Performance Evaluation of the SLEUTH Model in the Shenyang Metropolitan Area of Northeastern China

Performance evaluation is crucial for the development and improvement of an urban cellular automata model, such as SLEUTH. In this paper, we employed multiple methods for map comparison and model validation to evaluate the simulation performance of the SLEUTH urban growth model in the Shenyang metropolitan area of China. These multiple methods included the relative operating characteristic (ROC) curve statistic, multiple-resolutions error budget, and landscape metrics. They were used to quantitatively examine model performance in terms of the amount and spatial location of urban development, urban spatial pattern and prediction ability. The assessment results showed that SLEUTH performed well in the way of the quantitative simulation of urban growth for this case study. Similar to other urban growth models, however, the simulation accuracy for spatial location of new development at the pixel scale and urban spatial pattern still needs to be improved greatly. These inaccuracies might be attributed to the structure and nature of SLEUTH, local urban development characteristics, and the temporal and spatial scale of its application. Finally, many valuable suggestions had been put forward to improve simulation performance of SLEUTH model for spatial location of urban development in the Shenyang metropolitan area.     

Uncertainty from Model Calibration: Applying a New Method to Transport Energy Demand Modelling

Uncertainties in energy demand modelling originate from both limited understanding of the real-world system and a lack of data for model development, calibration and validation. These uncertainties allow for the development of different models, but also leave room for different calibrations of a single model. Here, an automated model calibration procedure was developed and tested for transport sector energy use modelling in the TIMER 2.0 global energy model. This model describes energy use on the basis of activity levels, structural change and autonomous and price-induced energy efficiency improvements. We found that the model could reasonably reproduce historic data under different sets of parameter values, leading to different projections of future energy demand levels. Projected energy use for 2030 shows a range of 44–95% around the best-fit projection. Two different model interpretations of the past can generally be distinguished: (1) high useful energy intensity and major energy efficiency improvements or (2) low useful energy intensity and little efficiency improvement. Generally, the first lead to higher future energy demand levels than the second, but model and insights do not provide decisive arguments to attribute a higher likelihood to one of the alternatives.     

Using Resource Economics to Anticipate Forest Land Use Change in the U.S. Mid-Atlantic Region

Demands for forest, farm, and developed land are evolving in the U.S. mid-Atlantic region. The demand for land in developed uses, as well as demands for various forest and farm products are changing in response to population growth, demographic shifts, and market forces. As demand factors change so do relative land values. Land area in future forest, farm, and developed uses may shift as landowners re-evaluate relative net benefits from land use alternatives. This study examines the effects of various land demand and supply factors on the determination of land use patterns in the mid-Atlantic region. Driving variables include costs and benefits from various uses, population density, and measures of land quality. Model parameters are estimated using a binomial logit procedure. Results from the study are used to estimate proportions of forest area on a county by county basis. Simulated forest landscapes under hypothetical future conditions are prepared and illustrated using geographic information system (GIS) techniques.     

AgInput: An Agricultural Nutrient and Pesticide Source Model

Agriculture can be a major nonpoint source (NPS) of nutrient and pesticide contamination in the environment. Available databases do not provide accurate and dynamic data on fertilizer and pesticide application, which limits the ability of complex watershed models to simulate contaminant loads into impaired water bodies. A model for estimating agricultural nutrient and pesticide input for watershed modeling has been developed. Climate, soils, and major agricultural operations are considered within the model, so that it can be adapted to any watershed or subregion within a watershed. The timing of the agricultural operations is a function of the weather data, providing realistic results at daily, monthly, or annual application rates. The model also predicts irrigation demand and biomass production, which can be used to calibrate the model. Model output can be used in any watershed model that considers agricultural land uses. Two case studies were evaluated, using grape vineyards in the Napa River and strawberry production in Newport Bay as examples. The predicted time to maturity corresponded well with actual data. Irrigation and fertilizer needs were very sensitive to weather input. Although the model can generate weather from long-term averages, the simulated results are best when at least observed precipitation and temperature are provided, to capture extreme events. The model has data for 98 crops and 126 pesticides, based on the California Department of Pesticide Regulation database. The databases are easily modifiable by the user to adapt them to local conditions. The output from AgInput is much needed for watershed modeling and for development of total maximum daily loads (TMDLs), based on realistic targets of irrigation, nutrient, and pesticide inputs. The model is available for free download at .     

