coupling model of lake hydrodynamics and water quality, and under appropriate initial conditions and boundary conditions, combined with meteorological parameters, use numerical methods to solve the core mathematical equations of the coupled model, and obtain different time and space scales. The distribution of algae biomass and dissolved oxygen concentration, so as to realize the prediction of lake flooding.
在每年浅水湖泊湖泛高发的夏秋季节(4月-10月)，可设定每周一和周四按照步骤1-5进行未来三天湖泛的预测，将预报结果制作成湖泛监测预警半周报，发送至湖泊各级管理部门，可以有效的为管理部门快速决策和制定应急措施提供理论依据，提高政府的应对治理能力，最大程度降低其生态危害和健康风险，为浅水湖泊的日常管理工作提供有利的科学依据。In the summer and autumn season (April-October) when shallow lake flooding occurs frequently every year, you can set every Monday and Thursday to predict lake flooding in the next three days according to steps 1-5, and make the forecast results into lake flooding monitoring and early warning half. Weekly reports, sent to lake management departments at all levels, can effectively provide theoretical basis for management departments to make quick decisions and formulate emergency measures, improve the government's ability to deal with governance, minimize ecological hazards and health risks, and provide support for the daily management of shallow lakes. Provide a favorable scientific basis.
应当理解，前述构思以及在下面更加详细地描述的额外构思的所有组合只要在这样的构思不相互矛盾的情况下都可以被视为本公开的发明主题的一部分。另外，所要求保护的主题的所有组合都被视为本公开的发明主题的一部分。It should be understood that all combinations of the foregoing concepts, as well as additional concepts described in more detail below, may be considered part of the inventive subject matter of the present disclosure, provided such concepts are not mutually inconsistent. Additionally, all combinations of claimed subject matter are considered a part of the inventive subject matter of this disclosure.
结合附图从下面的描述中可以更加全面地理解本发明教导的前述和其他方面、实施例和特征。本发明的其他附加方面例如示例性实施方式的特征和/或有益效果将在下面的描述中显见，或通过根据本发明教导的具体实施方式的实践中得知。The foregoing and other aspects, embodiments and features of the present teachings can be more fully understood from the following description when taken in conjunction with the accompanying drawings. Other additional aspects of the invention, such as the features and/or advantages of the exemplary embodiments, will be apparent from the description below, or learned by practice of specific embodiments in accordance with the teachings of the invention.
附图说明Description of drawings
附图不意在按比例绘制。在附图中，在各个图中示出的每个相同或近似相同的组成部分可以用相同的标号表示。为了清晰起见，在每个图中，并非每个组成部分均被标记。现在，将通过例子并参考附图来描述本发明的各个方面的实施例，其中：The figures are not intended to be drawn to scale. In the drawings, each identical or nearly identical component that is illustrated in various figures may be represented by a like reference numeral. For purposes of clarity, not every component may be labeled in every drawing. Embodiments of the various aspects of the invention will now be described by way of example with reference to the accompanying drawings, in which:
图1是反距离权重插值算法示意图。Figure 1 is a schematic diagram of an inverse distance weight interpolation algorithm.
图2是浅水湖泊三维水动力—水质数值模型计算流程图。Fig. 2 is a calculation flow chart of the three-dimensional hydrodynamic-water quality numerical model for shallow lakes.
图3是太湖湖泛巡查区域及监测站点示意图。Figure 3 is a schematic diagram of the inspection area and monitoring stations in Taihu Lake.
图4是太湖湖泛预测模型网格划分图。Figure 4 is the grid division diagram of the lake flood prediction model of Taihu Lake.
图5是2014年6月12日太湖叶绿素浓度和溶解氧浓度的全湖插值分布图。Figure 5 is an interpolated distribution map of the whole lake of chlorophyll concentration and dissolved oxygen concentration in Taihu Lake on June 12, 2014.
图6是6月13-15日太湖叶绿素a浓度和溶解氧浓度的模型预测分布图。Figure 6 is the distribution map of model prediction of chlorophyll a concentration and dissolved oxygen concentration in Taihu Lake from June 13 to 15.
图7是太湖6月13日湖泛发生概率分布图及湖泛发生面积的确定。Figure 7 is the probability distribution map of Taihu Lake flooding on June 13 and the determination of the occurrence area of lake flooding.
图8是太湖湖泛重点预测区域的分段划分。Figure 8 shows the segmental division of the key prediction areas of Taihu lake flooding.
图9是太湖蓝藻及湖泛监测预警半周报范本。Figure 9 is a sample of the semi-weekly report on the monitoring and early warning of cyanobacteria and lake flooding in Taihu Lake.
前述图示1-9中，各坐标、标识或其他表示，均为本领域所公知的，并不在本例中再做赘述。In the foregoing illustrations 1-9, each coordinate, mark or other representation is well known in the art, and will not be repeated in this example.
具体实施方式Detailed ways
为了更了解本发明的技术内容，特举具体实施例并配合所附图式说明如下。In order to better understand the technical content of the present invention, specific embodiments are given together with the attached drawings for description as follows.
在本公开中参照附图来描述本发明的各方面，附图中示出了许多说明的实施例。本公开的实施例不必定意在包括本发明的所有方面。应当理解，上面介绍的多种构思和实施例，以及下面更加详细地描述的那些构思和实施方式可以以很多方式中任意一种来实施，这是应为本发明所公开的构思和实施例并不限于任何实施方式。另外，本发明公开的一些方面可以单独使用，或者与本发明公开的其他方面的任何适当组合来使用。Aspects of the invention are described in this disclosure with reference to the accompanying drawings, which show a number of illustrated embodiments. Embodiments of the present disclosure are not necessarily intended to include all aspects of the invention. It should be understood that the various concepts and embodiments described above, as well as those concepts and embodiments described in more detail below, can be implemented in any of a number of ways, which should be the concepts and embodiments disclosed by the present invention and not Not limited to any implementation. In addition, some aspects of the present disclosure may be used alone or in any suitable combination with other aspects of the present disclosure.
