Classification of Drainage Crossings on high-resolution digital elevation models: A deep learning approach

High-Resolution Digital Elevation Models (HRDEMs) have been used to delineate fine-scale hydrographic features in landscapes with relatively level topography. However, artificial flow barriers associated with roads are known to cause incorrect modeled flowlines, because these barriers substantially increase the terrain elevation and often terminate flowlines. A common practice is to breach the elevation of roads near drainage crossing locations, which, however, are often unavailable. Thus, developing a reliable drainage crossing dataset is essential to improve the HRDEMs for hydrographic delineation. The purpose of this research is to develop deep learning models for classifying the images that contain the locations of flow barriers. Based on HRDEMs and aerial orthophotos, different Convolutional Neural Network (CNN) models were trained and compared to assess their effectiveness in image classification in four different watersheds across the U.S. Midwest. Our results show that most deep learning models can consistently achieve over 90% accuracies. The CNN model with HRDEMs as the sole input feature was found to be the best-fit one. The addition of aerial orthophotos and their derived spectral indices is insignificant to or even worsens the model’s accuracy. The selected best-fit model exhibits excellent transferability over different geographic contexts. This work can be applied to improve elevation-derived hydrography mapping at fine spatial scales

Emerging Themes and Future Directions of Multi-Sector Nexus Research and Implementation

Water, energy, and food are all essential components of human societies. Collectively, their respective resource systems are interconnected in what is called the “nexus”. There is growing consensus that a holistic understanding of the interdependencies and trade-offs between these sectors and other related systems is critical to solving many of the global challenges they present. While nexus research has grown exponentially since 2011, there is no unified, overarching approach, and the implementation of concepts remains hampered by the lack of clear case studies. Here, we present the results of a collaborative thought exercise involving 75 scientists and summarize them into 10 key recommendations covering: the most critical nexus issues of today, emerging themes, and where future efforts should be directed. We conclude that a nexus community of practice to promote open communication among researchers, to maintain and share standardized datasets, and to develop applied case studies will facilitate transparent comparisons of models and encourage the adoption of nexus approaches in practice

Emerging Themes and Future Directions of Multi-Sector Nexus Research and Implementation

Water, energy, and food are all essential components of human societies. Collectively, their respective resource systems are interconnected in what is called the “nexus”. There is growing consensus that a holistic understanding of the interdependencies and trade-offs between these sectors and other related systems is critical to solving many of the global challenges they present. While nexus research has grown exponentially since 2011, there is no unified, overarching approach, and the implementation of concepts remains hampered by the lack of clear case studies. Here, we present the results of a collaborative thought exercise involving 75 scientists and summarize them into 10 key recommendations covering: the most critical nexus issues of today, emerging themes, and where future efforts should be directed. We conclude that a nexus community of practice to promote open communication among researchers, to maintain and share standardized datasets, and to develop applied case studies will facilitate transparent comparisons of models and encourage the adoption of nexus approaches in practice

Groundwater Pollution Risk Assessment under Scenarios of Climate and Land Use Change in the Northern Great Plains

Modeling groundwater vulnerability to pollution is critical for implementing programs to protect groundwater quality. Traditionally, groundwater vulnerability was modeled 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. For example, global warming may lead to northward shifts in cropping patterns and changes in crop mixes (and use of farm chemicals). Meanwhile, growing demands for biofuels are resulting in expanding corn acreage, and may lead to pressures to remove land from the Conservation Reserve Program (CRP) or otherwise open lands that are currently not cropped to cultivation. Such changes may have significant implications for groundwater quality. In this research, a modeling framework, which employs four sub-models linked within a GIS environment, was presented to evaluate the groundwater pollution risks under future climate and land use changes in North Dakota. The major sub-models include a groundwater vulnerability model and a biofuels-related land use change model, which were illustrated in two separate studies. 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. Advisers: James W. Merchant and Xun-Hong Che

Vadose Zone Mapping Using Geographic Information Systems and Geostatistics

Characteristics of the vadose zone media are important for assessing contamination potentials for projects such as landfill siting and planning. However, accurate vadose zone maps at the regional scale are often not available. This study successfully mapped low-permeable components of the vadose zone across the Elkhorn River Basin, Nebraska, using geographic information systems (GIS), groundwater level data, and a test hole database. The map has the potential to land use planners for searching and planning landfill sites

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

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 condi- tions. However, groundwater vulnerability is strongly dependent on factors such as depth-to-water, recharge and land use conditions thatmay change in response to future changes in climate and/or socio-economic condi- tions. 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

Post-disaster assessment of northeastern coastal region for the 2011 Sendai Earthquake and tsunami

The 2011 Sendai Earthquake has hit the north-east of Japan triggering a destructive tsunami that has caused extensive damage. A fast and effective post-disaster assessment is highly imperative for the recovery of this region. This study modeled the tsunami-affected areas of coastal Fukushima Prefecture using Landsat-7 ETM+ data and terrain analysis. The result shows that most of the coastal areas were significantly affected by the tsunami. The low-lying plains along the coast are particularly vulnerable to the tsunami

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 the LiDAR-derived topographic DEMs. The paper culminates in a proposal and discussion of establishing a national or statewide drainage structure dataset

Developing a Restorable Wetland Index for Rainwater Basin Wetlands in South-Central Nebraska: A Multi-Criteria Spatial Analysis

It is always challenging for decision makers to prioritize wetland conservation programs at the landscape scale. This study employed a GIS-based multi-criteria spatial decision support tool that identified locations with the highest restoration potential for wetland conservation programs in the Rainwater Basin in south-central Nebraska. Five indicators were considered to assess wetland restoration potential: (1) Vegetation characteristics; (2) Soil characteristics; (3) Water volume released from hydrological modification of agricultural irrigation pits; (4) Topographical depression status; and (5) Habitat condition. The results suggested 192 (1.6% of the total) hydric soil footprints as the highest prioritized locations for future wetland restoration programs. The results also identified 901 footprints (7.7% of the total) with medium-high restoration potential, 1,792 (15.2% of the total) footprints with medium-low restorable potential and 8,875 (75.5% of the total) footprints with low restorable potential. The methodology and statistical results contribute directly to the state’s Rainwater Basin Wetland Program Plan and are potentially applicable to the management of other wetlands across the region and globally