Wetland Inventory Research Articles

Calibrated Simulation of the Long‐Term Average Surficial Groundwater System and Derived Spatial Distributions of its Characteristics for the Contiguous United States

AbstractWhile the physical processes governing groundwater flow are well understood, and the computational resources now exist for solving the governing equations in three dimensions over continental‐scale domains, there remains substantial uncertainty about the subsurface distribution of the properties that control groundwater flow and transport for much of the contiguous United States (CONUS). The transmissivity of the shallow subsurface is a key parameter for the simulation of water table position, shallow groundwater flow, and base‐flow discharge, but is not well‐characterized at large regional to continental scales. We used a process‐based inversion of CONUS‐extent groundwater information to generate national data sets of (a) the transmissivity of the shallow groundwater system, (b) the depth to the water table, (c) groundwater discharge as base‐flow, and (d) long‐term average water content in the unsaturated zone. CONUS‐extent coverage was developed in the form of 75 subdomain models, with the spatial distribution of long‐term average transmissivity for each subdomain model calibrated against water‐levels derived from U.S. Geological Survey (USGS) observation wells, NHDPlusV2 first‐order perennial streams, and National Wetlands Inventory (NWI) freshwater wetlands. Estimated transmissivities were lower in the western CONUS than the eastern CONUS, and across the CONUS both transmissivity and depth to water correlate with recharge, elevation, and topographic slope. These generated data sets provide spatially distributed, long‐term average estimates of subsurface properties and hydrological states that we anticipate will complement other environmental modeling efforts as explanatory variables, boundary conditions, or transport pathways.

A Multi-Scale, Participatory Approach to Developing a Protected Area Wetland Inventory in South Africa

Given limited time, staffing and specialist expertise, management of wetlands within biodiversity-rich protected areas of developing countries is often held back by a lack of information on the extent and nature of wetland resources. Rapid, realistic and effective wetland ecosystem assessment methods are needed to develop a baseline for monitoring that detects trends and guides management. Lack of available in-house wetland expertise stimulated a novel team which harnessed wide-ranging complementary and, ultimately, indispensable expertise, spanning all branches of the park, including park management and rangers, Scientific Services and Biodiversity Social Projects. Within a year, the team developed a sufficiently comprehensive inventory which captured the variation of wetlands present in Mountain Zebra National Park, South Africa. A total of 267 wetlands were mapped, while 62 were visited in the field and assessed through rapid verification. Careful collation of existing data and imagery informed a catchment approach, an emphasis on wetland-landscape connectivity, and strategic targeting of a sub-set of important and representative sites deserving of targeted, high effort field assessment. The remaining wetlands not visited in the field were subject to desktop review. Overall, this resulted in a comprehensive overview assessment of the entire Park at multiple scales. The approach of convening a multi-disciplinary team supports strengthened governance and integrated implementation of the findings into park planning, management and rehabilitation. The process provides a potential template for rolling out similar work in other parks and other areas that have limited funding and capacity.

Digital soil mapping in a low-relief landscape to support wetland restoration decisions

Efforts to utilize conventional soil maps in wetland conservation and restoration planning are often hampered by the coarse scale of the soil maps relative to the scale of restoration decisions, the spatial aggregation of soil components, and the difficulty in accounting for uncertainty in soil maps. The goal of this study was to explore the potential of digital soil mapping (DSM) techniques to improve identification of wetland soils and map soil properties on a low relief depressional wetland landscape. Separate random forests models were constructed to predict natural soil drainage and texture class on forest and cropland. The models were trained using soil profile data collected from local soil surveyors and previous research. Environmental covariates included topographic metrics developed from a 3 m lidar digital elevation model, and attributes derived from soil survey maps, the agricultural ditch network, and the National Wetlands Inventory. The resulting soil class probability maps demonstrated better representation of soil-landscape relationships on depressional wetlands on forest than on cropland; an independent field validation of soil maps resulted in greater than 70% accuracy in predicting natural soil drainage and texture class on forested depressions. DSM techniques can be used to generate maps with greater spatial detail than conventional soil maps in low relief depressional wetland landscapes; these maps and the attribute importance measures derived from the models have potential to improve watershed models and inform our understanding of wetland hydrology in these landscapes.

