Strengthening America's Best Idea: An Independent Review of the National Park Service's Natural Resource Stewardship and Science Directorate

NRSS requested that an independent panel of the National Academy conduct a review of its effectiveness in five core functions, its relationships with key internal stakeholders, and its performance measurement system. Among other things, the National Park Service's Natural Resource Stewardship and Science Directorate (NRSS) is responsible for providing usable natural and social science information throughout the National Park Service (NPS). NRSS leadership requested this review of the directorate's performance on five core functions, its relationships with key internal NPS stakeholders, and its performance measurement system.Main FindingsThe panel determined that NRSS is a highly regarded organization that provides independent, credible scientific expertise and technical information. The panel also found that NRSS and NPS have additional opportunities to advance natural resource stewardship throughout the Service. If implemented, the panel's eight major recommendations will: (1) help the Service respond to the parks' environmental challenges while raising public awareness about the condition of these special places; (2) strengthen NRSS as an organization; (3) promote scientifically based decision-making at the national, regional, and park levels; and (4) improve the existing performance measurement system

Using Machine Learning to Understand the Hydrologic Impacts of Permafrost Thaw-Driven Land Cover Change

Discontinuous permafrost regions are experiencing a change in land cover distribution as a result of permafrost thaw. In wetlands interspersed with discontinuous permafrost, climate change is particularly problematic because temperature increases can result in significant permafrost thaw, thaw-driven landscape changes, and resultant changes in watershed hydrologic responses. The influence of land cover change on the short- and long-term hydrological responses of wetland-peatland complexes is poorly understood. A better understanding of the impacts of climate-related land cover evolution on the hydrology of wetland-covered watersheds requires information about the distribution of hydrologically important lands, their pattern, and the rate at which they change over time. Here, we first developed a machine learning-based land cover evolution model (TSLCM) to estimate the long-term evolution of dominant land covers for application to the discontinuous permafrost regions of Northern Canada. This model is applied to replicate historical land cover and estimate future land cover scenarios at the Scotty Creek Research Basin in the Northwest Territories, Canada. A significant challenge when analyzing land cover change effects on hydrological properties is generating time-dependent classified maps of the region of interest, and the challenges associated with preprocessing remotely sensed data for discriminating between wetlands and forest-covered regions. In this work, we focus on two important objectives supporting the improved classification of wetlands in discontinuous permafrost regions: the exclusive use of only RGB imagery, and the use of an image segmentation method to accelerate the automatic classification of land cover. A semantic segmentation neural network, a multi-layer perceptron (MLP), and watershed function algorithms are applied to develop the taiga wetland identification neural network (TWINN) for the hydrological classification of wetlands. TWINN is here demonstrated to accurately classify high-resolution imagery of discontinuous permafrost regions within the Northwest Territories into the water, forest, and wetlands, and also able to delineate the runoff area of wetlands. To study the effect of land cover evolution on runoff generation in the Scotty Creek basin, the products of TWINN and TSLCM are used to inform a process-based hydrological model where land cover change is represented explicitly. According to simulation results, land cover transitions can modify annual mean streamflow by as much as 7%, in addition to influences due to changing precipitation regimes alone

Status and trends of wetland studies in Canada using remote sensing technology with a focus on wetland classification: a bibliographic analysis

A large portion of Canada is covered by wetlands; mapping and monitoring them is of great importance for various applications. In this regard, Remote Sensing (RS) technology has been widely employed for wetland studies in Canada over the past 45 years. This study evaluates meta-data to investigate the status and trends of wetland studies in Canada using RS technology by reviewing the scientific papers published between 1976 and the end of 2020 (300 papers in total). Initially, a meta-analysis was conducted to analyze the status of RS-based wetland studies in terms of the wetland classification systems, methods, classes, RS data usage, publication details (e.g., authors, keywords, citations, and publications time), geographic information, and level of classification accuracies. The deep systematic review of 128 peer-reviewed articles illustrated the rising trend in using multi-source RS datasets along with advanced machine learning algorithms for wetland mapping in Canada. It was also observed that most of the studies were implemented over the province of Ontario. Pixel-based supervised classifiers were the most popular wetland classification algorithms. This review summarizes different RS systems and methodologies for wetland mapping in Canada to outline how RS has been utilized for the generation of wetland inventories. The results of this review paper provide the current state-of-the-art methods and datasets for wetland studies in Canada and will provide direction for future wetland mapping research.Peer ReviewedPostprint (published version

