Photoinduced reduction of divalent mercury in ice by organic matter

Reduction of divalent mercury and subsequent emission to the atmosphere has been identified as loss process from surface snow, but its mechanism and importance are still unclear. The amount of mercury that stays in the snow pack until spring is of significance, because during snow melt it may be released to the aquatic environment and enter the food web. Better knowledge of its fate in snow might further assist the interpretation of ice core data as paleo-archive. Experiments were performed under well-controlled laboratory conditions in a coated wall flow tube at atmospheric pressure and irradiated with light between 300 nm and 420 nm. Our results show that the presence of benzophenone and of oxalic acid significantly enhances the release of mercury from the ice film during irradiation, whereas humic acid is less potent to promote the reduction. Further it was found that oxygen or chloride, and acidic conditions lowered the photolytically induced mercury release in the presence of benzophenone, while the release got larger with increasing temperatures.     

Does Including Soil Moisture Observations Improve Operational Streamflow Forecasts in Snow‐Dominated Watersheds?

Changing climate and growing water demand are increasing the need for robust streamflow forecasts. Historically, operational streamflow forecasts made by the Natural Resources Conservation Service have relied on precipitation and snow water equivalent observations from Snow Telemetry (SNOTEL) sites. We investigate whether also including SNOTEL soil moisture observations improve April‐July streamflow volume forecast accuracy at 0, 1, 2, and 3‐month lead times at 12 watersheds in Utah and California. We found statistically significant improvement in 0 and 3‐month lead time accuracy in 8 of 12 watersheds and 10 of 12 watersheds for 1 and 2‐month lead times. Surprisingly, these improvements were insensitive to soil moisture metrics derived from soil physical properties. Forecasts were made with volumetric water content (VWC) averaged from October 1 to the forecast date. By including VWC at the 0‐month lead time the forecasts explained 7.3% more variability and increased the streamflow volume accuracy by 8.4% on average compared to standard forecasts that already explained an average 77% of the variability. At 1 to 3‐month lead times, the inclusion of soil moisture explained 12.3‐26.3% more variability than the standard forecast on average. Our findings indicate including soil moisture observations increased statistical streamflow forecast accuracy and thus, could potentially improve water supply reliability in regions affected by changing snowpacks.     

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.     

From land cover change to land function dynamics: a major challenge to improve land characterization

Land cover change has always had a central role in land change science. This central role is largely the result of the possibilities to map and characterize land cover based on observations and remote sensing. This paper argues that more attention should be given to land use and land functions and linkages between these. Consideration of land functions that provide a wide range of goods and services makes more integrated assessments of land change possible. The increasing attention to multifunctional land use is another incentive to develop methods to assess changes in land functions. A number of methods to quantify and map the spatial extent of land use and land functions are discussed and the implications for modeling are identified based on recent model approaches in land change science. The mixed use of land cover, land use and land function in maps and models leads to inconsistencies in land change assessments. Explicit attention to the non-linear relations between land cover, land use and land function is essential to consistently address land change. New methods to map and quantify land function dynamics will enhance our ability to understand and model land system change and adequately inform policies and planning.     

Predicting land cover using GIS, Bayesian and evolutionary algorithm methods

Modelling land cover change from existing land cover maps is a vital requirement for anyone wishing to understand how the landscape may change in the future. In order to test any land cover change model, existing data must be used. However, often it is not known which data should be applied to the problem, or whether relationships exist within and between complex datasets. Here we have developed and tested a model that applied evolutionary processes to Bayesian networks. The model was developed and tested on a dataset containing land cover information and environmental data, in order to show that decisions about which datasets should be used could be made automatically. Bayesian networks are amenable to evolutionary methods as they can be easily described using a binary string to which crossover and mutation operations can be applied. The method, developed to allow comparison with standard Bayesian network development software, was proved capable of carrying out a rapid and effective search of the space of possible networks in order to find an optimal or near-optimal solution for the selection of datasets that have causal links with one another. Comparison of land cover mapping in the North-East of Scotland was made with a commercial Bayesian software package, with the evolutionary method being shown to provide greater flexibility in its ability to adapt to incorporate/utilise available evidence/knowledge and develop effective and accurate network structures, at the cost of requiring additional computer programming skills. The dataset used to develop the models included GIS-based data taken from the Land Cover for Scotland 1988 (LCS88), Land Capability for Forestry (LCF), Land Capability for Agriculture (LCA), the soil map of Scotland and additional climatic variables.     

