Spatial Drivers Research Articles

Components of changes in nuptiality patterns among Ethiopian women: A decomposition analysis of demographic and health surveys

Analyzing shifts in nuptiality patterns plays a significant role in understanding how demographic variables respond to socioeconomic changes. However, there has been a lack of comprehensive research on the sources of changes in nuptiality patterns. This study examines how nuptiality patterns change and investigates the factors linked to changes in two birth cohorts. Data from the Ethiopian Demographic and Health Surveys conducted in 2000 and 2016 were used. Marriage was observed to be early and nearly universal in both birth cohorts. A large proportion of the difference in the cumulative incidence of first marriage between the cohorts was observed at age 15 and decreased after age 35. However, the age of entry into the first marriage was slightly delayed, with a 46% reduction in the rate of marital dissolution. In addition, the compositional and structural impacts of a covariate were responsible for initiating the cumulative incidence of transition to first marriage. Furthermore, women’s education, occupation, and regional variation were identified as the main contributors to the reduction in the cumulative incidence of first marriage. Results show that the changes in nuptiality patterns were explained by a reduction in cumulative incidence of marriage, a shift in the timing of entry into the first marriage, and marital dissolution across the two birth cohorts. Moreover, socioeconomic factors, spatial drivers, and discriminatory effects between the two cohorts were the sources of changes in nuptiality patterns in Ethiopia.

Spatial patterns and drivers of micro, small and medium-sized enterprises (MSMEs) within and across Indonesian cities: Evidence from highly granular data

The importance of micro-, small-, and medium-sized enterprises (MSMEs) for economies and livelihood is widely recognized, especially in emerging economies. However, there are scarcely any studies on their spatial determinants, particularly in Indonesia, where MSMEs account for more than 95 % of enterprises. This paper aims to assess the spatial drivers of MSMEs within and between Indonesian cities using multilevel modeling techniques that incorporate district (n=98) and nested village-level (n=4995) spatial units. The study's findings have implications for policy interventions, suggesting the need to enhance infrastructure and digital connectivity, promote employment accessibility, and consider planning mechanisms to influence MSMEs' spatial configuration.

Spatially Explicit Analysis of Landscape Structures, Urban Growth, and Economic Dynamics in Metropolitan Regions

Assuming that settlement morphologies and landscape structures are the result of economic transformations, the present study illustrates a statistical framework investigating metropolitan growth due to the inherent changes in landscape configurations vis à vis socio-demographic functions. Focusing on the evolution of their spatial drivers over time, metropolitan development was studied by adopting land parcels (or ‘patches’, as they are referred to in the ecological literature) as the elementary analysis unit—with the individual surface area and a specific shape indicator as the dependent variables and background socioeconomic attributes as predictors of landscape change over time. We specifically ran a Multiscale Geographically Weighted Regression (MGWR) testing the spatial dependence of the size and shape of landscape parcels on a vast ensemble of socioeconomic factors in a dense region (metropolitan Athens, Greece) with natural landscapes exposed to increasing human pressure. To investigate the spatial direction and intensity of the settlement expansion and landscape change, local regressions using the parcel area and fractal index (perimeter-to-area ratio) as the dependent variables and the elevation, distance from selected economic nodes, transport infrastructures, and natural amenities as the predictors were run separately for 1990 and 2018, representative of, respectively, a mono-centric configuration and a moderately polycentric organization of economic spaces. In a strictly mono-centric setting (1990), the parcel size showed a linear dependence on the distance from business districts, elevation, and wealth. Changes in the relationship between the parcel size and spatial (economic and non-economic) drivers may suggest a latent process of settlement de-concentration, and a possible shift toward polycentric development (2018), as documented in earlier studies. By integrating socioeconomic and ecological dimensions of landscape analysis and land evaluation, the empirical results of this study outline the increased complexity of dispersed landscape structures within dense metropolitan regions and along urban–rural gradients in Europe.