本发明给予一种富营养化浅水湖泊藻源性湖泛的短期预报方法，基于确定性模型原理，依据藻类、溶解氧浓度与各种物理因子、生化因子的相互作用关系，建立湖泊水动力学—水质数值耦合模型，并在适当的初始条件和边界条件下，结合气象参数，利用数值方法求解耦合模型的数学基本方程组这一核心，得到不同时间和空间尺度上藻类生物量和溶解氧浓度的分布，从而实现湖泛的预测。基于此方法实现的短期预测结果，可在每年浅水湖泊湖泛高发的夏秋季节(4月-10月)，设定成每周一和周四按照步骤1-5进行未来三天湖泛的预测，将预报结果制作成湖泛监测预警半周报，发送至湖泊各级管理部门，可以有效的为管理部门快速决策和制定应急措施提供理论依据，提高政府的应对治理能力，最大程度降低其生态危害和健康风险，为浅水湖泊的日常管理工作提供有利的科学依据。The present invention provides a short-term prediction method for algae-derived lake flooding of eutrophic shallow lakes, based on the principle of deterministic models, and according to the interaction relationship between algae, dissolved oxygen concentration and various physical factors and biochemical factors, the lake hydrodynamics are established - Water quality numerical coupling model, and under appropriate initial conditions and boundary conditions, combined with meteorological parameters, use numerical methods to solve the core of the mathematical basic equations of the coupling model, and obtain algae biomass and dissolved oxygen concentration on different time and space scales distribution, so as to realize the prediction of lake flooding. Based on the short-term prediction results achieved by this method, in the summer and autumn seasons (April-October) when shallow lake flooding occurs every year, it can be set to predict lake flooding for the next three days according to steps 1-5 every Monday and Thursday. The forecast results are made into a semi-weekly report on lake flood monitoring and early warning, and sent to the lake management departments at all levels, which can effectively provide a theoretical basis for the management department to make quick decisions and formulate emergency measures, improve the government's ability to deal with governance, and minimize its ecological hazards and Health risks, providing a favorable scientific basis for the daily management of shallow lakes.
作为本发明提出的一个较佳的实施方式，上述目的是这样实现的：在浅水湖泊湖泛易发水域设定监测点，对气象参数(气温、风速、风向)和水环境参数(水温、透明度、叶绿素a浓度、溶解氧溶度和营养盐浓度)进行测定；然后，利用空间网格划分和插值技术，将监测点的水环境参数插值到全湖，其空间分布作为水质数值模型的初始条件；再以未来三天风场时空分布作为模型驱动力，驱动三维水动力—水质数值模型进行运算，得到未来三天浅水湖泊叶绿素a和溶解氧浓度的时空分布；再利用叶绿素a和溶解氧浓度的时空分布，结合气象参数建立概率经验公式，计算未来三天湖泛易发水域发生湖泛的概率；最后，对于发生湖泛概率较大的区域，进一步确定发生湖泛的位置和面积。As a preferred embodiment that the present invention proposes, above-mentioned purpose is achieved like this: set monitoring point in shallow lake lake flood prone waters, to meteorological parameter (air temperature, wind speed, wind direction) and water environment parameter (water temperature, transparency) , chlorophyll a concentration, dissolved oxygen solubility and nutrient concentration) were measured; then, using spatial grid division and interpolation technology, the water environment parameters of the monitoring points were interpolated to the whole lake, and their spatial distribution was used as the initial condition of the water quality numerical model ; Then use the spatial and temporal distribution of the wind field in the next three days as the driving force of the model to drive the three-dimensional hydrodynamic-water quality numerical model to perform calculations to obtain the spatial and temporal distribution of chlorophyll a and dissolved oxygen concentrations in shallow lakes in the next three days; then use chlorophyll a and dissolved oxygen concentrations Based on the temporal and spatial distribution of the meteorological parameters, a probability empirical formula is established to calculate the probability of lake flooding in the waters prone to lake flooding in the next three days; finally, for areas with a high probability of lake flooding, the location and area of lake flooding will be further determined.
作为示例性的描述，下面结合附图所示，对前述方法的实施进行具体说明。As an exemplary description, the implementation of the aforementioned method will be specifically described below in conjunction with the accompanying drawings.
1、在湖泛易发水域设定监测点位，对气象参数和水环境参数进行测定1. Set monitoring points in lake-prone waters to measure meteorological parameters and water environment parameters
基于对浅水湖泊易发水域的认识，分别在重点关注湖区设置若干个监测点，并可进行人工原位巡查。在每一点位处，采用多参数水质仪(YSI6600-V2)(YellowSpring Instruments，USA)测定水质参数，如叶绿素a浓度、溶解氧浓度、溶解氧饱和度等，采用塞氏盘法测定水体透明度，并即时测定气温、风速、风向、水温等指标。采集表层水样(10-30cm深度)1升，样品灌装于采样瓶中立即放入带有冰盒的保温箱内，上岸后用于实验室内氮、磷营养盐和溶解性有机碳等指标的测定，样品的分析按照湖泊生态系统观测方法进行。Based on the understanding of the prone waters of shallow lakes, several monitoring points were set up in key lake areas, and manual in-situ inspections can be carried out. At each point, a multi-parameter water quality instrument (YSI6600-V2) (YellowSpring Instruments, USA) was used to measure water quality parameters, such as chlorophyll a concentration, dissolved oxygen concentration, dissolved oxygen saturation, etc., and the transparency of the water body was measured by the Sephardt disc method. And real-time measurement of air temperature, wind speed, wind direction, water temperature and other indicators. Collect 1 liter of surface water sample (10-30cm depth), fill the sample in a sampling bottle and immediately put it in an incubator with an ice box, and use it for nitrogen, phosphorus nutrients and dissolved organic carbon in the laboratory after landing The determination of indicators and the analysis of samples are carried out according to the lake ecosystem observation method.
