Watershed-based Approach Research Articles

Defining nutrient ecoregions for reference nitrogen and phosphorus concentrations in rivers from the major South American biomes

Delineating reference (i.e., baseline) riverine nutrient concentrations is essential to understand fundamental processes of biogeochemical transport from continents to the ocean, describe ecological conditions, and inform managers of best attainable conditions when attempting to control anthropogenic eutrophication. We used data from 434 Brazilian watersheds representative of major South American biomes covering over half the continental area, to estimate nutrient levels expected prior to anthropogenic development. We used a novel watershed-based approach to describe spatial patterns throughout Brazil and for the entire Amazon basin. This approach considered nitrogen (N) and phosphorus (P) independently and allowed removal of anthropogenic influences. The approach was useful where there were few unimpacted watersheds and low levels of urbanization had strong effects. We found reference total N concentrations were most closely related to biome, whereas total P levels related to percentage sand in soils in addition to climatic features influencing biomes. There was a wide range of N:P at this coarse level, suggesting P or co-limitation could occur in streams; many areas have intrinsically high background P and relatively low N, suggesting N-limitation of freshwaters could be widespread in South America, favoring nitrogen-fixing cyanobacterial blooms. We provide unique broad-scale analyses of spatial distribution of baseline nutrient levels for tropical and subtropical watersheds across continental scales.

A pathomic approach for tumor-infiltrating lymphocytes classification on breast cancer digital pathology images

Background and objectivesThe detection of tumor-infiltrating lymphocytes (TILs) could aid in the development of objective measures of the infiltration grade and can support decision-making in breast cancer (BC). However, manual quantification of TILs in BC histopathological whole slide images (WSI) is currently based on a visual assessment, thus resulting not standardized, not reproducible, and time-consuming for pathologists. In this work, a novel pathomic approach, aimed to apply high-throughput image feature extraction techniques to analyze the microscopic patterns in WSI, is proposed. In fact, pathomic features provide additional information concerning the underlying biological processes compared to the WSI visual interpretation, thus providing more easily interpretable and explainable results than the most frequently investigated Deep Learning based methods in the literature. MethodsA dataset containing 1037 regions of interest with tissue compartments and TILs annotated on 195 TNBC and HER2+ BC hematoxylin and eosin (H&E)-stained WSI was used. After segmenting nuclei within tumor-associated stroma using a watershed-based approach, 71 pathomic features were extracted from each nucleus and reduced using a Spearman's correlation filter followed by a nonparametric Wilcoxon rank-sum test and least absolute shrinkage and selection operator. The relevant features were used to classify each candidate nucleus as either TILs or non-TILs using 5 multivariable machine learning classification models trained using 5-fold cross-validation (1) without resampling, (2) with the synthetic minority over-sampling technique and (3) with downsampling. The prediction performance of the models was assessed using ROC curves. Results21 features were selected, with most of them related to the well-known TILs properties of having regular shape, clearer margins, high peak intensity, more homogeneous enhancement and different textural pattern than other cells. The best performance was obtained by Random-Forest with ROC AUC of 0.86, regardless of resampling technique. ConclusionsThe presented approach holds promise for the classification of TILs in BC H&E-stained WSI and could provide support to pathologists for a reliable, rapid and interpretable clinical assessment of TILs in BC.

Pore-network extraction using discrete Morse theory: Preserving the topology of the pore space.

Pore-scale modeling based on the 3D structural information of porous materials has enormous potential in assessing physical properties beyond the capabilities of laboratory methods. Such capabilities are pricey in terms of computational expenses, and this limits the applicability of the direct simulations to a small volume and requires high-performance computational resources, especially for multiphase flow simulations. The only pore-scale technique capable of dealing with large representative volumes of porous samples is pore-network (PNM) based modeling. The problem of the PNM approach is that 3D pore geometry first needs to be simplified into a graph of pores and throats that conserve topological and geometrical properties of the original 3D image. While significant progress has been achieved in terms of geometry representation, no methodology provides full conservation of the topological features of the pore structure. In this paper we present a pore-network extraction algorithm for binary 3D images based on discrete Morse theory and persistent homology that by design targets topology preservation. In addition to methodological developments, we also clarify the relationship between topological characteristics of constructed Morse chain complex and pore-network elements. We show that the Euler numbers calculated for PNMs based on our methodology coincide with those obtained using the direct topological analysis. The characteristics of the extracted pore network are calculated for several 3D porous binary images and compared with the results of maximum inscribed balls-based and watershed-based approaches as well as a hybrid approach to support our methodology.