Predictive mapping of wetland types and associated soils through digital elevation model analyses in the Canadian Prairie Pothole Region

Effective management strategies are needed to control phosphorus loading of prairie watersheds that contribute to the eutrophication issues of Lake Winnipeg. Prairie Pothole Region (PPR) wetlands provide many ecosystems services including reducing nutrient mobility. Preferential conservation of PPR wetlands with calcium carbonate (CaCO3)-enriched soils may be a more effective strategy for controlling phosphorus loading, as these soils have greater potential to retain phosphorus from agricultural runoff. The spatial distribution of CaCO3-enriched wetland soils is controlled by hydrologic processes that may be modellable using high-resolution digital elevation models (DEMs). Two modelling approaches were tested to map spatial distributions of wetlands and wetland soils expected to be enriched with CaCO3. The models were trained and tested with wetland salinity and soil profile information collected at three Saskatchewan PPR sites, near Swift Current, St. Denis, and Smith Creek. The first model was developed to approximate landscape-scale hydrologic processes from high-resolution DEMs to predict the distributions of fresh and solute-rich wetlands; the solute-rich wetlands represent wetlands expected to have CaCO3-enriched soils. Spill channel connections between wetlands were modelled to characterize wetlands in terms of the runoff contributions they receive, their potential for contributing runoff downslope, and their relative position within the landscape; solute-richness predictions were based on these characteristics. This model was successful and achieved acceptable predictive accuracies based on external validation tests. Digital soil mapping (DSM) methodologies were tested for predicting the spatial distribution of wetland soil classes within PPR landscapes. Target soil classes were defined by hydropedological units that reflect differences in soil CaCO3 enrichment. Multiple machine-learning techniques were tested, which incorporated many topographic attributes derived from the DEMs as predictor variables, including knowledge-based topographic attributes developed specifically to characterize the PPR’s morphology. Certain DSM models achieved acceptable predictive accuracy based on external validation tests and mapped soils in expected distributions, but none predicted the occurrence of wetlands with CaCO3-enriched soils distributed throughout their basins. Both modelling approaches could potentially be used to 1) identify wetlands with CaCO3-enriched soils to target for conservation efforts to maximize phosphorus retention and 2) create upscaled estimates of phosphorus retention across the PPR

Use of multiple LIDAR-derived digital terrain indices and machine learning for high-resolution national-scale soil moisture mapping of the Swedish forest landscape

Spatially extensive high-resolution soil moisture mapping is valuable in practical forestry and land management, but challenging. Here we present a novel technique involving use of LIDAR-derived terrain indices and machine learning (ML) algorithms capable of accurately modeling soil moisture at 2 m spatial resolution across the entire Swedish forest landscape. We used field data from about 20,000 sites across Sweden to train and evaluate multiple ML models. The predictor features (variables) included a suite of terrain indices generated from a national LIDAR digital elevation model and ancillary environmental features, including surficial geology, climate and land use, enabling adjustment of soil moisture class maps to regional or local conditions. Extreme gradient boosting (XGBoost) provided better performance for a 2-class model, manifested by Cohen's Kappa and Matthews Correlation Coefficient (MCC) values of 0.69 and 0.68, respectively, than the other tested ML methods: Artificial Neural Network, Random Forest, Support Vector Machine, and Naive Bayes classification. The depth to water index, topographic wetness index, and `wetland' categorization derived from Swedish property maps were the most important predictors for all models. The presented technique enabled generation of a 3-class model with Cohen's Kappa and MCC values of 0.58. In addition to the classified moisture maps, we investigated the technique's potential for producing continuous soil moisture maps. We argue that the probability of a pixel being classified as wet from a 2-class model can be used as a 0-100% index (dry to wet) of soil moisture, and the resulting maps could provide more valuable information for practical forest management than classified maps

Towards an\u2028 EU research and innovation policy agenda for nature-based solutions & re-naturing cities. Final report of the Horizon 2020 expert group on nature-based solutions and re-naturing cities.

