入海水道二期对洪泽湖地区洪水风险的影响研究

为探究淮河入海水道二期工程对洪泽湖的泄洪能力以及周边滞洪区的影响,在对研究区河网概化及地形处理的基础上,建立了研究区河网一维、洪泽湖湖区与周边滞洪区二维耦合的水动力数值模型,并采用1991,2003,2006,2007四年实测历史洪水资料对模型进行率定及验证。基于现状工况、规划工况以及不同洪水量级,设计了5个对比方案对研究区进行洪水演进数值模拟,对不同洪水量级进行横向对比,对不同工况进行纵向对比,并对不同方案进行淹没面积分析和洪水影响分析。结果表明,入海水道二期工程的启用可以有效降低洪泽湖水位,提高洪泽湖的防洪标准和周边滞洪区的启用标准,减少同等洪水量级下的受影响人口数和区域GDP,为洪泽湖的防洪安全建设以及周边蓄滞洪区的稳定发展创造了条件。     

淮河流域水资源短缺风险评估与时空分析

根据2003-2013年淮河流域五省的降水量、径流系数、人均水资源量、人均GDP、人口密度等相关数据,构建水资源短缺风险评价体系,用熵权法对指标赋值,运用可变模糊模型对淮河流域及各省的水资源短缺风险进行评估和时空差异分析。结果表明:2003-2013年间淮河流域水资源短缺风险值总体较高,且呈缓慢的增长趋势,2004年降水较少,风险值达到最高;十年间河南省风险增加最为明显,而山东省较为稳定,风险增加也最低;在淮河流域五个区域中,河南省的风险程度最高,达到3.52;江苏省、山东省次之;安徽省和湖北省相对较低,达到2.86和2.51。水资源短缺风险二级指标分析发现,危险性最强的是河南省,安徽省最小;水资源短缺易损性最强的是山东省,湖北省最小;水资源短缺暴露性最强的是江苏省,山东省最小;水资源短缺可恢复性最好的是湖北省,河南省最差。同时,所有评价指标中人口密度、人均GDP、降水量对水资源短缺风险的影响较大。     

基于MIKE FLOOD的洪泽湖周边滞洪区洪水演进模拟研究

基于实测断面资料建立了研究区的一维水动力模型,基于高精度DEM以及1∶10000地形图建立了研究区的二维水动力模型,并用MIKE FLOOD将一维模型和二维模型进行耦合,构建了洪泽湖周边滞洪区一、二维耦合的洪水演进数学模型。利用2003年历史洪水资料对模型参数进行了率定,并以2007年历史洪水资料进行了验证。以洪泽湖百年一遇设计洪水为模型上边界,二河闸、三河闸以及高良涧闸的现行调度方案的水位-流量关系为模型下边界,对洪泽湖百年一遇设计洪水方案进行模拟计算,当蒋坝水位达到14.33m时,洪泽湖周边滞洪区开始滞洪,得到开始滞洪后不同时段研究区内各类洪水风险要素的动态分布情况以及最大淹没水深、淹没历时,验证了模型的合理性,可用于蓄滞洪区洪水演算分析。     

《赤山湖湖条》解读与赤山湖管理分析

历史上赤山湖作为秦淮河流域的重要水利枢纽,它不仅承担着湖区周边及秦淮河下游的灌溉防洪功能,还在长期用水管理过程中,形成以"五尺水则"为代表的有效管理制度。本文通过对《赤山湖湖条》内容的解读,从工程管理、用水管理和经费管理等方面对赤山湖的水利管理进行历史考察,探究赤山湖功能发挥与湖区管理体系密不可分的关系,为现代水利工程尤其是河湖工程管理提供一定的借鉴和参考。     