Pore Segmentation Techniques for Low-Resolution Data: Application to the Neutron Tomography Data of Cement Materials.

The development of neutron imaging facilities provides a growing range of applications in different research fields. The significance of the obtained structural information, among others, depends on the reliability of phase segmentation. We focused on the problem of pore segmentation in low-resolution images and tomography data, taking into consideration possible image corruption in the neutron tomography experiment. Two pore segmentation techniques are proposed. They are the binarization of the enhanced contrast data using the global threshold, and the segmentation using the modified watershed technique—local threshold by watershed. The proposed techniques were compared with a conventional marker-based watershed on the test images simulating low-quality tomography data and on the neutron tomography data of the samples of magnesium potassium phosphate cement (MKP). The obtained results demonstrate the advantages of the proposed techniques over the conventional watershed-based approach.

An Unsupervised Fuzzy Clustering Approach for Early Screening of COVID-19 From Radiological Images.

A global pandemic scenario is witnessed worldwide owing to the menace of the rapid outbreak of the deadly COVID-19 virus. To save mankind from this apocalyptic onslaught, it is essential to curb the fast spreading of this dreadful virus. Moreover, the absence of specialized drugs has made the scenario even more badly and thus an early-stage adoption of necessary precautionary measures would provide requisite supportive treatment for its prevention. The prime objective of this article is to use radiological images as a tool to help in early diagnosis. The interval type 2 fuzzy clustering is blended with the concept of superpixels, and metaheuristics to efficiently segment the radiological images. Despite noise sensitivity of watershed-based approach, it is adopted for superpixel computation owing to its simplicity where the noise problem is handled by the important edge information of the gradient image is preserved with the help of morphological opening and closing based reconstruction operations. The traditional objective function of the fuzzy c-means clustering algorithm is modified to incorporate the spatial information from the neighboring superpixel-based local window. The computational overhead associated with the processing of a huge amount of spatial information is reduced by incorporating the concept of superpixels and the optimal clusters are determined by a modified version of the flower pollination algorithm. Although the proposed approach performs well but should not be considered as an alternative to gold standard detection tests of COVID-19. Experimental results are found to be promising enough to deploy this approach for real-life applications.

A deep learning-integrated phenotyping pipeline for vascular bundle phenotypes and its application in evaluating sap flow in the maize stem

Plant vascular bundles are responsible for water and material transportation, and their quantitative and functional evaluation is desirable in plant research. At the single-plant level, the number, size, and distribution of vascular bundles vary widely, posing a challenge to automatically and accurately identifying and quantifying them. In this study, a deep learning-integrated phenotyping pipeline was developed to robustly and accurately detect vascular bundles in Computed Tomography (CT) images of stem internodes. Two semantic indicators were used to evaluate and identify a suitable feature extraction network for semantic segmentation models. The epidermis thickness of maize stem was evaluated for the first time and adjacent vascular bundles were improved using an adaptive watershed-based approach. The counting accuracy (R2) of vascular bundles was 0.997 for all types of stem internodes, and the measured accuracy of size traits was over 0.98. Combining sap flow experiments, multiscale traits of vascular bundles were evaluated at the single-plant level, which provided an insight into the water use efficiency of the maize plant.

Mapping of Water–Energy–Food (WEF) Hotspots in an Urban Landscape: A Watershed-Based Approach

Mapping of Water–Energy–Food (WEF) Hotspots in an Urban Landscape: A Watershed-Based Approach

Watershed-based policy integration approach to constructing territorial rainstorm flood safety pattern

Watershed-based policy integration approach to constructing territorial rainstorm flood safety pattern

Variability of precipitation areal reduction factors in the conterminous United States

