Sub-regional Scale Research Articles

Life in the Fast Lane: Modeling the Fate of Glass Sponge Larvae in the Gulf Stream

Effective conservation management of deep-sea sponges, including design of appropriate marine protected areas, requires an understanding of the connectivity between populations throughout a species’ distribution. We provide the first consideration of larval connectivity among deep-sea sponge populations along the southeastern coast of North America, illustrate the influence of the Gulf Stream on dispersal, and complement published distribution models by evaluating colonization potential. Connectivity among known populations of the hexactinellid sponge Vazella pourtalesii was simulated using a 3-D biophysical dispersal model throughout its distribution from Florida, United States to Nova Scotia, Canada. We found no exchange with an estimated pelagic larval duration of 2 weeks between populations north and south of Cape Hatteras, North Carolina at surface, mid-water and seabed release depths, irrespective of month of release or application of a horizontal diffusion constant specific to cross-Gulf Stream diffusivity. The population north of Cape Hatteras and south of Cape Cod was isolated. There was some evidence that Gulf Stream eddies formed near Cape Hatteras could travel to the northwest, connecting the populations in the two sub-regions, however that would require a much longer pelagic duration than what is currently known. More likely almost all larval settlement will be in the immediate area of the adults. At sub-regional scales, connectivity was found from the Strait of Florida through to the Blake Plateau, southeastern United States, with the latter area showing potential for recruitment from more than one source population. The influence of the Charleston Bump, a shallow feature rising from the Blake Plateau, was substantial. Particles seeded just north of the Bump were transported greater distances than those seeded to the south, some of which were caught in an associated gyre, promoting retention at the seabed. To the north on the Scotian Shelf, despite weaker currents and greater distances between known occurrences, unidirectional transport was detected from Emerald Basin to the Northeast Channel between Georges and Browns Banks. These major conclusions remained consistent through simulations run with different averaging periods for the currents (decades to daily) and using two ocean model products (BNAM and GLORYS12V1).

Impacts of Aerosol Loading in the Hindu Kush Himalayan Region Based on MERRA-2 Reanalysis Data

The impacts of climate change have severely affected geosphere, biosphere and cryosphere ecosystems in the Hindu Kush Himalayan (HKH) region. The impact has been accelerating further during the last few decades due to rapid increase in anthropogenic activities such as modernization, industrialization and urbanization, along with energy demands. In view of this, the present work attempts to examine aerosol optical depth (AOD) over the HKH region using the long-term homogeneous MERRA-2 reanalysis data from January, 1980 to December, 2020. The AOD trends are examined statistically with student’s t-test (t). Due to a vast landmass, fragile topography and harsh climatic conditions, we categorized the HKH region into three sub-regions, namely, the northwestern and Karakoram (HKH1), the Central (HKH2) and the southeastern Himalaya and Tibetan Plateau (HKH3). Among the sub-regions, the significant enhancement of AOD is observed at several potential sites in the HKH2 region, namely, Pokhara, Nainital, Shimla and Dehradun by 55.75 × 10−4 ± 3.76 × 10−4, 53.15 × 10−4 ± 3.94 × 10−4, 51.53 × 10−4 ± 4.99 × 10−4 and 39.16 × 10−4 ± 4.08 × 10−4 AOD year−1 (550 nm), respectively, with correlation coefficients (Rs) of 0.86 to 0.93. However, at a sub-regional scale, HKH1, HKH2 and HKH3 exhibit 23.33 × 10−4 ± 2.28 × 10−4, 32.20 × 10−4 ± 2.58 × 10−4 and 9.48 × 10−4 ± 1.21 × 10−4 AOD year−1, respectively. The estimated trends are statistically significant (t > 7.0) with R from 0.81 to 0.91. Seasonally, the present study also shows strong positive AOD trends at several potential sites located in the HKH2 region, such as Pokhara, Nainital, Shimla and Dehradun, with minimum 19.81 × 10−4 ± 3.38 × 10−4 to maximum 72.95 × 10−4 ± 4.89 × 10−4 AOD year−1 with statistical significance. In addition, there are also increasing AOD trends at all the high-altitude background sites in all seasons.

