Availability Scenarios Research Articles

Hydrological impacts of dam regulation for hydropower production: The case of Lake Sibinacocha, Southern Peru

Study regionVilcanota-Urubamba river basin, Southern Peru. Study focusHydraulic infrastructure plays a fundamental role for energy production, drinking and irrigation water storage and flood control in regions with seasonal river flow. The high-Andean Lake Sibinacocha has been regulated since 1988 to increase energy production of the Machupicchu hydropower plant. In this study, river streamflow changes are evaluated by analyzing precipitation and discharge trends using indicators of hydrologic alteration and ecoflow for natural (1965–1987) and altered (1988–2016) flow regimes. New hydrological insights for the regionFor the altered flow regime, an ecodeficit of about 20% (compared to natural river flow) and an ecosurplus > 30% were found during the wet season (December-February) and dry season (June-August), respectively. These changes have reduced the risk of water shortage (dry season) and flood (wet season) and contribute to increasing water use including hydropower production, irrigation and drinking water. However, river alteration might lead to considerable impacts on riverine ecosystems.Despite major limitations related to data scarcity and complex environmental processes in the basin, our results highlight the usefulness of combined methods of hydrological alteration and ecoflow to effectively evaluate water regime changes in regulated basins. An integrated scientific approach is necessary to address uncertainties and develop meaningful future water availability scenarios that guide hydropower projects with improved water and energy security considering minimal impacts on human and natural systems.

Macroinvertebrate community structure and ecological status in Portuguese streams across climatic and water scarcity gradients

Climatic and hydrological variation is of utmost importance in regions of the globe facing water scarcity and river intermittency (e.g. areas under Mediterranean influence). The main aim of this study was to compare the macroinvertebrate community structure and its bioindicator value (i.e. waterbody ecological status) in streams from three Portuguese regions (Regions C, N and S), representing distinct climatic features and water availability scenarios. Results showed that, overall, sampling sites differed in their climatic, hydromorphological and physical and chemical features, and environmental (abiotic) and ecological (community dissimilarities) gradients among regions were clearly identified. Sites from Regions C (wettest) and S (driest) represented non-overlapping clusters of samples, both in terms of their environmental context and ecological (dis)similarity; sites from Region N occupied an intermediate position, and their macroinvertebrate community was highly variable locally. This coincided with overall higher ecological quality and uniformity in Region C, whereas Regions N and S were more heterogeneous and generally presented lower ecological quality. Our data showed that climate (and associated water scarcity) is coupled with other environmental drivers of the macroinvertebrate community structure, highlighting a shared influence of the three environmental components (climatic, hydromorphological, and physical and chemical) in the modulation of macroinvertebrate communities.

Semantic segmentation of water bodies in very high-resolution satellite and aerial images

This study evaluates the performance of convolutional neural networks for semantic segmentation of water bodies in very high-resolution satellite and aerial images from multiple sensors with particular focus on flood emergency response applications. Different model architectures (U-Net and DeepLab-V3+) are combined with encoder backbones (MobileNet-V3, ResNet-50 and EfficientNet-B4) and tested for their ability to delineate inundated areas under varying environmental conditions and data availability scenarios. An unprecedented reference dataset of 1120 globally sampled images with quality checked binary water masks is introduced and used to train, validate and test the models for water body segmentation. Furthermore, independent test datasets are developed to test the generalization ability of the trained models across regions, sensors (IKONOS, GeoEye-1, WorldView-2, WorldView-3 and four different airborne camera systems) and tasks (normal water and flood water segmentation). Results indicate that across all tested scenarios a U-Net model with Mobilenet-V3 backbone pre-trained on ImageNet performs best. While using R-G-B image bands performs well, adding the near infrared band (if available) slightly improves prediction results. Similarly, adding slope information from an independent digital elevation model increases accuracies. Train-time augmentation and contrast enhancement could improve transferability across sensors and in particular between satellite and aerial images. Moreover, adding noisy training data from freely available online resources could further improve performance with minimal annotation effort.