1. Nature-based solutions harness the power and sophistication of nature to turn environmental, social and economic challenges into innovation opportunities. They can address a variety of societal challenges in sustainable ways, with the potential to contribute to green growth, 'future-proofing' society, fostering citizen well-being, providing business opportunities and positioning Europe as a leader in world markets. \u2028 2. Nature-based solutions are actions which are inspired by, supported by or copied from nature. They have tremendous potential to be energy and resource-efficient and resilient to change, but to be successful they must be adapted to local conditions. \u2028 3. Many nature-based solutions result in multiple co-benefits for health, the economy, society and the environment, and thus they can represent more efficient and cost-effective solutions than more traditional approaches. \u2028 4. An EU Research & Innovation (R&I) agenda on nature-based solutions will enable Europe to become a world leader both in R&I and in the growing market for nature-based solutions. For this, the evidence base for the effectiveness of nature-based solutions needs to be developed and then used to implement solutions. Both need to be done in conjunction with stakeholders. The potential for transferability and upscaling of solutions also requires further investigation. There is also a need to develop a systemic approach that combines technical, business, finance, governance, regulatory and social innovation. \u2028 5. Four principal goals have been identified that can be addressed by nature-based solutions: �� Enhancing sustainable urbanisation through nature-based solutions can stimulate economic growth as well as improving the environment, making cities more attractive, and enhancing human well-being. \u2028 �� Restoring degraded ecosystems using nature-based solutions can improve the resilience of ecosystems, enabling them to deliver vital ecosystem services and also to meet other societal challenges. \u2028 �� Developing climate change adaptation and mitigation using nature-based solutions can provide more resilient responses and enhance the storage of carbon. \u2028 �� Improving risk management and resilience using nature-based solutions can lead to greater benefits than conventional methods and offer synergies in reducing multiple risks. \u2028 6. Based on the four goals, seven nature-based solutions for R&I actions are recommended to be taken forward by the European Commission and Member States: �� Urban regeneration through nature-based solutions \u2028 �� Nature-based solutions for improving well-being in urban areas \u2028 �� Establishing nature-based solutions for coastal resilience \u2028 �� Multi-functional nature-based watershed management and ecosystem restoration \u2028 �� Nature-based solutions for increasing the sustainability of the use of matter and energy \u2028 �� Nature-based solutions for enhancing the insurance value of ecosystems \u2028 �� Increasing carbon sequestration through nature-based solutions \u2028This report was produced by the Horizon 2020 Expert Group on 'Nature-Based Solutions and Re- Naturing Cities', informed by the findings of an e-consultation and a stakeholder workshop. \u202

2012 Annual Report of the Graduate School of Engineering and Management, Air Force Institute of Technology

The Graduate School\u27s Annual Report highlights research focus areas, new academic programs, faculty accomplishments and news, and provides top-level sponsor-funded research data and information

Science-based restoration monitoring of coastal habitats, Volume Two: Tools for monitoring coastal habitats

Healthy coastal habitats are not only important ecologically; they also support healthy coastal communities and improve the quality of people’s lives. Despite their many benefits and values, coastal habitats have been systematically modified, degraded, and destroyed throughout the United States and its protectorates beginning with European colonization in the 1600’s (Dahl 1990). As a result, many coastal habitats around the United States are in desperate need of restoration. The monitoring of restoration projects, the focus of this document, is necessary to ensure that restoration efforts are successful, to further the science, and to increase the efficiency of future restoration efforts