Many hydrologic and hydraulic (H&H) engineering applications require spatial rainfall distribution over a watershed, but point precipitation frequency estimates, such as those provided by NOAA Atlas 14, are only applicable for relatively small areas. For larger areas, areal reduction factors (ARFs) are commonly used to transform a point precipitation frequency estimate of a given duration and frequency to a corresponding areal estimate. The most common source of ARFs for the United States is Technical Paper 29 (TP-29), published in 1958, although there have been significant increases in record length and types of available data and several new methods for computing ARFs have been proposed over the last several decades. This study applied up-to-date precipitation data products and analysis methods with a watershed-based approach to investigate factors that affect ARF variabilities, and to compare ARFs across multiple US hydrologic regions. Our overall findings are in line with other recent studies showing that ARFs decrease with increasing area, increase with increasing duration, and decrease with increasing return period. In particular, we found a strong geographical variability across different US hydrologic regions, suggesting that ARF are specific to regional climate patterns and geographical characteristics and should not be applied arbitrarily to other locations. The results also reveal the importance of record length, especially for long return period ARFs. The study demonstrates the need to improve ARFs with new data and methods to support more reliable areal precipitation frequency estimates for H&H applications.

Individual tree crown detection and delineation across a woodland using leaf-on and leaf-off imagery from a UAV consumer-grade camera

Extraction of information about individual trees using remotely sensed data is essential to supporting ecological and commercial applications in forest environments. Data acquired by consumer-grade cameras onboard unmanned aerial vehicles (UAV) offer an affordable option of high-spatial resolution imagery that can be used to extract forest structural information at a tree level. The aim of this work is to investigate the potential and accuracy of UAV time-series data to automatically detect and delineate tree crowns across an entire woodland. The workflow (presented in a step-by-step manner) involves the construction of a canopy height model (CHM) based on digital elevation models derived from the UAV photogrammetric point clouds. A watershed-based approach is modified to automatically detect and delineate the tree crowns, based on the CHM and the brightness information from the UAV orthomosaics. The accuracy of the proposed method was evaluated by comparing its results against manually delineated tree crowns. The results show an overall accuracy of 63%, where conifer species were more accurately delineated (up to 80%), while broadleaf species returned lower accuracies (<50 % ). Continued research is necessary to improve the confidence of automated individual tree crown detection and delineation, especially over complex forest structures.

Improving watershed-based pore-network extraction method using maximum inscribed ball pore-body positioning

Pore-scale modeling is a rapidly evolving area of research because modeling multiphase flow directly on 3D pore geometries is of utmost importance in wide variety of research areas, including oil and gas development, hydrology and material sciences. Although there are numerous methods to model flow, only so-called pore-network models are computationally effective enough to perform simulations in large modeling domains, and they are orders of magnitude faster than direct modeling approaches. However, pore-network models require a simplification of the 3D pore geometry to perform simulations, which are usually referred to as pore-network extraction. Such extraction poses a separate problem because it must provide an accurate description of the pore space geometry and topology. Different methods have been proposed in the literature. Recently, watershed-based approaches have been popular due to their effectiveness in working with porous media images of any porosity. A watershed algorithm requires seed placement to segment the space into distinct pores. We propose a hybrid algorithm combining the power of watersheds in finding intersections between pores with the advantages of the maximum inscribed ball technique, which is very effective in finding pore centers. We rigorously verify and test our novel methodology on artificial and X-ray microtomography images of wide variety of porous materials: sphere packings, carbonate, soil, ceramic and sandstone samples. Comparison against a purely watershed-based method and results based solely on the maximum inscribed balls–based method (in terms of pore/throat total number, pore size distributions and connection statistics, and multiphase flow properties including capillary curves and relative permeabilities) revealed the accuracy of our novel technique, consistency with existing classical techniques and great potential in analysing 3D pore images of any complexity. On the other hand, comparison of extracted pore-network topology (as based on Euler number) revealed significant differences between different methodologies, which is rather surprising considering the similarities in two-phase flow properties. While analysing permeability results, we also compared two popular pore-throat partitioning models and advocated in favor of the weight model usually utilized within watershed-based extracted pore networks. Our results illuminate problems in current pore-network models and outline some potential ways to improve their accuracy in future research.

