Similar Spatial Patterns Research Articles

Trade-offs and synergies relationships of ecosystem services and their socio-ecological driving factors under different spatial scales in Shaoguan City, Guangdong, China.

Ecosystem services refer to the benefits that human obtain from natural ecosystems. Different ecosystem services are generated by the combination of social-ecological driving factors, and exhibit different spatial patterns across scales. The complex relationships and driving mechanisms among ecosystem services under different spatial scales remain unclear. With Shaoguan City from Guangdong Province as the study area, we analyzed the spatial patterns and relationships of four ecosystem services and their trade-offs/synergies (TOSs), quantified their responses to seven social-ecological drivers at the kilometer grid scale and sub-watershed scale, and proposed regional ecologi-cal management and planning strategies for cross-scale sustainable development. The results showed that the spatial distribution of ecosystem services in Shaoguan City exhibited spatial clustering and cross-scale variations. Habitat quality, water yield, and carbon storage exhibited similar spatial distribution pattern. High supply was mainly distributed in mountainous areas in the east, north, west, and south, while weak supply was distributed in plain areas in the central, northwest, south and northeast. In addition, the spatial clustering of these services intensified with increasing spatial scale. Ecosystem services displayed synergistic relationships at both spatial scales, and the intensity of the synergy changed with scale. At both the kilometer grid and sub-watershed scale, the primary drivers for ecosystem services were the normalized vegetation index and digital elevation model. The main driver for TOSs was the mean annual temperature at the kilometer grid scale, while it was the mean annual evapotranspiration at the sub-watershed scale. Based on the supply levels of ecosystem services, the study area could be divided into five distinct ecosystem service bundles, i.e., mountain ecological balance zone, forest ecological conservation zone, urban forest maintenance zone, ecologically sensitive zone, and ecological risk zone. All bundles exhibited both spatial heterogeneity and cross-scale variations. We integrated the cross-scale variations of four representative ecosystem services and their complex interactions and driving mechanisms in Shaoguan City into spatial planning to facilitate the sustainable ecosystem management across multiple scales, which could offer valuable references for the construction of ecological civilization in other regions.

Supply-demand matching assessment of the public service facilities in 15-minute community life circle based on residents' behaviors

15-minute community life circle focuses on residents' daily needs and aligns with “people-oriented” planning. However, the personalized needs of residents have been ignored in literatures, resulting in the mismatch between the supply and demand of public service facilities (PSFs) as well as the unfair supply of different population groups among communities. Based on community life circle, we proposed a multi-group huff three-step floating catchment area (MGH3SFCA) model to evaluate the supply-demand matching levels of PSFs for different age groups in Nanjing, China. The results showed that the supply-demand between PSFs and different age groups was relatively mismatched in the main urban area of Nanjing, and medical care facilities were seriously inadequate to fulfill the needs of any age group. Comprehensive accessibility varied depending on the frequency of visits to PSFs for each age group. Although age groups had a similar spatial pattern of comprehensive accessibility at community and building scales, the service coverage rate of PSFs was larger at the building scale, and the assessment of supply-demand matching of PSFs at the building scale was more accurate and reliable. The findings would provide important decision-making support for the targeted allocation of PSFs in 15-minute community life circle.

Frequency dependent whole-brain coactivation patterns analysis in Alzheimer's disease.

The brain in resting state has complex dynamic properties and shows frequency dependent characteristics. The frequency-dependent whole-brain dynamic changes of resting state across the scans have been ignored in Alzheimer's disease (AD). Coactivation pattern (CAP) analysis can identify different brain states. This paper aimed to investigate the dynamic characteristics of frequency dependent whole-brain CAPs in AD. We utilized a multiband CAP approach to model the state space and study brain dynamics in both AD and NC. The correlation between the dynamic characteristics and the subjects' clinical index was further analyzed. The results showed similar CAP patterns at different frequency bands, but the occurrence of patterns was different. In addition, CAPs associated with the default mode network (DMN) and the ventral/dorsal visual network (dorsal/ventral VN) were altered significantly between the AD and NC groups. This study also found the correlation between the altered dynamic characteristics of frequency dependent CAPs and the patients' clinical Mini-Mental State Examination assessment scale scores. This study revealed that while similar CAP spatial patterns appear in different frequency bands, their dynamic characteristics in subbands vary. In addition, delineating subbands was more helpful in distinguishing AD from NC in terms of CAP.

