Temporal Datasets Research Articles

Alpha and beta diversity mapping in Indian tropical deciduous forests using high-fidelity imaging spectroscopy

The present study evaluates the potential of airborne hyperspectral and space-borne multispectral satellite datasets to determine alpha (α) and beta (β) diversity combining in-situ floristic composition in tropical deciduous forests (TDFs) of Mudumalai Tiger Reserve (MTR), India. 43 forest plots (each of 0.04 ha) were sampled to record the species diversity in MTR. The very high-resolution AVIRIS-NG hyperspectral images and temporal Sentinel-2 multispectral images were used to extract spectral variables by associating each field plot with corresponding pixels of the satellite images. A high positive correlation of field-based α-diversity (Shannon-Wiener diversity index (H′)) with AVIRIS-NG based α-diversity (R2: 0.83 (2016); R2: 0.82 (2018)) was observed compared to Sentinel-2 based α-diversity (R2: 0.50 to 0.63) with high correlation during post-monsoon (R2: 0.63). Principal coordinate analysis of AVIRIS-NG based β-diversity signifies a low diversity (H′: <1.00) in the eastern parts (0.13% of MTR) in contrast to moderate (H′: 1.00 to 2.00; 13.39% of MTR) to high diversity (H′ >2.00; 86.49% of MTR) in the remaining parts of MTR. The study highlights the application of high spectral-spatial AVIRIS-NG images for precise tree diversity mapping and the high temporal-spatial Sentinel-2 satellite images for assessing the phenological variations of the forest species. The integration of high spatial-hyperspectral and temporal multispectral satellite datasets with field-based observations for fine-scale tree species diversity mapping at geographical and temporal scales is the novelty of the study that may effectively contribute in devising effective forest management plans for biodiversity conservation.

Evaluation of Feature Selection Methods for Preserving Machine Learning Performance in the Presence of Temporal Dataset Shift in Clinical Medicine.

Temporal dataset shift can cause degradation in model performance as discrepancies between training and deployment data grow over time. The primary objective was to determine whether parsimonious models produced by specific feature selection methods are more robust to temporal dataset shift as measured by out-of-distribution (OOD) performance, while maintaining in-distribution (ID) performance. Our dataset consisted of intensive care unit patients from MIMIC-IV categorized by year groups (2008-2010, 2011-2013, 2014-2016, and 2017-2019). We trained baseline models using L2-regularized logistic regression on 2008-2010 to predict in-hospital mortality, long length of stay (LOS), sepsis, and invasive ventilation in all year groups. We evaluated three feature selection methods: L1-regularized logistic regression (L1), Remove and Retrain (ROAR), and causal feature selection. We assessed whether a feature selection method could maintain ID performance (2008-2010) and improve OOD performance (2017-2019). We also assessed whether parsimonious models retrained on OOD data performed as well as oracle models trained on all features in the OOD year group. The baseline model showed significantly worse OOD performance with the long LOS and sepsis tasks when compared with the ID performance. L1 and ROAR retained 3.7 to 12.6% of all features, whereas causal feature selection generally retained fewer features. Models produced by L1 and ROAR exhibited similar ID and OOD performance as the baseline models. The retraining of these models on 2017-2019 data using features selected from training on 2008-2010 data generally reached parity with oracle models trained directly on 2017-2019 data using all available features. Causal feature selection led to heterogeneous results with the superset maintaining ID performance while improving OOD calibration only on the long LOS task. While model retraining can mitigate the impact of temporal dataset shift on parsimonious models produced by L1 and ROAR, new methods are required to proactively improve temporal robustness.