Scenario Analysis for the San Pedro River, Analyzing Hydrological Consequences of a Future Environment

Studies of future management and policy options based on different assumptions provide a mechanism to examine possible outcomes and especially their likely benefits and consequences. The San Pedro River in Arizona and Sonora, Mexico is an area that has undergone rapid changes in land use and cover, and subsequently is facing keen environmental crises related to water resources. It is the location of a number of studies that have dealt with change analysis, watershed condition, and most recently, alternative futures analysis. The previous work has dealt primarily with resources of habitat, visual quality, and groundwater related to urban development patterns and preferences. In the present study, previously defined future scenarios, in the form of land-use/land-cover grids, were examined relative to their impact on surface-water conditions (e.g., surface runoff and sediment yield). These hydrological outputs were estimated for the baseline year of 2000 and predicted twenty years in the future as a demonstration of how new geographic information system-based hydrologic modeling tools can be used to evaluate the spatial impacts of urban growth patterns on surface-water hydrology.     

Chemical monitoring of river water bodies in an EU outermost region: examples from the Azores archipelago (Portugal)

Chemical monitoring of water quality on a total of 16 rivers in the Azores archipelago (Portugal), since 2003, made it possible to identify the major pressures and spatial geochemical variations along main course of the rivers. River water pollution is to a large extent associated to point sources, namely domestic wastewater discharges, especially in urban areas, and diffuse sources, associated with pasture land, and explain the high values on BOD(5) and nutrients (P and N). Heavy metals and metalloids, as well as hydrocarbons and pesticides, are generally under the detection limits of the analytical methods. Generally, river water reflects pollution loads according to a simple model, derived from land use in the watershed: in the upper part conditions are pristine, in the intermediate portion of the basin pasture land dominates and near the coast urban discharges are increasingly important. Results stress the role that an approach based on the watershed scale, coupled with land use management measures, are crucial to water management procedures and a successful WFD implementation in small river basin districts like the Azores. The paper also shows the need for full compliance regarding EU directives on urban wastewater and nitrate pollution due to agriculture.     

Land-cover changes in an urban lake watershed in a mega-city, Central China

Urbanization can exert a profound influence on land covers and landscape characteristics. In this study, we characterize the impact of urbanization on land cover and lacustrine landscape and their consequences in a large urban lake watershed, Donghu Lake watershed (the largest urban lake in China), Central China, by using Landsat TM satellite images of three periods of 1987, 1993 and 1999 and ground-based information. We grouped the land covers into six categories: water body, vegetable land, forested land, shrub-grass land, open area and urban land, and calculated patch-related landscape indices to analyze the effects of urbanization on landscape features. We overlaid the land cover maps of the three periods to track the land cover change processes. The results indicated that urban land continuously expanded from 9.1% of the total watershed area in 1987, to 19.4% in 1993, and to 29.6% in 1999. The vegetable land increased from 7.0% in 1987, 11.9% in 1993, to 13.9% in 1999 to sustain the demands of vegetable for increased urban population. Concurrently, continuous reduction of other land cover types occurred between 1987 and 1999: water body decreased from 30.4% to 23.8%, and forested land from 33.6% to 24.3%. We found that the expansion of urban land has at least in part caused a decrease in relatively wild habitats, such as urban forest and lake water area. These alterations had resulted in significant negative environmental consequences, including decline of lakes, deterioration of water and air quality, and loss of biodiversity.     

Using environmental stressor information to predict the ecological status of Maryland non-tidal streams as measured by biological indicators

In Maryland, U.S., an interim framework has recentlybeen developed for using biologically based thresholds, or `biocriteria', to assess the health of nontidal streams statewide at watershed scales. The evaluation of impairment is based on indices of biological integrity from the Maryland Biological Stream Survey (MBSS). We applied logistic regression to quantify how the biotic integrity of streams at a local scale is affected by cumulative effects resulting from catchment land uses, point sources, and nearby transmission line rights-of-way. Indicators for land use were developed from the remote sensing National Land Cover Data and applied at different scales. We determined that the risk of local impairment in nontidal streams rapidly increases with increased urban land use in the catchment area. The average likelihood of failing biocriteria doubled with every 10% points increment in urban land, thus an increase in urban land use from 0 to 20% quadruples the risk of impairment. For the basins evaluated in this study, catchments with more than 40–50% urban land use had greater than 80% probability of failing biocriteria, on average. Inclusion of rights-of-way and point sources in the model did not significantly improve the fit for this data set, most likely because of their low numbers. The overall results indicate that our predictive modeling approach can help pinpoint stream ecosystems experiencing or vulnerable to degradation.     

