Uncertainty Analysis Research Articles

Robust retrofits for rural house envelope considering construction quality and occupant behavior uncertainties: A MOO-integrated Taguchi method

Due to the underdeveloped socio-economic environment, the uncertainties in construction quality and occupant behavior are more prominent for rural house than urban residential buildings. If these uncertainties are not considered, there are higher risks of inaccurate performance prediction, unrealistic retrofit decisions and causing serious performance gap. To address this challenge, this paper proposes a robust envelope retrofitting workflow that combine deterministic multi objective optimization (MOO) and Taguchi method. This workflow stands out for its utilizing mature deterministic MOO to fast approximate Alternative schemes, and utilizing Taguchi method to handle uncertainties, reducing some key disadvantages of traditional robust design optimization (RDO), such as heavy calculating burden, overly conservative solution and difficulty to predefine proper robust objectives or performance target value. The proposed workflow consists of four main steps, formulating Alternative schemes by deterministic MOO, screening highly sensitive uncertain parameters by uncertainty and global sensitivity analysis, formulating Typical uncertain scenarios by orthogonal experiments (OE) and calculating the comprehensive robustness indicators. Results show that by employing the proposed workflow, the selected robust optimal solution not only proves to be robust but also leads to preferable performance enhancements. Compared with the reference mode, the energy efficiency has been improved by 33.2% to 49.4%, the discomfort hours have been reduced by 0.4% to 26.5%, and the robustness has been enhanced by 36.1%.

Hail hazard modeling with uncertainty analysis and roof damage estimation of residential buildings in North America

This research presents a statistical approach for hail risk modeling that incorporates the uncertainties of hail model prediction to provide insight into assessing the roof damage of a residential house in hail events. By quantifying the inherent uncertainties in evaluating hailstorm characteristics, this study extends the current existing hail models. The hail data are sourced from the Community Collaborative Rain, Hail and Snow Network (CoCoRaHS) in the U.S. In the modeling process, the largest hail diameter reported in the CoCoRaHS database serves as a primary input variable to estimate the number of observations for the largest hail diameter, hailstorm duration, and hit rate. The assessment of hail risk in this study focuses on the probability of hail damage and resultant repair costs for five types of roofs in North America (unrated roof and impact-resistant roofs with UL 2218 rating classes 1 to 4). The probability of hail damage is calculated as the failure probability by integrating all individual hailstone hits having variable diameters during a hailstorm with fragility curves, which estimate the probability that hailstones will fracture asphalt shingles (allowing water infiltration) or that they dislodge enough granules to cause visible damage requiring replacement for aesthetic reasons. The results reveal that an impact-resistant roof (impact-resistant rating classes 1 to 4) is associated with lower hail risks, with 60 % to 98 % reduction on average compared to unrated roofs. This study provides a comprehensive uncertainty modeling approach for hail hazard and risk, enabling better-informed decision-making and risk management strategies.

Optimal Sizing and Economic Analysis of Community Battery Systems Considering Sensitivity and Uncertainty Factors

Efficient sizing and economic analysis of community battery systems is crucial for enhancing energy efficiency and sustainability in rooftop PV panel-rich communities. This paper proposes a comprehensive model that integrates key technical and economic factors to optimize the size and operation of the prosumer-owned battery, maximizing the financial returns over the life span of the battery. Sensitivity and uncertainty analyses were also conducted on a number of factors that are constantly changing over the years such as per-unit cost of the battery and interest rate. Monte Carlo simulations were utilized to replicate the unpredictable PV generations and the volatility of house load demands. The developed model is evaluated under three scenarios: a shared community battery for all houses, individual batteries for each house, and a combined system with an additional large load. Particle Swarm Optimization (PSO) is utilized to maximize the formulated objective function subject to the considered constraints. The findings indicate that integrating community batteries offered a substantial economic advantage compared to individual home batteries. The additional revenue stream of incorporating larger consumers looking to reduce their carbon footprint (e.g., commercial) returned a further augmented net present value (NPV). The influence of different tariff structures was also assessed and it was found that critical peak pricing (CPP) was the most prolific. The outcomes offer valuable insights for policymakers and stakeholders in the energy sector to facilitate a more sustainable future.