A Snapshot of Coal Mine Drainage Discharge Limits for Conductivity, Sulfate, and Manganese across the Developed World

Coal mine drainage (CMD) negatively affects aquatic ecosystems around the world. This article presents the current state of regulatory guidelines and discharge limits established for selected parameters of concern in CMD: specific conductance (SC), SO42−, and Mn. Though not exhaustive, this review provides a representative snapshot of the existing regulatory status in developed nations of North America, Australia, Asia, Europe, and Africa. It was found that most of the nations surveyed have a separate approach to regulating CMD apart from other water quality issues, varying from national or regional guidelines and standards to individual site licenses or permits. In addition, there is broad variability between nations and regions. The selected parameters have a range of required discharge concentrations and in the case of Mn, a variety of categories (i.e. dissolved, total, bioavailable). A few countries use toxicity testing to assist in determining discharge limits, while others take a watershed-based approach to determine mass loading of pollutants, considering the status of the receiving water body and dilution effects. Overall, the myriad approaches encountered underscores the complex and dynamic scientific, ecosystemic, and political landscape in which these regulatory systems have evolved.

Left ventricular segmentation based on a parallel watershed transformation towards an accurate heart function evaluation

Magnetic resonance imaging (MRI) has emerged as the golden reference for cardiac examination. This modality allows the assessment of human cardiovascular morphology, functioning, and perfusion. Although a couple of challenging issues, such as the cardiac magnetic resonance (MR) image's features and the large variability of images among several patients, still influences the cardiac cavities’ segmentation and needs to be carried out. In this study, the authors have profoundly reviewed and fully compared semi-automated segmentation methods performed on cardiac cine-MR short-axis images for the evaluation of the left ventricular functions. However, the number of parameters handled by the synthesised works is limited if not null. For the sake of ensuring the highest coverage of the left ventricle parameters computing, they have introduced a parallel watershed-based approach to segment the left ventricular allowing hence the computation of six parameters (end-diastolic volume, end-systolic volume, ejection fraction, cardiac output, stroke volume, and left ventricular mass). An algorithm is associated with the main considered measurements. The experimental results that were obtained through studying 20 patients’ MRI data base demonstrate their approach's accuracy in estimating real values of the parameters’ set thanks to a faithful segmentation of the myocardium.

Land-Use/Land-Cover Change Dynamics and Groundwater Quality in and around Shrimp Farming Area in Coastal Watershed, Cuddalore District, Tamil Nadu, India

The present study was envisaged mainly to ascertain the influence of aquaculture on salinization of coastal groundwater resources in the Cuddalore district of Tamil Nadu located between 11°30'N–11°20'N and 79°38'E–79°48'E. Watershed-based multidisciplinary approach combining GIS and Remote Sensing, and hydro-geochemistry has been applied. The land-use study revealed that though aquaculture was initiated after 1991, the groundwater quality in some locations showed elevated total dissolved solids and electrical conductivity content during that time itself, supporting the fact that aquaculture has been initiated in in situ saline area. Land-use change dynamics showed no defined relationship between area of culture and the groundwater quality, indicating that there was no salinity build-up due to shrimp farming. Besides major chemical compositions, the hydro-geochemical analysis using Chadha’s plot suggests that reverse ion exchange is dominant in the study area due to the natural geological condition and it controls the groundwater quality rather than sea water incursion to a large extent. Thus these analyses clearly bring out the fact that shrimp farming is not the main reason for the source of salinity in the study area.

Leaf Segmentation and Tracking in Arabidopsis thaliana Combined to an Organ-Scale Plant Model for Genotypic Differentiation.

A promising method for characterizing the phenotype of a plant as an interaction between its genotype and its environment is to use refined organ-scale plant growth models that use the observation of architectural traits, such as leaf area, containing a lot of information on the whole history of the functioning of the plant. The Phenoscope, a high-throughput automated platform, allowed the acquisition of zenithal images of Arabidopsis thaliana over twenty one days for 4 different genotypes. A novel image processing algorithm involving both segmentation and tracking of the plant leaves allows to extract areas of the latter. First, all the images in the series are segmented independently using a watershed-based approach. A second step based on ellipsoid-shaped leaves is then applied on the segments found to refine the segmentation. Taking into account all the segments at every time, the whole history of each leaf is reconstructed by choosing recursively through time the most probable segment achieving the best score, computed using some characteristics of the segment such as its orientation, its distance to the plant mass center and its area. These results are compared to manually extracted segments, showing a very good accordance in leaf rank and that they therefore provide low-biased data in large quantity for leaf areas. Such data can therefore be exploited to design an organ-scale plant model adapted from the existing GreenLab model for A. thaliana and subsequently parameterize it. This calibration of the model parameters should pave the way for differentiation between the Arabidopsis genotypes.

