Spatiotemporal Scope Research Articles

Text, time, and travel: temporal pathways of postsocialism and Islam

AbstractAs the concept of postsocialism faces increased scrutiny, there is a call to expand its spatiotemporal scope beyond socialist contexts in order to reclaim its analytical capacity. In Azerbaijan, the quiet resurgence of tezkirahs – biographical anthologies rooted in both the Islamic and Soviet traditions – presents an opportunity to explore how former Soviet citizens can bridge different histories, countries, and cultural traditions to nurture an expansive sense of collective presence and moral dignity after seventy years of communist rule and disconnect. These texts help Azerbaijanis chart their diverse roots in the former imperial domains of Persians, Turks, and Russians and absorb them into their vision of who they once were and could be again. Writers and readers of tezkirahs establish connections to non‐socialist pasts and places through what I refer to as temporal pathways, where traversing time becomes a journey to another place, and vice versa. By exploring this spatialized historical sensibility through the capacious ethnographic‐textual lens of an Islamic genre, this article sheds fresh light on postsocialist possibilities.

Geographic principles applied to population dynamics: A spatially interpolated integrated population model

Abstract A major impediment to wildlife conservation and management, from a quantitative perspective, is dealing with high degrees of uncertainty associated with population estimates. Integrated population models (IPMs) can help alleviate that challenge, but they are often limited to narrow spatial or temporal windows owing to the financial and logistical burdens of acquiring requisite datasets. To expand the spatiotemporal scope of practical IPM implementation, we developed a novel method that expresses demographic relatedness among sampled and unsampled locations using geographic principles of spatial autocorrelation. We interpolated demographic parameters at unsampled locations using parameter estimates from data‐informed locations. Errors attributable to the interpolative process were corrected using a joint likelihood and locally recorded count data (‘cheaper’ and broadly distributed). We evaluated the spatially interpolated IPM (SIIPM) for precision and accuracy under variable levels of spatial autocorrelation using simulated data and a Leave‐One‐Out Cross‐Validation (LOOCV) technique. Conventional IPMs and state‐space models (SSM) were fit to the same simulated datasets to provide a comparative assessment of the novel method. In a final, empirical demonstration we fit the SIIPM to data collected from Greater Sage‐Grouse (Centrocercus urophasianus; sage‐grouse) populations located in Nevada, U.S.A. during 2013–2021. SIIPMs outperformed conventional IPMs when fit to data possessing moderate‐to‐high levels of spatial autocorrelation. Under moderate levels of autocorrelation, the average improvement in parameter estimation was 13.6% for survival, 65.3% for recruitment and 23.7% for rate of population change (). When spatial autocorrelation was low, the SIIPM still outperformed contemporary approaches in areas that were geographically close (<67 km) to sampling locations. Under low autocorrelation‐near distance scenarios, we observed SIIPM parameters that were 30.8% (recruitment), 32.5% (; IPM comparison) and 54.0% (; SSM comparison) more precise than contemporary models. Spatial autocorrelation is often assumed but rarely tested when comparing population dynamics across regions of large geographic extent. We demonstrated that SIIPMs can improve precision of species' vital rate estimation when extrapolating model inference beyond populations for which long‐term monitoring data exists. Specific to sage‐grouse, these results support previous conclusions of broad‐scale spatial autocorrelation in population dynamics and a reproductive‐survival trade‐off previously documented at smaller scales.

Successive observation oriented scheduling and task planning of heterogeneous constellation

This paper put forward a method to address the problem, of how to select multiple satellites from a given heterogeneous constellation and plan their tasks to implement a temporary mission for ground or aerial targets successive observation, while hardly affecting the constellation’s original mission as much as possible. First, considering the observation geometric constraints of the satellite on the target, an observability analysis model for effective observation of the satellite on the target is established to characterize the observable spatio-temporal scope. Considering the time-length requirement of sustained observation on the target, a multi-satellite-to-target sustained observation model is constructed for the scheduling of satellites. Second, to address the problem of selecting task satellites in a given heterogeneous constellation, a mathematical model for task planning is established to maximize observation benefits and minimize task burdens. Third, a hybrid method of multi-satellite scheduling and task planning is designed with fault tolerance, based on dynamic programming and an improved contract network protocol algorithm. Next, a joint simulation software system is established through the secondary development of STK and Python, which includes satellite-target observability analysis, task satellite selection, task allocation and planning, and full process visible demonstration functions. Finally, simulation cases verified the effectiveness and superiority of the proposed technique.

