Land Cover Properties Research Articles

Decadal hydrological impact assessment of evolving land use and land cover in an Indian river basin: a multi-model approach

ABSTRACT All river basins have ever-evolving land use and land cover (LULC) attributes. The impact of these changes may not be significant on short time scales (i.e., monthly, seasonal, yearly), but over a decadal scale, they can substantially alter the hydrological processes of the basin. This study comprehensively quantifies the impacts of LULC changes over Cauvery basin in India using LULC maps from four decades spanning from 1980 to 2020. Simulations were performed using the Soil and Water Assessment Tool (SWAT) with various datasets. To isolate the effects of LULC changes, two sets of SWAT models were developed: A-set models for calibration and validation to establish basin parameters and B-set models to examine LULC change impacts while isolating other factors such as terrain and climate changes. Key findings include a significant increase in urban areas (0.87% in 1985 to 5.54% in 2015), a decline in vegetation cover (25.34% in 1985 to 21.32% in 2015), and an increase in the Curve Number and average annual surface runoff, highlighting the impact of LULC changes on hydrological processes. The A-set models achieved R-squared values of 0.831, 0.728, 0.715, and 0.757, while the B-set models showcased significant changes in hydrological parameters due to LULC changes.

Hydromechanical modeling of evolving post-wildfire regional-scale landslide susceptibility

Post-wildfire mass wasting is a major problem throughout many regions worldwide. Recent dramatic increases in global wildfire activities coupled with a shift in wildfire-prone elevation to higher altitudes raise the need to better predict post-fire rainfall-triggered landslides. Despite its importance, only a limited number of studies have investigated landslide susceptibility in areas hit by wildfires using hydromechanical models. However, most of these studies follow either qualitative or semi-quantitative approaches without explicitly considering the fire's effects on the impacted area's physical behavior. This study aims to develop and employ a physics-based framework to generate susceptibility maps of rainfall-triggered shallow landslides in areas disturbed by wildfire. A coupled hydromechanical model considering unsaturated flow and root reinforcement is integrated into an infinite slope stability model to simulate the triggering of shallow landslides from rainfall. The impact of fire is considered through its effects on soil and land cover properties, near-surface processes, and canopy interception. The developed model is then integrated into a geographic information system (GIS) to characterize the regional distribution of landslide potential and its variability considering topography, geology, land cover, and burn severity. The proposed framework was tested for a study site in Southern California. The site was burned in the San Gabriel Complex Fire in June 2016 and experienced widespread landsliding almost three years later following an extreme rainstorm in January 2019. The proposed framework could successfully model the location of observed shallow landslides. The model also revealed a significantly higher likelihood for slope failure in areas burned at moderate to high severities as opposed to unburned and low-burn severity areas. The findings of this study can be employed to predict the timing and general locations of rainfall-triggered shallow landslides following wildfires.

Optimizing landslide susceptibility mapping using machine learning and geospatial techniques

Landslides present a substantial risk to human lives, the environment, and infrastructure. Consequently, it is crucial to highlight the regions prone to future landslides by examining the correlation between past landslides and various geo-environmental factors. This study aims to investigate the optimal data selection and machine learning model, or ensemble technique, for evaluating the vulnerability of areas to landslides and determining the most accurate approach. To attain our objectives, we considered two different scenarios for selecting landslide-free random points (a slope threshold and a buffer-based approach) and performed a comparative analysis of five machine learning models for landslide susceptibility mapping, namely: Support Vector Machine (SVM), Logistic Regression (LR), Linear Discriminant Analysis (LDA), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). The study area for this research is an area in Polk County in Western North Carolina that has experienced fatal landslides, leading to casualties and significant damage to infrastructure, properties, and road networks. The model construction process involves the utilization of a dataset comprising 1215 historical landslide occurrences and 1215 non-landslide points. We integrated a total of fourteen geospatial data layers, consisting of topographic variables, soil data, geological data, and land cover attributes. We use various metrics to assess the models' performance, including accuracy, F1-score, Kappa score, and AUC-ROC. In addition, we used the seeded-cell area index (SCAI) to evaluate map consistency. The ensemble of the five models using Weighted Average produces outstanding results, with an AUC-ROC of 99.4% for the slope threshold scenario and 91.8% for the buffer-based scenario. Our findings emphasize the significant impact of non-landslide random sampling on model performance in landslide susceptibility mapping. Furthermore, by optimally identifying landslide-prone regions and hotspots that need urgent risk management and land use planning, our study demonstrates the effectiveness of machine learning models in analyzing landslide susceptibility and providing valuable insights for informed decision-making and disaster risk reduction initiatives.

