High Spatial Scales Research Articles

Playing the scales: A strategy adopted by resistance coalitions for public value creation

Local actors can defend public value by mobilizing resistance coalitions against threats from higher spatial scales. Drawing on existing scholarship on resistance movements and public value creation, this paper proposes a theoretical framework for understanding how local actors can protect public value from threats of rescaling driven solely by hegemonic discourses and presents a strategy that we call “playing the scales.” Our case study analyzes a Local Action Group’s resistance to a state-imposed and EU-framed regulation that threatened local forms of value creation on the Croatian peninsula of Pelješac. Employing a longitudinal participatory approach, our findings outline the strategy of playing the scales, which involved three key tactics: gathering relevant knowledge from various scales; crafting an alternative narrative; and leveraging this narrative across scales to reshape the dominant organizational logic. The study contributes to the understanding of public value creation by showing how it can be defended by resistance coalitions that are capable of playing the scales. It also sheds light on alternative dynamics of rescaling that, rather than oppositional resistance to higher scales or attempts to scale up micro-practices can also be driven by trans-scalar alliances that inscribe the interests of local communities into dominant discourses.

High-Resolution Rainfall Estimation Using Ensemble Learning Techniques and Multisensor Data Integration.

In Indonesia, the monitoring of rainfall requires an estimation system with a high resolution and wide spatial coverage because of the complexities of the rainfall patterns. This study built a rainfall estimation model for Indonesia through the integration of data from various instruments, namely, rain gauges, weather radars, and weather satellites. An ensemble learning technique, specifically, extreme gradient boosting (XGBoost), was applied to overcome the sparse data due to the limited number of rain gauge points, limited weather radar coverage, and imbalanced rain data. The model includes bias correction of the satellite data to increase the estimation accuracy. In addition, the data from several weather radars installed in Indonesia were also combined. This research handled rainfall estimates in various rain patterns in Indonesia, such as seasonal, equatorial, and local patterns, with a high temporal resolution, close to real time. The validation was carried out at six points, namely, Bandar Lampung, Banjarmasin, Pontianak, Deli Serdang, Gorontalo, and Biak. The research results show good estimation accuracy, with respective values of 0.89, 0.91, 0.89, 0.9, 0.92, and 0.9, and root mean square error (RMSE) values of 2.75 mm/h, 2.57 mm/h, 3.08 mm/h, 2.64 mm/h, 1.85 mm/h, and 2.48 mm/h. Our research highlights the potential of this model to accurately capture diverse rainfall patterns in Indonesia at high spatial and temporal scales.

Research on downscaling method of the enhanced TROPOMI solar-induced chlorophyll fluorescence data

Solar-induced chlorophyll fluorescence (SIF) is the release of plant energy during photosynthesis, which is significantly superior to the vegetation index in the characterization of vegetation growth. However, the existing satellite retrieved SIF data have the problems of low spatial resolution and spatial discontinuity. To solve these problems, this paper proposes multiple parameters downscaling method that considers the structural and physiological characteristics of SIF. Multiple linear regression (MLR), random forest (RF), and convolutional neural network (CNN) models were used to construct a downscaling model for the TROPOspheric Monitoring Instrument (TROPOMI) Enhanced SIF (eSIF) data. The theory of spatial scale invariance was applied to invert the 500 m spatial resolution SIF data products for Henan Province from 2012 to 2021 using Moderate-resolution Imaging Spectroradiometer (MODIS) data. The evaluation metrics for assessing downscaling accuracy include the determination coefficient (R 2), mean absolute error (MAE), and root mean squared error (RMSE). The experimental results demonstrate that the RF model outperforms others, achieving R 2, MAE, and RMSE values of 0.935, 0.041 mW/m2/nm2/sr, and 0.061 mW/m2/nm2/sr, respectively. These results successfully meet the downscaling requirements. The downscaling data products have better fitting effect with eSIF and new Global 'OCO-2′ SIF (GOSIF) data both in time and space. The correlation between downscaling SIF data and winter wheat yield is significantly better than that of GOSIF data products and shows strong correlation with Gross Primary Productivity (GPP). By considering the structural and physiological characteristics of SIF, the RF algorithm can effectively retrieve reliable 500 m spatial resolution SIF data, this provides methodological support for the application of SIF data at higher spatial scales.