Vulnerability of Mid-Atlantic Forested Watersheds to Timber Harvest Disturbance

Forested watersheds of the Mid-Atlantic Region are an important economic resource. They are also critical for maintaining water quality, sustaining important ecological services, and providing habitat to many animal and plant species of conservation concern. These forests are vulnerable to disturbance and fragmentation from changing patterns of land use in the Mid-Atlantic Region, and from harvests of commercially mature and relatively inexpensive timber. The U.S. Department of Agriculture Forest Service (USDA-FS) Forest Inventory and Analysis (FIA) compiles data on forest condition by state and county. We have transformed these FIA data to a U.S. Geological Survey (USGS) 6-digithdrologic unit code (HUC 6) watershed base, and projected trends in timber growth, inventory, and harvest to 2025 using a timber economics forecasting model (SRTS). We consider forest sustainability from the perspective of timber production, and from the perspective of landscape stability important to conservation values. Simulation data is combined with FIA planted pine acreage data to form a more complete picture of forest extent, composition, and silvicultural practice. Early recognition of prevailing economic trends which encourage the fragmentation of mature forests due to increasing timber harvests may provide managers and policy makers with a planning tool to mitigate undesirable impacts.     

Monitoring conservation effectiveness in a global biodiversity hotspot: the contribution of land cover change assessment

Tropical forests, which play critical roles in global biogeochemical cycles, radiation budgets and biodiversity, have undergone rapid changes in land cover in the last few decades. This study examines the complex process of land cover change in the biodiversity hotspot of Western Ghats, India, specifically investigating the effects of conservation measures within the Indira Gandhi Wildlife Sanctuary. Current vegetation patterns were mapped using an IRS P6 LISS III image and this was used together with Landsat MSS data from 1973 to map land cover transitions. Two major and divergent trends were observed. A dominant degradational trend can be attributed to agricultural expansion and infrastructure development while a successional trend, resulting from protection of the area, showed the resilience of the system after prolonged disturbances. The sanctuary appears susceptible to continuing disturbances under the current management regime but at lower rates than in surrounding unprotected areas. The study demonstrates that remotely sensed land cover assessments can have important contributions to monitoring land management strategies, understanding processes underpinning land use changes and helping to inform future conservation strategies.     

The Parameter Optimization in the Inverse Distance Method by Genetic Algorithm for Estimating Precipitation

This study aimed to analyze long-term urban land use (LU) change and its negative implications on the coastal environment of Iskenderun, Turkey. Information on urban LU change for the 144-year period between 1858 and 2002 was extracted from a historic city plan, black-white monoscopic aerial photographs, and Landsat ETM+ imagery. According to the results, areal extent of urban areas has increased approximately 40 times during the past 144 years. The population growth as a result of industrial developments and permanent migration was main driving force to the urbanization. Environmental problems that resulted from rapid development in the region such as air, water and soil pollution were highlighted and some recommendations were made in the light of qualitative and quantitative information on the urbanization and its negative consequences.     

“省公顷”下渭河干流甘肃段生态安全评价及预测

摸清流域生态需求和生态供给是流域生态治理和促进其高质量发展的基础。文中应用改进生态足迹法对2009—2018年渭河干流甘肃段生态足迹和生态承载力进行核算,并运用GM（1,1）模型对流域未来的生态供需趋势进行预测。结果显示,2009—2018年渭河干流甘肃段生态足迹和生态承载力呈波动下降趋势,生态足迹下降2.26%,下降趋势低于生态承载力的4.18%;生态赤字以每年0.58%的速率波动减小。如果继续当前的发展模式,那么预计到2028年,生态赤字较2018年降低16.27%,流域生态安全状况将得到缓解。     

A Spatially Explicit Model for Estimating Annual Average Loads of Nonpoint Source Nutrient at the Watershed Scale

The overloaded nonpoint source (NPS) nutrients in upper streams always result in the nutrient enrichment at lakes and estuaries downstream. As NPS pollution has become a serious environmental concern in watershed management, the information about nutrient output distribution across a watershed has been critical in the designing of regional development policies. But existing watershed evaluation models often encounter difficulties in application because of their complicated structures and strict requirements for the input data. In this paper, a spatially explicit and process-based model, Integrated Grid’s Exporting and Delivery model, was introduced to estimate annual in-stream nutrient levels. Each grid cell in this model was regarded as having potentials of both exporting new nutrients and trapping nutrients passing by. The combined nutrient dynamics of a grid is mainly determined by the grid’s features in land use/land cover, soil drainage, and geomorphology. This simple-concept model was tested at some basins in north Georgia in the USA. Stations in one basin were used to calibrate the model. Then an external validation was employed by applying the calibrated model to stations in the other neighbor basins. Model evaluation statistics implied the model’s validity and good performance in estimating the annual NPS nutrients’ fluxes at the watershed scale. This study also provides a promising prospect that in-stream annual nutrient loads can be accurately estimated from a few public available datasets.     

