Integrating Seasonal Information on Nutrients and Benthic Algal Biomass into Stream Water Quality Monitoring

Benthic chlorophyll a (BChl a) and environmental factors that influence algal biomass were measured monthly from February through October in 22 streams from three agricultural regions of the United States. At‐site maximum BChl a ranged from 14 to 406 mg/m² and generally varied with dissolved inorganic nitrogen (DIN): 8 out of 9 sites with at‐site median DIN >0.5 mg/L had maximum BChl a >100 mg/m². BChl a accrued and persisted at levels within 50% of at‐site maximum for only one to three months. No dominant seasonal pattern for algal biomass accrual was observed in any region. A linear model with DIN, water surface gradient, and velocity accounted for most of the cross‐site variation in maximum chlorophyll a (adjusted R² = 0.7), but was no better than a single value of DIN = 0.5 mg/L for distinguishing between low and high‐biomass sites. Studies of nutrient enrichment require multiple samples to estimate algal biomass with sufficient precision given the magnitude of temporal variability of algal biomass. An effective strategy for regional stream assessment of nutrient enrichment could be based on a relation between maximum BChl a and DIN based on repeat sampling at sites selected to represent a gradient in nutrients and application of the relation to a larger number of sites with synoptic nutrient information.     

新疆某县河流水质现状评价及其变化趋势分析

利用新疆某县境内主要河流的水质监测资料,依据水质评价标准《地表水环境质量标准》(GB 3838-2002),评价了某县境内主要河流的环境质量,采用季节性肯达尔法进行趋势分析检验。结果表明,新疆某县境内河流水质总体优良,水质级别为Ⅰ、Ⅱ类,可以作为饮用水源地、农田灌溉用水、一般工业用水的水源。从趋势分析检验结果看,水质基本稳定,没有受到明显的人为污染。     

夏季高原河流CDOM光学特性、组成及来源研究

对高原河流有色溶解有机物(CDOM)光学特性、组成及来源进行研究,有利于更好的了解高原河流水体生态环境,为高原河流生态系统中溶解有机物(DOM)循环过程的研究提供数据参考.利用2017年夏季无定河和湟水流域河流的实测数据对CDOM的吸收特性、荧光特性、水质参数以及它们之间关系进行分析,进而对高原干旱地区河流CDOM光学特性及其组成与来源进行研究.研究结果表明:两流域河流中CDOM吸收系数随波长的增加呈衰减趋势,但吸收光谱曲线差别较大;两流域CDOM在440 nm处的吸收系数[a(440)]与溶解有机碳(DOC)具有显著相关性(p0.01),而与叶绿素a浓度(Chla)不存在相关性.无定河a(440)与Chla浓度的相关性说明其河流现场产生(生物活动)作用较小.通过对比分析S_(275-295)、SUVA_(254)发现,无定河分子量和芳香性程度均大于湟水,且湟水与无定河CDOM的分子量变化要高于其它河流的研究.根据CDOM三维荧光光谱分析发现,类腐殖质荧光峰是两流域河流CDOM荧光的主要贡献者;通过荧光指数FI、腐殖化指数HIX、生物源指数BIX的研究发现,湟水以陆源输入为主,腐殖化程度高,无定河以陆源输入为主的同时存在一定的自生成分,人为生产活动干扰是其陆源输入的主要影响因素.通过冗余分析发现,湟水DOC、Chla、pH对类腐殖质荧光峰及荧光强度影响较大(p0.01);无定河DOC、电导率(EC)、浊度(Tur)与类腐殖质荧光峰及荧光强度显著正相关(p0.01).     

重庆市河水中粪源微生物污染特征及源解析

以重庆主要河流水体作为研究对象,使用传统培养技术评估了细菌总数、粪链球菌、肠球菌、脆弱拟杆菌、总大肠菌群及粪大肠菌群的污染水平;同时以拟杆菌作为特异性指示菌,选取人源性粪便专一指示菌引物（HF183）和猪源性粪便专一指示菌引物（Pig-2-Bac）进行源追踪.微生物培养结果表明：重庆市主要河流在春季有15.4%的研究断面未达到Ⅲ类水质,秋季有61.5%的研究断面未达到Ⅲ类水质.在春季,主城区河流主要受人类粪便污染,区县河流主要受动物粪便污染;在秋季,主城区和区县河流都主要受人类粪便污染.指示微生物指标间Pearson相关性分析结果表明粪链球菌、粪大肠菌群、肠球菌两两之间有显著相关性,肠球菌与脆弱拟杆菌有显著相关性.猪源性拟杆菌特异性生物标记Pig-2-Bac和人源拟杆菌特异性生物标记HF183对人和动物粪便污染区分成功率达100%;用这两种特异性引物对春季水样DNA进行扩增,发现13个采样点均未受到猪源粪便污染,唐家沱、朝天门、鸡冠石、合川受到人源粪便污染.     

Controls on sediment delivery in coastal plain rivers

Rivers crossing coastal plains are often inefficient conveyors of sediment, so that changes in upstream sediment dynamics are not evident at the river mouth. Extensive accommodation space and low stream power often result in extensive alluvial storage upstream of estuaries and correspondingly low sediment loads at the river mouth. However, gaging stations with sediment records are typically well upstream of the coast, and thus tend to overestimate sediment yields by under-representing the lower coastal plain and because there is often a net loss of sediment in lower coastal plain reaches. Studies of alluvial sediment storage have generally focused on accommodation space, but, using examples from Texas, we show that low transport capacity controlled largely by slope is a crucial factor.     

Simulated Impacts of Three Decadal Climate Variability Phenomena on Water Yields in the Missouri River Basin1

Mehta, Vikram M., Norman J. Rosenberg, and Katherin Mendoza, 2011. Simulated Impacts of Three Decadal Climate Variability Phenomena on Water Yields in the Missouri River Basin. Journal of the American Water Resources Association (JAWRA) 47(1):126‐135. DOI: 10.1111/j.1752‐1688.2010.00496.x Abstract: The Missouri River Basin (MRB) is the largest river basin in the United States (U.S.), and is one of the most important crop and livestock‐producing regions in the world. In a previous study of associations between decadal climate variability (DCV) phenomena and hydro‐meteorological (HM) variability in the MRB, it was found that positive and negative phases of the Pacific Decadal Oscillation (PDO), the tropical Atlantic sea‐surface temperature gradient variability (TAG), and the west Pacific warm pool (WPWP) temperature variability were significantly associated with decadal variability in precipitation and 2‐meter air temperature in the MRB, with combinations of various phases of these DCV phenomena associated with drought, flood, or neutral HM conditions. Here, we report on a methodology developed and applied to assess whether the aforementioned DCVs directly affect the hydrology of the MRB. The Hydrologic Unit Model of the U.S. (HUMUS) was used to simulate water yields in response to realistic values of the PDO, TAG, and WPWP at 75 widely distributed, eight‐digit hydrologic unit areas within the MRB. HUMUS driven by HM anomalies in both the positive and negative phases of the PDO and TAG resulted in major impacts on water yields, as much as ±20% of average water yield in some locations. Impacts of the WPWP were smaller. The combined and cumulative effects of these DCV phenomena on the MRB HM and water availability can be dramatic with important consequences for the MRB.     

Streamflow Hydrology Estimate Using Machine Learning (SHEM)

Continuity and accuracy of near real‐time streamflow gauge (streamgage) data are critical for flood forecasting, assessing imminent risk, and implementing flood mitigation activities. Without these data, decision makers and first responders are limited in their ability to effectively allocate resources, implement evacuations to save lives, and reduce property losses. The Streamflow Hydrology Estimate using Machine Learning (SHEM) is a new predictive model for providing accurate and timely proxy streamflow data for inoperative streamgages. SHEM relies on machine learning (“training”) to process and interpret large volumes (“big data”) of historic complex hydrologic information. Continually updated with real‐time streamflow data, the model constructs a virtual dataset index of correlations and groups (clusters) of relationship correlations between selected streamgages in a watershed and under differing flow conditions. Using these datasets, SHEM interpolates estimated discharge and time data for any indexed streamgage that stops transmitting data. These estimates are continuously tested, scored, and revised using multiple regression analysis processes and methodologies. The SHEM model was tested in Idaho and Washington in four diverse watersheds, and the model's estimates were then compared to the actual recorded data for the same time period. Results from all watersheds revealed a high correlation, validating both the degree of accuracy and reliability of the model.     