Global Terrestrial Evapotranspiration Estimation from Visible Infrared Imaging Radiometer Suite (VIIRS) Data

It is a difficult undertaking to reliably estimate global terrestrial evapotranspiration (ET) using the Visible Infrared Imaging Radiometer Suite (VIIRS) at high spatial and temporal scales. We employ deep neural networks (DNN) to enhance the estimation of terrestrial ET on a global scale using satellite data. We accomplish this by merging five algorithms that are process-based and that make use of VIIRS data. These include the Shuttleworth–Wallace dual-source ET method (SW), the Priestley–Taylor-based ET algorithm (PT-JPL), the MOD16 ET product algorithm (MOD16), the modified satellite-based Priestley–Taylor ET algorithm (MS-PT), and the simple hybrid ET algorithm (SIM). We used 278 eddy covariance (EC) tower sites from 2012 to 2022 to validate the DNN approach, comparing it to Bayesian model averaging (BMA), gradient boosting regression tree (GBRT) and random forest (RF). The validation results demonstrate that the DNN significantly improves the accuracy of daily ET estimates when compared to three other merging methods, resulting in the highest average determination coefficients (R2, 0.71), RMSE (21.9 W/m2) and Kling–Gupta efficiency (KGE, 0.83). Utilizing the DNN, we generated a VIIRS ET product with a 500 m spatial resolution for the years 2012–2020. The DNN method serves as a foundational approach in the development of a sustained and comprehensive global terrestrial ET dataset. The basis for characterizing and analyzing global hydrological dynamics and carbon cycling is provided by this dataset.

Synergies Between NASA's Hyperspectral Aquatic Missions PACE, GLIMR, and SBG: Opportunities for New Science and Applications

AbstractWithin the next decade, NASA plans to launch three new missions with imaging spectrometers for aquatic science and applications: Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) in 2024, Geostationary Littoral Imaging Radiometer (GLIMR) in 2026, and Surface Biology and Geology (SBG) in 2028. Taken together, these missions will evaluate long‐term trends in phytoplankton biomass linked to climate change, and provide new spectral capabilities to assess aquatic biogeochemistry, biophysics, and habitats. Hyperspectral measurements of ocean color, paired with advanced retrieval algorithms, can provide new information on phytoplankton community composition and water quality. We compare the mission architecture and sensor characteristics to identify the synergistic opportunities to merge algorithms, field data, and calibration and validation techniques. Each mission has unique temporal and spatial characteristics to monitor the aquatic transitions from watershed to open ocean ecosystems. SBG provides observations at high spatial scales to monitor emergent, floating, submerged, and benthic habitats from inland to coastal waters. With global daily coverage, PACE can track the fate of material as it meanders offshore and provides an enhanced context for phytoplankton diversity and global biogeochemical cycling. GLIMR is optimized to resolve temporal processes that give rise to aquatic rates and fluxes including phytoplankton growth rates, physiology, and episodic events such as storms. Applications with high spectral, spatial, and temporal resolution from these NASA missions include assessing carbon dynamics and biogeochemical cycling across the land‐ocean continuum, harmful algal blooms, and oil spills.

Differences in levels of accessibility: The importance of spatial scale when measuring distributions of the accessibility of health and emergency services

This paper explores to what extent inequalities in travel times, measured via the Gini index, depend on the spatial scale at which (average) travel times are measured. By using the new concept of Dedicated Accessibility Points, for the Netherlands we calculated average travel times at four spatial levels, ranging from virtually individual addresses to the level of municipalities. Travel times by car and bicycle to three medical points of interest are calculated: pharmacies, family doctors, and hospitals, as well as travel times by car from three other points of interest: ambulance stations, fire stations and police stations. At the level of individual addresses the errors made due to spatial aggregation is absent, but at higher spatial scales it plays a role. The results show that the Gini index is heavily influenced by the spatial scale at which the indices are calculated, with smaller indices at higher spatial scales. We discuss the implications of these differences for research and policy.