2、利用空间网格划分和插值技术，将在监测点测定的水环境参数插值到全湖2. Using spatial grid division and interpolation technology, the water environment parameters measured at the monitoring points are interpolated to the whole lake
根据对湖泛易发水域地形的现场测定，采用三角形网格对全湖区域进行划分，网格分辨率可依照湖区关注程度和岸线弯曲程度，设置范围为1百米至1千米之间。本例中，优选采用反距离权重插值算法将监测点位的水环境参数值插值到全湖每一个计算网格上，得到全湖水环境参数的初始分布情况。According to the on-site measurement of the topography of the flood-prone waters of the lake, the whole lake area is divided by a triangular grid. The grid resolution can be set between 100 meters and 1 kilometer according to the degree of attention in the lake area and the curvature of the shoreline. . In this example, it is preferable to use the inverse distance weight interpolation algorithm to interpolate the water environment parameter values of the monitoring points to each calculation grid of the whole lake to obtain the initial distribution of the water environment parameters of the whole lake.
反距离权重插值算法的基本实现方法为(以叶绿素a浓度为例)：如图1所示，若某一网格中心点O的叶绿素a浓度未知，欲选取3-5个距离O点最近的监测点，对O点进行加权计算。首先需要搜索距离O点最近的3-5个监测点：1)以d1为搜索半径画圆，有A、B、C、D、E、F共6个监测点包含在圆内，说明半径d1值偏大；2)缩小半径为d2(比如取d1/2)，有A、C共2个点包含在圆内，说明半径d2偏小；3)扩大半径为d3(比如取d2*1.7)，有A、B、C共3个点包含在圆内，搜索结束；如果点数过多，则返回2)继续搜索；如果点数不够，则返回3)继续搜索。The basic implementation method of the inverse distance weight interpolation algorithm is (taking the concentration of chlorophyll a as an example): as shown in Figure 1, if the chlorophyll a concentration of a certain grid center point O is unknown, it is desired to select 3-5 points closest to point O Monitoring point, carry out weighted calculation on O point. First, you need to search for the 3-5 monitoring points closest to point O: 1) Draw a circle with d1 as the search radius, and there are 6 monitoring points A, B, C, D, E, and F included in the circle, indicating the radius d1 The value is too large; 2) Reduce the radius to d2 (for example, take d1/2), and there are 2 points A and C included in the circle, indicating that the radius d2 is too small; 3) Expand the radius to d3 (for example, take d2*1.7) , there are 3 points A, B, and C included in the circle, and the search ends; if there are too many points, return to 2) to continue searching; if the points are not enough, return to 3) to continue searching.
监测点找出之后，按照距离近，则权重小，距离远，则权重大的原则，计算O点的叶绿素a浓度，即：After the monitoring point is found, according to the principle that the weight is small if the distance is short, and the weight is large if the distance is long, the concentration of chlorophyll a at point O is calculated, namely:
其中ChlaO、ChlaA、ChlaB、ChlaC分别代表点O、A、B、C的叶绿素a浓度值，r1、r2、r3分别代表点O距A、B、C三点的距离。Among them, Chla O , Chla A , Chla B , and Chla C represent the chlorophyll a concentration values of points O, A, B, and C respectively, and r 1 , r 2 , and r 3 represent the distances from point O to points A, B, and C, respectively. .
3、采用三维水动力—水质数值模型计算未来三天浅水湖泊叶绿素a和溶解氧浓度的时空分布。3. Using a three-dimensional hydrodynamic-water quality numerical model to calculate the spatial and temporal distribution of chlorophyll a and dissolved oxygen concentrations in shallow lakes in the next three days.
1)水动力模型1) Hydrodynamic model
描述浅水湖泊水流运动的三维水动力守恒型方程组为：The three-dimensional hydrodynamic conservation equations describing the water flow movement in shallow lakes are:
式中：u、v、w为沿x、y、σ方向的流速分量；ζ为水位；D＝h+ζ为全水深；ρ为密度；g为重力加速度；f为柯氏系数；Am、Km为水平、垂向紊动粘性系数。In the formula: u, v, w are flow velocity components along x, y, σ directions; ζ is water level; D=h+ζ is full water depth; ρ is density; g is gravity acceleration; f is Korotkoff coefficient; A m , K m is the horizontal and vertical turbulent viscosity coefficient.