Regional-scale cotton yield forecast via data-driven spatio-temporal prediction (STP) of solar-induced chlorophyll fluorescence (SIF)

Solar-induced chlorophyll fluorescence (SIF), as a direct probe of vegetation photosynthesis, has recently been an effective indicator for crop yield estimation in late-season. Spatio-temporal prediction of SIF (STP-SIF) from mid- to late-season could be a promising solution to forecast crop yields in mid-season. However, STP-SIF has not been well explored in assessing its applicability in crop yield forecasting. In the study, we first explored the potential driving mechanism and designed time-series data-driven deep learning approaches for the STP-SIF issue. We compared the feasibility of four styles of explanatory variables, six network structures, and the corresponding eight approaches in SIF prediction in the main cotton-planting area in Northern Xinjiang, taking the two tasks of one and two months before harvest as examples. The STP-SIF products of the five approaches, namely Spe_CNN-LSTM (a hybrid network combining convolutional neural network and long short-term memory considering spectra), Spe_CNN3D (a three-dimensional CNN considering spectra), SpaSpeSIF_CNN3D (a three-dimensional CNN considering spectra and SIF), SpaSpeSIF_HCNN (a hybrid network combining CNN2D and CNN3D considering spectra and SIF), and SIF_CNN-LSTM (a CNN-LSTM network considering SIF), showed a similar spatial pattern to the referenced SIF, indicating that these proposed approaches could be better for STP-SIF. Discussion of the spatial scale effect of the STP-SIF issue preliminary showed that our proposed methods had little spatial scale dependence and hence could be suitable for STP-SIF in different spatial extents at diverse resolutions. Further, we performed the experiments for the regional-scale cotton yield forecast one to two months before harvest based on the STP-SIF products of August and September. The best cotton yield prediction accuracies, with R2 of 0.70 and 0.66 for one and two months before harvest, were obtained respectively by the combination of the known SIF and NDWI (Normalized Difference Water Index) and the predicted SIF by SIF_CNN-LSTM. This study offers a baseline for the STP-SIF issue and reveals the feasibility of the STP-SIF products for accurate crop yield forecast.

Bridging the spatial gaps of the Ammonia Monitoring Network using satellite ammonia measurements

Abstract. Ammonia (NH3) is a key precursor to fine particulate matter (PM2.5) and a primary form of reactive nitrogen. The limited number of NH3 observations hinders the further understanding of its impacts on air quality, climate, and biodiversity. Currently, NH3 ground monitoring networks are few and sparse across most of the globe, and even in the most established networks, large spatial gaps exist between sites and only a few sites have records that span longer than a decade. Satellite NH3 observations can be used to discern trends and fill spatial gaps in networks, but many factors influence the syntheses of the vastly different spatiotemporal scales between surface network and satellite measurements. To this end, we intercompared surface NH3 data from the Ammonia Monitoring Network (AMoN) and satellite NH3 total columns from the Infrared Atmospheric Sounding Interferometer (IASI) in the contiguous United States (CONUS) and then performed trend analyses using both datasets. We explored the sensitivity of correlations between the two datasets to factors such as satellite data availability and distribution over the surface measurement period, as well as agreement within selected spatial and temporal windows. Given the short lifetime of atmospheric ammonia and consequently sharp gradients, smaller spatial windows show better agreement than larger ones except in areas of relatively uniform, low concentrations where large windows and more satellite measurements improve the signal-to-noise ratio. A critical factor in the comparison is having satellite measurements across most of the measurement period of the monitoring site. When IASI data are available for at least 80 % of the days of AMoN's 2-week sampling period within a 25 km spatial window of a given site, IASI NH3 column concentrations and the AMoN NH3 surface concentrations have a correlation of 0.74, demonstrating the feasibility of using satellite NH3 columns to bridge the spatial gaps existing in the surface network NH3 concentrations. Both IASI and AMoN show increasing NH3 concentrations across the CONUS (median: 6.8 % yr−1 versus 6.7 % yr−1) in the last decade (2008–2018), suggesting the NH3 will become a greater contributor to nitrogen deposition. NH3 trends at AMoN sites are correlated with IASI NH3 trends (r = 0.66) and show similar spatial patterns, with the highest increases in the Midwest and eastern US. In spring and summer, increases in NH3 were larger than 10 % yr−1 in the eastern US and Midwest (cropland dominated) and the western US (pastureland dominated), respectively. NH3 hotspots are defined as regions where the IASI NH3 column is larger than the 95th percentile of the 11-year CONUS map (6.7 × 1015 molec.cm-2), they also experience increasing concentrations over time, with a median of NH3 trend of 4.7 % yr−1. IASI data show large NH3 increases in urban areas (8.1 % yr−1), including 8 of the top 10 most populous regions in the CONUS, where AMoN sites are sparse. A comparison between IASI NH3 concentration trends and state-level NH3 emission trends is then performed to reveal that positive correlations exist in states with strong agricultural NH3 emissions, while there are negative correlations in states with low NH3 emissions and large NOx emissions, suggesting the different roles of emission and partitioning in NH3 increases. The increases in NH3 could have detrimental effects on nearby eco-sensitive regions through nitrogen deposition and on aerosol chemistry in the densely populated urban areas, and therefore they should be carefully monitored and studied.