Research on temperature performance prediction of vortex tubes based on artificial neural networks

Abstract This study constructs a hybrid neural network model by integrating the physical constraints of the Bernoulli equation and Nikolaev's formula. The model is designed to explore and predict the variation pattern of the cold end temperature in a vortex tube. The input parameters include inlet pressure, inlet temperature, and cold mass fraction, with the cold end temperature as the output parameter. The network employs a multilayer feedforward model and the Levenberg-Marquardt learning algorithm, using a hyperbolic tangent function as the activation function. To evaluate the statistical validity of the developed model, the coefficient of determination (R²) and root mean square error (RMSE) are utilized, along with an analysis of the model's uncertainty and robustness. The hybrid model achieves an R² of 0.9936 and an RMSE of 0.3392, demonstrating strong performance in terms of uncertainty and robustness. These results indicate that the model accurately predicts the cold end temperature variation in the vortex tube. Furthermore, the findings reveal an optimal pressure range (0.49 MPa to 0.76 MPa) and cold mass fraction range (0.1 to 0.2) that minimize the cold end temperature.

Uncertainty Analysis of Irrigation Canals Operation

Uncertainty Analysis of Irrigation Canals Operation

Fate and transport of ten plant protection products of emerging concern in a coastal lagoon: Application and evaluation of a multimedia level III fugacity model

Multimedia fugacity models are effective tools for studying the environmental behaviour and occurrence of contaminants of emerging concern (CECs) and assessing associated risks, especially when experimental data is limited. These models describe processes controlling chemical partitioning, transport, and reactions in environmental media using mathematical statements based on the concept of fugacity.To aid in identifying and prioritizing CECs for future local monitoring, we present here the application of a level III multimedia fugacity model assuming non-equilibrium between compartments and steady-state conditions. This model estimated predicted environmental concentrations (PECs), persistence, distribution, and transport of ten plant protection products (PPPs) in the Venice Lagoon, a complex coastal environment under high anthropogenic pressure.The model was evaluated through uncertainty and sensitivity analysis using the Monte Carlo approach and by comparing PECs with PPP concentrations measured during four sampling campaigns. Results showed good agreement with field data, with the highest concentrations in water and sediments estimated for glyphosate, followed by imidacloprid, metaflumizone, and triallate. The model indicated accumulation of all investigated PPPs in sediments. For most chemicals, advection outflow and degradation in the water column were the main removal mechanisms, while volatilization was significant only for oxadiazon and triallate.Sensitivity and uncertainty analysis revealed that degradation rates, organic carbon/water partitioning coefficients (KOC), and parameters describing air-water interactions had the strongest influence on the model's results, followed by inputs accounting for sediment sinking and resuspension. The lack of data on PPP degradation in brackish waters accounted for most of the uncertainty in model results.This work shows how a relatively simple multimedia model can offer new insights into the environmental behaviour of PPPs in a complex transitional waterbody such as the Venice lagoon, providing useful data for the identification of the CECs to be prioritised in future local monitoring efforts.

RNN-Based Monthly Inflow Prediction for Dez Dam in Iran Considering the Effect of Wavelet Pre-Processing and Uncertainty Analysis