采用无结构网格下的有限体积模型对控制方程进行求解。求解区域平面采用无结构网格(三角形)剖分，使计算边界更加精确地拟合岸界；垂直方向采用σ坐标转换，使整个水域具有相同的垂向分层数。控制方程为时均化的N-S方程与封闭它的湍流闭合子模型，离散方法采用有限体积思想，所有物理变量设置在三角单元的中心。采用内、外模分离技术对控制方程组的在每一个三角单元上的积分形式进行数值求解，先求解二维外模式获得自由表面和垂向平均流速，其中外模式的时间离散采用牛顿向前差分格式，空间离散采用二阶精度的Roe型数值通量和一阶精度的分片常数逼近重构方式；再求解三维内模式获得三维流场，其中时间离散采用牛顿向前差分格式，空间对流项采用二阶精度的迎风格式，垂直扩散项采用显式格式。由于内外模时间步长不同，在计算过程中必须在每一个内模步长中进行外、内模流场的一致性检验。The governing equations are solved using a finite volume model on an unstructured grid. The plane of the solution area is subdivided by unstructured grid (triangle), so that the calculation boundary can fit the shore boundary more accurately; the vertical direction adopts σ coordinate transformation, so that the whole water area has the same vertical layer number. The governing equation is the time-averaged N-S equation and the turbulence closed submodel that closes it. The discrete method adopts the finite volume idea, and all physical variables are set at the center of the triangular unit. Using the internal and external model separation technology to numerically solve the integral form of the governing equations on each triangular unit, first solve the two-dimensional external model to obtain the free surface and vertical average flow velocity, and the time discretization of the external model adopts Newton forward The difference format, the spatial discretization adopts the second-order precision Roe-type numerical flux and the first-order precision slice constant approximation reconstruction method; and then solves the three-dimensional internal model to obtain the three-dimensional flow field, in which the time discretization adopts the Newton forward difference scheme, and the spatial convection The term is in an upwind form with second-order precision, and the vertical diffusion term is in an explicit form. Since the time steps of the inner and outer models are different, the consistency check of the flow fields of the outer and inner models must be carried out in each step of the inner model during the calculation process.
2)水质模型2) Water quality model
在水动力要素影响下，水质参数浓度的输运方程为：Under the influence of hydrodynamic factors, the transport equation of water quality parameter concentration is:
其中：C代表物质浓度。当C为叶绿素a浓度时，动力反应项SK分别考虑藻类的生长、死亡和沉降作用。当C为溶解氧浓度时，动力反应项SK分别考虑大气富氧、水体中光合作用对溶解氧的增加以及水生植物呼吸作用、BOD降解、硝化反应耗氧和底泥需氧量对溶解氧的减少作用。水质参数输运模型的求解过程采取与水动力模型内模式相同的离散方式。Where: C represents the substance concentration. When C is the concentration of chlorophyll a, the dynamic response term S K considers the growth, death and sedimentation of algae, respectively. When C is the concentration of dissolved oxygen, the kinetic reaction term S K considers the effect of oxygen enrichment in the atmosphere, the increase of dissolved oxygen by photosynthesis in the water body, and the effect of aquatic plant respiration, BOD degradation, oxygen consumption on nitrification reaction, and sediment oxygen demand on dissolved oxygen. reduction effect. The solution process of the water quality parameter transport model adopts the same discretization method as that of the internal model of the hydrodynamic model.
3)模型驱动力3) Model driving force
驱动水动力—水质耦合模型运行的外力作用主要为未来三天的风应力(Vx，Vy)，Vx，Vy分别为x、y方向上的风速值，单位为m/s。The external force driving the operation of the hydrodynamic-water quality coupling model is mainly the wind stress (V x , V y ) in the next three days, where V x and V y are the wind speed values in the x and y directions, respectively, in m/s.
风应力获取方式：在中国天气网How to obtain wind stress: on China Weather Network
(http://www.weather.com.cn/html/weather/101190201.shtml)上查询未来三天风力的蒲福风级和风向，根据表1的转换规则，将未来三天天气预报中每天的风力蒲福风级转换成合成风速值V，根据表2的转换规则，将未来三天天气预报中每天的风向转换成角度θ，则Vx＝-Vcosθ，Vy＝-Vsinθ。(http://www.weather.com.cn/html/weather/101190201.shtml) to query the Beaufort wind scale and wind direction of the wind force in the next three days, according to the conversion rules in Table 1, the wind force of each day in the weather forecast for the next three days The Beaufort wind scale is converted into a synthetic wind speed value V. According to the conversion rules in Table 2, the wind direction in the weather forecast for the next three days is converted into an angle θ, then V x = -Vcosθ, V y = -Vsinθ.
表1 蒲福风级与风速的转换Table 1 Conversion of Beaufort wind scale and wind speed
表2 风向与度数的转换Table 2 Conversion of wind direction and degree
4)模型的运算流程4) The calculation process of the model
如图2所示的流程，通过模型运算，得到未来三天全湖每个计算网格内叶绿素a和溶解氧浓度随时间变化的数值，进而得到全湖的时空分布。As shown in Figure 2, through model calculation, the values of chlorophyll a and dissolved oxygen concentration in each calculation grid of the whole lake in the next three days are obtained over time, and then the temporal and spatial distribution of the whole lake is obtained.
4、建立概率经验公式，计算未来三天湖泛易发水域发生湖泛的概率4. Establish a probability empirical formula to calculate the probability of lake flooding in lake flood-prone waters in the next three days
湖泛的发生与很多因素相关，但是在短期预报中，可以不考虑在较长时间尺度上影响湖泛发生的水体营养盐等因素，只筛选出在几天内快速影响湖泛发生的水体中藻类浓度、溶解氧浓度及风速、降雨等气象因子的作用，因此藻源性湖泛的概率预报模型可表示为The occurrence of lake flooding is related to many factors, but in the short-term forecast, factors such as nutrients and salts that affect the occurrence of lake flooding on a longer time scale can be ignored, and only those water bodies that rapidly affect the occurrence of lake flooding within a few days can be screened out. Algae concentration, dissolved oxygen concentration, wind speed, rainfall and other meteorological factors, so the probability prediction model of algal flooding can be expressed as
F＝f1(N1t)*f2(V)*f3(R)*f4(N2t) (6)F＝f 1 (N1 t )*f 2 (V)*f 3 (R)*f 4 (N2 t ) (6)
其中f1(N1t)为由t时刻藻类数量引起的概率，f2(V)为由风速条件引起的概率，f3(R)为由降雨条件引起的概率，f4(N2t)为由t时刻溶解氧溶度引起的概率。四种概率分别按表3进行赋值。在全湖每个计算网格上利用前述概率预报模型，可得到全湖湖泛发生的概率分布。where f 1 (N1 t ) is the probability caused by the algae quantity at time t, f 2 (V) is the probability caused by the wind speed condition, f 3 (R) is the probability caused by the rainfall condition, f 4 (N2 t ) is Probability due to dissolved oxygen solubility at time t. The four probabilities are assigned according to Table 3. Using the aforementioned probability forecast model on each calculation grid of the whole lake, the probability distribution of flooding in the whole lake can be obtained.