Integrating three-dimensional greenness into RSEI improved the scientificity of ecological environment quality assessment for forest

Normalized Difference Vegetation Index (NDVI) is widely used to represent the greenness indicator for the Ecological Environment Quality (EEQ) assessment based on the traditional Remote Sensing Ecological Index (RSEI). However, NDVI saturation issues are reported in agriculture and forest ecosystems at high greenness and biomass, creating a challenge when using NDVI to reflect the greenness components for forest EEQ assessment. In this paper, three-dimensional greenness (TDG) was obtained by the Forest Canopy Height (FCH) and Fractional Vegetation Cover (FVC) to quantify the forest greenness. The NDVI of RSEI was replaced with TDG to establish an improved remote sensing ecological index (TDRSEI) for the forest EEQ evaluation in the Central Yunnan, China. Moreover, we analyzed the difference between RSEI and TDRESI by qualitative and quantitative means and discussed deeply the optical saturation of NDVI by the quadratic function. The results shown that there were similar spatial distribution patterns and a strong correlation between TDG and FCH, Leaf Area Index (LAI), FVC, and NDVI, the TDG can be used to replace NDVI for reflecting forest greenness. The standard deviation of TDRSEI from the same FCH pixels was all less than that of RSEI, the absolute correlation coefficient between TDRSEI and ecological components were all greater than 0.63, and the mean values of TDRSEI (0.73, 0.84, and 0.90) from the relatively high FCH (20 m, 25 m, and 30 m) were greater than RSEI (0.71, 0.78, and 0.85), showing that the TDRSEI were more stable than RSEI for forest EEQ assessment and it can improve the EEQ in the high FCH. In Central Yunnan, China, the forest EEQ from TDRSEI and RSEI maps both increased with the growth of FCH, the distribution of RSEI had no significant difference between the low FCH and the high FCH, while the mean values of TDRSEI increased linearly with FCH growth. Besides, the ecological saturation points of TDRSEI and RSEI corresponding to the FCH were 19.50 m and 34.02 m, respectively. Therefore, TDRSEI method integrates the three-dimensional greenness to evaluate the forest EEQ objectively.

