Spatial and Temporal Variation of the Near-Surface Wind Environment in the Sahara Desert, North Africa

The Sahara Desert is the largest source of dust on Earth, and has a significant impact on global atmospheric changes. Wind is the main dynamic factor controlling the transport and intensity of dust in the Sahara Desert. This study comprehensively analyzed the spatial and temporal variation in the wind regime of the Sahara Desert from 1980 to 2019 using data from 17 meteorological stations to improve awareness of global atmospheric changes and the intensity of regional aeolian activities. All wind speed parameters decreased from northwest to southeast. While there were significant differences in the trends of temporal variation in wind speed among the different regions, there was an overall decreasing trend across the Sahara Desert, with an average wind speed of 0.09 m s−1 10 a−1. This decrease was closely related to wind frequency. The easterly, westerly, and northerly winds dominated, with more complex wind direction in the northern region. Seasonal differences in wind direction were observed in all regions. The wind direction frequency of wind speeds >6 m s−1 exceeded those with wind speeds <6 m s−1 in the western and northern regions, whereas other regions showed an opposite pattern. The highest drift potential (DP) and resultant drift potential (RDP) were found in the western and northern regions, and during spring and winter. There was a trend of decreasing annual variation in DP and RDP in all regions. The directional variability (RDP/DP) indicated mostly intermediate and high variability in wind direction. Resultant drift direction (RDD) indicated that a mainly southwest wind direction. No apparent trends in temporal variation in RDD and RDP/DP were observed. Total DP was strongly influenced by DP and the magnitude and frequency of strong winds in the prevailing wind direction. No strong correlation between wind regimes and dune types was observed in this desert, indicating the complexity of factors affecting dune morphology.

Spatial and Temporal Variation of Droughts in the Mongolian Plateau during 1959–2018 Based on the Gridded Self-Calibrating Palmer Drought Severity Index

Drought monitoring is challenging, but it is required for improving agricultural production, protecting the ecological environment, and reducing economic losses in drought-prone regions such as the Mongolian Plateau (MP). This study is a systematic analysis of the spatiotemporal changes in the characteristics of drought events (drought duration, severity, intensity, frequency, peak, and starting season) at the sub-regional scale between 1959 and 2018 based on the run theory and using the gridded self-calibrating Palmer Drought Severity Index (scPDSI) dataset. Principal component analysis and Varimax rotation and the Mann–Kendall trend and Sen’s slope were used for the sub-regional division and drought trend analysis, respectively. In addition, wavelet analysis was employed to analyze drought periodicity and determine the influence of large-scale climate indices on regional drought variation. The study results indicate clear differences in the spatial patterns of drought characteristics in the MP. The northern part suffered from droughts with longer duration and higher severity, whereas more drought events with shorter duration and less severity occurred in the southern part. Most of the MP experienced a relatively wet trend in 1996–2018 compared to the period of 1959–1995. The frequency of spring drought events showed an increasing trend in 1996–2018, unlike in 1959–1995. Some drought events simultaneously affected two or several sub-regions. The wavelet analysis results indicated that the drought periodicity in the MP was 10–64 months. The Arctic Oscillation (Pacific Decadal Oscillation) was significantly correlated with drought in the southern (northern) part.

Economic analysis of the environmental sustainability of agriculture: recent studies using quasi-experimental methods

PurposeThe purpose of this review article is to demonstrate how the quasi-experimental approach has been used to study environmental and natural resource issues related to agricultural production.Design/methodology/approachThis review article first provides a short introduction to the quasi-experimental approach using the potential outcomes framework and then uses studies on the environmental sustainability of agricultural production to illustrate how quasi-experimental methods have been applied. Papers reviewed consist of studies that estimate the environmental externalities from agricultural production, evaluate agri-environmental and other related policies and programs, and demonstrate issues related to on-farm resource use and climate adaptation.FindingsDifference-in-differences (DID) and two-way fixed effects methods that utilize the spatial and temporal variation in panel data are widely used to estimate the causal impact of changes in agricultural production and policy on the environment. Utilizing the discontinuities and limits created by agricultural policies and regulations, local treatment effects on land and other input use are estimated using regression discontinuity (RD) or instrumental variable (IV) methods with cross-sectional data.Originality/valueChallenges faced by the food systems have made agricultural sustainability more critical than ever. Over the past three decades, the quasi-experimental approach has become the powerhouse of applied economic research. This review article focuses on quasi-experimental studies on the environmental sustainability of agriculture to provide methodological insights and to highlight gaps in the economics literature of agricultural sustainability.