Air–sea CO2exchange in the Baltic Sea—A sensitivity analysis of the gas transfer velocity

Air–sea gas fluxes are commonly estimated using wind-based parametrizations of the gas transfer velocity. However, neglecting gas exchange forcing mechanisms – other than wind speed – may lead to large uncertainties in the flux estimates and the carbon budgets, in particular, in heterogeneous environments such as marginal seas and coastal areas. In this study we investigated the impact of including relevant processes to the air–sea CO 2 flux parametrization for the Baltic Sea. We used six parametrizations of the gas transfer velocity to evaluate the effect of precipitation, water-side convection, and surfactants on the net CO 2 flux at regional and sub-regional scale. The differences both in the mean CO 2 fluxes and the integrated net fluxes were small between the different cases. However, the implications on the seasonal variability were shown to be significant. The inter-annual and spatial variability were also found to be associated with the forcing mechanisms evaluated in the study. In addition to wind, water-side convection was the most relevant parameter controlling the air–sea gas exchange at seasonal and inter-annual scales. The effect of precipitation and surfactants seemed negligible in terms of the inter-annual variability. The effect of water-side convection and surfactants resulted in a reduction of the downward fluxes, while precipitation was the only parameter that resulted in an enhancement of the net uptake in the Baltic Sea. • Variability of air–sea CO 2 fluxes is modulated by mechanisms other than wind speed. • Gas transfer velocity parameterizations are not adequate for coastal/marginal seas. • Water-side convection enhances air–sea fluxes during winter in the Baltic Sea. • Surfactants suppress CO 2 downward fluxes during summer. • Precipitation represents a net increase of the carbon uptake.

Application of a risk-based approach to continuous underwater noise at local and subregional scales for the Marine Strategy Framework Directive

The Marine Strategy Framework Directive’s (MSFD) goal of achieving Good Environmental Status (GES) in European waters presents Member States (MS) with a significant challenge in measurement and monitoring of a suite of descriptors of GES, some of which are poorly understood. To address the burden for MSs of monitoring and measuring environmental status and trends over vast areas, provisions have been made for the use of risk-based approaches to assessment and monitoring. Here, a standardised risk-based approach is described, aligned with the articles of the directive and with the DAPSI(W)R(M) conceptual frame. This risk approach is then applied at two different spatial scales for the risks associated with continuous underwater noise (D11C2). The first case is a subregional scale application for the North East Atlantic assessing risk under data poor conditions while the second case focuses on the Bay of Biscay, using the same process but under data rich conditions. The parallel applications of the risk-based approach illustrate that it is a powerful tool that can provide useful outputs, even where significant data gaps and limitations in understanding exist. We demonstrate the adaptability and wider applicability of the risk-based approach and finally make recommendations as to how improvements in data availability and accessibility could increase the confidence in the outputs.

Satellite based Budyko framework reveals the human imprint on long-term surface water partitioning across India

The Budyko curve, relating a catchment’s water and energy balance, provides a useful tool to analyse how physio-climatic and socio-economic characteristics may impact long-term runoff. Often a parametric form of the curve, the Tixeront-Fu’s equation, is used to represent the catchment’s long-term water partitioning behaviour. Tixeront-Fu’s parameter ω, typically derived from observed climate and runoff data, can further be related to catchments’ physio-climatic characteristics to enable understanding the main drivers of their water balance. At times, prior analyses have reported potentially conflicting controls of characteristics on ω. Based on the rationale that several hydrological processes act across varying spatio-temporal scales, we hypothesize that the impact of a physio-climatic factor on ω is driven by its broader regional setting. We test our hypothesis by developing relationships between ω and a curated database of 33 physio-climatic and socio-economic characteristics for 520 regional divisions of India. We employ two related data-space splitting algorithms: classification and regression trees (CART) and random forest (RF) to study the effects of potential controlling factors within their regional context. The algorithms diagnose a hierarchy of representative vegetation, climate, soil, land use land cover, topography and anthropogenic controls. The most important characteristics controlling ω were found to be: correlation between monthly time series of P and PET, long-term temperature, long-term precipitation, and the synchronicity of P and PET. Human influences quantified in terms of population density and the extent of cultivated area were found to be important factors influencing sub-regional scale hydrological processes. These anthropogenic factors displayed predictive importance similar to vegetation and topographic factors. Our results indicate the dominant influence of climate and its seasonality on long-term water partitioning across Indian landmasses with landscape features including human influences playing a secondary but important role.

