氯盐类融雪剂对环境的影响及其对策研究

融雪剂是清除道路积雪的有效方法，而氯盐类融雪剂是目前使用最多的一类融雪剂产品。结合国内外长期使用氯盐类融雪剂后，对城市植物、水体、土壤以及基础设施产生的不良影响进行了分析，并在此基础上分析了其环境危害的机理以及国内外的研究现状，提出了减轻其环境危害行之有效的措施。     

融雪剂侵蚀城市春天

春天,本是万物复苏的季节,然而有些靠近马路的绿化带里却树木枯萎,绿篱发黄。建筑和建筑防腐专家尹震华教授通过调查解开了症结,树木草地等植被大面积死亡的主要元凶并不是冬季的严寒而是冬季施洒的融雪剂。融雪剂危害不容忽视由于价格低廉、融雪效果好,融雪剂近年来被广泛使用。同时     

保护城市生态需要科学融雪

正进入冬季,部分地方遭遇大范围降雪天气。面对艰巨的除雪任务,撒融雪剂几乎成为大雪过后迅速恢复交通的优先选择。然而,众所周知,融雪剂不但带来高额的经济负担,也可能引发诸多环境问题,那到底该不该用呢?中国每年的融雪剂消耗量高达几十万吨。在哈尔滨、沈阳、北京、天津等北方城市,每年都会耗用几千吨甚至万余吨。使用融雪剂固然会给行车安全带来保障,但同时也会产生以下诸多问题:融雪剂中的氯盐渗透到地面后,会使钢筋腐蚀、混凝土冻融破坏。北京原西直门立交桥1979年建成并投入     

融雪剂会“融”掉环保吗?

2009年冬天雪天频频降临我国北方一些地区。除雪过程中使用了大量融雪剂,交通畅通了,出行方便了,但是你可知道融雪剂会对环境造成一定伤害?     

我国城市交通污染分析及其对策研究

随着城市化和城市交通机动化的加快，交通污染已成为城市乃至全球大气环境和声环境的主要污染源之一，并且这种趋势仍在继续发展。通过对城市交通污染产生原因的系统分析及对身体健康危害的分析，以成都市为例，提出了我国城市汽车尾气污染和噪声污染控制技术的发展方向，并探讨了减少交通污染的对策和建议。     

环境噪声污染与健康生活

随着近代工业的发展，环境污染也随着产生，噪声哥染就是环境污染的一种，已经成为对人类的一大危害。咄声污染与水污染、大气污染被看成是全球三个主要环境问题。噪声是一类引起人烦躁、或音量过强而危害人体健周的声音，已成为当代社会一大公害。近年来，噪声诉讼身件不断增加，尤其是随着工业化的发展，城市噪声大幅压增加。世界上很多国家和地区高度重视噪声污染的防控瑞士规定晚上10点以后禁止大声喧哗，星期日不准使用害草机。新加坡对噪声污染环境、影响公众生活有严格规定违规者将面临巨额罚款甚至法律制裁。     

华南城市群酸雨的现状对比分析

酸雨是现代环境问题中最为重要的问题之一，全球工业化的大规模生产，特别是化石燃料的使用导致SO2的大量排放，城市区域形成的酸雨机率特别大，各国政府和环保部门已经高度重视。本文着重调查了中国华南地区的长沙、南昌、贵阳、福州、广州五大城市的酸雨现状并进行分析和比较，探讨了酸雨的分布特点、成因和危害。     

乌鲁木齐河流域融雪期积雪物理特性观测分析

融雪水作为干旱内陆河流主要补给来源,是干旱地区农业灌溉生存与发展的主要制约因素,积雪物理特性是研究融雪规律、融雪径流形成与侵蚀过程的基础。本研究日在天山北坡乌鲁木齐河流域下游试验区对林冠下与开阔地气象要素以及积雪物理特性(含水率、积雪密度、积雪深度、雪层温度等)进行观测,分析季节性积雪融雪期雪层物理特性垂直轮廓线和时间变化特征及其与气温的相互联系。研究得出:融雪期各雪层物理特性存在差异性,雪层平均含水率与气温指数相关且林冠下相关性更好。     

我国城市大气污染现状与特点

中国城市化和工业化的快速发展与能源消耗的迅速增加，给中国城市带来了很多空气污染问题。20世纪70年代期间，煤烟型污染排放成为中国工业城市的特点；80年代，许多南方城市遭受严重的酸雨危害；近年来，汽车尾气排放的NOx、CO及随后形成的光化学烟雾，使得许多大城市的空气质量恶化。城市空气污染影响着城市居民的健康和城市的发展。为控制空气污染和保护大气环境质量，我国政府已经实施了许多规划。本文概述了当前中国城市特别是重点城市的空气质量状况，描述一些主要城市空气污染物包括总悬浮颗粒、PM10、PM2．5、SO2、酸雨、NOx/NO2的特点。尽管采取了很多应对措施，但目前我国城市的空气质量依然不容乐观，文中还讨论了未来城市空气污染控制面临的问题。     