In recent years, deep learning (DL) methods, such as recurrent neural networks (RNN). have been used for streamflow prediction. In this study, the monthly inflow into the Dez Dam reservoir from 1955 to 2018 in southwestern Iran was simulated using various types of RNNs, including long short-term memory (LSTM), bidirectional long short-term memory (Bi-LSTM), gated recurrent unit (GRU), and stacked long short-term memory (Stacked LSTM). It was observed that considering flow discharge, temperature, and precipitation as inputs to the models yields the best results. Additionally, wavelet transform was employed to enhance the accuracy of the RNNs. Among the RNNs, the GRU model exhibited the best performance in simulating monthly streamflow without using wavelet transform, with RMSE, MAE, NSE, and R2 values of 0.061 m3/s, 0.038 m3/s, 0.556, and 0.642, respectively. Moreover, in the case of using wavelet transform, the Bi-LSTM model with db5 mother wavelet and decomposition level 5 was able to simulate the monthly streamflow with high accuracy, yielding RMSE, MAE, NSE, and R2 values of 0.014 m3/s, 0.008 m3/s, 0.9983, and 0.9981, respectively. Uncertainty analysis was conducted for the two mentioned superior models. To quantify the uncertainty, the concept of the 95 percent prediction uncertainty (95PPU) and the p-factor and r-factor criteria were utilized. For the GRU, the p-factor and r-factor values were 82% and 1.28, respectively. For the Bi-LSTM model, the p-factor and r-factor values were 94% and 1.06, respectively. The obtained p-factor and r-factor values for both models are within the acceptable and reliable range.

Sectoral Size‐Resolved Particle Number Emissions With Speciation: Emission Profile‐Based Quantification and a Case Study in the Yangtze River Delta Region, China

AbstractSizes and number concentrations are critical parameters for the impact of atmospheric particles on climate and human health. However, comprehensive studies focusing on size‐resolved particle number (PN) emissions from various sectors are scarce. This study aims to fill this research gap by developing sectoral size‐resolved PN emissions for major species including sulfate, organic mass (OM), and black carbon (BC). The size‐resolved emission profiles derived from various measurements in the literature were integrated with a particle mass emission inventory (EI) for 13 major sectors in the Yangtze River Delta region of China as a case study. The particle number size distribution (PNSD) of emitted particles exhibited two distinct peaks: one at approximately 10 nm and the other in the range of 40–60 nm. The primary contributors to PN emissions in the region in 2017 were power plants, gasoline vehicles, diesel vehicles, and cooking sources. In terms of species, OM dominated PN emissions, followed by primary sulfate and then BC. A regional size‐resolved aerosol model employing the size‐resolved PN EI developed here (referred to as the BIN‐SPE experiment) provided reasonably accurate temporal variations of the total PN concentration and captured the PNSD within the size range of 10–300 nm. Uncertainty analysis of sectoral PN emissions across size ranges was carried out and the performance of the BIN‐SPE experiment was compared with those of three commonly used PN emission parameterizations. Our model evaluations highlight future needs for in‐depth investigations into more advanced size‐resolved emissions and secondary OM formation mechanisms.

The Environmental Impact of Low-Pressure Anhydrous Dyeing Technology for Polyester: Carbon Footprint Quantification and Evaluation

Many emerging dyeing technologies have recently entered laboratory research or industrial production phases. However, only a few studies have evaluated and compared the environmental impact of different dyeing systems. This study calculated the carbon footprint of low-pressure anhydrous dyeing for polyester and compared with conventional water bath dyeing. The results showed that the carbon footprint of the anhydrous dyeing process was 1670 kgCO2 eq/1000 kg polyester. Steam occupied the most significant proportion, accounting for 55.5%, followed by electricity at 35.5%. The waste has the lowest environmental impact, with 0.55%. Uncertainty analyses showed that the data and results in this article were reliable. The results of the sensitivity analyses indicated that more attention should be paid to the data quality of steam and electricity. Compared with the traditional water bath dyeing process, the low-pressure anhydrous dyeing system can reduce greenhouse gas emissions by more than 30% and, more importantly, can avoid almost all the consumption of clean water. This anhydrous dyeing system can save water and reduce carbon emissions, which is of positive significance to the sustainable development of the textile and fashion industry. Further studies could use the water footprint as an indicator to evaluate this dyeing system in terms of water consumption and potential environmental impact.