LIFE RIPARIAS Early Alert: Using GBIF-mediated data to better manage invasive alien species

Invasive alien species represent one of the major threats to biodiversity. Prevention is better than cure, but once invasive species get established, early management interventions are important to prevent further damage to nature and increasing management costs. Managers need to be informed as swiftly as possible when invasive species pop up in their area. Moreover, an effective response also requires information on species in neighbouring areas. This way, managers can anticipate recolonisation and coordinate the response across higher spatial scales. Open data are indispensible to efficiently organize such early detection of and rapid response to invasive species. Here, we present LIFE RIPARIAS Early Alert, a new open source tool that aggregates data from diverse sources and sends tailored e-mail alerts to managers. The tool focusses on the project area of the EU co-funded project LIFE RIPARIAS (Reaching Integrated and Prompt Action in Response to Invasive Alien Species, LIFE19 NAT/BE/000953), which aims to improve dataflows for the management of invasive alien species in river basins across regional administrative borders in Belgium. Data on invasive alien species in Belgium are scattered across a multitude of sources, including project surveys, official monitoring schemes, scientific projects and citizen science reporting portals. We facilitated the standardization and publication of these sources as open datasets to the Global Biodiversity Information Facility (GBIF). The early alert tool then harvests these observations from GBIF, visualizes them on a map as well as in table view, with their exact coordinates and a link to the original record. The tool allows for date range filtering and the selection of newly reported (and published) observations through a simple intuitive user interface. Users can create e-mail alerts to be notified of new observations for their species group(s), area(s) or source dataset(s) of interest. Currently, only observations from LIFE RIPARIAS target species (Branquart et al. 2022)—equally published as a checklist on GBIF—are displayed. These include non-native crayfish, invasive aquatic plants and a range of high-impact alert list species that should not yet occur in the area but will be rapidly removed should they arrive. The tool is currently being tested by the Belgian managerial community. In time, we hope to increase both the taxonomic scope as well as the number of sources. To our knowledge the RIPARIAS early alert tool is one of the first of its kind in Europe. It is unique in displaying data from multiple data sources at the highest spatial resolution, considering the species from the European Union Invasive Species Regulation (European Commission 2019) and actively promoting and supporting data owners to publish their data openly.

Global spatio-temporal change assessment in interregional water stress footprint in China by a high resolution MRIO model

Developing effective strategies to alleviate increasing water stress in China requires an understanding of how consumption and production drive water stress footprints (WSF) at a high resolution and multiple spatial and temporal scales. However, current Chinese multi-regional input-output (CMRIO) models have limited resolution. Here, we build a high-resolution international MRIO model covering 31 Chinese provinces, 163 sectors, to address this issue, and then analyze the impact of changes in China's interprovincial and international trade patterns on the WSF from 2012 to 2017. We find that China's water stress embodied in inter-provincial trade has increased year after year, to 5606 km3 H2O-eq in 2017, exceeding 50 % of the total domestic footprint. Domestic water stress transfer is most apparent in the outsourcing of water stress from eastern coastal regions to Central and Western regions, with the top interregional supply chain paths mainly associated with the demand of processed rice and tobacco products. China has transformed into a net exporter of water stress in 2017, with water stress exports to developing countries accounting for 54 % of total exports, up from 51 % in 2012. With deepening globalization, trade between China and developing countries has boosted bilateral economic development, while also exacerbating water stress in China. In addition to agricultural cultivation, industrial products such as plastics and steel exported to meet international industries further contribute to water stress in Northern China. Further identify hotspots of water stress consumption is needed to prioritize actions to relieve regional water stress in a more effective manner, and our study can provide key information.

Estimation of soybean grain yield from multispectral high-resolution UAV data with machine learning models in West Africa