Isolating Anthropogenic Wetland Loss by Concurrently Tracking Inundation and Land Cover Disturbance across the Mid-Atlantic Region, U.S.

Global trends in wetland degradation and loss have created an urgency to monitor wetland extent, as well as track the distribution and causes of wetland loss. Satellite imagery can be used to monitor wetlands over time, but few efforts have attempted to distinguish anthropogenic wetland loss from climate-driven variability in wetland extent. We present an approach to concurrently track land cover disturbance and inundation extent across the Mid-Atlantic region, United States, using the Landsat archive in Google Earth Engine. Disturbance was identified as a change in greenness, using a harmonic linear regression approach, or as a change in growing season brightness. Inundation extent was mapped using a modified version of the U.S. Geological Survey’s Dynamic Surface Water Extent (DSWE) algorithm. Annual (2015–2018) disturbance averaged 0.32% (1095 km2 year−1) of the study area per year and was most common in forested areas. While inundation extent showed substantial interannual variability, the co-occurrence of disturbance and declines in inundation extent represented a minority of both change types, totaling 109 km2 over the four-year period, and 186 km2, using the National Wetland Inventory dataset in place of the Landsat-derived inundation extent. When the annual products were evaluated with permitted wetland and stream fill points, 95% of the fill points were detected, with most found by the disturbance product (89%) and fewer found by the inundation decline product (25%). The results suggest that mapping inundation alone is unlikely to be adequate to find and track anthropogenic wetland loss. Alternatively, remotely tracking both disturbance and inundation can potentially focus efforts to protect, manage, and restore wetlands.

The Second Generation Canadian Wetland Inventory Map at 10 Meters Resolution Using Google Earth Engine

Recently, there has been a significant increase in efforts to better inventory and manage important ecosystems across Canada using advanced remote sensing techniques. In this study, we improved the method and results of our first-generation Canadian wetland inventory map at 10-m resolution. Iin order to increase wetland classification accuracy, the main contributions of this new study are adding more training data to the classification process and training Random Forest (RF) models on the Google Earth Engine (GEE) platform within the boundaries of ecozones rather than provinces. A considerable effort has been devoted to data collection, preparation, standardization of datasets for each ecozone. The data cleaning reveals a data gap in several Northern ecozones. Accordingly, high-resolution optical data, from Worldview-2 and Pleiades, were acquired to delineate wetland training data based on visual interpretation in those regions. By using this well-distributed training data, this second generation wetland inventory map represents an improvement of 7% compared to the first generation map. Accuracy varied from 76% to 91% in different ecozones depending on available resources. Furthermore, the results of RF variable importance, which was carried out for each ecozone, demonstrate that and NDVI extracted from Sentinel-1 and Sentinel-2 data, respectively, were the most important features for wetland mapping.

Comparison of USACE Three-Factor Wetland Delineations to National Wetland Inventory Maps

Wetlands are mapped across the USA for compliance with §404 of the Clean Water Act using field-collected data and protocols in the 1987 Federal Wetlands Delineation Manual (3-factor method). The National Wetlands Inventory (NWI) maps wetlands and deepwater habitats for management and policy-making using aerial image analysis with limited field verification. There have been few comparisons of maps other than for limited geographic areas or wetland types. We compared 3-factor wetland delineations to NWI maps for 1751 assessment areas (AA) in different regions. We did not assess the accuracy of either product, but instead compared mapped area and polygon count for existing data at sites, then aggregated results to broader scales and compared with ancillary data to identify factors correlated with map differences. In a subset of NWI polygons eliminating non-wetland Cowardin types, 74% of NWI polygons were mapped in common with 3-factor polygons. NWI identified greater area in 33% of AA and greater total area across all sites. Approximately 27% of AA had 3-factor but no NWI polygons, while 6.7% of AA had features mapped only by NWI. Multiple factors likely contributed to differences including polygon size and temporal mismatches between maps, suggesting caution be used when comparing products.