Evaluation of analytical uncertainty in quantitative determination of elements - The case of boron

BackgroundThe uncertainty including accuracy and precision is the most vital factor that determines the overall quality of quantitative analysis. The uncertainty has been, however, evaluated relatively within the same analytical technique. Given this background, the present study evaluates the uncertainty on quantitative elemental analysis with a quasi-absolute approach. The objectives of this study are (1) to investigate the analytical uncertainty of prompt gamma-ray analysis (PGA), a chemical interference-free method in principle, on the quantitative analysis of boron and (2) to evaluate the applicability of inductively coupled plasma optical emission spectrometry (ICP-OES), a common technique for quantitative elemental analysis including boron. ResultsPGA provided analytical quantity that is equivalent to the true quantity. The quantity values determined for a series of boron-containing samples are all well above the detection limit of the PGA system, the quantity resolution of which is also much smaller than the minimum difference in quantity among the samples. These facts confirm that the evaluation of analytical uncertainty with the present PGA system is statistically meaningful. The analytical uncertainty in both methods was adequately evaluated by comparing the results from PGA and ICP-OES for a series of boron-containing materials with different physical/chemical properties (i.e. CrB2, B4C, and solidified products of stainless steel-B4C melt) and the major sources of uncertainty in both methods are specified. The conditions for sample preparation/pretreatment were optimized to lower the uncertainty. Significance and noveltyThis study proposes a new concept to perform the quasi-absolute evaluation of analytical uncertainty by employing a chemical interference-free technique. The proposed concept is not limited to the combination of PGA and ICP-OES as demonstrated in this study, but it is applicable to any combination of any analytical methods for any element. Hence, the concept demonstrated in this study could be beneficial to a wide range of analytical chemistry.

Application of INTDOSKIT tool for assessment of uncertainties on dose coefficients for ingestion of uranium by workers

The International Commission on Radiological Protection (ICRP) has developed the Human Alimentary Tract Model (HATM) to calculate radiation doses from the ingestion of radionuclides for the protection of workers and the public. In parallel, the ICRP's Occupational Intake of Radionuclides (OIR) series provides biokinetic models and dose coefficients based on a reference human, primarily for regulatory purposes. Although these coefficients are not usually checked for uncertainties, the investigation of such uncertainties is crucial to ensure their reliability in radiation protection. This study uses INTDOSKIT, a software tool developed using the R programming language and RStudio as the Integrated Development Environment (IDE), to calculate doses and explore uncertainties following ingestion of U-238 by workers. Two scenarios were investigated: Case I, using the latest HATM model and the systemic uranium model from ICRP publications 100 and 137 respectively, and Case II, based on data from literature, using older ICRP models. In both cases, intake of U-238 in its soluble form (Type F) was modeled and the results were validated using the ICRP OIR dataviewer software. Following validation, uncertainty and sensitivity analyses were performed. In Case I, uncertainties were assigned to the particle transport parameters in both the systemic model model and HATM as well as the uranium uptake fraction into the blood. While in Case II they were limited to the uptake fraction (f1) and the systemic uranium model. A Monte Carlo simulation of 60,000 runs was performed for both cases, sampling model parameters from their respective probability distributions to generate dose distributions. The influence of each parameter on these distributions was also analyzed. Probability distributions were inferred to the calculated dose values using the maximum likelihood estimation method and the Kolmogorov-Smirnov goodness-of-fit. The results showed that for both cases the committed effective dose coefficient, e (50), followed a lognormal distribution. Case I had a geometric mean of 3.2E-08 Sv/Bq and GSD of 2.0, while case II had a slightly lower geometric mean of 3.1E-08 Sv/Bq and GSD of 1.9. Sensitivity analysis showed that the main contributor to dose uncertainty was the fraction of uranium absorbed from the small intestine into the blood. Both cases showed similar trends, with slightly higher results in case I. Overall, this study demonstrates the effectiveness of INTDOSKIT in calculating dose coefficients and analyzing uncertainties. It suggests that while the ICRP reference values remain useful for protection, the incorporation of additional statistical measures and distribution characteristics could further enhance radiation protection strategies.