Robust Multi-Channel Far-Field Speaker Verification Under Different In-Domain Data Availability Scenarios

The popularity and application of smart home devices have made far-field speaker verification an urgent need. However, speaker verification performance is unsatisfactory under far-field environments despite its significant improvements enabled by deep neural networks (DNN). In this paper, we summarize our previous work and propose multiple training strategies and models for multi-channel far-field speaker verification with different in-domain data availability scenarios. The experiments are conducted on the FFSVC20 dataset, and we proposed the cross-device and cross-domain trials. We focus on single-channel and multi-channel speaker verification training based on the dataset. For single-channel speaker verification, considering the size of training data and availability of labels, we introduce three training scenarios and given our proposed training methods, including 1) given zero out-of-domain data and few in-domain labeled data; 2) given large-scale out-of-domain labeled data and few in-domain labeled data; 3) given large-scale out-of-domain labeled data and few in-domain unlabeled data. To this end, we propose a meta-learning approach, refined transfer learning methods, and semi-supervised learning for three scenarios, respectively. For multi-channel speaker verification, we first introduce two types of 3 dimension convolution (3D Conv) residual network (ResNet) models proposed in our previous works, including fully 3D ResNet and incorporating 3D Conv with 2D Conv ResNet (3D2D-ResNet). In this paper, we propose channel-wise 3D squeeze-and-excitation ResNet (C3DSE-ResNet) and spatial-wise 3D SE ResNet (S3DSE-ResNet) to further explore the channel dependencies and improve the 3D ConvNet performance. The results show that the proposed strategies and models can significantly boost performance under the far-field scenario.

Optimal Power Allocation Under Different Power Availability Scenarios for Multitarget Tracking With C-MIMO Radar Systems

Optimal Power Allocation Under Different Power Availability Scenarios for Multitarget Tracking With C-MIMO Radar Systems

Linking bioenergy production by agricultural residues to sustainable development goals: Prospects by 2030 in China

Large-scale bioenergy production utilizing agricultural residues (ARs) in multi-paths has potential energy, environmental, economic and social (EEES) impacts. If, how and to what extent these impacts integratively bring sustainability contributions at regional scale remains to be explored. Herein, the EEES impacts quantified based upon a multi-resource-technology-output life cycle framework are reflected as 17 indicators, and linked to the sustainable development goals (SDGs) and their sub-goals to explore the sustainability contributions and trade-offs in 31 Chinese provincial regions by 2030 in three scenarios of ARs availability. Results indicate that the ARs can contribute to 1.86 billion GJ net energy output and 32.1 billion yuan net profit by 2030 in the complete scenario with the highest ARs availability. Attributed to open burning avoidance and fossil energy offset, there are mitigation effects on 11 out of 13 life cycle environmental impact categories, however intensification effects on Freshwater eutrophication and Marine eutrophication. Regional sustainability score closely depends on ARs availability, while does not strictly parallel it. The ARs-abundant regions, such as Heilongjiang, Henan and Jilin, benefit outstandingly from bioenergy production to enhance regional sustainability (especially on SDG1, SDG7 and SDG13). The findings can provide decision-makers with references to soundly boosting agricultural bioenergy development to consolidate regional sustainability.

Climate change and land-use change impacts on future availability of forage grass species for Ethiopian dairy systems

Forage grasses are central feed resources for livestock globally. In Ethiopian dairy systems, they serve as feed sources during both wet and dry seasons, yet escalating climate change could threaten forage supply. Here, we investigate projected climate change impacts on three forage grasses currently recommended for Ethiopian dairy systems. We determine areas of geographical suitability for each species using three climate projections generated by General Circulation Models (GCMs) and calculate their ability to meet predicted dry matter demand under four scenarios for livestock intensification and land availability. By 2050, Buffel grass (Cenchrus ciliaris) is likely to be negatively affected by climate change in regions such as Tigray, while Rhodes grass (Chloris gayana) and Napier grass (Cenchrus purpureus) may have improved suitability under future climates. Our findings suggest that feed demands could theoretically be met by production of these forage grasses under current and future climates. However, if land availability is reduced and herd composition shifts towards higher-productivity exotic breeds, forage resources will not meet cattle demand even with improved agronomic management.