Soybean (Glycine max (L.) Merr.) is a leguminous and oil crop with rapidly growing importance and demand in Africa following the increasing demand for oil and livestock and poultry feed in sub-Saharan Africa. However, soybean productivity is low in most countries of sub-Saharan Africa, especially in West Africa, where productivity is below one ton per ha. Hence, concerted soybean varietal development and testing efforts have been underway by the International Institute of Tropical Agriculture (IITA), collaborating with the various African and US-based soybean breeding programs. Integrating new varietal evaluation approaches based on advanced phenotyping techniques into IITA's soybean breeding program is crucial for designing efficient crop genetic improvement techniques. Hence, this work aims to investigate machine learning (ML) models and Unmanned Aerial vehicles (UAVs) to aid rapid high throughput phenotypic workflow for soybean yield estimation. We acquired multispectral images through a Sequoia® camera aboard a senseFly eBee X UAV from five variety trials during the 2020 growing season in Nigeria. UAV-based spectral bands, canopy height, vegetation indices (VI), and texture features were generated by gray level co-occurrence matrix (GLCM) and integrated to predict crop grain yield using five machine learning (ML) regression models, including Cubist, Extreme Gradient Boosting (XGBoost), Stochastic Gradient Boosting (GBM), Support vector machine (SVM), and Random Forest (RF). The main findings are the textural information generated using gray level co-occurrence matrix (GLCM) slightly outperformed predictors based mainly on vegetation indices (VI) and provided a promising alternative to the conventional use of VI in crop yield estimation. All the five ML models performed moderately well in predicting grain yield for all the soybean trials investigated, though the Cubist and RF model stood out, with R2 reaching 0.89. The study provides a framework to perform crop breeding trial assessments more effectively and consistently at high spatial scales that African crop breeding programs did not commonly apply. The workflow can also be successfully modified and applied for high throughput phenotyping of breeding platforms in other crops.

Transient hydrology-induced elastic deformation and land subsidence in Australia constrained by contemporary geodetic measurements

Earth's lithospheric deformation in response to geophysical and climatic processes manifests as a wide range of spatio-temporal elastic/viscoelastic deformation, including seismic and tectonic motion, mantle flow, and climate-induced hydrologic-cyrospheric-atmospheric mass transports. We use increasingly accurate contemporary geodetic measurements, including those from GPS, Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GFO) missions, to quantify the competing processes of the hydrological cycle and geodynamics within the Australian continental lithosphere over the last two decades, 2002 to 2020. Observed precipitation and temperature anomalies reveal that anthropogenic climate change may have exerted significant impact on the terrestrial water storage (TWS) evolution in Australia, as evidenced by anomalous fluctuation during the 2010-2012 El Niño Southern Oscillation (ENSO) events. In particular, enhanced TWS inferred from the geodetic observations is shown to be highly correlated with increased precipitation during the 2010-2012 La Niña event. The GPS observed vertical land motion cannot be completely explained by the elastic deformation induced by surface water storage variations across Australia, nor by the Glacial Isostatic Adjustment (GIA) geophysical process. We suggest that the geodetic-observed land subsidence of Australia at high spatial scales could be related to anthropogenic aquifer groundwater depletion, while the long wavelength signals may be associated with reference frame error and then tectonic deformation.

Viewpoint: Irrigation water management in a space age

AbstractClimate change and rapid population growth are already putting increasing demand and pressure on the world's freshwater resources. Irrigated agriculture is responsible for about 70% of global freshwater withdrawals, consuming the most amount of water. However, the diverted water in irrigation systems is often not utilized efficiently because of poor water management at different spatial scales, resulting in a system efficiency of only 30–50% in most Asian countries. Typically, improving water management in irrigated areas requires accurate information on various water balance parameters while also considering a changing climate across different spatial scales. There have been technical limitations in getting accurate and reliable information on various key water balance parameters with the conventional approaches used in the recent past. In the twenty‐first century, considerable advances have been made in using satellite imagery, including processing and geospatial algorithms, to estimate hydro‐meteorological fluxes and relevant components at different spatial scales. This paper provides a perspective on the application of innovative and non‐conventional approaches to water resources management in the Murray Darling basin, Australia, the Indus basin, Pakistan and the Amu Darya basin, Uzbekistan. Examples of the state‐of‐the‐art tools described in this paper include: (i) using geoinformatics to monitor the diagnostic and operational performance of large irrigation schemes; (ii) quantifying groundwater and surface water to better manage these two resources using geoinformatics; (iii) forecasting irrigation supply and demand at high spatial and temporal scales using hydrological modelling based on the nodal network; (iv) forecasting crop yield production by satellite remote sensing. The approaches in this study clearly demonstrate that new monitoring and planning tools and methods are highly effective in improving irrigation water management in the ‘space age’ (for the purposes of this paper, space age refers to a period in which earth observation satellites are available to accurately monitor agricultural practices and water balance parameters such as soil moisture and evapotranspiration). The application of these innovative tools can assist in strategizing, diagnosing, monitoring and improving the performance of irrigation systems to grow more crop per drop of water while minimizing environmental impacts and dealing with climate change impacts.