滇池环湖截污干渠错峰技术模拟与分析

为了实现滇池流域截污效果的最大化,基于前期建立的东岸排水管网SWMM模型,结合该区域1995-2016年间降雨资料,研究环湖截污干渠的错峰调蓄技术。根据雨水干渠液位高度执行不同控制模式:当水位低于6.76 m时执行典型污染物浓度阈值控制模式;当水位高于6.76 m时执行典型污染物阈值控制的同时执行液位-污染物通量控制模式。采用SS和TN作为干渠截流的典型污染物控制指标,其控制浓度阈值分别取12和5 mg/L。模拟重现期为0.5~1 a降雨时SS、TN浓度-负荷通量调蓄方案下干渠负荷收集情况,结果表明两种调蓄方案均能有效提高雨水干渠的负荷收集率,提高污水干渠出口浓度,降低雨水径流对污水厂的高水量和低浓度冲击负荷。随着重现期增大,干渠对污染物的高效和最大化收集效果越明显。但是TN调控下污染负荷收集效果优于SS,因此选择TN作为干渠截流的最优典型污染物控制指标。     

Spatial ecology of Bay of Quinte walleye (Sander vitreus): Annual timing,extent, and patterns of migrations in eastern Lake Ontario

Walleye (Sander vitreus) are the top nearshore predator in the Bay of Quinte and eastern Lake Ontario, where they have strong ecological and socio-economic impacts. The population is known to migrate seasonally; however, the precise timing and extent are not well defined. This study used acoustic telemetry to provide a fisheries-independent measure of timing, extent, and seasonal distribution of migration in Lake Ontario and to examine the influence of sex, spawning river, size, and year. Annual detection histories were used to determine the timing of migrations into and out of the spawning rivers, departure from the Bay of Quinte post-spawn, and the pre-spawn return to the Bay of Quinte. Sequence analysis was subsequently used to examine how fish occupy defined regions of eastern Lake Ontario annually and identify patterns in migration strategy. Spawning site fidelity was high for both rivers (91–97%) and annual residency within the Bay of Quinte was low (9.5%). Females spent less time in spawning rivers, migrated to the main lake earlier, and generally travelled further than males. Larger fish also migrated to the main lake first and travelled further, and differences in timing between spawning rivers were minor. Annual differences in timing were observed and were most likely related to environmental differences between years. Cluster analysis was used to identify groups of fish which utilized unique annual migration strategies and demonstrated sex and size had an important influence on the variability in annual spatial occupancy, but the importance of spawning river and year was minimal.     

Dreissena in Lake Ontario 30 years post-invasion

We examined three decades of changes in dreissenid populations in Lake Ontario and predation by round goby (Neogobius melanostomus). Dreissenids (almost exclusively quagga mussels, Dreissena rostriformis bugensis) peaked in 2003, 13 years after arrival, and then declined at depths <90 m but continued to increase deeper through 2018. Lake-wide density also increased from 2008 to 2018 along with average mussel lengths and lake-wide biomass, which reached an all-time high in 2018 (25.2 ± 3.3 g AFTDW/m²). Round goby densities were estimated at 4.2 fish/m² using videography at 10 to 35 m depth range in 2018. This density should impact mussel populations based on feeding rates, as indicated in the literature. While the abundance of 0–5 mm mussels appears to be high in all three years with measured length distributions (2008, 2013, 2018), the abundance of 5 to 12 mm dreissenids, the size range most commonly consumed by round goby, was low except at >90 m depths. Although the size distributions indicate that round goby is affecting mussel recruitment, we did not find a decline in dreissenid density in the nearshore and mid-depth ranges where goby have been abundant since 2005. The lake-wide densities and biomass of quagga mussels have increased over time, due to both the growth of individual mussels in the shallower depths, and a continuing increase in density at >90 m. Thus, the ecological effects of quagga mussels in Lake Ontario are likely to continue into the foreseeable future.     

