Stream Segments Research Articles

Striking a Balance between Conservation and Development: A Geospatial Approach to Watershed Prioritisation in the Himalayan Basin

This study was undertaken in the Himalayan basin, in the river Lohawati, Uttarakhand, to study its hydro-morphological characteristics and prioritise the watersheds using geospatial tools. Advanced Spaceborne Thermal Emission and Reflection (ASTER-30 m) data and the Survey of India’s topographic sheets were used to analyse the study area comprehensively. Nine watersheds were identified within the basin in order to calculate the hydro-morphological characteristics in terms of basic, shape, texture, and relief aspects. The basin was identified as being elongated, with a total drainage area of 337.48 km2. The interaction between the terrain, rock formations, and precipitation levels produced a branching structure in the areas drainage system that ranged from dendritic to sub-dendritic. The basin had been classified as a fifth-order basin, comprising a network of 500 stream segments spanning a total length of 492.41 km. In each of the watersheds, the primary streams are of the first order, followed by those of the second order, and so forth. The physiography and lithology of the basin have a significant influence on this pattern. The calculated elongation ratio, circulatory ratio, form factor, shape index, and shape factor ranged from 0.57 to 0.80, 0.35 to 0.64, 0.26 to 0.50, 1.98 to 3.89, and 0.57 to 1.77, respectively. These values indicate that watersheds are elongated, suggesting moderate lag times. The parameters, including drainage density (0.98 to 1.62), stream frequency (1.07 to 1.59), infiltration number (1.04 to 2.59), drainage texture (0.67 to 2.82), and drainage intensity (0.93 to 1.12), pointed towards the coarser drainage texture, higher infiltration, and minimal runoff characteristics of the basin. In light of the relief characteristics of the basin, a higher basin relief, relief ratio, and relative relief were observed for the watersheds, indicating the possibility of higher erosion and deforestation rates. Using the Weighted Sum Analysis (WSA) method, the computed factors were utilised to rank the watersheds based on their potential for erosion. Based on the WSA approach, watersheds were classified into high-, moderate-, and low-prioritisation zones. This further indicates that 36.14% (121.95 km2) of watersheds are in the high-priority zone, and that 48.84% (164.91 km2) and 15.00% (50.62 km2) of watersheds are in the moderate- and low-priority zones, respectively. The WSA is a practical strategy to prioritise watersheds when making appropriate decisions.

Aquatic Risks at the Landscape Scale: A Case Study for Pyrethroid Use in Pome Fruit Orchards in Belgium.

Procedures for environmental risk assessment for pesticides are under continuous development and subject to debate, especially at higher tier levels. Spatiotemporal dynamics of both pesticide exposure and effects at the landscape scale are largely ignored, which is a major flaw of the current risk assessment system. Furthermore, concrete guidance on risk assessment at landscape scales in the regulatory context is lacking. In this regard, we present an integrated modular simulation model system that includes spatiotemporally explicit simulation of pesticide application, fate, and effects on aquatic organisms. As a case study, the landscape model was applied to the Rummen, a river catchment in Belgium with a high density of pome fruit orchards. The application of a pyrethroid to pome fruit and the corresponding drift deposition on surface water and fate dynamics were simulated. Risk to aquatic organisms was quantified using a toxicokinetic/toxicodynamic model for individual survival at different levels of spatial aggregation, ranging from the catchment scale to individual stream segments. Although the derivation of landscape-scale risk assessment end points from model outputs is straightforward, a dialogue within the community, building on concrete examples as provided by this case study, is urgently needed in order to decide on the appropriate end points and on the definition of representative landscape scenarios for use in risk assessment.