Influence of urbanization on winter surface temperatures in a topographically asymmetric Tropical City, Bhubaneswar, India

Urban areas experience significant alterations in their local surface energy balance due to changes in the thermal properties of impervious surfaces, albedo, land use, and land cover. In addition, the embedded influence of urbanization and heat-trapping in the urban canopy cause city temperature warmer compared to its surroundings peri-urban regions. However, the influence of urbanization on winter surface temperatures remains unclear. In this study, the urbanization influence on winter surface temperature in Bhubaneswar, a tropical two-tier city in India, is assessed using a high-resolution (4 km × 4 km) urban canopy model coupled with the Weather Research and Forecasting model. Numerical experiments are conducted with no urban coupling (CTL) and with coupling of a single-layer urban canopy model (UCM) for the winters of 2004 and 2015. The study suggests that both model simulations exhibit a similar warm bias in mean surface temperature (~ 2.2 °C), but UCM’s surface temperature better agrees with the observations compared to CTL. The warm bias in both experiments is primarily contributed by a higher nighttime warm bias (~ 3.2 °C). The study reveals that urbanization contributes to ~ 0.4 °C increase in surface temperature in 2015, especially in the eastern lowland regions of the city, while the impact is minimal in 2004. In the western region, the influence is nullified, possibly due to lower surface specific humidity affecting longwave radiation in a higher terrain setting. This study underscores the significance of terrain and local microclimate conditions in shaping winter urban surface temperatures, shedding light on the complex interplay between urbanization and climate.

Towards reducing the high cost of parameter sensitivity analysis in hydrologic modeling: a regional parameter sensitivity analysis approach

Abstract. Land surface models have many parameters that have a spatially variable impact on model outputs. In applying these models, sensitivity analysis (SA) is sometimes performed as an initial step to select calibration parameters. As these models are applied to large domains, performing sensitivity analysis across the domain is computationally prohibitive. Here, using a Variable Infiltration Capacity model (VIC) deployment to a large domain as an example, we show that watershed classification based on climatic attributes and vegetation land cover helps to identify the spatial pattern of parameter sensitivity within the domain at a reduced cost. We evaluate the sensitivity of 44 VIC model parameters with regard to streamflow, evapotranspiration and snow water equivalent over 25 basins with a median size of 5078 km2. Basins are clustered based on their climatic and land cover attributes. Performance in transferring parameter sensitivity between basins of the same cluster is evaluated by the F1 score. Results show that two donor basins per cluster are sufficient to correctly identify sensitive parameters in a target basin, with F1 scores ranging between 0.66 (evapotranspiration) and 1 (snow water equivalent). While climatic attributes are sufficient to identify sensitive parameters for streamflow and evapotranspiration, including the vegetation class significantly improves skill in identifying sensitive parameters for the snow water equivalent. This work reveals that there is opportunity to leverage climate and land cover attributes to greatly increase the efficiency of parameter sensitivity analysis and facilitate more rapid deployment of land surface models over large spatial domains.

Multi-Dimensional Surface Water Quality Analyses in the Manawatu River Catchment, New Zealand

Land Use and Land Cover (LULC) properties give vital information about pollution signatures in rivers, and they help develop best management practices (BMPs) for effective water resource management. This work employs multivariate statistical methods, receptor modeling, connectivity analysis, and univariate trend analysis to investigate pollution sources across spatiotemporal scales in the Manawatu River, New Zealand. A positive matrix factorization (PMF) method was applied to interpret possible contamination sources. A 25-year dataset (1989–2014) comprising 12 water quality variables from three sites was used. Runoff connectivity analyses identified high-producing grassland (HG) as the most dominant pollution class in all sub-catchments. Univariate analyses revealed that nutrients and sediments were higher than in the initial monitoring years. The PMF analysis found possible pollutants causing impairment, which required attention from waste managers. PMF also showed that point, natural, and agricultural sources significantly contributed to pollution downstream of the river. In the midstream, the erosion, point, and agricultural sources were significant contributing factors. Agricultural pollution and soil erosion were the main contributors to the upstream sub-catchment area. This study suggests that BMPs with a high retention capacity are needed in specific locations in the catchment area to filter high concentrations of pollutants generated.

