Ruopu Li

Modeling vulnerability of groundwater to pollution under future scenarios of climate change and biofuels-related land use change: A case study in North Dakota, USA

Modeling groundwater vulnerability to pollution is critical for implementing programs to protect groundwater quality. Most groundwater vulnerability modeling has been based on current hydrogeology and land use conditions. However, groundwater vulnerability is strongly dependent on factors such as depth-to-water, recharge and land use conditions that may change in response to future changes in climate and/or socio-economic conditions. In this research, a modeling framework, which employs three sets of models linked within a geographic information system (GIS) environment, was used to evaluate groundwater pollution risks under future climate and land use changes in North Dakota. The results showed that areas with high vulnerability will expand northward and/or northwestward in Eastern North Dakota under different scenarios. GIS-based models that account for future changes in climate and land use can help decision-makers identify potential future threats to groundwater quality and take early steps to protect this critical resource.

A Geospatial Approach for Prioritizing Wind Farm Development in Northeast Nebraska, USA

Being cleaner and climate friendly, wind energy has been increasingly utilized to meet the ever-growing global energy demands. In the State of Nebraska, USA, a wide gap exists between wind resource and actual energy production, and it is imperative to expand the wind energy development. Because of the formidable costs associated with wind energy development, the locations for new wind turbines need to be carefully selected to provide the greatest benefit for a given investment. Geographic Information Systems (GIS) have been widely used to identify the suitable wind farm locations. In this study, a GIS-based multi-criteria approach was developed to identify the areas that are best suited to wind energy development in Northeast Nebraska, USA. Seven criteria were adopted in this method, including distance to roads, closeness to transmission lines, population density, wind potential, land use, distance to cities, slope and exclusionary areas. The suitability of wind farm development was modeled by a weighted overlay of geospatial layers corresponding to these criteria. The results indicate that the model is capable of identifying locations highly suited for wind farm development. The approach could help identify suitable wind farm locations in other areas with a similar geographic background.

Drainage Structure Datasets and Effects on LiDAR-Derived Surface Flow Modeling

With extraordinary resolution and accuracy, Light Detection and Ranging (LiDAR)-derived digital elevation models (DEMs) have been increasingly used for watershed analyses and modeling by hydrologists, planners and engineers. Such high-accuracy DEMs have demonstrated their effectiveness in delineating watershed and drainage patterns at fine scales in low-relief terrains. However, these high-resolution datasets are usually only available as topographic DEMs rather than hydrologic DEMs, presenting greater land roughness that can affect natural flow accumulation. Specifically, locations of drainage structures such as road culverts and bridges were simulated as barriers to the passage of drainage. This paper proposed a geospatial method for producing LiDAR-derived hydrologic DEMs, which incorporates data collection of drainage structures (i.e., culverts and bridges), data preprocessing and burning of the drainage structures into DEMs. A case study of GIS-based watershed modeling in South Central Nebraska showed improved simulated surface water derivatives after the drainage structures were burned into

A Geospatial Approach for Assessing Groundwater Vulnerability to Nitrate Contamination in Agricultural Settings

Groundwater is the principal source of drinking water for at least one third of Earth’s human inhabitants. Thus, protection of groundwater is a critical issue in many locales. Nitrates and other contaminants that impact human health are of particular concern. Mapping of aquifer vulnerability to pollution is a critical first step in implementing groundwater management protection programs; however, mapping is often constrained by generalizations inherent in model formulation and availability of data. In this study, a groundwater vulnerability model, which employs data extracted from widely available national and statewide geospatial datasets, is used to evaluate regional groundwater pollution risk in the Elkhorn River Basin, Nebraska, USA. The model, implemented in a geographic information system (GIS), is specifically structured to address risks of nitrate contamination in agricultural landscapes; thus, land use is a key factor. Modeled groundwater vulnerability was found to be positively correlated with nitrate concentrations obtained from sampled wells. The results suggest that the approach documented here could be used effectively to model regional groundwater pollution risk in other areas.