Impacts of Land Use Intensity on Ecosystem Services: A Case Study in Harbin City, China

Land use intensity (LUI) is an important indicator for assessing human activities, and quantitatively studying the impact of LUI on ESs can help to realize the scientific management of urban ecosystems as well as sustainable development. In this study, we quantified five important ecosystem service bundles in the study area with the aid of the R-language “kohonen” package and used bivariate spatial autocorrelation modeling to examine the effects of LUI on the ESs in Harbin City from 2000 to 2020. These ESs include food supply (FP), water conservation (WC), soil conservation (SC), carbon storage (CS), water purification (WP), and habitat quality (HQ). The results show the following: (1) The LUI in Harbin City had a trend from 2000 to 2020 of “decreasing and then growing”, with a spatial distribution pattern of “high in the west and low in the east.” (2) Except for FP, all other ESs exhibit a similar spatial pattern of “west-low-east-high”; WC and WP exhibit a trend of continuous increase, SC exhibits a trend of decreasing and then increasing, and CS and HQ are generally more stable, with less fluctuation. The built-up area is situated in the high-value area of LUI, and the area exhibits a significant expansion trend. (3) Ecological conservation bundles, FP–WP synergistic bundles, ecological transition bundles, CS–WP–HQ synergistic bundles, and FP bundles are the five ecosystem service bundles that were discovered in Harbin. (4) From 2000 to 2020, there is a predominately “low LUI-high ESs” and “high LUI-low ESs” aggregation type, with a substantial positive correlation between LUI and FP and a significant negative correlation between LUI and other ESs. Harbin City should strengthen the management of ESs in the western part of the city and, at the same time, maintain the favorable ecological conditions in the ecological barriers of Zhangguangcai Range and Xiaoxing’an Mountains.

Spatio-temporal changes in global root zone soil moisture from 1981 to 2017

Root zone soil moisture (RZSM) has a direct impact on ecosystem function, vegetation growth and food security, and plays a vital role in global climate system, water and carbon cycles. However, large variations and uncertainties still exist in RZSM across the globe under the warming climate. In this study, we applied comparison map profile (CMP), Theil-Sen regression and partial correlation analysis to investigate the spatial and temporal changes of RZMS and its driving factors from 1981 to 2017 by using three soil moisture products–ERA5, GLDAS and MERRA-2. Results showed that RZSM derived from three products presented a similar spatial pattern that the highest RZSM values occurred in tropical forest and cold areas, followed by subtropical, while the relatively low RZSM values were observed in arid and semiarid regions. Globally, RZSM decreased in all of three datasets with a rate of −0.14 × 10-3 m3 m−3 yr−1 on average (p < 0.001), which was largely correlated with temperature anomalies. Spatially, the RZSM trends greatly varied, with 21–31% of global land areas experiencing a significant decreasing trend and 7–24% for an increasing trend, respectively, confirming their different sensitivities to climate change. Temperature-driven RZSM dominated 19–29% of global land areas and was primarily distributed in northern high-latitude areas. The areas dominated by evapotranspiration were mainly in arid and semiarid areas, accounting for 29–44% of global land areas. Precipitation dominates the remaining 36–45% of global land areas mainly in eastern America and Europe, suggesting variations in the dominance of environmental factors on the spatial patterns of RZSM trend. Our findings will deepen our understanding of the impacts of climate change on the long-term trend of global soil moisture, and will be greatly critical to global soil water resource protection and management under the warming climate.

Multidimensional Valuation and Spatial Differentiation of Cultivated Land Resources at the County Scale: A Case Study of Guangxi, China

Revealing the multidimensional value of cultivated land resources, improving calculation methods, and exploring their spatial distribution characteristics and influencing factors are crucial for the enhancement of natural resource accounting systems and the protection of cultivated land resources. This study takes Guangxi, China, as an example and constructs a comprehensive multidimensional value evaluation system for cultivated land resources at the county scale. The income capitalization method, substitution market method, and value equivalent correction method were applied to calculate the economic, social, and ecological values of cultivated land resources in 111 counties in 2020. Furthermore, spatial autocorrelation analysis was employed to study their distribution characteristics and influencing factors. The results show that: (1) In 2020, the total value of cultivated land resources in Guangxi was USD 2859.3 billion, with the economic, social, and ecological values were USD 1674.2 billion, USD 417.7 billion and USD 767.3 billion USD, respectively. (2) The economic and social value of cultivated land per hectare at the county scale in Guangxi demonstrates similar spatial distribution patterns. High-value counties exhibit a strip-like distribution along the eastern edge, following a north–south orientation, while low-value counties form clusters in the central and western regions. (3) There is a significant spatial clustering effect of all values of cultivated land resources in local space, with “high-high” and “low-low” clustering. (4) The level of economic development in counties has a negative impact on the ecological value of cultivated land resources, while it has a positive impact on their social value. (5) Natural conditions, agricultural infrastructure conditions and agricultural development approaches are the key factors affecting the value of cultivated land resources.