Abundance, cover use, and clustering of brown trout spawning redds during stream habitat rehabilitation

AbstractAbundance, placement beneath cover and clustering of brown trout spawning redds were monitored throughout five spawning seasons (2016–2020) in a 4.8‐km reach of a Minnesota coldwater stream to assess the potential impacts of concurrent stream habitat rehabilitation projects. We anticipated that redd abundance (redds/100 m stream segments) and placement beneath cover (e.g. logs and branches, boulders, overhanging bank vegetation, aquatic macrophytes) would increase and redd clustering (placement within 1.5 m of other redds) would decline as stream sections were rehabilitated. Repeated redd counts were conducted for the entire stream reach during each of the five spawning seasons, and redd cover and clustering were documented. Before‐after comparison of impact (BACI) tests were used to compare redd abundances in control versus rehabilitated stream sections, and before‐after distributional tests compared redd placement beneath cover and redd clustering within rehabilitated sections. In total, 1895 brown trout spawning redds were documented within the study reach during the five spawning seasons combined. Redd abundance increased nearly fivefold throughout the 5‐year study period (from 3.5 to 15.5 redds/100 m), but no significant difference was detected in redd abundances between rehabilitated versus control stream sections. Redd placement beneath cover (63% beneath cover in total) also increased throughout the study, but at a rate nine to 45 times greater in stream sections rehabilitated during the study than in control or previously rehabilitated sections. Redd clustering remained unchanged (5‐year means = 35 to 46% of all redds clustered) in rehabilitated sections, but increased significantly through time (0% to 35%) in the control section. Stream habitat rehabilitation did not increase spawning redd abundance or reduce redd clustering in rehabilitated stream sections, but redd placement beneath cover increased after rehabilitation to levels present in other stream sections.

STUDY OF DRAINAGE SYSTEM AND ITS HYDROLOGICAL IMPLICATIONS USING GEO-SPATIAL TECHNIQUES: A MORPHOMETRIC ANALYSIS IN MUTHUGAD WATERSHED OF GRAHWAL HIMALYA, UTTARAKHAND

Measurements of the shape or geometry of any natural form, including those of plants, animals, and relief features, are referred to as morphometry. Morphometry is the accurate measurement of landforms. One of the crucial requirements for effective management and planning of water resources within the watershed is an understanding of the behavior of the surface drainage network. In order to comprehend and interpret the dynamics of the watersheds drainage system and to understand the drainage network features, morphometric study of a watershed is a vital stage in watershed development and management. Watershed characterization includes morphometry as a key component. Aspects that are dimensional, aerial, and relief are typically present for the conservation of surface and ground water, watershed development and management strategies are crucial. Applying mathematical equations and statical measures to data from topographic maps and satellite images, morphometric attributes reflect a measurement of the earths surface and are processed in accordance with the principles of quantitative analysis. A study of drainage morphometry and its impact on the hydrology of the Mathugad watershed, Grahwal Himalaya, Uttarakhand (India) has been made. Total basin covers 77.12 sq km area. Shuttle Radar Topographic Mission (SRTM) data and on 1:50000 scale and Survey of India Toposheet as reference were utilized to create slope maps, aspect grids, and digital elevation models (DEMs) for the thorough investigation. Geographic information systems (GIS) were employed to assess the linear, areal, and relief aspects of the morphometric parameters. Numerous morphometric features of the Mathugad watershed have been intended by applying GIS techniques and using SRTM data. The research shows that the rainfall has a moderate to large impact on how stream segments develop in the basin area thrusting and faultings controlling influence is primarily responsible for the basins elongated structure. Relief ratio indicates that the discharge capability of these watersheds is very high and the groundwater potential is meager. Dendritic drainage pattern in the area shows that the area consists of homogeneous rock material which is structurally undisturbed. Form factor and circulatory ratio statics indicates basin shape is elongated the results clearly indicate relations among various morphometric attributes of the basin and help to understand their role in sculpturing the surface of the region. These studies are very useful for planning rainwater harvesting and watershed management.