PTKE: Translation-based temporal knowledge graph embedding in polar coordinate system

Knowledge graph embedding has received widespread attention in recent years. Most existing models represent time-independent facts as low dimensional embeddings. Nevertheless, knowledge graphs with temporal information provide more accurate and timely data. Hence, we propose Polar Temporal Knowledge Graph Embedding (PTKE), a novel temporal knowledge graph (TKG) embedding model which belongs to the translation-based model family and embeds time-aware facts into polar coordinate system. PTKE defines time as a constraint of the entity and synchronously embeds the starting and ending timestamps. The fact is divided into the modulus and the angular parts to avoid generating similar time-constrained entities. We use the modulus part to distinguish different time-constrained entities, and the angular part to distinguish time-constrained entities with the same modulus. Experiments on the temporal datasets show that PTKE outperforms prior state-of-the-art static knowledge graph (SKG) embedding models and temporal knowledge graph (TKG) embedding models in the link prediction task and the relation prediction task. Furthermore, the analysis of different time units and semantic expressive ability test on time embeddings prove that PTKE has a great ability on time expression.

Horizontal Geolocation Error Evaluation and Correction on Full-Waveform LiDAR Footprints via Waveform Matching

The geolocation accuracy of spaceborne LiDAR (Light Detection And Ranging) data is important for quantitative forest inventory. Geolocation errors in Global Ecosystem Dynamics Investigation (GEDI) footprints are almost unavoidable because of the instability of orbital parameter estimation and GNSS (Global Navigation Satellite Systems) positioning accuracy. This study calculates the horizontal geolocation error of multiple temporal GEDI footprints using a waveform matching method, which compares original GEDI waveforms with the corresponding simulated waveforms from airborne LiDAR point clouds. The results show that the GEDI footprint geolocation error varies from 3.04 m to 65.03 m. In particular, the footprints from good orbit data perform better than those from weak orbit data, while the nighttime and daytime footprints perform similarly. After removing the system error, the average waveform similarity coefficient of multi-temporal footprints increases obviously in low-waveform-similarity footprints, especially in weak orbit footprints. When the waveform matching effect is measured using the threshold of the waveform similarity coefficient, the waveform matching method can significantly improve up to 32% of the temporal GEDI footprint datasets from a poor matching effect to a good matching effect. In the improvement of the ratio of individual footprint waveform similarity, the mean value of the training set and test set is about two thirds, but the variance in the test set is large. Our study first quantifies the geolocation error of the newest version of GEDI footprints (Version 2). Future research should focus on the improvement of the detail of the waveform matching method and the combination of the terrain matching method with GEDI waveform LiDAR.

Anthropogenic interventions on land neutrality in a critically vulnerable estuarine island ecosystem: a case of Munro Island (India)

All landscapes, including estuarine islands, normally try to restore their geomorphic isostasy in all anthropogenic interventions on land dynamics. Munroe Island has been experiencing drastic environmental degradation, such as land subsidence, severe tidal/monsoon flooding, subsidence of build-ups and a drastic decay in agricultural productivity. This paper examines the role of anthropo-geomorphic interventions causing for the land degradation in Munroe Island through a multidisciplinary approach. Multidated, multiresolution satellite products and published maps, spanning a period of about six decades from 1960 to 2021, were used to understand the different geomorphic and geographical processes in the study area. Evaluation of the temporal bathymetric datasets, salinity measurements of the river and estuary, borehole data logs of the area and electrical resistivity surveys of the island were analyzed to find out the causative factors for the disturbances in the land neutrality, along with the tidal hydrodynamic changes in the region. The study shows about 14% of the total land area was vanished during the study period, and more than 25% of the area is under stress, leading to further land degradation. More than 500 households are forced to vacate their residence due to land subsidence/flooding. Lack of required freshwater and sediment supply from the Kallada river after the construction of the Thenmala reservoir in the Kallada river as well as the uncontrolled sand mining prevailed are the key factors for the environmental degradation of Munroe Island. The paper describes the role and colinkages of human-induced hydrogeomorphic interventions on a geomorphic system, in charge of the environmental degradation and land subsidence crisis of an estuarine island ecosystem and discusses the concerns related to the management strategies of such region.

Analysis and management of herbicidal mixtures in a high-intensity agricultural landscape in Belgium.