Observational angular analysis of Moon-based Earth observations

ABSTRACT Different from existing satellite platforms, a Moon-based platform provides a novel method for monitoring the Earth, owing to its high temporal and spatial scales. In this study, the elevation angle and azimuth angle for Earth features were calculated based on a geometric relationship and through a series of coordinate transformations of both Earth features and a Moon-based platform. According to the latitude of the Earth, choosing regions which are sensitive to climate change – the Tibet Plateau, Arctic region and Antarctic region as reference examples for the study. The results of the examples of the Earth showed that a Moon-based platform could synchronously observe the polar regions. Furthermore, using four extreme cases when the Moon rotates along with the Earth as examples, the distribution of observation angles showed a peculiarity of spatial continuity. By combining the angular coverages in the Arctic and Antarctic regions, a Moon-based platform could observe almost the entire polar region, and variations in the observation angle in one orbital period had the characteristics of temporal continuity. On comparing the polar-orbiting satellites and Deep Space Climate Observatory, a Moon-based platform had more angular combinations and observational frequencies. The findings of this study provide significant information for Moon-based Earth monitoring.

Monitoring irrigation dynamics in paddy fields using spatiotemporal fusion of Sentinel-2 and MODIS

Rapid and accurate monitoring of irrigation dynamics in paddy fields (the start, end, duration and irrigation peak, etc.) at field scales is crucial to the fine management of agricultural water resources, especially in typical areas with water shortages. However, there is still a lack of sufficient research to depict irrigation dynamics in paddy fields at high temporal and spatial levels. To this end, this study fused Sentinel-2 and MODIS images to map the spatio-temporal dynamics of irrigation events in paddy fields. A popular spatiotemporal fusion algorithm (enhanced spatial and temporal adaptive reflectance fusion model, ESTARFM) was used to generate 25 high-spatial resolution (10 m) remote sensing images based on 9 Sentinel-2 images and 24 MODIS images. Random forest algorithm was used to extract the spatial distribution of irrigated paddy fields. Water body index and vegetation index were employed to identify the start, end and duration of irrigation in paddy fields. Penman-Monteith model was used to estimate water surplus and deficit of irrigation during the critical irrigation period with daily observation data from meteorological stations. This study was carried out in rice-growing areas in the middle and lower reaches of the Yellow River in China. The results indicated that the spatial distribution difference of irrigation events in paddy fields with the shortest 3-day interval could be monitored in collaboration with Sentinel-2 and MODIS images. The start, end, and duration of irrigation in paddy fields presented significant spatial differences at field scales. A large amount of water from groundwater and Yellow River was needed, because the total water shortage of irrigation in paddy fields in the study area was about 80.79% of the total water demand. In addition, paddy fields with higher water demand were more concentrated spatially, as were paddy fields with lower water demand. The feasibility of spatiotemporal fusion of multi-source remote sensing data makes it possible to continuously monitor irrigation dynamics in paddy fields on high spatial resolution scales, which is conducive to the construction of spatiotemporal database and big data platform of agricultural irrigation information. This would not only help in promoting the high-quality development of agricultural water resources management but also alleviating the contradiction of regional water resources.

Spatiotemporal dynamics drive synergism of land use and climatic extreme events in insect meta-populations