Mediterranean-Scale Drought: Regional Datasets for Exceptional Meteorological Drought Events during 1975–2019

Drought is one of the most complex climate-related phenomena and is expected to progressively affect our lives by causing very serious environmental and socioeconomic damage by the end of the 21st century. In this study, we have extracted a dataset of exceptional meteorological drought events between 1975 and 2019 at the country and subregional scales. Each drought event was described by its start and end date, intensity, severity, duration, areal extent, peak month and peak area. To define such drought events and their characteristics, separate analyses based on three drought indices were performed at 12-month timescale: the Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI), and the Reconnaissance Drought Index (RDI). A multivariate combined drought index (DXI) was developed by merging the previous three indices for more understanding of droughts’ features at the country and subregional levels. Principal component analysis (PCA) was used to identify five different drought subregions based on DXI-12 values for 312 Mediterranean stations and a new special score was defined to classify the multi-subregional exceptional drought events across the Mediterranean Basin (MED). The results indicated that extensive drought events occurred more frequently since the late 1990s, showing several drought hotspots in the last decades in the southeastern Mediterranean and northwest Africa. In addition, the results showed that the most severe events were more detected when more than single drought index was used. The highest percentage area under drought was also observed through combining the variations of three drought indices. Furthermore, the drought area in both dry and humid areas in the MED has also experienced a remarkable increase since the late 1990s. Based on a comparison of the drought events during the two periods—1975–1996 and 1997–2019—we find that the current dry conditions in the MED are more severe, intense, and frequent than the earlier period; moreover, the strongest dry conditions occurred in last two decades. The SPEI-12 and RDI-12 have a higher capacity in providing a more comprehensive description of the dry conditions because of the inclusion of temperature or atmospheric evaporative demand in their scheme. A complex range of atmospheric circulation patterns, particularly the Western Mediterranean Oscillation (WeMO) and East Atlantic/West Russia (EATL/WRUS), appear to play an important role in severe, intense and region-wide droughts, including the two most severe droughts, 1999–2001 and 2007–2012, with lesser influence of the NAO, ULMO and SCAND.

Surface water extent dynamics from three periods of continuous Landsat time series; subregional differences across Argentine plains

<p>The Pampean region in Argentina is an extensive plain characterized by abundant shallow lakes that fulfill many environmental, ecological, and social functions. This study aims to detect the multiannual lake area changes in this region during 2001-2009 using remote sensing, including lakes as small as ≥10,000 m<sup>2</sup> or 1 ha. Landsat scenes of the wet (2008-2009), normal (2006), and dry (2008-2009) seasons were obtained, and using remote sensing techniques, the number and area of shallow lakes were calculated. The spatiotemporal variation of shallow lakes was studied in different climate periods in eight singular subregions. Spatial associations between annual precipitation and lake number and area were analyzed through the development of a Geographic Information System (GIS) at a subregional scale. During the study period the total lake area in the Pampean region decreased by 5257.39 km<sup>2 </sup>(62 %), but each subregion showed different responses to climatic events. In seven of them, the differences between climate periods prove to be statistically significant (P>0.01). The relationship between precipitation and lake number and area revealed the domain of positive association. We conclude that climate factors play a dominant role in lake changes across the Pampean plains. However, other factors such as origin, topographic and edaphic characteristics intensify or mitigate changes in surface hydrology.</p>

Territorial impacts of sea-level rise in marsh environments. The case of the Bay of Cádiz, Spain