Water bodies located in anthropogenically influenced environments, such as agricultural landscapes, may be exposed to different chemicals simultaneously or sequentially. Yet, current environmental risk assessments focus on single active substances for unintended mixtures. For 3.5 years, the present study monitored the mixture of herbicides, within an intensively managed agricultural catchment, accompanied by a stewardship program. Twelve herbicides and one metabolite were monitored on a daily to sub-daily basis, generating a unique, high temporal resolution data set, enabling an assessment of cumulative exposure in a worst-case scenario. Analyses focused on the number of events at which the herbicide mixture concentration exceeded the regulatory accepted concentration for algae and macrophytes, based on concentration addition, and the potential factors influencing the frequency of these events are considered. A low number of individual herbicides drove the toxicity and only two of these overlapped for the two organism groups, algae and macrophytes. The observed exceedance events coincided with seasonal influences, and low rainfall during the 2011 season correlated with a highly reduced number of these events. The major influence was found to be the implementation of the stewardship program, which directed farmers to use more advanced farming techniques, avoid spillages, and other point sources. The number of exceedance events was reduced by more than half for algae (9% of the daily mean samples in 2010 and 4% in 2013) and by approximately 10 times for macrophytes (36% in 2010 to 3% in 2013). This high-resolution monitoring data set illustrates how knowledge of the influencing factors can help reduce unintended exposure to chemicals and achieve real-world improvements. Overall, a single-substance assessment is protective of mixture effects. Where mixture effects do play a role, local measures to manage point sources are more effective than changes to the desk-based environmental risk assessments that focus on diffuse sources. Integr Environ Assess Manag 2023;19:1297-1306. © 2022 Cambridge Environmental Assessments RSK ADAS Ltd and Bayer AG. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

MONITORING SUITABILITY OF URBAN ROADS FOR EARTHQUAKE RESPONSE BASED ON MORPHOLOGICAL COMPONENTS USING NONLINEAR AUTOREGRESSIVE WITH EXTERNAL INPUT (NARX)

Abstract. Earthquake is one of the hazardous disasters in Iran and caused huge causalities in the last century. Among different effective criteria in earthquake management especially in the response phase, morphological components of the urban structure have the main role. This paper aims to assess and monitor the suitability of urban roads for emergency response after an earthquake. The main contribution is the definition of key morphological factors, assessment, and monitoring of urban road suitability using nonlinear autoregressive with external input (NARX) as a time series artificial neural network (ANN). In this framework, first, the effective criteria are detected and analyzed based on experts’ opinions, then the suitability of urban roads for emergency earthquake response is assessed for three temporal datasets for the 2010, 2015, and 2020 years. The proposed method has been implemented in Tehran; the capital of Iran based on designed ANNs. The RMSE and R of time series ANNs are 0.97629 and 0.027 for 2010, 0.91479 and 0.13 for 2015, 0.93569 and 0.056 for 2020. The results show that the suitability of urban roads has been improved significantly between 2010 to 2015, while according to the new expansion of roads in the west of Tehran, the level of suitability has been decreased from 2015 to 2020. As this region is going to be populated recent so some new strategies should be improved for urban traffic network structure.

Probable nexus between Methane and Air Pollution in Bangladesh using Machine Learning and Geographically Weighted Regression Modeling