Трансправительственное сетевое управление и соконкуренция в сфере управления космическим пространством в Азиатском регионе

With the facilitation of globalization and technological advancements, the growing complexity of global issues has expanded the spatiotemporal scope of their impact. In international relations, sub-units beneath the primary actors – states – have emerged as critical actors with a demand for effective and real-time responses to global affairs. Transgovernmental network governance, operating by the sub-units in a decentralized and centrifugal architecture, has emerged as a diverse and flexible governance model that transcends interstate interactions. Simultaneously, international regimes have expanded based on small-scale cooperation. When niches exist in specific issue areas, major powers have initiated the construction of transgovernmental network governance, positioning themselves as pivotal actors. In the realm of space governance in Asia, China and Japan have each established transgovernmental network governance mechanisms promoting capacity building and cooperation centred around themselves – the Asia-Pacific Space Cooperation Organization (APSCO) and the Asia-Pacific Regional Space Agency Forum (APRSAF). Case studies of the aforementioned organizations further reveal that when plural transgovernmental networks operate within the same issue area, the governance landscape takes on a derived form of coexistence between competition and cooperation, which is the transgovernmental network governance of “co-competition.” Across multiple analytical levels, such characteristics indicate a flexible space of proliferation, expansion, and transformation

THE STUDY OF SOIL EROSION UNDER A SYSTEMIC CONCEPTION

This article aimed to understand how erosion processes can be analyzed from a systemic point of view. Soil erosion is considered here a naturally occurring phenomenon, which can be (and usually is) influenced by human activities, constituting one of the main environmental problems of the planet, in addition to being the main modeling agent of the terrestrial surface. The consequences of the accelerated action of erosion processes are numerous, such as: reduction of natural fertility and productive capacity of soils; silting and pollution of water bodies; impoverishment of rural communities; rural exodus; among many others. Understanding the dynamics of this phenomenon becomes fundamental because a comprehension of a spatio-temporal scope, coupled with the knowledge of the possible problems that may occur with its accelerated performance, provides support for the development of sustainable management. Given that soils are the 'foundation' of the fauna, flora, and mainly of human subsistence, it is a valuable resource. The analytical procedures of this manuscript followed the construction of a system based on a case study, exemplifying how the systemic approach can be developed in the studies of erosive processes. By analyzing soil erosion from a systemic approach, it was possible to observe how the different flows of matter and energy occurred between the variables, explain the importance of understanding erosion processes in an integrated way, and avoid fragmented and reductionist interpretations.

Efficient Sensor Scheduling Strategy Based on Spatio-Temporal Scope Information Model

In this paper, based on the information entropy and spatio-temporal correlation of sensing nodes in the Internet of Things (IoT), a Spatio-temporal Scope Information Model (SSIM) is proposed to quantify the scope of the valuable information of sensor data. Specifically, the valuable information of sensor data decays with space and time, which can be used to guide the system to make efficient sensor activation scheduling decisions for regional sensing accuracy. A simple sensing and monitoring system with three sensor nodes is investigated in this paper, and a single-step scheduling decision mechanism is proposed for the optimization problem of maximizing valuable information acquisition and efficient sensor activation scheduling in the sensed region. Regarding the above mechanism, the scheduling results and approximate numerical bounds on the node layout between different scheduling results are obtained through theoretical analyses, which are consistent with simulation. In addition, a long-term decision mechanism is also proposed for the aforementioned optimization issues, where the scheduling results with different node layouts are derived by modeling as a Markov decision process and utilizing the Q-learning algorithm. Concerning the above two mechanisms, the performance of both is verified by conducting experiments using the relative humidity dataset; furthermore, the differences in performance and limitations of the model are discussed and summarized.

The Contributions of Marine Sediment Cores to Volcanic Hazard Assessments: Present Examples and Future Perspectives

The rigorous assessment of volcanic hazards relies on setting contemporary monitoring observations within an accurate, longer-term geological context. Revealing that geological context requires the detailed fieldwork, mapping and laboratory analysis of the erupted materials. However, many of the world’s most dangerous volcanic systems are located on or near coasts (e.g., the Phlegraean Fields and Vesuvius in Italy), islands (e.g., the volcanic archipelagos of the Pacific, south-east Asia, and Eastern Caribbean), or underwater (e.g., the recently erupting Hunga Tonga–Hunga Ha’apai volcano), meaning that much of their erupted material is deposited on the sea bed. The only way to sample this material directly is with seafloor sediment cores. This perspectives paper outlines how marine sediment cores are a vital yet underused resource for assessing volcanic hazards by: (1) outlining the spatio-temporal scope of the marine volcanic record and its main deposit types, (2) providing existing examples where marine sediments have contributed to volcanic hazard assessments; (3) highlighting the Sunda Arc, Indonesia as an example location where marine sediment cores are yet to contribute to hazard assessments, and (4) proposing that marine sediment cores can contribute to our understanding of very large eruptions that have a global impact. Overall, this perspectives paper aims to promote the utility of marine sediment cores in future volcanic hazard assessments, while also providing some basic information to assist researchers who are considering integrating marine sediment cores into their volcanological research.

