Spatiotemporal Uncertainty Research Articles

Spatiotemporal Uncertainty and Sensitivity Analysis of the SIMPLE Model Applied to Common Beans for Semi-Arid Climate of Mexico

Simulation models are used to estimate, forecast, optimize and identify limiting factors and analyze changes in crop production. In order to obtain a functional and reliable mathematical model, it is necessary to know the source of uncertainty and identify the most influential parameters. This study aimed to carry out an uncertainty analysis (UA) and a global spatiotemporal sensitivity analysis (SA) for the parameters of the SIMPLE model, which uses 13 parameters, has two state variables and uses daily weather data to simulate crop growth and development. A Monte Carlo simulation was performed for the UA, and Sobol’s method was used for the SA. Four automatic weather stations representing the climatic conditions of the different bean-producing areas in Zacatecas, Mexico, and a four-year historical series of each station for irrigated and rainfed common bean crops were analyzed. From the UA the coefficients of variation (CV) for thermal time were 11.49% and 11.47%, for biomass the CV were 47.94% and 37.80% and for yield the CV were 49.52% and 39.70% for irrigated and rainfed beans, respectively. From the SA, the most influential parameters for irrigated beans were Tsum > Swater > Tbase > I50A > Topt and for rainfed beans, Tsum > Tbase > I50A > Topt > Swater, according to indices calculated on biomass and thermal time. In conclusion, UA was able to accurately quantify the uncertainty of the biomass, and SA allowed the identification of the most influential of the parameters of the SIMPLE model applied to a common bean crop.

Propagation of hydropeaking waves in heterogeneous aquifers: effects on flow topology and uncertainty quantification

Topological flow properties are proxies for mixing processes in aquifers and allow us to better understand the mechanisms controlling transport of solutes in the subsurface. However, topological descriptors, such as the Okubo–Weiss metric, are affected by the uncertainty in the solution of the flow problem. While the uncertainty related to the heterogeneous properties of the aquifer has been widely investigated in the past, less attention has been given to the one related to highly transient boundary conditions. We study the effect of different transient boundary conditions associated with hydropeaking events (i.e., artificial river stage fluctuations due to hydropower production) on groundwater flow and the Okubo–Weiss metric. We define deterministic and stochastic modeling scenarios applying four typical settings to describe river stage fluctuations during hydropeaking events: a triangular wave, a sine wave, a complex wave that results of the superposition of two sine waves, and a trapezoidal wave. We use polynomial chaos expansions to quantify the spatiotemporal uncertainty that propagates into the hydraulic head in the aquifer and the Okubo–Weiss. The wave-shaped highly transient boundary conditions influence not only the magnitude of the deformation and rotational forces of the flow field but also the temporal dynamics of dominance between local strain and rotation properties. Larger uncertainties are found in the scenario where the trapezoidal wave was imposed due to sharp fluctuation in the stage. The statistical moments that describe the propagation of the uncertainty highly vary depending on the applied boundary condition.HighlightsDeterministic and stochastic scenarios to describe the groundwater flow field under river stage fluctuations during hydropeaking.Propagation of uncertainty of highly transient boundary conditions in the Okubo–Weiss metric.Highly transient boundary conditions can significantly affect mixing potential.

Representing and modeling spatio-temporal uncertainty using belief function theory in flood extent mapping

In our world of rapid change, floods are a growing threat. In this context, flood extent mapping is important for damage and accurate and timely information about flood-affected areas are needed. Due to the suitability of remote sensing in mapping flood inundation, multi-source satellite images have been used in recent years. This paper presents a new approach to representing and modeling uncertainty in spatio-temporal information of satellite images using belief function theory for flooded areas extraction. Interval approaches and the associated arithmetic operations are used for modeling the uncertainty of spatial locations and temporal durations from 2D dimensional model in order to take the direction of flow in the flood. Based on topological spatial relationships, the similarity for the spatio-temporal inundation events is performed and the similar intervals are fused. Detailed examples are given to illustrate each step. A case study of flood risk in Chad Lake from 2003 to 2019 using Sentinel-2, MODIS and Landsat-8 images shows how spatio-temporal uncertainty can be problematic and bias the results of even the simplest flood mapping, and how coupling a Dempster–Shafer approach with topological relationships nevertheless offers a useful solution.