Distribution, habitat and population structure of the threatened Manning River helmeted turtle, Myuchelys purvisi

AbstractMany freshwater turtle species are threatened with extinction, but the implementation of effective conservation measures may be hindered by ignorance of their biology and ecology. We analysed the available monitoring data for a listed Endangered but poorly known species, the Manning River helmeted turtle (Myuchelys purvisi: Chelidae), to elucidate its geographic distribution and evaluate its relationships to broad‐scale environmental factors. We found M. purvisi to be common and widely distributed in the Manning River system, with a balanced population structure including a wide mix of size classes and a substantial juvenile component. The occurrence of the species was significantly positively associated with a larger stream size, which probably confers greater habitat availability and lower vulnerability to drying during drought. Mean carapace length was significantly lower in stream segments with higher average annual air temperature, suggesting that warmer conditions favour recruitment. However, occurrence and mean carapace length were not related to the proportion of conservation land in the immediate stream environs, suggesting that persistence of the species is not favoured simply by occurrence in protected areas, and threats may need active management. We provide some suggestions for further investigation of the species' ecology and vulnerability to potential threats.

Using Deep-Learned Vector Representations for Page Stream Segmentation by Agglomerative Clustering

Page stream segmentation (PSS) is the task of retrieving the boundaries that separate source documents given a consecutive stream of documents (for example, sequentially scanned PDF files). The task has recently gained more interest as a result of the digitization efforts of various companies and organizations, as they move towards having all their documents available online for improved searchability and accessibility for users. The current state-of-the-art approach is neural start of document page classification on representations of the text and/or images of pages using models such as Visual Geometry Group-16 (VGG-16) and BERT to classify individual pages. We view the task of PSS as a clustering task instead, hypothesizing that pages from one document are similar to each other and different to pages in other documents, something that is difficult to incorporate in the current approaches. We compare the segmentation performance of an agglomerative clustering method with a binary classification model based on images on a new publicly available dataset and experiment with using either pretrained or finetuned image vectors as inputs to the model. To adapt the clustering method to PSS, we propose the switch method to alleviate the effects of pages of the same class having a high similarity, and report an improvement in the scores using this method. Unfortunately, neither clustering with pretrained embeddings nor clustering with finetuned embeddings outperformed start of document page classification for PSS. However, clustering with either pretrained or finetuned representations is substantially more effective than the baseline, with finetuned embeddings outperforming pretrained embeddings. Finally, having the number of documents K as part of the input, in our use case a realistic assumption, has a surprisingly significant positive effect. In contrast to earlier papers, we evaluate PSS with the overlap weighted partial match F1 score, developed as a Panoptic Quality in the computer vision domain, a metric that is particularly well-suited to PSS as it can be used to measure document segmentation.

Wastewater discharges alter microbial community composition in surface waters of the canadian prairies

Microbial communities are an important component of freshwater biodiversity that is threatened by anthropogenic impacts. Wastewater discharges pose a particular concern by being major sources of anthropogenic contaminants and microorganisms that may influence the composition of natural microbial communities. Nevertheless, the effects of wastewater treatment plant (WWTP) effluents on microbial communities remain largely unexplored. In this study, the effects of wastewater discharges on microbial communities from five different WWTPs in Southern Saskatchewan were investigated using rRNA gene metabarcoding. In parallel, nutrient levels and the presence of environmentally relevant organic pollutants were analyzed. Higher nutrient loads and pollutant concentrations resulted in significant changes in microbial community composition. The greatest changes were observed in Wascana Creek (Regina), which was found to be heavily polluted by wastewater discharges. Several taxa occurred in greater relative abundance in the wastewater-influenced stream segments, indicating anthropogenic pollution and eutrophication, especially taxa belonging to Proteobacteria, Bacteroidota, and Chlorophyta. Strong decreases were measured within the taxa Ciliphora, Diatomea, Dinoflagellata, Nematozoa, Ochrophyta, Protalveolata, and Rotifera. Across all sample types, a significant decline in sulfur bacteria was measured, implying changes in functional biodiversity. In addition, downstream of the Regina WWTP, an increase in cyanotoxins was detected which was correlated with a significant change in cyanobacterial community composition. Overall, these data suggest a causal relationship between anthropogenic pollution and changes in microbial communities, possibly reflecting an impairment of ecosystem health.