表3 叶绿素、溶解氧含量、风速、降雨与湖泛发生概率的对应表Table 3 Correspondence between chlorophyll, dissolved oxygen content, wind speed, rainfall and probability of lake flooding
5、对于发生湖泛概率较大的区域，进一步确定发生湖泛的位置和面积5. For areas with a high probability of lake flooding, further determine the location and area of lake flooding
对于所关注的湖泛易发湖区，可事先划分成一些区段，每个区段包含若干计算网格。分别利用公式(6)计算每区段水域内所覆盖的计算网格上湖泛发生的概率，如果该区段存在概率大于50％的网格，则预报该段水域发生湖泛；该区段内概率大于50％的网格面积之和，即发生湖泛的面积。For the lake prone lake area concerned, it can be divided into some sections in advance, and each section contains several calculation grids. Use formula (6) to calculate the probability of lake flooding on the calculation grid covered in each section of water area, if there is a grid with a probability greater than 50% in this section, it is predicted that lake flooding will occur in this section of water area; The sum of grid areas with inner probability greater than 50% is the area where flooding occurs.
以下所列举但并非是排他性的例子中，以太湖湖泛易发水域的监测预报为例，具体说明前述方法的实施。Among the examples listed below but not exclusive, the monitoring and forecasting of the prone waters of Taihu Lake is taken as an example to specifically illustrate the implementation of the aforementioned methods.
步骤1、在湖泛易发水域设定监测点位，对气象参数和水环境参数进行现场测定。Step 1. Set monitoring points in lake prone waters, and conduct on-site measurement of meteorological parameters and water environment parameters.
本例中，基于对太湖湖泛易发水域的认识，分别在湖西、竺山湾、梅梁湾、贡湖湾以及湖东水源地等五个湖区设置24个监测点，如图3所示。2014年6月12日周四，为了给当日的湖泛预报提供基础数据，并对太湖各重点湖区的水质及湖泛发生情况有全面的掌握，驾驶巡查快艇，沿图3所示的路线先后至24个巡查点位进行现场观察和测量。在每一点位处，采用多参数水质仪(YSI6600-V2)(YellowSpring Instruments，USA)测定叶绿素a浓度、溶解氧浓度、溶解氧饱和度等水质参数，采用塞氏盘法测定水体透明度，并即时测定气温、风速、风向、水温等指标。测量的各指标信息如表4所示。采集表层水样(10-30cm深度)1升，样品灌装于采样瓶中立即放入带有冰盒的保温箱内，上岸后用于实验室内氮、磷营养盐和溶解性有机碳等指标的测定，样品的分析按照湖泊生态系统观测方法进行。测量的各指标数据如表5所示。In this example, based on the understanding of the flood-prone waters of Taihu Lake, 24 monitoring points were set up in the five lake areas of the west of the lake, Zhushan Bay, Meiliang Bay, Gonghu Bay and the water source in the east of the lake, as shown in Figure 3 . On Thursday, June 12, 2014, in order to provide basic data for the lake flooding forecast of the day, and to have a comprehensive grasp of the water quality and lake flooding occurrences in key lake areas of Taihu Lake, the patrol speedboat was driven along the route shown in Figure 3. To 24 inspection points for on-site observation and measurement. At each point, a multi-parameter water quality instrument (YSI6600-V2) (YellowSpring Instruments, USA) was used to measure water quality parameters such as chlorophyll a concentration, dissolved oxygen concentration, and dissolved oxygen saturation. Determination of air temperature, wind speed, wind direction, water temperature and other indicators. The information of each index measured is shown in Table 4. Collect 1 liter of surface water sample (10-30cm depth), fill the sample in a sampling bottle and immediately put it in an incubator with an ice box, and use it for nitrogen, phosphorus nutrients and dissolved organic carbon in the laboratory after landing The determination of indicators and the analysis of samples are carried out according to the lake ecosystem observation method. The measured index data are shown in Table 5.
步骤2、将在监测点测定的水环境参数插值到全湖作为数值模型的初始条件。Step 2. Interpolate the water environment parameters measured at the monitoring points to the whole lake as the initial conditions of the numerical model.
根据对太湖湖泛易发水域地形的现场测定，将全湖划分为6353个计算网格，如图4所示。其中作为湖泛易发水域的西部湖区和北部湖区，网格分辨率为≈300m，其余水域网格分辨率为≈1000m。采用反距离权重插值算法将24个监测点位的水环境参数值插值到全湖6353个计算网格上，得到叶绿素a浓度、溶解氧浓度的全湖初始分布，如图5所示。According to the on-site measurement of the topography of the pan-prone waters of Taihu Lake, the whole lake is divided into 6353 calculation grids, as shown in Figure 4. Among them, the western lake area and northern lake area, which are prone to lake flooding, have a grid resolution of ≈300m, and the grid resolution of other water areas is ≈1000m. The inverse distance weight interpolation algorithm was used to interpolate the water environment parameter values of 24 monitoring points to 6353 calculation grids of the whole lake, and the initial distribution of chlorophyll a concentration and dissolved oxygen concentration of the whole lake was obtained, as shown in Figure 5.