Optimal allocation method of oxygen enriched combustion-carbon capture low-carbon integrated energy system considering uncertainty of carbon-source-load

In the context of green grid, it is crucial to enhance the low-carbon transformation of integrated energy systems. To improve the degree of low-carbon in these systems, this paper proposes an optimal allocation method of oxygen enriched combustion-carbon capture low-carbon integrated energy system considering the uncertainty of carbon-source-load. Firstly, this paper provides an analysis of uncertainties of carbon-source-load and uses budget uncertainty sets to model the three uncertain parameters. Secondly, in the integrated energy system, the by-product O2 of hydrogen production by electrocution and the oxygen storage device are used to provide oxygen-enriched combustion conditions for the gas turbine to reduce the CO2 generated in the combustion process. At the same time, the carbon capture device is used to collect CO2, and the oxygen enriched combustion-carbon capture integrated energy system is formed. Finally, an improved multi-populations quantum-behaved particle swarm optimization algorithm is proposed to solve the proposed model. This method considers the uncertainty of carbon emission intensity, which makes the optimal allocation of integrated energy system more inclined to low-carbon rather than economic. In addition, in terms of model structure, this method integrates oxygen enriched combustion-carbon capture into the integrated energy system to form a low-carbon model, which directly improves the low-carbon performance of the system. The simulation results show that the carbon emission of the proposed method is reduced by 38.8 % compared with other methods.

Performance Assessment of Hybrid PV/PVT Collectors Incorporating Natural Water-Cooling Circulation

The present work investigates outdoor recitals and characteristics of hybrid PVT collectors and compares using non-cooled Photo-Voltaic (PV) collectors on a clear day at Khamshet, Pune, India. The hybrid PVT is designed, fabricated, and mounted on the terraces of the institute to ensure maximum radiation will fall on PV and PVT collector. The spiral circular thermal absorber is manufactured and placed at the backside of the photovoltaic to lower the surface temperature by extracting heat through water flowing through the absorber. The experimentation is performed at 0.03 kg/sec of water and natural cooling circulation is adopted for experimental work. The uncertainty analysis is also performed to ensure the accuracy of the results. The investigation observed that the PVT collector is superior to the PV system from electrical and thermal efficiency viewpoints. The cutback in PV module temperature was observed in a variation of 8.7-13.7%, which justifies using the water-cooling technique. The maximum electrical and thermal efficiency of 6.93 % and 52.7% were found for PVT collectors while sole maximum electrical efficiency of 5.62 % was found for PV collectors. This study concludes that the PVT collector has better performance characteristics than the PV collector and can be further enhanced using different fluid and thermal absorber designs.

Prediction of Soil Temperature in Wheat Field Using Machine Learning Models

ABSTRACT Wheat is a very vital cereal crop in the development of a nation’s economy and agricultural sector. A crucial element that profoundly affects the growth and productivity of wheat is the soil temperature. As an insulating layer, straw mulch controls the soil temperature. Field measurement of the soil temperature is time-consuming and costly; therefore, the present study investigates the applicability of random forest (RF), support vector machine (SVM) and generalized regression neural network (GRNN) models for estimating soil temperature in wheat fields covered with straw mulch using meteorological, soil cover and agronomical parameters. A field study was conducted on the wheat cultivar with two soil cover treatments for measuring the soil temperature at three depths (5, 50 and 80 cm). Soil plant analysis development value; soil cover and depth; daily meteorological data such as average air temperature, average relative humidity, sunshine hours and vapor pressure were used as inputs for machine learning models in training and testing. Statistical performance indicators showed applicability of three models with a root mean square error (RMSE) ranging from 0.245–0.595°C, 0.867–0.886°C and 0.557–0.812°C, Nash-Sutcliffe efficiency (NSE) ranging from 0.917–0.987, 0.823–0.836 and 0.804–0.935 and Lin’s concordance correlation coefficient (CCC) ranging from 0.949–0.991, 0.887–0.897 and 0.881–0.957 for the RF, SVM and GRNN, respectively. Results indicated better performance of the RF model for predicting the soil temperature at different depths in the case of both crop and soil cover conditions. Also, the uncertainty analysis indicated the better performance of RF with R-factor and P-factor value of 0.40 and 0.84, respectively.