Mapping fine-scale carbon sequestration benefits and landscape spatial drivers of urban parks using high-resolution UAV data

Urban parks offer dual ecological benefits by increasing carbon sinks and reducing carbon emissions and are considered an important natural strategy for cities to achieve “double carbon” goals. However, rapid, efficient, and accurate quantification of the carbon sequestration benefits of urban parks poses a significant challenge. To address this, we utilized unmanned aerial vehicle (UAV) multispectral data to construct large-area, high-resolution models of urban park carbon sequestration benefits, replacing traditional, time-consuming, and laborious large-scale field surveys. Additionally, we explored the relationship between urban park landscape patterns and the benefits of carbon sequestration. First, we used data from 12 tree species to calculate the carbon storage based on tree species and compared these results with those calculated based on forest type. Second, three prediction models were constructed using multispectral vegetation index only, texture features only, and a combination of both, in conjunction with gradient boosting decision Trees (GBDT), random forest (RF), and backpropagation (BP) neural network to generate carbon sequestration benefit maps for the entire park. These maps allowed us to determine how variations in urban park landscape structures affect carbon stocks. The results show that UAV multispectral imagery provides a fast and accurate method for measuring the carbon sequestration benefits of urban parks and offers an alternative method for generating carbon sequestration benefit maps. This research reveals the benefits of urban park carbon sequestration and explores the spatial patterns within landscapes. The findings are of great significance for guiding the estimation of urban carbon sequestration benefits and achieving carbon neutrality.

A wrap-around movement path randomization method to distinguish social and spatial drivers of animal interactions.

Studying the spatial-social interface requires tools that distinguish between social and spatial drivers of interactions. Testing hypotheses about the factors determining animal interactions often involves comparing observed interactions with reference or 'null' models. One approach to accounting for spatial drivers of social interactions in reference models is randomizing animal movement paths to decouple spatial and social phenotypes while maintaining environmental effects on movements. Here, we update a reference model that detects social attraction above the effect of spatial constraints. We explore the use of our 'wrap-around' method and compare its performance to the previous approach using agent-based simulations. The wrap-around method provides reference models that are more similar to the original tracking data, while still distinguishing between social and spatial drivers. Furthermore, the wrap-around approach results in fewer false-positives than its predecessor, especially when animals do not return to one place each night but change movement foci, either locally or directionally. Finally, we show that interactions among GPS-tracked griffon vultures (Gyps fulvus) emerge from social attraction rather than from spatial constraints on their movements. We conclude by highlighting the biological situations in which the updated method might be most suitable for testing hypotheses about the underlying causes of social interactions. This article is part of the theme issue 'The spatial-social interface: a theoretical and empirical integration'.

Small-area population forecasting in a segregated city using density-functional fluctuation theory

Policy decisions concerning housing, transportation, and resource allocation would all benefit from accurate small-area population forecasts. However, despite the success of regional-scale migration models, developing neighborhood-scale forecasts remains a challenge due to the complex nature of residential choice. Here, we introduce an innovative approach to this challenge by extending density-functional fluctuation theory (DFFT), a proven approach for modeling group spatial behavior in biological systems, to predict small-area population shifts over time. The DFFT method uses observed fluctuations in small-area populations to disentangle and extract effective social and spatial drivers of segregation, and then uses this information to forecast intra-regional migration. To demonstrate the efficacy of our approach in a controlled setting, we consider a simulated city constructed from a Schelling-type model. Our findings indicate that even without direct access to the underlying agent preferences, DFFT accurately predicts how broader demographic changes at the city scale percolate to small-area populations. In particular, our results demonstrate the ability of DFFT to incorporate the impacts of segregation into small-area population forecasting using interactions inferred solely from steady-state population count data.

Research on the distribution and influencing factors of fine mode aerosol optical depth (AODf) in China

Analyzing the distribution and causes of fine mode aerosol optical depth (AODf) is important for assessing the impact of air pollution on human health and sustainable development. Based on remote sensing data and station information, the study uses Theil Sen's method and the Mann-Kendall test to analyze the spatio-temporal distribution and trends of AODf. Meanwhile, the study used the Extreme Gradient Boosting (XGBoost) model and the Shapley Additive Explanations (SHAP) algorithm to analyze the spatial drivers of AODf and interactive effects. The results show a trend of an initial increase followed by a decline in AODf across China and in most city clusters, with AODf peaking around 2011. Areas with high AODf are mainly concentrated in the eastern and central regions. On a seasonal scale, the average AODf is highest in summer (0.25), followed by winter (0.23), autumn (0.21), and spring (0.19). The AODf, mainly in the eastern and central regions, showed rapid increases and decreases in the 2000–2013 and 2013–2020 periods, respectively. Secondly, the most important factor influencing AODf is sulfate (SO4), contributing 54.84%. This is followed by elevation (DEM) at 10.31%. The SO4, organic carbon (OC), and DEM to AODf are 20–54%, 10–44%, and 11–45% respectively at the city cluster level. Lastly, the response thresholds of AODf to the main factors show significant spatial heterogeneity. The thresholds for SO4 and OC to increase AODf are in the range of 5–16 μg/m3 and 1–10 μg/m3, respectively. The study is of great importance for a comprehensive understanding of the variation patterns of AODf and for the formulation of tailored air pollution control policies based on local conditions.