The impact of quality control on cortical morphometry comparisons in autism.

Structural magnetic resonance imaging (MRI) quality is known to impact and bias neuroanatomical estimates and downstream analysis, including case-control comparisons, and a growing body of work has demonstrated the importance of careful quality control (QC) and evaluated the impact of image and image-processing quality. However, the growing size of typical neuroimaging datasets presents an additional challenge to QC, which is typically extremely time and labour intensive. One of the most important aspects of MRI quality is the accuracy of processed outputs, which have been shown to impact estimated neurodevelopmental trajectories. Here, we evaluate whether the quality of surface reconstructions by FreeSurfer (one of the most widely used MRI processing pipelines) interacts with clinical and demographic factors. We present a tool, FSQC, that enables quick and efficient yet thorough assessment of outputs of the FreeSurfer processing pipeline. We validate our method against other existing QC metrics, including the automated FreeSurfer Euler number, two other manual ratings of raw image quality, and two popular automated QC methods. We show strikingly similar spatial patterns in the relationship between each QC measure and cortical thickness; relationships for cortical volume and surface area are largely consistent across metrics, though with some notable differences. We next demonstrate that thresholding by QC score attenuates but does not eliminate the impact of quality on cortical estimates. Finally, we explore different ways of controlling for quality when examining differences between autistic individuals and neurotypical controls in the Autism Brain Imaging Data Exchange (ABIDE) dataset, demonstrating that inadequate control for quality can alter results of case-control comparisons.

Hydrogeomorphic Response of Charcoals During River Transits and Its Impact on the Carbon Cycle

AbstractNatural or anthropogenic‐induced biomass burning produces large amounts of charcoals, which enter riverine or lacustrine systems mostly via surface runoff processes. Charcoal storage and cycling within large riverine systems can play a crucial role in mediating long‐term carbon sink across transient reservoirs, thereby influencing the global carbon budget. However, natural processes governing the transport and storage of charcoal particles in large terrestrial reservoirs such as the Indo‐Gangetic region still remain unknown. To understand charcoal movement and accumulation across upland and lowland transient reservoirs, we characterized spatial distribution and morphology of different charcoal forms (&gt;125 μm and &lt;125 μm) from bedload and floodplain sediments of the Yamuna sub‐basin (YSB), India. Both &gt;125 μm and &lt;125 μm charcoal forms in bedload and floodplain sediments did not exhibit similar spatial distribution patterns, indicating the segregation of charcoal particles influenced by variable flow regimes. Attrition with sediments breaks down fragile charcoals (leaves) quickly compared to the woody forms, resulting in dominant woody microforms in transient deposits. Higher stream power and limited stable bedform development in upland mountainous regions restrict charcoal storage. During lowland riverine transits, reduced stream power conditions allow increased floodwater inundation and finer clay substrate availability, facilitating an exponential increase in charcoal storage. However, increased discharge from peninsular rivers into the YSB leads to erosion and redistribution of sediment, including charcoal particles, and reduced charcoal storage in lowland transient areas. Such diverse dispersal pathways and fate of charcoal particles across riverine transits highlight the influence of regional hydrogeomorphic processes on the overall carbon cycle within transient reservoirs.

Spatiotemporal Variation of Anticyclonic Eddies in the South China Sea during 1993–2019

Based on the absolute dynamic topography data from the Copernicus Marine Environment Monitoring Service, this paper applies the Topographic Position Index to develop a new approach for mapping the anticyclonic eddies in the South China Sea (SCS). The results show that anticyclonic eddies are active in the deep basin of SCS, and the five selected parameters (number or frequency, lifetime, kinetic energy, amplitude, and area or radius) of anticyclonic eddies have a similar temporal variation and a similar spatial distribution pattern. (1) As for monthly variations, anticyclonic eddies are active in late spring and most active in summer. (2) The El Niño–Southern Oscillation had a stronger impact on the inter-annual variations of anticyclonic eddies in the SCS before 2013, resulting in a significant transition of inter-annual variations of these five parameters in around 2004. After 2013, most of these five parameters had a minimum in 2015 and a maximum in 2017. (3) Analyses show that the eddy activities in the SCS are significantly influenced by the monsoon wind and the western boundary current like Kuroshio. Therefore, the areas southwest of Taiwan Island and east of Vietnam are the two areas where the anticyclonic eddies are most active, with much larger eddy kinetic energy and much higher eddy amplitude.