How can sea-level rise affect territory? Has territorial planning adjusted to this new situation? This paper analyses the possible ramifications of changes in tidal recurrence over the course of this century in a potentially vulnerable marsh environment, such as the Bay of Cádiz, in southern Spain, where sea-level rise is already a fact. For that purpose, the regionalisation criterion is used as a basis for adjusting the magnitude of global problems to subregional scale. Geographic information systems are applied to portray the forecasted territorial changes according to the RCP4.5 and RCP8.5 scenarios presented by the Intergovernmental Panel on Climate Change (IPCC) for the years 2050 and 2100. Projections of sea-level rise show that the tide is travelling farther inland, not just invading unoccupied areas of marshes and beaches but also reaching urban and productive areas. Estimates indicate that the floodable area could expand by more than 20% in 2050, with a further 2,000 ha of flooded areas added in 2100 for either of the two scenarios used. The occurrence of these changes, regardless of the model used, would therefore entail an alteration of the environmental, social, cultural and economic values and elements of the Bay of Cádiz, with the Natural Park being the most affected area. In response to these consequences, the need to apply the resulting projections to other variables is insisted on, with a view to introducing territorial management tailored to this new and already present reality. Application of the regionalisation criterion to study repercussions of the climate crisis in the Bay of Cádiz could serve as a precedent for the development of adaptation strategies in other marsh environments at subregional or local level.

Remote Sensing-Based Statistical Approach for Defining Drained Lake Basins in a Continuous Permafrost Region, North Slope of Alaska

Lake formation and drainage are pervasive phenomena in permafrost regions. Drained lake basins (DLBs) are often the most common landforms in lowland permafrost regions in the Arctic (50% to 75% of the landscape). However, detailed assessments of DLB distribution and abundance are limited. In this study, we present a novel and scalable remote sensing-based approach to identifying DLBs in lowland permafrost regions, using the North Slope of Alaska as a case study. We validated this first North Slope-wide DLB data product against several previously published sub-regional scale datasets and manually classified points. The study area covered >71,000 km2, including a >39,000 km2 area not previously covered in existing DLB datasets. Our approach used Landsat-8 multispectral imagery and ArcticDEM data to derive a pixel-by-pixel statistical assessment of likelihood of DLB occurrence in sub-regions with different permafrost and periglacial landscape conditions, as well as to quantify aerial coverage of DLBs on the North Slope of Alaska. The results were consistent with previously published regional DLB datasets (up to 87% agreement) and showed high agreement with manually classified random points (64.4–95.5% for DLB and 83.2–95.4% for non-DLB areas). Validation of the remote sensing-based statistical approach on the North Slope of Alaska indicated that it may be possible to extend this methodology to conduct a comprehensive assessment of DLBs in pan-Arctic lowland permafrost regions. Better resolution of the spatial distribution of DLBs in lowland permafrost regions is important for quantitative studies on landscape diversity, wildlife habitat, permafrost, hydrology, geotechnical conditions, and high-latitude carbon cycling.

K-Means and C4.5 Decision Tree Based Prediction of Long-Term Precipitation Variability in the Poyang Lake Basin, China

The machine learning algorithms application in atmospheric sciences along the Earth System Models has the potential of improving prediction, forecast, and reconstruction of missing data. In the current study, a combination of two machine learning techniques namely K-means, and decision tree (C4.5) algorithms, are used to separate observed precipitation into clusters and classified the associated large-scale circulation indices. Observed precipitation from the Chinese Meteorological Agency (CMA) during 1961–2016 for 83 stations in the Poyang Lake basin (PLB) is used. The results from K-Means clusters show two precipitation clusters splitting the PLB precipitation into a northern and southern cluster, with a silhouette coefficient ~0.5. The PLB precipitation leading cluster (C1) contains 48 stations accounting for 58% of the regional station density, while Cluster 2 (C2) covers 35, accounting for 42% of the stations. The interannual variability in precipitation exhibited significant differences for both clusters. The decision tree (C4.5) is employed to explore the large-scale atmospheric indices from National Climate Center (NCC) associated with each cluster during the preceding spring season as a predictor. The C1 precipitation was linked with the location and intensity of subtropical ridgeline position over Northern Africa, whereas the C2 precipitation was suggested to be associated with the Atlantic-European Polar Vortex Area Index. The precipitation anomalies further validated the results of both algorithms. The findings are in accordance with previous studies conducted globally and hence recommend the applications of machine learning techniques in atmospheric science on a sub-regional and sub-seasonal scale. Future studies should explore the dynamics of the K-Means, and C4.5 derived indicators for a better assessment on a regional scale. This research based on machine learning methods may bring a new solution to climate forecast.