This paper investigates the probable nexus between methane (CH4) and air pollutants, a public health hazard in Bangladesh. The hypothesis considers that the concentration of CH4 is dependent on the ten air pollutants found in the five districts in Dhaka Division, a major urban and industrial area in Bangladesh. These pollutants are: Particular matters (PM2.5), Nitrogen dioxide (NO2), Nitrogen oxide (NOx), Aerosol optical thickness (AOT), Sulfur dioxide (SO2), Carbon monoxide (CO), Ozone (O3), Black carbon (BC), Formaldehyde (HCHO) and Dust. The study applies Machine Learning (ML) technique and Geographically Weighted Regression (GWR) Modeling. Temporal CH4 datasets from the Sentinel-5P sensor are classified to estimate the annual CH4 concentration during 2019-2021.Seven supervised classifiers of ML coupled with the GWR model are used to predict the statistical and spatial relationships. CH4 increases gradually during 2018-2021 in Dhaka, Gazipur, and Munshiganj Districts. It relates differently with various air pollutants, e.g., positively with BC, Dust, NO2, PM2.5, O3, and AOT, and negatively with NOx, CO, HCHO, and SO2.This study results that Rational quadratic (RMSE-0.001, MAE-0.001, R2-0.96), Random Forest (RMSE-0.004, MAE-0.003, R2-0.91), and Stepwise regression (RMSE-0.002, MAE-0.002, R2-0.87) are the suitable method in ML. The highest goodness-of-fit (R2) of 82%-96% is found in Dhaka and Narshingdi Districts. The key findings may help formulate the appropriate action plan to mitigate ongoing and future air pollution in Bangladesh. In addition, the methodology of the research may be applicable elsewhere nationally and internationally for air pollution research.

Touchformer: A Transformer-Based Two-Tower Architecture for Tactile Temporal Signal Classification.

Haptic temporal signal recognition plays an important supporting role in robot perception. This paper investigates how to improve classification performance on multiple types of haptic temporal signal datasets using a Transformer model structure. By analyzing the feature representation of haptic temporal signals, a Transformer-based two-tower structural model, called Touchformer, is proposed to extract temporal and spatial features separately and integrate them using a self-attention mechanism for classification. To address the characteristics of small sample datasets, data augmentation is employed to improve the stability of the dataset. Adaptations to the overall architecture of the model and the training and optimization procedures are made to improve the recognition performance and robustness of the model. Experimental comparisons on three publicly available datasets demonstrate that the Touchformer model significantly outperforms the benchmark model, indicating our approach's effectiveness and providing a new solution for robot perception.

Answers of SPARQL 1.1 queries along with their temporal validity intervals over temporal RDF datasets

Answers of SPARQL 1.1 queries along with their temporal validity intervals over temporal RDF datasets

Answers of SPARQL 1.1 queries along with their temporal validity intervals over temporal RDF datasets

Answers of SPARQL 1.1 queries along with their temporal validity intervals over temporal RDF datasets

Temporal Attention-Pyramid Pooling for Temporal Action Detection

Temporal action detection is a challenging task in video understanding, which is usually divided into two stages: proposal generation and classification. Learning proposal features is a crucial step for both stages. However, most methods ignore temporal information of proposals and consider background and action frames in proposals equally, leading to poor proposal features. In this paper, we propose a novel Temporal Attention-Pyramid Pooling (TAPP) method to learn proposal features of arbitrary length action proposals. The TAPP method exploits the attention mechanism to focus on the discriminative part of proposals, suppressing background influence on proposal features. It constructs a temporal pyramid structure to convert arbitrary length proposal feature sequences to multiple fixed-length sequences while retaining the temporal information. In the TAPP method, we design a multi-scale temporal function and apply it to the temporal pyramid to generate final proposal features. Based on the TAPP method, we construct a temporal action proposal generation model and an action proposal classification model, and then we perform extensive experiments on two mainstream temporal action detection datasets for the temporal action proposal and temporal action detection tasks to verify our models. On the THUMOS'14 dataset, our models based on the TAPP significantly outperform the previous state-of-the-art methods for both tasks.