Spatial and temporal variation of the ambient noise environment of the Sikkim Himalaya

Ambient noise characteristics are perused to assess the station performance of 27 newly constructed broadband seismic stations across Sikkim Himalaya and adjoining Himalayan foreland basin, installed to study the seismogenesis and subsurface structure of the region. Power spectral densities obtained at each station, compared against the global noise limits, reveal that observed vertical component noise levels are within the defined global limits. However, the horizontal components marginally overshoot the limits due to the tilt effect. Ambient noise conditions significantly vary with different installation techniques, analysis revealing that seismic sensors buried directly in the ground have reduced long-period noise in comparison to pier installations. Tectonic settings and anthropogenic activities are also noted to cause a significant rise across short-period and microseism noise spectrum, varying spatially and temporally across the region. Day-time records higher cultural noise than night-time, while the microseism noise dominates during the monsoonal season. An assessment of the effect of the nationwide lockdown imposed due to COVID-19 pandemic revealed a significant decrease in the short-period noise levels at stations installed across the foreland basin marked with higher anthropogenic activity. Our study summarizes the overall ambient noise patterns, validating the stability and performance of the seismic stations across the Sikkim Himalayas.

Four-Year Field Survey of Black Band Disease and Skeletal Growth Anomalies in Encrusting Montipora spp. Corals around Sesoko Island, Okinawa

The Indo-Pacific zooxanthellate scleractinian coral genus Montipora is the host of many coral diseases. Among these are cyanobacterial Black Band Disease (BBD) and Skeletal Growth Anomalies (GAs), but in general data on both diseases are lacking from many regions of the Indo-Pacific, including from Okinawa, southern Japan. In this study, we collected annual prevalence data of Black Band Disease (BBD) and Skeletal Growth Anomalies (GAs) affecting the encrusting form of genus Montipora within the shallow reefs of the subtropical Sesoko Island (off the central west coast of Okinawajima Island) from summer to autumn for four years (2017 to 2020). In 2020 Montipora percent coverage and colony count were also assessed. Generalized Linear Models (GLM) were used to understand the spatial and temporal variation of both BBD and GAs in the nearshore (NE) and reef edge (RE) sites, which revealed higher probability of BBD occurrence in RE sites. BBD prevalence was significantly higher in 2017 in some sites than all other years with site S12 having significant higher probability during all four surveyed years. In terms of GAs, certain sites in 2020 had higher probability of occurrence than during the other years. While the general trend of GAs increased from 2017 to 2020, it was observed to be non-fatal to colonies. In both diseases, the interaction between sites and years was significant. We also observed certain BBD-infected colonies escaping complete mortality. BBD progression rates were monitored in 2020 at site S4, and progression was related to seawater temperatures and was suppressed during periods of heavy rain and large strong typhoons. Our results suggest that higher BBD progression rates are linked with high sea water temperatures (SST > bleaching threshold SST) and higher light levels (> 1400 µmol m−2 s−1), indicating the need for further controlled laboratory experiments. The current research will help form the basis for continued future research into these diseases and their causes in Okinawa and the Indo-Pacific Ocean.

Temporal and Spatial Variation of Aboveground Biomass of Pinus densata and Its Drivers in Shangri-La, CHINA