Land Use Classification: A Surface Energy Balance and Vegetation Index Application to Map and Monitor Irrigated Lands

Irrigated agriculture consumes the largest share of available fresh water, and awareness of the spatial distribution and application rates is paramount to a functional and sustainable communal consumptive water use. This remote sensing study leverages surface energy balance fluxes and vegetation indices to classify and map the spatial distribution of irrigated and non-irrigated croplands. The purpose is to introduce a classification scheme applicable across a wide variation in regional climate and inter-growing seasonal precipitation. The rationale for climate and inter-growing seasonal adaptability is founded in the derivation and calibration of the scheme based on the wettest growing season. Therefore, the scheme becomes a more efficient classifier during normal and dry growing seasons. Using empirical distribution functions, two indices are derived from evapotranspiration fluxes and vegetation indices to contrast and classify irrigated croplands from non-irrigated. The synergy of the two indices increases the classification proficiency by adding another classifying layer which re-characterizes misclassified croplands by the base index. The scheme was applied to a region with wide climate variation and to multiple years of growing seasons. The results presented, in cross validation with groundtruth, show an accurate and consistent approach to classify irrigation with overall accuracy of 92.1%, applicable from humid to semi-arid climate, and from dry to normal and wet growing seasons.

Evaluating Hydrologically Connected Surface Water and Groundwater Using a Groundwater Model

Determination of the nature and extent of the connection between groundwater and surface water is of paramount importance to managing water supplies. The development of analyses that detail the surface water-groundwater system may lead to more effective utilization of available water. A tool was developed to help determine the effects of groundwater and surface water interactions. The software tool includes two graphic user interfaces to allow full compatibility with numerical MODFLOW groundwater models. This case study shows the tool, in conjunction with MODFLOW groundwater models and carefully designed scenarios, can successfully calculate the rates of stream-groundwater interactions, thereby providing the basis for designating management areas with the most significant hydrologic impact. This tool can be applied in other regions with similar settings and needs for integrated water management.

Modeling Urban PM2.5 Concentration by Combining Regression Models and Spectral Unmixing Analysis in a Region of East China

Understanding the spatial distribution of PM2.5 concentration and its contributing environmental variables is critical to develop strategies of addressing adverse effects of the particulate pollution. In this study, a range of meteorological and land use factors were incorporated into a linear regression (LR) model and a logistic model-based regression (LMR) model to simulate the annual and winter PM2.5 concentrations. The vegetation cover, derived from a linear spectral unmixing analysis (LSUA), and the normalized difference built-up index (NDBI), were found to improve the goodness of fit of the models. The study shows that (1) both the LR and the LMR agree on the predicted spatial patterns of PM2.5 concentration and (2) the goodness of fit is higher for the models established based on the annual PM2.5 concentration than that based on the winter PM2.5. The modeling results show that higher PM2.5 concentration coincided with the major urban area for the annual average but focused on the suburban and rural areas for the winter. The methods introduced in this study can potentially be applied to similar regions in other developing countries.

GEOCODING LOCATIONS OF HISTORIC RECLAMATION RESEARCH SITES USING GOOGLE EARTH

The American Society of Mining and Reclamation (ASMR) has been publishing conference proceedings and journal articles on land reclamation and the protection of soil and water resources for more than three decades. Much of the technical work presented in the ASMR conferences and journals contain specific mining sites that are associated with geographic locations. However, the geographic contexts of these articles were often not made directly available to the readers. This deficiency affects the abilities of related professionals to explore the technical reclamation knowledge in terms of its geographic background. Therefore, it is critical to develop quality-assured geographic references to the papers published by ASMR. This study used Google Earth and ArcGIS software to create a series of placemarks that link past ASMR technical articles to the actual locations. These placemarks can be freely distributed and integrated into the website for web map display.