Soil arthropod communities associated with Berberis thunbergii invasion in a temperate deciduous forest harbour more detritivores

Abstract Barberry (Berberis thunbergii) is a widely established invasive shrub in temperate forests of the northeastern United States with the potential to alter soil arthropod communities through changes to soil nutrient cycling and acidity. We compared soil arthropod taxa and functional feeding groups between invaded and nearby control areas in a paired observational survey. Community ordination analysis suggested minimal differences between barberry and control samples. In contrast, pairwise differences revealed elevated densities of multiple taxa in barberry‐invaded soils. Among taxa that were able to be defined by functional feeding group and were collected at higher densities under barberry, all were detritivores. For example, barberry‐invaded soils harboured an average of 40% more Isopoda and more than twice as many Diplopoda per unit dry compared with control samples. Differences we observed between barberry and control samples despite the limited study spatiotemporal scope demonstrate the potential for invasions to restructure soil arthropod communities. Observed patterns potentially reflects elevated leaf litter decomposition rates associated with the invasion and results also highlight the need to integrate site‐specific environmental attributes when assessing the impact of invading plants on soil ecosystems.

Data availability and sector‐specific frameworks restrict drought impact quantification in the Intermountain West

AbstractAs is the case for many semi‐arid regions globally, drought in the Intermountain West of the United States is a recurrent, costly phenomenon that leaves few aspects of human and natural systems untouched. Here, we focus on drought impact data and evaluation challenges across four non‐agricultural sectors: water utilities, forest resources, public health, and recreation and tourism. There are marked commonalities in the way drought indicators—that is, hydrometeorological conditions—are tracked, but considerable differences in how impacts are measured, evaluated, and disseminated. For drought indicator data, researchers and practitioners have a veritable smorgasbord of data at their fingertips. Such data are often spatially and temporally continuous, available at a wide variety of scales, and readily accessible through government‐funded online portals. This is in stark contrast to drought impact data, which are typically collected opportunistically, if at all. These data are thus often limited in spatiotemporal scope and difficult to access relative to drought indicators. Concerningly, even within a given sector, the definition of drought impacts, quantitative or otherwise, can vary considerably, making it difficult to evaluate the true cost of drought. Far from being specific to the Intermountain West, these problems are found in most regions experiencing drought. We suggest such challenges are surmountable through the development of a common drought impact framework based around economic damages and purposeful, continuous, government‐funded drought impact data collection. These tractable changes will allow for a better quantification of drought's true impacts under both present conditions and climate change scenarios in the Intermountain West and beyond.This article is categorized under: Human Water > Value of Water Science of Water > Water Extremes Water and Life > Stresses and Pressures on Ecosystems

Five decades (1972-2020) of zooplankton monitoring in the upper San Francisco Estuary.

We present the longest available dataset (by 15 years) of estuarine zooplankton abundance worldwide. Zooplankton have been monitored throughout the upper San Francisco Estuary from 1972 -present due to its status as a central hub of California water delivery and home to commercially important and endangered fishes. We integrated data from five monitoring programs, including over 300 locations, three size-classes of zooplankton targeted with different gears, over 80,000 samples, and over two billion sampled organisms. Over the duration of this dataset, species invasions have driven community turnover, periodic droughts have occurred, and important fishes have declined, likely due in part to reduced food supply from zooplankton. Data from the individual surveys have been used in prior studies on issues related to species invasions, flows, fish diets and population dynamics, zooplankton population dynamics, and community ecology. Our integrated dataset offers unparalleled spatio-temporal scope to address these and other fundamental ecological questions.