Developing resilience to climate change impacts in Antarctica: An evaluation of Antarctic Treaty System protected area policy

Antarctica is increasingly vulnerable to climate change impacts, with the continent predicted to warm by ∼4 °C by 2100 under a ‘business as usual’ greenhouse gas emission scenario. Simultaneously, human activity, primarily in the form of scientific research and the fishing and tourism industries, is putting increasing pressure on Antarctic and Southern Ocean environments and ecosystems. We evaluate the effectiveness of the Antarctic area protection system in promoting resilience to climate change impacts. Under the framework of the Antarctic Treaty System (ATS), terrestrial and marine areas can be designated to protect locations of scientific, environmental, historic and intrinsic value and to facilitate operational coordination to minimise environmental impact. However, climate change is not mentioned explicitly in the Protocol on Environmental Protection to the Antarctic Treaty and is little considered in guidelines for the designation and management of the region’s existing protected areas. Climate change impacts are considered in only 17% of Antarctic Specially Protected Area (ASPA) management plans and, at a time when threats to Antarctic environments are increasing, the last decade has seen an 84% decline in ASPA designation rate compared with levels in the 1980s. Nevertheless, momentum is building within the Scientific Committee on Antarctic Research (SCAR) and the ATS’s Committee on Environmental Protection (CEP) to deliver an evidence-based, integrated response to climate change that includes the use of protected areas. The Antarctic scientific community is well-placed to support decision-makers in the use of existing conservation management tools through provision of climate change forecasts at sub-regional scales, data on anticipated environmental change, and predicted species and ecosystems responses. Ultimately, reducing global greenhouse gas emission will provide the greatest protection from climate change impacts within Antarctica.

Integrated energy-water-land nexus planning in the Colorado River Basin (Argentina)

Integrated energy-water-land (EWL) planning promotes synergies and avoids conflicts in ways that sector-specific planning approaches cannot. Many important decisions that influence emerging EWL nexus issues are implemented at regional (e.g., large river basin, electricity grid) and sub-regional (e.g., small river basin, irrigation district) scales. However, actual implementation of integrated planning at these scales has been limited. Simply collecting and visualizing data and interconnections across multiple sectors and sub-regions in a single modeling platform is a unique endeavor in many regions. This study introduces and applies a novel approach to linking together multiple sub-regions in a single platform to characterize and visualize EWL resource use, EWL system linkages within and among sub-regions, and the EWL nexus implications of future policies and investments. This integrated planning methodology is applied in the water-stressed Colorado River Basin in Argentina, which is facing increasing demands for agricultural and fossil fuel commodities. Guided by stakeholders, this study seeks to inform basin planning activities by characterizing and visualizing (1) the basin’s current state of EWL resources, (2) the linkages between sectors within and among basin sub-regions, and (3) the EWL nexus implications of planned future agricultural development activities. Results show that water scarcity, driven in part by human demands that have historically reached 60% of total surface water supply, poses a substantial constraint to economic development in the basin. The Colorado basin has the potential to serve as a testbed for crafting novel and generalizable sub-regional EWL planning approaches capable of informing the EWL planning dialogue globally.

Assessment of CMIP6 Performance and Projected Temperature and Precipitation Changes Over South America

We evaluate the performance of a large ensemble of Global Climate Models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) over South America for a recent past reference period and examine their projections of twenty-first century precipitation and temperature changes. The future changes are computed for two time slices (2040–2059 and 2080–2099) relative to the reference period (1995–2014) under four Shared Socioeconomic Pathways (SSPs, SSP1–2.6, SSP2–4.5, SSP3–7.0 and SSP5–8.5). The CMIP6 GCMs successfully capture the main climate characteristics across South America. However, they exhibit varying skill in the spatiotemporal distribution of precipitation and temperature at the sub-regional scale, particularly over high latitudes and altitudes. Future precipitation exhibits a decrease over the east of the northern Andes in tropical South America and the southern Andes in Chile and Amazonia, and an increase over southeastern South America and the northern Andes—a result generally consistent with earlier CMIP (3 and 5) projections. However, most of these changes remain within the range of variability of the reference period. In contrast, temperature increases are robust in terms of magnitude even under the SSP1–2.6. Future changes mostly progress monotonically from the weakest to the strongest forcing scenario, and from the mid-century to late-century projection period. There is an increase in the seasonality of the intra-annual precipitation distribution, as the wetter part of the year contributes relatively more to the annual total. Furthermore, an increasingly heavy-tailed precipitation distribution and a rightward shifted temperature distribution provide strong indications of a more intense hydrological cycle as greenhouse gas emissions increase. The relative distance of an individual GCM from the ensemble mean does not substantially vary across different scenarios. We found no clear systematic linkage between model spread about the mean in the reference period and the magnitude of simulated sub-regional climate change in the future period. Overall, these results could be useful for regional climate change impact assessments across South America.