Hydrological and empirical modeling framework for farmed prairie potholes in the prairie pothole region of Iowa

Closed surface depressions, also known as “potholes” play an important role in the hydrologic cycle and provide multiple environmental services including flood mitigation, water quality improvements, and wildlife habitat. In the Prairie Pothole Region, which covers approximately 715,000 km2, including parts of three Canadian provinces (Saskatchewan, Manitoba, and Alberta) and five states in the U.S. (Minnesota, Iowa, North and South Dakota, and Montana), these potholes are typically farmed and are a dominant feature in the landscape. These potholes are also different than the traditional prairie pothole wetlands as the natural vegetation (Typha spp., Scirpus spp., Carex spp., etc.) has been replaced by agricultural crops (mainly Corn and Soybean). In this study, we evaluated the Annualized Agriculture Non-Point Source (AnnAGNPS) model for simulating the inundation behavior of farmed potholes, in the Prairie Pothole Region (PPR) of Iowa. Performance analyses considered the entire growing season (GS), corresponding to the span in which there was observed data, and only days in which water storage (WS) was observed. Our results demonstrate that the AnnAGNPS model can be used to predict the inundation depth of drained and farmed potholes, which is useful for assessing the landscape impacts of these features. We then investigated the influence of different land use practices on depth, duration, and aerial extent of ponding in the two potholes using AnnAGNPS. Three management scenarios were compared — current: conventionally tilled farmed conditions in corn/soybean rotation with surface inlets in the potholes connecting to a subsurface drainage system; retired: pothole is converted to a mixture of grass, weeds, and low-growing brush, with surface inlets removed and the drainage system underneath the potholes disconnected; and conserved: conservation tillage throughout the field with surface inlets and drainage maintained in potholes. The average annual water depth for the conserved scenario was 7-8% lower than the average annual water depth for the current scenario. It was also observed that the potholes tend to flood more frequently in early stages of plant development, which could lead to delays in management operations and reduced yields. Next, we assessed the capability of USGS DEMs for modeling pothole inundation in the prairie pothole region of Iowa. We used three DEMs: a 1m DEM prepared from LiDAR data which is readily available for the state of Iowa, USGS 1/9 arc-second DEM (~3m) which covers about 25 percent of the conterminous United States (U.S.) and 1/3 arc-second seamless DEM (~10m) which covers the entire U.S. Modeling performance was evaluated using Nash-Sutcliffe efficiency (NSE), Percent bias (PBIAS), Ratio of the root mean square error (RSR) and R2 statistical performance criteria. Results show that the water depth simulated from AnnAGNPS model based on 1m DEM which is prepared from the LiDAR data gave Nash-Sutcliffe efficiency (NSE) values of 0.77 and 0.24 in the Walnut pothole and 0.56 and 0.30 in the Bunny pothole, for the GS calibration and validation periods, respectively. The estimates of water depths using USGS 3m and 10m DEMs was also found to be very similar to LiDAR 1m DEM based predictions and are also representative of field conditions. The developed AnnAGNPS model was then used to simulate the water depths for ten years (2007 – 2016) growing season (May to October) in the three potholes termed Bunny, Walnut and Lettuce. An empirical model based on artificial neural network (ANN) technology was developed on the expanded dataset and tested on the actual water depth observations collected in 2018 at another three potholes termed Turkey, Hen, and Plume. The R2 statistics were 0.604 and 0.563 during training and validation periods, respectively. A low root mean square error (RMSE) value of 0.057 and mean absolute error (MAE) value of 0.023 were found during both training and validation of the ANN model. In general, results suggest that the ANN models are able to predict the water depth fluctuations in the potholes during the growing season. These models can be a vital tool to augment the monitoring efforts of prairie potholes and can help stakeholders - farmers and state/federal agencies for management planning and making an informed decision about farming the potholes