A spatio-temporal analysis of rainfall and drought monitoring in the Tharparkar region of Pakistan

The Tharpakar desert region of Pakistan supports a population approaching two million, dependent on rain-fed agriculture as the main livelihood. The almost doubling of population in the last two decades, coupled with low and variable rainfall, makes this one of the world’s most food-insecure regions. This paper examines satellite-based rainfall estimates and biomass data as a means to supplement sparsely distributed rainfall stations and to provide timely estimates of seasonal growth indicators in farmlands. Satellite dekadal and monthly rainfall estimates gave good correlations with ground station data, ranging from R = 0.75 to R = 0.97 over a 19-year period, with tendency for overestimation from the Tropical Rainfall Monitoring Mission (TRMM) and underestimation from Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) datasets. CHIRPS was selected for further modeling, as overestimation from TRMM implies the risk of under-predicting drought. The use of satellite rainfall products from CHIRPS was also essential for derivation of spatial estimates of phenological variables and rainfall criteria for comparison with normalized difference vegetation index (NDVI)-based biomass productivity. This is because, in this arid region where drought is common and rainfall unpredictable, determination of phenological thresholds based on vegetation indices proved unreliable. Mapped rainfall distributions across Tharparkar were found to differ substantially from those of maximum biomass (NDVImax), often showing low NDVImax in zones of higher annual rainfall, and vice versa. This mismatch occurs in both wet and dry years. Maps of rainfall intensity suggest that low yields often occur in areas with intense rain causing damage to ripening crops, and that total rainfall in a season is less important than sustained water supply. Correlations between rainfall variables and NDVImax indicate the difficulty of predicting drought early in the growing season in this region of extreme climatic variability. Mapped rainfall and biomass distributions can be used to recommend settlement in areas of more consistent rainfall

Air pollution scenario over China during COVID-19

The unprecedented slowdown in China during the COVID-19 period of November 2019 to April 2020 should have reduced pollution in smog-laden cities. However, moderate resolution imaging spectrometer (MODIS) satellite retrievals of aerosol optical depth (AOD) show a marked increase in aerosols over the Beijing–Tianjin–Hebei (BHT) region and most of Northeast and Central China, compared with the previous winter. Fine particulate (PM2.5) data from ground monitoring stations show an increase of 19.5% in Beijing during January and February 2020, and no reduction for Tianjin. In March and April 2020, a different spatial pattern emerges, with very high AOD levels observed over 50% of the Chinese mainland, and including peripheral regions in the northwest and southwest. At the same time, ozone monitoring instrument (OMI) satellite-derived NO2 concentrations fell drastically across China. The increase in PM2.5 while NO2 decreased in BTH and across China is likely due to enhanced production of secondary particulates. These are formed when reductions in NOx result in increased ozone formation, thus increasing the oxidizing capacity of the atmosphere. Support for this explanation is provided by ground level air quality data showing increased volume of fine mode aerosols throughout February and March 2020, and increased levels of PM2.5, relative humidity (RH), and ozone during haze episodes in the COVID-19 lockdown period. Backward trajectories show the origin of air masses affecting industrial centers of North and East China to be local. Other contributors to increased atmospheric particulates may include inflated industrial production in peripheral regions to compensate loss in the main population and industrial centers, and low wind speeds. Satellite monitoring of the extraordinary atmospheric conditions resulting from the COVID-19 shutdown could enhance understanding of smog formation and attempts to control it

The accumulation of species and recovery of species composition along a 70 year successional gradient in a tropical secondary forest

The majority of global forests are secondary and are at different stages of succession. Assessing the dynamics of species richness and similarity, and quantifying the importance of environmental filtering, dispersal limitation and other stochastic processes are essential to understanding the mechanisms of succession for forest restoration. In this article, we explored the accumulation of species, the relative importance of spatial distance, environmental factors and stand age in determining species composition of a tropical secondary forest succession in Hong Kong. Twenty-eight plots with median age of 7, 20, 39, 61 and over 70 years in the secondary forest were established and surveyed, and the indicator species for each age class were identified. Species composition shows large variation both within and between age classes, while species richness in the old growth forest (>70 yr) was significantly lower than in the mid-age classes. Rarefied species richness showed a rapid accumulation during early succession, but species richness levelled off from 20 yrs onwards. Variation partitioning indicated that spatial distance alone explained 33% of the variation in species composition, followed by environmental distance (8%) and stand age (1%). The results of Nonmetric Multidimensional Scaling suggested that idiosyncratic successional pathways and alternative stable states might be prevalent. Our results suggested dispersal limitation was the main limiting factor in explaining the turnover of species during forest succession, while environmental filtering played a lesser role in shaping species distributions. Our results highlight the importance of active restoration in overcoming the barriers of succession in secondary vegetation in the tropics

