Efforts are increasingly being made to classify the world's wetland resources, an important ecosystem and habitat that is diminishing in abundance. There are multiple remote sensing classification ...methods, including a suite of nonparametric classifiers such as decision-tree (DT), rule-based (RB), and random forest (RF). High-resolution satellite imagery can provide more specificity to the classified end product, and ancillary data layers such as the Normalized Difference Vegetation Index, and hydrogeomorphic layers such as distance-to-a-stream can be coupled to improve overall accuracy (OA) in wetland studies. In this paper, we contrast three nonparametric machine-learning algorithms (DT, RB, and RF) using a large field-based dataset (
= 228) from the Selenga River Delta of Lake Baikal, Russia. We also explore the use of ancillary data layers selected to improve OA, with a goal of providing end users with a recommended classifier to use and the most parsimonious suite of input parameters for classifying wetland-dominated landscapes. Though all classifiers appeared suitable, the RF classification outperformed both the DT and RB methods, achieving OA >81%. Including a texture metric (homogeneity) substantially improved the classification OA. However, including vegetation/soil/water metrics (based on WorldView-2 band combinations), hydrogeomorphic data layers, and elevation data layers to increase the descriptive content of the input parameters surprisingly did not markedly improve the OA. We conclude that, in most cases, RF should be the classifier of choice. The potential exception to this recommendation is under the circumstance where the end user requires narrative rules to best manage his or her resource. Though not useful in this study, continuously increasing satellite imagery resolution and band availability suggests the inclusion of ancillary contextual data layers such as soil metrics or elevation data, the granularity of which may define its utility in subsequent wetland classifications.
•We provide insights on hydrologic model calibration using remotely sensed ET data.•The commonly followed lumped approach to use ET data produces an equifinal model.•We propose a spatially explicit ...approach that noticeably improves model performance.•Including the often disregarded biophysical parameters can influence model accuracy.•We demonstrate how the model state shifts within the continuum of equifinality.
A hydrologic model, calibrated using only streamflow data, can produce acceptable streamflow simulation at the watershed outlet yet unrealistic representations of water balance across the landscape. Recent studies have demonstrated the potential of multi-objective calibration using remotely sensed evapotranspiration (ET) and gaged streamflow data to spatially improve the water balance. However, methodological clarity on how to “best” integrate ET data and model parameters in multi-objective model calibration to improve simulations is lacking. To address these limitations, we assessed how a spatially explicit, distributed calibration approach that uses (1) remotely sensed ET data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and (2) frequently overlooked biophysical parameters can improve the overall predictability of two key components of the water balance: streamflow and ET at different locations throughout the watershed. We used the Soil and Water Assessment Tool (SWAT), previously modified to represent hydrologic transport and filling-spilling of landscape depressions, in a large watershed of the Prairie Pothole Region, United States. We employed a novel stepwise series of calibration experiments to isolate the effects (on streamflow and simulated ET) of integrating biophysical parameters and spatially explicit remotely sensed ET data into model calibration. Results suggest that the inclusion of biophysical parameters involving vegetation dynamics and energy utilization mechanisms tend to increase model accuracy. Furthermore, we found that using a lumped, versus a spatially explicit, approach for integrating ET into model calibration produces a sub-optimal model state with no potential improvement in model performance across large spatial scales. However, when we utilized the same MODIS ET datasets but calibrated each sub-basin in the spatially explicit approach, water yield prediction uncertainty decreased, including a distinct improvement in the temporal and spatial accuracy of simulated ET and streamflow. This further resulted in a more realistic simulation of vegetation growth when compared to MODIS Leaf-Area Index data. These findings afford critical insights into the efficient integration of remotely sensed “big data” into hydrologic modeling and associated watershed management decisions. Our approach can be generalized and potentially replicated using other hydrologic models and remotely sensed data resources – and in different geophysical settings of the globe.
In traditional watershed delineation and topographic modeling, surface depressions are generally treated as spurious features and simply removed from a digital elevation model (DEM) to enforce flow ...continuity of water across the topographic surface to the watershed outlets. In reality, however, many depressions in the DEM are actual wetland landscape features with seasonal to permanent inundation patterning characterized by nested hierarchical structures and dynamic filling-spilling-merging surface-water hydrological processes. Differentiating and appropriately processing such ecohydrologically meaningful features remains a major technical terrain-processing challenge, particularly as highresolution spatial data are increasingly used to support modeling and geographic analysis needs. The objectives of this study were to delineate hierarchical wetland catchments and model their hydrologic connectivity using high-resolution lidar data and aerial imagery. The graph-theory-based contour tree method was used to delineate the hierarchical wetland catchments and characterize their geometric and topological properties. Potential hydrologic connectivity between wetlands and streams were simulated using the least-cost-path algorithm. The resulting flow network delineated potential flow paths connecting wetland depressions to each other or to the river network on scales finer than those available through the National Hydrography Dataset. The results demonstrated that our proposed framework is promising for improving overland flow simulation and hydrologic connectivity analysis.