Ecosystems are increasingly threatened by co-occurring stressors associated with anthropogenic global change. Spatial stressor patterns range from local to regional to global, and temporal stressor patterns from discrete to continuous. To date, most multiple stressor studies covered short periods and focused on local effects and interactions. However, it remains largely unknown how stressors with different spatiotemporal patterns interact in their effects over longer periods. In particular, at higher spatial scales, biotic dynamics in ecological networks complicate the understanding of stressor interactions.We used a spatially explicit meta-population model for a generic freshwater insect, parameterized based on traits of the European damselfly Coenagrion mercuriale, to simulate scenarios of discrete climatic extreme events and continuous land use-related stress. Climatic extreme events were modeled as recurring mortality in all patches, whereas land use permanently influenced meta-populations via patch qualities and network connectivity.We found that the risk of discrete climatic extreme events to meta-populations depended strongly on the proportion of land use types, with effects ranging from negligible to extinction. Land use-related stress limited recovery in meta-populations from effects of climatic extreme events, resulting in synergistic stressor interactions. Moreover, the spatial configuration of land use type influenced the combined stressor effects with clustered configurations resulting in lower effects compared to a random configuration. Finally, we found that combined stressor effects can vary with the time point at which they were determined, indicating that inconclusive results in multiple stressor research can partly be due to differences in the time of determination.We conclude that conservation should focus on regional landscape management to mitigate risks on meta-populations from future, intensified extreme climate events. Reducing land use effects, thus improving patch quality and network connectivity, can compensate for effects of additional discrete stressors and, in turn, synergistic interactions.

High salinity gradients and intermediate spatial scales shaped similar biogeographical and co-occurrence patterns of microeukaryotes in a tropical freshwater-saltwater ecosystem.

Microeukaryotes play key ecological roles in the microbial web of aquatic ecosystems. However, large knowledge gaps urgently need to be filled regarding the biogeography with associated shaping mechanisms and co-occurrence patterns of microeukaryotes under freshwater-saltwater gradients, especially true in tropical regions. Here, we investigated microeukaryotes of six mixed freshwater-saltwater regions in the Pearl River Estuary and surrounding coasts in southern China, with salinity ranging 0.1-32.0% and distances spanned up to 500 km, using molecular ecological methods. Results indicate that the biogeography of abundant and rare microeukaryotic communities was similar, both their co-occurrence patterns and biogeographical patterns were driven by deterministic and stochastic processes. The environmental factors with higher selective pressure than dispersal limitation meant that the role of deterministic process in structuring communities was more significant than that of stochastic process, and salinity played important role in structuring both microeukaryotic communities and networks. The abundant communities had stronger influence on entire microeukaryotic communities and seemed to be more sensitive to environmental changes than their rare counterparts, while rare ones had stronger interspecific relationships. Finally, the geographic scale and environmental gradients of study regions should firstly be clarified in future research on the ecological processes of microeukaryotes before conclusions are drawn.

Snowpack signals in North American tree rings

Climate change has contributed to recent declines in mountain snowpack and earlier runoff, which in turn have intensified hydrological droughts in western North America. Climate model projections suggest that continued and severe snowpack reductions are expected over the 21st century, with profound consequences for ecosystems and human welfare. Yet the current understanding of trends and variability in mountain snowpack is limited by the relatively short and strongly temperature forced observational record. Motivated by the urgent need to better understand snowpack dynamics in a long-term, spatially coherent framework, here we examine snow-growth relationships in western North American tree-ring chronologies. We present an extensive network of snow-sensitive proxy data to support high space/time resolution paleosnow reconstruction, quantify and interpret the type and spatial density of snow related signals in tree-ring records, and examine the potential for regional bias in the tree-ring based reconstruction of different snow drought types (dry versus warm). Our results indicate three distinct snow-growth relationships in tree-ring chronologies: moisture-limited snow proxies that include a spring temperature signal, moisture-limited snow proxies lacking a spring temperature signal, and energy-limited snow proxies. Each proxy type is based on distinct physiological tree-growth mechanisms related to topographic and climatic site conditions, and provides unique information on mountain snowpack dynamics that can be capitalized upon within a statistical reconstruction framework. This work provides a platform and foundational background required for the accelerated production of high-quality annually resolved snowpack reconstructions from regional to high (12 km) spatial scales in western North America and, by extension, will support an improved understanding of the vulnerability of snowmelt-derived water resources to natural variability and future climate warming.