Urban Stream Corridors and Forest Patches—The Connections: A Case Study of Bloomington, IN

Streams and forests are ecosystems connected through hydrology, but few studies have looked at the connectivity between streams and forests in the context of urban development. City-made decisions affect connections between streams and forests by isolating both ecosystems. Streams are often channelized or buried to increase potential development areas. Forests often become fragmented and may be removed unless protected. Historical choices in land usage affect the sites and sizes of current urban streams, forests, and development. This affects the distribution of impervious surfaces, which separates streams from forests. Despite these barriers to stream/forest interactions, cities can experience stream/forest connectivity. Seven Bloomington watersheds are ranked on their proportions of buried streams, channelized streams, forested hydrology, forested streams, urban forest patch cover, and impervious surface cover, along with the historical presence of urbanization. Watersheds demonstrate stream/forest connectivity, with five watersheds containing 50% or greater forested stream segments. Bloomington canopy cover reduces stormwater runoff by approximately 127 kiloliters per year. These forested areas reduce flooding, reduce nutrient loading, and reduce stream conditions associated with urban stream syndrome. Understanding urban stream/forest connectivity can improve green infrastructure design and green space design, which improve urban resilience and better connect residents to the environment.

Bryophyte assembly rules across scales

Abstract Understanding how species assemble into communities is a central issue in community ecology. So far, most studies have focused on the assembly mechanisms of vascular plant communities, while the role of deterministic (environmental filtering and biotic interactions) and stochastic (e.g. dispersal limitation) processes structuring bryophyte assemblages remains poorly understood. To evaluate how different assembly processes shape bryophyte communities in mountain streams, we examined functional trait patterns across spatial scales and along environmental gradients. To do so, we sampled 754 microhabitat plots (0.25 m2) nested within 165 sites (100‐m long stream segments) and 13 sub‐basins, located in the northwest and central‐west of Portugal. At each spatial scale, observed functional diversity indices (Functional Richness and Rao's quadratic entropy) were compared to random expectations derived from null models, followed by the analysis of changes in functional trait patterns along environmental gradients by fitting a series of generalized additive mixed models. At local scales (site and microhabitat plot), coexisting species tended to be more functionally similar than expected, suggesting the prevalence of environmental filtering effects. In contrast, no significant deviations from random expectations were detected at the broadest spatial scale (sub‐basin), indicating the prevalence of stochastic processes. We found contrasting assembly processes along environmental gradients: environmental filtering prevailed in stressful environments, while competitive interactions were more important in favourable conditions. Synthesis. Our results highlight the role of environmental filtering in bryophyte community assembly at fine spatial scales, emphasizing the importance of measuring environmental conditions at the same spatial scales where biotic interactions take place. In line with the stress‐dominance hypothesis, the relative importance of environmental filtering increased with abiotic stress. Thus, analysing functional trait patterns across different spatial scales and environmental gradients may contribute to a better understanding of the mechanisms underlying community assembly.

Estimating biotic integrity to capture existence value of freshwater ecosystems

The US Environmental Protection Agency (EPA) uses a water quality index (WQI) to estimate benefits of proposed Clean Water Act regulations. The WQI is relevant to human use value, such as recreation, but may not fully capture aspects of nonuse value, such as existence value. Here, we identify an index of biological integrity to supplement the WQI in a forthcoming national stated preference survey that seeks to capture existence value of streams and lakes more accurately within the conterminous United States (CONUS). We used literature and focus group research to evaluate aquatic indices regularly reported by the EPA's National Aquatic Resource Surveys. We chose an index that quantifies loss in biodiversity as the observed-to-expected (O/E) ratio of taxonomic composition because focus group participants easily understood its meaning and the environmental changes that would result in incremental improvements. However, available datasets of this index do not provide the spatial coverage to account for how conditions near survey respondents affect their willingness to pay for its improvement. Therefore, we modeled and interpolated the values of this index from sampled sites to 1.1 million stream segments and 297,071 lakes across the CONUS to provide the required coverage. The models explained 13 to 36% of the variation in O/E scores and demonstrate how modeling can provide data at the required density for benefits estimation. We close by discussing future work to improve performance of the models and to link biological condition with water quality and habitat models that will allow us to forecast changes resulting from regulatory options.