Entangled footprints: Understanding urban neighbourhoods by measuring distance, diversity, and direction of flows in Singapore

Traditional approaches to human mobility analysis in Geography often rely on census or survey data that is resource-intensive to collect and often has a limited spatio-temporal scope. The advent of new technologies (e.g. geosocial media platforms) provides opportunities to overcome these limitations and, if properly leveraged, can yield more granular insights about human mobility. In this paper, we use an anonymized Twitter dataset collected in Singapore from 2012 to 2016 to investigate this potential to help understand the footprints of urban neighbourhoods from both a spatial and a relational perspective.We construct home-to-destination networks of individual users based on their inferred home locations. In aggregated form, these networks allow us to analyze three specific mobility indicators at the neighbourhood level, namely the distance, diversity, and direction of urban interactions. By mapping these three indicators of the spatial footprint of each neighbourhood, we can capture the nuances in the position of individual neighbourhoods within the larger urban network. An exploratory spatial regression reveals that socio-economic characteristics (e.g. share of rental housing) and the built environment (i.e. land use) only partially explain these three indicators and a residual analysis points to the need to explicitly include each neighbourhood's position within the transportation network in future work.

Investigating habitat heterogeneity of Late Pleistocene archaeological sites in eastern Africa from stable isotopes

ABSTRACT Site-specific habitat reconstructions in the form of faunal enamel stable carbon and oxygen isotope data allow for a finer assessment of the context of Homo sapiens in eastern Africa. To date, these studies have focused on a small collection of sites within a constrained spatiotemporal scope. Here, I analyse a compilation of faunal stable isotopes from the Kibish Formation and Porc Epic Cave, Ethiopia, and Rusinga and Mfangano Islands, Karungu, Lukenya Hill, and Panga ya Saidi, Kenya. New data for primate and notably Homo sapiens at Porc Epic are presented. Faunal isotope data indicate that the Lake Victoria and northern Lake Turkana basins were dominated by open grasslands between ~ 105 ka and 50 ka. Sites near the Ethiopian Rift and closer to the coast were at least in part buffered from the environmental changes that occurred further inland. In the following period, ~ 49 ka – 20 ka, inland sites see more wooded conditions while Panga ya Saidi at the coast becomes drier. This compilation provides evidence for spatial and temporal trends in local habitats necessary for understanding the mechanisms through which human populations exchanged genes, ideas, and behaviours during the Late Pleistocene.

Addressing data integration challenges to link ecological processes across scales

Data integration is a statistical modeling approach that incorporates multiple data sources within a unified analytical framework. Macrosystems ecology – the study of ecological phenomena at broad scales, including interactions across scales – increasingly employs data integration techniques to expand the spatiotemporal scope of research and inferences, increase the precision of parameter estimates, and account for multiple sources of uncertainty in estimates of multiscale processes. We highlight four common analytical challenges to data integration in macrosystems ecology research: data scale mismatches, unbalanced data, sampling biases, and model development and assessment. We explain each problem, discuss current approaches to address the issue, and describe potential areas of research to overcome these hurdles. Use of data integration techniques has increased rapidly in recent years, and given the inferential value of such approaches, we expect continued development and wider application across ecological disciplines, especially in macrosystems ecology.

Bayesian stable isotope mixing models effectively characterize the diet of an Arctic raptor.

Bayesian stable isotope mixing models (BSIMMs) for δ13 C and δ15 N can be a useful tool to reconstruct diets, characterize trophic relationships, and assess spatiotemporal variation in food webs. However, use of this approach typically requires a priori knowledge on the level of enrichment occurring between the diet and tissue of the consumer being sampled (i.e. a trophic discrimination factor or TDF). Trophic discrimination factors derived from captive feeding studies are highly variable, and it is challenging to select the appropriate TDF for diet estimation in wild populations. We introduce a novel method for estimating TDFs in a wild population-a proportionally balanced equation that uses high-precision diet estimates from nest cameras installed on a subset of nests in lieu of a controlled feeding study (TDFCAM ). We tested the ability of BSIMMs to characterize diet in a free-living population of gyrfalcon Falco rusticolus nestlings by comparing model output to high-precision nest camera diet estimates. We analysed the performance of models formulated with a TDFCAM against other relevant TDFs and assessed model sensitivity to an informative prior. We applied the most parsimonious model inputs to a larger sample to analyse broad-scale temporal dietary trends. Bayesian stable isotope mixing models fitted with a TDFCAM and uninformative prior had the best agreement with nest camera data, outperforming TDFs derived from captive feeding studies. BSIMMs produced with a TDFCAM produced reliable diet estimates at the nest level and accurately identified significant temporal shifts in gyrfalcon diet within and between years. Our method of TDF estimation produced more accurate estimates of TDFs in a wild population than traditional approaches, consequently improving BSIMM diet estimates. We demonstrate how BSIMMs can complement a high-precision diet study by expanding its spatiotemporal scope of inference and recommend this integrative methodology as a powerful tool for future trophic studies.