A Watershed-Based Approach to Link Watershed Health with Effective Riparian Planting and Stream Restoration using Emergent Technologies at Clean Water Services

A Watershed-Based Approach to Link Watershed Health with Effective Riparian Planting and Stream Restoration using Emergent Technologies at Clean Water Services

Groundwater loading of nitrate-nitrogen and phosphorus from watershed source areas to an Iowa Great Lake

Abstract Groundwater discharge to a lake can be an important component to water and nutrient budgets. In this study, we evaluated groundwater loading of nitrate-nitrogen (NO 3 -N) and phosphorus (P) to West Lake Okoboji, Iowa, using a watershed-based approach based on groundwater recharge and land cover class. Our objectives were to assess groundwater level fluctuations and nutrient concentrations under representative land use classes and develop an allocation model for groundwater nutrient loads based on land cover class. Monitoring wells were installed at 21 locations around the lake and sampled during a three-year study period. Groundwater quality varied among the land cover types with average NO 3 -N concentrations the highest beneath cropped fields (8.8 mg l − 1 ) and residential areas (2 mg l − 1 ), and P concentrations ranging between 0.05 and 0.1 mg l − 1 throughout the region. NO 3 -N loads were the highest under cropped fields and this source accounted for approximately 90% of the NO 3 -N, whereas P loads were more evenly distributed among source areas. Groundwater recharge averaged approximately 76 mm year − 1 for vegetated areas and substantially less for urban areas. Based on mass balance, groundwater discharge may account for 80% of the NO 3 -N in the lake compared to 10% of the P. Results are instructive to more effectively target implementation of conservation practices to major nutrient loading areas for reduction of NO 3 -N and P delivered to the lake.

Urbanization and watershed sustainability: Collaborative simulation modeling of future development states

Summary Urbanization has a significant impact on water resources and requires a watershed-based approach to evaluate impacts of land use and urban development on watershed processes. This study uses a simulation with urban policy scenarios to model and strategize transferable recommendations for municipalities and cities to guide urban decisions using watershed ecohydrologic principles. The watershed simulation model is used to evaluation intensive (policy in existing built regions) and extensive (policy outside existing build regions) urban development scenarios with and without implementation of Best Management practices (BMPs). Water quantity and quality changes are simulated to assess effectiveness of five urban development scenarios. It is observed that optimal combination of intensive and extensive strategies can be used to sustain urban ecosystems. BMPs are found critical to reduce storm water and water quality impacts on urban development. Conservation zoning and incentives for voluntary adoption of BMPs can be used in sustaining urbanizing watersheds.

Maintaining a Healthy Water Supply While Growing a Healthy Food Supply: Legal Tools for Cleaning Up Agricultural Water Pollution