Temperature change and urbanisation in a multi-nucleated megacity: China’s Pearl River Delta

This paper investigates urbanisation and temperature indices, over four decades in a multi-nucleated megacity, China’s Pearl River Delta (PRD). Daily mean minimum, maximum and mean temperatures (Tmin, Tmax and Tmean) have increased considerably above background warming rates. However, only weak to moderate relationships are observed between trends in local urban surface area surrounding climatic stations, and Tmin used as a proxy for UHI development. While 5 out of 21 stations with high increase in both Tmin and degree of urbanisation, showed moderate relationship (R=0.51), another 8 stations with low urbanisation showed significant increase in Tmin. This suggests warming from factors other than local urban development on Tmin. Since PRD contains 123 cities over 1 million population, and a semi-continuous urbanised area due to merging of urban centres, a regional heat island circulation, or heat dome model is invoked. The observed increase in Tmax above background warming rates for 14 stations, is more difficult to explain as Tmax is not generally affected by urbanisation. This is attributed to high surface energy flux in the afternoon, and anthropogenic energy use in dense urban districts. The regional heat dome circulation over PRD suggests local temperatures will increase further, even without further local developments

A spatio-temporal analysis of trends in rainfall from long term satellite rainfall products in the Sudano Sahelian zone of Nigeria

Rainfall and its variability drive the rural economies across the Sudano-Sahelian zone of northern Nigeria, where drought strategies largely determine crop yields. The increasing scarcity of rain gauges in West Africa generally limits assessments of the degree and spatial extent of hardship arising from rainfall deficiency. However, the improved availability and robustness of satellite-based rainfall products since the early 1980s, offers an alternative source of rainfall data which is spatially, and often temporally, more complete than rain gauges. This research evaluates four satellite-based rainfall products for their ability to represent both long term rainfall trends such as recovery from decadal droughts, and trends in seasonal rainfall variables relevant to crop yield prediction. The Climate Hazards group Infrared Precipitation with Stations (CHIRPS) rainfall product at 5 km resolution, was observed to be consistently most representative of ground station rainfall across northern Nigeria over a 35-year period 1981–2015, followed by TARCAT. CHIRPS was found to give a good overall prediction of rainfall amounts at dekadal, monthly and seasonal time scales, and was therefore used in the study to represent the typical performance of satellite rainfall datasets. The CHIRPS-observed increase in growing season length since the 1970s and 80s drought decades, was accompanied by significant rainfall increases in the later part of the growing season, especially marked in northern and northeastern states. This is especially important for the main subsistence crops sorghum and millet as the risk of late drought impedes swelling of the grain, affecting dry weight production. The CHIRPS data also indicate a significant decrease in dry spells in the northwest and southern parts of the study area, which would have favourable outcomes for crop production in the densely populated rural hinterlands of the cities of Sokoto, Jos and Abuja. In view of the continued intra-and inter-annual rainfall variability across northern Nigeria, and amid rapid rural population growth recently, a return to the rainfall levels of the drought decades, would require informed response. The study suggests that satellite rainfall estimates can offer such information, especially since we observed high spatial variability in rainfall distributions and trends

Improvement of Aerosol Optical Depth Retrieval over Hong Kong from a Geostationary Meteorological Satellite Using Critical Reflectance with Background Optical Depth Correction