根据中国天气网上显示的天气预报，6月13-15日蒲福风向风级分别为东北风2级，东风2级和东风2级，按照表1和表2的转换原则，三天的风速V＝2.5m/s，2.5m/s，2.5m/s；风向θ＝45，90，90。According to the weather forecast displayed on the China Weather Network, the Beaufort wind direction and wind scale from June 13 to 15 are northeast wind 2, east wind 2 and east wind 2 respectively. According to the conversion principles in Table 1 and Table 2, the wind speed V for three days =2.5m/s, 2.5m/s, 2.5m/s; wind direction θ=45, 90, 90.
步骤3、计算6月13-15日太湖叶绿素a浓度和溶解氧浓度的时空分布。Step 3. Calculate the temporal and spatial distribution of chlorophyll a concentration and dissolved oxygen concentration in Taihu Lake from June 13 to 15.
将步骤2中准备好的数据分别带入三维水动力—水质模型，按照图2所示的流程进行计算，得到未来三天全湖每个计算网格内叶绿素a和溶解氧浓度随时间变化的数值，即得到了全湖的时空分布，如图6所示。Bring the data prepared in step 2 into the three-dimensional hydrodynamic-water quality model, and perform calculations according to the process shown in Figure 2, and obtain the time-varying chlorophyll a and dissolved oxygen concentrations in each calculation grid of the whole lake in the next three days. value, that is, the temporal and spatial distribution of the whole lake is obtained, as shown in Figure 6.
步骤4、计算每个网格上未来三天太湖发生湖泛的概率。Step 4. Calculate the probability of lake flooding in Taihu Lake in the next three days on each grid.
根据中国天气网上显示的天气预报，6月13-15日风速为2级，天气分别为晴到多云、晴到多云、阴，对照表3，f(V)＝1.0，f(R)max＝1.0；若对于第i号网格，Chlai＝25.2μg/L，DOi＝1.5mg/L，则f(N1t)＝0.6，f(N2t)＝1.0，利用公式(6)，第i号网格发生湖泛的概率According to the weather forecast displayed on China Weather Online, the wind speed will be level 2 on June 13-15, and the weather will be sunny to cloudy, sunny to cloudy, and cloudy respectively. Compared with Table 3, f(V)＝1.0, f(R) max ＝ 1.0; if for grid i, Chla i =25.2μg/L, DO i =1.5mg/L, then f(N1 t )=0.6, f(N2 t )=1.0, using formula (6), the Probability of lake flooding in grid i
F＝f1(N1t)*f2(V)*f3(R)*f4(N2t)＝0.6F＝f 1 (N1 t )*f 2 (V)*f 3 (R)*f 4 (N2 t )＝0.6
在6353个网格上运用公式(6)，即可得到全太湖发生湖泛的概率分布，如图7所示。Using formula (6) on 6353 grids, the probability distribution of flooding in the whole Taihu Lake can be obtained, as shown in Figure 7.
步骤5、根据概率分布确定发生湖泛的位置和面积。Step 5. Determine the location and area of lake flooding according to the probability distribution.
如图8所示，将容易发生湖泛的竺山湾及西部沿岸带离岸2km范围水域分成4段，太滆港到沙塘港为第1段(太滆沙塘段)，沙塘港到符渎港为第2段(沙塘符渎段)，符渎港到陈东港为第3段(符渎陈东段)，陈东港到八房港为第4段(陈东八房段)；将贡湖湾北面取水口沿岸带离岸2公里范围分成2段，吴塘门港到壬子港为第5段(吴塘壬子段)，蠡河到新开港为第6段(蠡河新开段)；将梅梁湾沿岸带作为第7段。As shown in Figure 8, Zhushan Bay, which is prone to lake flooding, and the waters within 2km of the western coastal zone are divided into four sections. Taige Port to Shatang Port is the first section (Taige Shatang Section), To Fudu Port is the second section (Shatang Fudu Section), from Fudu Port to Chendong Port is the third section (Fudu Chendong Section), from Chendonggang to Bafanggang is the 4th section (Chendong Bafang Section ); Divide the 2km offshore zone of the water intake in the north of Gonghu Bay into two sections, the fifth section from Wutangmen Port to Renzi Port (Wutang Renzi Section), and the sixth section from Lihe River to Xinkai Port (Xinkai section of Lihe River); take the coastal zone of Meiliang Bay as section 7.
根据步骤4中太湖发生湖泛的概率分布，如果该区段存在概率大于50％的网格，则预报该段水域发生湖泛；该区段内概率大于50％的网格面积之和，即发生湖泛的面积。如图7所示，在梅梁湾区段中存在网格发生湖泛的概率为60％，因此预报梅梁湾在未来三天发生湖泛。其中，概率大于50％的网格约有5-10个，按照一个网格面积为0.3*0.3/2＝0.045km2计算，未来三天发生湖泛的面积为0.2-0.5km2。According to the probability distribution of flooding in Taihu Lake in step 4, if there are grids with a probability greater than 50% in this section, it is predicted that flooding will occur in this section of water; the sum of the grid areas with a probability greater than 50% in this section is The area where flooding occurs. As shown in Figure 7, the probability of lake flooding in grids in the Meiliang Bay section is 60%, so it is predicted that lake flooding will occur in Meiliang Bay in the next three days. Among them, there are about 5-10 grids with a probability greater than 50%. According to the calculation of a grid area of 0.3*0.3/2=0.045km 2 , the area of lake flooding in the next three days will be 0.2-0.5km 2 .