Assessment of Wetland Change on the Delmarva Peninsula from 1984 to 2010

Stubbs, Q.; Yeo, I.-Y.; Lang, M.; Townshend, J.; Sun, L.; Prestegaard, K., and Jantz, C., 2020. Assessment of wetland change on the Delmarva Peninsula from 1984 to 2010. Journal of Coastal Research, 36(3), 575–589. Coconut Creek (Florida), ISSN 0749-0208.The decline in wetland extent and condition emphasizes the need for sound wetland restoration and conservation policies, which require baseline information on wetland status, change, and change drivers. Multiple wetland maps are available, but they can be quite inconsistent bcause of different input and generation techniques, dates, and objectives. Moderate-resolution (30 m2) regional land-cover data sets (LCDs) were analyzed to (1) quantify historical wetland changes on the Delmarva Peninsula at multiple spatial scales between 1984 and 2010, (2) identify differences in the spatial area of wetland change and discuss the source of and implications for these differences, and (3) investigate the extent to which drivers of wetland change can be identified using existing LCDs. The following regional LCDs were considered: the National Oceanic and Atmospheric Administration Coastal Change Analysis Program (C-CAP), the U.S. Geological Survey (USGS) Chesapeake Bay Land Cover Data Series (CBLCD), and the USGS National Land Cover Database. The C-CAP and CBLCD had the highest spatial agreement at 97%, and an average of 76% spatial agreement with the U.S. Fish and Wildlife Service National Wetland Inventory. The highest percentages of net wetland loss occurred between 1992 and 2001, whereas net wetland gain occurred between 2001 and 2010. Wetlands were predominantly converted (e.g., lost) to croplands/grass/shrubs (67%) and water (11%), which could be linked to drivers such as agriculture and sea-level rise.

The relationship between biodiversity and wetland cover varies across regions of the conterminous United States.

Identifying the factors that determine the spatial distribution of biodiversity is a major focus of ecological research. These factors vary with scale from interspecific interactions to global climatic cycles. Wetlands are important biodiversity hotspots and contributors of ecosystem services, but the association between proportional wetland cover and species richness has shown mixed results. It is not well known as to what extent there is a relationship between proportional wetland cover and species richness, especially at the sub-continental scale. We used the National Wetlands Inventory (NWI) to model wetland cover for the conterminous United States and the National Land Cover Database to estimate wetland change between 2001 and 2011. We used a Bayesian spatial Poisson model to estimate a spatially varying coefficient surface describing the effect of proportional wetland cover on the distribution of amphibians, birds, mammals, and reptiles and the cumulative distribution of terrestrial endemic species. Species richness and wetland cover were significantly correlated, and this relationship varied both spatially and by taxonomic group. Rather than a continental-scale association, however, we found that this relationship changed more closely among ecoregions. The species richness of each of the five groups was positively associated with wetland cover in some or all of the Great Plains; additionally, a positive association was found for mammals in the Southeastern Plains and Piedmont of the eastern U.S. Model results indicated negative association especially in the Cold Deserts and Northern Lakes & Forests of Minnesota and Wisconsin, though these varied greatly between groups. Our results highlight the need for wetland conservation initiatives that focus efforts at the level II and III ecoregional scale rather than along political boundaries.

Remote Sensing of Boreal Wetlands 1: Data Use for Policy and Management

Wetlands have and continue to undergo rapid environmental and anthropogenic modification and change to their extent, condition, and therefore, ecosystem services. In this first part of a two-part review, we provide decision-makers with an overview on the use of remote sensing technologies for the ‘wise use of wetlands’, following Ramsar Convention protocols. The objectives of this review are to provide: (1) a synthesis of the history of remote sensing of wetlands, (2) a feasibility study to quantify the accuracy of remotely sensed data products when compared with field data based on 286 comparisons found in the literature from 209 articles, (3) recommendations for best approaches based on case studies, and (4) a decision tree to assist users and policymakers at numerous governmental levels and industrial agencies to identify optimal remote sensing approaches based on needs, feasibility, and cost. We argue that in order for remote sensing approaches to be adopted by wetland scientists, land-use managers, and policymakers, there is a need for greater understanding of the use of remote sensing for wetland inventory, condition, and underlying processes at scales relevant for management and policy decisions. The literature review focuses on boreal wetlands primarily from a Canadian perspective, but the results are broadly applicable to policymakers and wetland scientists globally, providing knowledge on how to best incorporate remotely sensed data into their monitoring and measurement procedures. This is the first review quantifying the accuracy and feasibility of remotely sensed data and data combinations needed for monitoring and assessment. These include, baseline classification for wetland inventory, monitoring through time, and prediction of ecosystem processes from individual wetlands to a national scale.