Solid-Phase Reference Baths for Fiber-Optic Distributed Sensing.

Observations from Raman backscatter-based Fiber-Optic Distributed Sensing (FODS) require reference sections of the fiber-optic cable sensor of known temperature to translate the primary measured intensities of Stokes and anti-Stokes photons to the secondary desired temperature signal, which also commonly forms the basis for other derived quantities. Here, we present the design and the results from laboratory and field evaluations of a novel Solid-Phase Bath (SoPhaB) using ultrafine copper instead of the traditional mechanically stirred liquid-phase water bath. This novel type is suitable for all FODS applications in geosciences and industry when high accuracy and precision are needed. The SoPhaB fully encloses the fiber-optic cable which is coiled around the inner core and surrounded by tightly interlocking parts with a total weight of 22 kg. The SoPhaB is thermoelectrically heated and/or cooled using Peltier elements to control the copper body temperature within ±0.04 K using commercially available electronic components. It features two built-in reference platinum wire thermometers which can be connected to the distributed temperature sensing instrument and/or external measurement and logging devices. The SoPhaB is enclosed in an insulated carrying case, which limits the heat loss to or gains from the outside environment and allows for mobile applications. For thermally stationary outside conditions the measured spatial temperature differences across SoPhaB parts touching the fiber-optic cable are <0.05 K even for stark contrasting temperatures of 40 K between the SoPhaB’s setpoint and outside conditions. The uniform, stationary known temperature of the SoPhaB allows for substantially shorter sections of the fiber-optic cable sensors of less than <5 bins at spatial measurement resolution to achieve an even much reduced calibration bias and spatiotemporal uncertainty compared to traditional water baths. Field evaluations include deployments in contrasting environments including the Arctic polar night as well as peak summertime conditions to showcase the wide range of the SoPhaB’s applicability.

Spatio-Temporal Statistical Characterization of Boundary Kinematic Phenomena of Triaxial Sand Specimens.

This paper follows up on a reference paper that inspired MDPI’s Topic “Stochastic Geomechanics: From Experimentation to Forward Modeling”, where global and local deformation effects on sand specimens are fully described from high resolution boundary displacement fields, and supported by its experimental database, which is open to the scientific community for further study. This paper introduces the use of spatio-temporal statistics from a subset of such an experimental database to characterize the specimens’ spatio-temporal displacement fields, populated by repeating a set of triaxial compression tests on drained, dry, vacuum-consolidated sand specimens, tested under similar experimentally controlled conditions. A three-dimensional digital image correlation (3D-DIC) technique was used to measure the specimens’ boundary displacement fields throughout the course of shearing under axial compression. Spatio-temporal first- and second-order statistics were computed for different data dimensionality conditions (0D, 0D-T, 1D-T, 3D-T) to identify and characterize the dominant failure mechanisms across different testing specimens. This allowed us to quantify localization phenomena’s spatio-temporal uncertainty. Results show that the uncertainty captured along the deformation process across different dimensionality conditions can be directly associated with different failure mechanisms, including localization patterns, such as the onset and evolution of shear, compression, and expansion bands. These spatio-temporal observations show the dependencies between locally distinctive displacement regions over a specimen’s surface, and across different times during a specimen’s shearing process. Results of this work provide boundary spatio-temporal statistics of experimental evidence in sands, which sets the basis for the development of research on the numerical simulation of sand’s constitutive behavior. Moreover, it allows to add a new understanding on the effect of uncertainty on the mechanistic interpretation of sands’ kinematic phenomena.

Multiperiod stochastic programming for biomass supply chain design under spatiotemporal variability of feedstock supply

Spatiotemporal uncertainties in the collection of crop residues pose great challenges to the development of a long-term and economic biomass-to-biofuel supply chain network (BSCN). A multiperiod stochastic programming (SP) model considering uncertain collectible corn stover removal and farmer participation rates is developed. The SP model is compared with the deterministic programming for the expected scenario (DPES) model to provide decision-making support for BSCN in two different periods. With the statistical results of separate deterministic programming models for each scenario generated randomly based on the normal distribution as a reference, the economic performance of the SP and DPES models is compared in the model development period and then confirmed in the model validation period. A county-level case study with a 10-year development and a 3-year validation period is applied. The economic performance of the SP model is comparable to that of the DPES model in the development period, and the SP model achieves much higher cost savings in the validation period. Although biomass transportation cost is the most unstable cost component, the variation in bioethanol production cost is largely consistent with that in biomass purchase cost. The SP model demonstrates stronger robustness to uncertainty than the DPES model.

Incoherent scatter radar observation of high frequency enhanced ion and plasma lines by X / O mode pump around critical altitude

During an experiment involving the alternating O / X mode pump, the Incoherent Scatter Radar (ISR) observation demonstrated that the high frequency enhanced ion line (HFIL) and plasma line (HFPL) did not immediately appear, but were delayed by a few seconds after the pump onset. By examining the initial behaviors of the ion line, plasma line and electron temperature as well as ionosphere condition, it is shown that (1) the HFIL and HFPL are delayed not only in the X mode pump but also in the O mode pump; (2) the HFIL can not be observed until the electron temperature is enhanced. The analysis suggests that (1) the leakage of the X mode to the O mode pump may not be ignored; (2) the spatiotemporal uncertainty, the spatiotemporal change in the profiles of ion mass and electron density, may play an important role in the lack of the Bragg condition; (3) nevertheless, the absence of parametric decay instability can not be ruled out due to the lack of matching condition caused by the spatiotemporal uncertainty.

CSMC

The rise concern about mobile communication performance has driven the growing demand for the construction of mobile network signal maps which are widely utilized in network monitoring, spectrum management, and indoor/outdoor localization. Existing studies such as time-consuming and labor-intensive site surveys are difficult to maintain an update-to-date finegrained signal map within a large area. The mobile crowdsensing (MCS) paradigm is a promising approach for building signal maps because collecting large-scale MCS data is low-cost and with little extra-efforts. However, the dynamic environment and the mobility of the crowd cause spatio-temporal uncertainty and sparsity of MCS. In this work, we leverage MCS as an opportunity to conduct the city-wide mobile network signal map construction. We propose a fine-grained city-wide Cellular Signal Map Construction (CSMC) framework to address two challenges including (i) the problem of missing and unreliable MCS data; (ii) spatio-temporal uncertainty of signal propagation. In particular, CSMC captures spatio-temporal characteristics of signals from both inter- and intra- cellular base stations and conducts missing signal recovery with Bayesian tensor decomposition to build large-area fine-grained signal maps. Furthermore, CSMC develops a context-aware multi-view fusion network to make full use of external information and enhance signal map construction accuracy. To evaluate the performance of CSMC, we conduct extensive experiments and ablation studies on a large-scale dataset with over 200GB MCS signal records collected from Shanghai. Experimental results demonstrate that our model outperforms state-of-the-art baselines in the accuracy of signal estimation and user localization.

A pseudoproxy assessment of why climate field reconstruction methods perform the way they do in time and space

Abstract. Spatiotemporal paleoclimate reconstructions that seek to estimate climate conditions over the last several millennia are derived from multiple climate proxy records (e.g., tree rings, ice cores, corals, and cave formations) that are heterogeneously distributed across land and marine environments. Assessing the skill of the methods used for these reconstructions is critical as a means of understanding the spatiotemporal uncertainties in the derived reconstruction products. Traditional statistical measures of skill have been applied in past applications, but they often lack formal null hypotheses that incorporate the spatiotemporal characteristics of the fields and allow for formal significance testing. More recent attempts have developed assessment metrics to evaluate the difference of the characteristics between two spatiotemporal fields. We apply these assessment metrics to results from synthetic reconstruction experiments based on multiple climate model simulations to assess the skill of four reconstruction methods. We further interpret the comparisons using analysis of empirical orthogonal functions (EOFs) that represent the noise-filtered climate field. We demonstrate that the underlying features of a targeted temperature field that can affect the performance of CFRs include the following: (i) the characteristics of the eigenvalue spectrum, namely the amount of variance captured in the leading EOFs; (ii) the temporal stability of the leading EOFs; (iii) the representation of the climate over the sampling network with respect to the global climate; and (iv) the strength of spatial covariance, i.e., the dominance of teleconnections, in the targeted temperature field. The features of climate models and reconstruction methods identified in this paper demonstrate more detailed assessments of reconstruction methods and point to important areas of testing and improving real-world reconstruction methods.