Monitoring Spatial and Temporal Differences in Andean Snow Depth Derived From Satellite Tri-Stereo Photogrammetry

Quantifying the high elevation winter snowpack in mountain environments is crucial for lowland water supply, though it is notoriously difficult to accurately estimate due to a lack of observations and/or uncertainty in the distribution of meteorological variables in space and time. We compare high spatial resolution (3 m), satellite-derived snow depth maps for two drought years (2017 and 2019) in a high mountain catchment of the central Chilean Andes, applying a recently updated methodology for spaceborne photogrammetry. Regional weather station observations revealed an 80% reduction in precipitation for 2019 (the second driest winter since 1950) relative to 2017, though only a 10% reduction in total snow-covered area is seen in the satellite imagery. We threshold surface height changes based upon uncertainty of stable (snow-free) terrain differences for topographic characteristics of the catchment (slope, aspect, roughness etc). For a conservative analysis of change, outside of the topographically-derived confidence intervals, we calculate a mean 0.48 ± 0.28 m reduction of snow depth and a 39 ± 15% reduction in snow volume for 2019, relative to 2017 (for 23% of the total catchment area). Our findings therefore quantify, for the first time in the Andes, the relationship of high-resolution mountain snow depth observations with low elevation precipitation records and characterise its inter-annual variability over high elevation, complex terrain. The practical use of such detailed snow depth information at high elevations is of great value to lowland communities and our findings highlight the clear need to relate the high spatial (Pléiades) and temporal (in-situ) scales within the available datasets in order to improve estimates of region-wide snow volumes.

A Technical Overview of the New York State Mesonet Standard Network

AbstractThe New York State Mesonet (NYSM) is a network of 126 standard environmental monitoring stations deployed statewide with an average spacing of 27 km. The primary goal of the NYSM is to provide high-quality weather data at high spatial and temporal scales to improve atmospheric monitoring and prediction, especially for extreme weather events. As compared with other statewide networks, the NYSM faced considerable deployment obstacles with New York’s complex terrain, forests, and very rural and urban areas; its wide range of weather extremes; and its harsh winter conditions. To overcome these challenges, the NYSM adopted a number of innovations unique among statewide monitoring systems, including 1) strict adherence to international siting standards and metadata documentation; 2) a hardened system design to facilitate continued operations during extreme, high-impact weather; 3) a station design optimized to monitor winter weather conditions; and 4) a camera installed at every site to aid situational awareness. The network was completed in spring of 2018 and provides data and products to a variety of sectors including weather monitoring and forecasting, emergency management, agriculture, transportation, utilities, and education. This paper focuses on the standard network of the NYSM and reviews the network siting, site configuration, sensors, site communications and power, network operations and maintenance, data quality control, and dissemination. A few example analyses are shown that highlight the benefits of the NYSM.

An improved dust identification index (IDII) based on MODIS observation

ABSTRACT Satellite remote sensing may serve as an ideal technique to detect dust storms for high temporal and spatial scales. In this paper, we propose an improved dust identification index (IDII) based on Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. The IDII algorithm was used to monitor 129 dust storm events over the West Asia region from 2016 to 2018. Ground-based observations of synoptic stations, RGB images, and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), and Ozone Monitoring Instrument (OMI) were implemented for validating IDII algorithm. In addition, the performance of the proposed algorithm was compared with the Global Dust Detection Index (GDDI). The results show that the accuracy (Ac), Probability Of Correct positive Detection (POCD), and Probability Of False-positive Detection (POFD) for the IDII and GDDI are 82%, 85%, 33%, and 71%, 74%, 27%, respectively. Also over the water as a challenging object, it is clearly seen that the IDII performs much better than GDDI method. Our results suggest that the proposed approach can deal with the common limitations of dust detection algorithms, i.e. dust detection over different surfaces (land and water), seasonal changes, and similarity between dust and other objects like clouds.

A present-day atlas of the human brain

Neuroscience Defining brain regions and demarking their spatial extent are important goals in neuroscience. A modern map of the brain's cellular structure, a cytoarchitectonic atlas, should provide maps of areas in three dimensions, integrate recent knowledge about brain parcellation, consider variations between individual brains, rely on reproducible workflows, and provide web-based links to other resources and databases. Amunts et al. created such an atlas based on serial histological sections of brain. They developed a computational framework and refined the current boundaries of the human brain based on cytoarchitectural patterns. This technique can easily be transferred to build brain atlases for other species or a spatial framework for other organs, other modalities, or multimodal maps for regions of interest at higher spatial scales. This research makes similar future attempts simultaneously reproducible and flexible. Science , this issue p. [988][1] [1]: /lookup/doi/10.1126/science.abb4588