Machine learning approach for assessment of arsenic levels using physicochemical properties of water, soil, elevation, and land cover

Groundwater is an essential resource; around 2.5 billion people depend on it for drinking and irrigation. Groundwater arsenic contamination is due to natural and anthropogenic sources. The World Health Organization (WHO) has proposed a guideline value for arsenic concentration in groundwater samples of 10[Formula: see text]g/L. Continuous consumption of arsenic-contaminated water causes various carcinogenic and non-carcinogenic health risks. In this paper, we introduce a geospatial-based machine learning method for classifying arsenic concentration levels as high (1) or low (0) using physicochemical properties of water, soil type, land use land cover, digital elevation, subsoil sand, silt, clay, and organic content of the region. The groundwater samples were collected from multiple sites along the river Ganga's banks of Varanasi district in Uttar Pradesh, India. The dataset was subjected to descriptive statistics and spatial analysis for all parameters. This study assesses the various contributing parameters responsible for the occurrence of arsenic in the study area based on the Pearson correlation feature selection method. The performance of machine learning models, i.e., Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), Decision Tree, Random Forest, Naïve Bayes, and Deep Neural Network (DNN), were compared to validate the parameters responsible for the dissolution of arsenic in groundwater aquifers. Among all the models, the DNN algorithm outclasses other classifiers as it has a high accuracy of 92.30%, a sensitivity of 100%, and a specificity of 75%. Policymakers can utilize the accuracy of the DNN model to approximate individuals prone to arsenic poisoning and formulate mitigation strategies based on spatial maps.

Satellite Image Processing Using Radiometric Resolution & Spectral Resolution

Abstract: This paper presents a detailed comparison of various image processing techniques for analysing satellite images. The satellite images are large in size, acquired from long distances and are affected by noise and other environmental conditions. Hence it is necessary to process them so that they can be used by the researchers for analysis. Spectral resolution basically is to measure changes in things that impact our environments like water quality or vegetation etc. Satellite images are widely used in many real time applications such as in agriculture land detection, navigation and in geographical information systems. In this paper, a review of spectral resolution requirements for urban mapping evaluated how spectral resolution of high-spatial resolution optical remote sensing data influences detailed mapping of urban land cover. A comprehensive regional spectral library and low altitude data from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) were used to characterize the spectral properties of urban land cover.

Multi-Domain Fusion Graph Network for Semi-Supervised PolSAR Image Classification

The expensive acquisition of labeled data limits the practical use of supervised learning on polarimetric synthetic aperture radar (PolSAR) image analysis. Semi-supervised learning has attracted considerable attention as it can utilize few labeled data and very many unlabeled data. The scattering response of PolSAR data is strongly spatial distribution dependent, which provides rich information about land-cover properties. In this paper, we propose a semi-supervised learning method named multi-domain fusion graph network (MDFGN) to explore the multi-domain fused features including spatial domain and feature domain. Three major factors strengthen the proposed method for PolSAR image analysis. Firstly, we propose a novel sample selection criterion to select reliable unlabeled data for training set expansion. Multi-domain fusion graph is proposed to improve the feature diversity by extending the sample selection from the feature domain to the spatial-feature fusion domain. In this way, the selecting accuracy is improved. By few labeled data, very many accurate unlabeled data are obtained. Secondly, multi-model triplet encoder is proposed to achieve superior feature extraction. Equipped with triplet loss, limited training samples are fully utilized. For expanding training samples with different patch sizes, multiple models are obtained for the fused classification result acquisition. Thirdly, multi-level fusion strategy is proposed to apply different image patch sizes for different expanded training data and obtain the fused classification result. The experiments are conducted on Radarsat-2 and AIRSAR images. With few labeled samples (about 0.003–0.007%), the overall accuracy of the proposed method ranges between 94.78% and 99.24%, which demonstrates the proposed method’s robustness and excellence.