按照步骤1-步骤5的操作内容，后期可以制作成“太湖蓝藻及湖泛监测预警半周报”(如图9所示)，发送至湖泊各级管理部门，不仅为太湖的日常管理工作提供有利的科学依据，而且可以提高政府的应对治理能力，在湖泛发生之前制定有效的应急措施，最大程度降低湖泛的生态危害和健康风险。According to the operation content of step 1-step 5, the "Taihu Lake Cyanobacteria and Lake Flood Monitoring and Early Warning Semi-weekly Report" (as shown in Figure 9) can be produced in the later stage and sent to the management departments at all levels of the lake, which not only provides benefits for the daily management of Taihu Lake In addition, it can improve the government's ability to deal with governance, formulate effective emergency measures before the occurrence of lake flooding, and minimize the ecological hazards and health risks of lake flooding.
虽然本发明已以较佳实施例揭露如上，然其并非用以限定本发明。本发明所属技术领域中具有通常知识者，在不脱离本发明的精神和范围内，当可作各种的更动与润饰。因此，本发明的保护范围当视权利要求书所界定者为准。Although the present invention has been disclosed above with preferred embodiments, it is not intended to limit the present invention. Those skilled in the art of the present invention can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, the scope of protection of the present invention should be defined by the claims.
表5 6月12日太湖巡查氮、磷营养盐和BOD数据Table 5 Data of Nitrogen, Phosphorus Nutrients and BOD in Taihu Lake Inspection on June 12
Claims (6)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510059526.1A CN104615885B (en) | 2015-02-04 | 2015-02-04 | A kind of general short-term earthquake prediction method in nutrition enriched shallow lake algae source property lake |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510059526.1A CN104615885B (en) | 2015-02-04 | 2015-02-04 | A kind of general short-term earthquake prediction method in nutrition enriched shallow lake algae source property lake |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN104615885A true CN104615885A (en) | 2015-05-13 |
| CN104615885B CN104615885B (en) | 2018-05-15 |
Family
ID=53150326
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510059526.1A Active CN104615885B (en) | 2015-02-04 | 2015-02-04 | A kind of general short-term earthquake prediction method in nutrition enriched shallow lake algae source property lake |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104615885B (en) |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108108911A (en) * | 2018-01-09 | 2018-06-01 | 中国科学院南京地理与湖泊研究所 | Large-size lake subregion water quality objective formulating method based on aquatic ecosystem health |
| CN109460869A (en) * | 2018-11-06 | 2019-03-12 | 北京英视睿达科技有限公司 | Water environment method for early warning and device |
| CN109598439A (en) * | 2018-12-03 | 2019-04-09 | 河海大学 | A method of improved using wind speed auxiliary shallow lake water environment |
| CN109856357A (en) * | 2019-03-19 | 2019-06-07 | 广西科学院 | A short-term red tide early warning method and application based on buoy online monitoring data |
| CN109858699A (en) * | 2019-01-31 | 2019-06-07 | 北京英视智慧生态科技有限公司 | Water quality quantitative simulation method, apparatus, electronic equipment and storage medium |
| CN110097232A (en) * | 2019-05-09 | 2019-08-06 | 重庆中航科技有限公司 | A kind of wisdom water utilities water pollutant resolution prediction technique |
| CN111289716A (en) * | 2020-03-18 | 2020-06-16 | 中国人民解放军海军军医大学 | Water quality detection system and method |
| CN111581586A (en) * | 2020-04-28 | 2020-08-25 | 生态环境部卫星环境应用中心 | Lake and reservoir water quality anisotropic interpolation method and device based on registration model |
| CN113158762A (en) * | 2021-02-23 | 2021-07-23 | 中国科学院南京地理与湖泊研究所 | Risk assessment method for cyanobacterial bloom accumulation in shore zone of large lake reservoir |
| CN114858987A (en) * | 2022-03-30 | 2022-08-05 | 河海大学 | River and lake water quantity and quality monitoring and management system based on Internet of things |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101962961A (en) * | 2010-09-20 | 2011-02-02 | 中国科学院南京地理与湖泊研究所 | Method for determining ecological dredging range of water body pollution bottom sediment |
| CN102135531A (en) * | 2010-12-24 | 2011-07-27 | 中国科学院南京地理与湖泊研究所 | Method for forecasting blue-green algae water bloom in large-scale shallow lake within 72 hours |
| CN103175946A (en) * | 2013-03-04 | 2013-06-26 | 中国科学院南京地理与湖泊研究所 | System and method for automatically monitoring lake-flooding of shallow lake |
-
2015
- 2015-02-04 CN CN201510059526.1A patent/CN104615885B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101962961A (en) * | 2010-09-20 | 2011-02-02 | 中国科学院南京地理与湖泊研究所 | Method for determining ecological dredging range of water body pollution bottom sediment |
| CN102135531A (en) * | 2010-12-24 | 2011-07-27 | 中国科学院南京地理与湖泊研究所 | Method for forecasting blue-green algae water bloom in large-scale shallow lake within 72 hours |
| CN103175946A (en) * | 2013-03-04 | 2013-06-26 | 中国科学院南京地理与湖泊研究所 | System and method for automatically monitoring lake-flooding of shallow lake |
Non-Patent Citations (5)
| Title |
|---|
| LI W ET.: "Forecasting short-term cyanobacterial blooms in Lake Taihu ，China，using a coupled hydrodynamic-algal biomass model", 《ECOHYDROLOGY》 * |
| 王成林等: "基于气象条件的太湖湖泛预警研究", 《环境监控与预警》 * |
| 秦伯强等: "高频无线自动监测技术在湖泊蓝藻水华监测和预测中的应用", 《科研信息化技术与应用》 * |
| 罗妮娜: "用BP网络预测杭州西湖富营养化的短期变化趋势", 《中国优秀博硕士学位论文全文数据库 (硕士) 工程科技Ⅰ辑》 * |
| 陆桂华等: "太湖湖泛成因及防控关键技术", 《 中国水文科技新发展——2012中国水文学术讨论会论文集》 * |
Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108108911A (en) * | 2018-01-09 | 2018-06-01 | 中国科学院南京地理与湖泊研究所 | Large-size lake subregion water quality objective formulating method based on aquatic ecosystem health |
| CN108108911B (en) * | 2018-01-09 | 2020-09-15 | 中国科学院南京地理与湖泊研究所 | Large lake subarea water quality target making method based on water ecosystem health |
| CN109460869A (en) * | 2018-11-06 | 2019-03-12 | 北京英视睿达科技有限公司 | Water environment method for early warning and device |
| CN109598439B (en) * | 2018-12-03 | 2021-06-04 | 河海大学 | A method of using wind speed to assist shallow lake water environment improvement |
| CN109598439A (en) * | 2018-12-03 | 2019-04-09 | 河海大学 | A method of improved using wind speed auxiliary shallow lake water environment |
| CN109858699A (en) * | 2019-01-31 | 2019-06-07 | 北京英视智慧生态科技有限公司 | Water quality quantitative simulation method, apparatus, electronic equipment and storage medium |
| CN109858699B (en) * | 2019-01-31 | 2021-09-14 | 北京清丰众智生态科技有限公司 | Water quality quantitative simulation method and device, electronic equipment and storage medium |
| CN109856357A (en) * | 2019-03-19 | 2019-06-07 | 广西科学院 | A short-term red tide early warning method and application based on buoy online monitoring data |
| CN110097232A (en) * | 2019-05-09 | 2019-08-06 | 重庆中航科技有限公司 | A kind of wisdom water utilities water pollutant resolution prediction technique |
| CN111289716A (en) * | 2020-03-18 | 2020-06-16 | 中国人民解放军海军军医大学 | Water quality detection system and method |
| CN111581586B (en) * | 2020-04-28 | 2021-02-05 | 生态环境部卫星环境应用中心 | Lake and reservoir water quality anisotropic interpolation method and device based on registration model |
| CN111581586A (en) * | 2020-04-28 | 2020-08-25 | 生态环境部卫星环境应用中心 | Lake and reservoir water quality anisotropic interpolation method and device based on registration model |
| CN113158762A (en) * | 2021-02-23 | 2021-07-23 | 中国科学院南京地理与湖泊研究所 | Risk assessment method for cyanobacterial bloom accumulation in shore zone of large lake reservoir |
| CN113158762B (en) * | 2021-02-23 | 2024-05-28 | 中国科学院南京地理与湖泊研究所 | Method for evaluating cyanobacterial bloom accumulation risk of coastal zone of large-scale lake reservoir |
| CN114858987A (en) * | 2022-03-30 | 2022-08-05 | 河海大学 | River and lake water quantity and quality monitoring and management system based on Internet of things |
Also Published As
| Publication number | Publication date |
|---|---|
| CN104615885B (en) | 2018-05-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104615885B (en) | A kind of general short-term earthquake prediction method in nutrition enriched shallow lake algae source property lake | |
| Mihanović et al. | Exceptional dense water formation on the Adriatic shelf in the winter of 2012 | |
| Ferrarin et al. | Cross-scale operational oceanography in the Adriatic Sea | |
| CN102135531A (en) | Method for forecasting blue-green algae water bloom in large-scale shallow lake within 72 hours | |
| Hu | A review of the models for Lake Taihu and their application in lake environmental management | |
| Park et al. | Comparison of precipitation distributions in precipitation data sets representing 1km spatial resolution over South Korea produced by PRISM, IDW, and cokriging | |
| Tosic et al. | Hydrodynamic modelling of a polluted tropical bay: Assessment of anthropogenic impacts on freshwater runoff and estuarine water renewal | |
| Zhang et al. | Application of EFDC model to grading the eutrophic state of reservoir: Case study in Tianjin Erwangzhuang Reservoir, China | |
| CN101865689A (en) | A Salt Tidal Prediction Method for Watershed | |
| Lass et al. | On the dynamics of the Pomeranian Bight | |
| Frishfelds et al. | Transport of microplastics from the daugava estuary to the open sea | |
| CN105701066A (en) | Estuary-tidal-range fast prediction method | |
| Pan et al. | Assessment Methods of Small Watershed Ecosystem Health. | |
| Gemmrich et al. | Wind waves in the Strait of Georgia | |
| Sorgente et al. | Impact of freshwater inflow from the Volturno River on coastal circulation | |
| Bessa et al. | Variability of the mixed layer depth and the ocean surface properties in the Cape Ghir region, Morocco for the period 2002–2014 | |
| Wang et al. | Numerical study of the hydrodynamic and sediment transport process in a subtropical water reservoir: the impacts of storms and winds | |
| Sheng et al. | Modeling Lagrangian residual velocity in a tide-dominated long-narrow bay: case study of the inner Xiangshan Bay | |
| Boucouvala et al. | MM5 simulations of a SCOS97-NARSTO episode | |
| Pierini et al. | Atmospheric influence over the residence time in the Bahia Blanca Estuary, Argentina | |
| Chi et al. | MIKE11 model in water quality research of Songhua River in Jiamusi City | |
| Sui et al. | Circulation, dispersion and hydrodynamic connectivity over the Scotian Shelf and adjacent waters | |
| Zazzini | Turbulent dispersion in the Ocean Surface Boundary Layer | |
| Hama Karim et al. | Hydraulic Properties of Sulejów Reservoir in Poland as a Driving Factor of Sedimentation Processes | |
| Yu et al. | Calculation of Water Volume for Sediment Transport in the Sediment-laden River of the Main Stream of Liaohe River, Northern China. |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |