PREDICTIVE MODEL FOR NITRATE LOAD IN THE BULL RUN WATERSHED,OREGON1

ABSTRACT: Predictive models for nitrate in four streams in the Bull Run Watershed in the Cascade Mountains of Oregon were developed from a record of 17 years of nitrate samples. The models are time series regression models written in terms of Log(nitrate load). The independent variables are logarithm of 14-day mean daily stream discharge, current day's precipitation, logarithm of the previous day's precipitation, total precipitation for the previous seven days, a hydrograph position variable that indicates rising or falling limb, and average maximum air temperature for the preceding 14 days. The models describe annual cycle and seasonable trends and variations in nitrate load, but are unable to describe large day to day variations like those associated with hydrograph peaks.     

南充市空气中可吸入颗粒物污染水平分析

本文在连续1年又5个月同步采样的基础上，测得南充市市辖三城区5个采样监测点环境空气中PM10的浓度及其时空变化进行了分析。结果表明，PM10的日平均浓度范围为0．020～1．030mg／m^3，超标率达38．0％；周日均浓度在0.040～0．520mg／m^3之间，超标率达到了37．8％。空气污染最重为嘉陵城区，其次为高坪城区，再次为石油学院、炼油厂和市府二院。浓度季节变化特征表现为冬季最高，其次为秋季，再次为春季，最轻为夏季。     

BASIN SCALE WATER MANAGEMENT AND FORECASTING USING ARTIFICIAL NEURAL NETWORKS1

ABSTRACT: Water scarcity in the Sevier River Basin in south‐central Utah has led water managers to seek advanced techniques for identifying optimal forecasting and management measures. To more efficiently use the limited quantity of water in the basin, better methods for control and forecasting are imperative. Basin scale management requires advanced forecasts of the availability of water. Information about long term water availability is important for decision making in terms of how much land to plant and what crops to grow; advanced daily predictions of streamflows and hydraulic characteristics of irrigation canals are of importance for managing water delivery and reservoir releases; and hourly forecasts of flows in tributary streams to account for diurnal fluctuations are vital to more precisely meet the day‐to‐day expectations of downstream farmers. A priori streamflow information and exogenous climate data have been used to predict future streamflows and required reservoir releases at different timescales. Data on snow water equivalent, sea surface temperatures, temperature, total solar radiation, and precipitation are fused by applying artificial neural networks to enhance long term and real time basin scale water management information. This approach has not previously been used in water resources management at the basin‐scale and could be valuable to water users in semi‐arid areas to more efficiently utilize and manage scarce water resources.     

SUMMER LOW FLOWS IN NEW ENGLAND DURING THE 20TH CENTURY1

ABSTRACT: High springtime river flows came earlier by one to two weeks in large parts of northern New England during the 20th Century. In this study it was hypothesized that late spring/early summer recessional flows and late summer/early fall low flows could also be occurring earlier. This could result in a longer period of low flow recession and a decrease in the magnitude of low flows. To test this hypothesis, variations over time in the magnitude and timing of low flows were analyzed. To help understand the relation between low flows and climatic variables in New England, low flows were correlated with air temperatures and precipitation. Analysis of data from 23 rural, unregulated rivers across New England indicated little evidence of consistent changes in the timing or magnitude of late summer/early fall low flows during the 20th Century. The interannual variability in the timing and magnitude of the low flows in northern New England was explained much more by the interannual variability in precipitation than by the interannual variability of air temperatures. The highest correlation between the magnitude of the low flows and air temperatures was with May through November temperatures (r =?0.37, p= 0.0017), while the highest correlation with precipitation was with July through August precipitation (r = 0.67, p > 0.0001).     

Long-term changes in microbial water quality indicators in a hydro-power plant reservoir: The role of natural factors and socio-economic changes

Long-term changes, from 1984 to 2010, in the indicators of microbial pollution (total viable count, coliforms, Escherichia coli, enterococci, and Clostridium perfringens) are analysed in the Riga Hydropower Plant Reservoir, an essential source of drinking water for Riga, the capital of Latvia. Counts in microbial indicators fluctuated seasonally and were related to physicochemical parameters (nitrogen compounds, turbidity, temperature, and pH). The changes in microbial pollution were brought about by two major socio-economic developments. Firstly, Latvia’s independence from the USSR in 1991 which facilitated a distinct reduction in most microorganism counts due to a sharp decline in industrial and agricultural production. This resulted in a significant drop in point and nonpoint pollution in the river basin. A further development was Latvia joining the European Union in 2004. The corresponding focus on water management, including wastewater treatment, was a major priority of environmental investment and lead to improvements in microbial water quality.Electronic supplementary materialThe online version of this article (10.1007/s13280-020-01470-1) contains supplementary material, which is available to authorized users.     

青藏高原东北部更尕海沉积软体动物壳体同位素初步研究

文章通过建立更尕海湖泊沉积岩芯软体动物化石属种组合,对比分析了软体动物壳体碳氧同位素的种内、种间变化。结果显示,壳体氧同位素种内变化较小,通常小于0.3‰;相同属不同种的壳体氧同位素种间差异约为0.3‰,但较相同属种的波动幅度大:不同属种的壳体氧同位素的种间差异最大,且波动幅度更大一些。壳体碳同位素的种内和种间差异均较氧同位素大,尤其是碳同位素的种间差异更为显著,可能与软体动物的"生命效应"有关。合理评价软体动物壳体同位素种内和种间变化对于理解软体动物壳体同位素所记录的气候变化具有重要的意义。     