Despite continuous efforts to retrieve aerosol optical depth (AOD) using a conventional 5-channelmeteorological imager in geostationary orbit, the accuracy in urban areas has been poorer than other areas primarily due to complex urban surface properties and mixed aerosol types from different emission sources. The two largest error sources in aerosol retrieval have been aerosol type selection and surface reflectance. In selecting the aerosol type from a single visible channel, the season-dependent aerosol optical properties were adopted from longterm measurements of Aerosol Robotic Network (AERONET) sun-photometers. With the aerosol optical properties obtained fromthe AERONET inversion data, look-up tableswere calculated by using a radiative transfer code: the Second Simulation of the Satellite Signal in the Solar Spectrum (6S). Surface reflectance was estimated using the clear sky composite method, awidely used technique for geostationary retrievals. Over East Asia, the AOD retrieved from the Meteorological Imager showed good agreement, although the values were affected by cloud contamination errors. However, the conventional retrieval of the AOD over Hong Kong was largely underestimated due to the lack of information on the aerosol type and surface properties. To detect spatial and temporal variation of aerosol type over the area, the critical reflectance method, a technique to retrieve single scattering albedo (SSA), was applied. Additionally, the background aerosol effect was corrected to improve the accuracy of the surface reflectance over Hong Kong. The AOD retrieved froma modified algorithmwas compared to the collocated data measured by AERONET in Hong Kong. The comparison showed that the new aerosol type selection using the critical reflectance and the corrected surface reflectance significantly improved the accuracy of AODs in Hong Kong areas,with a correlation coefficient increase from0.65 to 0.76 and a regression line change from MI [basic algorithm] = 0.41AERONET + 0.16 to MI [new algorithm] = 0.70AERONET + 0.01

Spatial and environmental constraints on natural forest regeneration in the degraded landscape of Hong Kong

Tropical forests are the main reservoirs for global biodiversity and climate control. As secondary forests are now more widespread than primary forests, understanding their functioning and role in the biosphere is increasingly important. This includes understanding how they achieve stability, how they accumulate species and build biodiversity and how they cycle nutrients and carbon. This study investigates how we can restore tropical secondary forests to resemble high biomass, highly biodiverse and stable ecosystems seen today only in primary, undisturbed forests. The study used historic aerial photographs and recent high-resolution satellite images from 1945 to 2014 to map forest patches with five age categories, from 14-years to over 70-years, in Hong Kong's degraded tropical landscape. A forest inventory comprising 28 quadrats provided a rare opportunity to relate patterns of species composition at different stages during the succession with topographic and soil characteristics. The topographic variables accounted for 15% of the variance in species abundance, and age of forest stands explained 29%. Species richness rapidly increased after the first 15 years, but was lower in old-growth, than in medium age forest. This is attributed to the inability of late-successional species to disperse into the young forests as the natural dispersal agents (birds, mammals) have been lost. Light-loving pioneers which are unable to tolerate the shade of older forests, cannot regenerate in their own shade, therefore species diversity declines after a few decades. For ecosystem restoration in tropical secondary forests, introduction of late-successional species is necessary to assist natural succession, given the absence of native fauna, seed dispersal agents, and the surrounding altered environment. We also show that remote sensing can play a pivotal role in understanding the recovery and functioning of secondary forest regeneration as its contribution to the biosphere is increasingly important

Characteristics of Fine Particulate Matter (PM2.5) over urban, suburban and rural areas of Hong Kong

In urban areas, Fine Particulate Matter (PM2.5) associated with local vehicle emissions can cause respiratory and cardiorespiratory disease and increased mortality rates, but less in rural areas. However, Hong Kong may be a special case since the whole territory often suffers from regional haze from nearby mainland China, as well as local sources. Therefore, to understand which areas of Hong Kong may be affected by damaging levels of fine particulates, PM2.5 data were obtained from March 2005 to February 2009 for urban, suburban and rural air quality monitoring stations; namely Central (city area, commercial area, and urban populated area), Tsuen Wan (city area, commercial area, urban populated, and residential area), Tung Chung (suburban and residential area), Yuen Long (urban and residential area), and Tap Mun (remote rural area). To evaluate the relative contributions of regional and local pollution sources, the study aims to test the influence of weather conditions on PM2.5 concentrations. Thus meteorological parameters including temperature, relative humidity, wind speed, and wind directions were obtained from the Hong Kong Observatory.. The results showed that Hong Kong’s air quality is mainly affected by regional aerosol emissions, either transported from the land or ocean, as similar patterns of variations in PM2.5 concentrations were observed over urban, suburban, and rural areas of Hong Kong. Only slightly higher PM2.5 concentrations were observed over urban sites, such as Central, compared to suburban and rural sites, which could be attributed to local automobile emissions. Results showed that meteorological parameters have potential to explain 80% of the variability in daily mean PM2.5 concentrations at Yuen Long, 77% at Tung Chung, 72% at Central, 71% at Tsuen Wan, and 67% at Tap Mun during the spring to summer part of the year. The results provide not only a better understanding of the impact of regional long-distance transport of air pollutants on Hong Kong’s air quality but also a reference for future regional-scale collaboration on air quality management