Optimization Modeling for Regional Energy System Management Coupled with Energy–Water Nexus and Carbon Emission Reduction: A Case Study

Under global climate change and resource crisis, the correlation and restriction between energy, water, and carbon emission are increasingly prominent, which has posed great challenges for the sustainable development of energy systems. This article develops a dual-interval stage-fuzzy credibility-constrained programming model for regional energy system management problems with energy–water nexus (EWN) and carbon emission reduction, in which uncertainties expressed as discrete intervals, probability distributions, and dual-interval fuzzy numbers can be addressed. It can provide a trade-off among economic cost, system risk, and environmental objective. Then, the hybrid programming method is applied to a case of Guangdong Province, China to verify its applicability, where a series of scenarios with respect to water resources and carbon emission constraints are explored and examined. The robust solutions of optimal strategies for electricity generation, capacity expansion, water supply, and carbon capture are investigated and obtained. Results disclose that different scenarios associated with water resource availability and carbon emissions would deeply affect the EWN system planning: (1) Compared with low carbon emission standard, the proportion of renewable power generation under high carbon emission scenario would increase by [1.39, 1.68] %, [1.69, 2.35] %, and [1.49, 1.94] % in periods 1–3, respectively. (2) Scarce water availability scenario would restrict coal-fired power generation and simulate the development of renewable energy. These findings can be useful for decision makers to gain deep insights into energy system management in a sustainable way considering energy structure adjustment, multiple uncertainties, EWN, and carbon emission reduction.

Mathematical modeling of drinking water availability in Kharkiv region (Ukraine) at different dynamics of global climate warming

Water purity and availability determines health and life quality of humans, biodiversity and existence of plants and animals. The results of global climate change have been registered all over the world as progressive warming with fast heat waves, accelerated glacier ice melting, variations in the global ocean streams and heat balance, droughts and lack of drinking water, damage to plants and animals. Mathematical modeling of the water exchange in local ecosystems is a very important constituent of detailed analysis of different scenarios of water availability at various trends in the weather change.
 The work is aimed at mathematical modelling of water balance in an urban ecosystem accounting for global climate changes. A brief review of the models is presented, and a synthetic model for the water balance on the urban territory of Kharkiv city (Ukraine) based on the statistical dependencies, compartmental system dynamics approach and hydrological equation with probabilistic description of the input parameters is developed. The monthly and year averaged temperature and precipitation curves, time series on downpours, droughts and storms over the Kharkiv region and Kharkiv city during 1908−2012 years were collected from the open databases and analyzed. Gradual increase in the annual temperature was confirmed.
 Different scenarios of the regional development (population growth and industry development with increased water demands) and weather changes were tested, and availability of water has been estimated. It was established by numerical simulations, the water insufficiency in the region in 2040 could reach 10−17 % if the mean annual air temperature increases in 0.5−2.5 °T. This will cause damage for plants, animals, and human health. The obtained results are important for decision making by official planning authorities and regional administration

An adaptive multi-objective reservoir operation scheme for improved supply-demand management

The complex hydrological connectivity of integrated river-reservoir catchment command regions in conjunction with abrupt societal development and population growth impose adverse water availability scenario in the global reservoirs. In this context, to formulate optimal reservoir operation policies, numerous reservoir simulation and optimization models are currently on board. However, lack of combined hydrologic and hydraulic conceptualizations in the existing reservoir operation framework fetches considerable uncertainty; thereby leading to formulation of erroneous reservoir operation rule. This study proposed an integrated Soil and Water Assessment Tool (SWAT) based hydrologic, HEC-ResSim based hydraulic, and genetic algorithm (GA) based optimization framework (SWAT-HEC-ResSim-GA) for formulating the optimal reservoir operation rule of the Kangsabati reservoir located in the eastern India. Unlike the existing reservoir operation rules, the present study incorporates a 10-daily-scale environmental flow estimation approach to quantify the water demand. The proposed reservoir operation framework generated rule curve based HEC-ResSim simulation revealed improved reservoir performance with reservoir time reliability, volume reliability, and resiliency magnitudes of 0.631, 0.736, and 0.591, respectively. Finally, this study endorses to use the developed methodology for supply–demand management, and subsequent, reservoir operation in any global reservoir catchment-command regions with utmost accuracy.