三峡库区典型消落带土壤微生物生物量碳、氮的变化特征及其影响因素探讨

本文以三峡库区王家沟一典型消落带为研究对象,选择180、175、165和155 m这4个高程以探讨水位变化对土壤微生物生物量碳(SMBC)和微生物生物量氮(SMBN)的影响.其中,175、165和155m高程坐落在消落带内,分别表现为短、中和长期淹水,180 m高程作为对照,为永不淹水的陆地.土壤样品的采集深度为0~20 cm,每周采集一次.结果表明,180 m高程处土壤有机碳(SOC)和全氮(TN)均无明显的季节变化,而175 m高程处SOC和TN季节变化明显,表现为春夏季高于秋冬季;各高程上的SMBC和SMBN及其分配比例呈现出秋高夏低的季节变化形态,表明消落带夏季高温低湿的土壤环境限制了微生物活性及土壤有机碳氮的周转速率.数据分析表明,与180 m高程相比,消落带上的175 m和165 m高程SOC、TN、SMBC及微生物商、SMBN及其分配比例均得到不同程度的升高,而155 m高程除了SMBN及其分配比例与对照无显著差异外,其他指标均显著低于对照,表明与未淹水对照点相比较,中短期淹水有利于提高消落带土壤碳氮含量及周转速率和微生物生物量,而长期受到江水淹没胁迫的土壤则会抑制土壤碳氮以及SMBC含量,并降低SOC的周转速率.相关分析表明,SMBC和SMBN均与地下5 cm处温度和p H呈极显著负相关,说明地下5cm处的温度以及p H对土壤微生物生物量有强烈的影响.     

基于增强回归树的城市PM2.5日均值变化分析:以常州为例

利用2014年12月至2015年11月常州市区6个国控监测站空气污染物浓度逐时数据,分析了PM_(2.5)浓度季节变化特征,采用增强回归树模拟分析了PM10、4种气态污染物和7个气象因子对ρ(PM_(2.5))日变化的贡献.结果表明,常州市区PM_(2.5)污染季节差异明显,冬季污染严重且持续时间长,夏季污染较轻.四季ρ(PM_(2.5))空间分布特征存在一定差异,但各季内不同监测站差异较小.增强回归树对ρ(PM_(2.5))日均值进行模拟和验证得到,训练数据的相关性为0.981,交叉验证的相关性为0.957.此外,模拟值与实测值的标准化平均偏差为1.80%,标准化平均误差为10.41%,可见模型拟合效果较好.PM10、气态污染物、气象因子和区域输送及扩散这4种影响类型对全年ρ(PM_(2.5))日均值差异的贡献率分别为23.4%、28%、36.2%和12.6%,表明在对ρ(PM_(2.5))日均值差异的影响上,气象因子二次形成一次源区域输送及扩散.在对ρ(PM_(2.5))日均值差异贡献率大于5%的因子中,ρ(PM_(2.5))日均值与PM10、相对湿度、CO和O3正相关,与温度、SO2和混合层高度负相关,与大气压和NO2关系较复杂.区域输送及扩散方面,东南风向、偏西风向和偏北风向等上风向周边城市的污染物输送对常州市区PM_(2.5)污染存在较大的负面影响.     

Adaptation of Climate Model Projections of Streamflow to Account for Upstream Anthropogenic Impairments

A statistical procedure is developed to adjust natural streamflows simulated by dynamical models in downstream reaches, to account for anthropogenic impairments to flow that are not considered in the model. The resulting normalized downstream flows are appropriate for use in assessments of future anthropogenically impaired flows in downstream reaches. The normalization is applied to assess the potential effects of climate change on future water availability on the Rio Grande at a gage just above the major storage reservoir on the river. Model‐simulated streamflow values were normalized using a statistical parameterization based on two constants that relate observed and simulated flows over a 50‐year historical baseline period (1964–2013). The first normalization constant is a ratio of the means, and the second constant is the ratio of interannual standard deviations between annual gaged and simulated flows. This procedure forces the gaged and simulated flows to have the same mean and variance over the baseline period. The normalization constants can be kept fixed for future flows, which effectively assumes that upstream water management does not change in the future, or projected management changes can be parameterized by adjusting the constants. At the gage considered in this study, the effect of the normalization is to reduce simulated historical flow values by an average of 72% over an ensemble of simulations, indicative of the large fraction of natural flow diverted from the river upstream from the gage. A weak tendency for declining flow emerges upon averaging over a large ensemble, with tremendous variability among the simulations. By the end of the 21st Century the higher‐emission scenarios show more pronounced declines in streamflow.     

Comparison and Evaluation of Gridded Precipitation Datasets in a Kansas Agricultural Watershed Using SWAT

Gridded precipitation datasets are becoming a convenient substitute for gauge measurements in hydrological modeling; however, these data have not been fully evaluated across a range of conditions. We compared four gridded datasets (Daily Surface Weather and Climatological Summaries [DAYMET], North American Land Data Assimilation System [NLDAS], Global Land Data Assimilation System [GLDAS], and Parameter‐elevation Regressions on Independent Slopes Model [PRISM]) as precipitation data sources and evaluated how they affected hydrologic model performance when compared with a gauged dataset, Global Historical Climatology Network‐Daily (GHCN‐D). Analyses were performed for the Delaware Watershed at Perry Lake in eastern Kansas. Precipitation indices for DAYMET and PRISM precipitation closely matched GHCN‐D, whereas NLDAS and GLDAS showed weaker correlations. We also used these precipitation data as input to the Soil and Water Assessment Tool (SWAT) model that confirmed similar trends in streamflow simulation. For stations with complete data, GHCN‐D based SWAT‐simulated streamflow variability better than gridded precipitation data. During low flow periods we found PRISM performed better, whereas both DAYMET and NLDAS performed better in high flow years. Our results demonstrate that combining gridded precipitation sources with gauge‐based measurements can improve hydrologic model performance, especially for extreme events.