Unveiling falling urban trees before and during Typhoon Higos (2020): empirical case study of potential structural failure using tilt sensor

Urban trees in a densely populated environment may pose risks to the public’s safety in terms of the potential danger of injuries and fatalities, loss of property, impacts on traffic, etc. The biological and mechanical features of urban trees may change over time, thereby affecting the stability of the tree structure. This can be a gradual process but can also be drastic, especially after typhoons or heavy rainstorms. Trees may fall at any time with no discernible signs of failure being exhibited or detected. It is always a challenge in urban tree management to develop a preventive alert system to detect the potential failure of hazardous urban trees and hence be able to have an action plan to handle potential tree tilting or tree collapse. Few studies have considered the comparison of tree morphology to the tilt response relative to uprooting failure in urban cities. New methods involving numerical modeling and sensing technologies provide tools for an effective and deeper understanding of the interaction of root-plate movement and windstorm with the application of the tailor-made sensor. In this study, root-plate tilt variations of 889 trees with sensors installed during Typhoon Higos (2020) are investigated, especially the tilting pattern of the two trees that failed in the event. The correlation of tree response during the typhoon among all trees with tilt measurements was also evaluated. The results from two alarm levels developed in the study, i.e., Increasing Trend Alarm and Sudden Increase Alarm indicated that significant root-plate movement to wind response is species-dependent. These systems could help inform decision making to identify the problematic trees in the early stage. Through the use of smart sensors, the data collected by the alert system provides a very useful analysis of the stability of tree structure and tree health in urban tree management

Classification of aerosols over Saudi Arabia from 2004–2016

Knowledge of aerosol size and composition is very important for investigating the radiative forcing impacts of aerosols, distinguishing aerosol sources, and identifying harmful particulate types in air quality monitoring. The ability to identify aerosol type synoptically would greatly contribute to the knowledge of aerosol type distribution at both regional and global scales, especially where there are no data on chemical composition. In this study, aerosol classification techniques were based on aerosol optical properties from remotely-observed data from the Ozone Monitoring Instrument (OMI) and Aerosol Robotic Network (AERONET) over Saudi Arabia for the period 2004–2016 and validated using data from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO). For this purpose, the OMI-based Aerosol Absorption Optical Depth (AAOD) and Ultra-Violet Aerosol Index (UVAI), and AERONET-based AAOD, Ångström Exponent (AE), Absorption Ångström Exponent (AAE), Fine Mode Fraction (FMF), and Single Scattering Albedo (SSA) were obtained. Spatial analysis of the satellite-based OMI-AAOD showed the dominance of absorbing aerosols over the study area, but with high seasonal variability. The study found significant underestimation by OMI AAOD suggesting that the OMAERUV product may need improvement over bright desert surfaces such as the study area. Aerosols were classified into (i) Dust, (ii) Black Carbon (BC), and (iii) Mixed (BC and Dust) based on the relationships technique, between the aerosol absorption properties (AAE, SSA, and UVAI) and size parameters (AE and FMF). Additionally, the AE vs. UVAI and FMF vs. UVAI relationships misclassified the aerosol types over the study area, and the FMF vs. AE, FMF vs. AAE and FMF vs. SSA relationships were found to be robust. As expected, the dust aerosol type was dominant both annually and seasonally due to frequent dust storm events. Also, fine particulates such as BC and Mixed (BC and Dust) were observed, likely due to industrial activities (cement, petrochemical, fertilizer), water desalination plants, and electric energy generation. This is the first study to classify aerosol types over Saudi Arabia using several different aerosol property relationships, as well as over more than one site, and using data over a much longer time-period than previous studies. This enables classification and recognition of specific aerosol types over the Arabian Peninsula and similar desert regions