Improving wetland ecosystem health in China

Abstract To counter widespread wetland loss and deterioration due to rapid socioeconomic development, China implemented a series of wetland conservation and restoration policies that have resulted in an increase in wetland area since 2000. Although wetland loss has been contained, changes in wetland ecosystem health are poorly quantified across China. We report on China’s first nationwide assessment of the spatial-temporal dynamics of wetland ecosystem health based on physical, biological, and chemical indicators from the first wetland inventory (1995–2003) to the second wetland inventory (2009–2013), and identify the potential effects of wetland ecosystem health changes to provide guidance for future wetland conservation management. We found that the wetland ecosystem health comprehensive index increased by 7.2%, indicating that China’s wetland conservation and restoration policies significantly promoted an improvement in wetland ecosystem health. These improvements were concentrated in the middle reaches of the Yangtze River and in the eastern and northern Qinghai-Tibetan Plateau. Deteriorations in wetland ecosystem health were focused in southern provinces and Heilongjiang, which were mainly attributed to population increase and conversion of wetland to cropland, respectively. Although wetland protection and restoration policies have improved wetland ecosystem health without affecting agricultural production, wetland conservation did not prevent the reduction in natural wetland area and the decline in biodiversity throughout China. This analysis demonstrated that a more proactive conservation strategy is required to greatly strengthen wetland conservation management, by expanding the wetland protected area network, strictly obeying wetland ecological redlines, strengthening degraded wetland restoration, and raising population awareness of wetland protection. This study provides a proactive wetland conservation strategy, and indicates that assessment of wetland ecosystem health changes and their driving factors could contribute to a better understanding and further coordination of the relationship between wetland ecological conditions and socioeconomic development.

Mapping Forested Wetland Inundation in the Delmarva Peninsula, USA Using Deep Convolutional Neural Networks

The Delmarva Peninsula in the eastern United States is partially characterized by thousands of small, forested, depressional wetlands that are highly sensitive to weather variability and climate change, but provide critical ecosystem services. Due to the relatively small size of these depressional wetlands and their occurrence under forest canopy cover, it is very challenging to map their inundation status based on existing remote sensing data and traditional classification approaches. In this study, we applied a state-of-the-art U-Net semantic segmentation network to map forested wetland inundation in the Delmarva area by integrating leaf-off WorldView-3 (WV3) multispectral data with fine spatial resolution light detection and ranging (lidar) intensity and topographic data, including a digital elevation model (DEM) and topographic wetness index (TWI). Wetland inundation labels generated from lidar intensity were used for model training and validation. The wetland inundation map results were also validated using field data, and compared to the U.S. Fish and Wildlife Service National Wetlands Inventory (NWI) geospatial dataset and a random forest output from a previous study. Our results demonstrate that our deep learning model can accurately determine inundation status with an overall accuracy of 95% (Kappa = 0.90) compared to field data and high overlap (IoU = 70%) with lidar intensity-derived inundation labels. The integration of topographic metrics in deep learning models can improve the classification accuracy for depressional wetlands. This study highlights the great potential of deep learning models to improve the accuracy of wetland inundation maps through use of high-resolution optical and lidar remote sensing datasets.

Mapping and Monitoring the Selected Wetlands of Punjab, India, Using Geospatial Techniques

Wetland inventories especially on their spatial extent are a prerequisite for management and conservation of any wetland. The advancement in geospatial techniques has offered a wide range of methodological applications to prepare the inventories and to understand the dynamics of wetlands. The freely available Landsat imagery has been widely used in extracting spatial and temporal information about wetlands. The literature suggests that wetland has declined all over the globe over the past few decades. This study aims to prepare land use/land cover information of three wetlands of Punjab (Harike, Ropar, and Nangal) through direct on screen digitization and through digital processing using automatic digital indices as well. Evaluation of the performance of two band indices, normalized difference water index (NDWI) and modified normalized difference water index (MNDWI) is also taken up in the present study. Landsat data of two periods-1990/91 and of 2018 are used for the study to perform two band indices. The result indicates that the NDWI and MNDWI are less time consuming and serve the purpose of mapping and monitoring wetlands with higher accuracy.