Trade-off efficiency: Linking urban socioecological quality with land use efficiency from return on investment perspective

Effectively coordinating urban development and urban socioecology is crucial for achieving sustainable development goals. However, efficacious measurements and assessment frameworks in this field are still insufficient. This study proposes a novel metrics, termed trade-off efficiency, which measures the relationship between urban development and urban socioecology from a return on investment perspective. Using three major Chinese cities, Zhengzhou, Nanchang and Changsha as a case study, we initially quantified regional socioecological states, including urban heat island effect, air quality, habitat quality, and green space accessibility, at a 1 km2 grid level. We also measured regional land use efficiency at the same resolution. The super-slack-based measure model was employed to examine the trade-off efficiency. The results revealed that the regional socioecological quality, in general, exhibits an opposite spatial pattern to the land use efficiency, underscoring the practicality of quantifying the trade-off efficiency from a return on investment perspective. The trade-off efficiency, which decreases from the inside to the outside, demonstrates a similar spatial pattern as the land use efficiency, implying that urban development and urban socioecology may conform to the Environmental Kuznets Curve. The proposed framework, with its fine resolution, holds the potential to serve specific urban planning and assist decision-makers in targeting low-efficiency areas.

Karst Groundwater Vulnerability Determined by Modeled Age and Residence Time Tracers

AbstractKarst aquifers are a vital groundwater resource globally, but features such as rapid recharge and conduit flow make them highly vulnerable to land‐surface contamination. We apply environmental age tracers to the south‐central Texas Edwards aquifer, a karst resource in a rapidly urbanizing and drought‐prone region, to assess vulnerability to land‐surface contamination and risks unique to karst aquifers. We show that vulnerability of Edwards aquifer groundwater follows similar spatial and depth patterns common to porous‐media type aquifers, despite complicated karst hydrogeologic features. Shallow and unconfined parts are more vulnerable to land‐surface contamination than the deeper and confined parts, although even the oldest groundwater is mixed with some recent recharge. When modeled age‐tracer results are coupled with other independent geochemical tracers of water‐rock interaction specific to karst settings, they can yield insight into residence time and associated vulnerability.

Hyperspectral Mixture Models in the CHIME Mission Implementation for Topsoil Texture Retrieval

AbstractThis study is part of the requirements consolidation study for the European Copernicus Hyperspectral Imaging Mission for Environment (CHIME). It explores the value added by existing hyperspectral data of similar characteristics to CHIME, namely AVIRIS‐NG and PRecursore IperSpettrale della Missione Applicativa (PRISMA), for detecting topsoil texture properties. The spatial variability is retrieved using the linear spectral mixture analysis, an image‐based algorithm that breaks down the hyperspectral data set into fractional abundance of spectral classes within each pixel. The fractional abundance of image‐based endmembers is broken into categories to find intervals having a spatial relation with texture components in terms of fine (clay and silt) or coarse (sand) abundance. The fraction maps obtained show similar spatial patterns to the USDA soil texture classification, obtained with a geostatistical approach. Specifically, AVIRIS CHIME‐like FAM1 &gt; 0.45 presented an agreement of 86% with clay and/or silt higher than 45% which, according to the United Stated Agriculture Department (USDA) intervals, correspond to loam‐clay loam classes. Similar results are obtained with PRISMA with FAM2 0.20–0.35, overlapping 63% of the kriging‐based USDA clay‐loam class. The fractional abundance categories showing the highest overlap percentages are correlated with the short‐wave infrared spectral range, showing average coefficients of 0.7 where wavelengths are over 1,500 nm. From 1700 nm, CHIME‐like shows values of 0.8. In conclusion, this exploratory research and results leverage the opportunity of extending the processing chain to a larger number of case studies to better understand the physical relation between the spectral reflectance captured by new spaceborne hyperspectral sensors and the spatial patterns of soil texture classes.