A comprehensive framework for assessing the spatial drivers of flood disasters using an optimal Parameter-based geographical Detector–machine learning coupled model

Flood disasters pose serious threats to human life and property worldwide. Exploring the spatial drivers of flood disasters on a macroscopic scale is of great significance for mitigating their impacts. This study proposes a comprehensive framework for integrating driving-factor optimization and interpretability, while considering spatial heterogeneity. In this framework, the Optimal Parameter-based Geographic Detector (OPGD), Recursive Feature Estimation (RFE), and Light Gradient Boosting Machine (LGBM) models were utilized to construct the OPGD–RFE–LGBM coupled model to identify the essential driving factors and simulate the spatial distribution of flood disasters. The SHapley Additive ExPlanation (SHAP) interpreter was employed to quantitatively explain the driving mechanisms behind the spatial distribution of flood disasters. Yunnan Province, a typical mountainous and plateau area in Southwest China, was selected to implement the proposed framework and conduct a case study. For this purpose, a flood disaster inventory of 7332 historical events was prepared, and 22 potential driving factors related to precipitation, surface environment, and human activity were initially selected. Results revealed that flood disasters in Yunnan Province exhibit high spatial heterogeneity, with geomorphic zoning accounting for 66.1% of the spatial variation in historical flood disasters. The OPGD–RFE–LGBM coupled model offers clear advantages over a single LGBM in identifying essential driving factors and quantitatively analyzing their impacts. Moreover, the simulation performance shows a slight improvement (a 6% average decrease in RMSE and an average increase of 1% in R2) even with reduced factor data. Factor explanatory analysis indicated that the combination of the essential driving factor sets varied across different subregions; nevertheless, precipitation-related factors, such as precipitation intensity index (SDII), wet days (R10MM), and 5-day maximum precipitation (RX5day), were the main driving factors controlling flood disasters. This study provides a quantitative analytical framework for the spatial drivers of flood disasters at large scales with significant heterogeneity, offering a reference for disaster management authorities in developing macro-strategies for disaster prevention.

Network-based strategies to combat HCV: Examining social and spatial drivers of transmission among PWID in New Delhi.

People who inject drugs (PWID) account for some of the fastest-growing HCV epidemics globally. While individual risk factors for infection are understood, less is known about network and spatial factors critical for elimination strategies. Two thousand five hundred twelve PWID in New Delhi, India, were recruited (2017-19) through network referrals. Biometrics identified duplicates and cross-network linkages. Participants completed semi-annual surveys and blood tests for HCV antibodies and RNA. Poisson regression and network analyses identified predictors of incident HCV and compared network-based intervention approaches. Baseline HCV antibody prevalence was 65.1%, of whom 79.6% were HCV RNA-positive. We observed 92 HCV seroconversions over 382.25 person-years (incidence: 24.1 per 100 person-years). Of the 92 seroconverters, 67% (62) were directly connected to an RNA-positive participant, and all were within one degree of separation from an RNA-positive participant. Individual-level factors associated with seroconversion included age, sexual activity, and injection behaviours. After adjusting for individual-level factors, seroconversion was significantly associated with number of RNA-positive partners (adjusted incidence rate ratio [AIRR] = 1.30) and injecting at a particular venue (AIRR = 2.53). This association extended to indirect ties, with 17% reduced odds of seroconversion for each degree of separation from the venue (AIRR = 0.83). Network analyses comparing intervention strategies found that targeting venues identified more cases compared to a treat-a-friend approach. We observed a fast-growing HCV epidemic driven by viremia within individuals' immediate networks and indirect social and spatial ties, demonstrating the importance of achieving broad, sustained virologic response and rethinking network-based interventions to include venues.