Improved drought monitoring in teleconnection to the climatic escalations: A hydrological modeling based approach

Catchment-scale efficient agricultural drought monitoring and irrigation planning mostly depend on the accuracy of evapotranspiration (ET) estimates under different crop-growth phases. As indirect ET estimation under limited data availability scenario and complex paddy-field dynamics could not be sufficiently addressed by the conventional curve number (CN) based SWAT model, SWAT incorporates an add-in pothole module (SWAT-P) that conceptualizes the paddy-field hydrology with the empirical coefficient or equations resulting in poor ET estimates. To address these limitations, this study integrates an improved pothole methodology in SWAT (SWAT-EP) accounting the vadose-zone soil water dynamics under alternate ponding and drying conditions, ET variation under moisture abundant and stress conditions, and role of irrigation return flow generated in the paddy fields. The proposed approach was validated in the Kangsabati River integrated reservoir-catchment-command (12,014.70 km2) of the eastern India, and the results reveal that SWAT-EP outperformed the existing SWAT-P in reproducing the catchment-scale streamflow and ET flux with respect to the FAO-56 Penman-Monteith (PM) based ET estimates in all the four cropping seasons. The SWAT-EP derived Standardized Evapotranspiration Drought Index (SEDI) based drought severity and duration well-corroborate with the benchmark MOD16A2 derived drought estimates; whereas, the SWAT-P tends to overestimate the drought severity in the command area. The predictive uncertainty in drought monitoring was the lowest by SWAT-EP with relatively lower uncertainty was observed in the crop-growing locations of Kharagpur and Mohanpur. Moreover, the teleconnection between drought and climatic escalations corresponds to a better reproducibility of El Niño and La Niña phases by the SWAT-EP, while the SWAT-P performed un-satisfactorily across different spatiotemporal domains. This study endorses to adopt the proposed SWAT-EP model for river basin-scale drought monitoring and irrigation planning with prior validation in the diversified climatic and topographic conditions.

Prediction of Emergency Mobility Under Diverse IoT Availability

INTRODUCTION: Prediction of emergency mobility needs to consider more scenarios as Internet of Things (IoT) develops at a high speed, which influences the quality and quantity of data, manageable resources and algorithms. OBJECTIVES: This work investigates differences in dynamic emergency mobility prediction when facing dynamic temporal IoT data with different quality and quantity considering diverse computing resources and algorithm availability. METHODS: A node construction scheme under a small range of traffic networks is adopted in this work, which can effectively convert the road to graph network structure data which has been proved to be feasible and used for the small-scale traffic network data here. Besides, two different datasets are formed using public large scale traffic network data. Representative widely used and proven algorithms from typical types of methods are selected respectively with different datasets to conduct experiments. RESULTS: The experimental results show that the graphed data and neural network algorithm can deal with the dynamic time series data with complex nodes and edges in a better way, while the non-neural network algorithm can predict the with a simple graph network structure. CONCLUSION: Our proposed graph construction with graph neural network improves dynamic emergency mobility prediction. The prediction should consider the scenarios of availability of computing resources, quantity and quality of data among other IoT features to improve the results. Later, automation and data enrichment should be improved.