Magnetic signature and X-ray fluorescence for mapping trace elements in soils originating from basalt and sandstone

The knowledge of the lithological context is necessary to interpret trace elements concentrations in the soil. Soil magnetic signature (χ) and soil X-ray fluorescence (XRF) are promising approaches in the study of the spatial variability of trace elements and the environmental monitoring of soil quality. This research aimed to assess the efficiency of measurements of χ and XRF sensors for spatial characterization of zinc (Zn), manganese (Mn), and copper (Cu) contents in soils of a sandstone-basalt transitional environment, using machine learning modeling. The studied area consisted of the Western Plateau of São Paulo (WPSP), with soils originating from sandstone and basalt. A total of 253 soil samples were collected at a depth of 0.0–0.2 m. The soils were characterized by particle size and chemical analysis: organic matter (OM), cation exchange capacity (CEC), ammonium oxalate-extracted iron (Feo), sodium dithionite-citrate-bicarbonate-extracted iron (Fed), and sulfuric acid-extracted iron (Fet). Hematite (Hm), goethite (Gt), kaolinite (Kt), and gibbsite (Gb) contents were obtained by X-ray diffraction (XRD). Magnetite (Mt) and maghemite (Mh) contents were obtained by soil χ, while trace elements contents were obtained by XRF and predicted by χ. Descriptive analysis, the test of means, and correlation were performed between attributes. Zn, Mn, and Cu contents were predicted using the machine learning algorithm random forest, and the spatial variability was obtained using the ordinary kriging interpolation technique. Landscape dissections influenced iron oxides, which had the highest contents in slightly dissected environments. Trace elements contents were not influenced by landscape dissections, demonstrating that lithological knowledge is necessary to characterize trace elements in soils. The prediction models developed through the machine learning algorithm random forest showed that χ can be used to characterize trace elements. The similar spatial pattern of trace elements obtained by XRF and χ measurements confirm the applicability of these sensors for mapping it under lithological and landscape transition, aiming for sustainable strategic planning of land use and occupation.

Asymmetrical Precipitation Sensitivity to Temperature Across Global Dry and Wet Regions

AbstractGlobal warming is expected to increase precipitation extremes. However, the response of extreme precipitation to global warming in various climates remains unclear. Here, we analyzed changes in the sensitivities of extreme and mean precipitation to temperature across the dry and wet regions of the world during 1960–1999 and 2060–2099 using global climate models. Both extreme and mean precipitation exhibited similar spatial patterns; however, the magnitude of sensitivity for extreme precipitation was approximately three times higher (19%/K) than for mean precipitation (6%/K). A higher precipitation sensitivity to temperature was observed in the dry regions than in the wet regions. Dry regions exhibited a four‐ to five‐fold higher temperature sensitivity for mean precipitation, and marginally higher temperature sensitivity for extreme precipitation than wet regions. These findings highlight the importance of implementing adaptive strategies to alleviate the effects of global warming on dryland ecosystems.

Direct observations of NOxemissions over the San Joaquin Valley using airborne flux measurements during RECAP-CA 2021 field campaign