Groundwater mounding due to recharge from ephemeral streams

Recharge from ephemeral streams (called Masil in Farsi and Wadi in Arabic) is the main process of groundwater recharge in arid regions of the world where groundwater is the most important source of water supply. Despite advances in mathematical models of simulating infiltration from these streams, there is no versatile analytical model to simulate the groundwater mound due to recharge from an ephemeral stream. The goal of this paper is to present a semi-analytical model of the groundwater mound due to recharge from an ephemeral stream of an arbitrary geometric shape in aerial view. A three-dimensional anisotropic unconfined aquifer is considered, and a point recharge is imposed to the water table at first. The point- recharge solution is obtained via the Laplace and Fourier transforms. The image well method is employed to incorporate the lateral no-flow boundary that represents the margin of the unconfined aquifer. The point-recharge solution is then extended to simulate the groundwater mound due to recharge from the stream of an arbitrary shape by approximating the stream by multiple straight segments and integration of the point-recharge solution along the desired directions of those segments and the principle of superposition. A constant head variable discharge well is also placed near the stream to calculate the sustainable yield which is the average discharge of a constant-head well as compared to an equivalent constant-flux well. The results of this work are presented in the form of groundwater mound-time curve and groundwater mound spatial maps. The scaled sensitivity of groundwater head to recharge from the stream-time curves and depletion volume of the nearby well-time curve are also obtained. The influences of aquifer parameters on the groundwater mound and sustainable development of the aquifer near the ephemeral stream are analyzed. This study shows that: (1) Groundwater mound follows the shape of the stream and spreads spatially from the stream over time and the groundwater mound is higher inside the convex part of the stream due to interference from the neighboring stream segments; (2) The groundwater head collected from an observation well in a fine-grain aquifer is more valuable to estimate the recharge from the ephemeral stream than a coarse-grain aquifer; (3) The sustainable yield of a production well in an alluvial aquifer is maximum near the stream in a coarse-grain non-layered aquifer than the (coarse- or fine-grain) layered aquifer. The results of this work can be utilized as a valuable tool for managing the unconfined aquifers in the arid and semi-arid regions when ephemeral streams are the primary sources of recharge.

LayoutQT—Layout Quadrant Tags to embed visual features for document analysis

The relative position of text blocks plays a crucial role in document understanding. However, the task of embedding layout information in the representation of a page instance is not trivial. Computer Vision and Natural Language Processing techniques have been advancing in extracting content from document images considering layout features. We propose a set of Layout Quadrant Tags (LayoutQT) as a new way of encoding layout information in textual embedding. We show that this enables a standard NLP pipeline to be significantly enhanced without requiring expensive mid or high-level multimodal fusion. Given that our focus is on developing a low computational cost solution, we focused our experiments on the AWD-LSTM neural network. We evaluated our method for page stream segmentation and document classification tasks on two datasets, Tobacco800 and RVL-CDIP. In the former, our method improved the F1 score from 97.9% to 99.1% and in the latter the F1 score went from 80.4% to 83.6%. Similar levels of performance improvement were also obtained when we applied LayoutQT with BERT.

Random Forest models to estimate bankfull and low flow channel widths and depths across the conterminous United States.

Channel dimensions (width and depth) at varying flows influence a host of instream ecological processes, as well as habitat and biotic features; they are a major consideration in stream habitat restoration and instream flow assessments. Models of widths and depths are often used to assess climate change vulnerability, develop endangered species recovery plans, and model water quality. However, development and application of such models require specific skillsets and resources. To facilitate acquisition of such estimates, we created a dataset of modeled channel dimensions for perennial stream segments across the conterminous U.S. We used random forest models to predict wetted width, thalweg depth, bankfull width, and bankfull depth from several thousand field measurements of the National Rivers and Streams Assessment. Observed channel widths varied from <5 m to >2000 m and depths varied from <2 m to >125 m. Metrics of watershed area, runoff, slope, land use, and more were used as model predictors. The models had high pseudo R-squared values (0.70 to 0.91) and median absolute errors within ±6% to ±21% of the interquartile range of measured values across ten stream orders. Predicted channel dimensions can be joined to 1.1 million stream segments of the 1:100K resolution National Hydrography Dataset Plus (version 2.1). These predictions, combined with a rapidly growing body of nationally available data, will further enhance our ability to study and protect aquatic resources.