Development of a Multi-Scale Tomato Yield Prediction Model in Azerbaijan Using Spectral Indices from Sentinel-2 Imagery

This paper presents the development and update of a multi-scale yield prediction model for processing tomatoes. The study was carried out under the EU-funded programme “Support to Development of a Rural Business Information System (RBIS)”, and the performance of the updated crop-specific yield prediction models and their generated predictions at regional and national levels are presented. The model was built using Sentinel-2 satellite imagery to obtain cumulative values of six (6) selected vegetation indices (VIs). The data were collected on five (5) different dates for processing tomato fields in the Khachmaz region of Azerbaijan during summer 2021 (June to August) at 10- to 13-day intervals. In addition, a targeted field sampling campaign was conducted on selected Khachmaz pilot fields towards the end of the growing season to assess the potential of Sentinel-2 data to determine yield variability in tomato fields. Finally, actual recorded yields were collected at the field level to build the yield prediction regression model and evaluate its performance at different spatial scales, ranging from single field to national level, as well as under different data availability scenarios (number of consecutive Sentinel-2 images used). The results showed a high degree of correlation between all implemented VIs and processing tomato yield, with a coefficient of determination of up to 0.89 for the NDVI, providing valuable information for future estimates of tomato production across multiple spatial scales. The developed prediction model could also be used in the agri-food sector for national yield estimates to support policy and regulatory decisions at the national level.

Nonparametric Option Pricing with Generalized Entropic Estimators

We propose a family of nonparametric estimators for an option price that require only the use of underlying return data, but can also easily incorporate information from observed option prices. Each estimator comes from a risk-neutral measure minimizing generalized entropy according to a different Cressie–Read discrepancy. We apply our method to price S&P 500 options and the cross-section of individual equity options, using distinct amounts of option data in the estimation. Estimators incorporating mild nonlinearities produce optimal pricing accuracy within the Cressie–Read family and outperform several benchmarks such as Black–Scholes and different GARCH option pricing models. Overall, we provide a powerful option pricing technique suitable for scenarios of limited option data availability.

A systematic analysis of the costs and environmental impacts of critical materials recovery from hybrid electric vehicle batteries in the U.S.

SummaryCritical materials such as rare earth underpin technologies needed for a decarbonized global economy. Recycling can mitigate the supply risks created by the increasing demand and net import dependence, and enable a circular economy for critical materials. In this study, we analyze the feasibility and life-cycle impacts of recovering critical materials from spent nickel metal hydride batteries from hybrid electric vehicles in the U.S., accounting for stocks, battery scrappage, and end-of-life reverse logistics, given uncertain future availability scenarios. Our results show that the total collection and recycling costs depend strongly on future battery availability, with marginal costs exceeding marginal revenues when the availability of spent batteries declines. We quantify the potential of recycling to reduce primary imports, as well as the accompanying climate change and resource impacts. We explore the underlying reverse logistics infrastructure required for battery recycling and evaluate strategies for reducing associated capital investment risk.

Uncertainty quantification in LV state estimation under high shares of flexible resources

The ongoing electrification introduces new challenges to distribution system operators (DSOs). Controllable resources may simultaneously react to price signals, potentially leading to network violations. DSOs require reliable and accurate low-voltage state estimation (LVSE) to improve awareness and mitigate such events. However, the influence of flexibility activations on LVSE has not been addressed yet. It remains unclear if flexibility-induced uncertainty can be reliably quantified to enable robust DSO decision-making. In this work, uncertainty quantification in LVSE is systematically investigated for multiple scenarios of input information availability and flexibility usage, using real data. For that purpose, a Bayesian neural network (BNN) is compared to quantile regression. Results show that frequent flexibility activations can significantly deteriorate LVSE performance, unless secondary substation measurements are available. Moreover, it is demonstrated that the BNN captures flexibility-induced voltage drops by dynamically extending the prediction interval during activation periods, and that it improves interpretability regarding the cause of uncertainty.