Regional impervious surface estimation: an urban heat island application

This paper describes a GIS-based estimation method that can be used to forecast future amounts of impervious surface as a mitigation measure for urban heat island effect in a metropolitan region. The method is unique because it employs a regression model that links the existing amount of impervious surface to population and employment at the census tract level. This approach provides a means to forecast future amounts of impervious surface based on projected population and employment. The method also includes a detailed analysis of high-resolution aerial photography to divide impervious surfaces into different categories. Subdividing impervious surfaces is necessary to evaluate potential urban heat island mitigation policies for different types of impervious surface. The analysis here shows that the impervious surface in the metropolitan Atlanta region will increase to 2638 km2 2. “The Albedo is defined as the hemispherical reflectivity averaged over the solar spectrum. A perfect reflector has a = 1, and a perfect absorber has a = 0” (Pomerantz et al. 1999 Pomerantz, M. 1999. Reflective surfaces for cooler buildings and cities. Philosophical magazine B, 79: 1457–1476. [Taylor & Francis Online], [Web of Science ®] [Google Scholar], p. 1458). View all notes in 2030, an increase of 45% from 2000. The most common type of impervious surface is dark-coloured pavement. Within this study area, the analyses showed that two-thirds of impervious surfaces are dark. Replacing dark pavement with light pavement materials, therefore, represents an important opportunity to mitigate the urban heat island effect in the Atlanta region.     

Arsenic concentration in porewater of an alkaline coal ash disposal site: Roles of siderite precipitation/dissolution and soil cover

The geochemical behavior of As in porewaters of an alkaline coal ash disposal site was investigated using multilevel samplers. The disposal site was in operation from 1983 until 1994 and was covered with 0.3–0.5 m thick soils in 2001 when this study was initiated. Sequential extraction analyses and batch leaching experiments were also performed using the coal ash samples collected from the disposal site. The results suggest the important roles of siderite (FeCO₃) precipitation/dissolution and soil cover, which have been ignored previously. Arsenic levels in the porewater were very low (average of 10 μg L⁻¹) when the site was covered with soil due to coprecipitation with siderite. The soil cover enabled the creation of anoxic conditions, which raised the Fe concentration by the reductive dissolution of Fe-(hydr)oxides. Because of the high alkalinity generated from the alkaline coal ash, even a small increase in the Fe concentration (0.66 mg L⁻¹ on average) could cause siderite precipitation. When the soil cover was removed, however, an oxidizing condition was created and triggered the precipitation of dissolved Fe as (hydr)oxides. As a result, the dissolution of previously precipitated As-rich siderite caused higher As concentration in the porewater (average of 345 μg L⁻¹).     

How dynamics and drivers of land use/land cover change impact elephant conservation and agricultural livelihood development in Rombo,Tanzania

Land use/land cover (LULC) change affects the provision of ecosystem services for humans and habitat for wildlife. Hence, it is crucial to monitor LULC particularly adjacent to protected areas. In this study, we measured LULC change in Rombo, Tanzania, an area with high-potential agro-ecological zones that is dominated by human–elephant conflicts (HECs). We used remote sensing and geographical information system techniques, questionnaires and village meetings to assess spatio-temporal patterns of the LULC changes in the study area. Using Landsat imagery, digital elevation model (DEM) and ground truthing, we classified and monitored changes in LULC from the years 1987 to 2015. We found that within Rombo, settlements were increasing, while agricultural and agroforestry lands were decreasing and respondents’ perceptions varied along the altitudinal gradient. Patterns of HEC and LULC were observed to change along the gradient and the later threatened the agricultural land and ecological integrity for elephant habitat, leading to high tension and competition between elephants and people. This research offers baseline information for land use planning to balance wildlife conservation with livelihood development in Rombo and highlights that managing the impacts of LULC changes on HEC and elephant habitat loss is a matter of urgency.     

基于SARIMA模型的黑龙江省冰雪旅游国际需求预测

选取黑龙江省1990-2011年冰雪旅游入境人次的月度时间序列为研究样本,建立SARIMA模型对黑龙江省冰雪旅游国际需求进行分析预测。结果表明,SARIMA（4,1,4）（1,1,1）。模型的拟合度和短期预测效果都较好。通过应用SARIMA模型进行冰雪旅游预测,以期对黑龙江省旅游企业的冰雪旅游国际客源市场开发及政府部门制定冰雪旅游发展战略和政策调整提供重要的理论依据。     

Baseline assessment for environmental services payments from satellite imagery: a case study from Costa Rica and Mexico

In this study we evaluate the accuracy of four global and regional forest cover assessments (MODIS, IGBP, GLC2000, PROARCA) as tools for baseline estimation. We conduct this research at the national scale for Costa Rica and for two tropical dry forest study sites in Costa Rica (Santa Rosa) and Mexico (Chamela-Cuixmala). We found that at the national level, the total forest cover accuracy of the four land cover maps was inflated due to an overestimation of forest in areas with an evergreen canopy. However, the four maps greatly underestimated the extent of the deciduous forest (dry forest); an ecosystem that faces high deforestation pressure and poses complications to the mapping of its extent from remotely sensed data. For the tropical dry forest sites, all maps have low forest cover accuracies (mean for Santa Rosa: 27%; mean for Chamela-Cuixmala: 56%). This has implications for policy implementation.