Identifying interactive effects of spatial drivers in soil heavy metal pollutants using interpretable machine learning models

The accurate identification of spatial drivers is crucial for effectively managing soil heavy metals (SHM). However, understanding the complex and diverse spatial drivers of SHM and their interactive effects remains a significant challenge. In this study, we present a comprehensive analysis framework that integrates Geodetector, CatBoost, and SHapley Additive exPlanations (SHAP) techniques to identify and elucidate the interactive effects of spatial drivers in SHM within the Pearl River Delta (PRD) region of China. Our investigation incorporated fourteen environmental factors and focused on the pollution levels of three prominent heavy metals: Hg, Cd, and Zn. These findings provide several key insights: (1) The distribution of SHM is influenced by the combined effects of various individual factors and interactions within the source–flow–sink process. (2) Compared with the spatial interpretation of individual factors, the interaction between Hg and Cd exhibited enhanced spatial explanatory power. Similarly, interactions involving Zn mainly demonstrated increased spatial explanatory power, but there was one exception in which a weakening was observed. (3) Spatial heterogeneity plays a crucial role in determining the contributions of environmental factors to soil heavy metal concentrations. Although individual factors generally promote metal accumulation, their effects fluctuate when interactions are considered. (4) The SHAP interpretable method effectively addresses the limitations associated with machine-learning models by providing understandable insights into heavy metal pollution. This enables a comparison of the importance of environmental factors and elucidates their directional impacts, thereby aiding in the understanding of interaction mechanisms. The methods and findings presented in this study offer valuable insights into the spatial heterogeneity of heavy metal pollution in soil. By focusing on the effects of interactive factors, we aimed to develop more accurate strategies for managing SHM pollution.

SpAbundance: An R package for single‐species and multi‐species spatially explicit abundance models

Abstract Numerous modelling techniques exist to estimate abundance of plant and animal populations. The most accurate methods account for multiple complexities found in ecological data, such as observational biases, spatial autocorrelation, and species correlations. There is, however, a lack of user‐friendly and computationally efficient software to implement the various models, particularly for large data sets. We developed the spAbundance R package for fitting spatially explicit Bayesian single‐species and multi‐species hierarchical distance sampling models, N‐mixture models, and generalized linear mixed models. The models within the package can account for spatial autocorrelation using Nearest Neighbour Gaussian Processes and accommodate species correlations in multi‐species models using a latent factor approach, which enables model fitting for data sets with large numbers of sites and/or species. We provide three vignettes and three case studies that highlight spAbundance functionality. We used spatially explicit multi‐species distance sampling models to estimate density of 16 bird species in Florida, USA, an N‐mixture model to estimate black‐throated blue warbler (Setophaga caerulescens) abundance in New Hampshire, USA, and a spatial linear mixed model to estimate forest above‐ground biomass across the continental USA. spAbundance provides a user‐friendly, formula‐based interface to fit a variety of univariate and multivariate spatially explicit abundance models. The package serves as a useful tool for ecologists and conservation practitioners to generate improved inference and predictions on the spatial drivers of abundance in populations and communities.

Temporal and spatial drivers of the structure of macroinvertebrate assemblages associated with Laminaria hyperborea detritus in the northeast Atlantic

Kelp forests occur on more than a quarter of the world's coastlines, serving as foundation species supporting high levels of biodiversity. They are also a major source of organic matter in coastal ecosystems, with the majority of primary production released and exported as detritus. Kelp detritus also provides food and shelter for macroinvertebrates, which comprise important components of inshore food-webs. Hitherto, research on kelp detritus-associated macroinvertebrate assemblages remains relatively limited. We quantified spatiotemporal variability in the structure of detritus-associated macroinvertebrate assemblages within Laminaria hyperborea forests and evaluated the influence of putative drivers of the observed variability in assemblages across eight study sites within four regions of the United Kingdom in May and September 2015. We documented 5167 individuals from 106 taxa with Malacostraca, Gastropoda, Isopoda and Bivalvia the most abundant groups sampled. Assemblage structure varied across months, sites, and regions, with highest richness in September compared to May. Many taxa were unique to individual regions, with few documented in all regions. Finally, key drivers of assemblage structure included detritus tissue nitrogen content, depth, sea surface temperature, light intensity, as well as L. hyperborea canopy density and canopy biomass. Despite their dynamic composition and transient existence, accumulations of L. hyperborea detritus represent valuable repositories of biodiversity and represent an additional kelp forest component which influences secondary productivity, and potentially kelp forest food-web dynamics.