实现“绿色崛起”加快城市转型

绿色是美丽的，绿色是祥和的，绿色的存在意味着生态的平衡、文化的兴盛、自然的和谐、发展的永续。绿色发展强调的是经济与环境的发展高度统一和谐，是实现以人为本可持续发展的正确选择。如果把淮南比喻成一部“著作”，封面就是煤城，封底写上的是两型城市。而书的正文，则是淮南从煤炭资源型城市到资源节约型、环境友好型城市的绿色发展过程。     

Influence of manure application on surface energy and snow cover: model development and sensitivities

Winter landspreading is an important part of manure management in the U.S. Upper Midwest. Although the practice is thought to lead to excessive P runoff losses, surprisingly little has been learned from field experiments or current water quality models. We captured knowledge gained from winter manure landspreading experiments by modifying a mechanistic snow ablation model to include manure. The physically based, modified model simulated the observed delay in snow cover disappearance and surface energy balance changes caused by application of the manure. Additional model simulations of surface energy balance estimates of radiation and turbulent fluxes showed that during intense melting events the manure on top of snow significantly reduced the energy available for melt of the snow underneath, slowing melt. The effect was most pronounced when snowmelt was driven by both relatively high solar radiation and turbulent heat fluxes. High absorbed shortwave radiation caused significant warming of the manure, which led to substantial losses in turbulent fluxes and longwave radiation. Simulations of snowmelt also showed that manure applications between 45 and 100 Mg ha(-1) significantly reduced peak snowmelt rates, in proportion to the manure applied. Lower snowmelt rates beneath manure may allow more infiltration of meltwater compared with bare snow. This infiltration and attenuated snowmelt runoff may partially mitigate the enhanced likelihood of P runoff from unincorporated winter-spread manure.     

EFFECT OF SIMULATED CLIMATE CHANGE ON SNOWMELT RUNOFF MODELING IN SELECTED BASINS1

ABSTRACT: The projected increase in the concentration of CO₂ and other greenhouse gases in the atmosphere is likely to result in a global temperature increase. This paper reports on the probable effects of a temperature increase and changes in transpiration on basin discharge in two different mountain snowmelt regions of the western United States. The hydrological effects of the climate changes are modeled with a relatively simple conceptual, semi-distributed snowmelt runoff model. Based on the model results, it may be concluded that increased air temperatures will result in a shift of snowmelt runoff to earlier in the snowmelt season. Furthermore, it is shown that it is very important to include the expected change in climate-related basin conditions resulting from the modeled temperature increase in the runoff simulation. The effect of adapting the model parameters to reflect the changed basin conditions resulted in a further shift of streamflow to April and an even more significant decrease of snowmelt runoff in June and July. If the air temperatures increase by approximately 5°C and precipitation and accumulated snow amounts remain about the same, runoff in April and May, averaged for the two basins, is expected to increase by 185 percent and 26 percent, respectively. The runoff in June and July will decrease by about 60 percent each month. Overall, the total seasonal runoff decreases by about 6 percent. If increased CO₂ concentrations further change basin conditions by reducing transpiration by the maximum amounts reported in the literature, then, combined with the 5°C temperature increase, the April, May, June, and July changes would average +230 percent, +40 percent, ?55 percent, and ?45 percent, respectively. The total seasonal runoff change would be +11 percent.     

Effect of Snow Cover Conditions on the Hydrologic Regime: Case Study in a Pluvial‐Nival Watershed,Japan1

Abstract: Hydrologic monitoring in a small forested and mountainous headwater basin in Niigata Prefecture has been undertaken since 2000. An important characteristic of the basin is that the hydrologic regime contains pluvial elements year‐round, including rain‐on‐snow, in addition to spring snowmelt. We evaluated the effect of different snow cover conditions on the hydrologic regime by analyzing observed data in conjunction with model simulations of the snowpack. A degree‐day snow model is presented and applied to the study basin to enable estimation of the basin average snow water equivalent using air temperature at three representative elevations. Analysis of hydrological time series data and master recession curves showed that flow during the snowmelt season was generated by a combination of ground water flow having a recession constant of 0.018/day and diurnal melt water flow having a recession constant of 0.015/hour. Daily flows during the winter/snowmelt season showed greater persistence than daily flows during the warm season. The seasonal water balance indicated that the ratio of runoff to precipitation during the cold season (December to May) was about 90% every year. Seasonal snowpack plays an important role in defining the hydrologic regime, with winter precipitation and snowmelt runoff contributing about 65% of the annual runoff. The timing of the snowmelt season, indicated by the date of occurrence of the first significant snowmelt event, was correlated with the occurrence of low flow events. Model simulations showed that basin average snow water equivalent reached a peak around mid‐February to mid‐March, although further validation of the model is required at high elevation sites.     