Surface water storage in small yet abundant landscape depressions—including wetlands and other small waterbodies—is largely disregarded in conventional hydrologic modeling practices. No quantitative ...evidence exists of how their exclusion may lead to potentially inaccurate model projections and understanding of hydrologic dynamics across the world's major river basins. To fill this knowledge gap, we developed the first‐ever major river basin‐scale modeling approach integrating surface depressions and focusing on the 450,000‐km2 Upper Mississippi River Basin (UMRB) in the United States. We applied a novel topography‐based algorithm to estimate areas and volumes of ~455,000 surface depressions (>1 ha) across the UMRB (in addition to lakes and reservoirs) and subsequently aggregated their effects per subbasin. Compared to a “no depression” conventional model, our depression‐integrated model (a) improved streamflow simulation accuracy with increasing upstream abundance of depression storage, (b) significantly altered the spatial patterns and magnitudes of water yields across 315,000 km2 (70%) of the basin area, and (c) provided realistic spatial distributions of rootzone wetness conditions corresponding to satellite‐based data. Results further suggest that storage capacity (i.e., volume) alone does not fully explain depressions' cumulative effects on landscape hydrologic responses. Local (i.e., subbasin level) climatic and geophysical drivers and downstream flowpath‐regulating structures (e.g., reservoirs and dams) influence the extent to which depression storage volume in a subbasin causes hydrologic effects. With these new insights, our study supports the integration of surface depression storage and thereby catalyzes a reassessment of current hydrological modeling and management practices for basin‐scale studies.
Key Points
We present the first hydrologic model incorporating surface depression and wetland water storage in one of the world's major river basins
Surface depressions, often overlooked in conventional large‐scale hydrologic modeling, significantly alter landscape water yields
Cumulative hydrologic effects of surface depression storage on downstream waters depend on a combination of local drivers
The Prairie Pothole Region of North America is characterized by numerous, small, wetland depressions that perform important ecological and hydrological functions. Recent studies have shown that total ...wetland area in the region is decreasing due to cumulative impacts related to natural and anthropogenic changes. The impact of wetland losses on landscape hydrology is an active area of research and management. Various spatially distributed hydrologic models have been developed to simulate effects of wetland depression storage on peak river flows, frequently using dated geospatial wetland inventories. We describe an innovative method for identifying wetland depressions and quantifying their nested hierarchical bathymetric/topographic structure using high-resolution light detection and ranging (LiDAR) data. This contour tree method allows identified wetland depressions to be quantified based on their dynamic filling-spilling-merging hydrological processes. In addition, wetland depression properties, such as surface area, maximum depth, mean depth, storage volume, etc., can be computed for each component of a depression as well as the compound depression. We successfully applied the proposed method to map wetland depressions in the Little Pipestem Creek watershed in North Dakota. The methods described in this study will provide more realistic and higher resolution data layers for hydrologic modeling and other studies requiring characterization of simple and complex wetland depressions, and help prioritize conservation planning efforts for wetland resources.
Fragaria × ananassa (common name: strawberry) is a globally cultivated hybrid species belonging to Rosaceae family. Colletotrichum acutatum sensu lato (s.l.) is considered to be the second most ...economically important pathogen worldwide affecting strawberries. A collection of 148 Colletotrichum spp. isolates including 67 C. acutatum s.l. isolates associated with the phytosanitary history of UK strawberry production were used to characterize multi-locus genetic variation of this pathogen in the UK, relative to additional reference isolates that represent a worldwide sampling of the diversity of the fungus. The evidence indicates that three different species C. nymphaeae, C. godetiae and C. fioriniae are associated with strawberry production in the UK, which correspond to previously designated genetic groups A2, A4 and A3, respectively. Among these species, 12 distinct haplotypes were identified suggesting multiple introductions into the country. A subset of isolates was also used to compare aggressiveness in causing disease on strawberry plants and fruits. Isolates belonging to C. nymphaeae, C. godetiae and C. fioriniae representative of the UK anthracnose pathogen populations showed variation in their aggressiveness. Among the three species, C. nymphaeae and C. fioriniae appeared to be more aggressive compared to C. godetiae. This study highlights the genetic and pathogenic heterogeneity of the C. acutatum s.l. populations introduced into the UK linked to strawberry production.