Extracting Impervious Surface from Aerial Imagery Using Semi-Automatic Sampling and Spectral Stability

The quantification of impervious surface through remote sensing provides critical information for urban planning and environmental management. The acquisition of quality reference data and the selection of effective predictor variables are two factors that contribute to the low accuracies of impervious surface in urban remote sensing. A hybrid method was developed to improve the extraction of impervious surface from high-resolution aerial imagery. This method integrates ancillary datasets from OpenStreetMap, National Wetland Inventory, and National Cropland Data to generate training and validation samples in a semi-automatic manner, significantly reducing the effort of visual interpretation and manual labeling. Satellite-derived surface reflectance stability is incorporated to improve the separation of impervious surface from other land cover classes. This method was applied to 1-m National Agriculture Imagery Program (NAIP) imagery of three sites with different levels of land development and data availability. Results indicate improved extractions of impervious surface with user’s accuracies ranging from 69% to 90% and producer’s accuracies from 88% to 95%. The results were compared to the 30-m percent impervious surface data of the National Land Cover Database, demonstrating the potential of this method to validate and complement satellite-derived medium-resolution datasets of urban land cover and land use.

Coastal Watershed Forested Wetland Change and Opportunities for Enhanced Collaboration with the Forestry Community

U.S. Fish and Wildlife Service National Wetlands Inventory (NWI) Wetlands Status and Trends reports have documented greater net wetland loss rates in U.S. coastal watersheds than in the rest of the contiguous U.S., and this loss rate has increased through time. Atlantic and Gulf of Mexico coastal watersheds contain 30% of the country’s forested wetlands. Although forested wetlands in this region have a long history of supplying substantial benefits to society, a portion of these habitats are currently being transformed into different land cover types – including both uplands and other types of wetlands. Drivers of forested wetland change in this region are complex, but some loss of area appears to be associated with plantation forestry. Improved understanding of this relationship is necessary to better support informed management of this critical resource. This will require close collaboration between the wetland and forestry communities. Unfortunately, communication between these two communities has been hampered by misconceptions about how wetland and forestry data are collected, and differences in the way key terms are defined. This article summarizes recent Wetlands Status and Trends data, seeks to clarify definitions and current misconceptions involving this dataset, and describes opportunities for future collaboration between the forestry and wetland communities.

Big Data for a Big Country: The First Generation of Canadian Wetland Inventory Map at a Spatial Resolution of 10-m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform

Detailed information on the spatial distribution of wetlands is crucial for sustainable management and resource assessment. Furthermore, regularly updated wetland inventories are of particular importance given that wetlands comprise a dynamic, rather than permanent, land condition. Accordingly, satellite-derived wetland maps are greatly beneficial, as they capture a synoptic and multi-temporal view of landscapes. Leveraging state-of-the-art remote sensing data and tools, this study produces a high-resolution 10-m wetland inventory map of Canada, covering an approximate area of one billion hectares, using multi-year, multi-source (Sentinel-1 and Sentinel-2) Earth Observation (EO) data on the Google Earth Engine™ cloud computing platform. The whole country is mapped using a large volume of reference samples using an object-based random forest classification scheme with an overall accuracy approaching 80% and individual accuracies varying from 74% to 84% in different provinces. This nationwide wetland inventory map illustrates that 19% of Canada’s land area is covered by wetlands, most of which are peatlands dominate in the northern ecozones. Importantly, the resulting ever-demanding wetland inventory map of Canada provides unprecedented details on the extent, status, and spatial distribution of wetlands and thus, is useful for many stakeholders, including federal and provincial governments, municipalities, NGOs, and environmental consultants.