Although agriculture is one of the most significant and pernicious causes of water pollution in the U.S., federal environmental laws designed to protect water resources exclude or exempt most agricultural activities. State efforts to address water quality impacts from agriculture have met with little success. The challenge of finding a way to reduce agricultural water pollution without causing severe economic harm to farmers is one of the greatest environmental challenges of our time. Large scale industrialized agriculture, with its heavy reliance on fertilizer, pesticide and water inputs, is a major contributor to water pollution. Agricultural practices can cause serious adverse impacts to the quality of both groundwater and surfacewater. Rain or irrigation water that falls on farm fields, picks up water soluble pesticides such as atrazine and nutrients such as nitrites found in fertilizers, causing them to leach into groundwater. Rain and irrigation water that is not absorbed into the soil runs off of agricultural fields carrying with it a variety of pollutants which ultimately end up in surface water bodies. Run-off from farm fields frequently contains high levels of sediments from soil erosion from tilled fields, pesticides and fertilizers. Pesticides that enter waterbodies can adversely impact aquatic life. Nutrients from fertilizers can cause waterbodies to be hypereutrophied, which can severely impact submersed plants and aquatic animals. Run-off of sediments from soil erosion due to tilling can clog streams and fill in shallow areas in water bodies, thereby reducing habitat and light availability to submersed plants. A report by the National Water Quality Inventory identified agricultural nonpoint source (NPS) pollution as “the leading source of water quality impacts on surveyed rivers and lakes, the second largest source of impairments to wetlands, and a major contributor to contamination of surveyed estuaries and ground water.”The federal Clean Water Act (CWA), the primary federal authority for addressing water pollution, has been largely successful at reducing water pollution from point sources such as wastewater treatment plants and industrial discharges through the National Pollutant Discharge Elimination System (NPDES). A major shortcoming of the NPDES program, however is that it does not apply to NPS discharges, including most agricultural runoff. Although most pollutant discharges to waterbodies from agriculture are not subject to NPDES regulation, the federal CWA does require states to establish water quality standards and total maximum daily loads (TMDLs) that can be used by the states to address agricultural water pollution through regulatory or non-regulatory mechanisms. A TMDL tells you how much of a particular pollutant a particular water body can assimilate without a violation of a water quality standard. Thus, the establishment of TMDL’s is an important step in ensuring that state water quality standards are met. The greatest challenge, however, is the allocation of TMDLs among all point and nonpoint source dischargers, and the implementation of the TMDLs. For point source discharges, TMDLs are allocated and implemented through the NPDES permit program and may require pollution reductions beyond what would be required using only technology-based standards. For nonpoint sources such as agricultural, the allocation and implementation of TMDLs is much more daunting. This Article will explore a number of legal mechanisms that could play a role in ensuring that discharges from agricultural activities do not cause or contribute to violations of water quality standards. Specifically, this article will evaluate the relative effectiveness of: 1) narrative nutrient criteria and numeric nutrient criteria, 2) TMDL implementation through regulatory and nonregulatory mechanisms; and 2) Design-based standards such as Best Management Practices (BMPs) and performance-based standards in reducing water pollution form agriculture. The Article will draw on experiences from the State of Florida, including Everglades’s restoration efforts, and efforts to reduce agricultural pollution in the Chesapeake Bay basin to demonstrate the efficacy of a variety of approaches and will suggest a multifaceted, watershed-based approach comprised of a combination of regulatory and nonregulatory federal and state efforts.

Could a Trend in Stormwater Issues Be Developing in Pennsylvania?

As Pennsylvania experiences wet weather events that are becoming more frequent and intense, managing stormwater runoff is becoming an increasingly challenging issue for residents and municipalities. In fact, managing stormwater probably will soon become a mandatory requirement for many municipalities that have thus far been exempt from the Municipal Separate Storm Sewer System (MS4) permit program under the Clean Water Act (CWA). The Environmental Protection Agency (EPA) is required to propose a rule, as part of the May 2010 settlement of a lawsuit brought by the Chesapeake Bay Foundation over the impaired water quality of Chesapeake Bay, which would expand the MS4 program to include small municipalities in currently non-urbanized areas in order to achieve a more holistic, watershed-based approach to managing stormwater. Considering that Pennsylvania currently boasts having 2,565 municipal jurisdictions, with nearly 1,000 of those jurisdictions with an MS4 permit, an expansion of the MS4 program will likely have a significant impact on the breadth of stormwater control at the local level. The new EPA rule, originally scheduled to be finalized by 2012, has yet to be proposed, but EPA has suggested that a proposal could come in the near future. In addition to the new EPA rule, a heightened focus on better stormwater management has resulted in several trends, including attempts to use Total Maximum Daily Loads (TMDL) to control stormwater runoff and an increased use of stormwater infrastructure.In January 2009, the Chesapeake Bay Foundation, along with other conservation organizations and individuals, sued EPA in the federal district court for the District of Columbia, alleging that it failed to comply with the Clean Water Act and the terms of the Chesapeake Bay Agreements of 2000. The lawsuit was settled in May 2010.Green stormwater infrastructure generally refers to stormwater runoff reduction strategies using land use, site design, and urban retrofits (e.g., green roofs, porous pavements). In particular, green infrastructure has frequently been suggested or used as a way to reduce sewer overflows due to heavy volumes of stormwater runoff generated during wet weather.