Land use in acid sulphate soils degrades river water quality – Do the biological quality metrics respond?

Land use in the Acid Sulphate (AS) soils induces metal and acidity pollution of aquatic ecosystems in coastal areas worldwide. Increasing utilization of AS soils poses increasing risks for deterioration of water bodies. We studied the effects of the coverage of AS soils, together with other catchment land cover attributes, on aquatic assemblages of fish, diatoms and benthic invertebrates in 42 sites along 15 lowland rivers in Finland during three subsequent years. Low pH and increasing content of several metals in the river water were related to high amount of AS soils in the catchment. Especially increasing iron content and water color were correlated to amount of forested areas in the catchment, whereas lower water color values and higher arsenic, chromium and iron concentrations were associated with wetlands. The assemblage structure of all three biological groups was strongly spatially structured among rivers and varied less temporally. The spatial structure of fish and diatoms were strongly affected by the acidic water, whereas invertebrates were more affected by low alkalinity and increasing concentrations of organic matter and iron. Especially fish and benthic invertebrate bioassessment metrics demonstrated for AS soil induced degradation in acidity by responding to low pH and high acidity, while the response from the diatoms index was weaker. The high metal concentrations alone did not seem to add to the degradation in biometrics without further increase in acidification. Our results highlight the importance of recognizing AS soil areas in the catchment to target the mitigation effects. A holistic approach in the mitigation of the adverse effects from AS soils is needed, using several mitigation methods in the catchment, and directing main efforts and protection from human disturbance to catchment areas with the highest proportion of AS soils. Our results suggest that status assessment of AS rivers should be based on multiple biological quality elements and that their metrics could be improved for better detection of impacts from acidity and metal pressures. The effects of metals and their concentrations on aquatic assemblages should be further examined.

Improving estimation of urban land cover fractions with rigorous spatial endmember modeling

The spatial information has been widely utilized in Spectral Mixture Analysis (SMA) studies over the years. However, the spatial autocorrelation, as a prerequisite of these studies, was seldomly examined. In the complex urban systems, it remains largely unknown whether and how land cover spectra at different locations are spatially autocorrelated and to which extent spatial autocorrelation can explain the great spectral variability. This study proposed an SMA method with Spatial Endmember Modeling (SEMSMA) to improve the fraction estimation of major urban land cover types. SEMSMA used the Incremental Spatial Autocorrelation (ISA) analysis to quantify spatial autocorrelation patterns in broadband reflectance. With the patterns, SEMSMA predicted endmember signatures that incorporated both spatial (location-oriented) and non-spatial spectral variability via the Restricted Maximum Likelihood-Empirical Best Linear Unbiased Predictor (REML-EBLUP) and Monte Carlo sampling. The spectral variabilities were then addressed in pixel unmixing by an iterative mixture analysis method. Results of ISA demonstrated diverse spatial autocorrelation patterns in reflectance of urban land covers. These patterns can be related to the pathways that spatial processes acted on land cover properties. The predicted endmember signatures explained most of the spectral variability for all land cover types. Subpixel fraction estimates showed that SEMSMA outperformed multiple endmember SMA (MESMA) and spatially adaptive SMA (SASMA), based on the root mean square error (0.04–0.15), the mean absolute error (0.01–0.09), and the Systematic Error (SE) (−80.00–0.01). The low SE of SEMSMA revealed unbiased estimation of land cover fractions. Results of this study underscored the necessity of spatial autocorrelation examination and the advantage of rigorous spatial endmember modeling when incorporating spatial information into SMA.