Model Sensitivity to Topographic Uncertainty in Meso- and Microtidal Marshes

Light detection and ranging (Lidar) derived digital elevation models are widely used in modeling coastal marsh systems. However, the topographic error in these models can affect simulations of marsh coverage and characteristics. We investigated the relevance and impact of this error in micro- and mesotidal systems. Lidar-derived and RTK-adjusted topography were each used in a dynamic marsh model, and the resulting marsh coverages were examined. For two microtidal sites (Apalachicola, FL, USA, and Grand Bay, MS, USA) using solely lidar-derived topography, the model produced Cohen Kappa values of 0.1 for both estuaries when compared with National Wetland Inventory data indicating “very poor agreement.” Applying the RTK-adjusted topography improved the model marsh coverage results to “substantial agreement” with the values to 0.6 and 0.77, respectively. The mesotidal site in Plum Island, MA, USA, contained similar topographic errors, but the model produced a Cohen Kappa value of 0.73, which categorized it as “very good agreement” with no need for a further topographic adjustment given its present robust biomass productivity. The results demonstrate that marsh models are sensitive to topographic errors when the errors are comparable to the tidal range. The particular sensitivity of the modeling results to topographic error in microtidal systems highlights the need for close scrutiny of lidar-derived topography.

HIGH ALTITUDE WETLANDS IN ARUNACHAL PRADESH: A REVIEW ON ITS IMPORTANCE AND WAY FORWARD FOR CONSERVATION AND MANAGEMENT

Arunachal Pradesh (26° 40’- 29°27’N,91° 35’-97°24E), the land of the dawn-lit mountains is a region to the easternmost part of India. One of the most important biomes that this region has is the wetlands which if blessed with a healthy aquatic plants and animals. In a general term wetlands are water bodies which are inundated by large water body either permanently or seasonally. Arunachal Pradesh ranks second and has 2653 identified high altitude wetlands, after Jammu and Kashmir in number. Of all the wetlands, 1672 wetlands have more than 2.25 hectare of land areas according to National Wetland Inventory and Assessment, 2011. Wetlands are blessed with a sufficient area for flourishing high altitude with a splendid biodiversity of this part of region. In terms of ecological perspective these wetlands are very important and also from religious point of view. Many of the tribal communities residing here consider the wetland and lakes as sacred sites and are being used for different rituals. On the basis of altitude the wetlands of Arunachal Pradesh can be divided into High Altitude Wetlands and Low Altitude wetlands. In the recent times due to a high pressure for grazing grounds, exploitation of pristine forest resources, a high unregulated dumping of waste, soil and water pollution, different unauthorised construction for different developmental activities and settlement are some of the major threats being observed in the High Altitude Wetlands. An attempt will be made to identify high altitude lakes and wetlands in the state and its importance on the eastern Himalayan region. Moreover, special attention will be focused on conservation and management of high altitude wetlands from Govt side and NGOs.

Using Spatial Modeling to Improve Wetland Inventories in Great Smoky Mountains National Park

Wetlands are an ecologically important yet threatened plant community type in many natural areas. Inventory and monitoring are essential components of protecting, managing, and restoring wetland resources. However, many challenges currently exist for resource managers looking to perform a comprehensive inventory of wetlands throughout expansive, topographically complex, and/or remote natural areas. Here we present ongoing work in Great Smoky Mountains National Park (GRSM) that is addressing these challenges using spatial modeling to inform on-the-ground surveys. Wetland communities occur throughout GRSM at all elevations but tend to be relatively small and patchily distributed. While simple models based on low (<10%) slope are informative to wetland surveys at low elevations (<1067 m), such models become ineffective in steep topography at high elevations. To address this issue, we developed and implemented parkwide maximum entropy (Maxent) wetland habitat suitability models incorporating 24 environmental variables. Using these models to guide wetland survey efforts for three years, we identified and mapped 93 new wetland occurrences and more than doubled our previous high-elevation (≥1067 m) wetland inventory in roughly half the time. Maxent modeling is a relatively easy to use tool that can help resource managers plan, prioritize, and implement field inventories efficiently and effectively. We encourage natural areas managers to adopt similar spatial modeling approaches to guide the detection of unique, patchily distributed plant community types such as glades, barrens, balds, or rock outcrop communities.