Trends in Vehicle Re-Identification Past, Present, and Future: A Comprehensive Review

Vehicle Re-identification (re-id) over surveillance camera network with non-overlapping field of view is an exciting and challenging task in intelligent transportation systems (ITS). Due to its versatile applicability in metropolitan cities, it gained significant attention. Vehicle re-id matches targeted vehicle over non-overlapping views in multiple camera network. However, it becomes more difficult due to inter-class similarity, intra-class variability, viewpoint changes, and spatio-temporal uncertainty. In order to draw a detailed picture of vehicle re-id research, this paper gives a comprehensive description of the various vehicle re-id technologies, applicability, datasets, and a brief comparison of different methodologies. Our paper specifically focuses on vision-based vehicle re-id approaches, including vehicle appearance, license plate, and spatio-temporal characteristics. In addition, we explore the main challenges as well as a variety of applications in different domains. Lastly, a detailed comparison of current state-of-the-art methods performances over VeRi-776 and VehicleID datasets is summarized with future directions. We aim to facilitate future research by reviewing the work being done on vehicle re-id till to date.

Impact of Prior Terrestrial Carbon Fluxes on Simulations of Atmospheric CO2 Concentrations

AbstractTerrestrial ecosystems have a significant role in shaping the distribution of atmospheric CO2, but it is uncertain how much they affect CO2 concentrations. We assessed the impact of terrestrial fluxes derived from different biosphere models in the Multi‐scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) on atmospheric CO2 estimations based on a global chemical transport model (GEOS‐Chem) when these fluxes were applied as prior information. We examined the spatiotemporal uncertainty in terrestrial flux estimations from 15 MsTMIP terrestrial fluxes. We found high uncertainties in the terrestrial fluxes for temperate North America, tropical and temperate South America, southern Africa, Europe and tropical Asia. Sensitivity simulations showed that the annual mean CO2 concentration changed by 6.0–8.0 ppmv with the spread of the terrestrial fluxes. The interannual trend in the terrestrial fluxes could significantly affect the simulated trend of atmospheric CO2 concentrations. As a result of the spread in the prior terrestrial fluxes, large differences in the daily mean CO2 anomalies changed with an interquartile range of −1.0 to +1.0 ppmv and the magnitude of change in the sub‐daily CO2 concentrations was in the range 4.0–6.0 ppmv for China, the USA and Europe. Our results suggest an urgent need to increase the reliability of terrestrial flux estimates in CO2 simulations. Surface seasonal CO2 concentrations were simulated to change by ±2.0 ppmv in most regions of the world due to the differences in the ensemble mean fluxes, reflecting the impact of the uncertainties in the existing optimized terrestrial fluxes on CO2 simulations.

End-user Preference for and Understanding of Hurricane Forecast Graphs

Hurricane forecast graphics have the challenging task of communicating information about spatio-temporal uncertainty. This study assesses the impact of graph literacy and graph format on user preference and understanding. In a laboratory setting, we compared user responses to official National Hurricane Center advisory maps and alternative visualizations. Results indicate that prior experience with a visualization drives preference and that graph literacy, visualization format, and tropical cyclone characteristics, in combination, influence interpretations of hurricane forecast track. The findings from this study are expected to inform redesign efforts of hurricane risk communication products.

Machine-learning and HEC-RAS integrated models for flood inundation mapping in Baro River Basin, Ethiopia

This study presents an integrated machine-learning and HEC-RAS models for flood inundation mapping in Baro River Basin, Ethiopia. ANN and HEC-RAS models were integrated as a predictive hydrological and hydraulic model to generate runoff and the extent of flood, respectively. Daily rainfall and temperature data of 7-years (1999–2005), daily discharge (1999–2005) and 30 m × 30 m gridded Topographical Wetness Index (TWI) were used to train a predictive ANN hydrological model in RStudio. The predictive performance of the developed ANN hydrological model was evaluated in RStudio using Nash–Sutcliffe Efficiency (NSE) values of 0.86 and 0.88 during the training period (1999–2005) and testing period (2006–2008), respectively, with the corresponding observed daily discharge. The validated ANN predictive hydrological model was linked with HEC-RAS to generate the flood extent along the river course. The HEC-RAS model result was calibrated and validated using the water body delineated using Normal Difference Water Index (NDWI) from LANDSAT 8 imagery based on historical flood events of 2005 and 2008. It was found that about 96% of an agreement was made between the flood-prone areas generated in HEC-RAS and the water body delineated using NDWI. Therefore, it is logical to conclude that the integration of a machine-learning approach with the HEC-RAS model has improved the spatiotemporal uncertainties in traditional flood forecasting methods. This integrated model is powerful tool for flood inundation mapping to warn residents of this basin.