Using ensemble learning to take advantage of high-resolution radar backscatter in conjunction with surface features to disaggregate SMAP soil moisture product

ABSTRACT Remote sensing based retrieved of soil moisture from low frequency passive microwave observations is preferred in different aspects such as better spatial coverage and more measurement compared to traditional ground-based measurements. However, due to coarse spatial resolution of the observations, their applications are limited in local to regional studies. This paper provides a framework using random forest regression to disaggregate the daily SMAP enhanced soil moisture (SPL3SMP_E) utilizing several ancillary data to overcome the spatial resolution limits and cloudiness effects. Ancillaries were acquired from sentinel-1 radar, MODIS monthly NDVI, land cover, topography, and surface soil properties. To validate the downscaled results with 1-km spatial resolution, the OZNET soil moisture measurements and sparse TDR ground soil moisture measurements were collected from Murrumbidgee catchment (Australia) and Firozabad catchment (Iran), respectively. Downscaled soil moisture product unbiased root-mean-square error (UnbRMSE) of ensemble learning demonstrated a range of 0.023 and –0.07 cm3/cm3. The produced downscaled soil moisture exhibited better local heterogeneity when compared to the coarse data and tracked the dynamics of temporal changes in soil moisture. Furthermore, cumulative distribution function (CDF) analysis showed good accuracy of downscaled soil moisture in grassland and cropland. Taken together, the findings supported usefulness of the suggested methodology in downscaling the medium- resolution SMAP soil moisture product.

Composite Sequential Network With POA Attention for PolSAR Image Analysis

The scattering response of polarimetric synthetic aperture radar (PolSAR) data is strongly target orientation-dependent. Formulating the polarimetric matrix as sequential data by rotating the polarimetric matrix along the radar line of sight would provide rich information about land-cover properties. In this work, we propose a composite sequential network (CSN) with polarization orientation angle (POA) attention to model the polarimetric coherency matrix sequence and explore target scattering orientation diversity features. Three major factors strengthen the proposed method for PolSAR image analysis. First, CSN improves the feature comprehensiveness by extending the interpretation mode of PolSAR data from spatial polarization to spatial polarization orientation. In this way, CSN could describe polarimetric response dynamics at different orientations. Second, a two-stream composite network with both real- and complex-valued convolutional long short-term memory (ConvLSTM) network is proposed to process the diagonal and off-diagonal elements of the coherency matrix sequence, respectively. Compared to existing real-/complex-valued networks, the CSN explores the significant phase information of the off-diagonal elements by operations in the complex domain. Meanwhile, CSN prevents padding 0 meaninglessly in the imaginary part of the real-valued diagonal elements. Third, during the sequential modeling of the polarimetric matrix, a POA attention mechanism is proposed. Equipped with POA-sensitive decomposition loss, the CSN attends to substantial POA range derived by targets' physical scattering mechanism and learns features closely related to the scattering mechanism. Extensive experiments and analysis on land-cover classification demonstrate the proposed method's robustness and excellence.

The Performance of Three Exponential Decay Models in Estimating Tropical Cyclone Intensity Change After Landfall Over China

In this study, the performance of three exponential decay models in estimating intensity change of tropical cyclones (TCs) after landfall over China is evaluated based on the best-track TC data during 1980–2018. Results indicate that the three models evaluated can reproduce the weakening trend of TCs after landfall, but two of them (M1 and M2) tend to overestimate TC intensity and one (M3) tends to overestimate TC intensity in the first 12 h and underestimate TC intensity afterwards. M2 has the best performance with the smallest errors among the three models within 24 h after landfall. M3 has better performance than M1 in the first 20 h after landfall, but its errors increase largely afterwards. M1 and M2 show systematic positive biases in the southeastern China likely due to the fact that they have not explicitly included any topographic effect. M3 has better performance in the southeastern China, where it was originally attempted, but shows negative biases in the eastern China. The relative contributions of different factors, including landfall intensity, translational speed, 850-hPa moist static energy, and topography, to model errors are examined based on classification analyses. Results indicate that the landfall intensity contributes about 18%, translational speed, moist static energy and topography contribute equally about 15% to the model errors. It is strongly suggested that the TC characteristics and the time-dependent decay constant determined by environmental conditions, topography and land cover properties, should be considered in a good exponential decay model of TC weakening after landfall.