Application of a gene modular approach for clinical phenotype genotype association and sepsis prediction using machine learning in meningococcal sepsis

Sepsis is a major global health concern causing high morbidity and mortality rates. Our study utilized a Meningococcal Septic Shock (MSS) temporal dataset to investigate the correlation between gene expression (GE) changes and clinical features. The research used Weighted Gene Co-expression Network Analysis (WGCNA) to establish links between gene expression and clinical parameters in infants admitted to the Pediatric Critical Care Unit with MSS. Additionally, various machine learning (ML) algorithms, including Support Vector Machine (SVM), Naive Bayes, K-Nearest Neighbors (KNN), Decision Tree, Random Forest, and Artificial Neural Network (ANN) were implemented to predict sepsis survival. The findings revealed a transition in gene function pathways from nuclear to cytoplasmic to extracellular, corresponding with Pediatric Logistic Organ Dysfunction score (PELOD) readings at 0, 24, and 48 h. ANN was the most accurate of the six ML models applied for survival prediction. This study successfully correlated PELOD with transcriptomic data, mapping enriched GE modules in acute sepsis. By integrating network analysis methods to identify key gene modules and using machine learning for sepsis prognosis, this study offers valuable insights for precision-based treatment strategies in future research. The observed temporal-spatial pattern of cellular recovery in sepsis could prove useful in guiding clinical management and therapeutic interventions.

Contextualizing patterns in short-term disaster recoveries from the 2015 Nepal earthquakes: household vulnerabilities, adaptive capacities, and change

Disaster recovery is multidimensional and requires theoretical and methodological approaches from the interdisciplinary social sciences to illustrate short- and long-term recovery dynamics that can guide more informed and equitable policy and interventions. The 2015 Nepal earthquakes have had catastrophic impacts on historically marginalized ethnic groups and Indigenous households in rural locations, arising in the immediate aftermath and unfolding for years afterward. Analyzing factors that shape household recovery patterns can help identify vulnerabilities and adaptive capacities in addition to signaling potential future changes. We pursue this goal using survey data from 400 randomly selected households in 4 communities over 2 10-week intervals at 9 months and 1.5 years after the earthquakes. Building on previous research that used non-metric multidimensional scaling ordination to identify patterns among multiple indicators of recovery (Spoon et al. 2020a), we investigate associations among these patterns of recovery, hazard exposure, and four domains of household adaptive capacity: institutional participation, livelihood diversity, connectivity, and social memory. Our results suggest: (1) social inequality, high hazard exposure, and disrupted place-based livelihoods (especially for herders, farmers, and forest harvesters on the geographic margins) had strong associations with negative recovery outcomes and displacement; (2) inaccessibility and marginality appeared to stimulate ingenuity despite challenging circumstances through mutual aid and local knowledge; (3) recoveries were non-linear, differing for households displaced from their primary home and agropastoral practice and those displaced to camps; and (4) some households experienced rapid changes while others stagnated. We contribute a temporal dataset with a random sample collected following a disaster that uses a theoretically informed quantitative methodology to explore linear and non-linear relationships among multidimensional recovery, adaptive capacity and change and provide an example of how vulnerabilities interact with adaptive capacity.

Application of clustering to improve the accuracy of linear approximations

The paper presents an approach to increase the accuracy of modelling an object of research based on a temporal multimodal data set with linear approximations using clustering. The proposed approach can be applied for creating digital twins of a researched object. The purpose of the study as a whole is to create a digital twin of the researched object based on a set of temporal multimodal data with previously unknown relationships, which will allow predictions with greater accuracy than a single linear approximation. The input data set is considered as complete and synchronized. This paper focuses on the use of clustering to analyse the sets of temporal multimodal data that characterize the researched object. The paper presents a method for dividing the data space into intervals, where linear approximations will be more accurate, by clustering based on the values of data points and their statistical characteristics for independent variables that show a nonlinear relationship with the dependent variable. As a result, the accuracy in models that use a linear approxima-tion for a given value has increased (the value of the mean square error used as an accuracy metric has decreased by 11 persents). At the same time, linear models have much better accuracy due to algorithms for calculating parameters that are less prone to overfitting and are more numerically stable. However, the proposed method is more computationally expensive due to the need to perform clustering, calculate intermediary approximations and store more models that describe the system. If there is more data, modalities and variations in the behaviour of the system, their number can be much larger and can lead to some reduction in productivity and accuracy.