Analyzing long-term water quality of lakes in Rhode Island and the northeastern United States with an anomaly approach.

Addressing anthropogenic impacts on aquatic ecosystems is a focus of lake management. Controlling phosphorus and nitrogen can mitigate these impacts, but determining management effectiveness requires long-term datasets. Recent analysis of the LAke multi-scaled GeOSpatial and temporal database for the Northeast (LAGOS-NE) United States found stable water quality in the northeastern and midwestern United States; however, sub-regional trends may be obscured. We used the University of Rhode Island’s Watershed Watch Volunteer Monitoring Program (URIWW) dataset to determine if there were sub-regional (i.e., 3000 km2) water quality trends. URIWW has collected water quality data on Rhode Island lakes and reservoirs for over 25 yr. The LAGOS-NE and URIWW datasets allowed for comparison of water quality trends at regional and sub-regional scales, respectively. We assessed regional (LAGOS-NE) and sub-regional (URIWW) trends with yearly median anomalies calculated on a per-station basis. Sub-regionally, temperature and chlorophyll a increased from 1993 to 2016. Total nitrogen, total phosphorus, and the nitrogen:phosphorus ratio (N:P) were stable. At the regional scale, the LAGOS-NE dataset showed similar trends to prior studies of the LAGOS-NE with chlorophyll a, total nitrogen, and N:P all stable over time. Total phosphorus did show a very slight increase. In short, algal biomass, as measured by chlorophyll a in Rhode Island lakes and reservoirs increased, despite stability in total nitrogen, total phosphorus, and the nitrogen to phosphorus ratio. Additionally, we demonstrated both the value of long-term monitoring programs, like URIWW, for identifying trends in environmental condition, and the utility of site-specific anomalies for analyzing for long-term water quality trends.

Fine-scale variation in malaria prevalence across ecological regions in Madagascar: a cross-sectional study

BackgroundLarge-scale variation in ecological parameters across Madagascar is hypothesized to drive varying spatial patterns of malaria infection. However, to date, few studies of parasite prevalence with resolution at finer, sub-regional spatial scales are available. As a result, there is a poor understanding of how Madagascar’s diverse local ecologies link with variation in the distribution of infections at the community and household level. Efforts to preserve Madagascar’s ecological diversity often focus on improving livelihoods in rural communities near remaining forested areas but are limited by a lack of data on their infectious disease burden.MethodsTo investigate spatial variation in malaria prevalence at the sub-regional scale in Madagascar, we sampled 1476 households (7117 total individuals, all ages) from 31 rural communities divided among five ecologically distinct regions. The sampled regions range from tropical rainforest to semi-arid, spiny forest and include communities near protected areas including the Masoala, Makira, and Mikea forests. Malaria prevalence was estimated by rapid diagnostic test (RDT) cross-sectional surveys performed during malaria transmission seasons over 2013–2017.ResultsIndicative of localized hotspots, malaria prevalence varied more than 10-fold between nearby (< 50 km) communities in some cases. Prevalence was highest on average in the west coast region (Morombe district, average community prevalence 29.4%), situated near protected dry deciduous forest habitat. At the household level, communities in southeast Madagascar (Mananjary district) were observed with over 50% of households containing multiple infected individuals at the time of sampling. From simulations accounting for variation in household size and prevalence at the community level, we observed a significant excess of households with multiple infections in rural communities in southwest and southeast Madagascar, suggesting variation in risk within communities.ConclusionsOur data suggest that the malaria infection burden experienced by rural communities in Madagascar varies greatly at smaller spatial scales (i.e., at the community and household level) and that the southeast and west coast ecological regions warrant further attention from disease control efforts. Conservation and development efforts in these regions may benefit from consideration of the high, and variable, malaria prevalences among communities in these areas.