Geographically isolated wetlands (GIWs) are wetlands completely surrounded by uplands. While common throughout the United States (U.S.), there have heretofore been no nationally available, spatially ...explicit estimates of GIW extent, complicating efforts to understand the myriad biogeochemical, hydrological, and habitat functions of GIWs and hampering conservation and management efforts at local, state, and national scales. We used a 10‐m geospatial buffer as a proxy for hydrological or ecological connectivity of National Wetlands Inventory palustrine and lacustrine wetland systems to nationally mapped and available stream, river, and lake data. We identified over 8.3 million putative GIWs across the conterminous U.S., encompassing nearly 6.5 million hectares of wetland resources (average size 0.79 ± 4.81 ha, median size 0.19 ha). Putative GIWs thus represent approximately 16% of the freshwater wetlands of the conterminous U.S. The water regime for the majority of the putative GIWs was temporarily or seasonally flooded, suggesting a vulnerability to ditching or hydrologic ion, sedimentation, or alterations in precipitation patterns. Additional analytical applications of this readily available geospatially explicit mapping product (e.g., hydrological modeling, amphibian metapopulation, or landscape ecological analyses) will improve our understanding of the abundance and extent, effect, connectivity, and relative importance of GIWs to other aquatic systems of the conterminous U.S.
Geographically isolated wetlands (GIWs), those surrounded by uplands, exchange materials, energy, and organisms with other elements in hydrological and habitat networks, contributing to landscape ...functions, such as flow generation, nutrient and sediment retention, and biodiversity support. GIWs constitute most of the wetlands in many North American landscapes, provide a disproportionately large fraction of wetland edges where many functions are enhanced, and form complexes with other water bodies to create spatial and temporal heterogeneity in the timing, flow paths, and magnitude of network connectivity. These attributes signal a critical role for GIWs in sustaining a portfolio of landscape functions, but legal protections remain weak despite preferential loss from many landscapes. GIWs lack persistent surface water connections, but this condition does not imply the absence of hydrological, biogeochemical, and biological exchanges with nearby and downstream waters. Although hydrological and biogeochemical connectivity is often episodic or slow (e.g., via groundwater), hydrologic continuity and limited evaporative solute enrichment suggest both flow generation and solute and sediment retention. Similarly, whereas biological connectivity usually requires overland dispersal, numerous organisms, including many rare or threatened species, use both GIWs and downstream waters at different times or life stages, suggesting that GIWs are critical elements of landscape habitat mosaics. Indeed, weaker hydrologic connectivity with downstream waters and constrained biological connectivity with other landscape elements are precisely what enhances some GIW functions and enables others. Based on analysis of wetland geography and synthesis of wetland functions, we argue that sustaining landscape functions requires conserving the entire continuum of wetland connectivity, including GIWs.
The Prairie Pothole Region of North America is characterized by millions of depressional wetlands, which provide critical habitats for globally significant populations of migratory waterfowl and ...other wildlife species. Due to their relatively small size and shallow depth, these wetlands are highly sensitive to climate variability and anthropogenic changes, exhibiting inter- and intra-annual inundation dynamics. Moderate-resolution satellite imagery (e.g., Landsat, Sentinel) alone cannot be used to effectively delineate these small depressional wetlands. By integrating fine spatial resolution Light Detection and Ranging (LiDAR) data and multi-temporal (2009–2017) aerial images, we developed a fully automated approach to delineate wetland inundation extent at watershed scales using Google Earth Engine. Machine learning algorithms were used to classify aerial imagery with additional spectral indices to extract potential wetland inundation areas, which were further refined using LiDAR-derived landform depressions. The wetland delineation results were then compared to the U.S. Fish and Wildlife Service National Wetlands Inventory (NWI) geospatial dataset and existing global-scale surface water products to evaluate the performance of the proposed method. We tested the workflow on 26 watersheds with a total area of 16,576 km2 in the Prairie Pothole Region. The results showed that the proposed method can not only delineate current wetland inundation status but also demonstrate wetland hydrological dynamics, such as wetland coalescence through fill-spill hydrological processes. Our automated algorithm provides a practical, reproducible, and scalable framework, which can be easily adapted to delineate wetland inundation dynamics at broad geographic scales.
•A fully automated algorithm was developed to map wetland inundation dynamics.•Multiple wetland inundation maps (1-m) were produced for the Prairie Pothole Region.•Mapped wetlands show high accuracy when compared to existing surface water products.•The algorithm is scalable for mapping wetland inundation at large geographic scales.
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