Abstract. Nitrogen oxides (NOx) are principle components of air pollution and serve as important ozone precursors. As the San Joaquin Valley (SJV) experiences some of the worst air quality in the United States, reducing NOx emissions is a pressing need, yet quantifying current emissions is complicated due to a mixture of mobile and agriculture sources. We performed airborne eddy covariance flux measurements during the Re-Evaluating the Chemistry of Air Pollutants in California (RECAP-CA) field campaign in June 2021. Combining footprint calculations and land cover statistics, we disaggregate the observed fluxes into component fluxes characterized by three different land cover types. On average, we find emissions of 0.95 mg N m−2 h−1 over highways, 0.43 mg N m−2 h−1 over urban areas, and 0.30 mg N m−2 h−1 over croplands. The calculated NOx emissions using flux observations are utilized to evaluate anthropogenic emissions inventories and soil NOx emissions schemes. We show that two anthropogenic inventories for mobile sources, EMFAC (EMission FACtors) and FIVE (Fuel-based Inventory for Vehicle Emissions), yield strong agreement with emissions derived from measured fluxes over urban regions. Three soil NOx schemes, including the MEGAN v3 (Model of Emissions of Gases and Aerosols from Nature), BEIS v3.14 (Biogenic Emission Inventory System), and BDISNP (Berkeley–Dalhousie–Iowa Soil NO Parameterization), show substantial underestimates over the study domain. Compared to the cultivated soil NOx emissions derived from measured fluxes, MEGAN and BEIS are lower by more than 1 order of magnitude, and BDISNP is lower by a factor of 2.2. Despite the low bias, observed soil NOx emissions and BDISNP present a similar spatial pattern and temperature dependence. We conclude that soil NOx is a key feature of the NOx emissions in the SJV and that a biogeochemical-process-based model of these emissions is needed to simulate emissions for modeling air quality in the region.

Tan DEM‐X Preserves the Relationship Between Hilltop Curvature and Erosion Rate in the Qilian Shan

AbstractHilltop curvature (CHT) can record erosional responses to tectonic forces and/or climate change. Although CHT has a linear relationship with the erosion rate, which is a significant advantage over traditional indicators, it is not widely used, mainly because it requires high‐resolution terrain data. Previous studies show that terrain steepness is strongly correlated with catchment‐wide erosion rates in the Qilian Shan. This relationship enables us to test the potential of CHT based on medium‐resolution terrain data (e.g., Tan DEM‐X with a resolution of ∼12 m) as an indicator of the erosion rate. We selected three typical sites in the Qilian Shan with evenly spaced ridge valleys and a continuous soil cover. At each site, we produced a high‐resolution digital surface model (DSM) using uncrewed aerial vehicles (UAVs), and then compared CHT from the UAV DSM with that from Tan DEM‐X, via LSDTopoTools. The results show that both Tan DEM‐X and the UAV DSM record similar spatial patterns of CHT, although the magnitudes vary between the three selected areas. We then examined the relationship between CHT based on Tan DEM‐X and the catchment‐wide erosion rate in the Qilian Shan. CHT has a linear relationship with the erosion rate in an area of clastic rocks, where the erosion rate is below 400 mm/Kyr. CHT extracted by Tan DEM‐X is superior to the mean basin slope in predicting the erosion rate in this clastic rock landscape. Overall, our results demonstrate the utility of Tan DEM‐X in calculating CHT to indicate the erosion rate.

Trends in Warm Season Mesoscale Convective Systems Over Asia in 2001–2020

AbstractMesoscale convective systems (MCSs) frequently occur over Asia during the warm season, often producing intense precipitation with associated socioeconomic impacts. Here we reveal significant trends in MCS occurrence frequency and related precipitation in Asia during the warm season (March–September) in 2001–2020, using a tracking method that combines cloud and precipitation criteria with high‐resolution satellite data from the Global Precipitation Measurement mission. To examine whether there are differences between MCSs of different scales, both meso‐α scales (MαCSs) and meso‐β scales (MβCSs), with horizontal scales of 200–2,000 km and 20–200 km, are tracked. The distribution pattern of frequency and related precipitation of both MαCSs and MβCSs are quite similar and manifest positive trends over East Asia (EA) and Northeast Asia, and negative trend over Southeast Asia (SEA). The MCS precipitation trend contributes significantly to total precipitation trend, with MαCSs contributing the most. Our analysis indicates the trend in lower‐tropospheric water vapor flux convergence has a similar spatial pattern to the MCS frequency and related precipitation trend. Based on an atmospheric moisture flux decomposition analysis, the water vapor flux convergence trend can largely be explained by the change in horizontal wind convergence, while the specific humidity trend driven largely by temperature change plays a minor role. The trend in wind convergence in EA and SEA is possibly related to the evident trend in the lower‐tropospheric anticyclone over the western North Pacific and SEA, which might be due to the relatively stronger warming in the Indian Ocean during the past two decades.