Large-scale spatial drivers of avian schistosomes in Northern Michigan inland lakes.

Avian schistosomes are snail-borne trematode parasites (Trichobilharzia spp.) that can cause a nasty skin rash in humans when their cercariae mistake us for their normal bird hosts. We sought to investigate drivers of the spatial distribution of Trichobilharzia cercaria abundance throughout Northern Michigan lakes. For 38 sites on 16 lakes, we assessed several dozen potential environmental predictors that we hypothesized might have direct or indirect effects on overall cercaria abundance, based on known relationships between abiotic and biotic factors in wetland ecosystems. We included variables quantifying local densities of intermediate hosts, temperature, periphyton growth rates, human land use and hydrology. We also measured daily abundance of schistosome cercariae in the water over a 5-week period, supported by community scientists who collected and preserved filtered water samples for qPCR. The strongest predictor of cercaria abundance was Lymnaea host snail density. Lymnaea density was higher in deeper lakes and at sites with more deciduous tree cover, consistent with their association with cool temperature habitats. Contrary to past studies of human schistosomes, we also found a significant negative relationship between cercaria abundance and submerged aquatic vegetation, possibly due to vegetation blocking cercaria movement from offshore snail beds. If future work shows that these effects are indeed causal, then these results suggest possible new approaches to managing swimmer's itch risk in northern MI lakes, such as modifying tree cover and shallow-water vegetation at local sites.

Drivers and human impacts on topsoil bacterial and fungal community biogeography across Australia.

Soil microbial diversity mediates a wide range of key processes and ecosystem services influencing planetary health. Our knowledge of microbial biogeography patterns, spatial drivers and human impacts at the continental scale remains limited. Here, we reveal the drivers of bacterial and fungal community distribution in Australian topsoils using 1384 soil samples from diverse bioregions. Our findings highlight that climate factors, particularly precipitation and temperature, along with soil properties, are the primary drivers of topsoil microbial biogeography. Using random forest machine-learning models, we generated high-resolution maps of soil bacteria and fungi across continental Australia. The maps revealed microbial hotspots, for example, the eastern coast, southeastern coast, and westcoast were dominated by Proteobacteria and Acidobacteria. Fungal distribution is strongly influenced by precipitation, with Ascomycota dominating the central region. This study also demonstrated the impact of human modification on the underground microbial community at the continental scale, which significantly increased the relative abundance of Proteobacteria and Ascomycota, but decreased Chloroflexi and Basidiomycota. The variations in microbial phyla could be attributed to distinct responses to altered environmental factors after human modifications. This study provides insights into the biogeography of soil microbiota, valuable for regional soil biodiversity assessments and monitoring microbial responses to global changes.

Conflicts and opportunities for commercial tree plantation expansion and biodiversity restoration across Brazil.

Substantial global restoration commitments are occurring alongside a rapid expansion in land-hungry tropical commodities, including to supply increasing demand for wood products. Future commercial tree plantations may deliver high timber yields, shrinking the footprint of production forestry, but there is an as-yet unquantified risk that plantations may expand into priority restoration areas, with marked environmental costs. Focusing on Brazil-a country of exceptional restoration importance and one of the largest tropical timber producers-we use random forest models and information on the economic, social, and spatial drivers of historic commercial tree plantation expansion to estimate and map the probability of future monoculture tree plantation expansion between 2020 and 2030. We then evaluate potential plantation-restoration conflicts and opportunities at national and biome-scales and under different future production and restoration pathways. Our simulations show that of 2.8 Mha of future plantation expansion (equivalent to plantation expansion 2010-2020), ~78,000 ha (3%) is forecast to occur in the top 1% of restoration priority areas for terrestrial vertebrates, with ~547,500 ha (20%) and ~1,300,000 ha (46%) in the top 10% and 30% of priority areas, respectively. Just ~459,000 ha (16%) of expansion is forecast within low-restoration areas (bottom 30% restoration priorities), and the first 1 Mha of plantation expansion is likely to have disproportionate impacts, with potential restoration-plantation overlap starkest in the Atlantic Forest but prominent in the Pampas and Cerrado as well. Our findings suggest that robust, coherent land-use policies must be deployed to ensure that significant trade-offs between restoration and production objectives are navigated, and that commodity expansion does not undermine the most tractable conservation gains under emerging global restoration agendas. They also highlight the potentially significant role an engaged forestry sector could play in improving biodiversity outcomes in restoration projects in Brazil, and presumably elsewhere.