Modeling Runoff Response to Land Cover and Rainfall Spatial Variability in Semi-Arid Watersheds

Hydrologic response is an integrated indicator of watershed condition, and significant changes in land cover may affect the overall health and function of a watershed. This paper describes a procedure for evaluating the effects of land cover change and rainfall spatial variability on watershed response. Two hydrologic models were applied on a small semi-arid watershed; one model is event-based with a one-minute time step (KINEROS), and the second is a continuous model with a daily time step (SWAT). The inputs to the models were derived from Geographic Information System (GIS) theme layers of USGS digital elevation models, the State Soil Geographic Database (STATSGO) and the Landsat-based North American Landscape Characterization classification (NALC) in conjunction with available literature and look up tables. Rainfall data from a network of 10 raingauges and historical stream flow data were used to calibrate runoff depth using the continuous hydrologic model from 1966 to 1974. No calibration was carried out for the event-based model, in which six storms from the same period were used in the calculation of runoff depth and peak runoff. The assumption on which much of this study is based is that land cover change and rainfall spatial variability affect the rainfall-runoff relationships on the watershed. To validate this assumption, simulations were carried out wherein the entire watershed was transformed from the 1972 NALC land cover, which consisted of a mixture of desertscrub and grassland, to a single uniform land cover type such as riparian, forest, oak woodland, mesquite woodland, desertscrub, grassland, urban, agriculture, and barren. This study demonstrates the feasibility of using widely available data sets for parameterizing hydrologic simulation models. The simulation results show that both models were able to characterize the runoff response of the watershed due to changes of land cover.     

Study of the Climate Change Impacts on Water Quality in the Upstream Portion of the Cau River Basin,Vietnam

This paper presents simulations of climate change impacts on water quality in the upstream portion of the Cau River Basin in the North of Vietnam. The integrated modeling system GIBSI was used to simulate hydrological processes, pollutant and sediment wash-off in the river basin, and pollutant transport and transformation in the river network. Three projections for climate change based on emission scenarios B1, B2, and A2 of IPCC Special Report on Emission Scenarios (SRES) were considered. By assuming that the input pollution sources and watershed configuration were constant, based on 2008 data, water quality in the river network was simulated up to the terminal year 2050. For each climate change scenario, patterns of precipitation in wet and dry year were considered. The change in annual and monthly trends for dissolved oxygen (DO), biochemical oxygen demand (BOD), and ammonium ions (NH4+) load and concentration for different portions of the watershed have been analyzed. The results of these simulations show that climate change has more impact on changing the seasonal water quality parameters than on altering the average annual load of the pollutants. The percent change and change pattern in water quality parameters are different for wet and dry year, and the changes in wet year are smaller than those in dry year.     

污染场地土壤通用评估基准建立的理论和常用模型

污染场地土壤通用评估基准的建立与每个国家的经济水准及社会发展紧密相关.从国际相关领域的发展趋势来看,场地基准的建立基于风险基础之上.我国正在颁布污染场地风险评估技术导则(C-RAG),表明我国已选择风险基础上的污染场地管理模式.文章回顾了污染场地土壤通用评估基准建立的理论、方法及通用模型,并推荐污染场地评估的模型框架,...     

Artificial intelligence assisted intelligent planning framework for environmental restoration of terrestrial ecosystems