Tectono-geomorphic anomaly and its significance for monitoring seismotectonic activity in Lower Tista sub-basin, India, using geoinformatics

ABSTRACT The different morphotectonic parameters have been analyzed for estimating tectono-geomorphic anomaly index (TGAI). The analysis of river long cross-profile and hypsometric curve has been integrated with the TGAI and epicenter distribution for monitoring tectono-geomorphic anomaly in Lower Tista sub-basin, India. Higher value of TGAI is observed in Rathong chhu, Rel chhu, Upper rangpo chhu, and Great Rangit micro-basin region which shows anomaly of basin tilt, stream segmentation, compressed meander, and topographic curvature. Number of earthquake is much higher (17) between 2000 and 2022 when compared with the status before 2000 in this region. The foothill region is associated with moderate TGAI, which shows the anomaly of resistance difference in soft sedimentary rock, stream migration, upliftment, drainage deflection, and rejuvenation. In moderate region, number of earthquake is 22 between 2000 and 2022 which is twice than the past (11). The signature of tectono-geomorphic anomaly suggests that the activation of thrust fault and resultant few earthquake indicating the strain of the exiting thrust continuously building in the sub-surface, which have not released as expected. As a result, moderate and low TGAI region have not produced any major earthquake at present and may expect severe seismic hazard in future.

Semi-Supervised Dual Stream Segmentation Network for Fundus Lesion Segmentation.

Accurate segmentation of retinal images can assist ophthalmologists to determine the degree of retinopathy and diagnose other systemic diseases. However, the structure of the retina is complex, and different anatomical structures often affect the segmentation of fundus lesions. In this paper, a new segmentation strategy called a dual stream segmentation network embedded into a conditional generative adversarial network is proposed to improve the accuracy of retinal lesion segmentation. First, a dual stream encoder is proposed to utilize the capabilities of two different networks and extract more feature information. Second, a multiple level fuse block is proposed to decode the richer and more effective features from the two different parallel encoders. Third, the proposed network is further trained in a semi-supervised adversarial manner to leverage from labeled images and unlabeled images with high confident pseudo labels, which are selected by the dual stream Bayesian segmentation network. An annotation discriminator is further proposed to reduce the negativity that prediction tends to become increasingly similar to the inaccurate predictions of unlabeled images. The proposed method is cross-validated in 384 clinical fundus fluorescein angiography images and 1040 optical coherence tomography images. Compared to state-of-the-art methods, the proposed method can achieve better segmentation of retinal capillary non-perfusion region and choroidal neovascularization.

Low Flow Trends in Texas Stream Segments Serving Unique Hydrologic Functions

In recognition of the unique hydrologic functions they serve, certain stream segments in Texas have been designated as ecologically significant. In this study, we evaluated low flow trends in seven hydrologically unique stream segments spanning three climatic divisions in Texas from 1970 to 2019. Despite increasing mean annual temperatures, there are no trends in low flows or other hydrologic variables in the East Fork of the San Jacinto River in the Upper Coast climatic division, likely due to local moisture surplus effects from the Gulf of Mexico. In the Edwards Plateau climatic division, annual low flows and annual baseflows are decreasing in the South Fork of the Guadalupe River, the Sabinal River and the Frio River. While increasing mean annual temperatures appear to have a role in the drying of all three of these stream segments, increasing annual potential evapotranspiration may be an additional driver in the Sabinal and Frio Rivers. Analysis of the Standardized Streamflow Index indicates that all seven stream segments experienced their worst streamflow droughts in the 2010s. As such, the watersheds draining to the gages on these stream segments have minimal anthropogenic impacts, suggesting the influence of climate on the observed stream drying.