Aboveground Biomass Mapping and Analysis of Spatial Drivers in the Qinghai-Xizang Plateau Permafrost Zone: A Case Study of the Beilu River Basin.

Aboveground biomass (AGB) serves as a crucial measure of ecosystem productivity and carbon storage in alpine grasslands, playing a pivotal role in understanding the dynamics of the carbon cycle and the impacts of climate change on the Qinghai-Xizang Plateau. This study utilized Google Earth Engine to amalgamate Landsat 8 and Sentinel-2 satellite imagery and applied the Random Forest algorithm to estimate the spatial distribution of AGB in the alpine grasslands of the Beiliu River Basin in the Qinghai-Xizang Plateau permafrost zone during the 2022 growing season. Additionally, the geodetector technique was employed to identify the primary drivers of AGB distribution. The results indicated that the random forest model, which incorporated the normalized vegetation index (NDVI), the enhanced vegetation index (EVI), the soil-adjusted vegetation index (SAVI), and the normalized burn ratio index (NBR2), demonstrated robust performance in regards to AGB estimation, achieving an average coefficient of determination (R2) of 0.76 and a root mean square error (RMSE) of 70 g/m2. The average AGB for alpine meadows was determined to be 285 g/m2, while for alpine steppes, it was 204 g/m2, both surpassing the regional averages in the Qinghai-Xizang Plateau. The spatial pattern of AGB was primarily driven by grassland type and soil moisture, with q-values of 0.63 and 0.52, and the active layer thickness (ALT) also played a important role in AGB change, with a q-value of 0.38, demonstrating that the influences of ALT should not be neglected in regards to grassland change.

Assessing future land-uses under planning scenarios: A case study of The Brantas River Basin, Indonesia

Understanding land-use dynamics and patterns and how they respond to management scenarios helps to develop sustainable land use policies. This study simulated future land uses in Brantas River Basin (BRB), East Java, Indonesia using Land Change Modeler (LCM) under policy scenarios. Following identification of twelve important biophysical and socio-economic spatial drivers, the LCM accurately simulated land-use changes over the period 1995–2015, with validation against the 1995 land use map giving an overall accuracy of 85–88 %. Under a business-as-usual scenario, the LCM predicted that during the period 2015 to 2035 continuing deforestation may leave forest cover accounting for only 4 % of the total BRB area, and cause declines in dryland from 45 % to 40 % and in rice-field farming from 24 % to 22 %. The results of that scenario also indicate the prospect of massive urban development, increasing from 18 % of the BRB area in 2015 to 30 % in 2035, with increasing threats to food security and water resources. Both percentage changes and a mean-weighted fractal dimension index suggest increased risks of forest fragmentation and urban aggregation. A spatial planning-influenced scenario, which aims to reduce forest loss and support watershed protection, is predicted to lead to 5.5 % forest cover, 37 % dryland, 29 % rice-field farming and 24 % urban area in 2035. Sensitivity analysis of the LCM showed that the model results were more sensitive to drivers, spatial resolution, and policy scenarios than to uncertainty in model parameters. It is concluded that despite some limitations, the LCM successfully provided" insights into policy impacts on land-uses in BRB, and the roles of forest and rice-field protection in spatial planning are essential in controlling urban development for watershed sustainability.

Spatiotemporal variability of evapotranspiration in Alpine grasslands and its biotic and abiotic drivers

AbstractTo gain a deeper understanding of the water balances of Alpine grassland ecosystems, it is crucial to know the abiotic and biotic drivers of evapotranspiration. The abiotic drivers are very heterogeneous in mountain regions because elevation, slope and aspect control incoming Rs, and atmospheric layering affect air temperature, humidity and wind distribution. In a study with 24 lysimeter plots distributed over a study area of approx. 300 km2 in the Eastern Alps, we covered a wide range of topographic conditions. We investigated the effects of abiotic drivers on evapotranspiration by measuring evaporation from a free‐water body (Ew). For the biological modulation of crop evapotranspiration (ETc), we analysed two different grassland types (at peak biomass and at low biomass) and calculated the respective crop coefficients (Kc). Results showed that primarily physical drivers such as the accumulated solar radiation from sunrise to measurement (Rs_acc), followed by atmospheric pressure (P), wind speed (u) and vapour pressure deficit (VPD) influence both Ew and ETc. Moreover, ETc is also significantly influenced by standing biomass and the grassland type (i.e., resource use strategies of the vegetation types) and by the geographic location along the valley (i.e., entrance, middle and head of the valley). We suppose plant stress and/or ground winds to be the underlying factor for the significance of the geographic location, yet further research is needed. The current study helps towards a better understanding of the water balance in alpine grassland ecosystems, but we also show that some spatial drivers cannot yet be adequately addressed.