Using internet-based query and climate data to predict climate-sensitive infectious disease risks: a systematic review of epidemiological evidence.

The use of internet-based query data offers a novel approach to improve disease surveillance and provides timely disease information. This paper systematically reviewed the literature on infectious disease predictions using internet-based query data and climate factors, discussed the current research progress and challenges, and provided some recommendations for future studies. We searched the relevant articles in the PubMed, Scopus, and Web of Science databases between January 2000 and December 2019. We initially included studies that used internet-based query data to predict infectious disease epidemics, then we further filtered and appraised the studies that used both internet-based query data and climate factors. In total, 129 relevant papers were included in the review. The results showed that most studies used a simple descriptive approach (n=80; 62%) to detect epidemics of influenza (including influenza-like illness (ILI)) (n=88; 68%) and dengue (n=9; 7%). Most studies (n=61; 47%) purely used internet search metrics to predict the epidemics of infectious diseases, while only 3 out of the 129 papers included both climate variables and internet-based query data. Our research shows that including internet-based query data and climate variables could better predict climate-sensitive infectious disease epidemics; however, this method has not been widely used to date. Moreover, previous studies did not sufficiently consider the spatiotemporal uncertainty of infectious diseases. Our review suggests that further research should use both internet-based query and climate data to develop predictive models for climate-sensitive infectious diseases based on spatiotemporal models.

Delimiting the spatio-temporal uncertainty of climate-sensitive forest productivity projections using Support Vector Regression

As climate change makes many traditional empirical growth approaches not functional for forest dynamics modelling, new climate-sensitive models are needed. However, using these newly developed models for extrapolation, such as predicting forest productivity for new areas or future scenarios is still a difficult task. In this study, we proposed a method for delimiting the uncertainty of climate-sensitive extrapolations of forest productivity (site index, SI) using the regularisation approach implicit in distance-based Support Vector Regression. As a case study, we predicted forest productivity with a dataset of 165 permanent research plots of radiata pine forests in Galicia (NW of Spain) as a function of bioclimatic variables from the Worldclim 2 raster datasets. The developed model was based on the radial basis kernel and, after calibrating it using cross-validation, produced adequate performance metrics, explaining up to 56% of the site index’ variability. Then, we predicted forest productivity for the Galician territory basing on climate raster maps for current conditions and six future scenarios (using different Global Climate Models) and evaluated the resulting maps by delimiting the surfaces with predictions strongly regressed to the mean. This analysis revealed that the extrapolations for unseen climatic conditions were extremely regularised, even for current climate, being 60–99% of the territory regressed to the observational site index mean. In other words, the validity area delimited for the fitted model was narrow in comparison with the prediction extent. These results imply that the climatic conditions in these areas/scenarios were too different from the training datastet for making reliable predictions, at least under the optimum model setup defined by cross-validation. However, when we reduced the σ parameter, responsible for controlling distance-based regularisation, we observed a noticeable increase in validity area of the model, together with a drop in performance. This fact revealed the existence of a trade–off between highly specific models, with high performance and a small applicability area, and more generalisable models, with a broad validity area but lower performance. We concluded that the tested methodology could be a useful starting point for assessing the spatio-temporal uncertainty of forest productivity predictions in the future.

Overview of global status and challenges for end-of-life crystalline silicon photovoltaic panels: A focus on environmental impacts.

Recent developments in photovoltaic (PV) technology have enabled a reduction of fossil fuel usage and subsequent carbon dioxide (CO2) release from energy production. However, end-of-life (EoL) crystalline silicon (c-Si) PV panels have become an emerging waste issue. This overview attempts to update and forecast the global status of renewable energy capacity and c-Si PV waste generation under different scenarios and to present a summary of the recent literature on recycling technologies and life cycle assessment (LCA) of EoL c-Si PV panels with a focus on reclaimable resources. For most LCA studies in the 1980s and the 2000s, the EoL phase of PV systems has often neglected or oversimplified (e.g., disposal after low-rate recovery) the fact that various recycling procedures and reclaimable resources from each stage cannot be appropriately considered. A limited number of studies have been available since the 2010s that highlight the high-rate recovery from EoL PV panels. However, the differences in functional unit, system boundary and impact analysis methodology make it difficult to compare the results directly, and spatio-temporal uncertainties are yet to be thoroughly quantified due to the lack of workable localized data. More efforts are needed to identify complementary environmental impacts (i.e., burden and credit) from the individual recycling processes. Correspondingly impacts from transport need to be fully incorporated for the optimization of the recycling process which has been neglected in most of the previous studies.