Energetic Carrying Capacity of Submersed Aquatic Vegetation in Semi-Permanent Wetlands Important to Waterfowl in the Upper Midwest

Intensification of land use practices and climate change have resulted in extensive wetland loss and declines of native submersed aquatic vegetation (SAV) species across North America. Limited by a lack of biomass and energy estimates for wetlands containing SAV, conservation planners currently are unable to accurately account for its energetic contribution in bioenergetics models for waterfowl and other waterbirds. Therefore, we estimated energetic carrying capacity of 21 semi-permanent wetlands containing SAV and identified as important stopover locations for migrating waterfowl and other waterbirds in the Midwest, USA during 2015–2017. Energy density of SAV ( $$ \overline{x} $$ = 813 ± 257 EUD/ha) was generally less than managed emergent wetlands, varied by National Wetland Inventory class, and had large annual (98–4873 ΔEUD/ha) and spatial variation (8–7971 EUD/ha). We developed a visual rapid assessment index (R2m = 0.43) that may be useful to wetland managers or researchers to quickly index energy density from SAV in semi-permanent wetlands. Energetic carrying capacity of wetlands containing SAV will allow conservation planners to more precisely estimate energy supply on the landscape for waterfowl and evaluate trade-offs among alternative management strategies. Our results demonstrate negative effects of hydrologic connectivity on SAV communities in highly modified landscapes.

Modelling and Mapping Elusive Locations of Historic Water-Powered Grist Mills

Abstract. Grist mills are structures in which stone wheels are used to grind grain (e.g. corn, wheat) into a powder-like form for human consumption. Circular stone wheels provided the pressure to grind the grain and separate seed components. In most grist mills the energy for turning the heavy stones was derived from water power. The most visible part of some these mills was the externally mounted water wheel. However, other common configurations of the water ‘turbine’ internal to the structure were used (Figure 1). While grist mills existed in Europe at least as early as the 11th century such mills were not present in the United States until the last 1600s. No comprehensive census has been conducted for grist mills in the U.S. although an estimate of all water powered mills in eastern U.S. was 65,000 in 1840. Many of these water powered mills in the census estimate were for textile, sawmills, manufacturing, and other non-grain-grinding applications.Most often, the mill site included a small impoundment for creating a reliable source of water during low-flow stream conditions. These ponds were created in the late 1700s and especially in the 1800s as a reliable water source for turning grist or saw mills. By the middle of the 20th century the grist mills had all but vanished. Except for a few historic relics and conversion to touristic sites, the mill infrastructure has disappeared on the landscape while the mill ponds typically remain, serving other purposes, such as fishing or hunting lakes, or merely aesthetic environments.The local ecosystems around the mills sites have been artificially modified by the presence of these stream impoundments providing a new environment for fish, mammals, avian species, and of course, humans. While numerous positive ecosystem values may be observed from their continued presence, the risk of dam failure to both downstream systems and humans is substantial. In fact, such a catastrophic series of dam failures (51 in South Carolina) occurred in October of 2015 from a heavy rainfall event. In other instances the impoundments now store toxic sediment originating from sources farther upstream such as mining, military or industrial processes.Where are these historic mill sites and mill ponds? No inventory of mills or their ponds exist in South Carolina (and only for a few northeastern states). In this research we developed a systematic approach for identifying and mapping historic mill sites and ponds using a geohistorical framework and applied to South Carolina. The approach relied on 1) numerous geospatial sources, and 2) an analytical model of confidence mapping for predicting sites. The developed approach resulted in a new database of definitive and likely historic mill sites and mill pond locations throughout South Carolina. The sources of geospatial information was largely cartographic in nature including the following:USGS Historic Topographic Map SeriesRobert Mills 1820 atlas covering South CarolinaHistoric aerial photographyNational Hydrography Data (NHD)National Wetlands Inventory (NWI)National Inventory of DamsSC Department of Health and Environmental Control for Regulated DamsToponyms from the Geographic Names Information System (GNIS)U.S. Census TIGER street dataThe design for a predictive model (Figure 2) using diverse historic data with various scales and reliability is problematic. Most of the grist mills have disappeared from the landscape and do not exist in contemporary geospatial databases. Historic maps might include toponyms for the sites or ponds but their map projection, scale, and subsequent distortions in geometry are problematic. In addition, some of the sources for data are derived from a similar source and thus, are not statistically independent. The design of the analytical model for mapping confidence in mill site locations was calibrated and subsequently validated using several independent sources of information. Numerous field visits to known and suspected locations for mills sites were conducted throughout the physiographic regions of South Carolina.The model of likelihood included factors for the reliability of the source, the mapping scale and spatial accuracy of the source, correlation with other sources, and likelihood of correspondence with grist mills.