Understanding the drivers of forest aboveground biomass (AGB) is essential to further understanding the forest carbon cycle. In the upper Yangtze River region, where ecosystems are incredibly fragile, the driving factors that make AGB changes differ from other regions. This study aims to investigate AGB’s spatial and temporal variation of Pinus densata in Shangri-La and decompose the direct and indirect effects of spatial attribute, climate, stand structure, and agricultural activity on AGB in Shangri-La to evaluate the degree of influence of each factor on AGB change. The continuous sample plots from National Forest Inventory (NFI) and Landsat time series were used to estimate the AGB in 1987, 1992, 1997, 2002, 2007, 2012, and 2017. The structural equation model (SEM) was used to analyze the different effects of the four factors on AGB based on five scales: entire, 1987–2002, 2007–2017, low population density, and high population density. The results are as follows: (1) The AGB of Pinus densata in Shangri-La decreased from 1987 to 2017, with the total amount falling from 9.52 million tons to 7.41 million tons, and the average AGB falling from 55.49 t/ha to 40.10 t/ha. (2) At different scales, stand structure and climate were the drivers that directly affect the AGB change. In contrast, the agricultural activity had a negative direct effect on the AGB change, and spatial attribute had a relatively small indirect effect on the AGB change. (3) Analyzing the SEM results at different scales, the change of the contribution of the agricultural activity indicates that human activity is the main negative driver of AGB change in Shangri-La, especially at the high population density region. In contrast, the change of the contribution of the stand structure and climate indicates that the loss of old trees has an important influence on the AGB change. Forest resources here and other ecologically fragile areas should be gradually restored by adhering to policies, such as strengthening forest protection, improving forest stand quality, and limiting agricultural production activities.

Landsat Observations of Two Decades of Wetland Changes in the Estuary of Poyang Lake during 2000–2019

The stability of wetlands is threatened by the combined effects of global climate change and human activity. In particular, the vegetation cover status of lake wetlands has changed. Here, the change in vegetation cover at the estuary of Poyang Lake was monitored, and its influencing factors are studied to elucidate the dynamic change characteristics of vegetation at the inlet of this lake. Flood and water level changes are two of the main factors affecting the evolution of wetland vegetation at the estuary of Poyang Lake. Therefore, Landsat data from 2000 to 2019 were used to study the spatial and temporal variation in the Normalized Difference Vegetation Index (NDVI) in the vegetation cover area. Theil–Sen Median trend analysis and Mann–Kendall tests were used to study the long-term trend characteristics of NDVI. The response between NDVI and the explanatory variables at the estuary of Poyang Lake was quantified using regression tree analysis to study the regional climate, water level, and flood inundation duration. Results showed the following: (1) Vegetation in a large area of the study area improved significantly from 2000 to 2010 and only slightly from 2010 to 2019, and few areas with slight degradation of vegetation were found. In most of these areas, the vegetation from 2000 to 2010 exhibited a gradual change, from nothing to something, which started around 2004; (2) The main variable that separated the NDVI values was the mean water level in October. When the mean October water level was greater than 14.467 m, the study area was still flooded in October. Thus, the regional value of BestNDVI was approximately 0.3, indicating poor vegetation growth. When the mean water level in October was less than 14.467 m, the elevation of the study area was higher than the water level value, and after the water receded in October, the wetland vegetation exhibited autumn growth in that year. Thus, the vegetation in the study area grew more abundantly. These results could help manage and protect the degraded wetland ecology.

Modeling Influence of Soil Properties in Different Gradients of Soil Moisture: The Case of the Valencia Anchor Station Validation Site, Spain

The prediction of spatial and temporal variation of soil water content brings numerous benefits in the studies of soil. However, it requires a considerable number of covariates to be included in the study, complicating the analysis. Integrated nested Laplace approximations (INLA) with stochastic partial differential equation (SPDE) methodology is a possible approach that allows the inclusion of covariates in an easy way. The current study has been conducted using INLA-SPDE to study soil moisture in the area of the Valencia Anchor Station (VAS), soil moisture validation site for the European Space Agency SMOS (Soil Moisture and Ocean Salinity). The data used were collected in a typical ecosystem of the semiarid Mediterranean conditions, subdivided into physio-hydrological units (SMOS units) which presents a certain degree of internal uniformity with respect to hydrological parameters and capture the spatial and temporal variation of soil moisture at the local fine scale. The paper advances the knowledge of the influence of hydrodynamic properties on VAS soil moisture (texture, porosity/bulk density and soil organic matter and land use). With the goal of understanding the factors that affect the variability of soil moisture in the SMOS pixel (50 km × 50 km), five states of soil moisture are proposed. We observed that the model with all covariates and spatial effect has the lowest DIC value. In addition, the correlation coefficient was close to 1 for the relationship between observed and predicted values. The methodology applied presents the possibility to analyze the significance of different covariates having spatial and temporal effects. This process is substantially faster and more effective than traditional kriging. The findings of this study demonstrate an advancement in that framework, demonstrating that it is faster than previous methodologies, provides significance of individual covariates, is reproducible, and is easy to compare with models.