Statistical Models to Predict and Assess Spatial and Temporal Low‐Flow Variability in New England Rivers and Streams

In the northern hemisphere, summer low flows are a key attribute defining both quantity and quality of aquatic habitat. I developed one set of models for New England streams/rivers predicting July/August median flows averaged across 1985–2015 as a function of weather, slope, % imperviousness, watershed storage, glacial geology, and soils. These models performed better than most United States Geological Survey models for summer flows developed at a statewide scale. I developed a second set of models predicting interannual differences in summer flows as a function of differences in air temperature, precipitation, the North Atlantic Oscillation (NAO) index, and lagged NAO. Use of difference equations eliminated the need for transformations and accounted for serial autocorrelations at lag 1. The models were used in sequence to estimate time series for monthly low flows and for two derived flow metrics (tenth percentile [Q10] and minimum 3‐in‐5 year average flows). The first metric is commonly used in assessing risk to low‐flow conditions over time, while the second has been correlated with increased probability of localized extinctions for brook trout. The flow metrics showed increasing trends across most of New England for 1985–2015. However, application of summer flow models with average and extreme climate projections to the Taunton River, Massachusetts, a sensitive watershed undergoing rapid development, projected that low‐flow metrics will decrease over the next 50 years.     

Implications of Upstream Flow Availability for Watershed Surface Water Supply across the Conterminous United States

Although it is well established that the availability of upstream flow (AUF) affects downstream water supply, its significance has not been rigorously categorized and quantified at fine resolutions. This study aims to fill this gap by providing a nationwide inventory of AUF and local water resource, and assessing their roles in securing water supply across the 2,099 8‐digit hydrologic unit code watersheds in the conterminous United States (CONUS). We investigated the effects of river hydraulic connectivity, climate variability, and water withdrawal, and consumption on water availability and water stress (ratio of demand to supply) in the past three decades (i.e., 1981–2010). The results show that 12% of the CONUS land relied on AUF for adequate freshwater supply, while local water alone was sufficient to meet the demand in another 74% of the area. The remaining 14% highly stressed area was mostly found in headwater areas or watersheds that were isolated from other basins, where stress levels were more sensitive to climate variability. Although the constantly changing water demand was the primary cause of escalating/diminishing stress, AUF variation could be an important driver in the arid south and southwest. This research contributes to better understanding of the significance of upstream–downstream water nexus in regional water availability, and this becomes more crucial under a changing climate and with intensified human activities.     

基于山江海视角与SRP模型的桂西南-北部湾生态环境脆弱性评价

为揭示桂西南喀斯特-北部湾海岸带生态环境脆弱性,本研究基于山江海视角,采用生态敏感度-生态恢复力-生态压力度(SRP)模型、无纲量化模型、层次分析法,结合生态环境脆弱性指数,对该区域进行了生态环境脆弱性的评价,为该区域的生态环境治理和恢复提供科学的理论基础和技术指导。结果表明:(1) 2000～2018年三期生态环境脆弱性指数在0.14～0.96之间,平均值在0.52,整体属于中度脆弱。2000年轻度脆弱区面积比重最大为44.96%,中度脆弱区面积其次,重度脆弱区面积最小; 2010年轻度脆弱区和中度脆弱区的面积比重相似,潜在脆弱区面积和重度脆弱区面积减少; 2018年仍然是轻度脆弱区比重最大,重度脆弱区面积增加1 736 km2。(2)随着高程的增加,生态环境脆弱性程度整体有减轻的趋势,重度脆弱比重越来越小。(3)林地主要分布于微度脆弱区和轻度脆弱区,耕地、水域、建设用地和未利用地主要分布在轻度脆弱区,草地主要在微度脆弱区和轻度脆弱区。