Depth and temperature selection of lake charr (Salvelinus namaycush) ecotypes in Lake Superior revealed by popup satellite archival tags

Lake charr exhibit morphological diversity in large North American lakes, largely attributed to habitat partitioning. Bathythermal habitats of lean lake charr have been assessed but remain largely unknown for other lake charr ecotypes. Popup satellite archival tags (PSATs) were used to determine depth and temperature profiles of lean (n = 15), siscowet (n = 16) and redfin (n = 3) lake charr ecotypes in Lake Superior during segments of the year. Monthly median depths of leans were < 20 m throughout the year while the median depth of redfins was 24–32 m (June-September). Monthly median depth of siscowets ranged from 103 to 204 m but they exhibited four distinctive patterns: deep (>80 m), shallow (<10 m), extreme vertical movements (>80–0 m), and high frequency vertical movements between 100 and 125 m. Siscowets were the most stenothermal with habitat temperatures generally 4–5 °C but not exceeding 12 °C. Leans were found as low as 0 °C (January-March) and up to 15 °C (July-September). Median monthly habitat temperatures for redfins were 1–3 °C colder than leans during July-September. PSATs confirmed that siscowets exhibit extreme vertical movements and may feed near the surface. The timing and duration of the extreme vertical movements exhibited by siscowets did not appear to be associated with time of day (i.e., not diel vertical movements) and may be described as opportunistic movements presumably related to foraging. Data obtained from the PSATs reinforces the view that lake charr are highly adaptive and can thrive in cold, oligotrophic lakes by optimizing the use of the entire water column.     

A modified NRCS-CN method for eliminating abrupt runoff changes induced by the categorical antecedent moisture conditions

The popular Natural Resources Conservation Service Curve Number (NRCS-CN) (earlier known as Soil Conservation Service Curve Number (SCS-CN) method of rainfall-runoff modeling has often faced the criticism of exhibiting quantum jumps in runoff computations because of the sudden jumps appearing in CN-values derived from NEH-4 tables for three antecedent moisture conditions (AMC), viz., AMC-I, AMC-II, and AMC-III valid for dry, normal, and wet conditions, respectively. The variability of antecedent soil moisture within an AMC category is responsible for the abrupt jump and other deficiencies in the CN method for runoff estimation. This paper suggests a novel procedure to account for the antecedent moisture (M), preventing quantum jumps and eliminating deficiencies in determination of CN and, in turn, estimation of direct runoff. Its validity was verified utilizing the observed rainfall (P)-runoff (Q) events from 36 US watersheds, four sub-catchments of the Godavari basin, and small agricultural plots at Roorkee, India. The performance of the proposed model (M5) for runoff prediction was compared with the existing NRCS-CN (M1), Mishra and Singh (2002) (M2), Singh et al. (2015) (M3), and Verma et al. (2021) (M4) model using various performance indices. Using the CNs derived from observed events, model M5 was seen to have performed better than M1-M4 in terms of Nash Sutcliffe Efficiency (NSE), Root Mean Square Error (RMSE), and Percent Bias (PBIAS) for the data of US watersheds, and CN-P correlation improved as the coefficient of determination (R²) enhanced. Similarly, using the RS & GIS-based CNs on natural watersheds of the Godavari basin and considering AMC-I, the performance of M5 was again better than M1-M4 in terms of RMSE, Mean Bias Error (mBIAS), Mean Absolute Error (MAE), and Normalized-Nash Sutcliffe Efficiency (NNSE). Interestingly, there existed a significant (p < 0.05) relationship between the in-situ water content (w) measured for the experimental plots of Roorkee and the model input variable antecedent moisture (M), offering a physical touch to the conceptual model.     

基于互信息的湖泊日水位预测——以西洞庭湖为例

鉴于传统的湖泊水位预测在输入因子选择时具有一定的盲目性,以西洞庭湖为例,利用基于互信息的输入因子选择法建立了日水位预测模型。按河流生态功能将水文年划分为枯水期、汛前涨水期、汛期、汛后退水期4个时期,然后分期计算影响湖泊日水位的自变量与日水位的互信息,并引入广义相关系数将互信息归一化,选出各时期互信息最大的自变量因子作为模型的输入变量。经过模型计算与数据分析可得:F检验结果显著,回归值与实测值的相关度高,剩余标准差小。由此证明用互信息筛选出的因子作为模型的输入变量能取得较好的精度并在实际中易于操作。