Assessing agro-food system circularity using nutrient flows and budgets

Nutrient reuse and recycling is a key strategy towards more circular and sustainable food systems and depends on the specific conditions of the area under study, such as geography and the type of agricultural system. In this study we analysed nutrient flows and assessed the circularity of a livestock-dominated and export-oriented agro-food system at different system levels and spatial scales. We quantified the nitrogen (N), phosphorus (P) and potassium (K) flows and soil balances in the Belgian agro-food system at the sub-regional, regional and national scale, and assessed five P-based indicators that capture different aspects of circularity: total inputs, phosphorus use efficiency, share of reused to total input, recycling rate, and losses. We found that nutrient soil balances depend on the type of agricultural system: areas with intense livestock production accumulate up to 108 kgN/ha, 4.8 kgP/ha and 150 kgK/ha in their soil annually, whereas areas of mostly arable production have low N and K surpluses of <20 kg/ha and P deficits of < -10 kg/ha. We further found that Wallonia, the southern region of the country that is characterized by lower livestock densities and a partial reuse of sewage sludge, outperforms the Flemish region in the North in all five indicators. The food system in the whole of Belgium has a 34% phosphorus use efficiency rate and a 63% overall recycling rate, while 84% of the total inputs in agriculture are from secondary sources. Our results show that the type of production system is the most crucial determinant for circularity, and highlight the benefit of working at different levels and spatial scales to capture all aspects of circularity in agro-food systems.

Improving GIS-Based Heat Demand Modelling and Mapping for Residential Buildings with Census Data Sets at Regional and Sub-Regional Scales

Heat demand of buildings and related CO2 emissions caused by energy supply contribute to global climate change. Spatial data-based heat planning enables municipalities to reorganize local heating sectors towards efficient use of regional renewable energy resources. Here, annual heat demand of residential buildings is modeled and mapped for a German federal state to provide regional basic data. Using a 3D building stock model and standard values of building-type-specific heat demand from a regional building typology in a Geographic Information Systems (GIS)-based bottom-up approach, a first base reference is modeled. Two spatial data sets with information on the construction period of residential buildings, aggregated on municipality sections and hectare grid cells, are used to show how census-based spatial data sets can enhance the approach. Partial results from all three models are validated against reported regional data on heat demand as well as against gas consumption of a municipality. All three models overestimate reported heat demand on regional levels by 16% to 19%, but underestimate demand by up to 8% on city levels. Using the hectare grid cells data set leads to best prediction accuracy values at municipality section level, showing the benefit of integrating this high detailed spatial data set on building age.

A High-Speed, Light-Weight Scalar Magnetometer Bird for km Scale UAV Magnetic Surveying: On Sensor Choice, Bird Design, and Quality of Output Data

Magnetic surveying is a widely used and cost-efficient remote sensing method for the detection of subsurface structures at all scales. Traditionally, magnetic surveying has been conducted as ground or airborne surveys, which are cheap and provide large-scale consistent data coverage, respectively. However, ground surveys are often incomplete and slow, whereas airborne surveys suffer from being inflexible, expensive and characterized by a reduced signal-to-noise ratio, due to increased sensor-to-source distance. With the rise of reliable and affordable survey-grade Unmanned Aerial Vehicles (UAVs), and the developments of light-weight magnetometers, the shortcomings of traditional magnetic surveying systems may be bypassed by a carefully designed UAV-borne magnetometer system. Here, we present a study on the development and testing of a light-weight scalar field UAV-integrated magnetometer bird system (the CMAGTRES-S100). The idea behind the CMAGTRES-S100 is the need for a high-speed and flexible system that is easily transported in the field without a car, deployable in most terrain and weather conditions, and provides high-quality scalar data in an operationally efficient manner and at ranges comparable to sub-regional scale helicopter-borne magnetic surveys. We discuss various steps in the development, including (i) choice of sensor based on sensor specifications and sensor stability tests, (ii) design considerations of the bird, (iii) operational efficiency and flexibility and (iv) output data quality. The current CMAGTRES-S100 system weighs ∼5.9 kg (including the UAV) and has an optimal surveying speed of 50 km/h. The system was tested along a complex coastal setting in Brittany, France, targeting mafic dykes and fault contacts with magnetite infill and magnetite nuggets (skarns). A 2.0 × 0.3 km area was mapped with a 10 m line-spacing by four sub-surveys (due to regulatory restrictions). The sub-surveys were completed in 3.5 h, including &gt;2 h for remobilisation and the safety clearance of the area. A noise-level of ±0.02 nT was obtained and several of the key geological structures were mapped by the system.

Bayesian machine learning ensemble approach to quantify model uncertainty in predicting groundwater storage change

Agricultural water demand, groundwater extraction, surface water delivery and climate have complex nonlinear relationships with groundwater storage in agricultural regions. As an alternative to elaborate computationally intensive physical models, machine learning methods are often adopted as surrogate to capture such complex relationships due to their high computational efficiency. Inevitably, using only one machine learning model is prone to underestimate prediction uncertainty and subjected to poor accuracy. This study presents a novel machine learning-based groundwater ensemble modeling framework in conjunction with a Bayesian model averaging approach to predict groundwater storage change and improve overall model predicting reliability. Three different machine learning models have been developed namely artificial neural network, support vector machine and response surface regression. To explicitly quantify uncertainty from machine learning model parameter and structure, Bayesian model averaging is employed to produce a forecast distribution associated with each machine learning prediction. Model weights and variances are obtained based on model performance to construct ensemble models. Then, the developed individual and Bayesian model averaging machine learning ensemble models are applied, evaluated and validated at different spatial scales including subregional and regional scales in an overdrafted agricultural region-the San Joaquin River Basin, through independent training and testing dataset. Results shows the machine learning models have remarkable predicting capability without sacrificing accuracy but with higher computational efficiency. Compared to a single-model approach, the ensemble model is able to produce consistently reliable predictions across the basin, yet it does not always outperform the best model in the ensemble. Additionally, model results suggest that groundwater pumping for agricultural irrigation is the primary driving force of groundwater storage change across the region. The modeling framework can serve as an alternative approach to simulating groundwater response, especially in those agricultural regions where lack of subsurface data hinders physically-based modeling.

The Baltic Health Index (BHI): Assessing the social–ecological status of the Baltic Sea

Abstract Improving the health of coastal and open sea marine ecosystems represents a substantial challenge for sustainable marine resource management, since it requires balancing human benefits and impacts on the ocean. This challenge is often exacerbated by incomplete knowledge and lack of tools that measure ocean and coastal ecosystem health in a way that allows consistent monitoring of progress towards predefined management targets. The lack of such tools often limits capabilities to enact and enforce effective governance. We introduce the Baltic Health Index (BHI) as a transparent, collaborative and repeatable assessment tool. The Index complements existing, more ecological‐oriented, approaches by including a human dimension on the status of the Baltic Sea, an ecosystem impacted by multiple anthropogenic pressures and governed by a multitude of comprehensive national and international policies. Using a large amount of social–ecological data available, we assessed the health of the Baltic Sea for nine goals that represent the status towards set targets, for example, clean waters, biodiversity, food provision, natural products extraction and tourism. Our results indicate that the overall health of the Baltic Sea is suboptimal (a score of 76 out of 100), and a substantial effort is required to reach the management objectives and associated targets. Subregionally, the lowest BHI scores were measured for carbon storage, contaminants and lasting special places (i.e. marine protected areas), albeit with large spatial variation. Overall, the likely future status of all goals in the BHI averaged for the entire Baltic Sea is better than the present status, indicating a positive trend towards a healthier Baltic Sea. However, in some Baltic Sea basins, the trend for specific goals was decreasing, highlighting locations and issues that should be the focus of management priorities. The BHI outcomes can be used to identify both pan‐Baltic and subregional scale management priorities and to illustrate the interconnectedness between goals linked by cumulative pressures. Hence, the information provided by the BHI tool and its further development will contribute towards the fulfilment of the UN Agenda 2030 and its Sustainability Development Goals. A free Plain Language Summary can be found within the Supporting Information of this article.