The environmental restoration of terrestrial ecosystems helps to protect the natural world and enhances sustainable land resource development. Modern and efficient approaches for the conservation of ecological functions must be established for more severe land degradation. In this paper, artificial intelligence assisted intelligent planning framework has been proposed to manage the environmental restoration of the terrestrial ecosystem. Facilitating balance of ecosystem service provision, demand, and using machine learning to dynamically build Biological Retreat Configuration (BRCs) that helps better to apprehend the influence of urban growth on environment-related procedures. Such factors can be used as a theoretical reference in the combination of commercial development and eco-friendly conservation. The BRC of the metro area of Changsha Zhuzhou Xiangtan (CZX) has been developed in this study to classify ecological sources using the Bayesian network model efficiently. Using the Least Collective Resistance (LCR) model and circuit theory, the environmental passage and environmental strategy points were established. The BRC was developed by integrating seven environmental factors with 35 ecological policy points. The results showed that the supply and demand of organic unit services (EUS) were spatially decoupled with the deterioration in locations with a significant EUS trend. The urban agglomeration's environmental sources and ecological corridors have been primarily located in forests and waters. The terrestrial environmental pathway has been scattered around the outer edge of the region, while the aquatic green corridor has been extended over the whole town. The environmentally sensitive areas were located primarily around the borders of the growing region and the intersections between land development and forest area. Finally, environmental components have been mainly identified in existing zones of biological defense, which support the effectiveness of Machine Learning (ML) in green sources forecasting and offer novel insight into the development of urban BRCs. The proposed approach has proven to be effective for the planning of assessing environmental restoration in terrestrial ecosystems.     

Urban stream syndrome in a small,lightly developed watershed: a statistical analysis of water chemistry parameters,land use patterns,and natural sources

The relationships among land use patterns, geology, soil, and major solute concentrations in stream water for eight tributaries of the Kayaderosseras Creek watershed in Saratoga County, NY, were investigated using Pearson correlation coefficients and multivariate regression analysis. Sub-watersheds corresponding to each sampling site were delineated, and land use patterns were determined for each of the eight sub-watersheds using GIS. Four land use categories (urban development, agriculture, forests, and wetlands) constituted more than 99 % of the land in the sub-watersheds. Eleven water chemistry parameters were highly and positively correlated with each other and urban development. Multivariate regression models indicated urban development was the most powerful predictor for the same eleven parameters (conductivity, TN, TP, NO $_{3}^-$ , Cl^?, HCO $_{3}^-$ , SO $_{4}^{2-}$ , Na⁺, K⁺, Ca²⁺, and Mg²⁺). Adjusted R ² values, ranging from 19 to 91 %, indicated that these models explained an average of 64 % of the variance in these 11 parameters across the samples and 70 % when Mg²⁺ was omitted. The more common R ², ranging from 29 to 92 %, averaged 68 % for these 11 parameters and 72 % when Mg²⁺ was omitted. Water quality improved most with forest coverage in stream watersheds. The strong associations between water quality variables and urban development indicated an urban source for these 11 water quality parameters at all eight sampling sites was likely, suggesting that urban stream syndrome can be detected even on a relatively small scale in a lightly developed area. Possible urban sources of Ca²⁺ and HCO $_{3}^-$ are suggested.     

Correspondence of biological condition models of California streams at statewide and regional scales

We used boosted regression trees (BRT) to model stream biological condition as measured by benthic macroinvertebrate taxonomic completeness, the ratio of observed to expected (O/E) taxa. Models were developed with and without exclusion of rare taxa at a site. BRT models are robust, requiring few assumptions compared with traditional modeling techniques such as multiple linear regression. The BRT models were constructed to provide baseline support to stressor delineation by identifying natural physiographic and human land use gradients affecting stream biological condition statewide and for eight ecological regions within the state, as part of the development of numerical biological objectives for California’s wadeable streams. Regions were defined on the basis of ecological, hydrologic, and jurisdictional factors and roughly corresponded with ecoregions. Physiographic and land use variables were derived from geographic information system coverages. The model for the entire state (n?=?1,386) identified a composite measure of anthropogenic disturbance (the sum of urban, agricultural, and unmanaged roadside vegetation land cover) within the local watershed as the most important variable, explaining 56 % of the variance in O/E values. Models for individual regions explained between 51 and 84 % of the variance in O/E values. Measures of human disturbance were important in the three coastal regions. In the South Coast and Coastal Chaparral, local watershed measures of urbanization were the most important variables related to biological condition, while in the North Coast the composite measure of human disturbance at the watershed scale was most important. In the two mountain regions, natural gradients were most important, including slope, precipitation, and temperature. The remaining three regions had relatively small sample sizes (n?≤?75 sites) and had models that gave mixed results. Understanding the spatial scale at which land use and land cover affect taxonomic completeness is imperative for sound management. Our results suggest that invertebrate taxonomic completeness is affected by human disturbance at the statewide and regional levels, with some differences among regions in the importance of natural gradients and types of human disturbance. The construction and application of models similar to the ones presented here could be useful in the planning and prioritization of actions for protection and conservation of biodiversity in California streams.