Uranium Resources and Production: Global Availability and Indian Scenario

Uranium Resources and Production: Global Availability and Indian Scenario

Prediction of weaning weight of grazing beef by machine learning

Objective: To develop and validate models using the variables available at calving to predict the weaning weight (WW) of grazing beef calves. Design/Methodology/Approach: The WW was modelled using machine learning (ML) algorithms and ordinary least squares (OLS). The model included three variable availability scenarios and the best fit was identified using the coefficient of determination (r2), the mean squared error, and the bias. Results: ML algorithms achieved a better fit than OLS in all scenarios. ML had a 0.70, 0.67, and 0.78 r2 when the following modelling variables were available: B) dam age at calving and parity, calf sex and weight, weaning age, and calving date; I) in addition to the previous variables, dams’ weight at calving, type of calving, calf and cow racial purity; and A) in addition to the all the previous variables, type of service, cow and sire tags and sire breed. Study Limitations/Implications: The ML and OLS models were representative of a specific database. Modelling based on regional or national data should be studied. Using the lowest number of variables in this study, ML in scenario B provided an acceptable fitting for the prediction modelling of the WW of grazing beef calves. Findings/Conclusions: ML performed better than OLS, without causing an overfitting, based on the suitability of the WW predictions regarding a database that was not used to train the model.

Hot and dry conditions predict shorter nestling telomeres in an endangered songbird: Implications for population persistence

Climate warming is increasingly exposing wildlife to sublethal high temperatures, which may lead to chronic impacts and reduced fitness. Telomere length (TL) may link heat exposure to fitness, particularly at early-life stages, because developing organisms are especially vulnerable to adverse conditions, adversity can shorten telomeres, and TL predicts fitness. Here, we quantify how climatic and environmental conditions during early life are associated with TL in nestlings of wild purple-crowned fairy-wrens (Malurus coronatus), endangered songbirds of the monsoonal tropics. We found that higher average maximum air temperature (range 31 to 45 °C) during the nestling period was associated with shorter early-life TL. This effect was mitigated by water availability (i.e., during the wet season, with rainfall), but independent of other pertinent environmental conditions, implicating a direct effect of heat exposure. Models incorporating existing information that shorter early-life TL predicts shorter lifespan and reduced fitness showed that shorter TL under projected warming scenarios could lead to population decline across plausible future water availability scenarios. However, if TL is assumed to be an adaptive trait, population viability could be maintained through evolution. These results are concerning because the capacity to change breeding phenology to coincide with increased water availability appears limited, and the evolutionary potential of TL is unknown. Thus, sublethal climate warming effects early in life may have repercussions beyond individual fitness, extending to population persistence. Incorporating the delayed reproductive costs associated with sublethal heat exposure early in life is necessary for understanding future population dynamics with climate change.

Responses of digestive enzyme profiles to various scenarios of food availability in newly-hatched Stage I phyllosoma larvae of the tropical spiny lobster Panulirus ornatus

Stage I phyllosoma larvae of the spiny lobster Panulirus ornatus hatch in tropical oceanic waters with limited and variable food resources. To better understand how these larvae cope with food deprivation, this study examined specific (mU mg−1 protein) and total (mU larva−1) activities of major digestive enzymes (i.e., α-amylase, non-specific esterase, trypsin-like protease) during a series of ex situ experiments. Specifically, temporal changes in enzyme profiles were examined in three experiments simulating scenarios in which phyllosoma hatch and begin development in an environment where (1) prey is either continuously present or absent; (2) prey is initially present, but subsequently absent; (3) prey is initially absent, but subsequently present. Results indicated that the accessibility of suitable prey provides an overarching influence on digestive enzyme activities and substrate utilisation in Stage I phyllosoma of P. ornatus, with enzymatic responses to both intermittent prey availability and food deprivation being influenced by their nutritional history. In the absence of prey, larval digestive enzyme activities (mU larva−1) initially remained static from hatch but eventually declined with extended food deprivation. When prey became available, enzyme activities increased, with delayed access to prey having minimal impact on this enzymatic response. Furthermore, phyllosoma were able to adjust α-amylase and non-specific esterase activities (within 8 h) in response to the disappearance of prey, demonstrating adaptive changes to endure periods of food deprivation that were based on their nutritional history. The ability of phyllosoma to regulate enzyme activities from hatch is an important physiological strategy allowing them to survive in an environment characterised by highly variable zooplankton biomass and abundance, and explains why diets that differ greatly in nutrient composition have consistently been found effective for rearing phyllosoma in captivity.