Speech prosody supports speaker selection and auditory stream segregation in a multi-talker situation

To process speech in a multi-talker environment, listeners need to segregate the mixture of incoming speech streams and focus their attention on one of them. Potentially, speech prosody could aid the segregation of different speakers, the selection of the desired speech stream, and detecting targets within the attended stream. For testing these issues, we recorded behavioral responses and extracted event-related potentials and functional brain networks from electroencephalographic signals recorded while participants listened to two concurrent speech streams, performing a lexical detection and a recognition memory task in parallel. Prosody manipulation was applied to the attended speech stream in one group of participants and to the ignored speech stream in another group. Naturally recorded speech stimuli were either intact, synthetically F0-flattened, or prosodically suppressed by the speaker. Results show that prosody – especially the parsing cues mediated by speech rate – facilitates stream selection, while playing a smaller role in auditory stream segmentation and target detection.

Advancing debris flow hazard and risk assessments using debris flow modeling and radar derived rainfall intensity data

Debris flow hazard and risk assessments are critical tools in mitigating and planning for these events. Existing debris flow hazard assessments can provide a rapid view of the likelihood of debris flows in recently burned watersheds and along stream segments within the watershed. Furthermore, debris flow volumes can be predicted for these watersheds and along the stream segments. Advances in modeling and remote-sensing data can add further value to the rapid assessments. Here, modeled debris flow volumes and a more detailed understanding of rainfall conditions highlight a need to reconcile debris flow probabilities and volumes using local conditions. Modeled debris flow volumes are consistently lower than even the lowest predicted volumes from empirical models used in the debris flow hazard assessments. Watershed probability and volume relations also over predict based on our probabilities derived from rainfall intensity from Multi-Radar/Multi-Sensor System 1-hr data. Probability and volume measures need to be further considered as the conservative measures of the rapid assessments have implication for risk analyses required for planning and management decisions, and ultimately for design and cost of mitigation to manage risk.

Testing Hidden Assumptions of Representativeness in Reach‐Scale Studies of Hyporheic Exchange

AbstractField studies of hyporheic exchange in mountain systems are often conducted using short study reaches and a limited number of observations. It is common practice to assume these study reaches represent hyporheic exchange at larger scales or different sites and to infer general relationships among potential causal mechanisms from the limited number of observations. However, these assumptions of representativeness are rarely tested. In this study, we develop numerical models from four segments of mountain streams in different geomorphologic settings and extract shorter reaches to test how representative exchange metrics are in shorter reaches compared to their reference segments. We also map the locations of the representative reaches to determine if a pattern exists based on location. Finally, we compare variance of these shorter within‐site reaches to 29 additional reaches across the same basin to understand the impacts of inferring causal mechanisms, for example, the expectation that wide and narrow valley bottoms will yield different hyporheic exchange patterns. Our results show that the location and length strategy of the study reach must be considered before assuming an exchange metric to be representative of anything other than the exact segment studied. Further, it is necessary to quantify within and between site variations before making causal inferences based on observable characteristics, such as valley width or stream morphology. Our findings have implications for future field practices and how those practices are translated into models.

A Multitask Deep Learning Approach for Sensor-Based Human Activity Recognition and Segmentation

Deep learning for sensor-based human activity recognition (HAR) has been a focus of research in recent years. Sensor data stream segmentation is a core element in HAR, which has currently been treated as an independent preprocessing task, usually with a fixed-size window. This has led to two critical problems, namely the multi-class window problem caused by possible multiple activities within a fixed-size window and the fluctuation of prediction results due to noisy data and over-segmentation. To address these research challenges, in this paper, we conceive a novel Multi-Task deep learning approach to segmenting and recognizing human activity simultaneously. Specifically, we propose a multi-scale window method based on feature sequence generation to overcome the multi-class window problem. We develop a novel boundary offset prediction algorithm to adjust a windows boundary to tackle the over-segmentation issue. In addition, We design a multi-task framework to streamline and optimize the activity recognition and segmentation tasks simultaneously. We conduct extensive experiments on eight benchmark datasets to evaluate the proposed framework and associated methods. Initial results show that our approach outperforms the performance of current state-of-the-art HAR methods.