Analyzing the Spatiotemporal Uncertainty in Urbanization Predictions

With the availability of computational resources, geographical information systems, and remote sensing data, urban growth modeling has become a viable tool for predicting urbanization of cities and towns, regions, and nations around the world. This information allows policy makers, urban planners, environmental and civil organizations to make investments, design infrastructure, extend public utility networks, plan housing solutions, and mitigate adverse environmental impacts. Despite its importance, urban growth models often discard the spatiotemporal uncertainties in their prediction estimates. In this paper, we analyzed the uncertainty in the urban land predictions by comparing the outcomes of two different growth models, one based on a widely applied cellular automata model known as the SLEUTH CA and the other one based on a previously published machine learning framework. We selected these two models because they are complementary, the first is based on human knowledge and pre-defined and understandable policies while the second is more data-driven and might be less influenced by any a priori knowledge or bias. To test our methodology, we chose the cities of Jiaxing and Lishui in China because they are representative of new town planning policies and have different characteristics in terms of land extension, geographical conditions, growth rates, and economic drivers. We focused on the spatiotemporal uncertainty, understood as the inherent doubt in the predictions of where and when will a piece of land become urban, using the concepts of certainty area in space and certainty area in time. The proposed analyses in this paper aim to contribute to better urban planning exercises, and they can be extended to other cities worldwide.

Resource Distribution Under Spatiotemporal Uncertainty of Disease Spread: Stochastic versus Robust Approaches

Speeding up testing and vaccination is essential for controlling the coronavirus disease 2019 (COVID-19) pandemic. We develop mathematical frameworks for optimizing locations of distribution centers DCs and plans for distributing resources such as test kits and vaccines, under spatiotemporal uncertainties of disease infections and demand for the resources. We aim to balance operational cost (including costs of deploying facilities, shipping, and storage) and quality of service (reflected by demand coverage), while ensuring equity and fairness of resource distribution across multiple populations. We compare a sample-based stochastic programming (SP) approach with a distributionally robust optimization (DRO) approach using a moment-based ambiguity set. Numerical studies are conducted on instances of distributing COVID-19 vaccines in the United States and test kits in Michigan, to compare SP and DRO with a deterministic model using demand estimates and with the current resource distribution implemented in the real world. We demonstrate the results over distinct phases of the pandemic to estimate the cost and speed of resource distribution depending on scale and coverage, and show the ``demand-driven'' properties of the SP and DRO solutions.

MIFF

Human Mobility Extraction with cellular Signaling Data (SD) is essential for human mobility understanding, epidemic control, and wireless network planning. SD log the detailed interactions between cellphones and cellular towers, but suffer from a spatio-temporal uncertainty problem due to cellular network tower-level load rebalancing (switching users between towers) and cellphone usage activities. To date, most models focus on utilizing better data like RSSI or GPS, do not directly address uncertainty. To address the SD uncertainty issue, we utilize two insights based on (i) individuals' regular mobility patterns and (ii) common co-movement mobility patterns between cellphone users as suggested by fundamental human mobility nature. Accordingly, we design a Multi-Information Fusion Framework (MIFF) to assist in extracting road-level human mobility based on cell-tower level traces. To evaluate the effectiveness of MIFF, we conduct experiments on one-month SD obtained from a cellular service operator, and SD manually collected by handheld mobile devices in two cities in China. Four transportation modes, namely railways, cars, buses, and bikes are evaluated. Experimental results show that with MIFF, our road-level trajectory extraction accuracy can be improved by 5.0% on Point correct matching index and 68.5% on Geographic Error on average.

Observations on Long-Term Memory in Honey Bees

Honey bees (Apis mellifera) must adjust their foraging behavior in response to their continuously shifting food resources. Spatiotemporal uncertainty of food availability can arise from the diurnal...