DEVELOPMENT AND TESTING OF A SNOWMELT-RUNOFF FORECASTING TECHNIQUE1

ABSTRACT: The snowmelt-runoff model (SRM) was used to produce accurate simulations of streamfiow during the snowmelt period (April-September) for ten years on the Rio Grande Basin (3419 km²) near Del Norte, Colorado, U.S.A. In order to use SRM in the forecast situation, it was necessary to develop a family of snow cover depletion curves for each elevation zone based on accumulated snow water equivalent on April 1. Selection of an appropriate curve for a particular year from snow course measurements allows input of the daily snow cover extent to SRM for forecast purposes. Data from three years (1980, 1981, and 1985) were used as a quasi-forecast test of the procedure. In these years forecasted snow cover extent data were input to SRM, but observed temperature and precipitation data were used. The resulting six-month hydrographs were very similar to the hydrographs in the ten simulation years previously tested based on comparisons of performance evaluation criteria. Based on this result, the Soil Conservation Service (SCS) requested SRM forecasts for 1987 on the Rio Grande. Using the same procedure but with SCS estimated temperature and precipi-tation data, SRM produced a forecast hydrograph that had a r²= 0.82 and difference in seasonal volume of 4.4 percent. To approximate actual operational conditions, SRM computed daily flows were updated every seven days with measured flows. The resulting forecast hydrograph had a R²= 0.90 and a difference in volume of 3.5 percent. The method developed needs to be refined and tested on additional years and basins, but the approach appears to be applicable to operational runoff forecasting using remote sensing data.     

A Simple Process‐Based Snowmelt Routine to Model Spatially Distributed Snow Depth and Snowmelt in the SWAT Model1

Abstract: We present a method to integrate a process‐based (PB) snowmelt model that requires only daily temperature and elevation information into the Soil and Water Assessment Tool (SWAT) model. The model predicts the spatiotemporal snowpack distribution without adding additional complexity, and in fact reduces the number of calibrated parameters. To demonstrate the utility of the PB model, we calibrate the PB and temperature‐index (TI) SWAT models to optimize agreement with stream discharge on a 46‐km² watershed in northwestern Idaho, United States, for 10 individual years and use the calibrated parameters for the year with the best agreement to run the model for 15 remaining years. Stream discharge predictions by the PB and TI model were similar, although the PB model simulated snowmelt more accurately than the TI model for the remaining 15‐year period. Spatial snow distributions predicted by the PB model better matched observations from LandSat imagery and a SNOTEL station. Results for this watershed show that including PB snowmelt in watershed models is feasible, and calibration of TI‐based watershed models against discharge can incorrectly predict snow cover.     

Defining the Diurnal Pattern of Snowmelt Using a Beta Distribution Function

Snow is an important component of the hydrologic cycle for many regions worldwide. In addition to vital water resources, snowmelt can be important for forest ecosystem dynamics and flood risk. However, standard design events in the United States lack a design snowmelt event, including only precipitation events, though snowmelt has been shown to be larger than rainfall. In this article, we present a method using hourly snow water equivalent data to develop and test a function for representing the diurnal pattern of snowmelt. A two‐parameter beta distribution function is modified for the purposes of this study and found to fit the pattern of snowmelt well with a root mean squared error of 0.008. Soil moisture sensors were additionally utilized to assess the timing of the snowmelt water outflow from the base of the snowpack that supports the shape of the function, but suggests that the timing of losses recorded on snow pillows lag as much as 3 h. Further testing of the function showed the shape of the function to be accurate. The methods developed and tested in this paper can be applied for design purposes comparing snowmelt and rainfall events or to improve hydrological models investigating processes such as streamflow or groundwater recharge.     