A wheat integrative regulatory network from large-scale complementary functional datasets enables trait-associated gene discovery for crop improvement

Gene regulation is central to all aspects of organism growth, and understanding it using large-scale functional datasets can provide a whole view of biological processes controlling complex phenotypic traits in crops. However, the connection between massive functional datasets and trait-associated gene discovery for crop improvement is still lacking. In this study, we constructed a wheat integrative gene regulatory network (wGRN) by combining an updated genome annotation and diverse complementary functional datasets, including gene expression, sequence motif, transcription factor (TF) binding, chromatin accessibility, and evolutionarily conserved regulation. wGRN contains 7.2 million genome-wide interactions covering 5947 TFs and 127 439 target genes, which were further verified using known regulatory relationships, condition-specific expression, gene functional information, and experiments. We used wGRN to assign genome-wide genes to 3891 specific biological pathways and accurately prioritize candidate genes associated with complex phenotypic traits in genome-wide association studies. In addition, wGRN was used to enhance the interpretation of a spike temporal transcriptome dataset to construct high-resolution networks. We further unveiled novel regulators that enhance the power of spike phenotypic trait prediction using machine learning and contribute to the spike phenotypic differences among modern wheat accessions. Finally, we developed an interactive webserver, wGRN (http://wheat.cau.edu.cn/wGRN), for the community to explore gene regulation and discover trait-associated genes. Collectively, this community resource establishes the foundation for using large-scale functional datasets to guide trait-associated gene discovery for crop improvement.

Factors influencing the temporal variability of atmospheric methane emissions from Upper Silesia coal mines: a case study from the CoMet mission

Abstract. Methane is a powerful greenhouse gas responsible for around 20 % of radiative forcing (relative to the pre-industrial era) caused by all long-lived greenhouse gases (WMO, 2021). About 60 % of the global emissions are from anthropogenic sources, and coal mining is one of the largest contributors. Emissions are either estimated by bottom-up approaches (based on inventories) or top-down approaches (based on atmospheric measurements). Combining those with an accurate error estimation allows us to better characterise model errors e.g. caused by transport mechanisms. Here we provide a detailed description of factors influencing the coal mine methane emission variability. We use high-frequency (up to hourly) temporal data from seven coal mines in the Upper Silesian Coal Basin during the Carbon dioxide and Methane (CoMet 1.0) mission from 14 May to 13 June 2018. Knowledge of these factors for the individual ventilation shaft is essential for linking the observations achieved during the CoMet 1.0 mission with models, as most publicly available data in the bottom-up worldwide inventories provide annual emissions only. The methane concentrations in examined shafts ranged from 0.10 % to 0.55 %±0.1 % during the study period. Due to the changing scope of mining works performed underground, they were subjected to a significant variation on a day-to-day basis. The yearly methane average emission rate calculated based on 1 month's set of temporal data of the analysed subset of mines was of the order of 142.68 kt yr−1 (σ=18.63 kt yr−1), an estimate 27 % lower than the officially published State Mining Authority (WUG) data and 36 % lower than reported to the European Pollutant Release and Transfer Register (E-PRTR). We also found that emissions from individual coal mine facilities were over- and underestimated by between 4 % to 60 %, compared to the E-PRTR, when short-term records were analysed. We show that the observed discrepancies between annual emissions based on temporal data and public inventories result from (1) the incorrect assumption that the methane emissions are time-invariant, (2) the methodology of measurements, and lastly, (3) the frequency and timing of measurements. From the emission monitoring perspective, we recommend using a standardised emission measurement system for all coal mines, similar to the Methane Fire Teletransmission Monitoring System (SMP-NT/A). Legal safety requirements require all coal mines to implement this system. After an adaptation, the system could allow for gas flow quantification, necessary for accurate and precise estimations of methane emissions at a high temporal resolution. Using this system will also reduce the emission uncertainty due to factors like frequency and timing of measurements. In addition, it would be beneficial to separately identify the emissions from individual ventilation shafts and methane drainage stations. That would bridge the gap between bottom-up and top-down approaches for coal mine emissions. The intermittent releases of unutilised methane from the drainage stations are currently not considered when constructing regional methane budgets.