A Probability Density-Based Visual Analytics Approach to Forecast Bias Calibration.

Biases inevitably occur in numerical weather prediction (NWP) due to an idealized numerical assumption for modeling chaotic atmospheric systems. Therefore, the rapid and accurate identification and calibration of biases is crucial for NWP in weather forecasting. Conventional approaches, such as various analog post-processing forecast methods, have been designed to aid in bias calibration. However, these approaches fail to consider the spatiotemporal correlations of forecast bias, which can considerably affect calibration efficacy. In this article, we propose a novel bias pattern extraction approach based on forecasting-observation probability density by merging historical forecasting and observation datasets. Given a spatiotemporal scope, our approach extracts and fuses bias patterns and automatically divides regions with similar bias patterns. Termed BicaVis, our spatiotemporal bias pattern visual analytics system is proposed to assist experts in drafting calibration curves on the basis of these bias patterns. To verify the effectiveness of our approach, we conduct two case studies with real-world reanalysis datasets. The feedback collected from domain experts confirms the efficacy of our approach.

Semi-continuous measurement of chromophoric organic aerosols using the PILS-EEM-TOC system

Atmospheric chromophoric organic aerosols play an important role in climate and air quality. However, up to now, their sources and chemical reaction processes have not been fully understood. Three-dimensional excitation-emission matrix (EEM) method is a direct and effective way to identify the types and sources of the complex atmospheric chromophores, which can also be used to study the chemical reactions involved in the generation and disappearance process. In this study, a PILS-EEM-TOC system (a Particle-Into-Liquid-Sampler coupled with the EEM spectroscopy and a total organic carbon analyzer) was successfully developed for the first time. The results showed that this system can provide the transient dynamic trend of types and concentrations of the water-soluble chromophores in ambient particulate matter (PM) with a time-resolution of up to 15 min. By comparing the PM samples collected by the filter method, the result suggests that the online analysis of the chromophores by the PILS-EEM-TOC system is comparable to the offline filter method. In this case of Xi'an, China, the primary emission and secondary processes of the atmospheric chromophores were observed, the obvious effects of sandstorm-precipitation processes on the types and concentrations of chromophores were also found. The result suggests that a type of highly-oxygenated HULIS chromophores can be used as a secondary source tracer, and a type of less-oxygenated HULIS chromophores can be used as dust source tracer. The PILS-EEM-TOC system has great potential application in a wider spatiotemporal scope to explore the origin, generation and disappearance mechanisms of atmospheric chromophores.

Seasonal habitat use indicates that depth may mediate the potential for invasive round goby impacts in inland lakes

Abstract The round goby (Neogobius melanostomus) is among the fastest‐spreading introduced aquatic species in North America and is radiating inland from the Great Lakes into freshwater ecosystems across the landscape. Predicting and managing the impacts of round gobies requires information on the factors influencing their distribution in habitats along the invasion front, yet this information is not available for many recently invaded ecosystems. We evaluated the seasonal habitat use and biomass of round gobies in an inland temperate lake to define the spatiotemporal scope of biological interactions at the leading edge of the round goby invasion. Using novel statistical approaches, we combined hierarchical models that control for imperfect species detection with flexible smooth terms to describe non‐linear relationships between round goby abundance and environmental gradients. Subsequently, we generated accurate detection‐corrected estimates of the standing stock biomass of round gobies. Our results show seasonally differentiated habitat niches, where suitable round goby habitat in summer months is restricted to shallow depths (<18.4 m) with a mixture of vegetative and mussel cover. We found high round goby biomass of 122 kg/ha in occupied habitats during the summer, with a total lake‐wide biomass of 766,000 kg. In winter, round gobies migrate to deep offshore habitats and disperse, dramatically altering their scope for biological interactions with resident aquatic species across summer and winter seasons. The results of this study indicate that the scope of biological interactions in inland lakes may be seasonally variable, with potential for high round goby biomass in shallow lakes or at the periphery of deep lakes in the summer months. Such shallow‐water habitats may therefore present higher risk of ecological impacts from round gobies in invaded lentic ecosystems. As round gobies expand inland, consideration of seasonal habitat use will be an important factor in predicting the impacts of this pervasive invader.

Building socially-enabled event-enriched maps

With the advancement of social sensing technologies, digital maps have recently witnessed a tremendous evolution with the aim of integrating enriched semantic layers from heterogeneous and diverse data sources. Current generations of digital maps are often crowd-sourced, allow interactive route planning, and may contain live updates, such as traffic congestion states. Within this context, we believe that the next generation of maps will introduce the concept of extracting Events of Interest (EoI) from crowdsourced data, and displaying them at different spatial scales based on their significance. This paper introduces Hadath1, a scalable and efficient system that extracts social events from unstructured data streams, e.g. Twitter. Hadath applies natural language processing and multi-dimensional clustering techniques to extract relevant events of interest at different map scales, and to infer the spatio-temporal scope of detected events. Hadath also implements a hierarchical in-memory spatio-temporal indexing scheme to allow efficient and scalable access to raw data, as well as to extracted clusters of events. Initially, data packets are processed to discover events at a local scale, then, the proper spatio-temporal scope and the significance of detected events at a global scale is determined. As a result, live events can be displayed at different spatio-temporal resolutions, thus allowing a smooth and unique browsing experience. Finally, to validate our proposed system, we conducted experiments on real-time and historical social media streams.

Learning Attention-Enhanced Spatiotemporal Representation for Action Recognition

Learning spatiotemporal features via 3D-CNN (3D Convolutional Neural Network) models has been regarded as an effective approach for action recognition. In this paper, we explore visual attention mechanism for video analysis and propose a novel 3D-CNN model, dubbed AE-I3D (Attention-Enhanced Inflated-3D Network), for learning attention-enhanced spatiotemporal representation. The contribution of our AE-I3D is threefold: First, we inflate soft attention in spatiotemporal scope for 3D videos, and adopt softmax to generate probability distribution of attentional features in a feedforward 3D-CNN architecture; Second, we devise an AE-Res (Attention-Enhanced Residual learning) module, which learns attention-enhanced features in a two-branch residual learning way, also the AE-Res module is lightweight and flexible, so that can be easily embedded into many 3D-CNN architectures; Finally, we embed multiple AE-Res modules into an I3D (Inflated-3D) network, yielding our AE-I3D model, which can be trained in an end-to-end, video-level manner. Different from previous attention networks, our method inflates residual attention from 2D image to 3D video for 3D attention residual learning to enhance spatiotemporal representation. We use RGB-only video data for evaluation on three benchmarks: UCF101, HMDB51, and Kinetics. The experimental results demonstrate that our AE-I3D is effective with competitive performance.

Risks for cyanobacterial harmful algal blooms due to land management and climate interactions

The frequency and severity of cyanobacteria harmful blooms (CyanoHABs) have been increasing with frequent eutrophication and shifting climate paradigms. CyanoHABs produce a spectrum of toxins and can trigger neurological disorder, organ failure, and even death. To promote proactive CyanoHAB management, geospatial risk modeling can act as a predictive mechanism to supplement current mitigation efforts. In this study, iterative AIC analysis was performed on 17 watershed-level biophysical parameters to identify the strongest predictors based on Sentinel-2-derived cyanobacteria cell densities (CCD) for 771 waterbodies in Georgia Piedmont. This study used a streamlined watershed delineation technique, a 1-meter LULC classification with ~88% accuracy, and a technique to predict CyanoHAB risk in small-to-medium sized waterbodies. Landscape characteristics were computed utilizing the Google Earth Engine platform that enabled large spatio-temporal scope and variable inclusion. Watershed maximum winter temperature, percent agriculture, percent forest, percent impervious, and waterbody area were the strongest predictors of CCD with a 0.33 R-squared. Warmer winter temperatures allow cyanobacteria to be photosynthetically active year-round, and trigger CyanoHABs when warmer temperatures and nutrients are introduced in early spring, typically referred to as Spring Bloom in southeast U.S. The risk models revealed an unexpected significant linear relationship between percent forest and CCD. It is due to the fact that land reclamation via reforestation in the piedmont have left legacy sediment and nutrients which are mobilized as surface runoff to the watershed after rain events. A Jenks Natural Break scheme assigned waterbodies to CyanoHAB risk groups, and of the 771 waterbodies, 24.38% were low, 37.35% and 38.26% were medium and high risk respectively. This research supplements existing cyanobacteria risk modeling methods by introducing a novel, scalable, and reproducible method to determine yearly regional risk. Future studies should include factors such as demographic, socioeconomic, labor, and site-specific environmental conditions to create more holistic CyanoHAB risk outputs.