REVISITING THE DEGREE-DAY METHOD FOR SNOWMELT COMPUTATIONS1

ABSTRACT: The simple, empirical degree-day approach for calculating snowmelt and runoff from mountain basins has been in use for more than 60 years. It is frequently suggested that the degree-day method be replaced by the more physically-based energy balance approach. The degree-day approach, however, maintains its popularity, applicability, and effectiveness. It is shown that the degree-day method is reliable for computing total snowmelt depths for periods of a week to the entire snowmelt season. It can also be used for daily snowmelt depths when utilized in connection with an adequate snowmelt runoff model for computing the basin runoff. The degree-day ratio is shown to vary seasonally as opposed to being constant as is often assumed. Additionally, in order to evaluate the degree-day ratio correctly, the changing snow cover extent in a basin during the snowmelt season must be taken into account. It is also possible to combine the degree-day approach with a radiation component so that short time interval (<24 hours) computations of snowmelt depth can be made. When snowmelt input is transformed to basin output (runoff) by a snowmelt runoff model, there is little difference between the degree-day approach and a radiation-based approach. This is fortuitous because the physically-based energy balance models will not soon displace the degree-day methods because of their excessive data requirements.     

Spatiotemporal Variability of Snow Depletion Curves Derived from SNODAS for the Conterminous United States, 2004‐2013

Assessment of water resources at a national scale is critical for understanding their vulnerability to future change in policy and climate. Representation of the spatiotemporal variability in snowmelt processes in continental‐scale hydrologic models is critical for assessment of water resource response to continued climate change. Continental‐extent hydrologic models such as the U.S. Geological Survey National Hydrologic Model (NHM) represent snowmelt processes through the application of snow depletion curves (SDCs). SDCs relate normalized snow water equivalent (SWE) to normalized snow covered area (SCA) over a snowmelt season for a given modeling unit. SDCs were derived using output from the operational Snow Data Assimilation System (SNODAS) snow model as daily 1‐km gridded SWE over the conterminous United States. Daily SNODAS output were aggregated to a predefined watershed‐scale geospatial fabric and used to also calculate SCA from October 1, 2004 to September 30, 2013. The spatiotemporal variability in SNODAS output at the watershed scale was evaluated through the spatial distribution of the median and standard deviation for the time period. Representative SDCs for each watershed‐scale modeling unit over the conterminous United States (n = 54,104) were selected using a consistent methodology and used to create categories of snowmelt based on SDC shape. The relation of SDC categories to the topographic and climatic variables allow for national‐scale categorization of snowmelt processes.     

USE OF TIME-LAPSE PHOTOGRAPHY TO ASSESS POTENTIAL INTERCEPTION IN ARIZONA PONDEROSA PINE1

ABSTRACT: The behavior of intercepted snow on a stand of uneven-aged ponderosa pine in east-central Arizona was evaluated with the use of a super 8-mm time lapse movie camera to determine the relative significance of snowfall interception in the water yield of this type forest. A snow load index was developed to estimate interception storage for two trees in the field of view for discrete time periods. The snow load index. photographs, and climatic data were combined to describe accumulation and to identify and rank according to relative magnitudes the basic processes of canopy snow removal. The rate of snow accumulation was nonlinear with initial storage being rapid. then slowing with time. Most of the intercepted snow eventually reached the snowpack on the ground by snowslide and wind erosion or by snowmelt and subsequent stemflow and drippmg of meltwater, and was therefore not considered a significant loss to the water budget on site. Some water apparently was disposed of by the evaporation of meltwater and sublimation of canopy snow, but these losses appeared to be comparatively minor.     

APPLICATION OF THE SNOWMELT RUNOFF MODEL USING MULTIPLE-PARAMETER LANDSCAPE ZONES ON THE TOWANDA CREEK BASIN,PENNSYLVANIA1

ABSTRACT: The Snowmelt Runoff Model (SRM) is designed to compute daily stream discharge using satellite snow cover data for a basin divided into elevation zones. For the Towanda Creek basin, a Pennsylvania watershed with relatively little relief, analysis of snow cover images revealed that both elevation and land use affected snow accumulation and melt on the landscape. The distribution of slope and aspect on the watershed was also considered; however, these landscape features were not well correlated with the available snow cover data. SRM streamflow predictions for 1990, 1993 and 1994 snowmelt seasons for the Towanda Creek basin using a combination of elevation and land use zones yielded more precise streamflow estimates than the use of standard elevation zones alone. The use of multiple-parameter zones worked best in non-rain-on-snow conditions such as in 1990 and 1994 seasons where melt was primarily driven by differences in solar radiation. For seasons with major rain-on-snow events such as 1993, only modest improvements were shown since melt was dominated by rainfall energy inputs, condensation and sensible heat convection. Availability of GIS coverages containing satellite snow cover data and other landscape attributes should permit similar reformulation of multiple-parameter watershed zones and improved SRM streamflow predictions on other basins.