Distinct roles of land cover in regulating spatial variabilities of temperature responses to radiative effects of aerosols and clouds

Surface temperature responses to radiative perturbations due to aerosols and clouds are complicated by the land surface properties. To disentangle these complexities, this study, from a terrestrial surface energy budget perspective, isolates the underlying biophysical processes from the instantaneous radiative effects of aerosols and clouds on surface temperature using the National Center for Atmospheric Research Community Earth System Model version 1.2.1. It is found that in comparison with the global heterogeneous distributions of instantaneous radiative perturbations at the surface induced by aerosols and clouds, the spatial variations of the corresponding surface temperature responses to aerosol direct radiative effects (DRE) during the daytime and cloud radiative effects (CRE) during the nighttime are amplified. It is because of the consistent global distribution of the local surface climate sensitivity (a function of land cover properties such as surface roughness and Bowen ratio) with those of daytime DRE and nighttime CRE. By applying identical anthropogenic aerosol and precursor emissions over eight major past, present and projected future anthropogenic aerosol emitting regions (i.e. Brazil, China, East Africa, India, Indonesia, South Africa, United States and Western Europe), surface temperature responses to aerosol radiative cooling in the daytime and cloud radiative warming in the nighttime over these regions positively regulated by local surface climate sensitivities are prominent.

Spatial Resolved Surface Ozone with Urban and Rural Differentiation during 1990-2019: A Space-Time Bayesian Neural Network Downscaler.

Long-term exposure to ambient ozone (O3) can lead to a series of chronic diseases and associated premature deaths, and thus population-level environmental health studies hanker after the high-resolution surface O3 concentration database. In response to this demand, we innovatively construct a space-time Bayesian neural network parametric regressor to fuse TOAR historical observations, CMIP6 multimodel simulation ensemble, population distributions, land cover properties, and emission inventories altogether and downscale to 10 km × 10 km spatial resolution with high methodological reliability (R2 = 0.89-0.97, RMSE = 1.97-3.42 ppbV), fair prediction accuracy (R2 = 0.69-0.77, RMSE = 5.63-7.97 ppbV), and commendable spatiotemporal extrapolation capabilities (R2 = 0.62-0.76, RMSE = 5.38-11.7 ppbV). Based on our predictions in 8-h maximum daily average metric, the rural-site surface O3 are 15.1±7.4 ppbV higher than urban globally averaged across 30 historical years during 1990-2019, with developing countries being of the most evident differences. The globe-wide urban surface O3 are climbing by 1.9±2.3 ppbV per decade, except for the decreasing trends in eastern United States. On the other hand, the global rural surface O3 tend to be relatively stable, except for the rising tendencies in China and India. Using CMIP6 model simulations directly without urban-rural differentiation will lead to underestimations of population O3 exposure by 2.0±0.8 ppbV averaged over each historical year. Our original Bayesian neural network framework contributes to the deep-learning-driven environmental studies methodologically by providing a brand-new feasible way to realize data fusion and downscaling, which maintains high interpretability by conforming to the principles of spatial statistics without compromising the prediction accuracy. Moreover, the 30-year highly spatial resolved monthly surface O3 database with multiple metrics fills in the literature gap for long-term surface O3 exposure tracing.

Ecosystem services value assessment and forecasting using integrated machine learning algorithm and CA-Markov model: an empirical investigation of an Asian megacity

The ecosystem services in an area are quite dependent on its ambient land use and land cover (LULC) attributes. Here we assess the spatio-temporal distribution of Ecosystem Services Value (ESV) for the years 1990, 2000, 2010, 2020, based on the then existing LULC aspects of the Kolkata Urban Agglomeration in eastern India. Further, these are simulated for 2030, 2040 and 2050 to determine the future potential ESV. The respective LULC layers were extracted from Landsat images using the support vector machine method and future projections were done using Markov Chain-Cellular Automata models. Results reveal that all LULC aspects are likely to significantly decrease except built-up tracts. The available ESV shall concomitantly decline by 29.7%, especially due to wetland loss. The ESV patterns also showed strong spatial correlation/clustering, with higher ESV patches locating along rivers/wetlands. These results can better inform management of high-value EVS components for sustaining/improving the urban environmental quality.