Change detection in a rural landscape: A case study of processes and main driving factors along with its response to thermal environment in Farim, Iran.

This study aims to investigate the alteration of Land Use/Land Cover (LULC) change and its response to changes in land surface temperature (LST) and heat island phenomena of a rural district known as Farim in the north of Iran from 1990 to 2020 using multi-date Landsat data. The random forest-based algorithm, supported by Google Earth Engine, is used to execute the LULC classification with an overall accuracy of more than 92%. Based on the LULC results, in terms of area changes, the classes of bare land, rice fields, and water bodies encountered an increase, but woods and dry farms decreased. The present study also incorporates the trends of land cover change that are analyzed using regression based on the temporal datasets of the three leading driving factors: temperature, precipitation, and population. The result demonstrates that the main changing factors of the mostly changed class (bare land) are population/precipitation and temperature/population. Additionally, the effect of LULC change on seasonal LST and urban heat island (UHI) is also analyzed in this study. The result witnessed a significant LST rise in the summer and winter seasons of about 12.87°C and 14.2°C, respectively over the study period. The Urban Thermal Field Variance Index (UTFVI), characterizing the heat island phenomenon, shows that the strongest UTFVI zone is in the central area and thenone UTFVI zone is in the surrounding region. Moreover, both seasons have seen a significant rise in none UTFVI zones compared to decreasing strongest UTFVI zone. The result of the present study will be helpful for urban planners and climate researchers who study future land cover change and its associated driving factors.

ClusTransition: An R package for monitoring transition in cluster solutions of temporal datasets.

Clustering analysis' primary purpose is to divide a dataset into a finite number of segments based on the similarities between items. In recent years, a significant amount of study has focused on the spatio-temporal aspects of clustering. However, clusters are no longer regarded as static objects since changes influence them in the underlying population. This paper describes an R package implementing the MONIC framework for tracing the evolution of clusters extracted from temporal datasets. The name of the package is clusTransition, which stands for Cluster Transition. The algorithm is based on re-clustering cumulative datasets that evolve at successive time-points and monitoring the transitions experienced by the clusters in these clustering solutions. This paper's contribution is to demonstrate how the package clusTransition is developed in the R programming language, and its workflow is discussed using hypothetical and real-life datasets.

Influence of sampling frequency and estimation method on phosphorus load uncertainty in the Western Lake Erie Basin, Ohio, USA

Accurate estimates of nutrient loads are necessary to identify critical source areas and quantify the impact of management practices on pollutant export. Previous studies have investigated nutrient load estimate uncertainty, but they often focus on nutrient loads estimated using an interpolation method for large-scale watersheds with short-term datasets. The study objective was to quantify uncertainty in soluble reactive phosphorus (SRP), total phosphorus (TP), and suspended solids (SS) load estimates from two small (<103 km2) agricultural watersheds in the western Lake Erie Basin resulting from different sampling frequencies. Each watershed had high temporal resolution datasets of discharge (15 min) and nutrient concentration (1 to 3 samples per day) collected over a 30-year period (1990–2020). Firstly, SRP, TP, and SS loads were calculated using the high temporal resolution datasets, which was assumed as “true loads”. Secondly, the high temporal concentration data were decomposed to semiweekly, weekly, biweekly, and monthly sampling and annual loads were estimated using four common load estimation methods to assess the effect of sampling frequency and load estimation method on load estimate error. Across the four different methods, the composite method had the lowest relative root mean square and absolute bias, but the rectangular interpolation method was the most precise. However, even with semiweekly sampling, the composite method resulted in an unacceptable level of precision (average imprecision = 39 %), while the interpolation method resulted in an unacceptable bias (average absolute bias = 16 %). Because neither method could provide acceptable accuracy and precision at the lowest decrease in sampling (e.t. semiweekly sampling), continued daily sampling is recommended in these watersheds.