Spatio-temporal pattern assessment of China's environmental performance and its spatial drivers: evidence from city-level data over 2003-2019.

A comprehensive assessment of China's environmental performance (EP) and an investigation into its driving factors are essential prerequisites for advancing environmental protection efforts. However, existing studies have often exhibited a one-sided EP evaluation approach and lacked a systematic perspective. Consequently, this study has adopted a holistic approach by integrating environmental protection and pollution within the same theoretical framework. We have employed the "P-S-R" model to comprehensively assess the EP of 272 cities from 2003 to 2019. Concurrently, we have applied the spatial Durbin model to analyze EP drivers utilizing three spatial matrices. The findings of this study reveal several vital insights. Firstly, the mean EP value for China is 0.1138, indicating a low level, but it demonstrates a consistent upward trend over the years. When comparing cities with high EP, they are predominantly situated in northern China, northeastern China, and certain areas along the southeastern coast. Secondly, from a spatial perspective, the directionality of EP exhibits a trend from "northeast to the southwest," with the center of gravity located in and around Zhumadian, Henan Province, gradually shifting towards the northeast. The majority of cities fall within the H-H and L-L clusters, displaying significant positive spatial autocorrelation effects. Thirdly, EP drivers encompass a wide range of factors, including economic development, urbanization, resource dependence, industrial structure, infrastructure construction, environmental regulation, government regulatory capacity, scientific and technological innovation, and foreign direct investment. These drivers also exhibit significant spillover effects. Finally, the characteristics of EP development vary between resource-based cities (RBCs) and non-resource-based cities (non-RBCs), as well as among the eastern, central, and western regions. Moreover, there are disparities in the driving factors' direct, indirect, and overall effects. Consequently, we must propose tailored strategies and recommendations to enhance EP, considering the heterogeneous effects of influencing factors across different city types, regions, and collaboration approaches.

Land intensification use scenarios based on urban land suitability assessment of the national park

In designing national parks, it is important to balance land use with ecological protection and local socio-economic development. In the past decades, the national park has undergone landscape fragmentation and degradation of ecological functions due to climate change and land use change, especially in national parks with urban areas. Therefore, ecological environmental protection projects should include the assessment of land use when planning urban area development in the national park region. Consequently, we used the Land Suitability Evaluation (LSE) method, with 13 indicators for natural, social and ecological variables, to assess the suitability of the newly created national park for urban land use on the Tibetan Plateau. Combining results of the suitability assessment and the Cellular Automata (CA) method, three urban intensification scenarios were simulated for 2040: (1) unplanned, (2) intensified, and (3) strictly regulated urban growth boundaries (UGBs). The impact of land use on the normalized difference vegetation index (NDVI) was analyzed by remote sensing and GIS tools, and the landscape patterns of the three urban growth scenarios were calculated. Close to 87 % of the area was basically suitable, nearly 13 % was unsuitable, and only 0.06 % (43.4 km2–also included in the basically suitable) was suitable for urban land use. In 2040, the built-up areas are predicted to be 3.38, 2.71 and 2.32 times greater than in 2020 under the unplanned (without considering ecological restrictions), the intensive development (emphasizing the driving force of transportation and tourism on the expansion of built-up areas) and the strict control (maximizing ecological restrictions) scenarios, respectively. An ideal development model was used to balance ecological protection and population growth and economic development. The NDVI analysis revealed that the impact of land use on vegetation cover in the intensive development scenario was ranked as winter pastures > tourist development zones > summer pastures. It is concluded that the intensification scenario, with transport and tourism as the main spatial drivers, is the most desirable for the future coordination of ecological conservation and socio-economic development in the national park. This scenario may alleviate the current environmental pressure of livestock grazing on winter pastures in the national park on the Tibetan Plateau. However, realizing this model requires appropriate engineering design for town development. Future research should assess the impacts of urban growth on the ecological environment, with the aim to achieve sustainable urban development tailored to the various geographic regions.