Identifying climate‐resistant vernal pools: Hydrologic refugia for amphibian reproduction under droughts and climate change

AbstractVernal pools of the northeastern United States provide important breeding habitat for amphibians but may be sensitive to droughts and climate change. These seasonal wetlands typically fill by early spring and dry by mid‐to‐late summer. Because climate change may produce earlier and stronger growing‐season evapotranspiration combined with increasing droughts and shifts in precipitation timing, management concerns include the possibility that some pools will increasingly become dry earlier in the year, potentially interfering with amphibian life‐cycle completion. In this context, a subset of pools that continues to provide wetland habitat later into the year under relatively dry conditions might function as ecohydrologic refugia, potentially supporting species persistence even as summer conditions become warmer and droughts more frequent. We used approximately 3000 field observations of inundation from 449 pools to train machine‐learning models that predict the likelihood of pool inundation based on pool size, day of the year, climate conditions, short‐term weather patterns, and soil, geologic and landcover attributes. Models were then used to generate predictions of pool wetness across five seasonal time points, three short‐term weather scenarios and four sets of downscaled climate projections. Model outputs are available through a website allowing users to choose the inundation thresholds, time points, weather scenarios and future climate projections most relevant to their management needs. Together with long‐term monitoring of individual pools at the site scale, this regional‐scale study can support amphibian conservation by helping to identify which pools may be most likely to function as ecohydrologic refugia from droughts and climate change.

Landscape cover type, not social dominance, is associated with the winter movement patterns of Snowy Owls in temperate areas

Abstract Migrating animals occur along a continuum from species that spend the nonbreeding season at a fixed location to species that are nomadic during the nonbreeding season, essentially continuously moving. Such variation is likely driven by the economics of territoriality or heterogeneity in the environment. The Snowy Owl (Bubo scandiacus) is known for its complex seasonal movements, and thus an excellent model to test these ideas, as many individuals travel unpredictably along irregular routes during both the breeding and nonbreeding seasons. Two possible explanations for this large variation in the propensity to move are (1) dominance hierarchies in which dominant individuals (adult females in this case) monopolize some key, consistent resources, and move less than subdominants; and (2) habitat heterogeneity in which individuals foraging in rich and less heterogenic environments are less mobile. We analyzed fine-scale telemetry data (global positioning system [GPS]/global system for mobile communication [GSM]) from 50 Snowy Owls tagged in eastern and central North America from 2013 to 2019, comparing space use during the winter period according to sex and age, and to land cover attributes. We used variograms to classify individuals as nomadic (58%) or range-resident (42%), and found that nomadic owls had ten times larger wintering areas than range-resident owls. The frequency of nomadism was similar in socially-dominant adult females, immatures, and males. However, nomadism increased from west to east, and north to south, and was positively associated with the use of water and negatively associated with croplands. We conclude that many individual Snowy Owls in Eastern North America are nomadic during the nonbreeding season and that movement patterns during this time are driven primarily by extrinsic factors, specifically heterogeneity in habitat and prey availability, as opposed to intrinsic factors associated with spacing behavior, such as age and sex.

Impact of urbanization on air quality in the Yangtze River Delta during the COVID-19 lockdown in China

The urban agglomeration of Yangtze River Delta (YRD) is symbol of China’s rapid urbanization during the past decades. Urbanization can significantly impact land-cover properties, surface heating, and emissions of air pollutants. To control the spread of COVID-19, China imposed very rigorous restrictions, leading to dramatic reductions in air pollutants (except O3) from satellite and ground-based data. As such, inter-transportation of air pollutants was weak during the lockdown, which was conducive to discuss the impacts of urbanization on the air quality. During the lockdown, the rates of surface PM2.5, PM10, SO2, NO2 and CO reductions in different urban types ranged from 6.6% to 62.4% in the YRD. Urbanization exerted great impacts on the pollutant variations in urban agglomerations despite such large decreases in primary pollution in YRD. Lower values of AOD and tropospheric NO2 columns were noticeably observed over large cities during the lockdown. The extents of surface PM, SO2, NO2 and CO reductions in large cities (first-tier and second-tier) were found to be larger (4.7%–10.6%) than those in small-medium cities (third-tier and fourth-tier) during the lockdown, which was also the case for the extent of the increase (33.0%–53.0%) in O3. PCA analysis revealed that the PM decreases in large cities made greater improvement in the air quality compared with the small cities during lockdown, while the urbanization had non-obvious influence on the photochemical reactions. It is imperative to adopt policies and programs to mitigate the air pollution in urban agglomerations in the fast urbanization process.