MODELLING THE IMPACT OF SOIL AND METEOROLOGICAL PA-RAMETERS ON CARBON DYNAMICS IN WETLAND ECOSYSTEMS

Wetlands are characterised by distinct hydrological regimes and have significant importance in the global carbon cycle, having the potential to reduce carbon emissions through long-term carbon storage in the soil. In this study, carbon dynamics were simulated using a process-based model DeNitrification-DeComposition (DNDC), for two locations along Dâmbovița River case study area. These scenarios took into consideration the interconnection of soil parameters, hydrology, meteorological conditions and vegetation type. The findings showed that soil CO2 emissions are positively and strongly correlated with air temperature and soil moisture, with changes in the water content of the soil regime having the greatest impact on CO2 fluxes. Also, the model simulations have been validated by statistical analysis of uncertainties with the values of CO2 fluxes measured in situ using the dynamic closed chamber method. By comparing DNDC outputs with field measurements, the performance of the model was evaluated in different environmental conditions and the results were consistent, which increased confidence in its application for assessing wetland ecosystems. These results contribute to a more comprehensive understanding of the carbon cycle in wetlands and an improved estimation of the effects of climate change on the dynamics of carbon in these ecosystems.

Fabrication and building energy-saving performance evaluation of polyethylene glycol/polymethyl methacrylate/expanded graphite thermal enhanced shape-stable phase change material

Integrating Phase Change Material (PCM) with building envelope is a promising way to reduce building energy consumption. However, leakage and insufficient PCM loading rate for existing encapsulation strategies limit its further application. In this work, a safer and easier way to fabricate Shape-Stable Phase Change Material (SSPCM) is proposed, whose products combine the outstanding leakage-proof property and high PCM loading rate. With the method of in-situ polymerization, not only the Polyethylene Glycol (PEG) is packaged by Polymethyl Methacrylate (PMMA), but the Expanded Graphite (EG) also has a chance to be added as the property regulator. The prepared SSPCMs are proved to be shape-stable over melting temperature with a leakage rate under 1 %. The characterization test reveals that with EG mass fraction increase from 0 to 2.5 %, the thermal conductivity augments from 0.23 to 1.73 W/(m·K) while the phase change latent enthalpy changes from 85.11 kJ/kg to 116.10 kJ/kg. Notably, the EG is more than a thermal conductivity enhancer, it also plays an important role in the modification of comprehensive properties. To optimize the building energy-saving performance of SSPCMs, the factors affecting building cooling electricity consumption like deployment position and thermal properties are evaluated in detail. The simulation results show that the optimum strategy is employing SSPCM with 2.0 wt% EG in the middle of the envelope, the cooling electricity consumption can be reduced by 8.64 % compared with the benchmark. The physical mechanism of energy-saving is also discussed from multiple angles. From a statistical view, sensitivity and uncertainty analysis of the simulation results is completed, to identify the influence of input parameters and validate the reliability of the simulation results. Finally, an economic analysis is carried out, proving the economic feasibility for further application. This research provides valuable insights for SSPCM’s application and design of energy-efficient buildings.

Uncertainty analysis of FLiNaK thermophysical properties on convective heat transfer characteristics in circular tube

Molten salts have attracted a spate of quantity of interests in energy and chemistry fields due to their large volumetric capacity, and thermodynamic stability at elevated temperature. However, there are significant uncertainties in the physical properties of molten salts, such as density, specific heat capacity, dynamic viscosity, and thermal conductivity, due to inexperienced measurement techniques and technology. These uncertainties could have certain impact on the evaluation of molten salts heat transfer characteristics. Therefore, an uncertainty analysis is performed regarding FLiNaK thermophysical properties on convective heat transfer characteristics in circular tube. Then, a polynomial chaos expansion (PCE) method is performed to study the uncertainty affecting the heat transfer characteristic. In the process of uncertainty analysis, tensor product quadrature nodes are used to calculate representative sample points and reduce computational costs. Three primary physical properties of molten salt are taken into account as the input parameters. The Sobol composition method is also used to analyze the contributions of each parameter to heat transfer. The results of the uncertainty analysis suggest that the uncertainties in dynamic viscosity, thermal conductivity, and specific heat capacity have a significant impact on the heat transfer of molten salt, contributing to 80 %, 14 %, and 6 % of the total variability, respectively. It also suggests that the polynomial chaos expansion methodology is both novel and reliable when applied to uncertainty analysis of molten salt.

Stable phytoplankton community compositions in Lake Mead (Nevada-Arizona, USA) during two decades of severe drought

Lake Mead, a large reservoir on the Colorado River and a critical drinking water source for the southwestern United States, typically exhibits high water quality, characterized by low nutrient and chlorophyll-a concentrations. This stability persists despite the inflow of highly treated wastewater since the 1960s and significant water level declines since 2000, driven by the ongoing Megadrought and basin-wide consumptive use. Such environmental changes may alter phytoplankton communities, potentially leading to increased cyanobacteria abundance, which could negatively impact water quality and the aquatic ecosystem through harmful algal blooms and toxin production. Here we analyzed 17 years of phytoplankton community structure and chlorophyll-a concentrations in Lake Mead, alongside quantitative water quality data, including nutrients, temperature, and water clarity, to assess the effects of environmental changes on phytoplankton communities. Contrary to the hypothesis that cyanobacteria abundance would have increased throughout the reservoir, our results indicate that phytoplankton community structures have remained largely stable, except for shallow areas where increases in temperature or phosphorus levels were observed. Additionally, we evaluated machine learning models for predicting changes in phytoplankton community structures. While the models confidently predicted changes in total phytoplankton biovolume and chlorophyll-a concentrations within the input parameter boundaries, predictions of peak biovolume showed considerable uncertainty, emphasizing the importance of incorporating uncertainty analysis in forecasting and communicating results. This study underscores the current buffering capacity of large, oligotrophic reservoirs like Lake Mead to maintain stable phytoplankton communities despite environmental changes. However, it also highlights the potential for significant community shifts if this buffering capacity is exceeded.

Measuring the low divergent luminous flux emitted by LED torch lamps using a simple detector and a spectrometer as an alternative to an integrating sphere

The widespread use of light emitting diodes (LEDs) in various applications has necessitated accurate measurement of their photometric parameters, particularly luminous flux. While traditional methods employing integrating spheres offer precise measurements, simpler and often cheaper techniques utilizing radiometric detectors and spectrometers present viable alternatives, especially for directional emissions. This article presents a comprehensive methodology for measuring luminous flux using a simple detector and a spectrometer, discussing equipment calibration, uncertainty analysis, and validation of the described method. The study delves into the intricacies of detector and spectrometer calibration, offering insights for traceability to a known reference standard in lumen measurement practices. Additionally, the article addresses uncertainties associated with luminous flux calculations and validates the proposed method against measurements from an independent laboratory. Limitations such as spectral response variations and temperature effects on measurements are mentioned. Moreover the article encompasses formula derivations and calculations, empowering readers to create and validate their own computation tools.

Combining earth observations with ground data to assess river topography and morphologic change: Case study of the lower Jamuna River

The Jamuna is a major braided river of Bangladesh and poses enormous challenges related to flooding, erosion, and sedimentation. Effective river management requires an understanding of the Jamuna’s sediment transport dynamics and channel morphology. Yet, developing that understanding is hindered by a lack of topographic data for the river corridor. In this study, the waterline method was applied to a 65 km reach of the Jamuna to derive river channel and floodplain topography above the low water level. The analysis relies on surface water detection from Sentinel-1 and Sentinel-2 data, along with in-situ water level observations, to generate river-floodplain DEMs for the years 2016–2019. Morphologic change was assessed through DEMs of difference for sequential years and for the overall study period. A quantitative uncertainty analysis was conducted to characterize errors in the waterline DEMs, vertical change detection, and gross and net volumetric changes. The results indicate that gross annual erosion and accretion volumes have a similar order of magnitude to contemporary estimates of the river’s sediment influx. The study highlights that localized and reach-scale lateral exchange is a key component of the overall sediment budget. Finally, the uncertainty analysis framework provides insight and recommendations for how errors can be assessed and reduced in future waterline applications.